From 98f8a2c6b51aac639e4172dae3447d7616599ce9 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 5 Sep 2023 19:41:06 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../00_Introduction_to_JupyterLab.ipynb       |  450 +-
 .../01_Getting_watershed_boundaries.ipynb     |  590 +-
 ...ct_geographical_watershed_properties.ipynb | 1436 ++---
 .../03_Extracting_forcing_data.ipynb          | 1698 +++---
 .../04_Emulating_hydrological_models.ipynb    | 1650 +++---
 .../05_Advanced_RavenPy_configuration.ipynb   | 1170 ++--
 docs/notebooks/06_Raven_calibration.ipynb     |  706 +--
 .../07_Making_and_using_hotstart_files.ipynb  |  458 +-
 ...tting_and_bias_correcting_CMIP6_data.ipynb | 4768 ++++++++---------
 ...drological_impacts_of_climate_change.ipynb |  436 +-
 docs/notebooks/10_Data_assimilation.ipynb     | 1020 ++--
 .../11_Climatological_ESP_forecasting.ipynb   |  592 +-
 ...2_Performing_hindcasting_experiments.ipynb |  648 +--
 .../Assess_probabilistic_flood_risk.ipynb     | 2612 ++++-----
 ...omparing_hindcasts_and_ESP_forecasts.ipynb |  746 +--
 .../Distributed_hydrological_modelling.ipynb  |  472 +-
 docs/notebooks/HydroShare_integration.ipynb   |  430 +-
 .../Hydrological_realtime_forecasting.ipynb   |  532 +-
 .../Managing_Jupyter_Environments.ipynb       |  278 +-
 docs/notebooks/Perform_Regionalization.ipynb  |  580 +-
 .../Running_HMETS_with_CANOPEX_dataset.ipynb  | 1980 +++----
 ...e_change_impact_study_on_a_watershed.ipynb | 2658 ++++-----
 docs/notebooks/time_series_analysis.ipynb     |  562 +-
 23 files changed, 13236 insertions(+), 13236 deletions(-)

diff --git a/docs/notebooks/00_Introduction_to_JupyterLab.ipynb b/docs/notebooks/00_Introduction_to_JupyterLab.ipynb
index dfc9c799..60651f93 100644
--- a/docs/notebooks/00_Introduction_to_JupyterLab.ipynb
+++ b/docs/notebooks/00_Introduction_to_JupyterLab.ipynb
@@ -1,227 +1,227 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 00 - Introduction to JupyterLab\n",
-    "\n",
-    "## Before going any further: \n",
-    "\n",
-    "These notebooks are best visualized by copying them to your writable-workspace on your PAVICS account, as files will be created and written on your writable-workspace that has write access. Please copy the tutorial notebooks (00-12) to that folder before continuing. \n",
-    "\n",
-    "Please see the PAVICS-Hydro documentation on the [PAVICS website](https://pavics.ouranos.ca) for more details."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## JupyterLab\n",
-    "\n",
-    "Welcome to this PAVICS-Hydro tutorial, where we will explore the various hydrological modelling and forecasting possibilities offered by PAVICS-Hydro. The platform uses the [Raven hydrological modelling framework](http://raven.uwaterloo.ca/) to emulate different hydrological models, and relies on various scientific Python libraries to analyze the results. This tutorial starts by exploring the JupyterLab environment that this notebook is currently running on.\n",
-    "\n",
-    "\n",
-    "## The file explorer\n",
-    "\n",
-    "The file explorer to the left of your screen works in much the same way as any file explorer on Windows, Mac or Linux. Here, we have files and folders that contain notebooks and data that we will want to use in our research or operations. You can:\n",
-    "\n",
-    "- Upload files here by using drag-and-drop OR using the button to that effect above the file explorer to send files from computer to the server (e.g. watershed boundaries, model files, input data, streamflow data, etc.) as required;\n",
-    "- Cut, Copy, Paste files from one folder to another in the JupyterLab server;\n",
-    "- Download files by right-clicking the file and saving to your computer locally;\n",
-    "- Open notebook files (*.ipynb*) in the editor to modify and run the codes within;\n",
-    "- Shutting down a running notebook by right-clicking and selecting \"Shut Down Kernel\".\n",
-    "\n",
-    "The file explorer allows users to manage the files and codes. To modify the codes and run them, we need to double-click on a notebook to open it in the file editor.\n",
-    "\n",
-    "## The file editor\n",
-    "\n",
-    "The file editor is what is being used right now to read the contents of this notebook! It is what is on the right side of your screen. If you open multiple notebooks, there will be as many tabs open on the top of the file editor. This is where the magic happens! Once a notebook has been opened, you can see that there are some \"Text\" cells and some \"Code\" cells. \n",
-    "\n",
-    "The **text cells** give context to what is happening and can be seen as meta-comments on top of the regular code comments, to ensure everything is clear to the users. The cell you are currently reading, for example, is a code cell. If you double-click it, you can modify its contents. To make it appear as text again, press the \"play\" or \"run\" button in the button list at the top of the file editor. These texts cells follow the Markdown templating.\n",
-    "\n",
-    "The **code cells** will actually perform the work on the PAVICS-Hydro server. For example, here is a simple code cell that will import a python package in our notebook. These code cells are in Python."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above cell does not do anything unless we tell the notebook that it needs to be run. To run a cell, you need to select it (click on it) and press the \"play\"/\"run\" button. This will tell the PAVICS-Hydro server that it needs to run this piece of code. To see if a code is running, the small brackets to the left of **code cells** will briefly turn to an asterisk, and will display a number once the code has finished running. If there is an error, there will be a red box with clear error messages under the executed cell.\n",
-    "\n",
-    "We can then see if importing the xarray package has worked by using a quick test. Run the below code. If it displays an error, then the importing has failed and should be run again. Also, there will be an empty space between the brackets. If it has worked, you should see a version in the brackets and the xarray version displayed under the cell!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(xr.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Order of operations and variables in memory\n",
-    "\n",
-    "Jupyter Notebooks function in the same way as scripts in most programming languages. That is to say:\n",
-    "\n",
-    "- Cells will execute the first line within that cell before the second one, etc.;\n",
-    "- If a cell tries to use a variable that has not been created yet, it will cause an error;\n",
-    "- If a cell creates a variable, then that variable will be available to all other cells from that point on;\n",
-    "- If a cell deletes a variable, then that variable is no longer available to any cell.\n",
-    "\n",
-    "To test this, you can try **skipping the next cell and running the following one, first**, which will return an error:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# SKIP THIS CELL FOR NOW!\n",
-    "\n",
-    "# Run this cell to create variable \"b\"\n",
-    "b = 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# You can also add comments by prefixing a line with a hashtag, like this.\n",
-    "a = b + 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will get an error saying that \"name 'b' is not defined\".\n",
-    "\n",
-    "This is normal, because the code is expecting that variable \"b\" exists somewhere in its memory, but it hasn't been created yet! So, let's now create variable \"b\" by running the cell that we skipped (Note that we are presenting the cells in this order because otherwise errors would break our quality testing checks!)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that variable \"b\" has been created, try and re-run the cell that gave an error previously. It should work, because now \"b\" exists! So you can see how ordering cells is important. It is also possible to use this to create small tests within a notebook, by changing some variable at key points between larger blocks of code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Managing files\n",
-    "\n",
-    "JupyterLab allows loading and saving files in the file explorer. Let's explore this capability.\n",
-    "\n",
-    "First we will create a random array of numbers and save them to a file. We will then read that variable back into memory and compare results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We will do this with the numpy package:\n",
-    "import numpy as np\n",
-    "\n",
-    "c = np.random.rand(100)  # Create 100 random values and store them in variable \"c\"\n",
-    "np.savetxt(\"array_c.txt\", c)  # Write the array to a file named \"array_c.txt\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you look in your workspace, you will see a new file named \"array_c.txt\". All files you generate will be written to the folder in which your notebook is running from.\n",
-    "\n",
-    "Now let's read it back in and verify that the values are the same. We can do this by taking the sum of the absolute differences between each element. If the sum is 0, that means we have succeeded:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = np.loadtxt(\"array_c.txt\")\n",
-    "print(sum(abs(c - d)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, files can be accessed easily by simply refering to them by their name if they are in the same folder as the notebook. If they aren't you also need to specify the folder path. Example \"writable-workspace/array_c.txt\". This is also true for all files, even those you upload yourself. You can also access data that can be found online on an accessible server using the URL, but we will get to that in a later notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## IMPORTANT: Multiple noteooks running concurrently\n",
-    "\n",
-    "Notebooks are independent instances and do not communicate with one another. This means that if you load a package or a variable in memory in one notebook, the information will not be available in your other notebooks. This means you would need to import the data in the different notebooks. This will have the drawback of consuming more memory on the server, which can slow down computations for everyone. Therefore, we ask that you kindly close and shut down the notebooks once you are done with them. This can be done by right-clicking the notebook in the file explorer and selecting \"Shut Down Kernel\". This will close the instance and free all memory the notebook was taking up. Thanks!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Important closing remarks\n",
-    "\n",
-    "JupyerLab environments make using codes easy and repeatable, without having to worry too much about packages, data access and other such elements that can be difficult to work with. However, there are some drawbacks:\n",
-    "\n",
-    "- You are running codes on a remote server, so it might be slower than on a high-performance local computer;\n",
-    "- You might require more resources than are available on the remote server;\n",
-    "- You might want to implement major changes that are not compatible with the Python packages available in the PAVICS-Hydro environment.\n",
-    "\n",
-    "To add packages, you can simply add a cell and \"! pip install **package**\" as required, which will add it to your local server. It will need to be re-added every time you close and re-spawn your server. You can also use the Jupyter conda plugin to install via conda (Settings --> Conda Packages Manager).\n",
-    "    \n",
-    "Otherwise, you can always install the PAVICS-Hydro environment on your local computer following the instructions found [here] (https://pavics-sdi.readthedocs.io/projects/raven/en/latest/index.html). Note that these instructions are for more advanced python users / developers.\n",
-    "    \n",
-    "In the next notebooks, we will start using your JupyterLab instance to start doing hydrological and hydroclimatological science!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 00 - Introduction to JupyterLab\n",
+        "\n",
+        "## Before going any further: \n",
+        "\n",
+        "These notebooks are best visualized by copying them to your writable-workspace on your PAVICS account, as files will be created and written on your writable-workspace that has write access. Please copy the tutorial notebooks (00-12) to that folder before continuing. \n",
+        "\n",
+        "Please see the PAVICS-Hydro documentation on the [PAVICS website](https://pavics.ouranos.ca) for more details."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## JupyterLab\n",
+        "\n",
+        "Welcome to this PAVICS-Hydro tutorial, where we will explore the various hydrological modelling and forecasting possibilities offered by PAVICS-Hydro. The platform uses the [Raven hydrological modelling framework](http://raven.uwaterloo.ca/) to emulate different hydrological models, and relies on various scientific Python libraries to analyze the results. This tutorial starts by exploring the JupyterLab environment that this notebook is currently running on.\n",
+        "\n",
+        "\n",
+        "## The file explorer\n",
+        "\n",
+        "The file explorer to the left of your screen works in much the same way as any file explorer on Windows, Mac or Linux. Here, we have files and folders that contain notebooks and data that we will want to use in our research or operations. You can:\n",
+        "\n",
+        "- Upload files here by using drag-and-drop OR using the button to that effect above the file explorer to send files from computer to the server (e.g. watershed boundaries, model files, input data, streamflow data, etc.) as required;\n",
+        "- Cut, Copy, Paste files from one folder to another in the JupyterLab server;\n",
+        "- Download files by right-clicking the file and saving to your computer locally;\n",
+        "- Open notebook files (*.ipynb*) in the editor to modify and run the codes within;\n",
+        "- Shutting down a running notebook by right-clicking and selecting \"Shut Down Kernel\".\n",
+        "\n",
+        "The file explorer allows users to manage the files and codes. To modify the codes and run them, we need to double-click on a notebook to open it in the file editor.\n",
+        "\n",
+        "## The file editor\n",
+        "\n",
+        "The file editor is what is being used right now to read the contents of this notebook! It is what is on the right side of your screen. If you open multiple notebooks, there will be as many tabs open on the top of the file editor. This is where the magic happens! Once a notebook has been opened, you can see that there are some \"Text\" cells and some \"Code\" cells. \n",
+        "\n",
+        "The **text cells** give context to what is happening and can be seen as meta-comments on top of the regular code comments, to ensure everything is clear to the users. The cell you are currently reading, for example, is a code cell. If you double-click it, you can modify its contents. To make it appear as text again, press the \"play\" or \"run\" button in the button list at the top of the file editor. These texts cells follow the Markdown templating.\n",
+        "\n",
+        "The **code cells** will actually perform the work on the PAVICS-Hydro server. For example, here is a simple code cell that will import a python package in our notebook. These code cells are in Python."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import xarray as xr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The above cell does not do anything unless we tell the notebook that it needs to be run. To run a cell, you need to select it (click on it) and press the \"play\"/\"run\" button. This will tell the PAVICS-Hydro server that it needs to run this piece of code. To see if a code is running, the small brackets to the left of **code cells** will briefly turn to an asterisk, and will display a number once the code has finished running. If there is an error, there will be a red box with clear error messages under the executed cell.\n",
+        "\n",
+        "We can then see if importing the xarray package has worked by using a quick test. Run the below code. If it displays an error, then the importing has failed and should be run again. Also, there will be an empty space between the brackets. If it has worked, you should see a version in the brackets and the xarray version displayed under the cell!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(xr.__version__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Order of operations and variables in memory\n",
+        "\n",
+        "Jupyter Notebooks function in the same way as scripts in most programming languages. That is to say:\n",
+        "\n",
+        "- Cells will execute the first line within that cell before the second one, etc.;\n",
+        "- If a cell tries to use a variable that has not been created yet, it will cause an error;\n",
+        "- If a cell creates a variable, then that variable will be available to all other cells from that point on;\n",
+        "- If a cell deletes a variable, then that variable is no longer available to any cell.\n",
+        "\n",
+        "To test this, you can try **skipping the next cell and running the following one, first**, which will return an error:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# SKIP THIS CELL FOR NOW!\n",
+        "\n",
+        "# Run this cell to create variable \"b\"\n",
+        "b = 5"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# You can also add comments by prefixing a line with a hashtag, like this.\n",
+        "a = b + 3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You will get an error saying that \"name 'b' is not defined\".\n",
+        "\n",
+        "This is normal, because the code is expecting that variable \"b\" exists somewhere in its memory, but it hasn't been created yet! So, let's now create variable \"b\" by running the cell that we skipped (Note that we are presenting the cells in this order because otherwise errors would break our quality testing checks!)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that variable \"b\" has been created, try and re-run the cell that gave an error previously. It should work, because now \"b\" exists! So you can see how ordering cells is important. It is also possible to use this to create small tests within a notebook, by changing some variable at key points between larger blocks of code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Managing files\n",
+        "\n",
+        "JupyterLab allows loading and saving files in the file explorer. Let's explore this capability.\n",
+        "\n",
+        "First we will create a random array of numbers and save them to a file. We will then read that variable back into memory and compare results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We will do this with the numpy package:\n",
+        "import numpy as np\n",
+        "\n",
+        "c = np.random.rand(100)  # Create 100 random values and store them in variable \"c\"\n",
+        "np.savetxt(\"array_c.txt\", c)  # Write the array to a file named \"array_c.txt\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you look in your workspace, you will see a new file named \"array_c.txt\". All files you generate will be written to the folder in which your notebook is running from.\n",
+        "\n",
+        "Now let's read it back in and verify that the values are the same. We can do this by taking the sum of the absolute differences between each element. If the sum is 0, that means we have succeeded:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "d = np.loadtxt(\"array_c.txt\")\n",
+        "print(sum(abs(c - d)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As you can see, files can be accessed easily by simply refering to them by their name if they are in the same folder as the notebook. If they aren't you also need to specify the folder path. Example \"writable-workspace/array_c.txt\". This is also true for all files, even those you upload yourself. You can also access data that can be found online on an accessible server using the URL, but we will get to that in a later notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## IMPORTANT: Multiple noteooks running concurrently\n",
+        "\n",
+        "Notebooks are independent instances and do not communicate with one another. This means that if you load a package or a variable in memory in one notebook, the information will not be available in your other notebooks. This means you would need to import the data in the different notebooks. This will have the drawback of consuming more memory on the server, which can slow down computations for everyone. Therefore, we ask that you kindly close and shut down the notebooks once you are done with them. This can be done by right-clicking the notebook in the file explorer and selecting \"Shut Down Kernel\". This will close the instance and free all memory the notebook was taking up. Thanks!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Important closing remarks\n",
+        "\n",
+        "JupyerLab environments make using codes easy and repeatable, without having to worry too much about packages, data access and other such elements that can be difficult to work with. However, there are some drawbacks:\n",
+        "\n",
+        "- You are running codes on a remote server, so it might be slower than on a high-performance local computer;\n",
+        "- You might require more resources than are available on the remote server;\n",
+        "- You might want to implement major changes that are not compatible with the Python packages available in the PAVICS-Hydro environment.\n",
+        "\n",
+        "To add packages, you can simply add a cell and \"! pip install **package**\" as required, which will add it to your local server. It will need to be re-added every time you close and re-spawn your server. You can also use the Jupyter conda plugin to install via conda (Settings --> Conda Packages Manager).\n",
+        "    \n",
+        "Otherwise, you can always install the PAVICS-Hydro environment on your local computer following the instructions found [here] (https://pavics-sdi.readthedocs.io/projects/raven/en/latest/index.html). Note that these instructions are for more advanced python users / developers.\n",
+        "    \n",
+        "In the next notebooks, we will start using your JupyterLab instance to start doing hydrological and hydroclimatological science!"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/01_Getting_watershed_boundaries.ipynb b/docs/notebooks/01_Getting_watershed_boundaries.ipynb
index 96eb76ff..81fdd2d7 100644
--- a/docs/notebooks/01_Getting_watershed_boundaries.ipynb
+++ b/docs/notebooks/01_Getting_watershed_boundaries.ipynb
@@ -1,303 +1,303 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 01 - Getting watershed boundaries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Region Selection and Map Preview with Ipyleaflet\n",
-    "In this notebook, you will extract a selected watershed from the HydroSHEDS database (see the reference manual for more information on HydroSHEDS). A GeoJSON with the watershed boundaries will be available for download and usable for other tasks such as extracting meteorological data covered in the next notebooks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the necessary libraries to format, send, and parse our returned results\n",
-    "import os\n",
-    "\n",
-    "import birdy\n",
-    "import geopandas as gpd\n",
-    "import ipyleaflet\n",
-    "import ipywidgets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you are running this locally (and not on the PAVICS-Hydro server), and your `notebook` is version prior to `5.3`, you might need to run this command `jupyter nbextension enable --py --sys-prefix ipyleaflet`.  For more information see https://ipyleaflet.readthedocs.io/en/latest/installation.html.\n",
-    "\n",
-    "This next box is all boilerplate, you do not need to understand it or play with it. Simply run it! Many such code snippets are provided throughout the notebooks to make your life easier. You can then modify some options to taylor the code to your needs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Create WPS instances# Set environment variable WPS_URL to \"http://localhost:9099\" to run on the default local server\n",
-    "pavics_url = \"https://pavics.ouranos.ca\"\n",
-    "raven_url = os.environ.get(\"WPS_URL\", f\"{pavics_url}/twitcher/ows/proxy/raven/wps\")\n",
-    "\n",
-    "raven = birdy.WPSClient(raven_url)\n",
-    "\n",
-    "# Build an interactive map with ipyleaflet\n",
-    "initial_lat_lon = (48.63, -74.71)\n",
-    "\n",
-    "leaflet_map = ipyleaflet.Map(\n",
-    "    center=initial_lat_lon,\n",
-    "    basemap=ipyleaflet.basemaps.OpenTopoMap,\n",
-    ")\n",
-    "\n",
-    "# Add a custom zoom slider\n",
-    "zoom_slider = ipywidgets.IntSlider(description=\"Zoom level:\", min=1, max=10, value=6)\n",
-    "ipywidgets.jslink((zoom_slider, \"value\"), (leaflet_map, \"zoom\"))\n",
-    "widget_control1 = ipyleaflet.WidgetControl(widget=zoom_slider, position=\"topright\")\n",
-    "leaflet_map.add_control(widget_control1)\n",
-    "\n",
-    "# Add a marker to the map\n",
-    "marker = ipyleaflet.Marker(location=initial_lat_lon, draggable=True)\n",
-    "leaflet_map.add_layer(marker)\n",
-    "\n",
-    "# Add an overlay widget\n",
-    "html = ipywidgets.HTML(\"\"\"Hover over a feature!\"\"\")\n",
-    "html.layout.margin = \"0px 10px 10px 10px\"\n",
-    "\n",
-    "control = ipyleaflet.WidgetControl(widget=html, position=\"bottomleft\")\n",
-    "leaflet_map.add_control(control)\n",
-    "\n",
-    "\n",
-    "def update_html(feature, **kwargs):\n",
-    "    html.value = \"\"\"\n",
-    "        <h2><b>USGS HydroBASINS</b></h2>\n",
-    "        <h4>ID: {}</h4>\n",
-    "        <h4>Upstream Area: {} sq. km.</h4>\n",
-    "        <h4>Sub-basin Area: {} sq. km.</h4>\n",
-    "    \"\"\".format(\n",
-    "        feature[\"properties\"][\"id\"],\n",
-    "        feature[\"properties\"][\"UP_AREA\"],  #\n",
-    "        feature[\"properties\"][\"SUB_AREA\"],\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using the map to select the outlet of the watershed\n",
-    "When using the \"leaflet_map\" command, an interative map will be displayed.\n",
-    "\n",
-    "Note that a blue marker will be displayed in the middle of the map, which can be dragged by interacting directly with it. Try dragging and placing the marker at the mouth of a river, over a large lake such as Lac Saint-Jean (next to Alma, east of the initial marker position), or anywhere else within North America. This coordinate will be used to find and extract the closest watershed outlet from the Hydrosheds database (see the reference manual for more info on Hydrosheds). The watershed ID and area will be displayed at the bottom left corner of the map.\n",
-    "\n",
-    "The user can zoom in and out on the map either by:\n",
-    "* Using the Zoom level on the top right corner;\n",
-    "* Using the + / - icons on the top left corner;\n",
-    "* Double-clicking on the map on the area to zoom in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the map in the notebook\n",
-    "leaflet_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Display the lat/lon coordinates of the marker location.\n",
-    "user_lonlat = list(reversed(marker.location))\n",
-    "print(user_lonlat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the shape of the watershed contributing to flow at the selected location.\n",
-    "resp = raven.hydrobasins_select(location=str(user_lonlat), aggregate_upstream=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Before continuing, wait for the process above to finish**\n",
-    "\n",
-    "This can be monitored when the \"[*]:\" on the left of the cell is replaced with a number."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extract the URL of the resulting GeoJSON feature\n",
-    "feat = resp.get(asobj=False).feature\n",
-    "print(\n",
-    "    \"This is the geoJSON file that can be used as the watershed contour in other toolboxes:\"\n",
-    ")\n",
-    "print(\"\")\n",
-    "print(feat)\n",
-    "print(\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## <span style=\"color:red\">BEFORE CONTINUING:</span>\n",
-    "\n",
-    "- If you are working in the writable-workspace, you will want to download the .geojson file at the link above and deposit it into your workspace on the left of your screen.\n",
-    "- If you are running this in the default workspace after logging in, the workspace is read-only so we will provide files for you, and you can ignore this file for the time being."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Print the properties from the extracted watershed\n",
-    "gdf = gpd.read_file(feat)\n",
-    "gdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Now we will add the extracted watershed to the map above!\n",
-    "\n",
-    "Scroll back up after executing the next cell to see the watershed displayed in blue on the map. You may reextract another  watershed by moving restarting the kernel or running all the cells from the beginning to reload the map."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Adding the GeoJSON to the map above.\n",
-    "user_geojson = ipyleaflet.GeoData(\n",
-    "    geo_dataframe=gdf,\n",
-    "    style={\n",
-    "        \"color\": \"blue\",\n",
-    "        \"opacity\": 1,\n",
-    "        \"weight\": 1.9,\n",
-    "        \"fillOpacity\": 0.5,\n",
-    "    },\n",
-    "    hover_style={\"fillColor\": \"#b08a3e\", \"fillOpacity\": 0.9},\n",
-    ")\n",
-    "\n",
-    "leaflet_map.add_layer(user_geojson)\n",
-    "user_geojson.on_hover(update_html)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Congratulations!\n",
-    "\n",
-    "You have successfully created a watershed boundary file that can be used in the following notebooks. If you already have the boundaries of your watershed of interest, then you can upload them to your workspace instead of using this notebook to generate them. a geojson file is accepted, as is a shapefile. For shapefiles, provide a zip containing all the shape data (.shp, .shx, .dbf, .prj, etc.).\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  },
-  "nbdime-conflicts": {
-   "local_diff": [
+  "cells": [
     {
-     "diff": [
-      {
-       "diff": [
-        {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "3.6.7"
-         ]
-        },
-        {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
-        }
-       ],
-       "key": "version",
-       "op": "patch"
-      }
-     ],
-     "key": "language_info",
-     "op": "patch"
-    }
-   ],
-   "remote_diff": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 01 - Getting watershed boundaries"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Region Selection and Map Preview with Ipyleaflet\n",
+        "In this notebook, you will extract a selected watershed from the HydroSHEDS database (see the reference manual for more information on HydroSHEDS). A GeoJSON with the watershed boundaries will be available for download and usable for other tasks such as extracting meteorological data covered in the next notebooks."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import the necessary libraries to format, send, and parse our returned results\n",
+        "import os\n",
+        "\n",
+        "import birdy\n",
+        "import geopandas as gpd\n",
+        "import ipyleaflet\n",
+        "import ipywidgets"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you are running this locally (and not on the PAVICS-Hydro server), and your `notebook` is version prior to `5.3`, you might need to run this command `jupyter nbextension enable --py --sys-prefix ipyleaflet`.  For more information see https://ipyleaflet.readthedocs.io/en/latest/installation.html.\n",
+        "\n",
+        "This next box is all boilerplate, you do not need to understand it or play with it. Simply run it! Many such code snippets are provided throughout the notebooks to make your life easier. You can then modify some options to taylor the code to your needs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": true
+      },
+      "outputs": [],
+      "source": [
+        "# Create WPS instances# Set environment variable WPS_URL to \"http://localhost:9099\" to run on the default local server\n",
+        "pavics_url = \"https://pavics.ouranos.ca\"\n",
+        "raven_url = os.environ.get(\"WPS_URL\", f\"{pavics_url}/twitcher/ows/proxy/raven/wps\")\n",
+        "\n",
+        "raven = birdy.WPSClient(raven_url)\n",
+        "\n",
+        "# Build an interactive map with ipyleaflet\n",
+        "initial_lat_lon = (48.63, -74.71)\n",
+        "\n",
+        "leaflet_map = ipyleaflet.Map(\n",
+        "    center=initial_lat_lon,\n",
+        "    basemap=ipyleaflet.basemaps.OpenTopoMap,\n",
+        ")\n",
+        "\n",
+        "# Add a custom zoom slider\n",
+        "zoom_slider = ipywidgets.IntSlider(description=\"Zoom level:\", min=1, max=10, value=6)\n",
+        "ipywidgets.jslink((zoom_slider, \"value\"), (leaflet_map, \"zoom\"))\n",
+        "widget_control1 = ipyleaflet.WidgetControl(widget=zoom_slider, position=\"topright\")\n",
+        "leaflet_map.add_control(widget_control1)\n",
+        "\n",
+        "# Add a marker to the map\n",
+        "marker = ipyleaflet.Marker(location=initial_lat_lon, draggable=True)\n",
+        "leaflet_map.add_layer(marker)\n",
+        "\n",
+        "# Add an overlay widget\n",
+        "html = ipywidgets.HTML(\"\"\"Hover over a feature!\"\"\")\n",
+        "html.layout.margin = \"0px 10px 10px 10px\"\n",
+        "\n",
+        "control = ipyleaflet.WidgetControl(widget=html, position=\"bottomleft\")\n",
+        "leaflet_map.add_control(control)\n",
+        "\n",
+        "\n",
+        "def update_html(feature, **kwargs):\n",
+        "    html.value = \"\"\"\n",
+        "        <h2><b>USGS HydroBASINS</b></h2>\n",
+        "        <h4>ID: {}</h4>\n",
+        "        <h4>Upstream Area: {} sq. km.</h4>\n",
+        "        <h4>Sub-basin Area: {} sq. km.</h4>\n",
+        "    \"\"\".format(\n",
+        "        feature[\"properties\"][\"id\"],\n",
+        "        feature[\"properties\"][\"UP_AREA\"],  #\n",
+        "        feature[\"properties\"][\"SUB_AREA\"],\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Using the map to select the outlet of the watershed\n",
+        "When using the \"leaflet_map\" command, an interative map will be displayed.\n",
+        "\n",
+        "Note that a blue marker will be displayed in the middle of the map, which can be dragged by interacting directly with it. Try dragging and placing the marker at the mouth of a river, over a large lake such as Lac Saint-Jean (next to Alma, east of the initial marker position), or anywhere else within North America. This coordinate will be used to find and extract the closest watershed outlet from the Hydrosheds database (see the reference manual for more info on Hydrosheds). The watershed ID and area will be displayed at the bottom left corner of the map.\n",
+        "\n",
+        "The user can zoom in and out on the map either by:\n",
+        "* Using the Zoom level on the top right corner;\n",
+        "* Using the + / - icons on the top left corner;\n",
+        "* Double-clicking on the map on the area to zoom in."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Load the map in the notebook\n",
+        "leaflet_map"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Display the lat/lon coordinates of the marker location.\n",
+        "user_lonlat = list(reversed(marker.location))\n",
+        "print(user_lonlat)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the shape of the watershed contributing to flow at the selected location.\n",
+        "resp = raven.hydrobasins_select(location=str(user_lonlat), aggregate_upstream=True)"
+      ]
+    },
     {
-     "diff": [
-      {
-       "diff": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Before continuing, wait for the process above to finish**\n",
+        "\n",
+        "This can be monitored when the \"[*]:\" on the left of the cell is replaced with a number."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Extract the URL of the resulting GeoJSON feature\n",
+        "feat = resp.get(asobj=False).feature\n",
+        "print(\n",
+        "    \"This is the geoJSON file that can be used as the watershed contour in other toolboxes:\"\n",
+        ")\n",
+        "print(\"\")\n",
+        "print(feat)\n",
+        "print(\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## <span style=\"color:red\">BEFORE CONTINUING:</span>\n",
+        "\n",
+        "- If you are working in the writable-workspace, you will want to download the .geojson file at the link above and deposit it into your workspace on the left of your screen.\n",
+        "- If you are running this in the default workspace after logging in, the workspace is read-only so we will provide files for you, and you can ignore this file for the time being."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Print the properties from the extracted watershed\n",
+        "gdf = gpd.read_file(feat)\n",
+        "gdf"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Now we will add the extracted watershed to the map above!\n",
+        "\n",
+        "Scroll back up after executing the next cell to see the watershed displayed in blue on the map. You may reextract another  watershed by moving restarting the kernel or running all the cells from the beginning to reload the map."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Adding the GeoJSON to the map above.\n",
+        "user_geojson = ipyleaflet.GeoData(\n",
+        "    geo_dataframe=gdf,\n",
+        "    style={\n",
+        "        \"color\": \"blue\",\n",
+        "        \"opacity\": 1,\n",
+        "        \"weight\": 1.9,\n",
+        "        \"fillOpacity\": 0.5,\n",
+        "    },\n",
+        "    hover_style={\"fillColor\": \"#b08a3e\", \"fillOpacity\": 0.9},\n",
+        ")\n",
+        "\n",
+        "leaflet_map.add_layer(user_geojson)\n",
+        "user_geojson.on_hover(update_html)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Congratulations!\n",
+        "\n",
+        "You have successfully created a watershed boundary file that can be used in the following notebooks. If you already have the boundaries of your watershed of interest, then you can upload them to your workspace instead of using this notebook to generate them. a geojson file is accepted, as is a shapefile. For shapefiles, provide a zip containing all the shape data (.shp, .shx, .dbf, .prj, etc.).\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    },
+    "nbdime-conflicts": {
+      "local_diff": [
         {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "3.6.10"
-         ]
-        },
+          "diff": [
+            {
+              "diff": [
+                {
+                  "key": 0,
+                  "op": "addrange",
+                  "valuelist": [
+                    "3.6.7"
+                  ]
+                },
+                {
+                  "key": 0,
+                  "length": 1,
+                  "op": "removerange"
+                }
+              ],
+              "key": "version",
+              "op": "patch"
+            }
+          ],
+          "key": "language_info",
+          "op": "patch"
+        }
+      ],
+      "remote_diff": [
         {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
+          "diff": [
+            {
+              "diff": [
+                {
+                  "key": 0,
+                  "op": "addrange",
+                  "valuelist": [
+                    "3.6.10"
+                  ]
+                },
+                {
+                  "key": 0,
+                  "length": 1,
+                  "op": "removerange"
+                }
+              ],
+              "key": "version",
+              "op": "patch"
+            }
+          ],
+          "key": "language_info",
+          "op": "patch"
         }
-       ],
-       "key": "version",
-       "op": "patch"
-      }
-     ],
-     "key": "language_info",
-     "op": "patch"
+      ]
     }
-   ]
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb b/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb
index 3fde2888..4f7858bd 100644
--- a/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb
+++ b/docs/notebooks/02_Extract_geographical_watershed_properties.ipynb
@@ -1,777 +1,777 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 02 - Extract geographical watershed properties"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extract geographical watershed properties automatically using PAVICS-Hydro's geospatial toolbox\n",
-    "\n",
-    "Hydrological models typically need geographical information about watersheds being simulated: latitude and longitude, area, mean altitude, land-use, etc. Raven is no exception. This notebook shows how to obtain this information using remote services that are made available for users in PAVICS-Hydro. These services connect to a digital elevation model (DEM) and a land-use data set to extract relevant information.\n",
-    "\n",
-    "The DEM used in the following is the [EarthEnv-DEM90](https://www.earthenv.org/DEM), while the land-use dataset is the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/). Other data sources could be used, given their availability through the Web Coverage Service (WCS) protocol.\n",
-    "\n",
-    "Since these computations happen on a specific Geoserver hosted on PAVICS, we need to establish a connection to that service. While the steps are a bit more complex, the good news is that you only need to change a few items in this notebook to tailor results to your needs. For example, this first code snippet is boilerplate and should not be changed.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# We need to import a few packages required to do the work\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"USE_PYGEOS\"] = \"0\"\n",
-    "import geopandas as gpd\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import rasterio\n",
-    "import rioxarray as rio\n",
-    "from birdy import WPSClient\n",
-    "\n",
-    "from ravenpy.utilities.testdata import get_file\n",
-    "\n",
-    "# This is the URL of the Geoserver that will perform the computations for us.\n",
-    "url = os.environ.get(\n",
-    "    \"WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\"\n",
-    ")\n",
-    "\n",
-    "# Connect to the PAVICS-Hydro Raven WPS server\n",
-    "wps = WPSClient(url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the previous notebook, we extracted the boundaries of a watershed, which were saved in the \"input.geojson\" file. We also downloaded the file and re-uploaded it to the workspace, so it should be available now to this workbook, too!\n",
-    "\n",
-    "We can now plot the outline of the watershed by loading it into `GeoPandas`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 02 - Extract geographical watershed properties"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extract geographical watershed properties automatically using PAVICS-Hydro's geospatial toolbox\n",
+        "\n",
+        "Hydrological models typically need geographical information about watersheds being simulated: latitude and longitude, area, mean altitude, land-use, etc. Raven is no exception. This notebook shows how to obtain this information using remote services that are made available for users in PAVICS-Hydro. These services connect to a digital elevation model (DEM) and a land-use data set to extract relevant information.\n",
+        "\n",
+        "The DEM used in the following is the [EarthEnv-DEM90](https://www.earthenv.org/DEM), while the land-use dataset is the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/). Other data sources could be used, given their availability through the Web Coverage Service (WCS) protocol.\n",
+        "\n",
+        "Since these computations happen on a specific Geoserver hosted on PAVICS, we need to establish a connection to that service. While the steps are a bit more complex, the good news is that you only need to change a few items in this notebook to tailor results to your needs. For example, this first code snippet is boilerplate and should not be changed.\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>COAST</th>\n",
-       "      <th>DIST_MAIN</th>\n",
-       "      <th>DIST_SINK</th>\n",
-       "      <th>ENDO</th>\n",
-       "      <th>HYBAS_ID</th>\n",
-       "      <th>LAKE</th>\n",
-       "      <th>NEXT_DOWN</th>\n",
-       "      <th>NEXT_SINK</th>\n",
-       "      <th>ORDER</th>\n",
-       "      <th>PFAF_ID</th>\n",
-       "      <th>SIDE</th>\n",
-       "      <th>SORT</th>\n",
-       "      <th>SUB_AREA</th>\n",
-       "      <th>UP_AREA</th>\n",
-       "      <th>id</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>141.3</td>\n",
-       "      <td>141.3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7120319552</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7120319551</td>\n",
-       "      <td>7120034330</td>\n",
-       "      <td>1</td>\n",
-       "      <td>724083033100</td>\n",
-       "      <td>R</td>\n",
-       "      <td>96044</td>\n",
-       "      <td>56.2</td>\n",
-       "      <td>73072.4</td>\n",
-       "      <td>USGS_HydroBASINS_lake_na_lev12.96044</td>\n",
-       "      <td>POLYGON ((-71.40830 48.44170, -71.42300 48.442...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# We need to import a few packages required to do the work\n",
+        "import os\n",
+        "\n",
+        "os.environ[\"USE_PYGEOS\"] = \"0\"\n",
+        "import geopandas as gpd\n",
+        "import matplotlib as mpl\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import rasterio\n",
+        "import rioxarray as rio\n",
+        "from birdy import WPSClient\n",
+        "\n",
+        "from ravenpy.utilities.testdata import get_file\n",
+        "\n",
+        "# This is the URL of the Geoserver that will perform the computations for us.\n",
+        "url = os.environ.get(\n",
+        "    \"WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\"\n",
+        ")\n",
+        "\n",
+        "# Connect to the PAVICS-Hydro Raven WPS server\n",
+        "wps = WPSClient(url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the previous notebook, we extracted the boundaries of a watershed, which were saved in the \"input.geojson\" file. We also downloaded the file and re-uploaded it to the workspace, so it should be available now to this workbook, too!\n",
+        "\n",
+        "We can now plot the outline of the watershed by loading it into `GeoPandas`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>COAST</th>\n",
+              "      <th>DIST_MAIN</th>\n",
+              "      <th>DIST_SINK</th>\n",
+              "      <th>ENDO</th>\n",
+              "      <th>HYBAS_ID</th>\n",
+              "      <th>LAKE</th>\n",
+              "      <th>NEXT_DOWN</th>\n",
+              "      <th>NEXT_SINK</th>\n",
+              "      <th>ORDER</th>\n",
+              "      <th>PFAF_ID</th>\n",
+              "      <th>SIDE</th>\n",
+              "      <th>SORT</th>\n",
+              "      <th>SUB_AREA</th>\n",
+              "      <th>UP_AREA</th>\n",
+              "      <th>id</th>\n",
+              "      <th>geometry</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>141.3</td>\n",
+              "      <td>141.3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7120319552</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7120319551</td>\n",
+              "      <td>7120034330</td>\n",
+              "      <td>1</td>\n",
+              "      <td>724083033100</td>\n",
+              "      <td>R</td>\n",
+              "      <td>96044</td>\n",
+              "      <td>56.2</td>\n",
+              "      <td>73072.4</td>\n",
+              "      <td>USGS_HydroBASINS_lake_na_lev12.96044</td>\n",
+              "      <td>POLYGON ((-71.40830 48.44170, -71.42300 48.442...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   COAST  DIST_MAIN  DIST_SINK  ENDO    HYBAS_ID  LAKE   NEXT_DOWN  \\\n",
+              "0      0      141.3      141.3     0  7120319552     0  7120319551   \n",
+              "\n",
+              "    NEXT_SINK  ORDER       PFAF_ID SIDE   SORT  SUB_AREA  UP_AREA  \\\n",
+              "0  7120034330      1  724083033100    R  96044      56.2  73072.4   \n",
+              "\n",
+              "                                     id  \\\n",
+              "0  USGS_HydroBASINS_lake_na_lev12.96044   \n",
+              "\n",
+              "                                            geometry  \n",
+              "0  POLYGON ((-71.40830 48.44170, -71.42300 48.442...  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "   COAST  DIST_MAIN  DIST_SINK  ENDO    HYBAS_ID  LAKE   NEXT_DOWN  \\\n",
-       "0      0      141.3      141.3     0  7120319552     0  7120319551   \n",
-       "\n",
-       "    NEXT_SINK  ORDER       PFAF_ID SIDE   SORT  SUB_AREA  UP_AREA  \\\n",
-       "0  7120034330      1  724083033100    R  96044      56.2  73072.4   \n",
-       "\n",
-       "                                     id  \\\n",
-       "0  USGS_HydroBASINS_lake_na_lev12.96044   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((-71.40830 48.44170, -71.42300 48.442...  "
+      "source": [
+        "# The contour can be generated using notebook \"01_Delineating watersheds, where it would be placed\n",
+        "# in the same folder as the notebooks and available in your workspace. The contour could then be accessed\n",
+        "# easily by defining it as follows:\n",
+        "\"\"\"\n",
+        "feature_url = \"input.geojson\"\n",
+        "\"\"\"\n",
+        "# However, to keep things tidy, we have also prepared a version that can be accessed easily for\n",
+        "# demonstration purposes:\n",
+        "feature_url = get_file(\"notebook_inputs/input.geojson\")\n",
+        "df = gpd.read_file(feature_url)\n",
+        "display(df)\n",
+        "df.plot()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generic watershed properties\n",
+        "\n",
+        "Now that we have delineated a watershed, lets find the zonal statistics and other properties using the `shape_properties` process. This process requires a `shape` argument defining the watershed contour, the exterior polygon. The polygon can be given either as a link to a geometry file  (e.g. a geojson file such as `feature_url`), or as data embeded in a string. For example, if variable `feature` is a `GeoPandas` geometry, `json.dumps(feature)` can be used to convert it to a string and pass it as the `shape` argument.\n",
+        "\n",
+        "Typically, we expect users will simply upload a shapefile and use this code to perform the extraction on the region of interest."
       ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "shape_resp = wps.shape_properties(shape=feature_url)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# The contour can be generated using notebook \"01_Delineating watersheds, where it would be placed\n",
-    "# in the same folder as the notebooks and available in your workspace. The contour could then be accessed\n",
-    "# easily by defining it as follows:\n",
-    "\"\"\"\n",
-    "feature_url = \"input.geojson\"\n",
-    "\"\"\"\n",
-    "# However, to keep things tidy, we have also prepared a version that can be accessed easily for\n",
-    "# demonstration purposes:\n",
-    "feature_url = get_file(\"notebook_inputs/input.geojson\")\n",
-    "df = gpd.read_file(feature_url)\n",
-    "display(df)\n",
-    "df.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generic watershed properties\n",
-    "\n",
-    "Now that we have delineated a watershed, lets find the zonal statistics and other properties using the `shape_properties` process. This process requires a `shape` argument defining the watershed contour, the exterior polygon. The polygon can be given either as a link to a geometry file  (e.g. a geojson file such as `feature_url`), or as data embeded in a string. For example, if variable `feature` is a `GeoPandas` geometry, `json.dumps(feature)` can be used to convert it to a string and pass it as the `shape` argument.\n",
-    "\n",
-    "Typically, we expect users will simply upload a shapefile and use this code to perform the extraction on the region of interest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "shape_resp = wps.shape_properties(shape=feature_url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the process has completed, we extract the data from the response, as follows. Note that you do not need to change anything here. The code will work and return the desired results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'id': 'USGS_HydroBASINS_lake_na_lev12.96044',\n",
-       " 'COAST': 0,\n",
-       " 'DIST_MAIN': 141.3,\n",
-       " 'DIST_SINK': 141.3,\n",
-       " 'ENDO': 0,\n",
-       " 'HYBAS_ID': 7120319552,\n",
-       " 'LAKE': 0,\n",
-       " 'NEXT_DOWN': 7120319551,\n",
-       " 'NEXT_SINK': 7120034330,\n",
-       " 'ORDER': 1,\n",
-       " 'PFAF_ID': 724083033100,\n",
-       " 'SIDE': 'R',\n",
-       " 'SORT': 96044,\n",
-       " 'SUB_AREA': 56.2,\n",
-       " 'UP_AREA': 73072.4,\n",
-       " 'area': 55919453.46888515,\n",
-       " 'centroid': [-71.41715786806483, 48.47239495054429],\n",
-       " 'perimeter': 45133.04400352313,\n",
-       " 'gravelius': 1.7025827715870572}"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Once the process has completed, we extract the data from the response, as follows. Note that you do not need to change anything here. The code will work and return the desired results."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "{'area': 55.91945346888515,\n",
-       " 'longitude': -71.41715786806483,\n",
-       " 'latitude': 48.47239495054429,\n",
-       " 'gravelius': 1.7025827715870572,\n",
-       " 'perimeter': 45133.04400352313}"
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'id': 'USGS_HydroBASINS_lake_na_lev12.96044',\n",
+              " 'COAST': 0,\n",
+              " 'DIST_MAIN': 141.3,\n",
+              " 'DIST_SINK': 141.3,\n",
+              " 'ENDO': 0,\n",
+              " 'HYBAS_ID': 7120319552,\n",
+              " 'LAKE': 0,\n",
+              " 'NEXT_DOWN': 7120319551,\n",
+              " 'NEXT_SINK': 7120034330,\n",
+              " 'ORDER': 1,\n",
+              " 'PFAF_ID': 724083033100,\n",
+              " 'SIDE': 'R',\n",
+              " 'SORT': 96044,\n",
+              " 'SUB_AREA': 56.2,\n",
+              " 'UP_AREA': 73072.4,\n",
+              " 'area': 55919453.46888515,\n",
+              " 'centroid': [-71.41715786806483, 48.47239495054429],\n",
+              " 'perimeter': 45133.04400352313,\n",
+              " 'gravelius': 1.7025827715870572}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'area': 55.91945346888515,\n",
+              " 'longitude': -71.41715786806483,\n",
+              " 'latitude': 48.47239495054429,\n",
+              " 'gravelius': 1.7025827715870572,\n",
+              " 'perimeter': 45133.04400352313}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "(properties,) = shape_resp.get(asobj=True)\n",
+        "prop = properties[0]\n",
+        "display(prop)\n",
+        "\n",
+        "area = prop[\"area\"] / 1000000.0\n",
+        "longitude = prop[\"centroid\"][0]\n",
+        "latitude = prop[\"centroid\"][1]\n",
+        "gravelius = prop[\"gravelius\"]\n",
+        "perimeter = prop[\"perimeter\"]\n",
+        "\n",
+        "shape_info = {\n",
+        "    \"area\": area,\n",
+        "    \"longitude\": longitude,\n",
+        "    \"latitude\": latitude,\n",
+        "    \"gravelius\": gravelius,\n",
+        "    \"perimeter\": perimeter,\n",
+        "}\n",
+        "display(shape_info)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(properties,) = shape_resp.get(asobj=True)\n",
-    "prop = properties[0]\n",
-    "display(prop)\n",
-    "\n",
-    "area = prop[\"area\"] / 1000000.0\n",
-    "longitude = prop[\"centroid\"][0]\n",
-    "latitude = prop[\"centroid\"][1]\n",
-    "gravelius = prop[\"gravelius\"]\n",
-    "perimeter = prop[\"perimeter\"]\n",
-    "\n",
-    "shape_info = {\n",
-    "    \"area\": area,\n",
-    "    \"longitude\": longitude,\n",
-    "    \"latitude\": latitude,\n",
-    "    \"gravelius\": gravelius,\n",
-    "    \"perimeter\": perimeter,\n",
-    "}\n",
-    "display(shape_info)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that these properties are a mix of the properties of the original file where the shape is stored, and properties computed by the process (area, centroid, perimeter and gravelius). Note also that the computed area is in m², while the \"SUB_AREA\" property is in km², and that there are slight differences between the two values due to the precision of HydroSHEDS and the delineation algorithm.\n",
-    "\n",
-    "## Land-use information\n",
-    "\n",
-    "Now we extract the land-use properties of the watershed using the `nalcms_zonal_stats` process. As mentioned, it uses a dataset from the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas), and retrieve properties over the given region. \n",
-    "\n",
-    "With the `nalcms_zonal_stats_raster` process, we also return the raster grid itself. Note that this is a high-resolution dataset, and to avoid taxing the system's resource, requests are limited to areas under 100,000km². "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "stats_resp = wps.nalcms_zonal_stats_raster(\n",
-    "    shape=feature_url, select_all_touching=True, band=1, simple_categories=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we will get the raster data and show it as a grid. Here the `birdy` client automatically transforms the returned `geotiff` file to a `DataArray` using either `gdal`, `rasterio`, or `rioxarray`, depending on what libraries are available in our runtime environment. Note that `pymetalink` needs to be installed for this to work. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading to /tmp/tmp8gyvvqhc/subset_1.tiff.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note that these properties are a mix of the properties of the original file where the shape is stored, and properties computed by the process (area, centroid, perimeter and gravelius). Note also that the computed area is in m², while the \"SUB_AREA\" property is in km², and that there are slight differences between the two values due to the precision of HydroSHEDS and the delineation algorithm.\n",
+        "\n",
+        "## Land-use information\n",
+        "\n",
+        "Now we extract the land-use properties of the watershed using the `nalcms_zonal_stats` process. As mentioned, it uses a dataset from the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas), and retrieve properties over the given region. \n",
+        "\n",
+        "With the `nalcms_zonal_stats_raster` process, we also return the raster grid itself. Note that this is a high-resolution dataset, and to avoid taxing the system's resource, requests are limited to areas under 100,000km². "
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f9b527808b0>"
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "stats_resp = wps.nalcms_zonal_stats_raster(\n",
+        "    shape=feature_url, select_all_touching=True, band=1, simple_categories=True\n",
+        ")"
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we will get the raster data and show it as a grid. Here the `birdy` client automatically transforms the returned `geotiff` file to a `DataArray` using either `gdal`, `rasterio`, or `rioxarray`, depending on what libraries are available in our runtime environment. Note that `pymetalink` needs to be installed for this to work. "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "features, statistics, raster = stats_resp.get(asobj=True)\n",
-    "grid = raster[0]\n",
-    "grid.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From there, it's easy to calculate the ratio and percentages of each land-use component. This code should also be left as-is unless you really know what you are doing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading to /tmp/tmp8gyvvqhc/subset_1.tiff.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.QuadMesh at 0x7f9b527808b0>"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "features, statistics, raster = stats_resp.get(asobj=True)\n",
+        "grid = raster[0]\n",
+        "grid.plot()"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "'Land use ratios'"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "From there, it's easy to calculate the ratio and percentages of each land-use component. This code should also be left as-is unless you really know what you are doing."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "{'Ocean': 0.0,\n",
-       " 'Forest': 0.9095753879046077,\n",
-       " 'Shrubs': 0.004920532612284039,\n",
-       " 'Grass': 0.005753721840562167,\n",
-       " 'Wetland': 0.0009589536400936945,\n",
-       " 'Crops': 0.045605319834619795,\n",
-       " 'Urban': 0.02361226831837261,\n",
-       " 'Water': 0.009573815849459998,\n",
-       " 'SnowIce': 0.0}"
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'Land use ratios'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'Ocean': 0.0,\n",
+              " 'Forest': 0.9095753879046077,\n",
+              " 'Shrubs': 0.004920532612284039,\n",
+              " 'Grass': 0.005753721840562167,\n",
+              " 'Wetland': 0.0009589536400936945,\n",
+              " 'Crops': 0.045605319834619795,\n",
+              " 'Urban': 0.02361226831837261,\n",
+              " 'Water': 0.009573815849459998,\n",
+              " 'SnowIce': 0.0}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "'Land use percentages'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'Ocean': '0.0 %',\n",
+              " 'Forest': '90.96 %',\n",
+              " 'Shrubs': '0.49 %',\n",
+              " 'Grass': '0.58 %',\n",
+              " 'Wetland': '0.1 %',\n",
+              " 'Crops': '4.56 %',\n",
+              " 'Urban': '2.36 %',\n",
+              " 'Water': '0.96 %',\n",
+              " 'SnowIce': '0.0 %'}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "lu = statistics[0]\n",
+        "total = sum(lu.values())\n",
+        "\n",
+        "land_use = {k: (v / total) for (k, v) in lu.items()}\n",
+        "display(\"Land use ratios\", land_use)\n",
+        "\n",
+        "land_use_pct = {k: f\"{np.round(v/total*100, 2)} %\" for (k, v) in lu.items()}\n",
+        "display(\"Land use percentages\", land_use_pct)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "'Land use percentages'"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Display the land-use statistics\n",
+        "Here we can display the land-use statistics according to the land cover map, as a function of land cover raster pixels over the catchment. Again, this does not need to be modified at all. It can also be simply deleted if the visualization tools are not required for your use-case."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "{'Ocean': '0.0 %',\n",
-       " 'Forest': '90.96 %',\n",
-       " 'Shrubs': '0.49 %',\n",
-       " 'Grass': '0.58 %',\n",
-       " 'Wetland': '0.1 %',\n",
-       " 'Crops': '4.56 %',\n",
-       " 'Urban': '2.36 %',\n",
-       " 'Water': '0.96 %',\n",
-       " 'SnowIce': '0.0 %'}"
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The land-use categories available are: [  1   5   6   8  10  14  15  16  17  18 127]\n",
+            "The number of occurrences of each land-use category is: [18949  9163 29747   313    72    61  2901   294  1502   609 51649]\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "unique, counts = np.unique(grid, return_counts=True)\n",
+        "print(\"The land-use categories available are: \" + str(unique))\n",
+        "print(\"The number of occurrences of each land-use category is: \" + str(counts))\n",
+        "\n",
+        "# Pixels values at '127' are NaN and can be ignored.\n",
+        "from matplotlib.colors import Normalize\n",
+        "\n",
+        "norm = Normalize()\n",
+        "norm.autoscale(unique[:-1])\n",
+        "cm = mpl.colormaps[\"tab20\"]\n",
+        "plt.bar(unique[:-1], counts[:-1], color=cm(norm(unique[:-1])))\n",
+        "\n",
+        "\n",
+        "# plt.bar(unique[:-1], counts[:-1])\n",
+        "plt.xticks(np.arange(min(unique[:-1]), max(unique[:-1]) + 1, 1.0))\n",
+        "plt.xlabel(\"Land-use categories\")\n",
+        "plt.ylabel(\"Number of pixels\")\n",
+        "plt.show()\n",
+        "\n",
+        "grid.where(grid != 127).sel(band=1).plot.imshow(cmap=\"tab20\")\n",
+        "grid.name = \"Land-use categories\"\n",
+        "plt.show()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lu = statistics[0]\n",
-    "total = sum(lu.values())\n",
-    "\n",
-    "land_use = {k: (v / total) for (k, v) in lu.items()}\n",
-    "display(\"Land use ratios\", land_use)\n",
-    "\n",
-    "land_use_pct = {k: f\"{np.round(v/total*100, 2)} %\" for (k, v) in lu.items()}\n",
-    "display(\"Land use percentages\", land_use_pct)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Display the land-use statistics\n",
-    "Here we can display the land-use statistics according to the land cover map, as a function of land cover raster pixels over the catchment. Again, this does not need to be modified at all. It can also be simply deleted if the visualization tools are not required for your use-case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The land-use categories available are: [  1   5   6   8  10  14  15  16  17  18 127]\n",
-      "The number of occurrences of each land-use category is: [18949  9163 29747   313    72    61  2901   294  1502   609 51649]\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "These values are not very helpful on their own, so the following relationship will be helpful to map the grid to specific land-uses. We can see from this example that we have mostly \"Temperate or sub-polar needleaf forest\" with some \"Sub-polar taiga needleleaf forest\" and a bit of \"Temperate or sub-polar boardleaf deciduous forest\". Exact percentages can be computed from the array of values as extracted and displayed above.\n",
+        "\n",
+        "- 0: Ocean\n",
+        "- 1: Temperate or sub-polar needleleaf forest\n",
+        "- 2: Sub-polar taiga needleleaf forest\n",
+        "- 3: Tropical or sub-tropical broadleaf evergreen forest\n",
+        "- 4: Tropical or sub-tropical broadleaf deciduous forest\n",
+        "- 5: Temperate or sub-polar broadleaf deciduous forest\n",
+        "- 6: Mixed forest\n",
+        "- 7: Tropical or sub-tropical shrubland\n",
+        "- 8: Temperate or sub-polar shrubland\n",
+        "- 9: Tropical or sub-tropical grassland\n",
+        "- 10: Temperate or sub-polar grassland\n",
+        "- 11: Sub-polar or polar shrubland-lichen-moss\n",
+        "- 12: Sub-polar or polar grassland-lichen-moss\n",
+        "- 13: Sub-polar or polar barren-lichen-moss\n",
+        "- 14: Wetland\n",
+        "- 15: Cropland\n",
+        "- 16: Barren lands\n",
+        "- 17: Urban\n",
+        "- 18: Water\n",
+        "- 19: Snow and Ice\n"
+      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Since the GeoTiff object was opened as an `xarray.Dataset` with the `.open_rasterio()` method, this makes it very easy to spatially reproject it with the `cartopy` library. Here we provide a sample projection, but this would need to be adapted to your needs."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import cartopy.crs as ccrs\n",
+        "\n",
+        "# Set a CRS transformation:\n",
+        "crs = ccrs.LambertConformal(\n",
+        "    central_latitude=49, central_longitude=-95, standard_parallels=(49, 77)\n",
+        ")\n",
+        "\n",
+        "ax = plt.subplot(projection=crs)\n",
+        "grid.name = \"Land-use categories\"\n",
+        "grid.where(grid != 127).sel(band=1).plot.imshow(ax=ax, transform=crs, cmap=\"tab20\")\n",
+        "plt.show()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "unique, counts = np.unique(grid, return_counts=True)\n",
-    "print(\"The land-use categories available are: \" + str(unique))\n",
-    "print(\"The number of occurrences of each land-use category is: \" + str(counts))\n",
-    "\n",
-    "# Pixels values at '127' are NaN and can be ignored.\n",
-    "from matplotlib.colors import Normalize\n",
-    "\n",
-    "norm = Normalize()\n",
-    "norm.autoscale(unique[:-1])\n",
-    "cm = mpl.colormaps[\"tab20\"]\n",
-    "plt.bar(unique[:-1], counts[:-1], color=cm(norm(unique[:-1])))\n",
-    "\n",
-    "\n",
-    "# plt.bar(unique[:-1], counts[:-1])\n",
-    "plt.xticks(np.arange(min(unique[:-1]), max(unique[:-1]) + 1, 1.0))\n",
-    "plt.xlabel(\"Land-use categories\")\n",
-    "plt.ylabel(\"Number of pixels\")\n",
-    "plt.show()\n",
-    "\n",
-    "grid.where(grid != 127).sel(band=1).plot.imshow(cmap=\"tab20\")\n",
-    "grid.name = \"Land-use categories\"\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These values are not very helpful on their own, so the following relationship will be helpful to map the grid to specific land-uses. We can see from this example that we have mostly \"Temperate or sub-polar needleaf forest\" with some \"Sub-polar taiga needleleaf forest\" and a bit of \"Temperate or sub-polar boardleaf deciduous forest\". Exact percentages can be computed from the array of values as extracted and displayed above.\n",
-    "\n",
-    "- 0: Ocean\n",
-    "- 1: Temperate or sub-polar needleleaf forest\n",
-    "- 2: Sub-polar taiga needleleaf forest\n",
-    "- 3: Tropical or sub-tropical broadleaf evergreen forest\n",
-    "- 4: Tropical or sub-tropical broadleaf deciduous forest\n",
-    "- 5: Temperate or sub-polar broadleaf deciduous forest\n",
-    "- 6: Mixed forest\n",
-    "- 7: Tropical or sub-tropical shrubland\n",
-    "- 8: Temperate or sub-polar shrubland\n",
-    "- 9: Tropical or sub-tropical grassland\n",
-    "- 10: Temperate or sub-polar grassland\n",
-    "- 11: Sub-polar or polar shrubland-lichen-moss\n",
-    "- 12: Sub-polar or polar grassland-lichen-moss\n",
-    "- 13: Sub-polar or polar barren-lichen-moss\n",
-    "- 14: Wetland\n",
-    "- 15: Cropland\n",
-    "- 16: Barren lands\n",
-    "- 17: Urban\n",
-    "- 18: Water\n",
-    "- 19: Snow and Ice\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the GeoTiff object was opened as an `xarray.Dataset` with the `.open_rasterio()` method, this makes it very easy to spatially reproject it with the `cartopy` library. Here we provide a sample projection, but this would need to be adapted to your needs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Terrain information from the DEM\n",
+        "\n",
+        "Here we collect terrain data, such as elevation, slope and aspect, from the DEM. We will do this using the `terrain_analysis` WPS service, which by default uses DEM data from [EarthEnv-DEM90](https://www.earthenv.org/DEM).\n",
+        "\n",
+        "Note here that while the feature outline is defined above in terms of geographic coordinates (latitude, longitude), the DEM is projected onto a 2D cartesian coordinate system (here NAD83, the Canada Atlas Lambert projection). This is necessary to perform slope calculations. For more information on this, see: https://en.wikipedia.org/wiki/Map_projection\n",
+        "\n",
+        "The DEM data returned in the process response here shows `rioxarray`-like access but using the URLs we can open the files however we like."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "\n",
-    "# Set a CRS transformation:\n",
-    "crs = ccrs.LambertConformal(\n",
-    "    central_latitude=49, central_longitude=-95, standard_parallels=(49, 77)\n",
-    ")\n",
-    "\n",
-    "ax = plt.subplot(projection=crs)\n",
-    "grid.name = \"Land-use categories\"\n",
-    "grid.where(grid != 127).sel(band=1).plot.imshow(ax=ax, transform=crs, cmap=\"tab20\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Terrain information from the DEM\n",
-    "\n",
-    "Here we collect terrain data, such as elevation, slope and aspect, from the DEM. We will do this using the `terrain_analysis` WPS service, which by default uses DEM data from [EarthEnv-DEM90](https://www.earthenv.org/DEM).\n",
-    "\n",
-    "Note here that while the feature outline is defined above in terms of geographic coordinates (latitude, longitude), the DEM is projected onto a 2D cartesian coordinate system (here NAD83, the Canada Atlas Lambert projection). This is necessary to perform slope calculations. For more information on this, see: https://en.wikipedia.org/wiki/Map_projection\n",
-    "\n",
-    "The DEM data returned in the process response here shows `rioxarray`-like access but using the URLs we can open the files however we like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "terrain_resp = wps.terrain_analysis(\n",
-    "    shape=feature_url, select_all_touching=True, projected_crs=3978\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'elevation': 165.37033101757254,\n",
-       " 'slope': 3.8477161303214786,\n",
-       " 'aspect': 5.4659402646877995}"
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "terrain_resp = wps.terrain_analysis(\n",
+        "    shape=feature_url, select_all_touching=True, projected_crs=3978\n",
+        ")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "properties, dem = terrain_resp.get(asobj=True)\n",
-    "\n",
-    "elevation = properties[0][\"elevation\"]\n",
-    "slope = properties[0][\"slope\"]\n",
-    "aspect = properties[0][\"aspect\"]\n",
-    "\n",
-    "terrain = {\"elevation\": elevation, \"slope\": slope, \"aspect\": aspect}\n",
-    "display(terrain)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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77747Eyq45ZZbRrW8ChOMhve85z3j2keCILY8fBRtDhljWLRoERYtWjTiPMViEddccw2uueaaJu7d+CFjgGg5br/9dnieh6OOOkpXE+yzzz44/fTTAQCHHHIIpkyZgs985jO47LLL4Ps+br75Zjz77LPj3uY555yDq6++Gueccw4uv/xyzJs3D7/61a9w3333jWr5IAhw4IEHjnv79Tz55JN47bXXAIj6aM45/uu//gsAcNBBB2HOnDlN2xZBbM/oEsEJLE9QAiHRgtx+++144YUXcPLJJ+PrX/86TjjhBPzmN79BEAQAhEv/3nvvRblcxtlnn43zzz8f7e3tuO2228a9zXK5jAcffBBHHnkkvvKVr+DUU0/FypUrceuttzbrsMbEtddei9NOOw2nnXYaVqxYgaVLl+r/7URKgnjXw2HKC8fzR8YAAIDx0fg8CIIgCGI7YmBgAF1dXXjidKDdH/96BiPgoJ8LJcGxlBa+26AwAUEQBNGyUJigOZAxQBAEQbQuTVIgnOxQzgBBEARBTHLIM0AQBEG0LBQmaA5kDBAEQRAty9YWHXq3QsYAQRAE0bpsZTnidyujNgaq1SrCMNyS+0IQBEG8SwiCoKHnB7H9MipjoFqtYu7cuaPWYScIgiAmN319fVi+fPkWNwgoTNAcRmUMhGGI1atX4x/9F9Afd2Il/28AwFu4D1MgJFYd+ChBNI+Z6xwtpjGgkkYAgNX8d/DQBgBYiZ9jGCsBAA/0j07bnSAI4t3A2V0/1e/f554NABhOaxjgbwAAKliFWc6HAQAumJ53iA8CAGIe4S08CABYjz/gzxBtcAO063kL6AEA7IbPIcJGsTxexSCWAQCm4xgsweUAgBqG9HJL+l9qyjEODAxg9uzZCMNwy3sHKEzQFMaUM1BEB0I2BT18XwCAjzJCrAUAzGIfRxvbAQBQYEIOymVAtydCCx3px/DnRDRqmc8uxSAXveL/pus3AIAhvILd2GcAAP38RbyJOwAAb+I+zMPnAQDr8CgAYCUexFqsAgDMw0ewXl7gL/PXxnI4BEEQTecj7C8BAPuybyOFGAylSFDj4l55qP/XAIAYHmIuqrsTVsMsRzTfSpCCSSOgw4nhsli8T2cAAN5O1qOXfwCAqg0X99h38Axm4hgAQFEOzHyUASQAgL2dr2KIrwEArOOP4/34N7E+CPnqAfwJR3SdAgB4gt/frNNBtAiUQEgQBEG0LFRa2BzGZAz87dp2fHdqASlEi9V2PgdcWp0pInhMrM6Rnq0Ci+Aw8XmPtxYuOwgAsDZ5U6+zh70fADANH9HTdvUPwPRkfwDAHH4u1vLHAAD7sasBADvyR7U1ux7PYiHuBQAcz76JdfgdACCR1vI0HIYahDUcYj12wEL5+TBWQ4Q7VuP/sBOOAAA8wo0LjyAIop6vumKkHqGG9emLAIB1EPeouewvsS/7tp73vaUBAIDLhpBwESYNEzGtx38WCe+W06ZjMBEjf5+FiLhoupXCAbi4r3rSQ7CDOwUb0w4AQDufhR7pJdiIP2EDngIAdEPcP8tsFkpsit6fKc5OYn/SAOv5M5nj2sG6B7cSlDPQHMbsGahZZtQUtwc+E2cy4Y7+TE1zWQxHXsAA0FcQF2pPMh0bE3EBr0+q+vMSK4vlEKHddcW8bCfsBnEBV9ISAGC2ezCGk2MBACFnqPIaAGB37wtYHQvj461UGAXr8Qf0yos8wkDmWNTFX8R07c47hf0YADCEZXoaALyXfRUA8L106qjOE0EQ7w4+wf4LZXkPKrI+lJm4B8z0a+hJ9wYATEvEa5UPoc8T8fqik73fTCneDACIEtFeOkzmougtAQC4bIOer5ZORbsrwqAxb0OUCiOCy9CBw1IEMhQb8operovNR5HPFNvAejEvTCtrHwW4MrDQ48xEGxdh3U6+JwBgPX8Kgcw1ICYfFCYgCIIgWhdKIGwKYzYGLo8d/KMnRugJB3wZEig7Q3B5KTNv2XsdDsSo3WFVcLk51xlEkYsM1pJ0d1X4sF4XA0fAano97Z7IcFW5spVkJ/3ZLu03wnVEYk4YvwfzdvgSAKB/8AsAgD8PfwUr4hUAgE62I95OnxHzYgNi6SmYgoP0+hyYzNdu7AsAaGOz9LRv+imGpQfkreRZAMDruAEP8H8b6ZQRBNEiMMZwME4CAEyBSOjrwB7oYLsCANqcbrQ76ulRQ8CEZ7PDFfeNIu9EJD8uAgicP4vlgofAmLjnFYM/AADceC1imRQYuMvhu6LCKkpmIUxFAmC79yRq8VwA5r5XTTtRYGIjMWtDij4xHW+jnQlPQApzz1J4zIUn77EJB1wmvK++DF+UsRNiDI7thG0HUM5AcxiXZyBg4oryWApX5gxwOCg77wAAXPkDSXkJqjKmPXgAadoFQLjHFF18NgDAT9sQyy+l5CR6HR4zZS9dxf8S6+LdiC2DoLvjmwAAx+lHFO0BANih528AAMXgt+ga/BoA4I0aMMcTD/53kncwzIVuQg1voZvNBwAwiB9IGbN09m8b60HREdMdlqAsXW3zfLHMHP49XOKK8p1Opx3LYxGieAGX4/f83lGcUYIgtib7soOxCsJFvx8+r0udP4Dj9DxTIPKZVMm0IubiXpDwIphseaemMQAuM0Fohwk3PmNDcJz1cqq/yX3znHe0YcAQoaftLrG9RDzgh8IFGIz2AQAUnDYUUuHar6VdqMpBls8KDeuNeaINAJdB3299JgZxRfTpHDBi8kFhAoIgCKJ1oRbGTWFcxsBlkRjuXxEY/wqzzmh78CQAwGH9cNiwnu67Qlsg5V3aDaYosFB7HACgzTPiFw6TYhupSI7pavs2GBPVApwHSBOR0BMU+hEUXhALyV3raL8Rvb03AgB2GurCmvWiWqA3fg/WRiJxZiDdCZFMxGl3puh9TNg0a/8ieZwcLlPeELG/7e56tLniVA5ahvWB7D90QqKqfqBqBYLYunyUfRVVvAUAOin4MPZLrOW/ByD0Sw52rgMADPG3McBFhUAI4en02DwkEJ7KSjoIztrkukrw5I3Gka8pmNYOGEymgIeHAQCq8XvQHjwMAPC9ZXrfUi68pZx72osAAJ4jEggL/lNIuRETAoCi/4y+f8ZxG4ry3pQyH5DCbglE4nbZKSDNcYNzQCcTBpDhXdY4XytAYYLmQJ4BgiAIomXhmKAx0LQ9aW0mZAx8LTSLX13s13H+MBF5AG3BK6ZkhoWArJ21ceQIP+E+OrzXAABuXUlOW/AQACDwn9PTCsWVcr3Z9flyE+ricH1TR9re2Q/XPREA0D9wMTD8WQDATPcNvFMT8p8VLlbY5a1HmHaKfYOs9wXQ4a1EJKerhMiUB5hSECP/9nQH+FI74c34LRxaOAkAsCH+KwDABex5rMBNAIBd2Gfx76nJfWg1FrDzM/8/xH+8jfaEIASfYCKvqIrVSKTM7oe8yxHK33UsPZhD6QbsF4hR+2B6nJZNB4AdHVFyvJELb0KCEL2u8ErWj7IdmR+gy/44kMj3HEAtFeXSRccsE8XzAACMRXCZTH5O5yLhIpnaZRsRJbvK7XWh4IuEw0B6XMEDeDIxsRwdjMFI3G8Qz0SVq3wEV2/Ps+6R3HpN6x6DHgqIZcL3F90h/L+kDcTkoWmegb+v7oAfdTwAADr5pR6VTVvwnhPJhYCuNvDYVMTSHTal8BO4rvwhJtO1MAek4eC6q5BKd7zjAn5Brd9sy5O/CeYYYyCOgDbxHEeh+F2UBn4NAHi7/xrs2P4dAMBwdDAAYCh6n15Xb+FRJKnYh5h3o7sg3H1xasQ8FFPbr0CPNHp2ig7AUChEjvacJdYb1mZhYOhzAIC3hrtxy9R/AgAsG/hnLIuFBOhN/Ojc87c9cDT7IgCgjJ0xn/0zACPw9FlnOXzppiyyTtRkMlOEIbTLkMtVCXUxI5rDBex5AEAq7yEJqljQtjMAIEw+gEi64OM0wrwpQtI8SaaLV96NSiT6qvT5v0OYiMTjWjwXg7H4XbdzkaVfSx1tBMwoPItY3o9qSS9CLqsIHHGtV9M2gJsbUSpDBtW0D4FMnA7c5WIf0l4wK4zK5brANmaOsxoeKpbzlgIAPHeF/sx3X0FZ3kvjtA2JTCbkch9SsEwIQ8Fg+h44MqkwAYOLsjw/LZRISDkDTYHCBARBEETLQjkDzaGpxsAFG48EAD3arQ8NcFnPqhIBAcCTGgGBuyKTQFMu/aeYN9o9U0YIAJ5fAZNuN8cxX6bjADKPz3zuGY+BVwDSWK0DcD2RbFgqHYWhIWF9T+0WZYhRNAuV2kcBAP2Vs9DT9l0AQBjP1y48RVfpBp3kCAAdHb8Qr/gFeuvOUd8uK7FDJLYxo/9reGv1PwIA9trhw+he/3MAYoQNAM/zr+vltofEw7PZbzAHItxRYN16epmJo2Rw4MqyKY4UkXTTFtCl5/2c8wYGuDjvr+L7AIDH+B1bfN+J1mVnJu4XH8S/oQPvBZC9/j7cJcKHSToVkazbB4DdeveT02cgisVy3V2iU18czUIpEp9Xw4NR8h8BIJKe1cg/jEW4s9dfCddprL/vCn6vE/mSVHg1u4sPo1+O5CtpFxLpGYjSIjaEhwAAyp5YpuS9gOFINRyqgUm11jjdQW/D4yV4jiiB3lj7C7Gc/6QOE9gE7lok0ktQ5WKEn8KEMGw4jGx8Yj0MW8ojQDQVxvnm7aKBgQF0dXWhv78fnZ2do175Lb1fQprjSufw9QM+To38pcoNcN2VKJfuBCC0A5JY1NcyR7jPfL8fUq0YjouMYeBJ28OxzBxlDDgedNAsrgGJNAziCIiEp1sbC0ERSKX7KImBWnUXAEC57VUMD4n3USxCCUnag2Lwv3J/1sF1+/X+BNLzV5AJu0E7UJCnMK4CQ2+L92+vPlTfsJat/yEA4E/xf2MFbgUA7ICFCCFmnoKDUJI3yN18cR43JEWsTp4BALSzOVjJRW3yL3g2rj9admM7AwBm4XB4Uu6pC/uiJMVMZjpGxzyVtckhH9I6DQC0McCR6PBBiggbuHDvrpTH9hp+jQ1YJ46DG7EpYvKirr/34rNwVegJfVjYJq6/ovdHOI74nXH54JvafSF4aoTPOMTNICj06wef/k0nZhCRpl2IIqEZMlw9Hv21kwAAsdRFYUjQU7pJrtNHkgrjN0pmoa0guq4mqQiDhfF7tDHAkCCWA6CEe7r7oKq8Cpz12sgYjudoTZXAWQ9ParaEaV9mkAQAvvMWomSmPA8v6s83hgcjkaEGtd1aWtT5EmL/IffBvFe5AxxcGwMOTJLDlcnYx4zjfWaMZxsPLQDaJzCsHYyBBQ9hi+5rK0BhAoIgCKJ1ITniprBFjYEz116l398y9Z+QcpOdqhJnAjmKVpLCgEiUYTIpzWEAvJXmPURCYGp5vpQNyxlMYqEVLnCsTF7mms+VZwAAApmEmKpkXA4Uy2Yd5eRVAEBtGCi3y/cVUQucJMY16br9OlRhb1ftj2sVVLgB0CbjCEH5txhc/1uxD9LlWBr8BnrDDwIA+vmLKDMRhkkwjG4mvBOpHN7s0fkd7C5HMk8PmOYkn3WWYx1/HAAwACGfvAv7rHbVr8VibIBws/bhWFQh3I+74FQAotZ6VyYSHovYAT4zCYCuvHyinNF8hCEkGJb7mKCCVfoz5WmYBpEoORUfwircI46D7Y4X+EsgJg/KCzAfX9QJbHtDNAaLsAHvdT4DAOjxQkBeqz1d/6A9A8rzxzngumYUzZzsiFpMFC+OI7wDAMDTjkwtf9F9WWybid+1wwa1bHBb4Zd63iA1OiQd5ev0usrSM7Cx9hdwZTJglM5ATY7mVTXScDIdLJkmt5Ho6qUUBTjSo+o5Q6gmO8rjk8mIbEcUZehgfW0hfFl9xSz1wJAX5LryxQPynn8MTCsUcs5RctycuYh3K+QZIAiCIFoWamHcHLaaMXDmum/ill7RRCixLGqlSui6K+HKOBkA+IGw6h3H1PaqeL4zwl4zli0vBLI5BTb2CN1xjZdAxfhTK6aIVCQfAuI1CdW8Yh/DyqsIQ7M+T+0fy3oHAADcXHz2RRgOA1FOuHyKK3IqCun+GORrAAAdbDa6XNmKtPCQnre7/V8AAIcEv8VbG0XZ31vRjtivJM7xQPgxAMA7SUmX08xxjsTGVHhlNvAlmIpD5G6q+KaHiIuRx1x/Fx33DLmPQZl5VHZEfXSVO4i5ORE+ZBMqqf6mKMkRlyvjmxvwDHbCWQCAaVityxcH8Cd0Y+/Msr/mVzaeJKKl2Jvtj11gclneC6H3EWED5rK/zMyboIZhLjwAvcxH0fujmDeaD98XvQWCwOToqHtFEgNJJPIH/KCiPQLqd845MvkFidRGiZJdMwl8ABC4r+myPgD6PuU678Cx7lmAyFPwvVcAAKX0SYTxe8R77//MtuR1z7ivk/vUSB4AkAIOEzeRMO6EIxVPE+6pjaCWCs+ggxTVuE8vylh2zD+W51zAmN6f+vvodg2FCZrCVvUMqLDBbdMuQJJsAAAdOnAB/cPy/Je0C88LTBhA9fdIY2TEhuyHvXrI513M9nw8FUaAQhkYKpnWK+SXnDBmtpFWzfRATeMmVOFbv+9YPugd1+y7vT+FDnOj6k6/K/YFPryhi8X+xH2ANKJSJCg4wgWvNB2K/u/1usqlOzFDVjd0h4ejGgvZ5V2nHg4A6Bn6Kt6pHgUAWJ9A91/3k4OwITVSqQAw1dlThwNq3EVf8LTYH+dtVJPdAAAbI1Fd0eNwDCViXTHvQSRPZgebjn7+pjwm48qc7Yls7j6+L15JbgcAzGDH67rxQf4K1uFRAMCOOA0A8HfOOnhMnOwKX48umUj5Dn8ZP0z3ALF9cST7W90JbxcId/88fF6HkABgDju7Ybl2R7jMY57AU7E9bNRSvq6zSifqbo4oLOnwgTLOa9VdkKZCxpw5GxF4IoQWxu/RGihKBChKZoFByZFHus5fSaID0OsCgCQVWgYu24CCt0RO69UCbCpRMGUBYllt5SJBIpNv7ez/BC4S2QTJyXm0J1bCbgIGxo3gkUInT3Jz2+QwHWfzdIjbnCEtCPe9UhV/V5nZMA/x7oLCBARBEETLQjoDzWGbGANnrPkRftb79wCMHLFNEPRrBUHAWPNaT8Azo+9CyUxnzIQS/BGE7rTHwE7us+tsrO2qdWUMZ27WoRIFvQCI5WGU262SJatkV4UZnLrupWobiXUalJci8F5ASTZsSngRRUeMIobSNmxU666eDACoRPuivSAUINuKd+p1dZRuwBRfrMOTI6kpuBJthV8BAEqD/4D+WLgcC6yMTtm33bHcFh1y8NHhvZ7Z95LUaVAqksPJLuhwTSKoSojakLiY4syS87KGcUibk2JXiOPYmK6FEoadxj6ELuwlDlO2m05QhSdLxrrYTD1qnOG8B191xclUMrLD/M9g8hJfjV/hXv5PILY8+7KD8T6Ic70r+xwKEN4i1SSol+2ty1EBIOHie+v12q1p6p0LV14wQ0kH1g2LZNZy8R79O7Nv5vr3lHTpBmauuwq1UHiNbD0QRaX60dzjYDJU5jpGEVCURYuQguOsB5cje9tL4LD1atfBZDKw46zXCY9F/xkAwHD4IYSpcPGHaQ/apGe0lvQi4eZGkacTYPaRI7FuZpt6rrkjrKbN3dgwjXMHJfcNsZyzAcD26xkgY6A5bDPPwCfXXg0A+K8Z8ofIA52lG4Zd8AvZTGGboM6FXxTevMzDV8Gc/JwBxqANAjt2z+xpdsWCWodtOFj6Bcp44RxolyHHNBa5AACQRGYZL8dQ4Tx/PwuucIsmaTeGYuGW73DXI5U3IZVhDIg+54Comy4X7tPThyvCxV4IHtXTKjLjuew/A98VLvwgPBCDTPZjUAYWgKo8P8W0E76s+kh5CR4TNy9H9qRo95YilhKwYdILzxFGQol3oCa/sG7PurHKfQ95Eb78oousE64VPy0xkVXtoDGzOUINjuy45oIhlW5UX1YrdDAjDjULp+J0JnQN3sFj6MefAABtEDf2DfJ/AHiG/65hW8Tm+RQTIaQ9cImeNt15j36vwkYJYrQ7omrAA8/EubtlvF1Jkw8l0xBKN3nZGYInXe3D1RPghkKwR8XzXXcVXFl5lCQztOs+it+npcy5fDjb1QOOM4hIGsRKs8DGd60eK3J++UnjvN7zSJTwUQKk8losF+9BLHsSVEKj0TGl+DMAQJjsgSEpgtQZPKl1BGppL0JVXSQNbYeFiFMp4MbbtLs/5AUtcqTOaP0tRT34laEj5hWPgTbvJSSq4otbvQ2c9Q3HuV1BOQNNgcIEBEEQBLGdc/fdd495maOOOgqlUmnzM2I7MAZOXSWaBd0x8/1wHKFC57r9eiTtuCbRz7X2tmgJRdkj6rzRtRr5530GZKsT1Lx5XgYxQ+Mk5pjRvmclDRZ7zDZDky+FRCUT+iZs4BXMPCrhpyjd/oBQYhTuOqCW7IyC+5pYl2ygFFoVGg4bRFIVAga++woCT/Rn3zj8KXEIdaMf3SDFossVSU5h3WdqRMHhIayTibbVJF1WhSt7wLe7MdrkpcYQ65FIyROJWEHajpocxfmsgGoqTmLME92XvccRI6Uqr+kGSD4rgssvpJbm503bCYtFiHPUg0PQjt0BABsgRrO74DxE2ABAJL2txRMAgG68V+siFOTyFG5o5Cx2L9qlJ6YL87VewDAfRpmJ9zr0xD1EVmyvJMMHgdWttCxH+w4LUUuMqHcKcW2E8Z4oBWsy+1ALPwA3ERobjrMOqbweE+u6VGEC13lHT1e6HgDgOW9qDZRU6nYkaS+CQCQCuu5qLbHuOO9oD4Mnq6IYC9HWLvRC7JK3JCmhWBRhNZlPCc/5s66s6m77NvzqSQCgGyiJ8/AqWGx0QwChg8DlzbCW7Iyq1CpwkeiQgq0v4DGlKpj9jUwt/iLzf5z2oU3eK5K0V98nUl7Cjd0/AgCcu+ECbHdMMEzQKp6Bk046aUzzM8awbNky7LLLLqOaf5sbA4qPv/kH3D1buGwZTImQ/fxWrnjbze76lhyxa9zxLCcEAGQvGjsEoe9Njlnv5mS6VejAfg55RSM3nCaA32beA1YeAoBoGBl55M3RURASw350MCLr4Q8AnmMyq5O0W7v2krQX1VDKqjJxcgr+H5AkIoaf8G5989tx6uEYGPwCAGB95Ry5spk6r8MWNQGAVBoKqWVcpFAx1ATccjUWXSFm5MpMarFtcXLa/GfAowMAAGEyHVM8ue+8qEuuavIcd7k+hqUgS2p9ud1ugooKncgvJOJAwDrkvBFidOv5O6QxoIyCClZiKkQb62G8AU9K4FbxFnoh3LqBjH2fxx7TGfIdbB7W86cAiLyEydbG+SOyFHAHLITPxfdSYD3aCHPhN2jgM8bgWA+rWN2G0k448hr15bSuwq8QyRh9NdlN9wAYCg9BKkNS6lp23begLtGUt+vqpCTtQSF4PLPfcTwPpcL/6HlrMuSQJLNQCkSfgjCW1SlWfxXfXaZlju0KgkIg82esO2rJ2BiIo4ouHQ58UWZYKDxiJJXTEkpFYUSE4S4IVd+E6INw2b5iuuyDkPJ2bdQU3ZcRSAGiarITKtJwShsCBEDB6UdZhmECmQ8AAG3Fe/R5CuP5Zn5ftEyOk53BeWNIZHthMukMrF69GtOmTdv8jAA6OjrGtO4RxsoEQRAEQWwvnHvuuaN2+QPA2WefPaZeC9uNZwDIyvoGjkgEst31qg7f45Y7nmUz8esZKTSwOVGNjLVYV02gsD0CyksQWeEAv814BhSVddn9VetwfCBws/tcbKugVBVWu7thte5rXi7cj+Ga0AngenReQFkKsojdFJZ8Kfitzm5WGc9J2oPO9u8AkB0g5fFFYQmzdhJd3YpvPQQAWLvxmxiMhPCPwyKd/VxwX9NhCyazwZPUXHgOM0IvLgZ1QpLrbNBNWBwmRkVx2ocu2fDFCw9BLTVu4R0KogFUNRYu6I3JTExxxUlOuK+TFOO0gKm+cNUGsrFVNS2gxsVIs8ymIlES17ygtQxKEKOtAnrhSBd0GTvpKoYa3kIIsb9tTPSjZ3DhwbinZjnHAgCm8ANwFrsXADAE0XlyByzERpmcGGFg3I2jtjcOYuL6i2Cy81UTq5gPooOJZLkYNdTS7I+QscaKEoUnR7y+s6bhMwc1JLBkg32R6Om7Qh485aYpmu8ug+sKCWzXWaWT+tTo2/df0A3QHDaI3h1uBACEFejKA1eOuMN4H6hkwSiZB18m9SrRI5vCCFVMucfq9uvGauAVxNKr6Qevau+DqFzYV8zP8nUVPEc0MPPSHriym6MvxcFUpQ8gQiwqlOe7q7Rmg6qIKASP62TMJO3TegnF4DHdRA3YPsMEWzOB8JFHHsG//uu/4qmnnsKqVatwxx13ZFz4bISHy1VXXYV/+Id/AAAsWLAADz/8cObzM844A7feeuuI273++uvHtJ/f//73xzQ/eQYIgiCIlkWVFk7kbywMDQ1hn332wbXXXpv7+apVqzJ/P/7xj8EYwymnnJKZ74ILLsjM98Mf/nC8p6ApbFeegY+/+QcAwC/n1JX35BladtmfHFHr8r1RwHnWO2A3OwFE7M+WDc7kINRfPNw0QHKt0Fr1HZMgmFr7NkKum06KVOsIh4Ci9Cz4hSeQxCKprTIEuFIFbWD402KGxCTwTWn7N73OJJ2uR0ylQCRrMmejHnn07lSBq5o0RRUMiRxOdHSKEdRw9XeZJEVV2sV5EYxZEowAwBI4aGwO47Ih3asdADwZr1SJja67Qpd8dbd/S9d9h/GeOi7c3iZ0EbqTnTEcvR8AMBjtoRu2uN6AjqO2++I6CpOdsC4UyVgbkhQdUgY5Qk2PaGtcHM8U5z0IVWIiOhBxUYLVyd6ja+LtZMQ2ZpLSIjn6amPTdKvmdghPRsoTTGMfkfMN4fOyD32ZTcV0P5TziJ9hJS3h69H2qwOrZKIH8Qo+xP4LADAsG1DV+DuZPIqQm3K0GOLCLsr8DRcuYpnM5nEHsRyTMOaikmTr2duCx7SKn+usQxqJ2H7eKNlh6zP1/krS3OOvIk1fzczr+UBQEN5H1zO/eyFHLuL/1epB1hKR3IbxhDCEWtlQ3R+SGGg3l4b2ZtoaKKr8MU66gLBfbzdQqRMJkCbCy1UMfotK9BE5vV3vg2pbzFgMrmSKWQLH+p0p2n2prmgl/LpsLQKpE+J5RnVUSzw7T2jPSZzMQbn0nwCAO2aKdal79WTk2GOPxbHHHjvi5319fZn/77rrLixcuLAhka9cLjfMO1qq1SquueYaLF68GGvWrEFa91B5+umnx7zO7coYUNha4oB5UKuHPrNKztMo37IbqYJgpPBA3jrU9riTXZ82OizRExUe4AXA8szpkECeAWAnP9pGhHoflLOJhcV2s71h+cxVrvaC149AdlsDgPb2e/R242hWZrvldiPqFA4DylPZORcoy9yUylp1Dr4G/21xIx2qHYdQCa7YNyTdx6CxdlnhK5ctW4t6fHcZnEBYIUkyA20l8aDx4/dpzfhEJg367jIUZcIi555OXvSdVXBlNYpdLdETPCPOT7w7BhPxMCowByFXHeJkJznO0StP/MbUQyR7LES8pqVxE/kA49bFUnBcOFK+tsaNIdQlO/ElLEIkp7exaQhk18c2B4AMWwQy3BI4G/Cdovg8StvQFfxRHnsnqjI8szYSxzaeHvPj4Xj2TQDAQd4/4hDvXwEAIU9RScVDce+SOBf90fvRn4jzM8D/jG5HhAkinmMcZtIHjW3NwcBY9odSifZFwetqWEct2RFOTXS97CzdJtbrvIVIhpM4D+C44npwnP7G3731e/cDIzGehMDGAREmiOXDM03b4ciE3DTt0VVPNr5cvmCNYxzfGAN2uFP1RGAIM/cnJVxWfy8qek/J+UW4LuUllHyTEKkk3dXvERA6HwAQOOv19LL/DGqJeCBV4v0gi30wpeP/J9ZviSu5LuBJo8VPVgrDBUCh+ETDsW9zmhQmGBgYyEwuFAooFAo5C4yet956C/feey9uvPHGhs9uvvlm3HTTTZg+fTqOPfZYXHbZZaNO+jv//PNx//3349RTT8X73//+EUMTY2G7NAYIgiAIYjRwTFCBUL7OltVsissuuwyLFi0a/4oB3Hjjjejo6MDJJ5+cmX7WWWdh7ty56Ovrw5IlS/DVr34Vzz77LO6///5Rrffee+/Fr371Kxx66KET2j+b7dIYOGYZx/17CEvHsUblsZV4p0bnmRJA1ugJOGixuUqeWGisp/qmRfWehBE9CJaFn1pWv930SI0C7I6FRTm4SSPzuZ2kmERmHao8KYlMGaVvravUCfRAKgzKQcpg5RNgsmyvVLpHz1soGbekYx1Tz87iNegCpMoxeAp4Mizh5OQqBe4L2iWZ8hI86aLnOrlqlq67Lriv6ulJ2oEkFRZvMXjKHLMc7Ufx++BBuCpVvTYABN7zcGSZVxSJkqc42QmxLLEqeMsRpyZpTHkEVBjCYf1iBATAZ/3okHLFoVVXvoMsCUt5CUOx8BL0eIMYkl6EFAVdEtfjmpGXajATchfdrhhiVdIyatJLoMoe25wCEnlOIp4gkBdYwkXSIwA4MgnSYZFuYlPyTelX4L6Bkv+8ODYm3OSXuB2oSFf86/wnWAHR6GkAq/GabAo1XvZgotxyT1yCOewceZyp3vedS/+LQOpcqHPeVbgTNVmWNhB+EP2JOsftGE7F8SXS1Z7AlZ4RgHOG2C4zlOckhPhei+4wKrLEz2VDuomQmFeMfquhCBv53jLdUCiK94AXyXBUwYxmK1Vx8wz8p1Eoie9zeND6rXJTJjhcPQGAaE6kOhVyHqAWfkhs352Dorw+40h6yarGy+D7QM1S+rXvEQrlMYxCc8+JokZPCGBK/exSx7bSz3QZYmIl3nqODHm5b+rfacq7dJiD86Iuawzj98l5l2lvSpJU4Kr7kQsEXr8+P+9WVqxYkcm+n6hXAAB+/OMf46yzzkKxmM0sveACk4g5f/58zJs3DwceeCCefvpp7L///ptd74477jjm0sHNsV0aAwBw1Aviqrt/D9bwI0oT4EOPjf2qPGgxNwZBndteGQE6tm8ZFszJ1xxQD+1CR35FQ9t08z5TZSBdiUktaxjE9TkPdQZJvQEjXlUc0WyA1RlFQcFMB0Q3xVCGPt0S4KtchTZAFgagnNj7+P8BAN5ac4V2T8bpzIba47bgIYSykyHnZXSWRPZrFO8KrmROARRl22UlCgMrzhsnc7RB4PkrkSYl+V70V+AIEHAZQ2Yb9PZS3mUZH716H3xnlXzvoeS8qLeT8G45rzj4dv8P8B3hQh2Kd9fzdVrSz4re0n8hkTXmQ9G+WjCpy1uFqtTDj+S5SQC0OaGeZkd0XVlTz7XUcgQmBWJi3g1HtYN2gDASLuuClI7erehhvTSQetN/xMzk4wCA1filrv3fGX8FQFRBMLmN6e5sDEgX/7r0jxiACEV0Yi8twDQLRqtfdYVsdzjaZKhHnVtAiFrVU3T/jEQ+tAfTNrQ7BXmcAiWLAwAFpwqkMjTCHf0dKqGhSjpFd+gsun9GJL8vhgRcam2EteMBAEG0Bp6Uy/bdlagOCe2KUviIdvP7ngilDVVOB8edYrmCsXzfWf/XGREiQPUVkJoFiYnxJslsxFLkyE1e0NNr8rdVf0+I5P/MEYYDT0umj0lqaZ0ghKNyEbBEhyWqodDBKAb/qx/aPO3QugUpn4LB8DDUo84ps+4RgfuGllh2mWnDrPo5MITgKgzoWoMleQ/5710Yjn11+7AMmtWboLOzc0yleJvjf//3f/Hiiy/itttu2+y8+++/P3zfx7Jly0ZlDHz729/Gl7/8ZfzgBz/AnDlzNjv/aNhujQGCIAiC2CzbaW+C6667DgcccAD22Wefzc77/PPPI4oizJgxY7PzAsCBBx6IarWKXXbZBeVyGb6fHZi98847Iyw5Mtu9MaA8BM1ChQ3+cJjxOETVrEtfoS17Zj53fNP9y64w6JY9cZKqCR/wBCjJgUTJMZ/LBGI4HhBLz7NdCaG8EPXhh00pZXnuaygExh2qEgTzjssrZrsnRipvJqepU23Q7ENvz9cwPHyo3N7relSkRkspn6L1ApLUjKB87xWd0JgkQByJEbiSba0OZ3vLKxigs7XjJNDb1TKziUmMdFi/dhGrkRCHr93KShMBEKNGj0u3r5W52VW8GQBQTuahI95T7Fu8KzqkLHSs5W2n6mTF2VNOQUU2iBqqHY3O4B25nPAuhOk07S0oOFV4XMkym+tahQaUV0Ch1ByTZDpcOUqrSGlal1X1iLmadqBXNgTyeBlT+EGZ9fQ4cxBYca82Jka+U7wPYmMqqi2G+Ntoh1j3AH9Rbj9Gvww5dKEPQ1L2tppMRbvsYJnKyo7AWZ3JtOeyQiBgCWKpMdHmGPVJez5PZsAn3NchA/syjKTngPPZWg0z5m1g8gfhWzLGw/J7C9JeFDyh9bCx9hdwmbiu1PfmOmsxtOEvxPLuK3ClroHrrMeg9DSoxDvlZgeg5YMBIE2nwHXekvOKY3fClVprYHDDSPLo4pp03EquIiqzwlGOW0GaimO2VRSDQCoX8v5MWEF7CLna326E2BkA0F74FTzpKYtT89BR+iW+94r2gKTpVHAIb5zD+02I0VJ73V7Y2l0LBwcH8fLLJll7+fLleOaZZ9DT04OddhIevIGBAfznf/4nvv3tbzcs/8orr+Dmm2/Gcccdh97eXixduhSXXHIJ9ttvv1HnAHzyk5/En//8Z1xxxRWYPn06JRBOhPc/bK6A3x3MdNa+qlSwz62d9W/TIb0zjg8k8vfbNse8T+vd/nL9KkafxpuXwlSGheOafXMcU3LYGQt98YH+U/RDOUm6kMbiZuGVGssmowrgy9BrNAi4VilUJA2VqpUwrQwKvwCUUvEAj6JZaGsT72OVO5F2IY7EQ7BcvkfHHMHMcbZ3AXEkqhNUWMTzX4UrhWOiqEsbBn6wUodKAtl+OU364fsyyzl8Hm54OACgFu2nKyuUMYAUSNAhl38tYyyUZQtnfYxsULem7SjdAF/eIEv+jjqrXGWtp2k7fN90OSwFD8nDjFCNDs6st+i+oYV0amkvAkfkF6SZFrWunPcVXZGR8nYt9+y5axv6QCS8qOctOCkiaUfs4OyMDt44uuj2VsnjNB3vhpKp6JCxLpf3YSBdnVmmzzkUHY5Kj4+wgwzvDMd76q6C5uHcBcgyUHBXV0iwtBNBXudR6bbncPW6WMrh1BlECfcwvXSn3O5eqCY76s/MtsXySVKEK8tdq8mOptLEfRPV+D1yGfGwFOJXYh8GasdrmWyHDVrfgcr/6NIyva6zBpHsPshYhGqY/b7jZA5qFRPmCqumdXJ9FUIczdLTOEz4wGFWJVPOg8rzTZlktbqHzmFQQmTqmAHRcdAO5RQDIdSUpNORJFYcEyKc57nK6H5Di8DxtANQeUcqXDBpnxzAk08+iYULF+r/v/AFIeF+7rnn4oYbbgAA3HrrreCc45Of/GTD8kEQ4H/+53/wve99D4ODg5g9ezaOP/54XHbZZXDd0VlZjz32GH73u9+NyuswWibxV0oQBEG0OlvbM7BgwYJMeXEef/M3f4O/+Zu/yf1s9uzZDeqDY2WPPfZApdJYtjsRyBgAcPDvOH57kBi+2E2PbG+AqgZwC9CuMum9Q3EGtHCPjdcFxNLrzXO8BLZXINNsI6eBUppmqwFU/XJiZ6Sp7br9GTeeV7dv9v9uYMIWSSg8Cfa+uV7WJdjeLV6jcKXedqBCIEk/XNeEKpT3grHsD64gt6FCGXEE08Sl0A/wHEEZx7zasq3Ki5Cm7RkXrj5WnQVeRiCzsd2c/uzl8i8yHhSVgR5HuzQ0pnGcdWK0BCngJN977goUZKKkqbCYAVe6sX0EmWTEQPantwV0bO9DlIgRaC3eHe2BuHkMRjKTPe1CIt3vJacfBSbla3kAyIz8Du1ururRc6f3B6h8xaLbi2FZd+6kZQQyWbBDytAO835Adh+cUb4RYTLXnC8lW6sSMXkbfOkyT9JupFaVh+3GB7INr+qbXyl2bL9Cv6/For5+p96DsX5ATO+vHaeTCbvluQmT2YjU9YB2HWapxLs3bCdOulFLdjYT5LlMWJv2EqgEO4f16w6G6hXIJuQpfYNC8I6W9w2lrDEgPT2ywoJJfQnGQl0lA0DLJzv+SiTW7urQmVx+cOivtehY4D2PQvComF49US+jhMIK7jJ4UuiqGPxWf14oPKKTBYet5fTyvAceTMhOHbfjmmv1f/YUP5gjlm7jRMLtNGdgS/Iv//IvuOSSS3D55Zdjr732asgZGE8iJBkDBEEQBNFCfPSjouLniCOOyEznnIMxhiTJN7I3BRkDkkOfEObh7w4xw+9AVhjZHoLCVMBTlUfWdL/XzOvIBKLIGuAmNfO5r5ZPzag7qlhqhSpPwDGtnJMQ0Kqjjon5myKoXyAKjdylaoDi5nzDXtEsDwitAUV9noOtgOgXjMcgKJrRvK3/YBuoagRfLGdVI9UIXOXuuZ5RcIvCbGtordymZKJdwFHnJDYjeI4AtVAkzikdAtcxaocqXwAAisGDmbirmcfsX2qNFlRymOuLkVuSzNCjOKWrAIhe9+r/ooxne87OugWvyzboz1NeRCobIyl1Rof1I4rFd9jV8S09cguj/TBcOwYAMEWWaw7VjkZFxsFj3qZj8C4vwpelfErmuOy9nmkdrRLjXGcDPFlmWENZX86+/LLKsBLTnH5M7/grfczDFaGzXi6JnJVK9aOZfIk2mTMQpdN17N2X30dilZkCQJxICWdvNcr+M5nPZu54uR4lDw+a/ZnWvkgnvsUyWXWHzq+gWhOldcPRwXrkX/Je0u9TGM+MOg+2doGN+a66dFMkx1mvE1fjdKYu61MjdYZQSyJ3dr+KqjztUWQ0OPS1Y7VAthNumbMRrhyB16oHIeEqYdbMo0bzVXaYyZEBtOy38moAQKkoZMh9/wX920uSLr0f5eLdYh/j9+nmRC4GdYIwYPQ/tBcMK3PP2bZga4cJtgcWL17c9HWSMVDHwY+ZagP1YPRKJmEm2gj4MuGuIPNv3A4grZj3irRqwgSx9CiOlPTpeI11yWliDAPAfG4nE9qomxCzXOm2MaBFTYaMMEpgyae6JRP6UGGPYhdQ2SCn+dBPzCQ0hox6qDue2V+/kJ9xzKzkKC3O5GWTHNV0O5dGTUvTbKWEkXn9rXa/hjKjPEmnZYwAVWseJfP0uVLJiMwWt0psV7D5UtT6XXeVvimKZcWXzK2buw5F8EAnsqXJLLQF4kccpzN03wWF56wy+xjN1zrxZf8XaG8TD13VUa+t+AsMVcUDeah2tE4w9HOkclMUMlLRNsp97iCFih+oahmHuZkKgFJZJpF5QLEoultWZYJcd+d3MTQsEtWGqiegJkMcbf7j+jiVlHXRfRkJl6JOvF1XUzhsWCe7dbSLJjBJDHTLdgVTnH70Dl4IABhYC3iekHitSVldW3Z7h6lfQ7UiDKu3+q/T/SoiGUrivCiSHiEqIZR8NkOs36tugLZRCQCuK3sauG/paW0lIQJWKJvrM4mBDnmvCKsrtfGsjJtS+0pENbGuyvBB2iBgiQk9Oc46JLGs1pGhF1uzI0z20IJetgR44IpKimKQ7SGgbj+u2681PNQ17nnL4KSymoC3Z8SNdIjMVaJFm09+3lpMRmPgsMMa9SQmCnUtJAiCIIjtnOeee66hIdGmeP755xHH+YOAPMgzMALvf5jj/441w3g1kvbaGuct7gowlQy3BnCs0baynhPpGai+nW1aopsvOUZ6WHkkYhg3fZpmNQPUdDuBUMkOu152VK3RdczWMkXxZ+8rYMIa9r5GVcCX87qBUWBUpWNpnG3MYo/21Znk3OyHLbus8HwgtTrAFcvmvXpVv4esZ8CgerLHiSkXBKxacKsePpShFddS1xP7PxX16JJHfyVSKeXqpKXMqEl5D2y3byBHXoG3VI/u2ou3a5eskmVO0yn68yjeDa6sCXecfu2tUO5aVfYFCN0EO/yg6uvViNEOZdh4znqUZOdIDhdMHnMiPQQRd3XjoIHa8Sj1C+ndGbPvQfsUdZ6E8l5YAQploSURrd5Vb4PDR1vhLrGfsjwvlOENM4+5IH25P0oVsGw5T5gLFOShdAIY3iDed+u80ZWIquI3UB0CuntFKKhYXoiNAyfIlUjvUXggIukGD5O58OVon/Oy0ang5vZoRuPrtTRxkk5H4D2LesomdxJVmTtZKBmPgbpmk8R47oLCE4gik3CoOxtG2U539jHofbNu4yo8kPBeuY9TjEqiuwrMSgDUpYxK0zwFuCzFddggUhWE5IEOUahrEjD3i/t2ZzjmpW04vJ4kCYT77bcfVq9ejR122GFU8x988MF45plnGroljgQZA5tgv/8WV8mSUyyjoAR40kPsW88LTynrukD4ZzNdhRfsB206grGmHsC220pl/ocV4/p3UvOgtV3qYSjdsTVTTeDYhofalySbB+GoFsY1IYpkk0YjuAO5tQ65v34ZYMqIsDQU7DBApvW0ffVZ7VvVMac57aLbpopOi4B41WGQKgCYCgAACJx1cF2py5726CxvAOYGad3cdF01Av1Qd9x11ntTyqMkntOkgiRRN+5Z2rWvohOcB/qm6nuv6Bgz5wGU574YPCgWid+HUOrwJ8ksDFVEjbIbrtSCM7XwA3oflFAOsKd2aefhsMHMA0PFlTn3GrpLAoArQwdKEwEAOgv3oly+p2HegkxadlxgaIOct+O7CGsif8M2ikpSvCnwXtS5EVFiDIcwmY6BytnZDfAb0a7OU5f57sFNtYqi2GlyYTzf/F7aOgFA7PvGjcfo+X2rda8yAOyQgC237Xuqh0U7EiWjDSCMRZ03r4prizm/1QZAsRMoy3tENAxI9Wn9+2exMXwdBwhkT4QognbhB8GrWnrYbHMP+O4yvY8xjPWhqmdUfw4xkxKvmqH7ETBmKgRUSML+fYxkQKoKF45AG6ZpTmfKrclkCRNwznHppZeiXC5vfmYAYZijkb8JyBggCIIgiO2cj3zkI3jxxRc3P6Pk4IMPRqmUnxybBxkDo0Sr9w2Ihj4AEKr8IdeECZwyUJBeGW8qIIXA4MkR1NBLQL9RssxIFyt0Bj+vkym2sujrrdk0Ma5t13IF2u56m0jmhTmeCRt47SYMorwUflmEB9Q+qn1zXKO9oKSNM2GGOm0BHRpw6zwCch+0roFrzkVQV4Wg9qEkB0KOA1SsEnZPVVuwV+Wxl+AH5r1y5yfJDJ30l0hZ1jSdqqfZGeE87QDkdEe6Vf3AhCrKHeZ9WFmJWlWO6FSnvFp2lKUSFsXIVeybClV43jI96kySWbpPfRLP1+EDlWA4XDvOnBP4erTvu6/oER9zxMWT8pL+3IaxGK6sJQ+cNUik4qF6DZz1aLOy+1XIptBhEkzztDFsSqV7tKdGjSrt43BSc626rAJXnWsrlFOzBp5aRtu6vlT4LInMtR60A54aiadC+VIcg0j0qwzeh8qw8F747quIpN5Cmk7RCY+qLt/el8BbqhUpU25G5MqNHtZm6aTAIrJdC+up/x2r34XLAcAcdLHYL1+FdyMM70GtpmTBV8CNhDcjTPbQlRGqWRdDpMNRPAr0b8B1VplmR9zoaHCr2kLDwoavl6cdSJla17atLJgsnoGHHnpoi66fjAGCIAiidZkkOQNbGjIGRsH8X3C8eK6wjT07XKNi5klWYTCWoVyemHi8Io1MTDyqZOP4DclwLNviWE9mVs6AnO75QJJYSXDWBa6TH+V2M+qEZZNACAA1WREXyhFNzQyKwJhZl9XfB+VpZh9rG+RyA2aEbyfApjk5DIxlGycxa9Sp5vGsfYylp6LQZdbNORDK6Sp5kqGiT4MfVJAmqoxwo84PUNrwKi9A70OmYVJd7I0ZXQTOzci0cxoQSYnQijxvQeEJVCui4Ust/KAuDXTcFxBID15Qkn0ZaiaRcXDorwCrhKy+5wFg6uNdZ10m1q0SKPVIPO3N1KLnkaLQkD9Q9F7RLavBQryzTsTbeXofumQqQCDzZ9wACKW3KYkBhyn9B6Dcpjwg4rVWOxSI3ysXfB2VeD+9TcdSYwREkp3a81oFcCzP1Oaa5eQll6ppjJnvHslOCPznAABR9F44ED8C5Y0pFB/PqAYqOFZbTbqE96dSOwLhWpGnMjx4n/ZIhBVRbjvS/tQ3FNMJt/a8yhPCTK5LkvRpL4vHV2mPQGYbqs25u96aZrwAdslsqvtEhPpzW3EzjySehbtni0TdE1esGHG+LcVk8QxsacgYGCWRvLnzFNoI0C7JHYBIGgDpsHFtV181dfvxBrMu9bkb1LnW5bwqNGDfzEaqKLET8zxfPIiSxNRe+4FJ9NPGgG8e6tFGExpgSVaACBCJT7G1bbWOoN1IF+vP2owBkIQmFOH6+UmIm7uZZ6oerH1XBllqyScXBoxLtqIkoC3RotQSePLQb7L+pVHAWJgxAPQ+INRZ13a4RVc0uFlDRc2jJJdtF7fvLssmg1lCSoBIhozkd18u3o2qbMIUxu/R0reJktu1hXLSqegs/39iG/4S3SyqIBMqa+FBWogpTnbKLKvq/QHohkoJrAOSuM6arOtenipmuejt4/KC7P+ALT+9ChF20x8XZJOqGnZBVVYcKINmuHIM0kRUMRSKFTDLCNOrl99FGtclxlpdSfMMA6M18bxOKC0Vf52Rms5DGRFJOkMnqDLd0dIkF8bRLti4fpXe9yG5Om2sOkZi3O5Q6nlAqqZzc03oxoEe4CRiH7zUJAq6znpUpBAT52bU4jiDcnuWZQ/kGjh6Xe4qHT7gCHRirPotcLuKxlmXH14gWgoyBgiCIIiWhTwDzYGMgVEy/xfiinnhL1nGpQ0A1RXCOwAAtTdgPAcOdLvi2BpkqJGtExjPQeIAvqrRt1UHR6kZsSktinpJ4mKPcb/bxxIPGQ9IzfLW2iN45b0IB02oQOkiOAHgq1IzH6iKKifhIbDliJ26VytpsB6vcZCql3OLQDpkfyCny2OLQzPNlnYGslKwDeu3wgLMqVgSrnLZihn58tR8R55vzpU9zQ1V++WpurQrTYB6z73jAr4vZWiTqXDYerk/jV2uCt5L+r3nvqbft3f2I6wKj8DQkOmNbq/DLi10mbwwHcCBpT0NIEpmohKJUsYSC+Ek4uJY/84JKFezZYa1Sl0ynNVuWzfbUuqW7ipTq58Y1cDAXQGXZdX+xPwVdRBmmmcl3Nkqm/LasD1RdsIqt7wUKmyUciHVK5YrwQ0qerqapq4XzoOM+9wk5AkPgWeVKwLmOqsMG+0ARRwZL4HrmlbgjmOpdFq/f6U1wSypUiVVrAhckZ2smjslaS+cRPxIfO9PuhzQbrDFdIJsPzxLrtiTichZL0xJnr8K0lSev6SUSVre6lDOQFMgY2CM7PFTjte+KH4xgYyVp1Ugkt5f5gFcPTD7zXtF2uiR09RbqJxnhXv0OvJc7g6QqF7jTPyY1U64KmQgvcPxMBDIh7ZbRKYfQUU+wFXVQOam6hsDwO58yLmZ13bt54YGPGOAqHXZ0qZJLbtcqGLv9jrkctEg9APCDlmonALUufWN2IuZWbntG3MGGkMD5rM64SNVTWA9S1UMOKyZhxXDq3rbqVPRoRz1ueeb98Xyq3Bqct9YaPQHtJxuGSWpT1AsPoBiyboZK6NIxpU9d4VezmH9OlvezhEouMuQOmJ6NRZhBt99U+v3Z09AqDvyDW4QD1G/AAwNzJLHvg5+oaKPSaHyLBynAo5H5D6u1vK+tej9prZfuajTdlNtEbya22uDWW53Pc1+bxmaOqRQd23q/eSVhpCC61bAZdUFrxOjUudYGQi2eJXj9GcenmFN9jSIZKyehUhTWdWS9GvDIIxMZYzjQvdbVNVCqgJGoYS1OG9DnM7MfOa7r+vzmz2mfqTymlf77vkmbOF6/cag9cxvnHnmelChsDSuYBx9cYgJ8tJLL+Ghhx7CmjVrGpQJv/71r495fWQMEARBEC3LZHQM/OhHP8JnP/tZ9Pb2oq+vD8watTDGyBjYWuz8/8Tl8/YPzRdQkvlQ8XogkZ7Xwk6ATCRHbY1ZXnkH0iTrHlcj9JFiWJnpyvWaM9JJ7ZGv3XFRegOKvUAgS6R5Yo3KQ6ErYBNZA0OeZpMQ648nYVbC5ChEyfSIjlnqi2ld5YScR43209iM1pkLDCmtB54fUtHHb527JDG11VxrM6zKJEepER1zK6ab4Sg6eeiERXtf1KjKAZil1mbrRqj5bPdwXga36k3vuSZrmyE0VSceUCiq9TZ2ZqxFB8B3hCs7SadlEs3Kwa8AAAVPZNbX4r0bmvTofZf7ppoBhaFJjOQIkMoLIHUaQyf135MKGXjOm1oi2DQDWp3pMGn/BnRIIDH/Zzw5St3Pg/aWqGuLW0+QODLflxdY34GVmKhc5jGyUtD16oCA0QUQ4QlxHqIa0Na5Ur8X2y2ZjH4m/hfHYa6RyHJYKa9Dks5AaiUsqgTBMN4xo5oIAGGyG9qkeqLnvg5P6lw4jqi2AQBuaRoUVHJuYr6n2NJv0OEfywPoFwBn9BL4TWcy5gx885vfxOWXX44vf/nLTVsnGQMTYIdPi6uo+jxDKJN62z4AxFIZtvY6dDtjVWLoWg//2jsmC5unJj6ufoQ8zbr+9UVrx2ath13K7OnihuQHQLFOWZQ5RiaZOUAi7wW1Daak0L5ha1EiKzTglQC/zewnIMsj7V4JKqcgNvtsfx7ZcsXqxu0boyiJGo85HKzLL5DHrKRw67GNJduVmefW1at0Nm3JMGa5mxPkVkWkOd8VYyZGnKaW61Utk1deKlFdEFWZWLH4QO58SQwkdTe3JJ1h2uc6a3SZYSl4JFeCVj2Ifd6mQwpxsjN8N6ftsyXOZBtYqneD3VNDvwYA12JDLyGpqfK8Nvgypm2Oe1XDgwiQ4SYr5ySPjAT4Jgxtx0UmpOTV5XKknonnB14/YtmbPIlnaaNIiUkB2eus2/LaK8NaiTeF1QriUBoLEXRoJY6MtLjr9uuQn22E6H1Lp+gQEAD93arqE8+S2/b9lQhU/5PEVAnZYT5lqPgFkxfj1EybcrsUMtNXhYoJtirr16/Haaed1tR1UtdCgiAIonXhTfhrMU477TT85je/aeo6yTPQBIrv4yjsIYYW4Wsjj1QAMUKxmwGpTOe4YskQq3nTkasEcqfL0Y0fmISeasW4/pQrvvq2kFUGsl0Yi1OMO14nEHIra9s39f42qmIiDQFuubztY9QVB3Zmt3UMOumPZ0fHdg05IEYjarHqQFZERp8TtW3WOCoVVOCksi+7HNm5/sqsC3oEed1NfZ7XWGmkdTKYkVVu5jsHfMgRur9SixWpevg42h3tHU9kty1f6xM3PWu0nVp18OKzN+TmAjDpGTAd6tZoAR7lmQCgm+TUo7wEdn2+55vEQUVUs0b5CeDIMIHrrNEub1W/zxEgkgmEDK9mrg0mo2Eq6Y1ZlQuOa0Y6UWMxhp4HqPNeuI2fA+ZGGcfGc8DYygZPQ7HNJIH6VlJr0G48f9Gwmd4uNYLimhH4KrcDYVV4H6IQ4Fy8DwLxGoZdWkuCuwF8/09ierR3g+iQw4Yy+gKx5SFRyazKE5amMNU3diIwz7/faM9WvfT4VmYyhgl22203XHrppfj973+PvfbaC76fdWf97d/+7ZjXScYAQRAEQbQQ//Ef/4H29nY8/PDDePjhhzOfMcbIGNiWMNFdBMHODLEcbQ8/b0Zpujbekv91S6LMz6xEvsra+QhmNJk36hyJTAtkW+ZX7kOaZMr+UVXtzK14vk5Is8r+wiErPyA1SYSelX9gKyIqD4nDLaW60DoObk2z0OWU1rTEilnm6jDkjNTtGneeNvaTz2yzbruZUY+aSY2OLC9DZsRk70POd1U/AlH5A+rLSOu+N9XeNo5M6WMh+D0Ao0cAiHitOqbYGgWrEXWSztCjfcZC+N7zchetIXvaAZeJEbrjSSXCpE8340nSKYBsfJSkM7S3Qanx2etyXVMmZ5NJPlN5IckMRFKa2JZMVsfZUL+uck9GyK3IeF8s5c20LiDquObaYU5djgzLvjquVf4ZmHkLRaP06Vl30lK3eHUDo/QJNEpru745jkK7ybGpbYSO7TuuucZU3L7c3o84FF6hMHwJtfCDAICe7n/F8LDQlqiFqoX0NCSy9HBw6BSdaxIE/SYHQXvXQiQy+zmO+nWbbtczUt92Sa1rXZ+HL9l2w2v79zre5VuN5cuXN32dZAw0GeZy1F40dySlP5DUGuf1O43bnTHjKlcPUb9oOgYC1oONG7em/TDS0qaOeV6lsZHnVTc3v2we6rG1/jTKPmj1MeW4UHkK/XDk1jLKuGnoqjjabGNmSQ8n2QRLtV77PGgjI84mNCmUAZDE2QeI1qW30G7jBLnGRR4j3YQy7mxlCFo3duaah7/ax4YEHvUw8gAfsn+BesA7/Zmae/3dRGio+U55u3YV2yED338BSSxq31PHhEy0TK0zCCSmM5/S6re7MNpGgFqOc/MwyxyOuv4yGhUv6Pe18ENQVoJyg7vuE+ZB7FkJbJuRsh4J9UBOoqyhnDGg68Ia3Er29IuAJ/chsn/TluCV+k3ZMtyZa1Il8cUAs8JqtpEQDjfOr9Yb1oDuPjWtH2FVdGKsVYDe6aLT4mC/eK1WD9XGluuu1uc1Tcz1bwtweaqbZwyEVrhNzytDC7UEcCLTxXNbMhnDBDZcHgDbXGxzM1ACIUEQBEG0GD/5yU+w1157oVQqoVQqYe+998ZPf/rTca+PPANNpvYSQ2J5Nn1pdPO3zTTtro+MF8AtWQqBykNgJcDZoxNg0/LDqBvlOJY7DxAquErZj9WVCGlXpuqsWDVuUSdGZpQwJLUT1MjFLj2Mq/l1+TxnxGzv84jJlxlJVPFq10IDVqmZlVCmP7PWGxSBKLSSptR6VfKZlw2H1FcJ8hQZ7QF1XvOOt75sTZFXijVSIlYcQese5NW11x+bUhVOUzGvm041krNsUMviMgaUyiv1NqJIeAmU+9hzX9fhhQw80GWEJioU6tI4e6RY7GhMaOSWVgRPsyNT3xNyurbnxslx16eJSXJVNzHmQA9veFIXMrASFgHpvudm+frfCGAtbzUUAkx4zImz4QFAeA7sxEG9TF1Cntpf+5rJJJKqcl7Lw9ImJc9LkfGQeEWgXX5FaWzuHR1yWrjxtxhYK7wEw4O7wA+Eh4nBXM+16lS5P0aiGFZ4Jo2zISwxb0V7CVR5JLH1+M53voNLL70Un//853HooYeCc47f/va3+MxnPoO1a9fi7//+78e8TjIGmsTAf6s7lpkWvgUk2UZh2YehnbnsA0X5Y1e9Cyprzc2gttGq9bU68OXGTpmJ52Umy12Ma+bBXdtoYpWu1eHQrmywM/HzWhArwsH8h7ktbMSc7INWTVMkNfO/baioGu36HgZ57mId1nDyH65p0viAGumhDWuyNga4VbBgGxN1RoJetwoTeEDgmXmVu1w9gNLE7G8c1ocapDwyGmWSR3o/UttZHZZwzEGJFtgy/m93q5MPZ8QmTBDFu+p1uanI+nfdVYikfHKxXEFRbtrxrA6Zw2YfbYMt5b+V+2O0/o1MdBZ1zuxclpHQnRR54+/EcczKHde45R3XyhVQ3xU3YS4eN7YEB8z67bCeVzTbYI4R9NICW9b+1B+LMih42mhkeqXsb0QtW+g027ArFtq65T6mr2rjuWRpj/i+yD+oVrsQFBp7DDAHCHTlRUUfbyqvSccFfr2bONCPvrwNfO4TDBO0YtLANddcg+9///s455xz9LS/+Iu/wPve9z4sWrSIjAGCIAhicjEZcwZWrVqFQw45pGH6IYccglWrGoXURgMZA03gndsYQjFAAg+h1f0AINqQndf+LM1JKgRM578kBCqqAdJIuSEqu5pb7lRmqYe5jcmGaQxUa2a9agSV5MiO2m5TnlghDktVMLFGP5uT7LV7zuvRmj0CZtnkqXqrvV4a1U4m1N3x6tan0NnsnqhwaFj/OG4K9ToC9ep1Np4v3MhiZqO9EKjRXMXIOcP6DpPYfHdqe3byYBIDBVnBkoTGTc9lV7mwZkb7tgaAH5j1Jtb3rEblPO1ACjlSZ4O5GUa2O9/zjbu4LD3/zDOqlhqr0gSRUeQL4he0JkAcqcTGfj3NdlX7BTNCt5NTdTdOS9WSj5AQqpNPo7rvcTPJiep7Y66lc5GTZGtPt/U5lOcvCeuqbHLwSo2eNHs79r6miTl+pQ7KHGBYfkV+kH99Biqswfr19VBuN+ckia1KBunxCWvmvKdpNimU2PLstttu+PnPf46vfe1rmem33XYb5s2bN651kjEwAd65RaUQA55QDEXcD0RSzj2pQN9AQ9kN0PGtkjvfEiBi1g1F/mCDzpwbqaLuwRUUrDis9Vmxo+6mCOHKzMxrueu1tn5OLD2ORIY/IEue6jXmg6zrX7tZHcvNmpoHj204qDAAY9A37qjS6N6197Heoq8vCasnt4uiMkzqjAI7XqxRy9fF9u14sl16BciM8joBJvWqZOTV+Sh2As6QOQbbpVsvB5ty4+rNhAa4Wc7+DpW+v2PloQAm6z/xAEe2Cq5VxWsCwOXWKEOJEbmrG6oxCkUjwFO0ohOOax7aunLFivc3dghU71bq49FhmjoRLnVNjeb7dnIMGfW5nfUfVY2rX4dTrHbJ3Arl5HUgdbysQaBDCpZrXwtw+aYSx/Gs4xnBqK5v/a2PQxkkOUaM6wNFeW9izJx35mS7hgLCKLCrd8qmkMTcA1SeRtEY7Um8jUfXE1URbEHPwDe+8Q2cccYZeOSRR3DooYeCMYZHH30U//M//4Of//zn41onGQMEQRBEy8LhgI+2Fjh3eWtE1CKccsopePzxx3H11VfjzjvvBOcce+65J/7whz9gv/32G9c6yRgYB+t+Ii485fLnNZiae2u0kEZAXJdACAA1qezqBkaAKB60xIjkq98FlOQohScA3yDnDc2lqxIFbcu8rSfr+tcjAzu5UVr1rpdv1XvWqCFvJJ7EaOgtn0TZ0UvG9cmz89VP0/tVyXft63BFmp8wl7dMfSxRh0mS7Gg+bx83hS1QZOP51sjf+l6UG1xtW6GSvXTGOBeytYBIMrO/QyVbq763uGpGs37bpnUcsq58893a399IyXh51QsA0Nb+gly33J/IhCciS1rbLTRmxvPUbDuOYGVjNo7g7URBnprjjMPGigzHzV7j2iOWJ9jF8kffQP53q6e5yOhq1IcFktAk/7l+tntnSfYTUhLgSc14DpKqSSy0f0e5+zfCZ5lrQGmOJNDHXu+xUAmQma6lSuK57retrks7CbIgPUC1jVm9EmLrcMABB+Cmm25q2vrIGCAIgiBaFs4DcD4BzwDnALZ/a2ZgYACdnZ36/aZQ840FMgZGSfia9Ab4QOm9cppoLY9kAEhkrNfWA3ALQCJHciq+aI8UkpoZPTuu9V5el46V7BMO5ceC1QiLMSs5DaYmOQkbR33lXsCX+1UbNLFDwIxGdXJbW3aaGtnao0odh7RHHpZcMbi1jrhxuYYR/AhSs+o487BLwvQ+WHoBdjMa7sIkXqoYP0zewEjxT7ucs94DIBbMlhHaywDivNvokb0qI+PZ8xNY5V9q1FiScdxwMJt7oUaYds6ATvB0KpnzEFseG1tjQn+3ep9N+1zXXaUbESmdAgAoS69TWMmuV5FpWS1/I3GtTr1PwRvr2V0XRq7ZyhmIQnOcOlnW8nowBp3vwaycFVs0wh5hZ9ptS0Yqk7XR508ltVq/Jcc327D1Ajx5n2ZDpmEYkE0yrE9MzOxDCqOnUOcR0hol9rEplVPXHFNcM78/fd+wcn58y5tlY6skRta9zNuWCYS8AEzAGECLGANTpkzBqlWrMG3aNHR3d+cqDnLOwRhDUi9DOgrIGBgljvwBc+sH4MqbdbzBmq8IMBkayNTqK5dmXf14INebRsZgiOTybmBuPsVus74E2UxfACiUzA/V/iGXp5mbsLoR8gQIrCQvez8Lyk1tJRKpH3pqJxjG5sai9pEnQFpXow2I+dR91XHNDUvdSG2pYG4l7+U94O3PgTq38CbuBw3JZ3UdDusfQpvCPrY0zfZmqDckHC/rji32NK5PGV6FTiAcsNaVmnW4dcmGgFkvY9nvRYdDciR2693a4SbugQyAJ8VnxKpUlUFJ/xDUw7PYbhLvkhiobhDvq/1mP3UvC5iwjwMgsRIz9ddpfZcq252ndQbxJr5v+6FsZ+3XP8zVeu0EV7svBYBMIw/m1kmL1/ersAy6NLakh3O0LdyS+e4Bk0zMPGt1dvKp2zjNXqf9ftTy3xb2A52nIxsEgDgPylh1PKNr8PiHGD7waAtm5LUADz74IHp6xA1k8eLFTV8/GQMEQRBEy8K5D87Hr6zPN6detZ1w2GGH6fdz587F7NmzG7wDnHOsWLFiXOsnY2CMOF1m9Dws28kXZpmGREpvABB6AfVaAp7l+nV84w1o21mWIsIkHSYVY+Er+WBFfeJcEmfLfnSdsQu0iaZlel+SmtmuW7AU0RIzwlQjqzSqq0G3k854dl5wZEZrxW61kGlgpL0Um2AkRb2Gz+q8AvWjcnsUaGsSqP8By0uzifvBpjwOTt0+5CkiqhGUZ8nUuoHlhlYjMA4UZSghGjDniqeAI0dtapTslcznaWRGkkGb5WLOSP7K7dpd8KzvNbW++/Giy0ot7Qo3EN6BzHw53wMAwL7+LG0MtZ/Fsqlnj2rGS6CPYxja08OYuVYznScVLDuS1p4wnv99q2up/hxpL4K96pznklfKhgEAwG3LehlUQnJSMdIbLGeQzaz9sUtfG5J2UeeNqNPEyMMbIYGwwQszQlnvtoAjAJ9Amx3eYpUEgDAGVMjA5p133sHcuXMpTLAl8XqslGdJaX/xGi4HXBnLDWYCkdTsj9cCJancGsneBNU3TZywsIMIKyjKdVoRA/9nhQysevXcB1+97kC33G/LIFEPFOabXIZ4KHuTUjcnu4Oia4v8WDoCtpEgJmZd4spVHHQY48TxASa1E3QLWUtH3u5EmHeDqX+o5x2/bvs8lt+DJdY+Uo+AXLc0t0IOrNGVa7te1TloWK3KMyhm3cYNN2SYY3MDM69tsOUds+tlW1bbuRP1D1973xvr9JUUbQm1qnxqMDEtiKwKgZpZbxKbddstg+1qCy26VAW0d9vKgM/TCCi0m5h1FJr1K4PYcc35i3JCIY6zCSO3TuAJiTHcPA/6OrDzcZycHBFg0w/JxDKMuZX1n9lPOySlvttNeOH170+FEa0QSWp19rS/e50rUrC+8xEM28y+qN+LYw0eJmhQjgeRQPju9wzYqNyAegYHB1Es5rQMHQVkDBAEQRBEC/CFL3wBAMAYw6WXXopy2SR2JEmCxx9/HPvuu++41k3GwDiIZCggzavuSAFXfT+9QCJHwSU56vc6gZqSLo4BXyaUOfYIUlYC9CwEyq+L9wPPAa5M4h58y7hGbQleNVJyfaDylngf1Iy8sU74S/PrynkyQoLVCNa+SmrTo19m3JSulUkNADXpKk5jM4/6PCibEUVUtUYXdka0daXq0Y/VtXCkaoRMslWO90CPfqxM9pFcxQqH1SXn2Rnsah6579Gw2d+kahJJM93q5P6qUApgtCb0fqfZedM43wMSVazwTc75s3UjgJxwCRo/U8vprpBo7FJnJzE61qjTHplqr4ZjvAFuYOYNypaOgqVQaH/3KuQS10y4jlueptCqUNFeAl9UzQCWNoEPPcK3EwhTy0s10jVQsPZBJc6p7yWB8aTVu+x1t0N5PXjt5neY1jUt09eqVTmgvs5MxYMdNnPNfmQ8CjlyzVn5afNZxhuQp75pLaM9Cpbeg1un/bBV4IFwdYx7+W3gzhgn//d//wdAeAb++Mc/IgjMCQ+CAPvssw+++MUvjmvd4/etEARBEMQ2RuQMTOxvLDzyyCM44YQTMHPmTDDGcOedd2Y+P++888AYy/x98IMfzMxTq9Vw0UUXobe3F21tbTjxxBOxcuVKbI7Fixdj8eLFOPfcc/Hf//3f+v/Fixfjvvvuww9/+EPqTbD14OBVYY6rcsPKUjP6caxyHMcHWHd2abcbKKr4ml162CZKjRq2Jq16t67MJ1cBT+UEOFljV/dFUKNS14xSuDVSh2PyB7QWuRVvtdupOmgcTQbtZl22nkJcN5BUIxV75GrX2tsx3voyL3vkUh9PTXO8HZlchJzeDXpddsMXez0j6Rqwxs9tRbz6WLLaR6fuc2B09ewKldNhL2OPbB3X1PurY7JVJl0v65lRnhU/AFTOUWyVlSpVwTg2++z5xjOS196a8+w53NRIuzjFSpLdaK5h5Q1IrOuzYOWeFLvM9grqd7jeeACi0OQS2C2TVSmll9Tlc1glgA25MNa+29+nY/2O0pxlMop/JUthNCd3pP43m8lhUOdV/fYC6JF6fQkhq7ujO5Yqpp0gzFzkqgbqvin116R2SzQuY8+/LXIGtjZDQ0PYZ5998Fd/9Vc45ZRTcuf56Ec/iuuvv17/b4/gAeDiiy/GPffcg1tvvRVTp07FJZdcgo997GN46qmn4Lqb93LY624WZAyMg8pzjdMSeROyM7+Bxsxit2R+sE7NPOx5zWrWY8moqgRDHgGFbrmtGlDZIKfX10Sr/VHd3yyZY5WMyFi2DlvfWHKSmNJo5G3UH5sTAAUZ9khDIOw3+6gMAp42JsYxSxo2KJvwgy209OFn8rOmHj2Q6W3Uk0k2rKsfz5t/pDBBrnRx3f4D4tgCdaO3dCBqG83n6qFhi9Co78cJRjASEvO16ETBSvbhoaWLrYeDfrD62f2xhYYUPLXFhrLHAAClNiMUlKbm2aDWYXeTrH8gKMOgoHIOWdaYsRv0oG5Zr5ANEyhj1Q3Md6i0GQqW2702hEynRzvxVaENW/s6Zo0NfOz3qdVkyfWzgl0AMo24HM+c03jYVIrkGaVplB+6s8k1Zq3E15EXlC9uNtRQL7SUMdptzQf7/DiNb23Nkbzqhy2NSCAcf5iAjzFMcOyxx+LYY4/d5DyFQgF9fX25n/X39+O6667DT3/6Uxx55JEAgJtuugmzZ8/GAw88gGOOOWZU+/HEE0/gP//zP/HGG28gDLMxqdtvv31U67ChMAFBEATRsgidgWACf8IqGhgYyPzVaiP0mB8FDz30EKZNm4bdd98dF1xwAdasWaM/e+qppxBFEY4++mg9bebMmZg/fz4ee+yxUa3/1ltvxaGHHoqlS5fijjvuQBRFWLp0KR588EF0dXWNa5/JMzAOuk4U5q9qYczjRvccIEr4uJKMVe73uutLjwasRKHBpdDrtd2PFVmemKeoZqv1pZFJbAIAt861HFetUijfcnXWhQTq3zO3buRqjTjqYZ7p2x4NZd2prK50y3ZlpjFw4IOjH1586Ekz70N7S8loy8RVyX2p1Xq2vuESYFzk9nGNBtczI9eMJKs6N/WNd1RTmFpj3TlghW8cowVhu3fVaM6uKXdSk7iVJsbDYYdbVBgmtbwMrnXeE1ve15bUtUoAVY2/7VlRIQU/MG55ICvHXO9y9qzmRdGw1aynZiUb5jUc4qalt12uqlQd44o5p37J6BuEOff0TZXN1WMfr308eS2S0zqtDXXeA+v+rH7TbskKAxatduYedI1l5ndo/yzywnX2542V0Bkcz1zzOoxQn1ico+5pe9rsEIW6D+19V+uqD86ePTvz/2WXXYZFixaNeT3HHnssTjvtNMyZMwfLly/HpZdeisMPPxxPPfUUCoUCVq9ejSAIMGXKlMxy06dPx+rVq0dYa5YrrrgCV199NS688EJ0dHTge9/7HubOnYtPf/rTmDFjxpj3GSBjYEL0nCku/P67malNTkwuAHOMEaDdaGXLvTYMQLruw7dM7b9i48tWLXBdbFA/FEbx4KoPP9g32jTKGhy2Ox/IyqgmOS5+m6RiPrez4b2yEVQSOyBf5X1j//uacwNZ8JxYz2MfNCclzwNo39zt3g5pXrhkM+fXdsszx8rMtoyQelcyAPiWHLRtTNX3vNfHYWW7KzLxa2t+Hd+u65MAyP2zzr+a1+5BoYwMr4jMebBDHHkd79qni/cDq7IPFfVAtB9WOnYdZb8jbRjk9DmwtQMylQ45D0PHtWS002weBCByB3yVA1Gw+ms4+Uau/s1wc55suV5tTFr1+fb3bmuKuNa5U3lC9eJkanDBeNZQ1eSFFOyQQd1vTM9iufO14WkZ+7qqIrLCSfZ5UEaBtc5tn4wfYGKPMvHlrlixItPgp1AYX8OFM844Q7+fP38+DjzwQMyZMwf33nsvTj755BGXG0k7II9XXnkFxx9/vN7PoaEhMMbw93//9zj88MPxjW98Y8z7TWECgiAIomWZWIhA/AGi05/9N15joJ4ZM2Zgzpw5WLZsGQCgr68PYRhi/fr1mfnWrFmD6dOnj2qdPT092LhRuMl23HFHLFmyBACwYcMGDA8Pb2rRESHPQJPwporXZL01WqoIFy4AOCp5yjWhA55mXfOVFWY5QI4g5IhheJ2V6MSgLX9bCth2I+o67bgxIapgjF89j/1qk4TWtqyRgz0KttGeBTu5rQLsdefWcx8e8nuxrcc/xEw+Wt3opd7t6ReynfTSHJe57TnIU8VTn9nzAlmPjK0G15b1SmakaT3XuI15aoWhEjMtb7s2uua7TnrXTiC090edEy9HwMypCxGVpBaGPo8JUJOJfDu8F6hKee7IlgjO0blIInM92t0F1T7Elay6YqC+D6fxHOR16LT3sR5bo6K+Xl/tj5gh2xTK9qzZypqACMHZHTrVuqJ+4zmxUaGgQh/gqUqIDea7d3zjkcl0KLSvv7yR+Sh+bvWewowegZ/dRn1TMjjQ9wW3MDmqCMbLunXrsGLFCu2+P+CAA+D7Pu6//36cfvrpAIBVq1ZhyZIluOqqq0a1zg9/+MO4//77sddee+H000/H3/3d3+HBBx/E/fffjyOOOGJc+0nGQBPoOpFj+CnxywhmA7G6Ef7ZPNiVG9AtiVwCQHQ71D0N3jbrG1oll4mzZU6p5Yqsxy9ZGc+R5SK11qFcrHZs1XHNzcB21z/+YeUrtUIGPBsv1cqlah8LRi9//n9t+9jhBx7leGKhySNIrYeKelDm6cjHYdZYUOV16vxGITLuWLuaoJ6GeLl0LRescCGT63eRNQhU9UcaoaFVbSaMwIyITxJa8Xxb997OnLdkZLUBUAASq4oFaGxtrebN62ZX7AMK0iCurQeK8r1dwmaXW+a1/PXKyEgs6323vgs7FyatCxm4genhwRwrPGF9R3bXwzyDzXWs0FFO1YXf1liFYVNvJKvvu2gllvtq4DBkjLzU+t6YZ8JsPAE8aXDocxOahzKzcoXs3JJcY8CukPGsclSr66aNPX1z1/a2/L2LBMKcWNyolx9bY4XBwUG8/PLL+v/ly5fjmWeeQU9PD3p6erBo0SKccsopmDFjBl577TV87WtfQ29vLz7+8Y8DALq6uvCpT30Kl1xyCaZOnYqenh588YtfxF577aWrCzbHtddei2pV/LC++tWvwvd9PProozj55JNx6aWXjul4FGQMEARBEC2LEA6agDGwucSgOp588kksXLhQ/68kgs8991x8//vfxx//+Ef85Cc/wYYNGzBjxgwsXLgQt912Gzo6TKLQ1VdfDc/zcPrpp6NSqeCII47ADTfcMCqNAQC6lTEAOI6DL33pS/jSl740puOoh4yBJlE+QFjG4WtMj/ydstEfUOGA+B0jOuKUjPRwWjXdCgPpNh1anR3dKRyrWYo9emuTDaxqA1lBkbykv9hKKtz/N41W/Qf+10zTCXlWeMLmgAe2vRdgJA5aLPbtqSNZJnRS30iIp9CjKdszkBcK8fysi9n2GNQnJvpFKxs+BWobGtenJKlZAERvWfsjsTs9Zqbbo+ScaggdDqhzgdsjwEzHyToCK8kxGgJCJRk9bNz4Wvti2KyrOC2bEOfWdaxMImTr1W3NgbrESce3QlMjXH/Km2J70eKa1YkwJ0QCZD0DOmwRA0x1/7RCxnZNvh0aUPvLco6Hp8CwrCjz2sz3nD1AOa+dmV+nNaE9Kn7jvA3rynPX2x6BTQhceUUrVMCRK2OsvSXWcrlNvbYmPAAm4BnAGD0DCxYsAN/EQd93332bXUexWMQ111yDa665ZkzbVixcuBBnn302Tj311HGXEtZDCYQEQRAE0ULstdde+Kd/+if09fXhlFNOwZ133tkgPDRWyDPQZIKdjcW47idMtylV1jzzhHocALDUvAeyNeibQ1n4avQSdBirvX2WiTdHg1kvgL2d+vcjoRLyWpkDHuB45ngxArCbJdnkGft2wpgaPyQxRkxK0x4BK65ul5LZI8h4yEwHZLxZ5g9U1uQndCrS2OxQEllJoHaTJkuy1i4PU/tWX1euRr92HN+uQbfRMXjbA2XJbKc559fWVdCJlpHxODg1k8uR59my9yGJAIdnP/es/ATbYxYNW9e+irU70Gp5GanqtDEJ1K1r3W2jFURzlCMdv867IM+702Ze07rfJgDwMHsP0Iqmdvmy0qBI60oE7fJYAOD53gDA0vawPQdKr8IqOXb8bI5CPWPR5dgScPhj7i+QXb71+Ld/+zd897vfxQMPPIBbbrkF5557LlzXxamnnoqzzjoLhx122JjXScbAFmTqORzrfmKEiQCRNKQuvqKlDeG2Qftphlflr0+7+60fX1EmogVTzE0hqQJ+e+PyiVVLrR4eYxH4aXX2vVcc69PHMO3mtrsAqgeIXVmQ535nVi25bRgwZgn3qBr2uu9BP6Ssh0doVRilOe7ieiMCkP+P4avLPOzkNoIOs440Ng9Su8uguuaKU4B0rVmH0q5Qr17R7K/XBlOB4pmQgkru89uMgVovXNOgl28dY0ZMqtgY/krjrHgVs0R17N4N6hhs+eRMJY5K1FPGgGeMFHs+z9ZNGMFwU3oSfrc1UbnXY2T1AtR7q8rDfsDnambUJ/fJ5ebfJnZuyaks000S1neb191Sr8Y3203tiiJ5/bqB2cf33Lht7yF2eeD4lm/izmxFHMfB0UcfjaOPPho/+MEPcM899+Dyyy/HddddhyRXnGLTkDFAEARBEC3K6tWrceutt+Kmm27Cc889h4MOOmhc6yFjYAsz9Rxhdq79sTCtS7tnQwOqyyGrGBdd+07yMx+oviPeJ2FWklZZ5aq2GwBKM+XnZaNlYMugatd4i1rCzcIrWp37lNuzvvtbzmgpL+EMsFQHncZ+7mmcLWPUnoZaY202j7OjlJH0CcwMcrkk34ORh1ewSu6ATEe7hgZS1v92WCVN8t3GqgTOKWT3XY2OVXmdSpQFRDMr7SXI6fxnY8tWiwl1n9eVy+n12QmU6r2THWnnNg+yNB30coWsSuem5JOZA0RKPrkCxPK36ljn39YcUQqFjnWueWySMVNr3oa6fwB7/LTxh22X/C09g2XVFesSAOuvm03dJvK8VduKyegZGBgYwC9+8QvccssteOihh7DLLrvgzDPPxK233orddtttXOskY2ArUdpTvCb9QGEX8T4dBlLrxqjjkrZ8rHJ7Jsh0zwuk+1llfAfd2e3ZAi8N64qzceHJxt53cSw9Q5xM5ZZOrIczcwHXqixI626Wdi15fRa5rlG3JZ8toaZMbHgTOQH2OjLxaLvm3W55rY4jbpyXW5nh4bCJ3QcF6AeqWzLXjC1JqyssKpZBwMR6ANP617HCIXyEDnylWeI16geqMhTmt1nbQPZc5R2zfmAxZNzqCnVdZyoonKwIFyDOiW24qesgtfIo8kIHTtWc93CjWYedba9zGIpZWW5VOZTXp4SHWQEye9/V/UCty87TAAPec93onmZ73mYZBp/MtxhHejDWV6ZsL4aAwAMmUFqYr+28fTN9+nRMmTIFp59+Oq644opxewNsyBggCIIgiBbirrvuwpFHHglnJCnUcUDGwFai7YPC5I7fYUilu9DtBCLVpGod4El9ATVCaHeBypvifWWtWRdPTTJgICVtPauXO0+N+zCpNGZ2N6sxUCtju4AVKhmu3n2vwgPqd2ePqXyrNtvuda+yyOOqpWVgrTfNabqT57oFxHx5I+28jnb1crl6mgpl2HX2VsJdZnRt3xVUIyNr5F8btNzxcrkkNGqbXlt+slso1Tb9KUBZDuSGXjXbsLdrqxXqXRlBm8CGWyP/7AeN82YaSNmqjXXnL7UqDMKK+AOyDaAyzZ3U/rKsZ6Am5cbb9pLL1FVd5HkkRmLejyb2G97zZ2b5JaeaKzpzXlUy4i+27/vFxMME2/fx5XH00UcjjmM8+OCDeOWVV3DmmWeio6MDb775Jjo7O9HenpNBvhnIGCAIgiBaFlFaOBEFwtYLE7z++uv46Ec/ijfeeAO1Wg1HHXUUOjo6cNVVV6FareIHP/jBmNdJxsBWxuvhSFxhiSfrTc5AWjFqhTaOVfut+6Bbtcu1dea9qmNmDnQIjXnbv2W/LVCJVY9/SHwXkRWH3VxVjlOnP6/LzlI0JGE5I9Soj9ToKU/hLVOfr0pUw7r+CkoFsZqvOGnnMtgeBZ2QmBNCzngOLPJG/W7BJMCOpF1h58JEG+RyVt5DNGTpF+SoBwJmgM9yEikd18p7CEeIayvlzrr2xHYOiNqm6veQJlZ7Z6uUNA7z582rxXd8wJWDNZUQqHIIAJEvYV8nWiYgtEqG5ecT9QrUsz30ESHGxt/93d/hwAMPxLPPPoupU6fq6R//+Mfx13/91+NaJxkD2xBWBAJZORCtM+EBO6vabhpjJ5QpFydzs6+AcJXucg39wEeD7ipXy7ra8zyHmUx/1RzHkoZmfv6D0k7W3KzIU073PMBk6uvkMZY1DlV4wn54q0Q3O2zEE7NcOJg1ZPIe/OFGsy/qOIJ2oCanKyOqsi7bfClzSOoBLeeN7MqEML+KIC950v6fWw9dreER54su2fuuZZLrKgzU5z4aRYf8kjlPiSXG4xfN8eta/rprwP4OVbWACgcClgBRxTqO0LzfXIUFIT0DEwkTtKBn4NFHH8Vvf/tbBEH2uOfMmYM///nP41onGQMEQRBEC+NjYtUErdd/OU3TXGGhlStXZhoijQUyBrYBbpdMJlzLdGMaAPB7xWu8XLzyxNRoxxXj6nVcKwFOvtbWAe+9hbwBY+X9D4tztnivkYvzbYXBhs/c7ChWKxtaCXDcGmFqVzlvrNXPjCgtjwNgRojKQ+CWTL1+NGzc3G59LT6A0hSrtM5uDWwpUTLHJMHp0bh1Odmll3ZDIDsJMpKJsX5n9piSupI5ty07areT7HSXbiUVbJf6WbXtthyx/b1o/QirjDOuWt/HCCWLNlo3Iuf54vrm8zQ2pZV26aLd/Mr+ndZrEhR3BTxLB8SR8tTMg07ei1OrDTp5CfLhwYTkiEelyb6dcdRRR+G73/0u/uM//gMAwBjD4OAgLrvsMhx33HHjWicZA9uYWMb843eA6J3sZ1F/440UkG5PebOws4KJ8WN3H/R86yZuZ/XbceYcw2CkHIA897sTWDf3vK+QQxsDXlt+HFoZBrZBYnc41Jr13MhWJzXzwEzj/AeiFjiKLO2FuiqFhtyIulBIxj2uJGyt/ADdryEyORr257YGg62PYYsKaSMhZ9/t0IjrWzkIlma/LeCkjzk1vy39jMgRN1LL1Pe4SKw+EG5gqn6iIcCv6zlihzoc31S72xLE9vZ3+wH91gnB1VdfjYULF2LPPfdEtVrFmWeeiWXLlqG3txc/+9nPxrVOMgYIgiCIlmXi1QStFyaYOXMmnnnmGfzsZz/D008/jTRN8alPfQpnnXUWSqXS5leQAxkD25DCrhzRSjHEqi43CVa6oZBrRqb2qJPCAc3n8CUc/7u/7GqYAoFy/1qjY/Wa0Raw3MJBTqjOdnOnMIPMjGMyJ2nQ8Ux2PtDosuapqVFPwvxGObYSniJvNFz/f6aPve0Nka9eQSS/AiaBLhwESr3W/uXIFduNjGzvg8Lu1Oepyhirg99Ibv08tUKemh2u785odqjuwCC8CLwuBDdS5YdjhQzUuY6r5vxVNpgqAwCobRCv5Z3NtFQqOSaVrDdEfbdpBC1ZTOTDuTcxY2BzUqDbKaVSCeeffz7OP//8pqyPjIFtzPBS816JsiQ5VQW2lCixZWiTD7Ooku2e58uHve3uL8gHf5pkHxTqgaj065NaNuatnkmui4ZyPuaaB5BTNsaAXe6WeSCqMrnAalPtQj/k7ByAvFBFPfXbsHMYHN+qbHHNtVmS4Qe37l5sS+fWx8qZZeh47UBiVcTUb9stmvfRUGM77vr93pQLf0TqflrKeLIrE+w2yhmJbz/7OXOA0ArV6FyC1BgGa38rXrsHgPIe4r1bApINZjlVhsh5S4a0iS3A3XffPep5TzzxxDGvn4wBgiAIomWZeJigNTwDJ5100qjmY4xRC+NWZNpnxbDk9S81ZqTldSEjthz7/0ac76VnMO3qjatArV+8V7XmnhWSiytAQdaN2yN71S0y7Ddubp42NrYBrBp1D7m/SHvErJeJTJZ5PFy/gHyxlskbJW8uTGCLHdn7nudpaJ8rRvmAqOVX+zZSQ6x6iWxAusQ3cQ9zA6v+Phk5bKDIyBvLn5IOgYzw07L1PHyrKiDjfVCJmTl6Aq5vzlNcbQwxAUChW7yG7wCelCO3qwqQZkMGu3yP7gObhE+wtLBFwgRpumVdRNtV7ymCIAiCILY+5BnYTphzFVn/2xMqgQ2srqUvgKDTlNZ5VkzbCUysXKv1dZmRe1Ix6yr0mJG0GpjEQ1lFSUeNzFPzXqsOwoy+63HrZIFHijnX96TPm081KEpj411wfVMG6Fv9UEq7mv2NZP5LuM7yYCjp7Qjwu81ySnvBsWWXVb6DrSiYWPNiBJXINOf9KH5aKufB8Rs9Dr6VBGifB+Y2nmPHt9o6W62TOTdtn/tfF69JDai9Ld6XdwICqSrL6K48JjgmmECI1snQPO644/Czn/0MXV3ClXT55ZfjwgsvRHd3NwBg3bp1+PCHP4ylS5duYi350GVHEHXseRvHi+eKp33QZR6uSpoXsNzChexDST288gRi3FK+PoHtIlbJo0Fv1sUey21rnfrIGB48MS59uxZf72vdA9/OuM/LxNf7W8i+z3PhF6ZnXwGR/a4eaG4p32ixwwjaS5s2buM9N2af5H88SZxA5pr8y7xKCltDYTRiPbacsE4GtL4rXdmRI3Bkk9l/ZtZlCzWp6yUcNBUo1VVAIhMPvS5jbLWIB3ubIqoJxv8o47x1HoP33XcfajVTdvOtb30Ln/zkJ7UxEMcxXnzxxXGtm8IEBEEQBNEC1Ldbbmb75dYxiQhiK6JGpC99ygwFtcrfoOU6duoS1VSimbUulXCYRla5oNU8R70yz3SetHHL1rb7zXbsWn47VKHr661kOx06SLLJbCOV6OltF82+qf20VQ6VaiYPAV+6ub1OcxzRO2aUaw+adcMgu4NitPmOfHvdKT5fcor1vajjrVvU9hhsKnGQudny0L3v2vQ+LD1DbNDxjHegQcoZ2evCcQE3zc4bVYDqevG+PM3MW1kBBD1yOcs7Q+Qz8WqCifQ1ePdAxgBBbILdr+N44S+zvv0ktHoBWJUFeZnztkaALSLktQNMPaBV9n6bcRXzEGDWutW8niVsZLvf8+R/taaBB/0QdEsAU9K8sXmAjpRXYBsvidxPfwR55FDGv6P1JkzAmDm+vOx/ngI7/7+xj27sttzaMEigfZ2OZ6o4bONHJWQ7DvCBR8c3qrI1P545vjHuk3tOmaXFoHQgImBInrNwozivgMjTqKwV7/e9l3KJNsdkMgYYY2B1scb6/8cLGQMEQRBE68JdTOhRxnMs2+0UzjnOO+88FArCZVStVvGZz3wGbW3CkrTzCcYKGQMEsRnq9R6eOZ4hskfl0iXOHStxri77H5DJaXZDH+VaVv9bI8mRqkte/4oYBSR12gJ5oUN7W/bgQYcMXOM+T8Ic7wA3Wf1+t+VxsKsB8jwKqUl4ZE62GY94Y2ad8y8TH/naXoLn/sIKH8jtHvSbLTe6ViP3p4/e/OhMe4hUR0erWVKamPCLnRBKEDbnnntu5v+zzz67YZ5zzjlnXOsmY4AgxojturVj1zZ2i16FbRjwBNj138f+kFKdCpOhESoWAqsXgt1jwDIM1HL1uQP1DyuvXNdpUL73uhqz3Nvnm2m1VUBRCjHV3jI9N9T6d96CZbSbi/dvKfa3DI6njhTXBGcQNaAQ51obW6ostSD1cuTn6loJhwDUC0kRIyJKCydQTdBCj8Hrr79+i627dc4CQRAEQdTB+QRzBnjr5AxsScgYIIgJMP8XXGeXI0ewptk96He8VKzv1YtYJjSgM+pTk/Tn1CUoArIqwFqfnQWv6/KtygSVzW57FvwuwCmaeRRFKTrk9QC1FWb6eBIEW5kDHjChg9RKnsxL0vRtaWsr3HvwY5PrnBHbHjIGCIIgiJZlMoUJtiR0FghigmyL9tLRgHnvBlZuQk4Kw0jytjyp8xpYZXlAtgGSVzbriQfMjcNedzps1lPaXbyfftHkHeHm5REAjbkZgEgYVN8htSweG2QMNAc6CwTRgrznRo6ln5SVBaHVDTHYxELIPmhGEh9SxoDj1VU/KB1+6/meDDZuw+sGaq9tej8mGyp0AACPf0h+b3VdD9V3oLpgEsTWhIwBgiAIomXh3AHH+LUCOCdVfoCMAYJoWfb8mZRMPp9lShjr1QHzGgwBwpugPnLtEkglc5zUqRzKe2bcb8oF7dJDFXJIhsy8r3+JUUfOOkZSPlRqhqQ6OFY8TOxRRo9BgM4CQbQ8IxkCmfa/dT0A6j8HTB8C15rPCRrnzWwvpy9DOmiJItGga9SQEUBsS8gYIAiCIFqWydTCeEtCZ4EgWpx5P+JYdoFUvUusLohKNyDKb6Lk+CYMkEbGY6A8BN4IyYi2l8CTyW5u2XgGktCst16pkCCaDYc7sZyBCSz7boKMAYIgCKJlodLC5kBngSDeZeTVqXvt4pU5VmvfmkkAZG5jDkFeWSEgvQj9Zn2AaLGs2iszx/RQqK0d/3EQBLH1oPQegngXMO9HHPN+xMFT4/JXf/UhArdk/hTMlfLD1l89PDEGQxqKv3hQ/NmEbwvZ5B0v5djle5QUR2xZOHfAuTuBv7E9Bh955BGccMIJmDlzJhhjuPPOO/VnURThy1/+Mvbaay+0tbVh5syZOOecc/Dmm29m1rFgwQIwxjJ/n/jEJ5pxOsYNGQMEQRBEy6JyBibyNxaGhoawzz774Nprr234bHh4GE8//TQuvfRSPP3007j99tvx0ksv4cQTT2yY94ILLsCqVav03w9/+MNxn4NmQGECgngXsft1HC+dL6Vv5YtTyLrzVVOjtGa8BtzJb4mcJ5HLUzM9HhKv42nHTBDbEwMDA5n/C4UCCoVCw3zHHnssjj322Nx1dHV14f77789Mu+aaa/D+978fb7zxBnbaaSc9vVwuo6+vrwl73hzIGCCIdxm7/zj7YF7+90w/6FmalR62DQBtGKTmNS//YPfr6MFPbE9MrJpAKWvMnj07M/Wyyy7DokWLJrBeQX9/Pxhj6O7uzky/+eabcdNNN2H69Ok49thjcdlll6Gjo2PC2xsvZAwQBEEQrQt3gYkYA1wsu2LFCnR2msYQeV6BsVKtVvGVr3wFZ555ZmbdZ511FubOnYu+vj4sWbIEX/3qV/Hss882eBW2JmQMEMS7nLlXm5H8sgtYg1wxINz9OtFQhRd8YLcfkBeAmBx0dnZmHtgTJYoifOITn0Capvj3f//3zGcXXHCBfj9//nzMmzcPBx54IJ5++mnsv//+TduHsUDGAEFMIub9yDzcXzqfZcoJ68MLBNEKcDjgE8iFn8iyIxFFEU4//XQsX74cDz744GaNjP333x++72PZsmWtYQzUJ1gQBNG6DIamVJCn9PsmmsfWvJa2NwVCZQgsW7YMixcvxtSpUze7zPPPP48oijBjxoym7stYGJUxoGIn9QkWBEG8i/h517beA+JdRF9fH4JgBE3rFmZwcBAvv/yy/n/58uV45pln0NPTg5kzZ+LUU0/F008/jV/+8pdIkgSrV68GAPT09CAIArzyyiu4+eabcdxxx6G3txdLly7FJZdcgv322w+HHnrotjosMM75qHyDtVoNtVptS+8PQRAE8S4gCAIUi8Uttv6BgQF0dXXhisJyFNn4Y/1VPoCv1eaiv79/VDkDDz30EBYuXNgw/dxzz8WiRYswd+7c3OUWL16MBQsWYMWKFTj77LOxZMkSDA4OYvbs2Tj++ONx2WWXoaenZ9zHMVFGHSYYqeaSIAiCILYVnE8wZ2CMCoQLFizApsbQmxtfz549Gw8//PCYtrk1oARCgiAIomXZ3nIGWhWSIyYIgiCISQ55BgiCIIiWhYOBK3GMcS5PkDFAEARBtDBbO2fg3QqdBYIgCIKY5JBngCAIgmhZtkcFwlaEjAGCIAiiZaGcgeZAJhFBEARBTHLIM0AQBEG0LBQmaA5kDBAEQRAtDAMm5OqnMAFAxgBBEATRwnA+wZwBTsYAQDkDBEEQBDHpIc8AQRAE0bJQNUFzIGOAIAiCaFnIGGgOFCYgCIIgiEkOeQYIgiCIloXLv4ksT5AxQBAEQbQwVE3QHChMQBAEQRCTHPIMEARBEC0LhQmaAxkDBEEQRMtCxkBzoDABQRAEQUxyyDNAEARBtDQ0up84ZAwQBEEQLQuFCZoDGQMEQRBEy8I5B5/AI51zMgcAyhkgCIIgiEkPeQYIgiCIloXCBM2BjAGCIAiiZeGYYJiAzAEAFCYgCIIgiEkPeQYIgiCIloU8A82BjAGCIAiiZSFjoDlQmIAgCIIgJjnkGSAIgiBalhQc6QRG9xNZ9t0EGQMEQRBEy0JhguZAYQKCIAiCmOSQZ4AgCIJoWUiOuDmQMUAQBEG0LFxmDUxkeYKMAYIgCKKFIWOgOVDOAEEQBEFMcsgzQBAEQbQsVE3QHMgzQBAEQbQsXCsNjP9vLDzyyCM44YQTMHPmTDDGcOedd2b3h3MsWrQIM2fORKlUwoIFC/D8889n5qnVarjooovQ29uLtrY2nHjiiVi5cuVET8WEIGOAIAiCIEbJ0NAQ9tlnH1x77bW5n1911VX4zne+g2uvvRZPPPEE+vr6cNRRR2Hjxo16nosvvhh33HEHbr31Vjz66KMYHBzExz72MSRJsrUOowHGqa6CIAiCaDEGBgbQ1dWFz7LXUGCd415PjQ/g+3xn9Pf3o7NzbOthjOGOO+7ASSedBEB4BWbOnImLL74YX/7yl8X6azVMnz4d3/rWt/DpT38a/f392GGHHfDTn/4UZ5xxBgDgzTffxOzZs/GrX/0KxxxzzLiPZSKQZ4AgCIJoWUTGwETCBGI8PDAwkPmr1Wpj3pfly5dj9erVOProo/W0QqGAww47DI899hgA4KmnnkIURZl5Zs6cifnz5+t5tgVkDBAEQRCTntmzZ6Orq0v/XXnllWNex+rVqwEA06dPz0yfPn26/mz16tUIggBTpkwZcZ5tAVUTEARBEC0LR4q0CToDK1asyIQJCoXCuNfJGMtug/OGaQ37MYp5tiTkGSAIgiBaFs6TCf8BQGdnZ+ZvPMZAX18fADSM8NesWaO9BX19fQjDEOvXrx9xnm0BGQMEQRAE0QTmzp2Lvr4+3H///XpaGIZ4+OGHccghhwAADjjgAPi+n5ln1apVWLJkiZ5nW0BhAoIgCKJl4UjAMf6SvLEuOzg4iJdffln/v3z5cjzzzDPo6enBTjvthIsvvhhXXHEF5s2bh3nz5uGKK65AuVzGmWeeCQDo6urCpz71KVxyySWYOnUqenp68MUvfhF77bUXjjzyyHEfx0QhY4AgCIJoWba2MfDkk09i4cKF+v8vfOELAIBzzz0XN9xwA770pS+hUqngc5/7HNavX48PfOAD+M1vfoOOjg69zNVXXw3P83D66aejUqngiCOOwA033ADXdcd9HBOFdAYIgiCIlkPpDHwKzyJgHZtfYARCvhHXYZ9x6Qy8m6CcAYIgCIKY5FCYgCAIgmhZUiRIJxAmmMiy7ybIGCAIgiBaFqEiOJGcgfFrFLyboDABQRAEQUxyyDNAEARBtCxbu5rg3QoZAwRBEETLwhGDI57Q8gSFCQiCIAhi0kOeAYIgCKJlSREjncDofiLLvpsgY4AgCIJoWShnoDlQmIAgCIIgJjnkGSAIgiBaFuEZmEgCIXkGADIGCIIgiBaGcgaaAxkDBEEQRMtCpYXNgXIGCIIgCGKSQ54BgiAIomXhiMARTWh5gowBgiAIooXhSCYU96cEQgGFCQiCIAhikkOeAYIgCKJl4YjAOYUJJgoZAwRBEETLQqWFzYHCBARBEAQxySHPAEEQBNGyUDVBcyBjgCAIgmhZRJhg/A90ChMIKExAEARBEJMc8gwQBEEQLYsIE4z/UUZhAgEZAwRBEETLkiJCOoFH2URCDO8myBggCIIgWhbyDDQHyhkgCIIgiEkOeQYIgiCIloWqCZoDGQMEQRBEyyLCBO6ElicoTEAQBEEQkx7yDBAEQRAti6gmGL9ngKoJBGQMEARBEC2LMAbG7+QmY0BAYQKCIAiCmOSQZ4AgCIJoWfgEPQOUQCggY4AgCIJoWThCcLAJLU9QmIAgCIIgJj3kGSAIgiBaFpFAOH7PACUQCsgYIAiCIFoWMgaaAxkDBEEQRMuSIkQ6weUJyhkgCIIgiFGz8847gzHW8HfhhRcCAM4777yGzz74wQ9u473ePOQZIAiCIFqWre0ZeOKJJ5Akif5/yZIlOOqoo3DaaafpaR/96Edx/fXX6/+DIJjAHm4dyBggCIIgWhaRMzCx5cfCDjvskPn/X/7lX7DrrrvisMMO09MKhQL6+vomsFdbHwoTEARBEJOegYGBzF+tVtvsMmEY4qabbsL5558PxkwS40MPPYRp06Zh9913xwUXXIA1a9ZsyV1vCmQMEARBEC2LCBNM7A8AZs+eja6uLv135ZVXbnbbd955JzZs2IDzzjtPTzv22GNx880348EHH8S3v/1tPPHEEzj88MNHZVxsSxjnnG/rnSAIgiCIsTAwMICuri4cgKPgwR/3emJEeAr3Y8WKFejs7NTTC4UCCoXCJpc95phjEAQB7rnnnhHnWbVqFebMmYNbb70VJ5988rj3c0tDOQMEQRDEpKezszNjDGyO119/HQ888ABuv/32Tc43Y8YMzJkzB8uWLZvoLm5RyBggCIIgWhbRqGj8Dm6OeFzLXX/99Zg2bRqOP/74Tc63bt06rFixAjNmzBjXdrYWZAwQBEEQLUuCEJhAPUEyDmMgTVNcf/31OPfcc+F55jE6ODiIRYsW4ZRTTsGMGTPw2muv4Wtf+xp6e3vx8Y9/fNz7uDUgY4AgCIIgxsADDzyAN954A+eff35muuu6+OMf/4if/OQn2LBhA2bMmIGFCxfitttuQ0dHxzba29FBCYQEQRBEy6ESCPfCgXAnMK5NEOOPeBL9/f1jyhl4t0GeAYIgCKJlSRBhYmGCZPMzTQLIGCAIgiBaFpEz4E5geTIGABIdIgiCIIhJD3kGCIIgiJaFPAPNgYwBgiAIomURcsLjd3KnE2pz9O6BwgQEQRAEMckhzwBBEATRssQI4ZBnYMKQMUAQBEG0LGQMNAcKExAEQRDEJIc8AwRBEETLEiGCAzbu5SfS5OjdBBkDBEEQRMsSogY2AWOAkzEAgMIEBEEQBDHpIc8AQRAE0bL0I9rWu/CugDwDBEEQRMsRBAH6+vqasq6+vj4EQdCUdbUq1MKYIAiCaEmq1SrCMJzweoIgQLFYbMIetS5kDBAEQRDEJIfCBARBEAQxySFjgCAIgiAmOWQMEARBEMQkh4wBgiAIgpjkkDFAEARBEJMcMgYIgiAIYpJDxgBBEARBTHL+/8ne2cHxmN8DAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'elevation': 165.37033101757254,\n",
+              " 'slope': 3.8477161303214786,\n",
+              " 'aspect': 5.4659402646877995}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "properties, dem = terrain_resp.get(asobj=True)\n",
+        "\n",
+        "elevation = properties[0][\"elevation\"]\n",
+        "slope = properties[0][\"slope\"]\n",
+        "aspect = properties[0][\"aspect\"]\n",
+        "\n",
+        "terrain = {\"elevation\": elevation, \"slope\": slope, \"aspect\": aspect}\n",
+        "display(terrain)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "crs = ccrs.LambertConformal(\n",
-    "    central_latitude=49, central_longitude=-95, standard_parallels=(49, 77)\n",
-    ")\n",
-    "\n",
-    "dem.name = \"Elevation\"\n",
-    "dem.attrs[\"units\"] = \"m\"\n",
-    "ax = plt.subplot(projection=crs)\n",
-    "dem.where(dem != -32768).sel(band=1).plot.imshow(ax=ax, transform=crs, cmap=\"gnuplot\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "'https://pavics.ouranos.ca/wpsoutputs/raven/274bbfe2-feed-11ed-8c70-0242ac130010/input.json'"
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "crs = ccrs.LambertConformal(\n",
+        "    central_latitude=49, central_longitude=-95, standard_parallels=(49, 77)\n",
+        ")\n",
+        "\n",
+        "dem.name = \"Elevation\"\n",
+        "dem.attrs[\"units\"] = \"m\"\n",
+        "ax = plt.subplot(projection=crs)\n",
+        "dem.where(dem != -32768).sel(band=1).plot.imshow(ax=ax, transform=crs, cmap=\"gnuplot\")\n",
+        "plt.show()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "'https://pavics.ouranos.ca/wpsoutputs/raven/274bbfe2-feed-11ed-8c70-0242ac130010/clipped_vftry25z.tiff'"
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'https://pavics.ouranos.ca/wpsoutputs/raven/274bbfe2-feed-11ed-8c70-0242ac130010/input.json'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "'https://pavics.ouranos.ca/wpsoutputs/raven/274bbfe2-feed-11ed-8c70-0242ac130010/clipped_vftry25z.tiff'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([[-32768, -32768,     86, ..., -32768, -32768, -32768],\n",
+              "       [   120,    103,     86, ..., -32768, -32768, -32768],\n",
+              "       [   120,    104,     93, ..., -32768, -32768, -32768],\n",
+              "       ...,\n",
+              "       [-32768, -32768, -32768, ..., -32768, -32768, -32768],\n",
+              "       [-32768, -32768, -32768, ..., -32768, -32768, -32768],\n",
+              "       [-32768, -32768, -32768, ..., -32768, -32768, -32768]], dtype=int16)"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# We can also access the files directly via their URLs:\n",
+        "properties, dem = terrain_resp.get(asobj=False)\n",
+        "display(properties, dem)\n",
+        "\n",
+        "# Let's read the data from band=1 as numpy array\n",
+        "display(rasterio.open(dem).read(1))"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "array([[-32768, -32768,     86, ..., -32768, -32768, -32768],\n",
-       "       [   120,    103,     86, ..., -32768, -32768, -32768],\n",
-       "       [   120,    104,     93, ..., -32768, -32768, -32768],\n",
-       "       ...,\n",
-       "       [-32768, -32768, -32768, ..., -32768, -32768, -32768],\n",
-       "       [-32768, -32768, -32768, ..., -32768, -32768, -32768],\n",
-       "       [-32768, -32768, -32768, ..., -32768, -32768, -32768]], dtype=int16)"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Overview\n",
+        "\n",
+        "A synthesis of all watershed properties can be created by merging the various dictionaries created. This allows users to easily access any of these values, and to provide them to Raven as needed."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# We can also access the files directly via their URLs:\n",
-    "properties, dem = terrain_resp.get(asobj=False)\n",
-    "display(properties, dem)\n",
-    "\n",
-    "# Let's read the data from band=1 as numpy array\n",
-    "display(rasterio.open(dem).read(1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "\n",
-    "A synthesis of all watershed properties can be created by merging the various dictionaries created. This allows users to easily access any of these values, and to provide them to Raven as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'area': 55.91945346888515,\n",
-       " 'longitude': -71.41715786806483,\n",
-       " 'latitude': 48.47239495054429,\n",
-       " 'gravelius': 1.7025827715870572,\n",
-       " 'perimeter': 45133.04400352313,\n",
-       " 'Ocean': 0.0,\n",
-       " 'Forest': 0.9095753879046077,\n",
-       " 'Shrubs': 0.004920532612284039,\n",
-       " 'Grass': 0.005753721840562167,\n",
-       " 'Wetland': 0.0009589536400936945,\n",
-       " 'Crops': 0.045605319834619795,\n",
-       " 'Urban': 0.02361226831837261,\n",
-       " 'Water': 0.009573815849459998,\n",
-       " 'SnowIce': 0.0,\n",
-       " 'elevation': 165.37033101757254,\n",
-       " 'slope': 3.8477161303214786,\n",
-       " 'aspect': 5.4659402646877995}"
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'area': 55.91945346888515,\n",
+              " 'longitude': -71.41715786806483,\n",
+              " 'latitude': 48.47239495054429,\n",
+              " 'gravelius': 1.7025827715870572,\n",
+              " 'perimeter': 45133.04400352313,\n",
+              " 'Ocean': 0.0,\n",
+              " 'Forest': 0.9095753879046077,\n",
+              " 'Shrubs': 0.004920532612284039,\n",
+              " 'Grass': 0.005753721840562167,\n",
+              " 'Wetland': 0.0009589536400936945,\n",
+              " 'Crops': 0.045605319834619795,\n",
+              " 'Urban': 0.02361226831837261,\n",
+              " 'Water': 0.009573815849459998,\n",
+              " 'SnowIce': 0.0,\n",
+              " 'elevation': 165.37033101757254,\n",
+              " 'slope': 3.8477161303214786,\n",
+              " 'aspect': 5.4659402646877995}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "all_properties = {**shape_info, **land_use, **terrain}\n",
+        "display(all_properties)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "all_properties = {**shape_info, **land_use, **terrain}\n",
-    "display(all_properties)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/03_Extracting_forcing_data.ipynb b/docs/notebooks/03_Extracting_forcing_data.ipynb
index fd940633..40895807 100644
--- a/docs/notebooks/03_Extracting_forcing_data.ipynb
+++ b/docs/notebooks/03_Extracting_forcing_data.ipynb
@@ -1,871 +1,871 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 03 - Extracting forcing data\n",
-    "\n",
-    "## Extracting meteorological data for a selected watershed\n",
-    "Using a GeoJSON file extracted from the HydroSHEDS database or given by the user, meteorological datasets can be extracted inside the watershed's boundaries using the PAVICS-Hydro ERA5 database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "import tempfile\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import fsspec  # noqa\n",
-    "import intake\n",
-    "import s3fs  # noqa\n",
-    "import xarray as xr\n",
-    "from clisops.core import subset\n",
-    "\n",
-    "from ravenpy.utilities.testdata import get_file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to extract data for our watershed, we need to know:\n",
-    "\n",
-    "- The spatial extent (as defined by the watershed boundaries);\n",
-    "- The temporal extent (as defined by the start and end days of the period of interest).\n",
-    "\n",
-    "Let's define those now:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This will be our input section, where we control what we want to extract.\n",
-    "# We know which watershed interests us, it is the input.geojson file that we previously generated!\n",
-    "\n",
-    "# The contour can be generated using notebook \"01_Delineating watersheds, where it would be placed\n",
-    "# in the same folder as the notebooks and available in your workspace. The contour could then be accessed\n",
-    "# easily by defining it as follows:\n",
-    "\"\"\"\n",
-    "basin_contour = \"input.geojson\"\n",
-    "\"\"\"\n",
-    "# However, to keep things tidy, we have also prepared a version that can be accessed easily for\n",
-    "# demonstration purposes:\n",
-    "basin_contour = get_file(\"notebook_inputs/input.geojson\")\n",
-    "\n",
-    "# Also, we can specify which timeframe we want to extract. Here let's focus on a 10-year period\n",
-    "reference_start_day = dt.datetime(1985, 12, 31)\n",
-    "reference_stop_day = dt.datetime(1987, 1, 1)\n",
-    "# Notice we are using one day before and one day after the desired period of 1986-01-01 to 1986-12-31.\n",
-    "# This is to account for any UTC shifts that might require getting data in a previous or later time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now provide a means to get some data to run our model. Typically, models will require precipitation and temperature data, so let's get that data. We will use a generally reliable dataset that is available everywhere to minimize missing values: the ERA5 Reanalysis.\n",
-    "\n",
-    "The code block below gathers the required data automatically. If you need other data or want to use another source, this cell will need to be replaced for your customized needs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the ERA5 data from the Wasabi/Amazon S3 server.\n",
-    "catalog_name = \"https://raw.githubusercontent.com/hydrocloudservices/catalogs/main/catalogs/atmosphere.yaml\"\n",
-    "cat = intake.open_catalog(catalog_name)\n",
-    "ds = cat.era5_reanalysis_single_levels.to_dask()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the ERA5 data. We will rechunk it to a single chunk to make it compatible with other codes on the platform, especially bias-correction.\n",
-    "We are also taking the daily min and max temperatures as well as the daily total precipitation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 03 - Extracting forcing data\n",
+        "\n",
+        "## Extracting meteorological data for a selected watershed\n",
+        "Using a GeoJSON file extracted from the HydroSHEDS database or given by the user, meteorological datasets can be extracted inside the watershed's boundaries using the PAVICS-Hydro ERA5 database."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import datetime as dt\n",
+        "import tempfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import fsspec  # noqa\n",
+        "import intake\n",
+        "import s3fs  # noqa\n",
+        "import xarray as xr\n",
+        "from clisops.core import subset\n",
+        "\n",
+        "from ravenpy.utilities.testdata import get_file"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If we want to extract data for our watershed, we need to know:\n",
+        "\n",
+        "- The spatial extent (as defined by the watershed boundaries);\n",
+        "- The temporal extent (as defined by the start and end days of the period of interest).\n",
+        "\n",
+        "Let's define those now:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# This will be our input section, where we control what we want to extract.\n",
+        "# We know which watershed interests us, it is the input.geojson file that we previously generated!\n",
+        "\n",
+        "# The contour can be generated using notebook \"01_Delineating watersheds, where it would be placed\n",
+        "# in the same folder as the notebooks and available in your workspace. The contour could then be accessed\n",
+        "# easily by defining it as follows:\n",
+        "\"\"\"\n",
+        "basin_contour = \"input.geojson\"\n",
+        "\"\"\"\n",
+        "# However, to keep things tidy, we have also prepared a version that can be accessed easily for\n",
+        "# demonstration purposes:\n",
+        "basin_contour = get_file(\"notebook_inputs/input.geojson\")\n",
+        "\n",
+        "# Also, we can specify which timeframe we want to extract. Here let's focus on a 10-year period\n",
+        "reference_start_day = dt.datetime(1985, 12, 31)\n",
+        "reference_stop_day = dt.datetime(1987, 1, 1)\n",
+        "# Notice we are using one day before and one day after the desired period of 1986-01-01 to 1986-12-31.\n",
+        "# This is to account for any UTC shifts that might require getting data in a previous or later time."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now provide a means to get some data to run our model. Typically, models will require precipitation and temperature data, so let's get that data. We will use a generally reliable dataset that is available everywhere to minimize missing values: the ERA5 Reanalysis.\n",
+        "\n",
+        "The code block below gathers the required data automatically. If you need other data or want to use another source, this cell will need to be replaced for your customized needs."
+      ]
+    },
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-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (time: 367, latitude: 1, longitude: 1)&gt;\n",
-       "dask.array&lt;rechunk-merge, shape=(367, 1, 1), dtype=float32, chunksize=(367, 1, 1), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates:\n",
-       "  * latitude   (latitude) float64 48.5\n",
-       "  * longitude  (longitude) float64 -71.5\n",
-       "  * time       (time) datetime64[ns] 1985-12-31 1986-01-01 ... 1987-01-01\n",
-       "Attributes:\n",
-       "    long_name:     Total precipitation\n",
-       "    units:         m\n",
-       "    grid_mapping:  crs</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 367</li><li><span class='xr-has-index'>latitude</span>: 1</li><li><span class='xr-has-index'>longitude</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d6169682-bc1c-461b-9464-ba5fa4c9b7ff' class='xr-array-in' type='checkbox' checked><label for='section-d6169682-bc1c-461b-9464-ba5fa4c9b7ff' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(367, 1, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.43 kiB </td>\n",
-       "                        <td> 1.43 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (367, 1, 1) </td>\n",
-       "                        <td> (367, 1, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 35 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,126.000852,83.294544)\">1</text>\n",
-       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">367</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-b8000513-70a6-4f00-b4c6-c84b1c3b2197' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8000513-70a6-4f00-b4c6-c84b1c3b2197' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>48.5</div><input id='attrs-e4937338-9d7f-4fe1-ad88-f8a7b6145477' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4937338-9d7f-4fe1-ad88-f8a7b6145477' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dacbe3d-fa44-4105-8285-43ab41e8c725' class='xr-var-data-in' type='checkbox'><label for='data-1dacbe3d-fa44-4105-8285-43ab41e8c725' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([48.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-71.5</div><input id='attrs-1294af79-e3b5-4b73-a46f-87ce67c8cf52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1294af79-e3b5-4b73-a46f-87ce67c8cf52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda5ea09-26b2-4fb6-b527-6b9e6e8d4923' class='xr-var-data-in' type='checkbox'><label for='data-eda5ea09-26b2-4fb6-b527-6b9e6e8d4923' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-71.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1985-12-31 ... 1987-01-01</div><input id='attrs-09d8ad0b-4013-4c3b-b576-7290b8c927b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09d8ad0b-4013-4c3b-b576-7290b8c927b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc31aef6-37c2-49f9-a263-fd09be48b2e8' class='xr-var-data-in' type='checkbox'><label for='data-dc31aef6-37c2-49f9-a263-fd09be48b2e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1985-12-31T00:00:00.000000000&#x27;, &#x27;1986-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1986-01-02T00:00:00.000000000&#x27;, ..., &#x27;1986-12-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1986-12-31T00:00:00.000000000&#x27;, &#x27;1987-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b7fadc9-beb8-401f-8709-2962edf0278b' class='xr-section-summary-in' type='checkbox'  ><label for='section-0b7fadc9-beb8-401f-8709-2962edf0278b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b82b151d-2dd3-42b9-9e22-bc61aa213ea1' class='xr-index-data-in' type='checkbox'/><label for='index-b82b151d-2dd3-42b9-9e22-bc61aa213ea1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([48.5], dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5fde8658-e71c-44e3-9452-d08dad6a2726' class='xr-index-data-in' type='checkbox'/><label for='index-5fde8658-e71c-44e3-9452-d08dad6a2726' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-71.5], dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9022f398-18c3-4976-a6eb-9614516dcf49' class='xr-index-data-in' type='checkbox'/><label for='index-9022f398-18c3-4976-a6eb-9614516dcf49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1985-12-31&#x27;, &#x27;1986-01-01&#x27;, &#x27;1986-01-02&#x27;, &#x27;1986-01-03&#x27;,\n",
-       "               &#x27;1986-01-04&#x27;, &#x27;1986-01-05&#x27;, &#x27;1986-01-06&#x27;, &#x27;1986-01-07&#x27;,\n",
-       "               &#x27;1986-01-08&#x27;, &#x27;1986-01-09&#x27;,\n",
-       "               ...\n",
-       "               &#x27;1986-12-23&#x27;, &#x27;1986-12-24&#x27;, &#x27;1986-12-25&#x27;, &#x27;1986-12-26&#x27;,\n",
-       "               &#x27;1986-12-27&#x27;, &#x27;1986-12-28&#x27;, &#x27;1986-12-29&#x27;, &#x27;1986-12-30&#x27;,\n",
-       "               &#x27;1986-12-31&#x27;, &#x27;1987-01-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=367, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-deb25bc5-d9b4-45ae-9c20-95ca34356cd3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-deb25bc5-d9b4-45ae-9c20-95ca34356cd3' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div></li></ul></div></div>"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the ERA5 data from the Wasabi/Amazon S3 server.\n",
+        "catalog_name = \"https://raw.githubusercontent.com/hydrocloudservices/catalogs/main/catalogs/atmosphere.yaml\"\n",
+        "cat = intake.open_catalog(catalog_name)\n",
+        "ds = cat.era5_reanalysis_single_levels.to_dask()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get the ERA5 data. We will rechunk it to a single chunk to make it compatible with other codes on the platform, especially bias-correction.\n",
+        "We are also taking the daily min and max temperatures as well as the daily total precipitation."
+      ]
+    },
+    {
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+      "execution_count": 4,
+      "metadata": {
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+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (time: 367, latitude: 1, longitude: 1)&gt;\n",
+              "dask.array&lt;rechunk-merge, shape=(367, 1, 1), dtype=float32, chunksize=(367, 1, 1), chunktype=numpy.ndarray&gt;\n",
+              "Coordinates:\n",
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+              "  * time       (time) datetime64[ns] 1985-12-31 1986-01-01 ... 1987-01-01\n",
+              "Attributes:\n",
+              "    long_name:     Total precipitation\n",
+              "    units:         m\n",
+              "    grid_mapping:  crs</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 367</li><li><span class='xr-has-index'>latitude</span>: 1</li><li><span class='xr-has-index'>longitude</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d6169682-bc1c-461b-9464-ba5fa4c9b7ff' class='xr-array-in' type='checkbox' checked><label for='section-d6169682-bc1c-461b-9464-ba5fa4c9b7ff' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(367, 1, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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+              "                        <td colspan=\"2\"> 1 chunks in 35 graph layers </td>\n",
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+              "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,126.000852,83.294544)\">1</text>\n",
+              "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">367</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></div></li><li class='xr-section-item'><input id='section-b8000513-70a6-4f00-b4c6-c84b1c3b2197' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8000513-70a6-4f00-b4c6-c84b1c3b2197' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>48.5</div><input id='attrs-e4937338-9d7f-4fe1-ad88-f8a7b6145477' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4937338-9d7f-4fe1-ad88-f8a7b6145477' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dacbe3d-fa44-4105-8285-43ab41e8c725' class='xr-var-data-in' type='checkbox'><label for='data-1dacbe3d-fa44-4105-8285-43ab41e8c725' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>stored_direction :</span></dt><dd>decreasing</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([48.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-71.5</div><input id='attrs-1294af79-e3b5-4b73-a46f-87ce67c8cf52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1294af79-e3b5-4b73-a46f-87ce67c8cf52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda5ea09-26b2-4fb6-b527-6b9e6e8d4923' class='xr-var-data-in' type='checkbox'><label for='data-eda5ea09-26b2-4fb6-b527-6b9e6e8d4923' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-71.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1985-12-31 ... 1987-01-01</div><input id='attrs-09d8ad0b-4013-4c3b-b576-7290b8c927b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09d8ad0b-4013-4c3b-b576-7290b8c927b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc31aef6-37c2-49f9-a263-fd09be48b2e8' class='xr-var-data-in' type='checkbox'><label for='data-dc31aef6-37c2-49f9-a263-fd09be48b2e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1985-12-31T00:00:00.000000000&#x27;, &#x27;1986-01-01T00:00:00.000000000&#x27;,\n",
+              "       &#x27;1986-01-02T00:00:00.000000000&#x27;, ..., &#x27;1986-12-30T00:00:00.000000000&#x27;,\n",
+              "       &#x27;1986-12-31T00:00:00.000000000&#x27;, &#x27;1987-01-01T00:00:00.000000000&#x27;],\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b7fadc9-beb8-401f-8709-2962edf0278b' class='xr-section-summary-in' type='checkbox'  ><label for='section-0b7fadc9-beb8-401f-8709-2962edf0278b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b82b151d-2dd3-42b9-9e22-bc61aa213ea1' class='xr-index-data-in' type='checkbox'/><label for='index-b82b151d-2dd3-42b9-9e22-bc61aa213ea1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([48.5], dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5fde8658-e71c-44e3-9452-d08dad6a2726' class='xr-index-data-in' type='checkbox'/><label for='index-5fde8658-e71c-44e3-9452-d08dad6a2726' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-71.5], dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9022f398-18c3-4976-a6eb-9614516dcf49' class='xr-index-data-in' type='checkbox'/><label for='index-9022f398-18c3-4976-a6eb-9614516dcf49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1985-12-31&#x27;, &#x27;1986-01-01&#x27;, &#x27;1986-01-02&#x27;, &#x27;1986-01-03&#x27;,\n",
+              "               &#x27;1986-01-04&#x27;, &#x27;1986-01-05&#x27;, &#x27;1986-01-06&#x27;, &#x27;1986-01-07&#x27;,\n",
+              "               &#x27;1986-01-08&#x27;, &#x27;1986-01-09&#x27;,\n",
+              "               ...\n",
+              "               &#x27;1986-12-23&#x27;, &#x27;1986-12-24&#x27;, &#x27;1986-12-25&#x27;, &#x27;1986-12-26&#x27;,\n",
+              "               &#x27;1986-12-27&#x27;, &#x27;1986-12-28&#x27;, &#x27;1986-12-29&#x27;, &#x27;1986-12-30&#x27;,\n",
+              "               &#x27;1986-12-31&#x27;, &#x27;1987-01-01&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=367, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-deb25bc5-d9b4-45ae-9c20-95ca34356cd3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-deb25bc5-d9b4-45ae-9c20-95ca34356cd3' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.DataArray 'tp' (time: 367, latitude: 1, longitude: 1)>\n",
+              "dask.array<rechunk-merge, shape=(367, 1, 1), dtype=float32, chunksize=(367, 1, 1), chunktype=numpy.ndarray>\n",
+              "Coordinates:\n",
+              "  * latitude   (latitude) float64 48.5\n",
+              "  * longitude  (longitude) float64 -71.5\n",
+              "  * time       (time) datetime64[ns] 1985-12-31 1986-01-01 ... 1987-01-01\n",
+              "Attributes:\n",
+              "    long_name:     Total precipitation\n",
+              "    units:         m\n",
+              "    grid_mapping:  crs"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "<xarray.DataArray 'tp' (time: 367, latitude: 1, longitude: 1)>\n",
-       "dask.array<rechunk-merge, shape=(367, 1, 1), dtype=float32, chunksize=(367, 1, 1), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * latitude   (latitude) float64 48.5\n",
-       "  * longitude  (longitude) float64 -71.5\n",
-       "  * time       (time) datetime64[ns] 1985-12-31 1986-01-01 ... 1987-01-01\n",
-       "Attributes:\n",
-       "    long_name:     Total precipitation\n",
-       "    units:         m\n",
-       "    grid_mapping:  crs"
+      "source": [
+        "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    ERA5_reference = subset.subset_shape(\n",
+        "        ds.sel(time=slice(reference_start_day, reference_stop_day)), basin_contour\n",
+        "    )\n",
+        "    ERA5_tas = ERA5_reference[\"t2m\"].resample(time=\"1D\")\n",
+        "    ERA5_tmin = ERA5_tas.min().chunk(-1, -1, -1)\n",
+        "    ERA5_tmax = ERA5_tas.max().chunk(-1, -1, -1)\n",
+        "    ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)\n",
+        "\n",
+        "ERA5_pr"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    ERA5_reference = subset.subset_shape(\n",
-    "        ds.sel(time=slice(reference_start_day, reference_stop_day)), basin_contour\n",
-    "    )\n",
-    "    ERA5_tas = ERA5_reference[\"t2m\"].resample(time=\"1D\")\n",
-    "    ERA5_tmin = ERA5_tas.min().chunk(-1, -1, -1)\n",
-    "    ERA5_tmax = ERA5_tas.max().chunk(-1, -1, -1)\n",
-    "    ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)\n",
-    "\n",
-    "ERA5_pr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now convert these variables to the desired format and save them to disk in netcdf files to use at a later time (in a future notebook!)\n",
-    "\n",
-    "First, we will want to make sure that the units we are working with are compatible with the Raven modelling framework. We will want precipitation to be in mm (per time period, here we are working daily so it will be in mm/day), and temperatures will be in °C. Let's check out the current units:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tmin units: K\n",
-      "Tmax units: K\n",
-      "Precipitation units: m\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Tmin units: {ERA5_tmin.units}\")\n",
-    "print(f\"Tmax units: {ERA5_tmax.units}\")\n",
-    "print(f\"Precipitation units: {ERA5_pr.units}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the units are in Kelvin for temperatures and in meters for precipitation. We will want to do some conversions!\n",
-    "\n",
-    "Let's start by applying offsets for temperatures and a conversion factor for precipitation:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with xr.set_options(keep_attrs=True):\n",
-    "    ERA5_tmin = ERA5_tmin - 273.15  # K to °C\n",
-    "    ERA5_tmin.attrs[\"units\"] = \"degC\"\n",
-    "\n",
-    "    ERA5_tmax = ERA5_tmax - 273.15  # K to °C\n",
-    "    ERA5_tmax.attrs[\"units\"] = \"degC\"\n",
-    "\n",
-    "    ERA5_pr = ERA5_pr * 1000  # m to mm\n",
-    "    ERA5_pr.attrs[\"units\"] = \"mm\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see the changes now by re-inspecting the datasets:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now convert these variables to the desired format and save them to disk in netcdf files to use at a later time (in a future notebook!)\n",
+        "\n",
+        "First, we will want to make sure that the units we are working with are compatible with the Raven modelling framework. We will want precipitation to be in mm (per time period, here we are working daily so it will be in mm/day), and temperatures will be in °C. Let's check out the current units:"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tmin units: degC\n",
-      "Tmax units: degC\n",
-      "Precipitation units: mm\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Tmin units: {ERA5_tmin.units}\")\n",
-    "print(f\"Tmax units: {ERA5_tmax.units}\")\n",
-    "print(f\"Precipitation units: {ERA5_pr.units}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So let's write them to disk for now. We will use the netcdf format as this is what Raven uses for inputs. It is possible you will get some warnings, this is OK and should not cause any problems. Since our model will run in lumped mode, we will average the spatial dimensions of each variable over the domain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with xr.set_options(keep_attrs=True):\n",
-    "    # Average the variables\n",
-    "    ERA5_tmin = ERA5_tmin.mean({\"latitude\", \"longitude\"})\n",
-    "    ERA5_tmax = ERA5_tmax.mean({\"latitude\", \"longitude\"})\n",
-    "    ERA5_pr = ERA5_pr.mean({\"latitude\", \"longitude\"})\n",
-    "\n",
-    "    # Ensure that the precipitation is non-negative, which can happen with some reanalysis models.\n",
-    "    ERA5_pr[ERA5_pr < 0] = 0\n",
-    "\n",
-    "    # Transform them to a dataset such that they can be written with attributes to netcdf\n",
-    "    ERA5_tmin = ERA5_tmin.to_dataset(name=\"tmin\", promote_attrs=True)\n",
-    "    ERA5_tmax = ERA5_tmax.to_dataset(name=\"tmax\", promote_attrs=True)\n",
-    "    ERA5_pr = ERA5_pr.to_dataset(name=\"pr\", promote_attrs=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tmin units: K\n",
+            "Tmax units: K\n",
+            "Precipitation units: m\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"Tmin units: {ERA5_tmin.units}\")\n",
+        "print(f\"Tmax units: {ERA5_tmax.units}\")\n",
+        "print(f\"Precipitation units: {ERA5_pr.units}\")"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f0b917289a0>]"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the units are in Kelvin for temperatures and in meters for precipitation. We will want to do some conversions!\n",
+        "\n",
+        "Let's start by applying offsets for temperatures and a conversion factor for precipitation:\n"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with xr.set_options(keep_attrs=True):\n",
+        "    ERA5_tmin = ERA5_tmin - 273.15  # K to °C\n",
+        "    ERA5_tmin.attrs[\"units\"] = \"degC\"\n",
+        "\n",
+        "    ERA5_tmax = ERA5_tmax - 273.15  # K to °C\n",
+        "    ERA5_tmax.attrs[\"units\"] = \"degC\"\n",
+        "\n",
+        "    ERA5_pr = ERA5_pr * 1000  # m to mm\n",
+        "    ERA5_pr.attrs[\"units\"] = \"mm\""
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Check and see if the precipitation makes sense:\n",
-    "ERA5_pr.pr.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Here we will write the files to disk in a temporary folder since the root folder containing these notebooks is read-only.\n",
-    "You can change the path here to your own preferred path in your writable workspace. Alternatively, if you copy this notebook to your writable-workspace as shown in the introduction documentation, you can save just the filename (no absolute path) and the file will appear \"beside\" the notebooks, ready to be read by the next series of notebooks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with xr.set_options(keep_attrs=True):\n",
-    "    # Write to disk.\n",
-    "    tmp = Path(tempfile.mkdtemp())\n",
-    "    ERA5_tmin.to_netcdf(tmp / \"ERA5_tmin.nc\")\n",
-    "    ERA5_tmax.to_netcdf(tmp / \"ERA5_tmax.nc\")\n",
-    "    ERA5_pr.to_netcdf(tmp / \"ERA5_pr.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We can also prepare a single file that merges all three variables into one netcdf file:\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    xr.merge([ERA5_tmin, ERA5_tmax, ERA5_pr]).to_netcdf(tmp / \"ERA5_weather_data.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have daily precipitation and minimum/maximum temperatures to drive our Raven Model, which we will do in the next notebook!\n",
-    "\n",
-    "Note that our dataset generated here is very short (1 year) but the same dataset for the period 1980-12-31 to 1991-01-01 has been pre-generated and stored on the server for efficiency.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
-  "nbdime-conflicts": {
-   "local_diff": [
+    },
     {
-     "diff": [
-      {
-       "diff": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see the changes now by re-inspecting the datasets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
         {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "3.6.7"
-         ]
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tmin units: degC\n",
+            "Tmax units: degC\n",
+            "Precipitation units: mm\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"Tmin units: {ERA5_tmin.units}\")\n",
+        "print(f\"Tmax units: {ERA5_tmax.units}\")\n",
+        "print(f\"Precipitation units: {ERA5_pr.units}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "So let's write them to disk for now. We will use the netcdf format as this is what Raven uses for inputs. It is possible you will get some warnings, this is OK and should not cause any problems. Since our model will run in lumped mode, we will average the spatial dimensions of each variable over the domain."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with xr.set_options(keep_attrs=True):\n",
+        "    # Average the variables\n",
+        "    ERA5_tmin = ERA5_tmin.mean({\"latitude\", \"longitude\"})\n",
+        "    ERA5_tmax = ERA5_tmax.mean({\"latitude\", \"longitude\"})\n",
+        "    ERA5_pr = ERA5_pr.mean({\"latitude\", \"longitude\"})\n",
+        "\n",
+        "    # Ensure that the precipitation is non-negative, which can happen with some reanalysis models.\n",
+        "    ERA5_pr[ERA5_pr < 0] = 0\n",
+        "\n",
+        "    # Transform them to a dataset such that they can be written with attributes to netcdf\n",
+        "    ERA5_tmin = ERA5_tmin.to_dataset(name=\"tmin\", promote_attrs=True)\n",
+        "    ERA5_tmax = ERA5_tmax.to_dataset(name=\"tmax\", promote_attrs=True)\n",
+        "    ERA5_pr = ERA5_pr.to_dataset(name=\"pr\", promote_attrs=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f0b917289a0>]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
         },
         {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
-       ],
-       "key": "version",
-       "op": "patch"
-      }
-     ],
-     "key": "language_info",
-     "op": "patch"
-    }
-   ],
-   "remote_diff": [
+      ],
+      "source": [
+        "# Check and see if the precipitation makes sense:\n",
+        "ERA5_pr.pr.plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Here we will write the files to disk in a temporary folder since the root folder containing these notebooks is read-only.\n",
+        "You can change the path here to your own preferred path in your writable workspace. Alternatively, if you copy this notebook to your writable-workspace as shown in the introduction documentation, you can save just the filename (no absolute path) and the file will appear \"beside\" the notebooks, ready to be read by the next series of notebooks."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with xr.set_options(keep_attrs=True):\n",
+        "    # Write to disk.\n",
+        "    tmp = Path(tempfile.mkdtemp())\n",
+        "    ERA5_tmin.to_netcdf(tmp / \"ERA5_tmin.nc\")\n",
+        "    ERA5_tmax.to_netcdf(tmp / \"ERA5_tmax.nc\")\n",
+        "    ERA5_pr.to_netcdf(tmp / \"ERA5_pr.nc\")"
+      ]
+    },
     {
-     "diff": [
-      {
-       "diff": [
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We can also prepare a single file that merges all three variables into one netcdf file:\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    xr.merge([ERA5_tmin, ERA5_tmax, ERA5_pr]).to_netcdf(tmp / \"ERA5_weather_data.nc\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now have daily precipitation and minimum/maximum temperatures to drive our Raven Model, which we will do in the next notebook!\n",
+        "\n",
+        "Note that our dataset generated here is very short (1 year) but the same dataset for the period 1980-12-31 to 1991-01-01 has been pre-generated and stored on the server for efficiency.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    },
+    "nbdime-conflicts": {
+      "local_diff": [
         {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "3.6.10"
-         ]
-        },
+          "diff": [
+            {
+              "diff": [
+                {
+                  "key": 0,
+                  "op": "addrange",
+                  "valuelist": [
+                    "3.6.7"
+                  ]
+                },
+                {
+                  "key": 0,
+                  "length": 1,
+                  "op": "removerange"
+                }
+              ],
+              "key": "version",
+              "op": "patch"
+            }
+          ],
+          "key": "language_info",
+          "op": "patch"
+        }
+      ],
+      "remote_diff": [
         {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
+          "diff": [
+            {
+              "diff": [
+                {
+                  "key": 0,
+                  "op": "addrange",
+                  "valuelist": [
+                    "3.6.10"
+                  ]
+                },
+                {
+                  "key": 0,
+                  "length": 1,
+                  "op": "removerange"
+                }
+              ],
+              "key": "version",
+              "op": "patch"
+            }
+          ],
+          "key": "language_info",
+          "op": "patch"
         }
-       ],
-       "key": "version",
-       "op": "patch"
-      }
-     ],
-     "key": "language_info",
-     "op": "patch"
+      ]
     }
-   ]
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/04_Emulating_hydrological_models.ipynb b/docs/notebooks/04_Emulating_hydrological_models.ipynb
index 8602101e..39987866 100644
--- a/docs/notebooks/04_Emulating_hydrological_models.ipynb
+++ b/docs/notebooks/04_Emulating_hydrological_models.ipynb
@@ -1,827 +1,827 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 04 - Emulating hydrological models\n",
-    "\n",
-    "## Using Ravenpy to emulate an existing hydrological model\n",
-    "\n",
-    "In this notebook, we will demonstrate the versatility of the Raven modelling framework to emulate one of eight hydrological models that are currently supported. We will walk through the different configuration parameters required to build the model and simulate streamflow on a catchments. We will also show how to import files from a pre-configured Raven configuration that users can inport into Ravenpy instead of using one of the default emulators.\n",
-    "\n",
-    "## A note on datasets\n",
-    "\n",
-    "There are numerous ways to run a Raven model and to pass its required input data. For this introduction to RavenPy, we will use our ERA5 data we generated in the previous notebook and we will configure the Raven model instance on the fly! In the next tutorials, we will see how users can import and use their own datasets to make the entire process flexible and tailored to the user needs.\n",
-    "\n",
-    "## Using templated model emulators\n",
-    "The first thing we need to run the raven model is... a Raven model! Raven is not a model per se, but a modelling framework that can be used to build hydrological models from their underlying components. For now, PAVICS-Hydro allows building a set of pre-determined models. The Python wrapper offers at present eight model emulators: GR4J-CN, HMETS, MOHYSE, HBV-EC, Canadian Shield, HYPR, Sacramento and Blended. For each of these, templated configuration files are available to facilitate launching the model with options passed by Python at run-time.\n",
-    "\n",
-    "In the next cell, we are going to import the possible models, and later, we will configure and run the GR4J-CN model. Please see the documentation for more details on the mandatory vs optional parameters, and what they represent. A small glimpse is provided here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the list of possible model templates.\n",
-    "from ravenpy.config.emulators import (\n",
-    "    GR4JCN,\n",
-    "    HBVEC,\n",
-    "    HMETS,\n",
-    "    HYPR,\n",
-    "    SACSMA,\n",
-    "    Blended,\n",
-    "    CanadianShield,\n",
-    "    Mohyse,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "\n",
-    "# Import other required packages:\n",
-    "import tempfile\n",
-    "from pathlib import Path\n",
-    "\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.utilities.testdata import get_file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this next step, we will define the hydrological response unit (HRU). For lumped models, there is only one unit so the following structure should be good. However, for distributed modelling, there will be more than one HRU, so we would use another tool to help us build the HRUs in that case. The HRU provides information on the area, elevation, and location of the catchment.\n",
-    "\n",
-    "For now, let's provide the basin properties such that Raven can run. These are the minimal values that must always be provided, but some models might require other inputs. Please see the Raven documentation for more information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the hydrological response unit. We can use the information from the tutorial notebook #02! Here we are using\n",
-    "# arbitrary data for a test catchment.\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next required inputs are the start and end dates for the simulation. The `start_date` and `end_date` arguments indicate when a simulation should start and end. As long as the forcing data covers the simulation period, it should work. If these parameters are not defined, then start and end dates default to the start and end of the driving data.\n",
-    "\n",
-    "To keep things simple, we will use a short 5-year period. Note that the dates are python datetime.datetime objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_date = dt.datetime(1985, 1, 1)\n",
-    "end_date = dt.datetime(1990, 1, 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are now ready to build our first Raven-based hydrological model. the model will be the GR4JCN model. The following code block will show and describe every step. However, more control options are available for users. Please see the documentation for a more detailed explanation on model options."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Import required packages. We already imported the GR4JCN emulator in the first cell, but let's keep it here for\n",
-    "# completeness.\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "\n",
-    "# Alternative variable names are useful for allowing Raven to read variables from NetCDF files even if the variable\n",
-    "# names are not those that are expected by Raven. For example, our ERA5-dernived temperature variables are named\n",
-    "# \"tmax\" and \"tmin\", whereas Raven expects \"TEMP_MAX\" and \"TEMP_MIN\". Therefore, instead of forcing users to rename\n",
-    "# their variables, we provide a mechanism to tell Raven which variables in the NetCDF files correspond to which\n",
-    "# meteorological variable.\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Here is where we build the raven configuration. We will first configure the model in memory based on the user\n",
-    "# preferences, and then we will write the Raven configuration files to disk such that Raven can read them and\n",
-    "# execute a simulation. In this case, we are building a GR4JCN model, but you could change this to any of the\n",
-    "# eight models that are preconfigured for direct emulation in Ravenpy.\n",
-    "\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"latitude\": hru[\"latitude\"],\n",
-    "        \"longitude\": hru[\"longitude\"],\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "run_name = \"test_NB_04\"\n",
-    "\n",
-    "# Get the weather data. It can either be to a data file that was already in the same folder/workspace as this\n",
-    "# notebook, as we generated in the previous notebook, or it could be a path to a file stored elsewhere.\n",
-    "\n",
-    "# Example for using the data we just generated in Notebook 03:\n",
-    "\"\"\"\n",
-    "ERA5_tmax = \"ERA5_tmax.nc\"\n",
-    "ERA5_tmin = \"ERA5_tmin.nc\"\n",
-    "ERA5_pr = \"ERA5_pr.nc\"\n",
-    "\"\"\"\n",
-    "\n",
-    "# In our case, we will prefer to link to existing, pre-computed and locally stored files to keep things tidy:\n",
-    "ERA5_tmax = get_file(\"notebook_inputs/ERA5_tmax.nc\")\n",
-    "ERA5_tmin = get_file(\"notebook_inputs/ERA5_tmin.nc\")\n",
-    "ERA5_pr = get_file(\"notebook_inputs/ERA5_pr.nc\")\n",
-    "\n",
-    "default_emulator_config = dict(\n",
-    "    # The HRU as defined earlier must be provided. This is the physical representation of the catchment that Raven\n",
-    "    # needs for certain models and processes.\n",
-    "    HRUs=[hru],\n",
-    "    # Model simulation start and end dates.\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    # Name of the simulation. Raven will prefix all .rvX files and model outputs with the runName.\n",
-    "    RunName=run_name,\n",
-    "    # Custom outputs allow pre-processing of certain statistics and variables after the model runs. Please see the\n",
-    "    # documentation for more details on possible options.\n",
-    "    CustomOutput=[\n",
-    "        rc.CustomOutput(\n",
-    "            time_per=\"YEARLY\",\n",
-    "            stat=\"AVERAGE\",\n",
-    "            variable=\"PRECIP\",\n",
-    "            space_agg=\"ENTIRE_WATERSHED\",\n",
-    "        )\n",
-    "    ],\n",
-    "    # Here we will prepare the weather gauge data to be fed to the model. The data could also come from other\n",
-    "    # sources such as the ERA5 reanalysis product. There are 2 ways to do this. We will show one way here, and\n",
-    "    # then show the alternative method in the following cell.\n",
-    "    #\n",
-    "    # METHOD 1: If you have one netcdf file of data per meteorological variable (such as what is generated in the\n",
-    "    #    03_Extracting_forcing_data.ipynb notebook), you can add them successively as follows. Note that we are\n",
-    "    #    creating a gauge for each variable. Raven will use the data from the three sources to provide forcing\n",
-    "    #    data to the simulation.\n",
-    "    #\n",
-    "    #   You can add the files you need! As long as there are timestamps associated with each value in the netcdf\n",
-    "    #   files, and the other required information (data_type, alt_names, other required information), the code\n",
-    "    #   will accept them and use what it needs. As per the Raven model itself, the gauge station elevation, latitude\n",
-    "    #   and longitude are required to let the model run. For lumped models such as the ones we are currently\n",
-    "    #   emulating, there is only one \"station\" that corresponds to the basin-averaged weahter. In this case, we\n",
-    "    #   set the station elevation to that of the mean catchment elevation to remove any adiabatic gradient\n",
-    "    #   modifications to the data. We also provide the catchment centroid latitude and longitude which we take\n",
-    "    #   from the only HRU defining the entire catchment. For multi-gauge basins and semi-distributed models, the\n",
-    "    #   latitude and longitude must be correctly identified for each station.\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ERA5_tmax,  # This is a path to the file of ERA5-derived maximum daily temperature.\n",
-    "            data_type=[\n",
-    "                \"TEMP_MAX\"\n",
-    "            ],  # Raven expects maximum temperature to be identified as \"TEMP_MAX\", so we indicate that this variable is maximum temperature.\n",
-    "            alt_names=alt_names,  # However, the variable name in the file is different to the one Raven expects: use alt-names.\n",
-    "            data_kwds=data_kwds,\n",
-    "        ),\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ERA5_tmin,\n",
-    "            data_type=[\"TEMP_MIN\"],\n",
-    "            alt_names=alt_names,\n",
-    "            data_kwds=data_kwds,\n",
-    "        ),\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ERA5_pr, data_type=[\"PRECIP\"], alt_names=alt_names, data_kwds=data_kwds\n",
-    "        ),\n",
-    "    ],\n",
-    ")\n",
-    "\n",
-    "\n",
-    "m = GR4JCN(\n",
-    "    # Raven requires parameters for the GR4JCN model. For now, we provide default values, but in a later notebook\n",
-    "    # we will show how to calibrate and find new parameters for our model.\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    # GR4JCN needs an extra constant parameter for the catchment, corresponding to the G50 parameter in the CEMANEIGE\n",
-    "    # description. Here is how we provide it.\n",
-    "    GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 208.480},\n",
-    "    **default_emulator_config,\n",
-    ")\n",
-    "\n",
-    "# We are now ready to write this newly-configured model to disk through the use of .rvX files that Raven will read.\n",
-    "\n",
-    "# In the following code snippet, there is a \"workdir\" path that must be provided. This indicates the path\n",
-    "# where the data used to run the model (RAVEN .RV files) will be made available from the PAVICS Jupyter environment.\n",
-    "# The default \"workdir\" setting puts model outputs in the temporary directory (\"/tmp\"),\n",
-    "# which is not visible from the Jupyter file explorer. Therefore, You can change the last subfolder,\n",
-    "# but '/notebook_dir/writable-workspace/' must be the beginning  of the path when running on the PAVICS platform.\n",
-    "\n",
-    "workdir = Path(tempfile.mkdtemp(prefix=\"NB4\"))\n",
-    "m.write_rv(workdir=workdir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 2- Alternatively, we could already have (or we could create) a single netcdf file containing all the required\n",
-    "#    forcing data. Since we computed such a merged file in Notebook 03, let's reuse it here\n",
-    "\n",
-    "# Example for using the data we just generated in Notebook 03:\n",
-    "\"\"\"\n",
-    "ERA5_full = ERA5_weather_data.nc\n",
-    "\"\"\"\n",
-    "\n",
-    "# In our case, we will prefer to link to existing, pre-computed and locally stored files to keep things tidy:\n",
-    "ERA5_full = get_file(\"notebook_inputs/ERA5_weather_data.nc\")\n",
-    "\n",
-    "#    Since our meteorological gauge data is all included in a single file, we need to tell the model which variables\n",
-    "#    we are providing. We will generate the list now and pass it later to Ravenpy as an argument to the model.\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "\n",
-    "# Set up the gauge using the second method, i.e., using a single file that contains all meteorological inputs. As\n",
-    "# you can see, a single gauge is added, but it contains all the information we need.\n",
-    "default_emulator_config[\"Gauge\"] = [\n",
-    "    rc.Gauge.from_nc(\n",
-    "        ERA5_full,  # Path to the ERA5 file containing all three meteorological variables\n",
-    "        data_type=data_type,  # Note that this is the list of all the variables\n",
-    "        alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
-    "        data_kwds=data_kwds,\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "#    Now that we have the single file containing tmax, tmin and pr, we can setup a single gauge that contains all three.\n",
-    "m = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 208.480},\n",
-    "    **default_emulator_config,\n",
-    ")\n",
-    "\n",
-    "# Now we will write the files to disk to prepare them for Raven. This step is not strictly necessary since the\n",
-    "# next step will also write files to disk automatically. We will leave it here so we can see the intermediate step\n",
-    "# and inspect the files if necessary.\n",
-    "\n",
-    "workdir = Path(tempfile.mkdtemp(prefix=\"NB4\"))\n",
-    "m.write_rv(workdir=workdir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It can be seen that both methods generated .rvX files in the indicated path. This makes the Ravenpy platform more flexible for various use-cases, where some data can be stored in independent files or databases (perhaps temperatures come from one source and precipitation from another source).\n",
-    "\n",
-    "The above code only created the .rvX files. The model did not actually run yet. To do so, we must ask it to, as follows. Don't worry about the warnings: Raven is informing us that it has generated some internal parameters to build the model configuration based on some of the parameters we provided."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If we want to import our own raven configuration files and forcing data, we can do so by importing them\n",
-    "# using the ravenpy.run  method. This will run the model exactly as the users will have designed it.\n",
-    "from ravenpy import OutputReader\n",
-    "from ravenpy.ravenpy import run\n",
-    "\n",
-    "# This is used to specify the raven configuration files prefixes. In this case, we will retake the previously created files\n",
-    "run_name = run_name\n",
-    "\n",
-    "# This is the path where the files were uploaded by the user. Model outputs will also be placed there in a\n",
-    "# subfolder called \"outputs\"\n",
-    "configdir = workdir\n",
-    "\n",
-    "# Run the model and get the path to the outputs folder that can be used in the output reader.\n",
-    "outputs_path = run(modelname=run_name, configdir=configdir)\n",
-    "\n",
-    "# Get the outputs using the Output Reader object.\n",
-    "outputs = OutputReader(run_name=run_name, path=outputs_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# If we already have a model configuration that we built in-memory (such as the \"m\" GR4JCN model we built above),\n",
-    "# then we can use the Emulator object to simply emulate the model we were working on and get outputs directly\n",
-    "from ravenpy import Emulator\n",
-    "\n",
-    "# Prepare the emulator by writing files on disk\n",
-    "e = Emulator(config=m)\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "outputs = e.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, the above demonstration shows how to use one of the eight emulators to run a Raven simulation. Ravenpy also contains other powerful tools to run other user-defined raven models by reading existing configuration files and running the model in Ravenpy. This means that users that already have Raven models of their systems can upload the configuration and hydrometeorological data netcdf files to their private account on the PAVICS-Hydro server and run their model there. Here is an example of how this could be done."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this stage, no matter the method we used, we have the outputs of the model simulations in the \"outputs\" object. Let's explore it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Show the files available in the outputs. Each of these can be accessed to get information about the simulation.\n",
-    "outputs.files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The outputs are as follow:\n",
-    "\n",
-    "- hydrograph: The actual simulated hydrograph (q_sim), in netcdf format. It also contains the observed discharge (q_obs) if observed streamflow was provided as a forcing file.\n",
-    "- storage: The state variables of the simulation duration, in netcdf format\n",
-    "- solution: The state variables at the end of the simulation, which are saved as a \".rvc\" file that can be used to hot-start a model (for forecasting, for example)\n",
-    "- messages: A list of messages returned by Raven when executing the run.\n",
-    "\n",
-    "You can explore the outputs using othe following syntax. This loads the data into memory to be used directly in another cell for processing or analysis.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The model outputs are actually already loaded as Python objects in memory, thus we can access the data directly.\n",
-    "print(\"----------------HYDROGRAPH----------------\")\n",
-    "display(outputs.hydrograph)\n",
-    "print(\"\")\n",
-    "print(\"-----------------STORAGE------------------\")\n",
-    "display(outputs.storage)\n",
-    "print(\"\")\n",
-    "print(\"-----------------SOLUTION-----------------\")\n",
-    "display(outputs.solution)\n",
-    "print(\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see in the \"hydrograph\" section that the model has generated a simulation using the forcing data we provided, but it only used the period between the start_date and end_date we asked it for. We can see that the dates of the ERA5 data we requested in the previous notebook cover the period 1980-01-01 to 1991-01-01. In our simulation, we only ask to run over the period from 1985-01-01 to 1990-01-01. Raven takes care of subsetting the data for the required period. We can look at the simulated streamflow from Raven to confirm this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the graphing utility built to handle Raven model outputs\n",
-    "from ravenpy.utilities.nb_graphs import hydrographs\n",
-    "\n",
-    "hydrograph_objects = outputs.hydrograph\n",
-    "hydrographs(hydrograph_objects)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, the simulated flow covers only the period we asked for. The results probably don't look good, but that's OK! We will soon calibrate our model to get reasonable parameters.\n",
-    "\n",
-    "We could also simply do basic plots using:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outputs.hydrograph.q_sim.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can inspect and work with other state variables in the model outputs. For example, say we want to investigate the snow water equivalent timeseries. We can first get the list of available state variables:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(list(outputs.storage.keys()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then plot the variable of interest:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot the \"Snow\" variable\n",
-    "outputs.storage[\"Snow\"].plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, PAVICS-Hydro makes it easy to build a hydrological model, run it with forcing data, and then interact with the results! In the next notebooks, we will see how to adjust configuration files (the .rvX files) to setup and run a model, and also how to calibrate its parameters.\n",
-    "\n",
-    "\n",
-    "\n",
-    "## Supplementary information on Hydrological response unit definition\n",
-    "Raven requires a description of the watershed streamflow is simulated in. Different models require different parameters, but minimally, area, elevation, latitude and longitude are required. These data need to be provided for a few reasons:\n",
-    "* Area is required since the size of the watershed will directly influence the simulated streamflow. Units are in square kilometers (km²).\n",
-    "* Elevation (average elevation of the watershed) is required, although in many models the value is not actually used and therefore can be set to an arbitrary number. We strongly recommend using the real elevation as that will ensure that the value is present if you decide to switch to another model that requires elevation. Elevation is expressed in meters above mean sea level.\n",
-    "* Latitude and longitude refer to the catchment centroid, and are used, among others, for evapotranspiration  computations. They are expressed in decimal degrees (°), with longitudes within [-180, 180].\n",
-    "\n",
-    "These values should be either precomputed externally, or they can be computed using the PAVICS-Hydro geophysical extraction toolbox that we used in the second tutorial notebook.\n",
-    "\n",
-    "## Supplementary information on model parameters\n",
-    "\n",
-    "Each model requires a set of tuning parameters to represent and compensate for unknown quantities in certain hydrological processes. Some models have more parameters than others, for example:\n",
-    "\n",
-    "* GR4JCN = 6 parameters\n",
-    "* HMETS = 21 parameters\n",
-    "* MOHYSE = 10 parameters\n",
-    "* HBVEC = 21 parameters\n",
-    "\n",
-    "These parameters are found through calibration by tuning their values until the simulated streamflow matches the observations as much as possible. PAVICS-Hydro provides an integrated calibration toolbox that will be explored in the the 6th step of this tutorial. For now, we simply provided a set of parameters but it is not yet fully calibrated. This explains the poor quality of the simulated hydrograph."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Explore!\n",
-    "With this information in mind, you can now explore running different models and parameters and on different periods, and display the simulated hydrographs. You can change the start and end dates, the area, latitude, and even add other options that you might find in the documentation or in later tutorials.\n",
-    "\n",
-    "If you want to run other models than GR4JCN, you can use these preset models:\n",
-    "\n",
-    "### HMETS:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = HMETS(\n",
-    "    params=(\n",
-    "        9.5019,\n",
-    "        0.2774,\n",
-    "        6.3942,\n",
-    "        0.6884,\n",
-    "        1.2875,\n",
-    "        5.4134,\n",
-    "        2.3641,\n",
-    "        0.0973,\n",
-    "        0.0464,\n",
-    "        0.1998,\n",
-    "        0.0222,\n",
-    "        -1.0919,\n",
-    "        2.6851,\n",
-    "        0.3740,\n",
-    "        1.0000,\n",
-    "        0.4739,\n",
-    "        0.0114,\n",
-    "        0.0243,\n",
-    "        0.0069,\n",
-    "        310.7211,\n",
-    "        916.1947,\n",
-    "    ),\n",
-    "    **default_emulator_config,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mohyse:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = Mohyse(\n",
-    "    params=(1.0, 0.0468, 4.2952, 2.658, 0.4038, 0.0621, 0.0273, 0.0453, 0.9039, 5.6167),\n",
-    "    **default_emulator_config,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### HBVEC:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = HBVEC(\n",
-    "    params=(\n",
-    "        0.059845,\n",
-    "        4.07223,\n",
-    "        2.00157,\n",
-    "        0.034737,\n",
-    "        0.09985,\n",
-    "        0.506,\n",
-    "        3.4385,\n",
-    "        38.32455,\n",
-    "        0.46066,\n",
-    "        0.06304,\n",
-    "        2.2778,\n",
-    "        4.8737,\n",
-    "        0.5718813,\n",
-    "        0.04505643,\n",
-    "        0.877607,\n",
-    "        18.94145,\n",
-    "        2.036937,\n",
-    "        0.4452843,\n",
-    "        0.6771759,\n",
-    "        1.141608,\n",
-    "        1.024278,\n",
-    "    ),\n",
-    "    **default_emulator_config,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### CanadianShield:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The CanadianShield model needs atleast 2 HRUs. We have to modify the default config before executing it.\n",
-    "default_emulator_config[\"HRUs\"] = [hru, hru]\n",
-    "\n",
-    "m = CanadianShield(\n",
-    "    params=(\n",
-    "        4.72304300e-01,\n",
-    "        8.16392200e-01,\n",
-    "        9.86197600e-02,\n",
-    "        3.92699900e-03,\n",
-    "        4.69073600e-02,\n",
-    "        4.95528400e-01,\n",
-    "        6.803492000e00,\n",
-    "        4.33050200e-03,\n",
-    "        1.01425900e-05,\n",
-    "        1.823470000e00,\n",
-    "        5.12215400e-01,\n",
-    "        9.017555000e00,\n",
-    "        3.077103000e01,\n",
-    "        5.094095000e01,\n",
-    "        1.69422700e-01,\n",
-    "        8.23412200e-02,\n",
-    "        2.34595300e-01,\n",
-    "        7.30904000e-02,\n",
-    "        1.284052000e00,\n",
-    "        3.653415000e00,\n",
-    "        2.306515000e01,\n",
-    "        2.402183000e00,\n",
-    "        2.522095000e00,\n",
-    "        5.80344900e-01,\n",
-    "        1.614157000e00,\n",
-    "        6.031781000e00,\n",
-    "        3.11129800e-01,\n",
-    "        6.71695100e-02,\n",
-    "        5.83759500e-05,\n",
-    "        9.824723000e00,\n",
-    "        9.00747600e-01,\n",
-    "        8.04057300e-01,\n",
-    "        1.179003000e00,\n",
-    "        7.98001300e-01,\n",
-    "    ),\n",
-    "    **default_emulator_config,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### HYPR:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = HYPR(\n",
-    "    params=(\n",
-    "        -1.856410e-01,\n",
-    "        2.92301100e00,\n",
-    "        3.1194200e-02,\n",
-    "        4.3982810e-01,\n",
-    "        4.6509760e-01,\n",
-    "        1.1770040e-01,\n",
-    "        1.31236800e01,\n",
-    "        4.0417950e-01,\n",
-    "        1.21225800e00,\n",
-    "        5.91273900e01,\n",
-    "        1.6612030e-01,\n",
-    "        4.10501500e00,\n",
-    "        8.2296110e-01,\n",
-    "        4.15635200e01,\n",
-    "        5.85111700e00,\n",
-    "        6.9090140e-01,\n",
-    "        9.2459950e-01,\n",
-    "        1.64358800e00,\n",
-    "        1.59920500e00,\n",
-    "        2.51938100e00,\n",
-    "        1.14820100e00,\n",
-    "    ),\n",
-    "    **default_emulator_config,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SACSMA:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = SACSMA(\n",
-    "    params=(\n",
-    "        0.0100000,\n",
-    "        0.0500000,\n",
-    "        0.3000000,\n",
-    "        0.0500000,\n",
-    "        0.0500000,\n",
-    "        0.1300000,\n",
-    "        0.0250000,\n",
-    "        0.0600000,\n",
-    "        0.0600000,\n",
-    "        1.0000000,\n",
-    "        40.000000,\n",
-    "        0.0000000,\n",
-    "        0.0000000,\n",
-    "        0.1000000,\n",
-    "        0.0000000,\n",
-    "        0.0100000,\n",
-    "        1.5000000,\n",
-    "        0.4827523,\n",
-    "        4.0998200,\n",
-    "        1.0000000,\n",
-    "        1.0000000,\n",
-    "    ),\n",
-    "    **default_emulator_config,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Blended:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = Blended(\n",
-    "    params=(\n",
-    "        2.930702e-02,\n",
-    "        2.211166e00,\n",
-    "        2.166229e00,\n",
-    "        0.0002254976,\n",
-    "        2.173976e01,\n",
-    "        1.565091e00,\n",
-    "        6.211146e00,\n",
-    "        9.313578e-01,\n",
-    "        3.486263e-02,\n",
-    "        0.251835,\n",
-    "        0.0002279250,\n",
-    "        1.214339e00,\n",
-    "        4.736668e-02,\n",
-    "        0.2070342,\n",
-    "        7.806324e-02,\n",
-    "        -1.336429e00,\n",
-    "        2.189741e-01,\n",
-    "        3.845617e00,\n",
-    "        2.950022e-01,\n",
-    "        4.827523e-01,\n",
-    "        4.099820e00,\n",
-    "        1.283144e01,\n",
-    "        5.937894e-01,\n",
-    "        1.651588e00,\n",
-    "        1.705806,\n",
-    "        3.719308e-01,\n",
-    "        7.121015e-02,\n",
-    "        1.906440e-02,\n",
-    "        4.080660e-01,\n",
-    "        9.415693e-01,\n",
-    "        -1.856108e00,\n",
-    "        2.356995e00,\n",
-    "        1.0e00,\n",
-    "        1.0e00,\n",
-    "        7.510967e-03,\n",
-    "        5.321608e-01,\n",
-    "        2.891977e-02,\n",
-    "        9.605330e-01,\n",
-    "        6.128669e-01,\n",
-    "        9.558293e-01,\n",
-    "        1.008196e-01,\n",
-    "        9.275730e-02,\n",
-    "        7.469583e-01,\n",
-    "    ),\n",
-    "    **default_emulator_config,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 04 - Emulating hydrological models\n",
+        "\n",
+        "## Using Ravenpy to emulate an existing hydrological model\n",
+        "\n",
+        "In this notebook, we will demonstrate the versatility of the Raven modelling framework to emulate one of eight hydrological models that are currently supported. We will walk through the different configuration parameters required to build the model and simulate streamflow on a catchments. We will also show how to import files from a pre-configured Raven configuration that users can inport into Ravenpy instead of using one of the default emulators.\n",
+        "\n",
+        "## A note on datasets\n",
+        "\n",
+        "There are numerous ways to run a Raven model and to pass its required input data. For this introduction to RavenPy, we will use our ERA5 data we generated in the previous notebook and we will configure the Raven model instance on the fly! In the next tutorials, we will see how users can import and use their own datasets to make the entire process flexible and tailored to the user needs.\n",
+        "\n",
+        "## Using templated model emulators\n",
+        "The first thing we need to run the raven model is... a Raven model! Raven is not a model per se, but a modelling framework that can be used to build hydrological models from their underlying components. For now, PAVICS-Hydro allows building a set of pre-determined models. The Python wrapper offers at present eight model emulators: GR4J-CN, HMETS, MOHYSE, HBV-EC, Canadian Shield, HYPR, Sacramento and Blended. For each of these, templated configuration files are available to facilitate launching the model with options passed by Python at run-time.\n",
+        "\n",
+        "In the next cell, we are going to import the possible models, and later, we will configure and run the GR4J-CN model. Please see the documentation for more details on the mandatory vs optional parameters, and what they represent. A small glimpse is provided here."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import the list of possible model templates.\n",
+        "from ravenpy.config.emulators import (\n",
+        "    GR4JCN,\n",
+        "    HBVEC,\n",
+        "    HMETS,\n",
+        "    HYPR,\n",
+        "    SACSMA,\n",
+        "    Blended,\n",
+        "    CanadianShield,\n",
+        "    Mohyse,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import datetime as dt\n",
+        "\n",
+        "# Import other required packages:\n",
+        "import tempfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.utilities.testdata import get_file"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this next step, we will define the hydrological response unit (HRU). For lumped models, there is only one unit so the following structure should be good. However, for distributed modelling, there will be more than one HRU, so we would use another tool to help us build the HRUs in that case. The HRU provides information on the area, elevation, and location of the catchment.\n",
+        "\n",
+        "For now, let's provide the basin properties such that Raven can run. These are the minimal values that must always be provided, but some models might require other inputs. Please see the Raven documentation for more information."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Define the hydrological response unit. We can use the information from the tutorial notebook #02! Here we are using\n",
+        "# arbitrary data for a test catchment.\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The next required inputs are the start and end dates for the simulation. The `start_date` and `end_date` arguments indicate when a simulation should start and end. As long as the forcing data covers the simulation period, it should work. If these parameters are not defined, then start and end dates default to the start and end of the driving data.\n",
+        "\n",
+        "To keep things simple, we will use a short 5-year period. Note that the dates are python datetime.datetime objects."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "start_date = dt.datetime(1985, 1, 1)\n",
+        "end_date = dt.datetime(1990, 1, 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We are now ready to build our first Raven-based hydrological model. the model will be the GR4JCN model. The following code block will show and describe every step. However, more control options are available for users. Please see the documentation for a more detailed explanation on model options."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "# Import required packages. We already imported the GR4JCN emulator in the first cell, but let's keep it here for\n",
+        "# completeness.\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "\n",
+        "# Alternative variable names are useful for allowing Raven to read variables from NetCDF files even if the variable\n",
+        "# names are not those that are expected by Raven. For example, our ERA5-dernived temperature variables are named\n",
+        "# \"tmax\" and \"tmin\", whereas Raven expects \"TEMP_MAX\" and \"TEMP_MIN\". Therefore, instead of forcing users to rename\n",
+        "# their variables, we provide a mechanism to tell Raven which variables in the NetCDF files correspond to which\n",
+        "# meteorological variable.\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Here is where we build the raven configuration. We will first configure the model in memory based on the user\n",
+        "# preferences, and then we will write the Raven configuration files to disk such that Raven can read them and\n",
+        "# execute a simulation. In this case, we are building a GR4JCN model, but you could change this to any of the\n",
+        "# eight models that are preconfigured for direct emulation in Ravenpy.\n",
+        "\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"latitude\": hru[\"latitude\"],\n",
+        "        \"longitude\": hru[\"longitude\"],\n",
+        "    }\n",
+        "}\n",
+        "\n",
+        "run_name = \"test_NB_04\"\n",
+        "\n",
+        "# Get the weather data. It can either be to a data file that was already in the same folder/workspace as this\n",
+        "# notebook, as we generated in the previous notebook, or it could be a path to a file stored elsewhere.\n",
+        "\n",
+        "# Example for using the data we just generated in Notebook 03:\n",
+        "\"\"\"\n",
+        "ERA5_tmax = \"ERA5_tmax.nc\"\n",
+        "ERA5_tmin = \"ERA5_tmin.nc\"\n",
+        "ERA5_pr = \"ERA5_pr.nc\"\n",
+        "\"\"\"\n",
+        "\n",
+        "# In our case, we will prefer to link to existing, pre-computed and locally stored files to keep things tidy:\n",
+        "ERA5_tmax = get_file(\"notebook_inputs/ERA5_tmax.nc\")\n",
+        "ERA5_tmin = get_file(\"notebook_inputs/ERA5_tmin.nc\")\n",
+        "ERA5_pr = get_file(\"notebook_inputs/ERA5_pr.nc\")\n",
+        "\n",
+        "default_emulator_config = dict(\n",
+        "    # The HRU as defined earlier must be provided. This is the physical representation of the catchment that Raven\n",
+        "    # needs for certain models and processes.\n",
+        "    HRUs=[hru],\n",
+        "    # Model simulation start and end dates.\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    # Name of the simulation. Raven will prefix all .rvX files and model outputs with the runName.\n",
+        "    RunName=run_name,\n",
+        "    # Custom outputs allow pre-processing of certain statistics and variables after the model runs. Please see the\n",
+        "    # documentation for more details on possible options.\n",
+        "    CustomOutput=[\n",
+        "        rc.CustomOutput(\n",
+        "            time_per=\"YEARLY\",\n",
+        "            stat=\"AVERAGE\",\n",
+        "            variable=\"PRECIP\",\n",
+        "            space_agg=\"ENTIRE_WATERSHED\",\n",
+        "        )\n",
+        "    ],\n",
+        "    # Here we will prepare the weather gauge data to be fed to the model. The data could also come from other\n",
+        "    # sources such as the ERA5 reanalysis product. There are 2 ways to do this. We will show one way here, and\n",
+        "    # then show the alternative method in the following cell.\n",
+        "    #\n",
+        "    # METHOD 1: If you have one netcdf file of data per meteorological variable (such as what is generated in the\n",
+        "    #    03_Extracting_forcing_data.ipynb notebook), you can add them successively as follows. Note that we are\n",
+        "    #    creating a gauge for each variable. Raven will use the data from the three sources to provide forcing\n",
+        "    #    data to the simulation.\n",
+        "    #\n",
+        "    #   You can add the files you need! As long as there are timestamps associated with each value in the netcdf\n",
+        "    #   files, and the other required information (data_type, alt_names, other required information), the code\n",
+        "    #   will accept them and use what it needs. As per the Raven model itself, the gauge station elevation, latitude\n",
+        "    #   and longitude are required to let the model run. For lumped models such as the ones we are currently\n",
+        "    #   emulating, there is only one \"station\" that corresponds to the basin-averaged weahter. In this case, we\n",
+        "    #   set the station elevation to that of the mean catchment elevation to remove any adiabatic gradient\n",
+        "    #   modifications to the data. We also provide the catchment centroid latitude and longitude which we take\n",
+        "    #   from the only HRU defining the entire catchment. For multi-gauge basins and semi-distributed models, the\n",
+        "    #   latitude and longitude must be correctly identified for each station.\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ERA5_tmax,  # This is a path to the file of ERA5-derived maximum daily temperature.\n",
+        "            data_type=[\n",
+        "                \"TEMP_MAX\"\n",
+        "            ],  # Raven expects maximum temperature to be identified as \"TEMP_MAX\", so we indicate that this variable is maximum temperature.\n",
+        "            alt_names=alt_names,  # However, the variable name in the file is different to the one Raven expects: use alt-names.\n",
+        "            data_kwds=data_kwds,\n",
+        "        ),\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ERA5_tmin,\n",
+        "            data_type=[\"TEMP_MIN\"],\n",
+        "            alt_names=alt_names,\n",
+        "            data_kwds=data_kwds,\n",
+        "        ),\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ERA5_pr, data_type=[\"PRECIP\"], alt_names=alt_names, data_kwds=data_kwds\n",
+        "        ),\n",
+        "    ],\n",
+        ")\n",
+        "\n",
+        "\n",
+        "m = GR4JCN(\n",
+        "    # Raven requires parameters for the GR4JCN model. For now, we provide default values, but in a later notebook\n",
+        "    # we will show how to calibrate and find new parameters for our model.\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    # GR4JCN needs an extra constant parameter for the catchment, corresponding to the G50 parameter in the CEMANEIGE\n",
+        "    # description. Here is how we provide it.\n",
+        "    GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 208.480},\n",
+        "    **default_emulator_config,\n",
+        ")\n",
+        "\n",
+        "# We are now ready to write this newly-configured model to disk through the use of .rvX files that Raven will read.\n",
+        "\n",
+        "# In the following code snippet, there is a \"workdir\" path that must be provided. This indicates the path\n",
+        "# where the data used to run the model (RAVEN .RV files) will be made available from the PAVICS Jupyter environment.\n",
+        "# The default \"workdir\" setting puts model outputs in the temporary directory (\"/tmp\"),\n",
+        "# which is not visible from the Jupyter file explorer. Therefore, You can change the last subfolder,\n",
+        "# but '/notebook_dir/writable-workspace/' must be the beginning  of the path when running on the PAVICS platform.\n",
+        "\n",
+        "workdir = Path(tempfile.mkdtemp(prefix=\"NB4\"))\n",
+        "m.write_rv(workdir=workdir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 2- Alternatively, we could already have (or we could create) a single netcdf file containing all the required\n",
+        "#    forcing data. Since we computed such a merged file in Notebook 03, let's reuse it here\n",
+        "\n",
+        "# Example for using the data we just generated in Notebook 03:\n",
+        "\"\"\"\n",
+        "ERA5_full = ERA5_weather_data.nc\n",
+        "\"\"\"\n",
+        "\n",
+        "# In our case, we will prefer to link to existing, pre-computed and locally stored files to keep things tidy:\n",
+        "ERA5_full = get_file(\"notebook_inputs/ERA5_weather_data.nc\")\n",
+        "\n",
+        "#    Since our meteorological gauge data is all included in a single file, we need to tell the model which variables\n",
+        "#    we are providing. We will generate the list now and pass it later to Ravenpy as an argument to the model.\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "\n",
+        "# Set up the gauge using the second method, i.e., using a single file that contains all meteorological inputs. As\n",
+        "# you can see, a single gauge is added, but it contains all the information we need.\n",
+        "default_emulator_config[\"Gauge\"] = [\n",
+        "    rc.Gauge.from_nc(\n",
+        "        ERA5_full,  # Path to the ERA5 file containing all three meteorological variables\n",
+        "        data_type=data_type,  # Note that this is the list of all the variables\n",
+        "        alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
+        "        data_kwds=data_kwds,\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "#    Now that we have the single file containing tmax, tmin and pr, we can setup a single gauge that contains all three.\n",
+        "m = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 208.480},\n",
+        "    **default_emulator_config,\n",
+        ")\n",
+        "\n",
+        "# Now we will write the files to disk to prepare them for Raven. This step is not strictly necessary since the\n",
+        "# next step will also write files to disk automatically. We will leave it here so we can see the intermediate step\n",
+        "# and inspect the files if necessary.\n",
+        "\n",
+        "workdir = Path(tempfile.mkdtemp(prefix=\"NB4\"))\n",
+        "m.write_rv(workdir=workdir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "It can be seen that both methods generated .rvX files in the indicated path. This makes the Ravenpy platform more flexible for various use-cases, where some data can be stored in independent files or databases (perhaps temperatures come from one source and precipitation from another source).\n",
+        "\n",
+        "The above code only created the .rvX files. The model did not actually run yet. To do so, we must ask it to, as follows. Don't worry about the warnings: Raven is informing us that it has generated some internal parameters to build the model configuration based on some of the parameters we provided."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# If we want to import our own raven configuration files and forcing data, we can do so by importing them\n",
+        "# using the ravenpy.run  method. This will run the model exactly as the users will have designed it.\n",
+        "from ravenpy import OutputReader\n",
+        "from ravenpy.ravenpy import run\n",
+        "\n",
+        "# This is used to specify the raven configuration files prefixes. In this case, we will retake the previously created files\n",
+        "run_name = run_name\n",
+        "\n",
+        "# This is the path where the files were uploaded by the user. Model outputs will also be placed there in a\n",
+        "# subfolder called \"outputs\"\n",
+        "configdir = workdir\n",
+        "\n",
+        "# Run the model and get the path to the outputs folder that can be used in the output reader.\n",
+        "outputs_path = run(modelname=run_name, configdir=configdir)\n",
+        "\n",
+        "# Get the outputs using the Output Reader object.\n",
+        "outputs = OutputReader(run_name=run_name, path=outputs_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# If we already have a model configuration that we built in-memory (such as the \"m\" GR4JCN model we built above),\n",
+        "# then we can use the Emulator object to simply emulate the model we were working on and get outputs directly\n",
+        "from ravenpy import Emulator\n",
+        "\n",
+        "# Prepare the emulator by writing files on disk\n",
+        "e = Emulator(config=m)\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "outputs = e.run()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "However, the above demonstration shows how to use one of the eight emulators to run a Raven simulation. Ravenpy also contains other powerful tools to run other user-defined raven models by reading existing configuration files and running the model in Ravenpy. This means that users that already have Raven models of their systems can upload the configuration and hydrometeorological data netcdf files to their private account on the PAVICS-Hydro server and run their model there. Here is an example of how this could be done."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "At this stage, no matter the method we used, we have the outputs of the model simulations in the \"outputs\" object. Let's explore it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Show the files available in the outputs. Each of these can be accessed to get information about the simulation.\n",
+        "outputs.files"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The outputs are as follow:\n",
+        "\n",
+        "- hydrograph: The actual simulated hydrograph (q_sim), in netcdf format. It also contains the observed discharge (q_obs) if observed streamflow was provided as a forcing file.\n",
+        "- storage: The state variables of the simulation duration, in netcdf format\n",
+        "- solution: The state variables at the end of the simulation, which are saved as a \".rvc\" file that can be used to hot-start a model (for forecasting, for example)\n",
+        "- messages: A list of messages returned by Raven when executing the run.\n",
+        "\n",
+        "You can explore the outputs using othe following syntax. This loads the data into memory to be used directly in another cell for processing or analysis.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The model outputs are actually already loaded as Python objects in memory, thus we can access the data directly.\n",
+        "print(\"----------------HYDROGRAPH----------------\")\n",
+        "display(outputs.hydrograph)\n",
+        "print(\"\")\n",
+        "print(\"-----------------STORAGE------------------\")\n",
+        "display(outputs.storage)\n",
+        "print(\"\")\n",
+        "print(\"-----------------SOLUTION-----------------\")\n",
+        "display(outputs.solution)\n",
+        "print(\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see in the \"hydrograph\" section that the model has generated a simulation using the forcing data we provided, but it only used the period between the start_date and end_date we asked it for. We can see that the dates of the ERA5 data we requested in the previous notebook cover the period 1980-01-01 to 1991-01-01. In our simulation, we only ask to run over the period from 1985-01-01 to 1990-01-01. Raven takes care of subsetting the data for the required period. We can look at the simulated streamflow from Raven to confirm this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import the graphing utility built to handle Raven model outputs\n",
+        "from ravenpy.utilities.nb_graphs import hydrographs\n",
+        "\n",
+        "hydrograph_objects = outputs.hydrograph\n",
+        "hydrographs(hydrograph_objects)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As you can see, the simulated flow covers only the period we asked for. The results probably don't look good, but that's OK! We will soon calibrate our model to get reasonable parameters.\n",
+        "\n",
+        "We could also simply do basic plots using:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "outputs.hydrograph.q_sim.plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can inspect and work with other state variables in the model outputs. For example, say we want to investigate the snow water equivalent timeseries. We can first get the list of available state variables:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(list(outputs.storage.keys()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And then plot the variable of interest:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the \"Snow\" variable\n",
+        "outputs.storage[\"Snow\"].plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As you can see, PAVICS-Hydro makes it easy to build a hydrological model, run it with forcing data, and then interact with the results! In the next notebooks, we will see how to adjust configuration files (the .rvX files) to setup and run a model, and also how to calibrate its parameters.\n",
+        "\n",
+        "\n",
+        "\n",
+        "## Supplementary information on Hydrological response unit definition\n",
+        "Raven requires a description of the watershed streamflow is simulated in. Different models require different parameters, but minimally, area, elevation, latitude and longitude are required. These data need to be provided for a few reasons:\n",
+        "* Area is required since the size of the watershed will directly influence the simulated streamflow. Units are in square kilometers (km²).\n",
+        "* Elevation (average elevation of the watershed) is required, although in many models the value is not actually used and therefore can be set to an arbitrary number. We strongly recommend using the real elevation as that will ensure that the value is present if you decide to switch to another model that requires elevation. Elevation is expressed in meters above mean sea level.\n",
+        "* Latitude and longitude refer to the catchment centroid, and are used, among others, for evapotranspiration  computations. They are expressed in decimal degrees (°), with longitudes within [-180, 180].\n",
+        "\n",
+        "These values should be either precomputed externally, or they can be computed using the PAVICS-Hydro geophysical extraction toolbox that we used in the second tutorial notebook.\n",
+        "\n",
+        "## Supplementary information on model parameters\n",
+        "\n",
+        "Each model requires a set of tuning parameters to represent and compensate for unknown quantities in certain hydrological processes. Some models have more parameters than others, for example:\n",
+        "\n",
+        "* GR4JCN = 6 parameters\n",
+        "* HMETS = 21 parameters\n",
+        "* MOHYSE = 10 parameters\n",
+        "* HBVEC = 21 parameters\n",
+        "\n",
+        "These parameters are found through calibration by tuning their values until the simulated streamflow matches the observations as much as possible. PAVICS-Hydro provides an integrated calibration toolbox that will be explored in the the 6th step of this tutorial. For now, we simply provided a set of parameters but it is not yet fully calibrated. This explains the poor quality of the simulated hydrograph."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explore!\n",
+        "With this information in mind, you can now explore running different models and parameters and on different periods, and display the simulated hydrographs. You can change the start and end dates, the area, latitude, and even add other options that you might find in the documentation or in later tutorials.\n",
+        "\n",
+        "If you want to run other models than GR4JCN, you can use these preset models:\n",
+        "\n",
+        "### HMETS:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "m = HMETS(\n",
+        "    params=(\n",
+        "        9.5019,\n",
+        "        0.2774,\n",
+        "        6.3942,\n",
+        "        0.6884,\n",
+        "        1.2875,\n",
+        "        5.4134,\n",
+        "        2.3641,\n",
+        "        0.0973,\n",
+        "        0.0464,\n",
+        "        0.1998,\n",
+        "        0.0222,\n",
+        "        -1.0919,\n",
+        "        2.6851,\n",
+        "        0.3740,\n",
+        "        1.0000,\n",
+        "        0.4739,\n",
+        "        0.0114,\n",
+        "        0.0243,\n",
+        "        0.0069,\n",
+        "        310.7211,\n",
+        "        916.1947,\n",
+        "    ),\n",
+        "    **default_emulator_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Mohyse:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "m = Mohyse(\n",
+        "    params=(1.0, 0.0468, 4.2952, 2.658, 0.4038, 0.0621, 0.0273, 0.0453, 0.9039, 5.6167),\n",
+        "    **default_emulator_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### HBVEC:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "m = HBVEC(\n",
+        "    params=(\n",
+        "        0.059845,\n",
+        "        4.07223,\n",
+        "        2.00157,\n",
+        "        0.034737,\n",
+        "        0.09985,\n",
+        "        0.506,\n",
+        "        3.4385,\n",
+        "        38.32455,\n",
+        "        0.46066,\n",
+        "        0.06304,\n",
+        "        2.2778,\n",
+        "        4.8737,\n",
+        "        0.5718813,\n",
+        "        0.04505643,\n",
+        "        0.877607,\n",
+        "        18.94145,\n",
+        "        2.036937,\n",
+        "        0.4452843,\n",
+        "        0.6771759,\n",
+        "        1.141608,\n",
+        "        1.024278,\n",
+        "    ),\n",
+        "    **default_emulator_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### CanadianShield:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The CanadianShield model needs atleast 2 HRUs. We have to modify the default config before executing it.\n",
+        "default_emulator_config[\"HRUs\"] = [hru, hru]\n",
+        "\n",
+        "m = CanadianShield(\n",
+        "    params=(\n",
+        "        4.72304300e-01,\n",
+        "        8.16392200e-01,\n",
+        "        9.86197600e-02,\n",
+        "        3.92699900e-03,\n",
+        "        4.69073600e-02,\n",
+        "        4.95528400e-01,\n",
+        "        6.803492000e00,\n",
+        "        4.33050200e-03,\n",
+        "        1.01425900e-05,\n",
+        "        1.823470000e00,\n",
+        "        5.12215400e-01,\n",
+        "        9.017555000e00,\n",
+        "        3.077103000e01,\n",
+        "        5.094095000e01,\n",
+        "        1.69422700e-01,\n",
+        "        8.23412200e-02,\n",
+        "        2.34595300e-01,\n",
+        "        7.30904000e-02,\n",
+        "        1.284052000e00,\n",
+        "        3.653415000e00,\n",
+        "        2.306515000e01,\n",
+        "        2.402183000e00,\n",
+        "        2.522095000e00,\n",
+        "        5.80344900e-01,\n",
+        "        1.614157000e00,\n",
+        "        6.031781000e00,\n",
+        "        3.11129800e-01,\n",
+        "        6.71695100e-02,\n",
+        "        5.83759500e-05,\n",
+        "        9.824723000e00,\n",
+        "        9.00747600e-01,\n",
+        "        8.04057300e-01,\n",
+        "        1.179003000e00,\n",
+        "        7.98001300e-01,\n",
+        "    ),\n",
+        "    **default_emulator_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### HYPR:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "m = HYPR(\n",
+        "    params=(\n",
+        "        -1.856410e-01,\n",
+        "        2.92301100e00,\n",
+        "        3.1194200e-02,\n",
+        "        4.3982810e-01,\n",
+        "        4.6509760e-01,\n",
+        "        1.1770040e-01,\n",
+        "        1.31236800e01,\n",
+        "        4.0417950e-01,\n",
+        "        1.21225800e00,\n",
+        "        5.91273900e01,\n",
+        "        1.6612030e-01,\n",
+        "        4.10501500e00,\n",
+        "        8.2296110e-01,\n",
+        "        4.15635200e01,\n",
+        "        5.85111700e00,\n",
+        "        6.9090140e-01,\n",
+        "        9.2459950e-01,\n",
+        "        1.64358800e00,\n",
+        "        1.59920500e00,\n",
+        "        2.51938100e00,\n",
+        "        1.14820100e00,\n",
+        "    ),\n",
+        "    **default_emulator_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### SACSMA:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "m = SACSMA(\n",
+        "    params=(\n",
+        "        0.0100000,\n",
+        "        0.0500000,\n",
+        "        0.3000000,\n",
+        "        0.0500000,\n",
+        "        0.0500000,\n",
+        "        0.1300000,\n",
+        "        0.0250000,\n",
+        "        0.0600000,\n",
+        "        0.0600000,\n",
+        "        1.0000000,\n",
+        "        40.000000,\n",
+        "        0.0000000,\n",
+        "        0.0000000,\n",
+        "        0.1000000,\n",
+        "        0.0000000,\n",
+        "        0.0100000,\n",
+        "        1.5000000,\n",
+        "        0.4827523,\n",
+        "        4.0998200,\n",
+        "        1.0000000,\n",
+        "        1.0000000,\n",
+        "    ),\n",
+        "    **default_emulator_config,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Blended:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "m = Blended(\n",
+        "    params=(\n",
+        "        2.930702e-02,\n",
+        "        2.211166e00,\n",
+        "        2.166229e00,\n",
+        "        0.0002254976,\n",
+        "        2.173976e01,\n",
+        "        1.565091e00,\n",
+        "        6.211146e00,\n",
+        "        9.313578e-01,\n",
+        "        3.486263e-02,\n",
+        "        0.251835,\n",
+        "        0.0002279250,\n",
+        "        1.214339e00,\n",
+        "        4.736668e-02,\n",
+        "        0.2070342,\n",
+        "        7.806324e-02,\n",
+        "        -1.336429e00,\n",
+        "        2.189741e-01,\n",
+        "        3.845617e00,\n",
+        "        2.950022e-01,\n",
+        "        4.827523e-01,\n",
+        "        4.099820e00,\n",
+        "        1.283144e01,\n",
+        "        5.937894e-01,\n",
+        "        1.651588e00,\n",
+        "        1.705806,\n",
+        "        3.719308e-01,\n",
+        "        7.121015e-02,\n",
+        "        1.906440e-02,\n",
+        "        4.080660e-01,\n",
+        "        9.415693e-01,\n",
+        "        -1.856108e00,\n",
+        "        2.356995e00,\n",
+        "        1.0e00,\n",
+        "        1.0e00,\n",
+        "        7.510967e-03,\n",
+        "        5.321608e-01,\n",
+        "        2.891977e-02,\n",
+        "        9.605330e-01,\n",
+        "        6.128669e-01,\n",
+        "        9.558293e-01,\n",
+        "        1.008196e-01,\n",
+        "        9.275730e-02,\n",
+        "        7.469583e-01,\n",
+        "    ),\n",
+        "    **default_emulator_config,\n",
+        ")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb b/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb
index 1523359e..38df9700 100644
--- a/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb
+++ b/docs/notebooks/05_Advanced_RavenPy_configuration.ipynb
@@ -1,587 +1,587 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 05 - Advanced RavenPy configuration"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this notebook, we will explore alternative ways to setup a Raven model and how to parameterize and customize a raven-based hydrological model\n",
-    "\n",
-    "## Running Raven using pre-existing configuration files\n",
-    "\n",
-    "To run Raven, we need configuration (`.rvX`) files defining hydrological processes, watersheds and meteorological data. If you already have those configuration files ready, or want to see how to import an existing Raven model into PAVICS-Hydro, this tutorial is for you. It shows how to run Raven from a Python programming environment using [RavenPy](https://ravenpy.readthedocs.io/en/latest/).\n",
-    "\n",
-    "Let's start by importing some utilities that will make our life easier to get data on the servers. If you already have raven model setups, you could simply upload the files here and create your own \"config\" list:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Utility that simplifies getting data hosted on the remote PAVICS-Hydro data server.\n",
-    "from ravenpy.utilities.testdata import get_file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## A note on datasets\n",
-    "\n",
-    "For this part of the tutorial, we will use pre-existing datasets that are hosted on the PAVICS-Hydro servers to setup the Raven model. This means that the .rv files are all built and the forcing file already exists. We could apply all of the same logic to a RavenPy model we would have built at the previous step, but this way lets us show that we can also work on an imported model. Let's import the configuration files:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the .rv files. It could also be the .rv files returned from the previous notebook, but here we are using a new basin that contains observed streamflow\n",
-    "# to make the calibration possible in the next notebook. Note that these configuration files also include links to the\n",
-    "# required hydrometeorological database (NetCDF file).\n",
-    "config = [\n",
-    "    get_file(f\"raven-gr4j-cemaneige/raven-gr4j-salmon.{ext}\")\n",
-    "    for ext in [\"rvt\", \"rvc\", \"rvi\", \"rvh\", \"rvp\"]\n",
-    "]\n",
-    "config"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So \"config\" is just a set of paths to the various .rvX files (.rvt, .rvc, .rvi. .rvh and .rvp). Therefore, if you have your own .rv files that describe your model, you can upload them and replace \"config\" with your own files!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Building a hydrological model on-the-fly using existing configuration files.\n",
-    "\n",
-    "Here we create a Raven model instance, configuring it using the pre-defined configuration files and running it by providing the full path to the NetCDF driving datasets. The configuration we provide is for a GR4J-CN model emulator that Raven will run for us. We provide the configuration files for GR4J-CN as well as the forcing data (precipitation, temperature, observed streamflow, etc.) that will be used to run the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ravenpy import OutputReader\n",
-    "from ravenpy.ravenpy import run\n",
-    "\n",
-    "run_name = \"raven-gr4j-salmon\"  # As can be seen in the config above, this is the name of the .rvX files.\n",
-    "configdir = config[\n",
-    "    0\n",
-    "].parent  # We can get the path to the folder containing the .rvX files this way\n",
-    "\n",
-    "# Run the model and get the path to outputs\n",
-    "outputs_path = run(modelname=run_name, configdir=configdir, overwrite=True)\n",
-    "\n",
-    "# Note. The modelname parameter can be confusing. You need to give the FILES extension name (run_name in our case),\n",
-    "# not the name of the model.\n",
-    "\n",
-    "outputs_path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read the output files at the output_path\n",
-    "\n",
-    "outputs = OutputReader(run_name=None, path=outputs_path)  # Get the outputs\n",
-    "# Note. We set up the run_name to None, because we didn't rename the output files. If you gave a different name to your file\n",
-    "# compared to the one above, you should change the run_name value to this new name. It's important though that you keep the end\n",
-    "# of the filename the same\n",
-    "\n",
-    "# Show the list of files that were retrived by the OutputReader\n",
-    "outputs.files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model should have run! But you also might have seen some warnings that Raven is giving us, depending on the input files used:\n",
-    "\n",
-    "- Some might be saying that we are providing rain and snow independently, but in the configuration files, we are asking the model to recompute the separation using an algorithm based on total precipitation and air temperature. This is OK, and we can live with this (alternatively, we could reconfigure the model to remove this but that will be for another notebook!).\n",
-    "\n",
-    "- Others could be saying that we supply PET data, but the model is configured to compute PET from the available temperature and latitude/longitude data. This is also acceptable to us for now, so these warnings can be disregarded.\n",
-    "\n",
-    "- And others might simply explain that our configuration provided some parameters but others were computed internally based on our parameter set rather than being explicitly set in our configuration, which is OK.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluating the model response\n",
-    "\n",
-    "That's it! The code above has launched the GR4J-CN model using weather data and the configuration we provided. There are many other options we could provide, but for now we left everything to the default options to keep things simple. We will explore those in a future tutorial as well.\n",
-    "\n",
-    "Now, let's look at the modeled hydrographs. Note that there is a \"q_obs\" hydrograph, representing the observations we provided ourselves. This is to facilitate the comparison between observations and simulations, and it is not required per se to run the model. The \"q_sim\" variable is the simulated streamflow and is the one we are interested in.\n",
-    "\n",
-    "Note that RavenPy assumes that model outputs are always saved in netCDF format, and relies on [xarray](http://xarray.pydata.org/en/stable/) to access data.\n",
-    "\n",
-    "To see results, we must first tell the model to read them from the files Raven has written in the output folder:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can visualize the simulated streamflow using xarray's built-in plotting tool, as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outputs.hydrograph.q_sim.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We also now have access to diagnostics! This is because along with the simulated discharge, the model has access to observed discharge to compute error metrics such as RMSE and NSE. Let's see where the file has been generated:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"-----------------DIAGNOSTICS-----------------\")\n",
-    "print(outputs.diagnostics)\n",
-    "print(\"\")\n",
-    "\n",
-    "print(\"-----------------NASH_SUTCLIFFE-----------------\")\n",
-    "print(outputs.diagnostics[\"DIAG_NASH_SUTCLIFFE\"])\n",
-    "print(\"\")\n",
-    "\n",
-    "print(\"-----------------RMSE-----------------\")\n",
-    "print(outputs.diagnostics[\"DIAG_RMSE\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the Nash-Sutcliffe value is quite poor. This is due to the short simulation period in the configuration (see the hydrograph above!) and the lack of a spin-up period, combined to a poor parameter set choice. We will improve upon all of these shortcomings in the next notebooks!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Advanced RavenPy configuration options\n",
-    "\n",
-    "Raven can perform many operations and has multiple configuration options. Here we provide a list of configuration options to explore which you can eventually use to taylor the codes to your own specifications. These can only be run on RavenPy-built hydrological models, and will not operate on Raven models imported by users since those configuration files are not modifiable for the time being.\n",
-    "\n",
-    "We will give an overview of the various configuration keywords after this code block, but users should read the Raven documentation for more options for each of these processes.\n",
-    "\n",
-    "Let's first define some variables we will need for all of our tests:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get required packages\n",
-    "import datetime as dt\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from ravenpy import Emulator\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "\n",
-    "# Observed weather data for the Salmon river. We extracted this using Tutorial Notebook 03 and the\n",
-    "# salmon_river.geojson file as the contour.\n",
-    "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
-    "\n",
-    "# Set alternate variable names in the timeseries data file\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Provide the type of data made available to Raven\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "\n",
-    "# Prepare the catchment properties\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Add some information regarding station data\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"latitude\": hru[\"latitude\"],\n",
-    "        \"longitude\": hru[\"longitude\"],\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Start and end dates of the simulation\n",
-    "start_date = dt.datetime(1985, 1, 1)\n",
-    "end_date = dt.datetime(1990, 1, 1)\n",
-    "\n",
-    "# Set parameters\n",
-    "parameters = [0.529, -3.396, 407.29, 1.072, 16.9, 0.947]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now perform a \"basic\" run, with no modifications."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run the model (See Notebook 04 for more details on implementation)\n",
-    "m = GR4JCN(\n",
-    "    params=parameters,\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts,\n",
-    "            data_type=data_type,  # Note that this is the list of all the variables\n",
-    "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"NB05_test1\",\n",
-    "    # GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 208.480},\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "outputs1 = Emulator(m).run()\n",
-    "\n",
-    "# Plot the generated hydrograph\n",
-    "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now run another model by adding some other properties. To start, we can add some Global Parameters to the model to make Raven adjust the simulations based on the information we provide. Some options of Global Parameters are indicated here, but more can be found in the official Raven documentation.\n",
-    "\n",
-    "Examples of GlobalParameter options (Note that some are only available for certain models and others can be mutually exclusive. Please refer to the documentation for this type of adjustment):\n",
-    "\n",
-    "### Temperature interval of transformation between rain and snow. Set the midpoint of the range and the width of the range, in degrees C:\n",
-    "\"RAINSNOW_TEMP\": midpoint_temp  // Ex: \"RAINSNOW_TEMP\": -1.0\n",
-    "\n",
-    "\"RAINSNOW_DELTA\": delta_temp    // Ex: \"RAINSNOW_DELTA\": 3.0\n",
-    "\n",
-    "### Maximum liquid water content of snow, as a percentage of SWE (0-1). Usually ~0.05.\n",
-    "\"SNOW_SWI\": saturation // Ex: \"SNOW_SWI\": 0.1\n",
-    "\n",
-    "### Average annual snow for the entire watershed in mm of SWE. Used in CemaNeige.\n",
-    "\"AVG_ANNUAL_SNOW\": average_snow_per_year // Ex: \"AVG_ANNUAL_SNOW\": 400.0\n",
-    "\n",
-    "There are many others, but this should clarify the implementation. Let's try some of them out!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run the model (See Notebook 04 for more details on implementation)\n",
-    "m = GR4JCN(\n",
-    "    params=parameters,\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts,\n",
-    "            data_type=data_type,  # Note that this is the list of all the variables\n",
-    "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"NB05_test2\",\n",
-    "    GlobalParameter={\"AVG_ANNUAL_SNOW\": 350.0},\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "outputs2 = Emulator(m).run()\n",
-    "\n",
-    "# Plot the generated hydrograph\n",
-    "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
-    "outputs2.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW\")\n",
-    "\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also adjust the time series data to play with the scaling of units.\n",
-    "\n",
-    "By default, RavenPy and Raven will detect units from the forcing data netcdf files. However, in some instances, units might be lacking, or their format might require some tinkering. One such case is for precipitation data that is cumulative in the netcdf file. In these cases, Raven can decumulate the precipitation, but the scaling might lead to undesirable results. For this reason, it is highly recommended to pass the scaling and offsetting variables directly. To do so, add some context in the data_kwds:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add some information regarding station data\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "    # HOW TO PROCESS THE PRECIPITATION DATA: For the Precip variable, we tell Raven we want to Deaccumulate\n",
-    "    # values, shift them in time by 6 hours (for UTC time zone management), and then apply a linear transform\n",
-    "    # to the values to get new scaled values. The linear transform can take two inputs:\n",
-    "    #     \"scale\"   is the \"a\" variable in the linear relationship y = ax + b. Usually used to multiply precipitation.\n",
-    "    #     \"offset\"  is the \"b\" variable in the linear relationship y = ax + b. Usually used to convert temperatures(K to °C)\n",
-    "    \"PRECIP\": {\n",
-    "        \"Deaccumulate\": True,\n",
-    "        \"TimeShift\": -0.25,\n",
-    "        \"LinearTransform\": {\n",
-    "            \"scale\": 1000.0  # # Converting meters to mm (multiply by 1000).\n",
-    "        },\n",
-    "    },\n",
-    "    \"TEMP_AVE\": {\n",
-    "        \"TimeShift\": -0.25,\n",
-    "    },\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In our example, our precipitation is not actually accumulated and the timestep is daily, so we don't need the \"Deaccumulate\" or the \"TimeShift\" parameters. So let's generate a new data_kwds that is applicable in our case. More complex cases that require \"Deaccumulate\" and \"TimeShift\" will be presented in later notebooks that use accumulated precipitation in forecasting applications, in Notebook 12."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add some information regarding station data\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "    # Let's simulate a very rough estimation of the impacts of climate change where precipitation is expected\n",
-    "    # to increase by 10% and temperatures to increase by 3°C. This will be applied to all data on the entire\n",
-    "    # period and is thus not realistic. We will explore more realistic methods in Notebook 08.\n",
-    "    \"PRECIP\": {\"LinearTransform\": {\"scale\": 1.1}},\n",
-    "    \"TEMP_AVE\": {\"LinearTransform\": {\"offset\": 3.0}},\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use this new setup to generate another series of streamflow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run the model (See Notebook 04 for more details on implementation)\n",
-    "m = GR4JCN(\n",
-    "    params=parameters,\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts,\n",
-    "            data_type=data_type,  # Note that this is the list of all the variables\n",
-    "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"NB05_test3\",\n",
-    "    GlobalParameter={\"AVG_ANNUAL_SNOW\": 350.0},\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "outputs3 = Emulator(m).run()\n",
-    "\n",
-    "# Plot the generated hydrograph\n",
-    "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
-    "outputs2.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW\")\n",
-    "outputs3.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW and Scaling\")\n",
-    "\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the scaling increased the flows almost everywhere except in the first year which is the warm-up period.\n",
-    "\n",
-    "Other options that can be implemented are indicated here, although more exist and are documented in the official Raven manual.\n",
-    "\n",
-    "\n",
-    "\n",
-    "### RainSnowFraction:\n",
-    "Algorithm to use to separate the total precipitation into rainfall and snowfall.\n",
-    "\n",
-    "Ex: RainSnowFraction='RAINSNOW_DINGMAN'\n",
-    "\n",
-    "### Evaporation\n",
-    "Evaporation: Formula to use to compute the evapotranspiration from the land HRUs.\n",
-    "\n",
-    "Ex: Evaporation=\"PET_OUDIN\"\n",
-    "\n",
-    "### Suppress model outputs / files\n",
-    "Boolean that indicates if you wish for Raven to provide information after the model evaluation by writing to file. For a single run this can be left to **False**, but for calibration and other intensive tasks, it is faster to leave it to **True**.\n",
-    "\n",
-    "Ex: SuppressOutputs=True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, let's see how to implement these commands:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run the model (See Notebook 04 for more details on implementation)\n",
-    "m = GR4JCN(\n",
-    "    params=parameters,\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts,\n",
-    "            data_type=data_type,  # Note that this is the list of all the variables\n",
-    "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"NB05_test3\",\n",
-    "    GlobalParameter={\"AVG_ANNUAL_SNOW\": 350.0},\n",
-    "    RainSnowFraction=\"RAINSNOW_DINGMAN\",\n",
-    "    Evaporation=\"PET_HARGREAVES_1985\",\n",
-    "    SuppressOutput=False,  # We can't read the hydrographs if they are not written to disk, so set to False here.\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "outputs4 = Emulator(m).run()\n",
-    "\n",
-    "# Plot the generated hydrograph\n",
-    "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
-    "outputs2.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW\")\n",
-    "outputs3.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW and Scaling\")\n",
-    "outputs4.hydrograph.q_sim.plot.line(\n",
-    "    x=\"time\", label=\"With AVG_ANNUAL_SNOW, Scaling and Options\"\n",
-    ")\n",
-    "\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### A note on the above results\n",
-    "\n",
-    "We can see that the results change significantly according to the options we have passed, namely the evaporation algorithm modified the hydrograph quite significantly. However, this is caused by the fact that the parameter set we have used has not been calibrated using this PET method, and thereore the model cannot be expected to perform as well. This means that when using these model options, it is important to recalibrate the model parameters such that they represent the actual model being used!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Finally, we can also ask Raven to supply custom outputs using this line in the model configuration:\n",
-    "\n",
-    "CustomOutput=rc.CustomOutput() and by providing a list of desired pre-processed variables. Here we ask for the yearly average of precipitation over the entire watershed:\n",
-    "\n",
-    "CustomOutput=rc.CustomOutput(\"YEARLY\", \"AVERAGE\", \"PRECIP\", \"ENTIRE_WATERSHED\")\n",
-    "\n",
-    "Please see the documentation for more details on using custom outputs.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 05 - Advanced RavenPy configuration"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this notebook, we will explore alternative ways to setup a Raven model and how to parameterize and customize a raven-based hydrological model\n",
+        "\n",
+        "## Running Raven using pre-existing configuration files\n",
+        "\n",
+        "To run Raven, we need configuration (`.rvX`) files defining hydrological processes, watersheds and meteorological data. If you already have those configuration files ready, or want to see how to import an existing Raven model into PAVICS-Hydro, this tutorial is for you. It shows how to run Raven from a Python programming environment using [RavenPy](https://ravenpy.readthedocs.io/en/latest/).\n",
+        "\n",
+        "Let's start by importing some utilities that will make our life easier to get data on the servers. If you already have raven model setups, you could simply upload the files here and create your own \"config\" list:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Utility that simplifies getting data hosted on the remote PAVICS-Hydro data server.\n",
+        "from ravenpy.utilities.testdata import get_file"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## A note on datasets\n",
+        "\n",
+        "For this part of the tutorial, we will use pre-existing datasets that are hosted on the PAVICS-Hydro servers to setup the Raven model. This means that the .rv files are all built and the forcing file already exists. We could apply all of the same logic to a RavenPy model we would have built at the previous step, but this way lets us show that we can also work on an imported model. Let's import the configuration files:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the .rv files. It could also be the .rv files returned from the previous notebook, but here we are using a new basin that contains observed streamflow\n",
+        "# to make the calibration possible in the next notebook. Note that these configuration files also include links to the\n",
+        "# required hydrometeorological database (NetCDF file).\n",
+        "config = [\n",
+        "    get_file(f\"raven-gr4j-cemaneige/raven-gr4j-salmon.{ext}\")\n",
+        "    for ext in [\"rvt\", \"rvc\", \"rvi\", \"rvh\", \"rvp\"]\n",
+        "]\n",
+        "config"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "So \"config\" is just a set of paths to the various .rvX files (.rvt, .rvc, .rvi. .rvh and .rvp). Therefore, if you have your own .rv files that describe your model, you can upload them and replace \"config\" with your own files!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Building a hydrological model on-the-fly using existing configuration files.\n",
+        "\n",
+        "Here we create a Raven model instance, configuring it using the pre-defined configuration files and running it by providing the full path to the NetCDF driving datasets. The configuration we provide is for a GR4J-CN model emulator that Raven will run for us. We provide the configuration files for GR4J-CN as well as the forcing data (precipitation, temperature, observed streamflow, etc.) that will be used to run the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from ravenpy import OutputReader\n",
+        "from ravenpy.ravenpy import run\n",
+        "\n",
+        "run_name = \"raven-gr4j-salmon\"  # As can be seen in the config above, this is the name of the .rvX files.\n",
+        "configdir = config[\n",
+        "    0\n",
+        "].parent  # We can get the path to the folder containing the .rvX files this way\n",
+        "\n",
+        "# Run the model and get the path to outputs\n",
+        "outputs_path = run(modelname=run_name, configdir=configdir, overwrite=True)\n",
+        "\n",
+        "# Note. The modelname parameter can be confusing. You need to give the FILES extension name (run_name in our case),\n",
+        "# not the name of the model.\n",
+        "\n",
+        "outputs_path"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Read the output files at the output_path\n",
+        "\n",
+        "outputs = OutputReader(run_name=None, path=outputs_path)  # Get the outputs\n",
+        "# Note. We set up the run_name to None, because we didn't rename the output files. If you gave a different name to your file\n",
+        "# compared to the one above, you should change the run_name value to this new name. It's important though that you keep the end\n",
+        "# of the filename the same\n",
+        "\n",
+        "# Show the list of files that were retrived by the OutputReader\n",
+        "outputs.files"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The model should have run! But you also might have seen some warnings that Raven is giving us, depending on the input files used:\n",
+        "\n",
+        "- Some might be saying that we are providing rain and snow independently, but in the configuration files, we are asking the model to recompute the separation using an algorithm based on total precipitation and air temperature. This is OK, and we can live with this (alternatively, we could reconfigure the model to remove this but that will be for another notebook!).\n",
+        "\n",
+        "- Others could be saying that we supply PET data, but the model is configured to compute PET from the available temperature and latitude/longitude data. This is also acceptable to us for now, so these warnings can be disregarded.\n",
+        "\n",
+        "- And others might simply explain that our configuration provided some parameters but others were computed internally based on our parameter set rather than being explicitly set in our configuration, which is OK.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluating the model response\n",
+        "\n",
+        "That's it! The code above has launched the GR4J-CN model using weather data and the configuration we provided. There are many other options we could provide, but for now we left everything to the default options to keep things simple. We will explore those in a future tutorial as well.\n",
+        "\n",
+        "Now, let's look at the modeled hydrographs. Note that there is a \"q_obs\" hydrograph, representing the observations we provided ourselves. This is to facilitate the comparison between observations and simulations, and it is not required per se to run the model. The \"q_sim\" variable is the simulated streamflow and is the one we are interested in.\n",
+        "\n",
+        "Note that RavenPy assumes that model outputs are always saved in netCDF format, and relies on [xarray](http://xarray.pydata.org/en/stable/) to access data.\n",
+        "\n",
+        "To see results, we must first tell the model to read them from the files Raven has written in the output folder:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can visualize the simulated streamflow using xarray's built-in plotting tool, as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "outputs.hydrograph.q_sim.plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We also now have access to diagnostics! This is because along with the simulated discharge, the model has access to observed discharge to compute error metrics such as RMSE and NSE. Let's see where the file has been generated:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"-----------------DIAGNOSTICS-----------------\")\n",
+        "print(outputs.diagnostics)\n",
+        "print(\"\")\n",
+        "\n",
+        "print(\"-----------------NASH_SUTCLIFFE-----------------\")\n",
+        "print(outputs.diagnostics[\"DIAG_NASH_SUTCLIFFE\"])\n",
+        "print(\"\")\n",
+        "\n",
+        "print(\"-----------------RMSE-----------------\")\n",
+        "print(outputs.diagnostics[\"DIAG_RMSE\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the Nash-Sutcliffe value is quite poor. This is due to the short simulation period in the configuration (see the hydrograph above!) and the lack of a spin-up period, combined to a poor parameter set choice. We will improve upon all of these shortcomings in the next notebooks!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Advanced RavenPy configuration options\n",
+        "\n",
+        "Raven can perform many operations and has multiple configuration options. Here we provide a list of configuration options to explore which you can eventually use to taylor the codes to your own specifications. These can only be run on RavenPy-built hydrological models, and will not operate on Raven models imported by users since those configuration files are not modifiable for the time being.\n",
+        "\n",
+        "We will give an overview of the various configuration keywords after this code block, but users should read the Raven documentation for more options for each of these processes.\n",
+        "\n",
+        "Let's first define some variables we will need for all of our tests:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get required packages\n",
+        "import datetime as dt\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from ravenpy import Emulator\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "\n",
+        "# Observed weather data for the Salmon river. We extracted this using Tutorial Notebook 03 and the\n",
+        "# salmon_river.geojson file as the contour.\n",
+        "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
+        "\n",
+        "# Set alternate variable names in the timeseries data file\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Provide the type of data made available to Raven\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "\n",
+        "# Prepare the catchment properties\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Add some information regarding station data\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"latitude\": hru[\"latitude\"],\n",
+        "        \"longitude\": hru[\"longitude\"],\n",
+        "    }\n",
+        "}\n",
+        "\n",
+        "# Start and end dates of the simulation\n",
+        "start_date = dt.datetime(1985, 1, 1)\n",
+        "end_date = dt.datetime(1990, 1, 1)\n",
+        "\n",
+        "# Set parameters\n",
+        "parameters = [0.529, -3.396, 407.29, 1.072, 16.9, 0.947]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now perform a \"basic\" run, with no modifications."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Run the model (See Notebook 04 for more details on implementation)\n",
+        "m = GR4JCN(\n",
+        "    params=parameters,\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts,\n",
+        "            data_type=data_type,  # Note that this is the list of all the variables\n",
+        "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"NB05_test1\",\n",
+        "    # GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 208.480},\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "outputs1 = Emulator(m).run()\n",
+        "\n",
+        "# Plot the generated hydrograph\n",
+        "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now run another model by adding some other properties. To start, we can add some Global Parameters to the model to make Raven adjust the simulations based on the information we provide. Some options of Global Parameters are indicated here, but more can be found in the official Raven documentation.\n",
+        "\n",
+        "Examples of GlobalParameter options (Note that some are only available for certain models and others can be mutually exclusive. Please refer to the documentation for this type of adjustment):\n",
+        "\n",
+        "### Temperature interval of transformation between rain and snow. Set the midpoint of the range and the width of the range, in degrees C:\n",
+        "\"RAINSNOW_TEMP\": midpoint_temp  // Ex: \"RAINSNOW_TEMP\": -1.0\n",
+        "\n",
+        "\"RAINSNOW_DELTA\": delta_temp    // Ex: \"RAINSNOW_DELTA\": 3.0\n",
+        "\n",
+        "### Maximum liquid water content of snow, as a percentage of SWE (0-1). Usually ~0.05.\n",
+        "\"SNOW_SWI\": saturation // Ex: \"SNOW_SWI\": 0.1\n",
+        "\n",
+        "### Average annual snow for the entire watershed in mm of SWE. Used in CemaNeige.\n",
+        "\"AVG_ANNUAL_SNOW\": average_snow_per_year // Ex: \"AVG_ANNUAL_SNOW\": 400.0\n",
+        "\n",
+        "There are many others, but this should clarify the implementation. Let's try some of them out!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Run the model (See Notebook 04 for more details on implementation)\n",
+        "m = GR4JCN(\n",
+        "    params=parameters,\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts,\n",
+        "            data_type=data_type,  # Note that this is the list of all the variables\n",
+        "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"NB05_test2\",\n",
+        "    GlobalParameter={\"AVG_ANNUAL_SNOW\": 350.0},\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "outputs2 = Emulator(m).run()\n",
+        "\n",
+        "# Plot the generated hydrograph\n",
+        "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
+        "outputs2.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW\")\n",
+        "\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can also adjust the time series data to play with the scaling of units.\n",
+        "\n",
+        "By default, RavenPy and Raven will detect units from the forcing data netcdf files. However, in some instances, units might be lacking, or their format might require some tinkering. One such case is for precipitation data that is cumulative in the netcdf file. In these cases, Raven can decumulate the precipitation, but the scaling might lead to undesirable results. For this reason, it is highly recommended to pass the scaling and offsetting variables directly. To do so, add some context in the data_kwds:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Add some information regarding station data\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "    # HOW TO PROCESS THE PRECIPITATION DATA: For the Precip variable, we tell Raven we want to Deaccumulate\n",
+        "    # values, shift them in time by 6 hours (for UTC time zone management), and then apply a linear transform\n",
+        "    # to the values to get new scaled values. The linear transform can take two inputs:\n",
+        "    #     \"scale\"   is the \"a\" variable in the linear relationship y = ax + b. Usually used to multiply precipitation.\n",
+        "    #     \"offset\"  is the \"b\" variable in the linear relationship y = ax + b. Usually used to convert temperatures(K to °C)\n",
+        "    \"PRECIP\": {\n",
+        "        \"Deaccumulate\": True,\n",
+        "        \"TimeShift\": -0.25,\n",
+        "        \"LinearTransform\": {\n",
+        "            \"scale\": 1000.0  # # Converting meters to mm (multiply by 1000).\n",
+        "        },\n",
+        "    },\n",
+        "    \"TEMP_AVE\": {\n",
+        "        \"TimeShift\": -0.25,\n",
+        "    },\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In our example, our precipitation is not actually accumulated and the timestep is daily, so we don't need the \"Deaccumulate\" or the \"TimeShift\" parameters. So let's generate a new data_kwds that is applicable in our case. More complex cases that require \"Deaccumulate\" and \"TimeShift\" will be presented in later notebooks that use accumulated precipitation in forecasting applications, in Notebook 12."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Add some information regarding station data\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "    # Let's simulate a very rough estimation of the impacts of climate change where precipitation is expected\n",
+        "    # to increase by 10% and temperatures to increase by 3°C. This will be applied to all data on the entire\n",
+        "    # period and is thus not realistic. We will explore more realistic methods in Notebook 08.\n",
+        "    \"PRECIP\": {\"LinearTransform\": {\"scale\": 1.1}},\n",
+        "    \"TEMP_AVE\": {\"LinearTransform\": {\"offset\": 3.0}},\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can use this new setup to generate another series of streamflow"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Run the model (See Notebook 04 for more details on implementation)\n",
+        "m = GR4JCN(\n",
+        "    params=parameters,\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts,\n",
+        "            data_type=data_type,  # Note that this is the list of all the variables\n",
+        "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"NB05_test3\",\n",
+        "    GlobalParameter={\"AVG_ANNUAL_SNOW\": 350.0},\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "outputs3 = Emulator(m).run()\n",
+        "\n",
+        "# Plot the generated hydrograph\n",
+        "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
+        "outputs2.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW\")\n",
+        "outputs3.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW and Scaling\")\n",
+        "\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the scaling increased the flows almost everywhere except in the first year which is the warm-up period.\n",
+        "\n",
+        "Other options that can be implemented are indicated here, although more exist and are documented in the official Raven manual.\n",
+        "\n",
+        "\n",
+        "\n",
+        "### RainSnowFraction:\n",
+        "Algorithm to use to separate the total precipitation into rainfall and snowfall.\n",
+        "\n",
+        "Ex: RainSnowFraction='RAINSNOW_DINGMAN'\n",
+        "\n",
+        "### Evaporation\n",
+        "Evaporation: Formula to use to compute the evapotranspiration from the land HRUs.\n",
+        "\n",
+        "Ex: Evaporation=\"PET_OUDIN\"\n",
+        "\n",
+        "### Suppress model outputs / files\n",
+        "Boolean that indicates if you wish for Raven to provide information after the model evaluation by writing to file. For a single run this can be left to **False**, but for calibration and other intensive tasks, it is faster to leave it to **True**.\n",
+        "\n",
+        "Ex: SuppressOutputs=True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, let's see how to implement these commands:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Run the model (See Notebook 04 for more details on implementation)\n",
+        "m = GR4JCN(\n",
+        "    params=parameters,\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts,\n",
+        "            data_type=data_type,  # Note that this is the list of all the variables\n",
+        "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"NB05_test3\",\n",
+        "    GlobalParameter={\"AVG_ANNUAL_SNOW\": 350.0},\n",
+        "    RainSnowFraction=\"RAINSNOW_DINGMAN\",\n",
+        "    Evaporation=\"PET_HARGREAVES_1985\",\n",
+        "    SuppressOutput=False,  # We can't read the hydrographs if they are not written to disk, so set to False here.\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "outputs4 = Emulator(m).run()\n",
+        "\n",
+        "# Plot the generated hydrograph\n",
+        "outputs1.hydrograph.q_sim.plot.line(x=\"time\", label=\"Base case\")\n",
+        "outputs2.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW\")\n",
+        "outputs3.hydrograph.q_sim.plot.line(x=\"time\", label=\"With AVG_ANNUAL_SNOW and Scaling\")\n",
+        "outputs4.hydrograph.q_sim.plot.line(\n",
+        "    x=\"time\", label=\"With AVG_ANNUAL_SNOW, Scaling and Options\"\n",
+        ")\n",
+        "\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### A note on the above results\n",
+        "\n",
+        "We can see that the results change significantly according to the options we have passed, namely the evaporation algorithm modified the hydrograph quite significantly. However, this is caused by the fact that the parameter set we have used has not been calibrated using this PET method, and thereore the model cannot be expected to perform as well. This means that when using these model options, it is important to recalibrate the model parameters such that they represent the actual model being used!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "\n",
+        "Finally, we can also ask Raven to supply custom outputs using this line in the model configuration:\n",
+        "\n",
+        "CustomOutput=rc.CustomOutput() and by providing a list of desired pre-processed variables. Here we ask for the yearly average of precipitation over the entire watershed:\n",
+        "\n",
+        "CustomOutput=rc.CustomOutput(\"YEARLY\", \"AVERAGE\", \"PRECIP\", \"ENTIRE_WATERSHED\")\n",
+        "\n",
+        "Please see the documentation for more details on using custom outputs.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/06_Raven_calibration.ipynb b/docs/notebooks/06_Raven_calibration.ipynb
index be86b701..11d59fac 100644
--- a/docs/notebooks/06_Raven_calibration.ipynb
+++ b/docs/notebooks/06_Raven_calibration.ipynb
@@ -1,356 +1,356 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4a5f03fb",
-   "metadata": {},
-   "source": [
-    "# 06 - Calibration of a Raven hydrological model"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "4a5f03fb",
+      "metadata": {},
+      "source": [
+        "# 06 - Calibration of a Raven hydrological model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d1ce69fb",
+      "metadata": {},
+      "source": [
+        "## Calibration of a Raven model\n",
+        "\n",
+        "In this notebook, we show how to calibrate a Raven model using the GR4J-CN predefined structure. Users can refer to the documentation for the parameterization of other hydrological model structures.\n",
+        "\n",
+        "Let's start by importing the packages that will do the work."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "565a7b6c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import datetime as dt\n",
+        "\n",
+        "import spotpy\n",
+        "\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities.calibration import SpotSetup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cbfe7818",
+      "metadata": {},
+      "source": [
+        "## Preparing the model to be calibrated on a given watershed\n",
+        "Our test watershed from the last notebook is selected for this test. It can be replaced with any desired watershed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bf6a2500",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from ravenpy.utilities.testdata import get_file\n",
+        "\n",
+        "# We get the netCDF for testing on a server. You can replace the getfile method by a string containing the path to your own netCDF\n",
+        "nc_file = get_file(\n",
+        "    \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n",
+        ")\n",
+        "\n",
+        "# Display the dataset that we will be using\n",
+        "print(nc_file)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e5611922",
+      "metadata": {},
+      "source": [
+        "The process is very similar to setting up a hydrological model. We first need to create the model with its configuration. We must provide the same information as before, except for the model parameters since those need to be calibrated."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2105f6ea",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Here, we need to give the name of your different dataset in order to match with Raven models.\n",
+        "alt_names = {\n",
+        "    \"RAINFALL\": \"rain\",\n",
+        "    \"SNOWFALL\": \"snow\",\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PET\": \"pet\",\n",
+        "    \"HYDROGRAPH\": \"qobs\",\n",
+        "}\n",
+        "\n",
+        "# The HRU of your watershed\n",
+        "hru = dict(area=4250.6, elevation=843.0, latitude=54.4848, longitude=-123.3659)\n",
+        "\n",
+        "# You can decide the evaluation metrics that will be used to calibrate the parameters of your model. You need atleast\n",
+        "# 1 evaluation metric, but you can do any combinaison of evaluations from this list :\n",
+        "#\n",
+        "# NASH_SUTCLIFFE,\n",
+        "# LOG_NASH,\n",
+        "# RMSE,\n",
+        "# PCT_BIAS,\n",
+        "# ABSERR,\n",
+        "# ABSMAX,\n",
+        "# PDIFF,\n",
+        "# TMVOL,\n",
+        "# RCOEFF,\n",
+        "# NSC,\n",
+        "# KLING_GUPTA\n",
+        "eval_metrics = (\"NASH_SUTCLIFFE\",)\n",
+        "\n",
+        "\n",
+        "# We need to create the desired model with its parameters the same way as in the Notebook 04_Emulating_hydrological_models.\n",
+        "model_config = GR4JCN(\n",
+        "    ObservationData=[rc.ObservationData.from_nc(nc_file, alt_names=\"qobs\")],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            nc_file,\n",
+        "            alt_names=alt_names,\n",
+        "            data_kwds={\"ALL\": {\"elevation\": hru[\"elevation\"]}},\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=dt.datetime(1990, 1, 1),\n",
+        "    EndDate=dt.datetime(1999, 12, 31),\n",
+        "    RunName=\"test\",\n",
+        "    EvaluationMetrics=eval_metrics,  # We add this code to tell Raven which objective function we want to pass.\n",
+        "    SuppressOutput=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "40c8371c",
+      "metadata": {},
+      "source": [
+        "## Spotpy Calibration\n",
+        "\n",
+        "Once you've created your model, you need to create a SpotSetup, which will be used to calibrate your model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0c089720",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# In order to calibrate your model, you need to give the lower and higher bounds of the model. In this case, we are passing\n",
+        "# the boundaries for a GR4JCN, but it's important to change them, if you are using another model. Note that the list of these\n",
+        "# boundaries for each model is at the end of this notebook.\n",
+        "low_params = (0.01, -15.0, 10.0, 0.0, 1.0, 0.0)\n",
+        "high_params = (2.5, 10.0, 700.0, 7.0, 30.0, 1.0)\n",
+        "\n",
+        "\n",
+        "spot_setup = SpotSetup(\n",
+        "    config=model_config,\n",
+        "    low=low_params,\n",
+        "    high=high_params,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b6351b2",
+      "metadata": {},
+      "source": [
+        "Now that the model is setup and configured and that `SpotSetup` object exists, we need to create a sampler from `spotpy` module  which will optimize the hydrological model paramaters. You can see that we are using the DDS algorithm to optimize the parameters:\n",
+        "\n",
+        "[Tolson, B.A. and Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 43(1)].\n",
+        "\n",
+        "If you want to use another algorithm, please refer to the Spotpy documentation here : https://spotpy.readthedocs.io/\n",
+        "\n",
+        "Finally, we run the sampler by the amount of desired repetitions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "77d168a1",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Number of total model evaluations in the calibration. This value should be over 500 for real optimisation,\n",
+        "# and upwards of 10000 evaluations for models with many parameters. This will take a LONG period of time so\n",
+        "# be sure of all the configuration above before executing with a high number of model evaluations.\n",
+        "model_evaluations = 10\n",
+        "\n",
+        "# Set up the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer to\n",
+        "# the spotpy documentation for more options. We recommend sticking to this format for efficiency of most applications.\n",
+        "sampler = spotpy.algorithms.dds(\n",
+        "    spot_setup, dbname=\"RAVEN_model_run\", dbformat=\"ram\", save_sim=False\n",
+        ")\n",
+        "\n",
+        "# Launch the actual optimization. Multiple trials can be launched, where the entire process is repeated and\n",
+        "# the best overall value from all trials is returned.\n",
+        "sampler.sample(model_evaluations, trials=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f789c674",
+      "metadata": {},
+      "source": [
+        "## Analysing the calibration results\n",
+        "The best parameters as well as the objective functions can be analyzed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ae1fc2c4",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get all the values of each iteration\n",
+        "results = sampler.getdata()\n",
+        "\n",
+        "print(\"The best Nash-Sutcliffe value is : \")\n",
+        "\n",
+        "# Get the raw resutlts directly in an array\n",
+        "bestindex, bestobjfun = spotpy.analyser.get_maxlikeindex(\n",
+        "    results\n",
+        ")  # Want to get the MAX NSE (change for min for RMSE)\n",
+        "best_model_run = list(\n",
+        "    results[bestindex][0]\n",
+        ")  # Get the parameter set returning the best NSE\n",
+        "optimized_parameters = best_model_run[\n",
+        "    1:-1\n",
+        "]  # Remove the NSE value (position 0) and the ID at the last position to get the actual parameter set.\n",
+        "\n",
+        "print(\"\\nThe best parameters are : \")\n",
+        "# Display the parameter set ready to use in a future run:\n",
+        "print(optimized_parameters)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d22ea8c6-173d-44e9-82c2-cf87a0227180",
+      "metadata": {},
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "In the next notebooks, we will apply the model to specific use-cases, including making and using hotstart files for forecasting, performing hindcasting and forecasting, applying data assimilation and evaluating the impacts of climate change on the hydrology of a watershed. In the meantime, you can explore calibration with any of the emulated models below with the provided low and high bounds. You can also provide your own for specific cases."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c4655f07",
+      "metadata": {},
+      "source": [
+        "## List of Model-Boundaries\n",
+        "\n",
+        "GR4J-CN :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (0.01, -15.0, 10.0, 0.0, 1.0, 0.0),</li>\n",
+        "  <li>high =  (2.5, 10.0, 700.0, 7.0, 30.0, 1.0)</li>\n",
+        "</ul>\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "HMETS  :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (0.3, 0.01, 0.5, 0.15, 0.0, 0.0, -2.0, 0.01, 0.0, 0.01, 0.005,\n",
+        "               -5.0, 0.0, 0.0, 0.0, 0.0, 0.00001, 0.0, 0.00001, 0.0, 0.0),</li>\n",
+        "  <li>high =  (20.0, 5.0, 13.0, 1.5, 20.0, 20.0, 3.0, 0.2, 0.1, 0.3, 0.1,\n",
+        "               2.0, 5.0, 1.0, 3.0, 1.0, 0.02, 0.1, 0.01, 0.5, 2.0)</li>\n",
+        "</ul>\n",
+        "\n",
+        "\n",
+        "Mohyse :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (0.01, 0.01, 0.01, -5.00, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01),</li>\n",
+        "  <li>high =  (20.0, 1.0, 20.0, 5.0, 0.5, 1.0, 1.0, 1.0, 15.0, 15.0)</li>\n",
+        "</ul>\n",
+        "\n",
+        "\n",
+        "HBV-EC  :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (-3.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.01, 0.05, 0.01,\n",
+        "               0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.05, 0.8, 0.8),</li>\n",
+        "  <li>high =  (3.0, 8.0, 8.0, 0.1, 1.0, 1.0, 7.0, 100.0, 1.0, 0.1, 6.0,\n",
+        "               5.0, 5.0, 0.2, 1.0, 30.0, 3.0, 2.0, 1.0, 1.5, 1.5)</li>\n",
+        "</ul>\n",
+        "\n",
+        "\n",
+        "CanadianShield :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (0.01, 0.01, 0.01, 0.0, 0.0, 0.05, 0.0, -5.0, -5.0, 0.5, 0.5,\n",
+        "               0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.005, -3.0, 0.5, 5.0, 0.0,\n",
+        "               0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.8, 0.0),</li>\n",
+        "  <li>high =  (0.5, 2.0, 3.0, 3.0, 0.05, 0.45, 7.0, -1.0, -1.0, 2.0, 2.0,\n",
+        "            100.0, 100.0, 100.0, 0.4, 0.1, 0.3, 0.1, 3.0, 4.0, 500.0, 5.0,\n",
+        "            5.0, 1.0, 8.0, 20.0, 1.5, 0.2, 0.2, 10.0, 10.0, 1.2, 1.2, 1.0)</li>\n",
+        "</ul>\n",
+        "\n",
+        "HYPR :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (-1.0, -3.0, 0.0, 0.3, -1.3, -2.0, 0.0, 0.1, 0.4, 0.0, 0.0,\n",
+        "            0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 1.5, 0.0, 0.0, 0.8),</li>\n",
+        "  <li>high =  (1.0, 3.0, 0.8, 1.0, 0.3, 0.0, 30.0, 0.8, 2.0, 100.0,\n",
+        "            0.5, 5.0, 1.0, 1000.0, 6.0, 7.0, 8.0, 3.0, 5.0, 5.0, 1.2)</li>\n",
+        "</ul>\n",
+        "\n",
+        "SACSMA :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (-3.0, -1.52287874, -0.69897, 0.025, 0.01, 0.075, 0.015, 0.04,\n",
+        "              0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.01, 0.8, 0.8),</li>\n",
+        "  <li>high =  (-1.82390874, -0.69897, -0.30102999, 0.125, 0.075, 0.3, 0.3, 0.6,\n",
+        "              0.5, 3.0, 80.0, 0.8, 0.05, 0.2, 0.1, 0.4, 8.0, 20.0, 5.0, 1.2, 1.2)</li>\n",
+        "</ul>\n",
+        "\n",
+        "Blended :\n",
+        "\n",
+        "<ul>\n",
+        "  <li>low  =  (0.0, 0.1, 0.5, -5.0, 0.0, 0.5, 5.0, 0.0, 0.0, 0.0, -5.0,\n",
+        "            0.5, 0.0, 0.01, 0.005, -5.0, 0.0, 0.0, 0.0, 0.3, 0.01, 0.5,\n",
+        "            0.15, 1.5, 0.0, -1.0, 0.01, 0.00001, 0.0, 0.0, -3.0, 0.5,\n",
+        "            0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),</li>\n",
+        "  <li>high =  (1.0, 3.0, 3.0, -1.0, 100.0, 2.0, 10.0, 3.0,\n",
+        "            0.05, 0.45, -2.0, 2.0, 0.1, 0.3, 0.1, 2.0, 1.0,\n",
+        "            5.0, 0.4, 20.0, 5.0, 13.0, 1.5, 3.0, 5.0, 1.0,\n",
+        "            0.2, 0.02, 0.5, 2.0, 3.0, 4.0, 1.2, 1.2, 0.02,\n",
+        "            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)</li>\n",
+        "</ul>\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "id": "d1ce69fb",
-   "metadata": {},
-   "source": [
-    "## Calibration of a Raven model\n",
-    "\n",
-    "In this notebook, we show how to calibrate a Raven model using the GR4J-CN predefined structure. Users can refer to the documentation for the parameterization of other hydrological model structures.\n",
-    "\n",
-    "Let's start by importing the packages that will do the work."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "565a7b6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "\n",
-    "import spotpy\n",
-    "\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities.calibration import SpotSetup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cbfe7818",
-   "metadata": {},
-   "source": [
-    "## Preparing the model to be calibrated on a given watershed\n",
-    "Our test watershed from the last notebook is selected for this test. It can be replaced with any desired watershed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf6a2500",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ravenpy.utilities.testdata import get_file\n",
-    "\n",
-    "# We get the netCDF for testing on a server. You can replace the getfile method by a string containing the path to your own netCDF\n",
-    "nc_file = get_file(\n",
-    "    \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n",
-    ")\n",
-    "\n",
-    "# Display the dataset that we will be using\n",
-    "print(nc_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5611922",
-   "metadata": {},
-   "source": [
-    "The process is very similar to setting up a hydrological model. We first need to create the model with its configuration. We must provide the same information as before, except for the model parameters since those need to be calibrated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2105f6ea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here, we need to give the name of your different dataset in order to match with Raven models.\n",
-    "alt_names = {\n",
-    "    \"RAINFALL\": \"rain\",\n",
-    "    \"SNOWFALL\": \"snow\",\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PET\": \"pet\",\n",
-    "    \"HYDROGRAPH\": \"qobs\",\n",
-    "}\n",
-    "\n",
-    "# The HRU of your watershed\n",
-    "hru = dict(area=4250.6, elevation=843.0, latitude=54.4848, longitude=-123.3659)\n",
-    "\n",
-    "# You can decide the evaluation metrics that will be used to calibrate the parameters of your model. You need atleast\n",
-    "# 1 evaluation metric, but you can do any combinaison of evaluations from this list :\n",
-    "#\n",
-    "# NASH_SUTCLIFFE,\n",
-    "# LOG_NASH,\n",
-    "# RMSE,\n",
-    "# PCT_BIAS,\n",
-    "# ABSERR,\n",
-    "# ABSMAX,\n",
-    "# PDIFF,\n",
-    "# TMVOL,\n",
-    "# RCOEFF,\n",
-    "# NSC,\n",
-    "# KLING_GUPTA\n",
-    "eval_metrics = (\"NASH_SUTCLIFFE\",)\n",
-    "\n",
-    "\n",
-    "# We need to create the desired model with its parameters the same way as in the Notebook 04_Emulating_hydrological_models.\n",
-    "model_config = GR4JCN(\n",
-    "    ObservationData=[rc.ObservationData.from_nc(nc_file, alt_names=\"qobs\")],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            nc_file,\n",
-    "            alt_names=alt_names,\n",
-    "            data_kwds={\"ALL\": {\"elevation\": hru[\"elevation\"]}},\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=dt.datetime(1990, 1, 1),\n",
-    "    EndDate=dt.datetime(1999, 12, 31),\n",
-    "    RunName=\"test\",\n",
-    "    EvaluationMetrics=eval_metrics,  # We add this code to tell Raven which objective function we want to pass.\n",
-    "    SuppressOutput=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40c8371c",
-   "metadata": {},
-   "source": [
-    "## Spotpy Calibration\n",
-    "\n",
-    "Once you've created your model, you need to create a SpotSetup, which will be used to calibrate your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0c089720",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# In order to calibrate your model, you need to give the lower and higher bounds of the model. In this case, we are passing\n",
-    "# the boundaries for a GR4JCN, but it's important to change them, if you are using another model. Note that the list of these\n",
-    "# boundaries for each model is at the end of this notebook.\n",
-    "low_params = (0.01, -15.0, 10.0, 0.0, 1.0, 0.0)\n",
-    "high_params = (2.5, 10.0, 700.0, 7.0, 30.0, 1.0)\n",
-    "\n",
-    "\n",
-    "spot_setup = SpotSetup(\n",
-    "    config=model_config,\n",
-    "    low=low_params,\n",
-    "    high=high_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b6351b2",
-   "metadata": {},
-   "source": [
-    "Now that the model is setup and configured and that `SpotSetup` object exists, we need to create a sampler from `spotpy` module  which will optimize the hydrological model paramaters. You can see that we are using the DDS algorithm to optimize the parameters:\n",
-    "\n",
-    "[Tolson, B.A. and Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 43(1)].\n",
-    "\n",
-    "If you want to use another algorithm, please refer to the Spotpy documentation here : https://spotpy.readthedocs.io/\n",
-    "\n",
-    "Finally, we run the sampler by the amount of desired repetitions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "77d168a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of total model evaluations in the calibration. This value should be over 500 for real optimisation,\n",
-    "# and upwards of 10000 evaluations for models with many parameters. This will take a LONG period of time so\n",
-    "# be sure of all the configuration above before executing with a high number of model evaluations.\n",
-    "model_evaluations = 10\n",
-    "\n",
-    "# Set up the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer to\n",
-    "# the spotpy documentation for more options. We recommend sticking to this format for efficiency of most applications.\n",
-    "sampler = spotpy.algorithms.dds(\n",
-    "    spot_setup, dbname=\"RAVEN_model_run\", dbformat=\"ram\", save_sim=False\n",
-    ")\n",
-    "\n",
-    "# Launch the actual optimization. Multiple trials can be launched, where the entire process is repeated and\n",
-    "# the best overall value from all trials is returned.\n",
-    "sampler.sample(model_evaluations, trials=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f789c674",
-   "metadata": {},
-   "source": [
-    "## Analysing the calibration results\n",
-    "The best parameters as well as the objective functions can be analyzed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ae1fc2c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get all the values of each iteration\n",
-    "results = sampler.getdata()\n",
-    "\n",
-    "print(\"The best Nash-Sutcliffe value is : \")\n",
-    "\n",
-    "# Get the raw resutlts directly in an array\n",
-    "bestindex, bestobjfun = spotpy.analyser.get_maxlikeindex(\n",
-    "    results\n",
-    ")  # Want to get the MAX NSE (change for min for RMSE)\n",
-    "best_model_run = list(\n",
-    "    results[bestindex][0]\n",
-    ")  # Get the parameter set returning the best NSE\n",
-    "optimized_parameters = best_model_run[\n",
-    "    1:-1\n",
-    "]  # Remove the NSE value (position 0) and the ID at the last position to get the actual parameter set.\n",
-    "\n",
-    "print(\"\\nThe best parameters are : \")\n",
-    "# Display the parameter set ready to use in a future run:\n",
-    "print(optimized_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d22ea8c6-173d-44e9-82c2-cf87a0227180",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    "In the next notebooks, we will apply the model to specific use-cases, including making and using hotstart files for forecasting, performing hindcasting and forecasting, applying data assimilation and evaluating the impacts of climate change on the hydrology of a watershed. In the meantime, you can explore calibration with any of the emulated models below with the provided low and high bounds. You can also provide your own for specific cases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4655f07",
-   "metadata": {},
-   "source": [
-    "## List of Model-Boundaries\n",
-    "\n",
-    "GR4J-CN :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (0.01, -15.0, 10.0, 0.0, 1.0, 0.0),</li>\n",
-    "  <li>high =  (2.5, 10.0, 700.0, 7.0, 30.0, 1.0)</li>\n",
-    "</ul>\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "HMETS  :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (0.3, 0.01, 0.5, 0.15, 0.0, 0.0, -2.0, 0.01, 0.0, 0.01, 0.005,\n",
-    "               -5.0, 0.0, 0.0, 0.0, 0.0, 0.00001, 0.0, 0.00001, 0.0, 0.0),</li>\n",
-    "  <li>high =  (20.0, 5.0, 13.0, 1.5, 20.0, 20.0, 3.0, 0.2, 0.1, 0.3, 0.1,\n",
-    "               2.0, 5.0, 1.0, 3.0, 1.0, 0.02, 0.1, 0.01, 0.5, 2.0)</li>\n",
-    "</ul>\n",
-    "\n",
-    "\n",
-    "Mohyse :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (0.01, 0.01, 0.01, -5.00, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01),</li>\n",
-    "  <li>high =  (20.0, 1.0, 20.0, 5.0, 0.5, 1.0, 1.0, 1.0, 15.0, 15.0)</li>\n",
-    "</ul>\n",
-    "\n",
-    "\n",
-    "HBV-EC  :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (-3.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.01, 0.05, 0.01,\n",
-    "               0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.05, 0.8, 0.8),</li>\n",
-    "  <li>high =  (3.0, 8.0, 8.0, 0.1, 1.0, 1.0, 7.0, 100.0, 1.0, 0.1, 6.0,\n",
-    "               5.0, 5.0, 0.2, 1.0, 30.0, 3.0, 2.0, 1.0, 1.5, 1.5)</li>\n",
-    "</ul>\n",
-    "\n",
-    "\n",
-    "CanadianShield :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (0.01, 0.01, 0.01, 0.0, 0.0, 0.05, 0.0, -5.0, -5.0, 0.5, 0.5,\n",
-    "               0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.005, -3.0, 0.5, 5.0, 0.0,\n",
-    "               0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.8, 0.0),</li>\n",
-    "  <li>high =  (0.5, 2.0, 3.0, 3.0, 0.05, 0.45, 7.0, -1.0, -1.0, 2.0, 2.0,\n",
-    "            100.0, 100.0, 100.0, 0.4, 0.1, 0.3, 0.1, 3.0, 4.0, 500.0, 5.0,\n",
-    "            5.0, 1.0, 8.0, 20.0, 1.5, 0.2, 0.2, 10.0, 10.0, 1.2, 1.2, 1.0)</li>\n",
-    "</ul>\n",
-    "\n",
-    "HYPR :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (-1.0, -3.0, 0.0, 0.3, -1.3, -2.0, 0.0, 0.1, 0.4, 0.0, 0.0,\n",
-    "            0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 1.5, 0.0, 0.0, 0.8),</li>\n",
-    "  <li>high =  (1.0, 3.0, 0.8, 1.0, 0.3, 0.0, 30.0, 0.8, 2.0, 100.0,\n",
-    "            0.5, 5.0, 1.0, 1000.0, 6.0, 7.0, 8.0, 3.0, 5.0, 5.0, 1.2)</li>\n",
-    "</ul>\n",
-    "\n",
-    "SACSMA :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (-3.0, -1.52287874, -0.69897, 0.025, 0.01, 0.075, 0.015, 0.04,\n",
-    "              0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.01, 0.8, 0.8),</li>\n",
-    "  <li>high =  (-1.82390874, -0.69897, -0.30102999, 0.125, 0.075, 0.3, 0.3, 0.6,\n",
-    "              0.5, 3.0, 80.0, 0.8, 0.05, 0.2, 0.1, 0.4, 8.0, 20.0, 5.0, 1.2, 1.2)</li>\n",
-    "</ul>\n",
-    "\n",
-    "Blended :\n",
-    "\n",
-    "<ul>\n",
-    "  <li>low  =  (0.0, 0.1, 0.5, -5.0, 0.0, 0.5, 5.0, 0.0, 0.0, 0.0, -5.0,\n",
-    "            0.5, 0.0, 0.01, 0.005, -5.0, 0.0, 0.0, 0.0, 0.3, 0.01, 0.5,\n",
-    "            0.15, 1.5, 0.0, -1.0, 0.01, 0.00001, 0.0, 0.0, -3.0, 0.5,\n",
-    "            0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),</li>\n",
-    "  <li>high =  (1.0, 3.0, 3.0, -1.0, 100.0, 2.0, 10.0, 3.0,\n",
-    "            0.05, 0.45, -2.0, 2.0, 0.1, 0.3, 0.1, 2.0, 1.0,\n",
-    "            5.0, 0.4, 20.0, 5.0, 13.0, 1.5, 3.0, 5.0, 1.0,\n",
-    "            0.2, 0.02, 0.5, 2.0, 3.0, 4.0, 1.2, 1.2, 0.02,\n",
-    "            1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)</li>\n",
-    "</ul>\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
diff --git a/docs/notebooks/07_Making_and_using_hotstart_files.ipynb b/docs/notebooks/07_Making_and_using_hotstart_files.ipynb
index e5091917..f992a396 100644
--- a/docs/notebooks/07_Making_and_using_hotstart_files.ipynb
+++ b/docs/notebooks/07_Making_and_using_hotstart_files.ipynb
@@ -1,232 +1,232 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 07 - Making and using hostart files"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 07 - Making and using hostart files"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create a hotstart file to resume a simulation from given hydrological conditions\n",
+        "\n",
+        "Hydrological models have state variables that describe the snow pack, soil moisture, underground reservoirs, etc. Typically, those cannot be measured empirically, so one way to estimate those values is to run the model for a period before the period we are actually interested in, and save the state variables at the end of this *warm-up* simulation.\n",
+        "\n",
+        "This notebook shows how to save those state variables and use them to configure another Raven simulation. These *states* are configured by the `:HRUStateVariableTable` and `:BasinStateVariables` commands, but `ravenpy` has a convenience function `set_solution` to update those directly from the `solution.rvc` simulation output.\n",
+        "\n",
+        "In the following, we run the model on two years then save the final states. Next, we use those final states to configure the initial state of a second simulation over the next two years. If everything is done correctly, these two series should be identical to a simulation over the full four years.\n",
+        "\n",
+        "## Model configuration\n",
+        "\n",
+        "At this point the following blocks of code should be quite familiar! If not, please go back to notebook \"04 - Emulating hydrological models\" to understand what is happening.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import packages\n",
+        "import datetime as dt\n",
+        "import warnings\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "\n",
+        "from ravenpy import Emulator, RavenWarning\n",
+        "from ravenpy.config import commands as rc\n",
+        "\n",
+        "# Import the GR4JCN model\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities.testdata import get_file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Start and end date for full simulation\n",
+        "# Make sure the end date is before the end of the hydrometeorological data NetCDF file.\n",
+        "start_date = dt.datetime(1986, 1, 1)\n",
+        "end_date = dt.datetime(1988, 1, 1)\n",
+        "\n",
+        "# Define HRU\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Get dataset:\n",
+        "ERA5_full = get_file(\"notebook_inputs/ERA5_weather_data.nc\")\n",
+        "\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"latitude\": hru[\"latitude\"],\n",
+        "        \"longitude\": hru[\"longitude\"],\n",
+        "    }\n",
+        "}\n",
+        "\n",
+        "# Model configuration\n",
+        "config = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ERA5_full,\n",
+        "            data_type=data_type,\n",
+        "            alt_names=alt_names,\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"full\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Silence the Raven warnings\n",
+        "warnings.simplefilter(\"ignore\", category=RavenWarning)\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "out1 = Emulator(config=config).run()\n",
+        "\n",
+        "# Plot the model output\n",
+        "out1.hydrograph.q_sim.plot(label=\"Part 1\")\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Now let's run the model for the next two years, setting the initial conditions to the final states of the first simulation.\n",
+        "\n",
+        "The path to the `solution.rvc` file can be found in `out1.files[\"solution\"]`.\n",
+        "The content itself can be displayed with `out1.solution`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The path to the solution (final model states)\n",
+        "hotstart = out1.files[\"solution\"]\n",
+        "\n",
+        "# Configure and run the model, this time with the next two years first 3 years (1988-1990).\n",
+        "conf2 = config.set_solution(hotstart)\n",
+        "conf2.start_date = dt.datetime(1988, 1, 1)\n",
+        "conf2.end_date = dt.datetime(1990, 1, 1)\n",
+        "conf2.run_name = \"part_2\"\n",
+        "\n",
+        "out2 = Emulator(config=conf2).run()\n",
+        "\n",
+        "# Plot the model output\n",
+        "out2.hydrograph.q_sim.plot(label=\"Part 2\")\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compare with simulation over entire period\n",
+        "\n",
+        "Now in theory, those two simulations should be identical to one simulation over the whole period of four years, let's confirm this."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "full = config.copy()\n",
+        "full.end_date = dt.datetime(1990, 1, 1)\n",
+        "full.run_name = \"full\"\n",
+        "\n",
+        "out = Emulator(config=full).run(overwrite=True)\n",
+        "\n",
+        "out.hydrograph.q_sim.plot(label=\"Full\", color=\"gray\", lw=4)\n",
+        "out1.hydrograph.q_sim.plot(\n",
+        "    label=\"Part 1\",\n",
+        "    color=\"blue\",\n",
+        "    lw=0.5,\n",
+        ")\n",
+        "out2.hydrograph.q_sim.plot(label=\"Part 2\", color=\"orange\", lw=0.5)\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And now if we look at the difference between both hydrographs, we can see that there are differences in the second part at machine precision levels, due to rounding in the hotstart file (note that the y-axis is 1e-6, which is essentially 0!). But the rest is perfect!\n",
+        "\n",
+        "Therefore, we can provide forecasting abilities by saving simulation final states and using those to initialize model states for the forecasting runs. This will be used in other notebooks such as notebook #12 on hindcasting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "delta1 = np.abs(out1.hydrograph.q_sim - out.hydrograph.q_sim)\n",
+        "delta2 = np.abs(out2.hydrograph.q_sim - out.hydrograph.q_sim)\n",
+        "\n",
+        "delta1.plot(label=\"Part 1\")\n",
+        "delta2.plot(label=\"Part 2\")\n",
+        "plt.title(\"Difference between two parts and full simulation\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a hotstart file to resume a simulation from given hydrological conditions\n",
-    "\n",
-    "Hydrological models have state variables that describe the snow pack, soil moisture, underground reservoirs, etc. Typically, those cannot be measured empirically, so one way to estimate those values is to run the model for a period before the period we are actually interested in, and save the state variables at the end of this *warm-up* simulation.\n",
-    "\n",
-    "This notebook shows how to save those state variables and use them to configure another Raven simulation. These *states* are configured by the `:HRUStateVariableTable` and `:BasinStateVariables` commands, but `ravenpy` has a convenience function `set_solution` to update those directly from the `solution.rvc` simulation output.\n",
-    "\n",
-    "In the following, we run the model on two years then save the final states. Next, we use those final states to configure the initial state of a second simulation over the next two years. If everything is done correctly, these two series should be identical to a simulation over the full four years.\n",
-    "\n",
-    "## Model configuration\n",
-    "\n",
-    "At this point the following blocks of code should be quite familiar! If not, please go back to notebook \"04 - Emulating hydrological models\" to understand what is happening.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import packages\n",
-    "import datetime as dt\n",
-    "import warnings\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from ravenpy import Emulator, RavenWarning\n",
-    "from ravenpy.config import commands as rc\n",
-    "\n",
-    "# Import the GR4JCN model\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities.testdata import get_file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start and end date for full simulation\n",
-    "# Make sure the end date is before the end of the hydrometeorological data NetCDF file.\n",
-    "start_date = dt.datetime(1986, 1, 1)\n",
-    "end_date = dt.datetime(1988, 1, 1)\n",
-    "\n",
-    "# Define HRU\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Get dataset:\n",
-    "ERA5_full = get_file(\"notebook_inputs/ERA5_weather_data.nc\")\n",
-    "\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"latitude\": hru[\"latitude\"],\n",
-    "        \"longitude\": hru[\"longitude\"],\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Model configuration\n",
-    "config = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ERA5_full,\n",
-    "            data_type=data_type,\n",
-    "            alt_names=alt_names,\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"full\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Silence the Raven warnings\n",
-    "warnings.simplefilter(\"ignore\", category=RavenWarning)\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "out1 = Emulator(config=config).run()\n",
-    "\n",
-    "# Plot the model output\n",
-    "out1.hydrograph.q_sim.plot(label=\"Part 1\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now let's run the model for the next two years, setting the initial conditions to the final states of the first simulation.\n",
-    "\n",
-    "The path to the `solution.rvc` file can be found in `out1.files[\"solution\"]`.\n",
-    "The content itself can be displayed with `out1.solution`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The path to the solution (final model states)\n",
-    "hotstart = out1.files[\"solution\"]\n",
-    "\n",
-    "# Configure and run the model, this time with the next two years first 3 years (1988-1990).\n",
-    "conf2 = config.set_solution(hotstart)\n",
-    "conf2.start_date = dt.datetime(1988, 1, 1)\n",
-    "conf2.end_date = dt.datetime(1990, 1, 1)\n",
-    "conf2.run_name = \"part_2\"\n",
-    "\n",
-    "out2 = Emulator(config=conf2).run()\n",
-    "\n",
-    "# Plot the model output\n",
-    "out2.hydrograph.q_sim.plot(label=\"Part 2\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Compare with simulation over entire period\n",
-    "\n",
-    "Now in theory, those two simulations should be identical to one simulation over the whole period of four years, let's confirm this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "full = config.copy()\n",
-    "full.end_date = dt.datetime(1990, 1, 1)\n",
-    "full.run_name = \"full\"\n",
-    "\n",
-    "out = Emulator(config=full).run(overwrite=True)\n",
-    "\n",
-    "out.hydrograph.q_sim.plot(label=\"Full\", color=\"gray\", lw=4)\n",
-    "out1.hydrograph.q_sim.plot(\n",
-    "    label=\"Part 1\",\n",
-    "    color=\"blue\",\n",
-    "    lw=0.5,\n",
-    ")\n",
-    "out2.hydrograph.q_sim.plot(label=\"Part 2\", color=\"orange\", lw=0.5)\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now if we look at the difference between both hydrographs, we can see that there are differences in the second part at machine precision levels, due to rounding in the hotstart file (note that the y-axis is 1e-6, which is essentially 0!). But the rest is perfect!\n",
-    "\n",
-    "Therefore, we can provide forecasting abilities by saving simulation final states and using those to initialize model states for the forecasting runs. This will be used in other notebooks such as notebook #12 on hindcasting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "delta1 = np.abs(out1.hydrograph.q_sim - out.hydrograph.q_sim)\n",
-    "delta2 = np.abs(out2.hydrograph.q_sim - out.hydrograph.q_sim)\n",
-    "\n",
-    "delta1.plot(label=\"Part 1\")\n",
-    "delta2.plot(label=\"Part 2\")\n",
-    "plt.title(\"Difference between two parts and full simulation\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb b/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb
index aa8e7332..eb5e303a 100644
--- a/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb
+++ b/docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb
@@ -1,2418 +1,2418 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 08 - Getting and bias-correcting CMIP6 climate data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Applying bias correction on climate model data to perform climate change impact studies on hydrology\n",
-    "\n",
-    "This notebook will guide you on how to conduct bias correction of climate model outputs that will be fed as inputs to the hydrological model `Raven` to perform climate change impact studies on hydrology.\n",
-    "\n",
-    "## Geographic data\n",
-    "In this tutorial, we will be using the shapefile or GeoJSON file for watershed contours as generated in previous notebooks. The file can be uploaded to your workspace here and used directly in the cells below. In this notebook, we present a quick demonstration of the bias-correction approach on a small and predetermined dataset, but you can use your own basin according to your needs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "from numba.core.errors import NumbaDeprecationWarning\n",
-    "\n",
-    "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "import tempfile\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import gcsfs\n",
-    "import intake\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import xclim\n",
-    "import xclim.sdba as sdba\n",
-    "from clisops.core import average, subset\n",
-    "\n",
-    "from ravenpy.utilities.testdata import get_file\n",
-    "\n",
-    "tmp = Path(tempfile.mkdtemp())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Application to a real catchment and test-case.\n",
-    "In this notebook, we will perform bias-correction on a real catchment using real data! You can change the input file for the contours, the catchment properties and other such parameters. The previous notebooks show how to extract basin area, latitude, and longitude, so use those to generate the required information if it is not readily available for your catchment.\n",
-    "\n",
-    "Let's first start by providing some basic information:\n",
-    "\n",
-    "- basin_contour: The shapefile or geojson of the watershed boundaries (if it is a shapefile, it has to be a zip-file containing the .shp, .shx and .prj files)\n",
-    "- reference_start_day: The start day of the reference period\n",
-    "- reference_end_day: The end day of the reference period\n",
-    "- future_start_day: The start day of the future period\n",
-    "- future_end_day: The end day of the future period\n",
-    "- climate_model: The name of the climate model. Must be selected from the available list for this notebook. However, if you want to use other data, the bias-correction step will still be applicable if you follow the same logic and formats as shown here.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# We get the basin contour for testing on a server. You can replace the getfile method by a string containing the path\n",
-    "# to your own geojson\n",
-    "\n",
-    "# Get basin contour\n",
-    "basin_contour = get_file(\"notebook_inputs/input.geojson\")\n",
-    "\n",
-    "reference_start_day = dt.datetime(1980, 12, 31)\n",
-    "reference_end_day = dt.datetime(1991, 1, 1)\n",
-    "# Notice we are using one day before and one day after the desired period of 1981-01-01 to 1990-12-31.\n",
-    "# This is to account for any UTC shifts that might require getting data in a previous or later time.\n",
-    "\n",
-    "future_start_day = dt.datetime(2080, 12, 31)\n",
-    "future_end_day = dt.datetime(2091, 1, 1)\n",
-    "# Notice we are using one day before and one day after the desired period of 1981-01-01 to 1990-12-31.\n",
-    "# This is to account for any UTC shifts that might require getting data in a previous or later time.\n",
-    "\n",
-    "\"\"\"\n",
-    "Choose a climate model from the list below, which have the daily data required for Raven. Depending on the period required, it is possible that some\n",
-    "models will cause errors that need to be adressed specifically using date conversions. In those cases, please select another model or adjust the datetime\n",
-    "data to your needs.\n",
-    "\n",
-    "ACCESS-CM2\n",
-    "ACCESS-ESM1-5\n",
-    "AWI-CM-1-1-MR\n",
-    "BCC-CSM2-MR\n",
-    "CESM2-WACCM\n",
-    "CMCC-CM2-SR5\n",
-    "CMCC-ESM2\n",
-    "CanESM5\n",
-    "EC-Earth3\n",
-    "EC-Earth3-CC\n",
-    "EC-Earth3-Veg\n",
-    "EC-Earth3-Veg-LR\n",
-    "FGOALS-g3\n",
-    "GFDL-CM4\n",
-    "GFDL-ESM4\n",
-    "INM-CM4-8\n",
-    "INM-CM5-0\n",
-    "IPSL-CM6A-LR\n",
-    "KACE-1-0-G\n",
-    "KIOST-ESM\n",
-    "MIROC6\n",
-    "MPI-ESM1-2-HR\n",
-    "MPI-ESM1-2-LR\n",
-    "MRI-ESM2-0\n",
-    "NESM3\n",
-    "NorESM2-LM\n",
-    "NorESM2-MM\n",
-    "\"\"\"\n",
-    "\n",
-    "climate_model = \"MIROC6\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get CMIP6 data from the cloud\n",
-    "\n",
-    "Accessing and downloading climate data can be a painful and time-consuming endeavour. PAVICS-Hydro provides a method to gather data quickly and efficiently, with as little user-input as possible. We use the PanGEO catalog for cloud climate data, and with a few simple keywords, we can automatically extract the required data from the climate model simulations. Furthermore, we can also automatically subset it to our precise location as defined by the watershed boundaries, and also extract only the time period of interest.\n",
-    "\n",
-    "Let's start by opening the catalog of available data:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 08 - Getting and bias-correcting CMIP6 climate data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Applying bias correction on climate model data to perform climate change impact studies on hydrology\n",
+        "\n",
+        "This notebook will guide you on how to conduct bias correction of climate model outputs that will be fed as inputs to the hydrological model `Raven` to perform climate change impact studies on hydrology.\n",
+        "\n",
+        "## Geographic data\n",
+        "In this tutorial, we will be using the shapefile or GeoJSON file for watershed contours as generated in previous notebooks. The file can be uploaded to your workspace here and used directly in the cells below. In this notebook, we present a quick demonstration of the bias-correction approach on a small and predetermined dataset, but you can use your own basin according to your needs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "\n",
+        "from numba.core.errors import NumbaDeprecationWarning\n",
+        "\n",
+        "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import datetime as dt\n",
+        "import tempfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import gcsfs\n",
+        "import intake\n",
+        "import numpy as np\n",
+        "import xarray as xr\n",
+        "import xclim\n",
+        "import xclim.sdba as sdba\n",
+        "from clisops.core import average, subset\n",
+        "\n",
+        "from ravenpy.utilities.testdata import get_file\n",
+        "\n",
+        "tmp = Path(tempfile.mkdtemp())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Application to a real catchment and test-case.\n",
+        "In this notebook, we will perform bias-correction on a real catchment using real data! You can change the input file for the contours, the catchment properties and other such parameters. The previous notebooks show how to extract basin area, latitude, and longitude, so use those to generate the required information if it is not readily available for your catchment.\n",
+        "\n",
+        "Let's first start by providing some basic information:\n",
+        "\n",
+        "- basin_contour: The shapefile or geojson of the watershed boundaries (if it is a shapefile, it has to be a zip-file containing the .shp, .shx and .prj files)\n",
+        "- reference_start_day: The start day of the reference period\n",
+        "- reference_end_day: The end day of the reference period\n",
+        "- future_start_day: The start day of the future period\n",
+        "- future_end_day: The end day of the future period\n",
+        "- climate_model: The name of the climate model. Must be selected from the available list for this notebook. However, if you want to use other data, the bias-correction step will still be applicable if you follow the same logic and formats as shown here.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# We get the basin contour for testing on a server. You can replace the getfile method by a string containing the path\n",
+        "# to your own geojson\n",
+        "\n",
+        "# Get basin contour\n",
+        "basin_contour = get_file(\"notebook_inputs/input.geojson\")\n",
+        "\n",
+        "reference_start_day = dt.datetime(1980, 12, 31)\n",
+        "reference_end_day = dt.datetime(1991, 1, 1)\n",
+        "# Notice we are using one day before and one day after the desired period of 1981-01-01 to 1990-12-31.\n",
+        "# This is to account for any UTC shifts that might require getting data in a previous or later time.\n",
+        "\n",
+        "future_start_day = dt.datetime(2080, 12, 31)\n",
+        "future_end_day = dt.datetime(2091, 1, 1)\n",
+        "# Notice we are using one day before and one day after the desired period of 1981-01-01 to 1990-12-31.\n",
+        "# This is to account for any UTC shifts that might require getting data in a previous or later time.\n",
+        "\n",
+        "\"\"\"\n",
+        "Choose a climate model from the list below, which have the daily data required for Raven. Depending on the period required, it is possible that some\n",
+        "models will cause errors that need to be adressed specifically using date conversions. In those cases, please select another model or adjust the datetime\n",
+        "data to your needs.\n",
+        "\n",
+        "ACCESS-CM2\n",
+        "ACCESS-ESM1-5\n",
+        "AWI-CM-1-1-MR\n",
+        "BCC-CSM2-MR\n",
+        "CESM2-WACCM\n",
+        "CMCC-CM2-SR5\n",
+        "CMCC-ESM2\n",
+        "CanESM5\n",
+        "EC-Earth3\n",
+        "EC-Earth3-CC\n",
+        "EC-Earth3-Veg\n",
+        "EC-Earth3-Veg-LR\n",
+        "FGOALS-g3\n",
+        "GFDL-CM4\n",
+        "GFDL-ESM4\n",
+        "INM-CM4-8\n",
+        "INM-CM5-0\n",
+        "IPSL-CM6A-LR\n",
+        "KACE-1-0-G\n",
+        "KIOST-ESM\n",
+        "MIROC6\n",
+        "MPI-ESM1-2-HR\n",
+        "MPI-ESM1-2-LR\n",
+        "MRI-ESM2-0\n",
+        "NESM3\n",
+        "NorESM2-LM\n",
+        "NorESM2-MM\n",
+        "\"\"\"\n",
+        "\n",
+        "climate_model = \"MIROC6\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get CMIP6 data from the cloud\n",
+        "\n",
+        "Accessing and downloading climate data can be a painful and time-consuming endeavour. PAVICS-Hydro provides a method to gather data quickly and efficiently, with as little user-input as possible. We use the PanGEO catalog for cloud climate data, and with a few simple keywords, we can automatically extract the required data from the climate model simulations. Furthermore, we can also automatically subset it to our precise location as defined by the watershed boundaries, and also extract only the time period of interest.\n",
+        "\n",
+        "Let's start by opening the catalog of available data:\n",
+        "\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>version</th>\n",
-       "      <td>736</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<p><strong>pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s)</strong>:</p> <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>unique</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>activity_id</th>\n",
+              "      <td>18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>institution_id</th>\n",
+              "      <td>36</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>source_id</th>\n",
+              "      <td>88</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>experiment_id</th>\n",
+              "      <td>170</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>member_id</th>\n",
+              "      <td>657</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>table_id</th>\n",
+              "      <td>37</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>variable_id</th>\n",
+              "      <td>700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>grid_label</th>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>zstore</th>\n",
+              "      <td>514818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dcpp_init_year</th>\n",
+              "      <td>60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>version</th>\n",
+              "      <td>736</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "# Prepare the filesystem that allows reading data. Data is read on the Google Cloud Services, which host a copy of the CMIP6 (and other) data.\n",
+        "fsCMIP = gcsfs.GCSFileSystem(token=\"anon\", access=\"read_only\")\n",
+        "\n",
+        "# Get the catalog info from the pangeo dataset, which basically is a list of links to the various products.\n",
+        "col = intake.open_esm_datastore(\n",
+        "    \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
+        ")\n",
+        "\n",
+        "# Print the contents of the catalog, so we can see the classification system\n",
+        "display(col)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Prepare the filesystem that allows reading data. Data is read on the Google Cloud Services, which host a copy of the CMIP6 (and other) data.\n",
-    "fsCMIP = gcsfs.GCSFileSystem(token=\"anon\", access=\"read_only\")\n",
-    "\n",
-    "# Get the catalog info from the pangeo dataset, which basically is a list of links to the various products.\n",
-    "col = intake.open_esm_datastore(\n",
-    "    \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
-    ")\n",
-    "\n",
-    "# Print the contents of the catalog, so we can see the classification system\n",
-    "display(col)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that there are a lot of climate models (source_id), experiments, members, and other classifications. Let's see the list of available models, for example (source_id):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "['CMCC-CM2-HR4',\n",
-       " 'EC-Earth3P-HR',\n",
-       " 'HadGEM3-GC31-MM',\n",
-       " 'HadGEM3-GC31-HM',\n",
-       " 'HadGEM3-GC31-LM',\n",
-       " 'EC-Earth3P',\n",
-       " 'ECMWF-IFS-HR',\n",
-       " 'ECMWF-IFS-LR',\n",
-       " 'HadGEM3-GC31-LL',\n",
-       " 'CMCC-CM2-VHR4',\n",
-       " 'GFDL-CM4',\n",
-       " 'GFDL-AM4',\n",
-       " 'IPSL-CM6A-LR',\n",
-       " 'E3SM-1-0',\n",
-       " 'CNRM-CM6-1',\n",
-       " 'GFDL-ESM4',\n",
-       " 'GFDL-ESM2M',\n",
-       " 'GFDL-CM4C192',\n",
-       " 'GFDL-OM4p5B',\n",
-       " 'GISS-E2-1-G',\n",
-       " 'GISS-E2-1-H',\n",
-       " 'CNRM-ESM2-1',\n",
-       " 'BCC-CSM2-MR',\n",
-       " 'BCC-ESM1',\n",
-       " 'MIROC6',\n",
-       " 'AWI-CM-1-1-MR',\n",
-       " 'EC-Earth3-LR',\n",
-       " 'IPSL-CM6A-ATM-HR',\n",
-       " 'CESM2',\n",
-       " 'CESM2-WACCM',\n",
-       " 'CNRM-CM6-1-HR',\n",
-       " 'MRI-ESM2-0',\n",
-       " 'SAM0-UNICON',\n",
-       " 'GISS-E2-1-G-CC',\n",
-       " 'UKESM1-0-LL',\n",
-       " 'EC-Earth3',\n",
-       " 'EC-Earth3-Veg',\n",
-       " 'FGOALS-f3-L',\n",
-       " 'CanESM5',\n",
-       " 'CanESM5-CanOE',\n",
-       " 'INM-CM4-8',\n",
-       " 'INM-CM5-0',\n",
-       " 'NESM3',\n",
-       " 'MPI-ESM-1-2-HAM',\n",
-       " 'CAMS-CSM1-0',\n",
-       " 'MPI-ESM1-2-LR',\n",
-       " 'MPI-ESM1-2-HR',\n",
-       " 'MRI-AGCM3-2-H',\n",
-       " 'MRI-AGCM3-2-S',\n",
-       " 'MCM-UA-1-0',\n",
-       " 'INM-CM5-H',\n",
-       " 'KACE-1-0-G',\n",
-       " 'NorESM2-LM',\n",
-       " 'FGOALS-f3-H',\n",
-       " 'FGOALS-g3',\n",
-       " 'MIROC-ES2L',\n",
-       " 'FIO-ESM-2-0',\n",
-       " 'NorCPM1',\n",
-       " 'NorESM1-F',\n",
-       " 'MPI-ESM1-2-XR',\n",
-       " 'CESM1-1-CAM5-CMIP5',\n",
-       " 'E3SM-1-1',\n",
-       " 'KIOST-ESM',\n",
-       " 'ACCESS-CM2',\n",
-       " 'NorESM2-MM',\n",
-       " 'ACCESS-ESM1-5',\n",
-       " 'IITM-ESM',\n",
-       " 'GISS-E2-2-G',\n",
-       " 'CESM2-FV2',\n",
-       " 'GISS-E2-2-H',\n",
-       " 'CESM2-WACCM-FV2',\n",
-       " 'CIESM',\n",
-       " 'E3SM-1-1-ECA',\n",
-       " 'TaiESM1',\n",
-       " 'AWI-ESM-1-1-LR',\n",
-       " 'EC-Earth3-Veg-LR',\n",
-       " 'CMCC-ESM2',\n",
-       " 'CMCC-CM2-SR5',\n",
-       " 'EC-Earth3-AerChem',\n",
-       " 'IPSL-CM6A-LR-INCA',\n",
-       " 'IPSL-CM5A2-INCA',\n",
-       " 'BCC-CSM2-HR',\n",
-       " 'EC-Earth3P-VHR',\n",
-       " 'CESM1-WACCM-SC',\n",
-       " 'CAS-ESM2-0',\n",
-       " 'EC-Earth3-CC',\n",
-       " 'MIROC-ES2H',\n",
-       " 'ICON-ESM-LR']"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that there are a lot of climate models (source_id), experiments, members, and other classifications. Let's see the list of available models, for example (source_id):"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the list of models. Replace \"source_id\" with any of the catalog categories (table_id, activity_id, variable_id, etc.)\n",
-    "list(col.df.source_id.unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this notebook, we will work with MIROC6, but you can use any other model from the list established previously.\n",
-    "\n",
-    "Now, we can be more selective about what we want to get from the CMIP6 project data:\n",
-    "\n",
-    "- source_id: The climate model, in this case 'MIROC6'\n",
-    "- experiment_id: The forcing scenario. Here we will use 'historical' (for the historical period) and for future data we could use any of the SSP simulations, such as 'ssp585' or 'ssp245'.\n",
-    "- table_id: The timestep of the model simulation. Here we will use 'day' for daily data, but some models have monthly and 3-hourly data, for example.\n",
-    "- variable_id: The codename for the variable of interest. Here we will want 'tasmin', 'tasmax', and 'pr' for minimum temperature, maximum temperature and total precipitation, respectively.\n",
-    "- member_id: The code identifying the model member. Some models are run multiple times with varying initial conditions to represent natural variability. Here we will only focus on the first member 'r1i1p1f1'.\n",
-    "\n",
-    "You can find more information about available data on the CMIP6 project webpage and [data nodes] (https://esgf-node.llnl.gov/projects/cmip6/).\n",
-    "\n",
-    "Let's now see what the PanGEO catalog returns when we ask to filter according to all of these criteria:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>activity_id</th>\n",
-       "      <th>institution_id</th>\n",
-       "      <th>source_id</th>\n",
-       "      <th>experiment_id</th>\n",
-       "      <th>member_id</th>\n",
-       "      <th>table_id</th>\n",
-       "      <th>variable_id</th>\n",
-       "      <th>grid_label</th>\n",
-       "      <th>zstore</th>\n",
-       "      <th>dcpp_init_year</th>\n",
-       "      <th>version</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>CMIP</td>\n",
-       "      <td>MIROC</td>\n",
-       "      <td>MIROC6</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>r1i1p1f1</td>\n",
-       "      <td>day</td>\n",
-       "      <td>tasmin</td>\n",
-       "      <td>gn</td>\n",
-       "      <td>gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>20191016</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['CMCC-CM2-HR4',\n",
+              " 'EC-Earth3P-HR',\n",
+              " 'HadGEM3-GC31-MM',\n",
+              " 'HadGEM3-GC31-HM',\n",
+              " 'HadGEM3-GC31-LM',\n",
+              " 'EC-Earth3P',\n",
+              " 'ECMWF-IFS-HR',\n",
+              " 'ECMWF-IFS-LR',\n",
+              " 'HadGEM3-GC31-LL',\n",
+              " 'CMCC-CM2-VHR4',\n",
+              " 'GFDL-CM4',\n",
+              " 'GFDL-AM4',\n",
+              " 'IPSL-CM6A-LR',\n",
+              " 'E3SM-1-0',\n",
+              " 'CNRM-CM6-1',\n",
+              " 'GFDL-ESM4',\n",
+              " 'GFDL-ESM2M',\n",
+              " 'GFDL-CM4C192',\n",
+              " 'GFDL-OM4p5B',\n",
+              " 'GISS-E2-1-G',\n",
+              " 'GISS-E2-1-H',\n",
+              " 'CNRM-ESM2-1',\n",
+              " 'BCC-CSM2-MR',\n",
+              " 'BCC-ESM1',\n",
+              " 'MIROC6',\n",
+              " 'AWI-CM-1-1-MR',\n",
+              " 'EC-Earth3-LR',\n",
+              " 'IPSL-CM6A-ATM-HR',\n",
+              " 'CESM2',\n",
+              " 'CESM2-WACCM',\n",
+              " 'CNRM-CM6-1-HR',\n",
+              " 'MRI-ESM2-0',\n",
+              " 'SAM0-UNICON',\n",
+              " 'GISS-E2-1-G-CC',\n",
+              " 'UKESM1-0-LL',\n",
+              " 'EC-Earth3',\n",
+              " 'EC-Earth3-Veg',\n",
+              " 'FGOALS-f3-L',\n",
+              " 'CanESM5',\n",
+              " 'CanESM5-CanOE',\n",
+              " 'INM-CM4-8',\n",
+              " 'INM-CM5-0',\n",
+              " 'NESM3',\n",
+              " 'MPI-ESM-1-2-HAM',\n",
+              " 'CAMS-CSM1-0',\n",
+              " 'MPI-ESM1-2-LR',\n",
+              " 'MPI-ESM1-2-HR',\n",
+              " 'MRI-AGCM3-2-H',\n",
+              " 'MRI-AGCM3-2-S',\n",
+              " 'MCM-UA-1-0',\n",
+              " 'INM-CM5-H',\n",
+              " 'KACE-1-0-G',\n",
+              " 'NorESM2-LM',\n",
+              " 'FGOALS-f3-H',\n",
+              " 'FGOALS-g3',\n",
+              " 'MIROC-ES2L',\n",
+              " 'FIO-ESM-2-0',\n",
+              " 'NorCPM1',\n",
+              " 'NorESM1-F',\n",
+              " 'MPI-ESM1-2-XR',\n",
+              " 'CESM1-1-CAM5-CMIP5',\n",
+              " 'E3SM-1-1',\n",
+              " 'KIOST-ESM',\n",
+              " 'ACCESS-CM2',\n",
+              " 'NorESM2-MM',\n",
+              " 'ACCESS-ESM1-5',\n",
+              " 'IITM-ESM',\n",
+              " 'GISS-E2-2-G',\n",
+              " 'CESM2-FV2',\n",
+              " 'GISS-E2-2-H',\n",
+              " 'CESM2-WACCM-FV2',\n",
+              " 'CIESM',\n",
+              " 'E3SM-1-1-ECA',\n",
+              " 'TaiESM1',\n",
+              " 'AWI-ESM-1-1-LR',\n",
+              " 'EC-Earth3-Veg-LR',\n",
+              " 'CMCC-ESM2',\n",
+              " 'CMCC-CM2-SR5',\n",
+              " 'EC-Earth3-AerChem',\n",
+              " 'IPSL-CM6A-LR-INCA',\n",
+              " 'IPSL-CM5A2-INCA',\n",
+              " 'BCC-CSM2-HR',\n",
+              " 'EC-Earth3P-VHR',\n",
+              " 'CESM1-WACCM-SC',\n",
+              " 'CAS-ESM2-0',\n",
+              " 'EC-Earth3-CC',\n",
+              " 'MIROC-ES2H',\n",
+              " 'ICON-ESM-LR']"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  activity_id institution_id source_id experiment_id member_id table_id  \\\n",
-       "0        CMIP          MIROC    MIROC6    historical  r1i1p1f1      day   \n",
-       "\n",
-       "  variable_id grid_label                                             zstore  \\\n",
-       "0      tasmin         gn  gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/...   \n",
-       "\n",
-       "   dcpp_init_year   version  \n",
-       "0             NaN  20191016  "
+      "source": [
+        "# Get the list of models. Replace \"source_id\" with any of the catalog categories (table_id, activity_id, variable_id, etc.)\n",
+        "list(col.df.source_id.unique())"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Build a query dictionary for all of our requests, for tasmin.\n",
-    "query = dict(\n",
-    "    experiment_id=\"historical\",\n",
-    "    table_id=\"day\",\n",
-    "    variable_id=\"tasmin\",\n",
-    "    member_id=\"r1i1p1f1\",\n",
-    "    source_id=climate_model,\n",
-    ")\n",
-    "col_subset = col.search(\n",
-    "    require_all_on=[\"source_id\"], **query\n",
-    ")  # Command that will return the filtered list\n",
-    "\n",
-    "# Show the filtered list:\n",
-    "display(col_subset.df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the list contains only one item: The daily tasmin variable, for the historical period of member r1i1p1f1 from the MIROC6 model, as requested! We can also see the path where that file resides on the \"zstore\", which is where it is stored on the Google Cloud service. We can now get the data:\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Get the object locator object\n",
-    "mapper = fsCMIP.get_mapper(col_subset.df.zstore[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final step is to open the dataset with xarray by using the 'open_zarr()' function. The following block performs multiple operations to get the data that we want:\n",
-    "\n",
-    "- It opens the data using xarray\n",
-    "- It extracts only the times that we need for the reference/historical period\n",
-    "- It then subsets it spatially by getting only the points within the catchment boundaries. If your catchments is too small and this fails, try with a larger basin or apply a buffer around your boundaries.\n",
-    "- Since we are running a lumped model, it take the spatial average.\n",
-    "- It will then remove unnecessary coordinates that could cause problems later ('height', in this case)\n",
-    "- It will then rechunk the data into a format that makes it much faster to read and process\n",
-    "\n",
-    "Finally, we will display the output of this entire process.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hwloc/linux: Ignoring PCI device with non-16bit domain.\n",
-      "Pass --enable-32bits-pci-domain to configure to support such devices\n",
-      "(warning: it would break the library ABI, don't enable unless really needed).\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For this notebook, we will work with MIROC6, but you can use any other model from the list established previously.\n",
+        "\n",
+        "Now, we can be more selective about what we want to get from the CMIP6 project data:\n",
+        "\n",
+        "- source_id: The climate model, in this case 'MIROC6'\n",
+        "- experiment_id: The forcing scenario. Here we will use 'historical' (for the historical period) and for future data we could use any of the SSP simulations, such as 'ssp585' or 'ssp245'.\n",
+        "- table_id: The timestep of the model simulation. Here we will use 'day' for daily data, but some models have monthly and 3-hourly data, for example.\n",
+        "- variable_id: The codename for the variable of interest. Here we will want 'tasmin', 'tasmax', and 'pr' for minimum temperature, maximum temperature and total precipitation, respectively.\n",
+        "- member_id: The code identifying the model member. Some models are run multiple times with varying initial conditions to represent natural variability. Here we will only focus on the first member 'r1i1p1f1'.\n",
+        "\n",
+        "You can find more information about available data on the CMIP6 project webpage and [data nodes] (https://esgf-node.llnl.gov/projects/cmip6/).\n",
+        "\n",
+        "Let's now see what the PanGEO catalog returns when we ask to filter according to all of these criteria:\n",
+        "\n"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
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-       ".xr-no-icon {\n",
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-       "  stroke: currentColor;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tasmin&#x27; (time: 3653, geom: 1)&gt;\n",
-       "dask.array&lt;rechunk-merge, shape=(3653, 1), dtype=float32, chunksize=(3653, 1), chunktype=numpy.ndarray&gt;\n",
-       "Coordinates: (12/19)\n",
-       "  * time       (time) datetime64[ns] 1980-12-31T12:00:00 ... 1990-12-31T12:00:00\n",
-       "    lon        (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    lat        (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "  * geom       (geom) int64 0\n",
-       "    COAST      (geom) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    DIST_MAIN  (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    ...         ...\n",
-       "    PFAF_ID    (geom) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    SIDE       (geom) object dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    SORT       (geom) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    SUB_AREA   (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    UP_AREA    (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    id         (geom) object dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    cell_measures:  area: areacella\n",
-       "    cell_methods:   area: mean time: minimum\n",
-       "    comment:        minimum near-surface (usually, 2 meter) air temperature (...\n",
-       "    long_name:      Daily Minimum Near-Surface Air Temperature\n",
-       "    original_name:  T2\n",
-       "    standard_name:  air_temperature\n",
-       "    units:          K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tasmin'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3653</li><li><span class='xr-has-index'>geom</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-022f59d1-fb5f-4454-8d82-8b30b5fba09a' class='xr-array-in' type='checkbox' checked><label for='section-022f59d1-fb5f-4454-8d82-8b30b5fba09a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(3653, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 14.27 kiB </td>\n",
-       "                        <td> 14.27 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (3653, 1) </td>\n",
-       "                        <td> (3653, 1) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-6c6a2381-6107-4f4f-b424-ae784f78483b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c6a2381-6107-4f4f-b424-ae784f78483b' class='xr-section-summary' >Coordinates: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-12-31T12:00:00 ... 1990-12-...</div><input id='attrs-d6296ab6-c276-4640-a8c6-1babdafe8448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6296ab6-c276-4640-a8c6-1babdafe8448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-631c426d-af9f-4ed9-8a77-19e3ec04413c' class='xr-var-data-in' type='checkbox'><label for='data-631c426d-af9f-4ed9-8a77-19e3ec04413c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-12-31T12:00:00.000000000&#x27;, &#x27;1981-01-01T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1981-01-02T12:00:00.000000000&#x27;, ..., &#x27;1990-12-29T12:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-12-30T12:00:00.000000000&#x27;, &#x27;1990-12-31T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-1d32a6f7-da37-4d90-b289-01219a1fc622' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d32a6f7-da37-4d90-b289-01219a1fc622' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6b23a6b-1a71-49d8-8547-95fc91fe91c1' class='xr-var-data-in' type='checkbox'><label for='data-c6b23a6b-1a71-49d8-8547-95fc91fe91c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-0a5899aa-af31-4af3-8a72-12448aac7782' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a5899aa-af31-4af3-8a72-12448aac7782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06b09edb-c277-43d7-8369-ba8b53d6e741' class='xr-var-data-in' type='checkbox'><label for='data-06b09edb-c277-43d7-8369-ba8b53d6e741' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-dc9e14bc-68e8-461a-ace0-ed7375f2525a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc9e14bc-68e8-461a-ace0-ed7375f2525a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa1d9a10-3246-4dcb-b534-97c26375051b' class='xr-var-data-in' type='checkbox'><label for='data-aa1d9a10-3246-4dcb-b534-97c26375051b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>COAST</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-1a96ba39-bbd7-494e-8107-f005d91161f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a96ba39-bbd7-494e-8107-f005d91161f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fa5e3e8-5862-4182-b5fc-9e4997fab3a5' class='xr-var-data-in' type='checkbox'><label for='data-2fa5e3e8-5862-4182-b5fc-9e4997fab3a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIST_MAIN</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-4e67f7ed-5d3d-4c48-9d34-1cd653e60073' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e67f7ed-5d3d-4c48-9d34-1cd653e60073' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a822ca33-9696-4380-8071-c163b24ac4ea' class='xr-var-data-in' type='checkbox'><label for='data-a822ca33-9696-4380-8071-c163b24ac4ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIST_SINK</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-29dee96d-05f2-4d4c-abe4-f99c2b50b57b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29dee96d-05f2-4d4c-abe4-f99c2b50b57b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1eefa1f-0659-416b-9695-b4bb3c8ba7bd' class='xr-var-data-in' type='checkbox'><label for='data-a1eefa1f-0659-416b-9695-b4bb3c8ba7bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENDO</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-bb3c3c63-df14-435a-b326-d5dee05c4702' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb3c3c63-df14-435a-b326-d5dee05c4702' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2588b63-a479-4909-b4a2-1e1b2da6cb35' class='xr-var-data-in' type='checkbox'><label for='data-c2588b63-a479-4909-b4a2-1e1b2da6cb35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HYBAS_ID</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d00a27e5-1543-4723-867e-5f76a7baaaf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d00a27e5-1543-4723-867e-5f76a7baaaf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2700eaf2-20fc-42cb-a686-9bfdf4ac06e1' class='xr-var-data-in' type='checkbox'><label for='data-2700eaf2-20fc-42cb-a686-9bfdf4ac06e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LAKE</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-8e86202a-c161-4287-8571-7bdf4f082bf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e86202a-c161-4287-8571-7bdf4f082bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b8f548b-1ddd-4323-8123-28b82b722f8b' class='xr-var-data-in' type='checkbox'><label for='data-2b8f548b-1ddd-4323-8123-28b82b722f8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NEXT_DOWN</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-b4bbbdf8-b4e9-4b79-93ed-bdaf13789cbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4bbbdf8-b4e9-4b79-93ed-bdaf13789cbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe548ef4-6f67-455e-b2ea-196b4726eb8e' class='xr-var-data-in' type='checkbox'><label for='data-fe548ef4-6f67-455e-b2ea-196b4726eb8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NEXT_SINK</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-873a1248-1e26-465e-a981-413c814aa20d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-873a1248-1e26-465e-a981-413c814aa20d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7add80c8-2fcd-45cb-b190-e268cbc62b25' class='xr-var-data-in' type='checkbox'><label for='data-7add80c8-2fcd-45cb-b190-e268cbc62b25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ORDER</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-f49331c9-e55b-499c-9308-7650e7092b4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f49331c9-e55b-499c-9308-7650e7092b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee4f56ae-5013-4693-a9ad-356fecf16a76' class='xr-var-data-in' type='checkbox'><label for='data-ee4f56ae-5013-4693-a9ad-356fecf16a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PFAF_ID</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-79942194-69ac-4782-9de5-c33e39bf4420' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79942194-69ac-4782-9de5-c33e39bf4420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d09cce2-272f-462c-a9d3-847932cd9f97' class='xr-var-data-in' type='checkbox'><label for='data-8d09cce2-272f-462c-a9d3-847932cd9f97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIDE</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-382cff4b-9b8a-4e10-a552-9bb4958cad5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-382cff4b-9b8a-4e10-a552-9bb4958cad5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b72b5f67-64bb-4c9c-9a3d-ab15207e7f9e' class='xr-var-data-in' type='checkbox'><label for='data-b72b5f67-64bb-4c9c-9a3d-ab15207e7f9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SORT</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-735c12ed-6bcc-4dbd-a4e8-4aaefa960ee8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-735c12ed-6bcc-4dbd-a4e8-4aaefa960ee8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5f50bbb-bbad-466b-81cb-feee4914f5c0' class='xr-var-data-in' type='checkbox'><label for='data-f5f50bbb-bbad-466b-81cb-feee4914f5c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SUB_AREA</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e0be4ed1-f463-4b70-acce-1c9b0307e699' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0be4ed1-f463-4b70-acce-1c9b0307e699' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c93420d1-4718-4316-af03-38ef8727c9fe' class='xr-var-data-in' type='checkbox'><label for='data-c93420d1-4718-4316-af03-38ef8727c9fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UP_AREA</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c71bb2a4-fa75-4fc1-a27d-926028fe4fbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c71bb2a4-fa75-4fc1-a27d-926028fe4fbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fef9e742-54f3-4a93-95a1-2cfe26c13724' class='xr-var-data-in' type='checkbox'><label for='data-fef9e742-54f3-4a93-95a1-2cfe26c13724' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-47b0622c-871f-4ba0-b9ab-3972b47833a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b0622c-871f-4ba0-b9ab-3972b47833a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-823713bc-9964-45f4-8b2b-d41f3281712f' class='xr-var-data-in' type='checkbox'><label for='data-823713bc-9964-45f4-8b2b-d41f3281712f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
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-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2d205e1-d732-4911-a624-e54c630c0225' class='xr-section-summary-in' type='checkbox'  ><label for='section-e2d205e1-d732-4911-a624-e54c630c0225' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-002f6a54-f07c-4714-b86c-0c9cce7aab6b' class='xr-index-data-in' type='checkbox'/><label for='index-002f6a54-f07c-4714-b86c-0c9cce7aab6b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1980-12-31 12:00:00&#x27;, &#x27;1981-01-01 12:00:00&#x27;,\n",
-       "               &#x27;1981-01-02 12:00:00&#x27;, &#x27;1981-01-03 12:00:00&#x27;,\n",
-       "               &#x27;1981-01-04 12:00:00&#x27;, &#x27;1981-01-05 12:00:00&#x27;,\n",
-       "               &#x27;1981-01-06 12:00:00&#x27;, &#x27;1981-01-07 12:00:00&#x27;,\n",
-       "               &#x27;1981-01-08 12:00:00&#x27;, &#x27;1981-01-09 12:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;1990-12-22 12:00:00&#x27;, &#x27;1990-12-23 12:00:00&#x27;,\n",
-       "               &#x27;1990-12-24 12:00:00&#x27;, &#x27;1990-12-25 12:00:00&#x27;,\n",
-       "               &#x27;1990-12-26 12:00:00&#x27;, &#x27;1990-12-27 12:00:00&#x27;,\n",
-       "               &#x27;1990-12-28 12:00:00&#x27;, &#x27;1990-12-29 12:00:00&#x27;,\n",
-       "               &#x27;1990-12-30 12:00:00&#x27;, &#x27;1990-12-31 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3653, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>geom</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e6171c2-9a88-4333-a144-76fbbcdc41d0' class='xr-index-data-in' type='checkbox'/><label for='index-6e6171c2-9a88-4333-a144-76fbbcdc41d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=1, step=1, name=&#x27;geom&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eda96ba8-1ff2-4571-9a27-09db99b2802e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eda96ba8-1ff2-4571-9a27-09db99b2802e' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: minimum</dd><dt><span>comment :</span></dt><dd>minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: min&#x27;)</dd><dt><span>long_name :</span></dt><dd>Daily Minimum Near-Surface Air Temperature</dd><dt><span>original_name :</span></dt><dd>T2</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>activity_id</th>\n",
+              "      <th>institution_id</th>\n",
+              "      <th>source_id</th>\n",
+              "      <th>experiment_id</th>\n",
+              "      <th>member_id</th>\n",
+              "      <th>table_id</th>\n",
+              "      <th>variable_id</th>\n",
+              "      <th>grid_label</th>\n",
+              "      <th>zstore</th>\n",
+              "      <th>dcpp_init_year</th>\n",
+              "      <th>version</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>CMIP</td>\n",
+              "      <td>MIROC</td>\n",
+              "      <td>MIROC6</td>\n",
+              "      <td>historical</td>\n",
+              "      <td>r1i1p1f1</td>\n",
+              "      <td>day</td>\n",
+              "      <td>tasmin</td>\n",
+              "      <td>gn</td>\n",
+              "      <td>gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>20191016</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  activity_id institution_id source_id experiment_id member_id table_id  \\\n",
+              "0        CMIP          MIROC    MIROC6    historical  r1i1p1f1      day   \n",
+              "\n",
+              "  variable_id grid_label                                             zstore  \\\n",
+              "0      tasmin         gn  gs://cmip6/CMIP6/CMIP/MIROC/MIROC6/historical/...   \n",
+              "\n",
+              "   dcpp_init_year   version  \n",
+              "0             NaN  20191016  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "<xarray.DataArray 'tasmin' (time: 3653, geom: 1)>\n",
-       "dask.array<rechunk-merge, shape=(3653, 1), dtype=float32, chunksize=(3653, 1), chunktype=numpy.ndarray>\n",
-       "Coordinates: (12/19)\n",
-       "  * time       (time) datetime64[ns] 1980-12-31T12:00:00 ... 1990-12-31T12:00:00\n",
-       "    lon        (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    lat        (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "  * geom       (geom) int64 0\n",
-       "    COAST      (geom) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    DIST_MAIN  (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    ...         ...\n",
-       "    PFAF_ID    (geom) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    SIDE       (geom) object dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    SORT       (geom) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    SUB_AREA   (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    UP_AREA    (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    id         (geom) object dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    cell_measures:  area: areacella\n",
-       "    cell_methods:   area: mean time: minimum\n",
-       "    comment:        minimum near-surface (usually, 2 meter) air temperature (...\n",
-       "    long_name:      Daily Minimum Near-Surface Air Temperature\n",
-       "    original_name:  T2\n",
-       "    standard_name:  air_temperature\n",
-       "    units:          K"
+      "source": [
+        "# Build a query dictionary for all of our requests, for tasmin.\n",
+        "query = dict(\n",
+        "    experiment_id=\"historical\",\n",
+        "    table_id=\"day\",\n",
+        "    variable_id=\"tasmin\",\n",
+        "    member_id=\"r1i1p1f1\",\n",
+        "    source_id=climate_model,\n",
+        ")\n",
+        "col_subset = col.search(\n",
+        "    require_all_on=[\"source_id\"], **query\n",
+        ")  # Command that will return the filtered list\n",
+        "\n",
+        "# Show the filtered list:\n",
+        "display(col_subset.df)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Get the CMIP6 data from Google Cloud and read it in memory using xarray. This is done via \"lazy loading\" and is not actually reading the data in memory\n",
-    "# yet, but is keeping track of what it will need to get, eventually.\n",
-    "ds = xr.open_zarr(mapper, consolidated=True)\n",
-    "\n",
-    "# Convert to numpy.datetime64 object to be compatbile\n",
-    "if type(ds.time[0].values) is not type(np.datetime64(\"1980-01-01\")):\n",
-    "    ds = ds.convert_calendar(\"standard\")\n",
-    "\n",
-    "# Extract only the dates that we really want. Again, this is done via lazy loading, and is not actually using memory at this point.\n",
-    "ds = ds.sel(time=slice(reference_start_day, reference_end_day))\n",
-    "\n",
-    "# Use the clisops subsetting tools to extract the data for the watershed boundaries and take the spatial average\n",
-    "ds = average.average_shape(ds, basin_contour)\n",
-    "\n",
-    "# Correct the coordinates that are unnecessary for our variable\n",
-    "ds = ds.reset_coords(\"height\", drop=True)\n",
-    "\n",
-    "# Rechunk the data so it is much faster to read (single chunk rather than 1 chunk per day)\n",
-    "historical_tasmin = ds[\"tasmin\"].chunk(-1)\n",
-    "\n",
-    "# Show the end result!\n",
-    "display(historical_tasmin)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that we have a single chunk of 10 years of tasmin data, as expected! However, you might also have noticed that there is no metadata, such as units and variable properties left in the data array. We can fix that by wrapping the code in a block that forces xarray to keep the metadata.\n",
-    "\n",
-    "Also, since we will need to use this block of code for each variable, it might become tedious. Therefore, to simplify the code, we can combine everything into a function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def extract_and_average(mapper, start, end, geometry):\n",
-    "    with xr.set_options(keep_attrs=True):\n",
-    "        ds = xr.open_zarr(mapper, consolidated=True)\n",
-    "\n",
-    "        # Convert to numpy.datetime64 object to be compatbile\n",
-    "        if type(ds.time[0].values) is not type(np.datetime64(\"1980-01-01\")):\n",
-    "            ds = ds.convert_calendar(\"standard\")\n",
-    "\n",
-    "        # Compute average over region\n",
-    "        out = average.average_shape(ds.sel(time=slice(start, end)), geometry)\n",
-    "\n",
-    "        # Convert geometry variables into attributes\n",
-    "        attrs = {\n",
-    "            key: out[key].values.item()\n",
-    "            for key in out.coords\n",
-    "            if key not in [\"time\", \"time_bnds\", \"lon\", \"lat\"]\n",
-    "        }\n",
-    "        out = out.isel(geom=0).reset_coords(attrs.keys(), drop=True)\n",
-    "        out.attrs.update(attrs)\n",
-    "\n",
-    "        return out.chunk(-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Much better! we have all the information we need. Let's repeat the process for the 3 variables and for the reference and future periods using ssp585. You probably don't have to change anything in this following block of code, but you can taylor it to your needs knowing how everything is built now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "historical tasmin\n",
-      "historical tasmax\n",
-      "historical pr\n",
-      "ssp585 tasmin\n",
-      "ssp585 tasmax\n",
-      "ssp585 pr\n"
-     ]
-    }
-   ],
-   "source": [
-    "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    # Load the files from the PanGEO catalogs, for reference and future variables of temperature and precipitation.\n",
-    "    out = {}\n",
-    "    for exp in [\"historical\", \"ssp585\"]:\n",
-    "        if exp == \"historical\":\n",
-    "            period_start = reference_start_day\n",
-    "            period_end = reference_end_day\n",
-    "        else:\n",
-    "            period_start = future_start_day\n",
-    "            period_end = future_end_day\n",
-    "\n",
-    "        out[exp] = {}\n",
-    "        for variable in [\"tasmin\", \"tasmax\", \"pr\"]:\n",
-    "            print(exp, variable)\n",
-    "            query = dict(\n",
-    "                experiment_id=exp,\n",
-    "                table_id=\"day\",\n",
-    "                variable_id=variable,\n",
-    "                member_id=\"r1i1p1f1\",\n",
-    "                source_id=climate_model,\n",
-    "            )\n",
-    "            col_subset = col.search(require_all_on=[\"source_id\"], **query)\n",
-    "            mapper = fsCMIP.get_mapper(col_subset.df.zstore[0])\n",
-    "            ds = xr.open_zarr(mapper, consolidated=True)\n",
-    "\n",
-    "            out[exp][variable] = extract_and_average(\n",
-    "                mapper, period_start, period_end, basin_contour\n",
-    "            )[variable]\n",
-    "\n",
-    "# We can now extract the variables that we will need later:\n",
-    "historical_tasmax = out[\"historical\"][\"tasmax\"]\n",
-    "historical_tasmin = out[\"historical\"][\"tasmin\"]\n",
-    "historical_pr = out[\"historical\"][\"pr\"]\n",
-    "future_tasmax = out[\"ssp585\"][\"tasmax\"]\n",
-    "future_tasmin = out[\"ssp585\"][\"tasmin\"]\n",
-    "future_pr = out[\"ssp585\"][\"pr\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Reference data to prepare bias correction\n",
-    "We have extracted the historical period and future period data from the GCM. Now we need the reference data to use as the baseline for bias-correction. Here we will use ERA5 and we will gather it again, since we can't be sure that the dates we selected in the 3rd notebook are still valid.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Regenerate the ERA5 data to be sure. In an operational context, you could combine everything onto one notebook and ensure that the\n",
-    "# dates and locations are constant!\n",
-    "\n",
-    "# Get the ERA5 data from the Wasabi/Amazon S3 server.\n",
-    "catalog_name = \"https://raw.githubusercontent.com/hydrocloudservices/catalogs/main/catalogs/atmosphere.yaml\"\n",
-    "cat = intake.open_catalog(catalog_name)\n",
-    "ds = cat.era5_reanalysis_single_levels.to_dask()\n",
-    "\n",
-    "\"\"\"\n",
-    "Get the ERA5 data. We will rechunk it to a single chunck to make it compatible with other codes on the platform, especially bias-correction.\n",
-    "We are also taking the daily min and max temperatures as well as the daily total precipitation.\n",
-    "\"\"\"\n",
-    "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    ERA5_reference = subset.subset_shape(\n",
-    "        ds.sel(time=slice(reference_start_day, reference_end_day)), basin_contour\n",
-    "    ).mean({\"latitude\", \"longitude\"})\n",
-    "    ERA5_tmin = ERA5_reference[\"t2m\"].resample(time=\"1D\").min().chunk(-1, -1, -1)\n",
-    "    ERA5_tmax = ERA5_reference[\"t2m\"].resample(time=\"1D\").max().chunk(-1, -1, -1)\n",
-    "    ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the list contains only one item: The daily tasmin variable, for the historical period of member r1i1p1f1 from the MIROC6 model, as requested! We can also see the path where that file resides on the \"zstore\", which is where it is stored on the Google Cloud service. We can now get the data:\n",
+        "\n"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f39904c4730>]"
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Get the object locator object\n",
+        "mapper = fsCMIP.get_mapper(col_subset.df.zstore[0])"
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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N9YaHhyM8PNzXXSIiIiKDkNwHSC5hYWGIi4vDmjVrXF5fs2YNevfu7fY9vXr1qrH86tWr0bNnT4SGhuL666/Hvn37sHv3bufPww8/jLvvvhu7d+82ZKuO32O/JSLy0bZjBfg+NVvrMMhgRCVAY8aMwcWLF0WvdNy4cbhw4YKo9c6ZMwfz5s1DRkYGRo8ejaysLMTHxzvX88wzzziXj4+Px6lTpzBmzBhkZGRg3rx5mDt3Ll599VUAQEREBLp16+byc9VVV6FRo0bo1q0bwsLCRO8DKccE3qsjIvk8NecPvPbDXhw4W6R1KGQgohKgzz//HGVlZaJXOnPmTBQWFnpcbvDgwUhISMCECRPQo0cPbNq0CUlJSWjbti0AICcnx2VMoNjYWCQlJWHjxo3o0aMHPvjgA0yfPh2DBg0SHRsRka8sNjtWH8hFYZlZ61CoipzCcq1DIAMR1QdIEAR06tRJ9MzvUlqLRo4ciZEjR7r924IFC2q81rdvX+zatUv0+t2tg7Ql8L4XGdyM9cfw+bqjuC6qEVaN/ovW4RCRF0QlQPPnz5e8YjkGSiQi0qNf95wFABw+V6JxJETkLVEJ0LPPPqt0HBRA2AeIiJTAoUBICs2eAiP5MbEgUgdv4hIZHxMgP8K+NUREROIwASJNMWkjI2JbK5HxMQEi1en9Pn1hmRlPfbUdP6Sd1joUIiJSCBMgomoS1h7FtuPn8er/9mgdChERKUTyXGAXL17Exx9/jHXr1iEvL88515bDiRMnZAuO/JNQ5a6XHjtuF5dbtA6BiIgUJjkBGj58OJKTkzFkyBC0atVK9OCIRO6wDxB5UlRmwWvL9uDRm67B/d1aah0O6RgvRySF5ATo999/x4oVK3DHHXcoEQ8FAFZSJMVna49g1YFzWHXgHE5+/JDW4QDgY/BE/kByH6AmTZqgadOmSsRCPtLj7SQiX+WXVmgdAhH5IckJ0AcffIB3331X0uSoRET+hF81iIxP8i2wTz/9FMePH0dUVBTatWuH0NBQl79LmaiUSOC9BDIgHrZExic5ARowYIACYVAg4bdnIiLSmuQE6L333lMiDpKBEZ+oYodoMiIetkTGJzkBckhLS0NGRgZMJhO6dOmCm266Sc64yI/pPU1jZ3LyRO/HcKDiuUtSSE6A8vLy8MQTT2Djxo246qqrIAgCioqKcPfdd2PJkiVo0aKFEnGSn9JjHyAjtqQREZE0kp8Ce+mll1BcXIwDBw7gwoUL+PPPP7F//34UFxdj1KhRSsRIfobf0YiISGuSW4BWrlyJtWvXonPnzs7XunTpgpkzZ6Jfv36yBkfSsPlXHv5ajmVmK9JO/Ynb2zdDaDCnASSiwCa5FrTb7TUefQeA0NDQGvOCERmRv94CG7EwDUPm7sC0NUe0DoWISHOSE6C//vWvePnll3H27Fnna2fOnMHo0aNxzz33yBoc+akqDSz+mWro05ZjBQCAxTuyNI6EiEh7khOgGTNmoKSkBO3atcO1116LDh06IDY2FiUlJfjvf/+rRIxEqvLXW2AkHx4hRMYnuQ9QTEwMdu3ahTVr1uDQoUMQBAFdunTBvffeq0R8RKrz11tgJB8eITrFzJQk8HocoPvuuw/33XefnLEQERERqUJUAjR9+nSMGDECERERmD59ep3L8lF47Rix5ULQ4UBAvAVGpG+CIMDEYeTJR6ISoM8++wxPP/00IiIi8Nlnn9W6nMlkYgJERESKmbnhGOZvzcTyF+5Am2b1Xf6mdkp0vrQCv+3NwYAerRFZv+bT0aRvohKgzMxMt7+TvrDlgoj83ZRVhwEAH6/MQOLTcS5/U7s9+bmvU7EnuxAbDudhwdBbVd46+UryU2ATJkxAWVlZjdcvXbqECRMmyBIUkRiCIMBu198tNCIKDHuyCwEAGw/naxsIeUVyAjR+/HiUlpbWeL2srAzjx4+XJSjyb1VbqrxNXwRBwKOJ2/DQf7cwCSIiIskkPwVWW+ezPXv2oGnTprIEReTJRbMNuy9/+8otLkf0VfW0DYiINMdOACSF6ASoSZMmMJlMMJlM6NSpk0sSZLPZUFpaivj4eEWCJP8ix9Nqenx6zCh4kSB/wD6P5CvRCVBCQgIEQcBzzz2H8ePHIzIy0vm3sLAwtGvXDr169VIkSKK68GlYaZg6EhFJSICeffZZAEBsbCx69+7tdkJUIqm8bchR8iLOhIrImDg2EEkhuQ9Q3759nb9funQJFovF5e+NGzf2PSryTgCd+1UTJ7mbwnl3jciYeGucpJD8FFhZWRlefPFFXH311WjYsCGaNGni8kNERESkd5IToP/85z9Yv349EhMTER4ejjlz5mD8+PGIjo7GwoULlYiRxOKXH1n4eyu6n+8eEZEokm+B/frrr1i4cCHuuusuPPfcc+jTpw86dOiAtm3b4ttvv8XTTz+tRJzkp7x+IqzqLTCZr+j+3oru57une+dLK9CkfhiCgpiK+sRN8bEPEEkhuQXowoULiI2NBVDZ3+fChQsAgDvvvBObNm2SNzoiEVjlkdq87Wuy/cR5xH24FiO/3SVzRFSXS2ab1iGQDklOgNq3b4+TJ08CALp06YLvv/8eQGXL0FVXXSVnbOSvZGiCkGMsodrwSyQp5atNJwAAKw/kahxJ4PhszRF0fnclNhzO0zoU0hnJCdDQoUOxZ88eAMC4ceOcfYFGjx6N//znP7IHSKQ2f78FxvzOd7zVok/uPpXP1x0FALz38wF1gyHdk9wHaPTo0c7f7777bhw6dAipqam49tprceONN8oaHAUAOZINXouIfLIr60/8lH4GY/tdh8h6xh3jzc+/u5DMJLcALVy4EBUVFc7/t2nTBgMHDkTnzp35FJjWAigRULKVhl/uKdAMTNyGhSmnMCkpQ+tQiFTj1S2woqKiGq+XlJRg6NChsgRF5EnV/IdzAknDb8na0XvZH88v1ToE0QLhrDdb7Xhj2V6s2JujdSh+SXICVNts8KdPn3aZH4yIiEhN/pYULU3NxpKd2fj3d3xqUAmi+wDddNNNztng77nnHoSEXHmrzWZDZmYm7r//fkWCJP+l92/E/sjfLhJa8PYxeL2Xvb8/AGA0+SUVnhcir4lOgAYMGAAA2L17N/r374+GDRs6/+aYDX7QoEGyB0jkTtULEPvsEBGRVKIToPfeew8A0K5dOwwePBgRERGyBJCYmIgpU6YgJycHXbt2RUJCAvr06VPr8snJyRgzZgwOHDiA6OhovPbaa4iPj3f+ffny5Zg4cSKOHTsGi8WCjh07YuzYsRgyZIgs8eqaQb69yR0m8x9Sm7ePwRvkFDUsfhkiKST3AXr22WdlS36WLl2KV155BW+99RbS09PRp08fPPDAA8jKynK7fGZmJh588EH06dMH6enpePPNNzFq1CgsW7bMuUzTpk3x1ltvISUlBXv37sXQoUMxdOhQrFq1SpaYSR94ISEtcdZxfeLHQlKIagFq2rQpjhw5gubNm6NJkyZ1fvtxTI0hxrRp0zBs2DAMHz4cAJCQkIBVq1Zh1qxZmDRpUo3lZ8+ejTZt2iAhIQEA0LlzZ6SmpmLq1KnO22933XWXy3tefvllfP3119iyZQv69+8vOjZSh7cVFis6MpIVe3MwZ8sJlFUYY0qG9YfOYdqaI5j2eA90imqkdThucTBK8pWoBOizzz5Do0aVJ4Ej+fCV2WxGWloa3njjDZfX+/Xrh23btrl9T0pKCvr16+fyWv/+/TF37lxYLBaEhroO4CUIAtavX4/Dhw9j8uTJtcZSUVHhMrZRcXGx1N3RhwCqD6pOhcFciNQm9eJrlKd4HOfScwtSAQAvLErDurF3aRaPVMyJSApRCdCzzz7r9ndfFBQUwGazISoqyuX1qKgo5Oa6nycnNzfX7fJWqxUFBQVo1aoVAKCoqAitW7dGRUUFgoODkZiYiPvuu6/WWCZNmoTx48f7uEdEFCgC5RZYcblV6xAkmbslEze0jsRV9cO0DoUMQPJUGEDlY+8//vgjMjIyYDKZ0LlzZzzyyCMuj8aLVf2bVG3jDNW1fPXXGzVqhN27d6O0tBTr1q3DmDFj0L59+xq3xxzGjRuHMWPGOP9fXFyMmJgYqbtCagqM6w8RSbDxcD5eWbobC4beqnUoZACSM5b9+/fjkUceQW5uLq677joAwJEjR9CiRQv88ssvuOGGG0Stp3nz5ggODq7R2pOXl1ejlcehZcuWbpcPCQlBs2bNnK8FBQWhQ4cOAIAePXogIyMDkyZNqjUBCg8PR3h4uKi4SV7ezupe9V1yfxlnKzqRcW08nK91CGQQkp8CGz58OLp27YrTp09j165d2LVrF7Kzs9G9e3eMGDFC9HrCwsIQFxeHNWvWuLy+Zs0a9O7d2+17evXqVWP51atXo2fPnjX6/1QlCIJLHx8yPiXvQLBxiYh0IUButWpFcgvQnj17kJqaiiZNmjhfa9KkCT766CPccsstktY1ZswYDBkyBD179kSvXr3w5ZdfIisryzmuz7hx43DmzBnnJKvx8fGYMWMGxowZg+effx4pKSmYO3cuFi9e7FznpEmT0LNnT1x77bUwm81ISkrCwoULMWvWLKm7SgqR45z2tuWIiPwDW2rJV5IToOuuuw7nzp1D165dXV7Py8tz3nYSa/DgwTh//jwmTJiAnJwcdOvWDUlJSWjbti0AICcnx2VMoNjYWCQlJWH06NGYOXMmoqOjMX36dJcRqC9evIiRI0fi9OnTqFevHq6//nosWrQIgwcPlrqrZBByJ0NqVqzf/ZGFCqsNQ++IVXGrRO4FSuduw+BjbYqSnABNnDgRo0aNwvvvv4/bb78dALB9+3ZMmDABkydPdnmEvHHjxh7XN3LkSIwcOdLt3xYsWFDjtb59+2LXrtofKf3www/x4YcfetyuP9h7uhCjFqdrHYZPAnkcoAqrDW/+uA8A8Pcbo9G8Ifuhqe30n2X4fO1RDO/THte1FD/eDceg0Q8mbeQtyQnQ3/72NwDA448/7qwEHAfg3//+d+f/TSYTbDZjDPplVMO/TkVegE6W5w9Vns1+ZS/KLTxXtDBiYRoO5hTj5z1nceTDB0S/z18vukzsKJBIToA2bNigRBwk0uaj+ci+cAlP3dYGl8yBe9F0uQDp8Fp0scKK+VszcX+3VuhwdUOPy6t5PeVF7opDuZUt1marXeNI9MFfEzvD4uehKMkJUN++fZWIg0QaMncHAKBba8+3F/2Z3uuFySsPYWHKKUxdfQQnP37I7TImjbpx8iJHRCQyAdq7dy+6deuGoKAg7N27t85lu3fvLktgVLezheV+8RiEv16Kd2X96XEZPsmmPZPJpP9smtxyNGT69cfH1lpFiUqAevTogdzcXFx99dXo0aMHTCaT22+R7PdDYsh94ffn+o+UxdYwosAlKgHKzMxEixYtnL+THgR2xe0P1y2tboEREZHIBMgxLk/134m0IrUVaVnaaRw5V4I3HrhelU7Aek7Q2An6Ct4Cc2XEkjBizKLx2FSU5KkwJk2ahHnz5tV4fd68eZg8ebIsQVHg8PYWhMtDYCJWMfZ/e/DFphPYdvy8V9tTAvMQ0jt/uv6yzx1VJzkB+uKLL3D99dfXeL1r166YPXu2LEGRONWvn7yeelZYZlFlO2KSG60uLuz3QkTkRQKUm5uLVq1a1Xi9RYsWyMnJkSUoIk+8vYTzWyCReHUl8lnny5Cw9ggKy8zqBeQDQ/a5YzOxoiSPAxQTE4OtW7ciNtZ17qKtW7ciOjpatsCoboH+Jb5qK4Yekxoxnw/rNjKyR2ZuwZ9lFhw8W4wvn+mp+vYdpw9bNMlbkhOg4cOH45VXXoHFYsFf//pXAMC6devw2muvYezYsbIHSOIZsRrwei4wecPQHEeCJqP58/Lt5B0nL2iyfX+rA9xicqcoyQnQa6+9hgsXLmDkyJEwmyubPiMiIvD6669j3LhxsgdI/kfuc5p1hP/6fV8OVuzlrXW1VD+X/Onc0mNLMWlLcgJkMpkwefJkvPPOO8jIyEC9evXQsWNHhIdzJmtSjz9VzABvh9XmhW93aR0C6ZTzFpimUZCRSe4E7ZCbm4sLFy7g2muvRXh4OO/DaoC3MqTT62EqZ1yrDuRi5oZjtZ6TejxXBUHAWz/uw8wNx1Tdrhxn0CMztuDj3w/JsCb90fPTjFIZshM0KUpyAnT+/Hncc8896NSpEx588EHnk1/Dhw9nHyDShNz1r9x55e7sQlWTjv/7Jg1TVh3GH5na9M3wxv4zxfj2jyxMWXVY61Ak23O6CLOTj2sdBvkjfslVlOQEaPTo0QgNDUVWVhbq16/vfH3w4MFYuXKlrMFR7dxdTgPrVPFyAEWZoxBjwMytWLT9VM1Yqg7mqEBk+SUVbl/XY8vhJQvnEDQqrQ4nPR7HsjNK85pBSU6AVq9ejcmTJ+Oaa65xeb1jx444dapmJU8UaL5IPo4DZ4tdXvv2jyyNoiE1ZV8ow4z1R1Gk0oCbStPz9dfRqqrnGEnfJHeCvnjxokvLj0NBQQE7QpNqXKfCEF8DKv2d0W4XMMmL/iDsn+AfBszcivMXzThwthiz/hmndTiSeZNL6DEB+Sn9DP4sM2PoHbGeF6aAJbkF6C9/+QsWLlzo/L/JZILdbseUKVNw9913yxoc+T+1K0+lN6enEaoD4Q6B3py/WDk0SMoJ/cw556/qugX2ytLdGP/rQZw6f1HFiMhoJLcATZkyBXfddRdSU1NhNpvx2muv4cCBA7hw4QK2bt2qRIwkkcVmx74zRejeOhIhwV4/6KcYOS71OvzSqTt6/GZO+lZjfkEJSXRRmQUrD+Tg/m6tEFkvVNa46lLXl4fiS1bV4lAEv8UoSvLVsUuXLti7dy9uvfVW3Hfffbh48SIGDhyI9PR0XHvttUrESG4IQu3nxvu/HMDAxG0Y/+tBdYPSiJQLvRxPYxWUViDl+Hmf18WB2bTH64srb45IRxm+uHgXXl+2D6MWp8saU0DT6FtMRk4x3vxxH/KKyzXZvloktQBZLBb069cPX3zxBcaPH69UTOQjR4fbb7afwgcDuol6z4z1R5G0LxdL/u92NI5Q79ubEf3lkw0oM9vw5ZA49OvaUutwamX0i/t/1x1VfBuciqVuUspn89ECAEDykXyFonFl8MNb1x74fDOAyglvFw2/TeNolCOpBSg0NBT79+8PjMcPA8zU1UdwMKcYC7edVHW73raCeHvhkuPYLTNXPrK94bB8FT1vV9X06ZojWocQeLw4ELU6dnnKKO9QbrHnhQxM8i2wZ555BnPnzlUiFtIBs82/qxU9joKslADaVa8/V2/zYX4F1I9AOs7V5u9lK7kTtNlsxpw5c7BmzRr07NkTDRo0cPn7tGnTZAuOqDb+0H9Gq8rF3y7e+04XYeiCnXjt/uvweM8YVbZp/KOvFtUyQj039us4NDIIyQnQ/v37cfPNNwMAjhxxbaLmrTH1CBAUqQD4CUqh78tgbaejvqOWbuR3aSgorcBrP+xVLQHyW7Vk5WcKLwEAWl9Vr8bf9Fzt6zk20p7kBGjDhg1KxEEByutOqIL737UWSLfY9OKSWf1pNALluioIQLnFhjs+Xg8AWPz87WgQHozu11zlsoxe6Tk20p7kBKiq7OxsmEymGtNiENXFf5IE+S6D/lIiWqiw2FXfZiB9XoVVpvV48qvtAIATEx/UKpwrJJ5+/nDbnOQluRO01WrFO++8g8jISLRr1w5t27ZFZGQk3n77bVgs/jH/DZEaWB3Lo8KqfgIUSNwlDro4dnURhH/z9yKW3AL04osv4scff8Qnn3yCXr16AQBSUlLw/vvvo6CgALNnz5Y9SFKPUe6ZKz2Tuj8zyEcsms1vWhT1xyj1QW2qxs/59qg6yQnQ4sWLsWTJEjzwwAPO17p37442bdrgiSeeYAKkEnd1vhE7oat96RJzrfSlojTCpdgIMUrhy1Ff+VlLLxFP2zTemXiF1SatRa3okkYt/5cLua5zmrmxZyXlFoxbvg8P3xit64FdlSD5FlhERATatWtX4/V27dohLCxMjphIpOoJj//0rfGMrT6e8RuvcjwdfUY9OvecLkKX91Y5/x9AVUrAmrH+GH7bm4MR36TJvu6Scgtsdv0eRJIToH//+9/44IMPUFFR4XytoqICH330EV588UVZgyMSwx8qaSWS10BJEgNjL9VjrtanysjnV9XviIFyPkiVV1JR6998qZfOFl7CDe+vxqBZ2wAAy9JO46mvtqOwzOz1OuUm+RZYeno61q1bh2uuuQY33ngjAGDPnj0wm8245557MHDgQOeyy5cvly9SUoVRWg2MXCk7BFKLnb/xl1tg+88UYdmu01qH4RVHXcXERp9W7M0BAOzOLgQAjP3fHgBAwtqjeP/hrlqF5UJyAnTVVVdh0KBBLq/FxHDwMRLPdQwfz5XXL3vOon3zBujWOlLBqLyh74q3tmTWKBdnsbTYH31/8uL97b9bPC6j132Vmvg4zoc/L5qR/WeZy1hGpJ7icv08LS45AZo/f74ScZBEeq2U5JZ68gJGLU4HAJz8+CG3y0gpC6W/LXrbqHMotwTtWzSUNxjyTKHsSY/n58e/H8LVjcLx3J2xot9T62jiBm69vH3SOlRY7fj+/3rh1timWofjt2p9JkdHh47kPkCkH/72Td6do3mlWodQB/k+gZHf7pJlPWIuTDqqf7QXIIVxOLcEs5OPY8JvByW9T895jre36x3jRiUfyZMzHL+kxMevp0OKCVAAOZxbgtN/ltW5jAGfpDcsJSqCVQfOKbBWdRm5dcFBzGl0scJao8OxUkorrKpsRwtiD5fqrb9+cJiRj5gA+ZG6xgEqKK1A/4RNuHOyvuZy87YOktqPKFBsP3Fe6xCMxc0pY7ba8fgXKZiy6pCim+763ir0+WS9otuQg7vzi2cc+QMmQAHiZMFFt68fyyvFf9cddf6fDUC+UaqP0Z7sQrz2wx7klZRLet8by/bisVnbXAa30/tnrGo+62Zbv+/PwY7MC5i54bjimz9XXPsjyFQ3qa3VRnnCldTj02SopB25Wj3unZYsy3rU5m2iYdTGokdmbgUA5JdUYP7QW0W/b8nObADAjpMXFInLH1lsBj1IFKDnW+KOc5mflrHoqcVeVAI0ffp00SscNWqU18GQNFIqJz1XZO6UW2w4cq5EVMKin9NJecfz3bfkueMyCFwgFZIUbs4LbyvolOP+d/vRn48bP9412Uj5/M1WO/JLK9D6qnrKBSQzUQnQZ599JmplJpOJCZCG5Mis1U6Uagt5+Nep2HKsAJ1bNVY3IBUpfXEx6sXLoGHjn3P/0DoERbg7jvRwbBntS52/GzBzKw7mFGPZC70R17aJ1uGIIioByszMVDoOIhdbjhUAADJyit3+XQ8VsLtLteJJjZfpAa8VytPznEdy4+jLVN3By3X1T+lnENe2iSEm52YnaDI8KUmHPhIndRig/nFLT30ESP/0crycKy7HoFnb8PPuM6LfU2a2InHjMRzP1/N4Z9J4So718WlV8ioBOn36NBITE/HGG29gzJgxLj9SJSYmIjY2FhEREYiLi8PmzZvrXD45ORlxcXGIiIhA+/btMXv2bJe/f/XVV+jTpw+aNGmCJk2a4N5778WOHTskx0X6po+TSP0Mg0+y+KfMgosot9i0DsNQfD0T5M6bJvx2EGmn/sTLS3aLfs8nKw/jk5WHcc+n7h9G0bqe00tyqRTJT4GtW7cODz/8MGJjY3H48GF069YNJ0+ehCAIuPnmmyWta+nSpXjllVeQmJiIO+64A1988QUeeOABHDx4EG3atKmxfGZmJh588EE8//zzWLRoEbZu3YqRI0eiRYsWzvnJNm7ciCeffBK9e/dGREQEPvnkE/Tr1w8HDhxA69atpe6uzrlWAXI0OarfbOnfJ1id1OwDZKC8Sc0jQg/FsvPkBfxjdgpimzfAhlfv0jocUfRwXdRBCC6KL0mf4yr1FJ/O1JLkFqBx48Zh7Nix2L9/PyIiIrBs2TJkZ2ejb9+++Mc//iFpXdOmTcOwYcMwfPhwdO7cGQkJCYiJicGsWbPcLj979my0adMGCQkJ6Ny5M4YPH47nnnsOU6dOdS7z7bffYuTIkejRoweuv/56fPXVV7Db7Vi3bl2tcVRUVKC4uNjlx//ooaqXj+s3E3mrQvE5oN6qYDKiX3afBVDZCkTSGfks9JRIGrnWrnUqMB19YJIToIyMDDz77LMAgJCQEFy6dAkNGzbEhAkTMHnyZNHrMZvNSEtLQ79+/Vxe79evH7Zt2+b2PSkpKTWW79+/P1JTU2GxuM++y8rKYLFY0LRp7ZPeTZo0CZGRkc4fzm4fGMxWOxbvyELWedfpQaScoOeKy3X7+LNR+wARkXHpKcHxRHIC1KBBA1RUVI5eGh0djePHr4yWWlBQIHo9BQUFsNlsiIqKcnk9KioKubm5bt+Tm5vrdnmr1Vrrtt944w20bt0a9957b62xjBs3DkVFRc6f7Oxs0fuhJn+/H+stSZ2gq/z+1eYTGLd8H/4yxdvpQUy4beI6PPnVdmw+ml/nkoVlFpSZrXjw882YvFLZKRaMTuvDnGeZK60/j9r43AeIn7QijFSqkvsA3X777di6dSu6dOmChx56CGPHjsW+ffuwfPly3H777ZIDqN7nRBCEOvuhuFve3esA8Mknn2Dx4sXYuHEjIiIial1neHg4wsPDpYStOb1WSmK4zuPl5TpkiKO2ebO8aTnZdvw8+nRsUevfBQhYlnYaB3OKcTCnGK/ff71XFbD3j8GzOYjkY+T6x0i0Lmatt680yQnQtGnTUFpa+cje+++/j9LSUixduhQdOnQQPWAiADRv3hzBwcE1Wnvy8vJqtPI4tGzZ0u3yISEhaNasmcvrU6dOxcSJE7F27Vp0795ddFx6Vr3S8eeRoI3G0wXBLmg7xcKRcyWabVsqrb+Z81TxTOvPqCojJWOZBRdRWm7FDddEah2K4mq75ujp45KcALVv3975e/369ZGYmOjVhsPCwhAXF4c1a9bg0Ucfdb6+Zs0aPPLII27f06tXL/z6668ur61evRo9e/ZEaGio87UpU6bgww8/xKpVq9CzZ0+v4gtURkmUXFqRtIui5iu1BCNXsXrbklNaYZUpAm0VlplxVf0wRbehpwqa/MvdUzcCAHa8eQ+ublz7XQkHg1THhiW5D1D79u1x/nzNWweFhYUuyZEYY8aMwZw5czBv3jxkZGRg9OjRyMrKQnx8PIDKvjnPPPOMc/n4+HicOnUKY8aMQUZGBubNm4e5c+fi1VdfdS7zySef4O2338a8efPQrl075ObmIjc319lqRUT6Vtc3+h4T1tR669IbSiX8RhgFVyx3rT1GanVxqLEfGu5D1oWyGq9tPVbgF6OJG+nYkJwAnTx5EjZbzQG7KioqcOaM+BEwAWDw4MFISEjAhAkT0KNHD2zatAlJSUlo27YtACAnJwdZWVnO5WNjY5GUlISNGzeiR48e+OCDDzB9+nTnGEBA5cCKZrMZjz32GFq1auX8qfqovFEZ6LgSzft98nY2eDlL8cpFTq3RT6Xceqh6Cfaj6zFmbTzueSGRlKqs/f2BBV3tXR3B6P24r3qYPD3nD8zdckK7YNzR1QctP9G3wH755Rfn76tWrUJk5JV7mDabDevWrUO7du0kBzBy5EiMHDnS7d8WLFhQ47W+ffti165dta7v5MmTkmMwIqn34HVeD/jECNcaLSaUNECxkEr0ngh4Q8w+VT3H1HwQoMxsRf2w2i+vtZ2by9LOoOiSBb/vz8X3/9fL0Odw7eMA6WevRCdAAwYMAFDZtOsYB8ghNDQU7dq1w6effiprcOSq+oHj7elc15N2fFpICmknshynvRafz5zNJ7B81xksGn4bmjZQtv+N2rxNDHRUh4titHiN7mRBGbpEN/bqvTM3VLZwJqw9giYK93cLdKITILvdDqDyNtTOnTvRvHlzxYIiZQmC8b8RKnnrYslO78eBUvrpGG9vgfniwxUZAICZG47hnb91kWmt8tHjtd2v+gC5bb3UY6lLo+UeiDk6LpntaFJf8VAUYJxjQ/JTYJmZmUrEQQGk6kVcjnpUzqRD2qjOVaoxjyEIumlb8/baXGFVZ7JOI1xbPZWhPyQIddHT3tV1/ptM4paTm1zbUqPOUPtY1dOx49Vs8MnJyfj73/+ODh06oGPHjnj44Yc9zuJOvvPlwKn6jVRPB6C3vN0HT+/LK6nwcs3SafU5+Nu1WYvEUukyrLDaMPzrnfh620lZ1udLg5SRDxcjH+t6GGtJrmuOXklOgBYtWoR7770X9evXx6hRo/Diiy+iXr16uOeee/Ddd98pEWNAG710N56dt0O1LN0Ax6xb5RZ5Wiek7b/4z0RPFfH5i2atQ6iT5hW/DJuXUvm7298f0k5jbUYe3vvlgO/BQF/Hn1aM2L9RjY9N7kTF47Gmo2NR8i2wjz76CJ988glGjx7tfO3ll1/GtGnT8MEHH+Cpp56SNcBAJggCfkyvHFrgWF4p2jVvINt6jf5cWNWTbMrKw1h3KA/fPX8bel+rTd80Rzh6vdAYsfJXg7flovQXhdJyfQxcWVBagWFf76zxuh6Oc8dnV1csev9Cp4NiDGiSW4BOnDiBv//97zVef/jhh9k/SEECas6hVf3k1vm57pYc3/bXHcoDAHz4W4bP66La6eGi54/cJWF6unCfyL9Y80UeC7Lw975ieic5AYqJicG6detqvL5u3TrExMTIEhRVkvPcqFqf+uspdzCnGFnna46wWht35SutOVjKbY6a5e5N5edti4Xmt5Uk8FQsekgOPMV44aIZaaf+BADkFF1SIaK6uXQGDtCLbvVzQOtyGLN0Nw7lus7Rd7jqnH06+JgcZWSzC5LLSw/nqSeiE6DnnnsOJSUlGDt2LEaNGoUXXngB33zzDRYtWoT4+Hi8/PLLLlNSkPzUGG3YAMcsgNorr79M2eDhjXX/Wa3997bylfQYfJWd8adbYEa5fg+atQ3F5RaM/X6P1qHIXmZGSqj1anm655kT9HDWlpmtuOPj9fj3d7UPQFyVp2NNT8eO6D5AX3/9NT7++GO88MILaNmyJT799FN8//33AIDOnTtj6dKltU5iSvqi9QVE6+3Lp+rj/B6SU8H1Mfgbx6/GmPs6KRSXY5tXfpfj25haH5sRDg+x5VlUZnE775PH9evi0lc7PZ3DYkNRs0w9JwEi1+NzJL5bl5GH3OJyJO3LlfQ+fR/BlUQnQFUr+EcffdRlBndShh4OfikEQUBhmQVNPIwWbBOqJg5ebsu7tymutriqv15cbsX7vx5ULRY9XbB8pYemdaOVp+stMO3KUBAErDqQi67RkYhpashR/kgkI5wikvoAGeG5fn/hblbgGp2gddYf5J2f9+OmD9Zg7cFzdS734W/KXvgdzFY7Tp1304GzDtIOcf8/H8xWu+rb9NyaplIgKtHTLQGlJe3LRfyiXejzyQYIgoBpa45g9QFpLQsOvl6OZL8tKKHF1Z9rDiMdz5Ieg+/UqZPHJOjChQs+BUSVUw7M3HAM/4vvpfq2falUFm3PAgBMXX0Y93aJqnW5DYfzvd/IZWIqr398kYI92YVY+NytotfrdVKp83Pe28/1sdnbnL/rfR/lIqYCV/q7oNzrl78PkHd2ZF4ZaX39oTxMX3cUAHDy44e8j0Xkzil9Ya7eylZ3LJ7p4XTzaSDE2taphx27TFICNH78eJdZ4EkZU1YdBgCMl/EWiZST01/syS4EACxNvTK3V12V4JFzJUg+kidhC/oeCFGObe49XeT8ffGOLEwaeIPvK/WRHhqi5X1CUwc7JJH3nfivyC0ulycYnVDiHDfekeGZnq4/khKgJ554AldffbVSsZCPjHiyqHIy1LKN6slQv882+b6pOnZI7fO+6v7JdWyUma2oHyZ5/FRJPA4kq6MKVAsbDufhzJ+X8M/b24p+j+ucWPqgduKn9PaUmKhYL5+VvxJdk7H/j7EZ8VtmXYx0n9mfWKwCUHcfd0MxYrU2dH7lyMw9Yq5Ct9bGapFXu7jrqvfkrkGk1LE7T/7pcRmtxynylpHCFt0J2qgfhqFVK3KXTtBQpvKWI1EyyqHi+76Kn2C2+mPwtS0jJyU+BzUSTyMMhKgHuUWebyFZbXacL63QTR8gJY6eutap1xngJ688pGAk8vHpuDHAiSq6BchuV/9pEKqb14+QGyRBqZOEfahaMbkmkb4WhHEKUq66yM3DibL7Zc9ZRIRIHqRel7Q+1x5N3IZ9Z4oweZC8fbfk2C9fj0m9XV+1/qyVInuHfB3Vm8rezCdZ+XLgiD2I1a5U9HQyiJHnoeOmL3uj5PgsclXOdhVq+Xd+2q/4NvTE3TkgV5eDfWcqO7H/vPvsle35wWTIYplcWmmNVdfohdRT3kilzARIxzyNIFu9jhRw5cmnuihdEahym0TxLbj3/MJUt6/LNfKrnM6XVsi+Tn/9lqsUb/MYudMTf/7c9NISrrcWKbXY7QJST3nu06RHTIB0zJvHRJfvOq1AJMYmZ0W3p8pj4XKT+xpVdaRpuSrnQPgW7e54+e6PLDRtEIb7u7X0eV3VqfGAgtyfmxzr832vpa1B8afAdNLPSm6e6o7Xl+3F/9KuXHcc5WCEfNA/brQHiOonWPUDTPSjlXo5swxP2imuZbHLlbgF4rFzIr8Ub/64D/GL0rQORT8UOg6KLlnw4OebkbjxmHZBeMkfzw0xiW7V5AcA8ksrUFBH67OeyokJkIHp6DhyIeUAV6r5Oqfokvv3ebe52qKQdVEln7RcsTdHlvVU7wP08+4zuG3iWlG3Xo3qwkWz1+/1+haYgl+f9VpvAMC8LZk4mFOMT1Ye1joUyQL1Flh1m47ko+eHa1XpL+grJkAGUvVw8uVi6XhnSbnFp3iU4msi8E3KSfSatL7K+twvd7ZQvpFoHd+U6gpd6frxzR/3YcG2k4puo/r+vbxkN84VV2Dkt7sU3a4SrDY7ysw2RbchCPr6xisXOXbJXcJgtol/2rjcYr8ci7hoqi8n+21BuW+BGfy40WIeQamYABlYjVtgJvEV06SkDNzw/mq5QwIAHM0rhUVCRVbdH5me55Orq/L6cEWGx/cfyytFZoG0iVJ9IeZz8aW+M1vt+O6PLB/WIE5tMVp9GCbjp/QzePyLFK/f763lu86ovk2tGOViKuVLwo/pnj8/Tw+SyCkQ+sfJQU+lxARIY7/uOYudJ6VPIOvLPGGCIOCLTSe8fr8YW44WeP3ewjLlW6Y2HJIy51dt9NPmrVbla1dgIKBXlu6WfZ1i5CvwlFx1JpPnWyNuH4NXKB65GCWhqtpvy99Gw1eDEp+z2WrHz7vP1NlPSC1MgDR05FwJXlqcjn/MFvftt+qtodIKq9uxQnwe21imG9k2hUfMq/NWU43hAZSKxf0Ai26XFFGT+FLZqFW56+G+vl0AUo6fR7FKt3B9OSX0eAtML/HIdsz62/74ueQj+Xh5yW48mrhV61CYAGnp9J9XmmdXHciV/P7qF1U9TbB3/mKFIq0FvlK88ldxl2cnH8ei7afU2yDUGQnak01H8vHkV9sxMHGb1qHIQp3H4OVenw4OBB/J/9h63SvkdFKusi+4f1BFTUyANFS14vu/b9KwYGumYrOJu8wGrcJ5+PqyfXju652il7fbBczaeBx/nDjv87b1+E1MgIj5wkR8wo7P7kzhJXz8+yG8rfKoyWJb9iqsNszccAwHzxYrFsuxvFJRy50tvIR/zd+B5CP5isVSF29akAJ18mlln37TrlXaq/XJuzrVGeEYZgKkI+//ehAbJVTSej/ANh72vC82u4CV+3Mxb2smJq88hMFfbhe1bimVg7v5v9T+BiumcpRSgZaWW70Pxifigvxq0wlMWXUYD07frHA8NVVYbbBW6YQ/bvk+bDycj2fn7RD1ftlbSzyOEm68S50sF3uN74At2HYS5RZlngL0l8ae2nZj2urDmL81s873pmfpf3RoJkA6kywiafCZBo9qF5VZcDy/5jf2JTuzEb8oTdSTW9/vzEbWefWe6pCbHGXrLucVBEG1i6jYin3/GeVafupSbrHhpglr8NdPk52vnfNiRHW5fLHpOM4U1t3UL7YlyyduvgTISWzLoJaJgbuW4cU7lH9y0kHJfS8ut6h2i+1Efimmrz/m8UGc32Qae0xJTIC0JPGKaORvFT0+WI17Pk3GiWpJUPJh8U9jvbZsL/4yZQOAuu+n19UwptXtMT1OReCPjp4rRZnZ5vL4s5Ytpd+KGJpg+4maT4H6EvL50gqMW75X0cEpqx99C1NOKrKd5btOI+2U9KdkxSq+JF9Lqvwth65rHP71TrfDi+zIvIDu76/G68v2yhyBe6UVWrU+y48JkM74UvHVVdFrPSuy41yuPsaPGhcnlwEkNZoIVq7k1d161HzSqLbN6LHflRKM0JH17Z/2Y/GObDwyU7mnbKqXQ/XWgGVp7uckrHq6uztiqh5H6Vl/Ysz3ezBolvpjRMll4+E8DJn7h8sDL95am5GHlftrPiwzfd1RAMD3qXXPA3kot9irFnR/PreZABmJ/uveGrx9Us3jeuv4m5jRfeU+qeW4MBrg2oqJSZ5vVUqx/4xyk8s6yDnpphE+o6MebqmpsQ9j/7fH522fEnmxFrtOd19O5PxC5O44+9f8ndh8tAD/+d9eWbZ0yU2fJTH7cL60AvcnbHa2oEtRYwRtA5wDYjEB8nP3TkvG+78ccHlNyhg6vtqVVSjvCr3g0gn68u9iK766x5oRX1hy1Rlu+wB5ua4Nh/IwZ/OVATHFDF0vpmO7FH/77xZZ16c0tep+X05Dd8k4b5nKL2HtEZf/Vy3hC2VmvPbDlSSwtrkJ6/Lb3hz8d72YSWE983pEbD8/bJgAaUjuhkV36zuWVyr7/FBzt2Tin3P+EPUEhRHmgwEqB9Zz5191PjkkoXYQ5H0M3jWp866WGrpgJz5ckYG0UxeweEcWOr39O1bu13/HRT3Qc0KhRmRytAJ4c/vbl2Ey5G71TVh71PWFKmUy/tcDLrekLDblPhWlW2R4C4wUIaUCKLpkwdc+dDR0GQfIw7I2u4DP1x7FtuPup7P44LeD2HKsQNQTFAIE/Jh+pSKQrYXJy5O+tu0/+ZX7x++ltmDVVhnpeQySnKJyjFu+DwAQv8h4k5p6Imerpmp9gHwaflq+MNTmabfdTRukx2T0RL7rPIO+zI2oteqxe1PaehwUFwBCtA6AXNWWbb/43S6cK/Y8d4och9lP6Wfw2eXm3ZMfP+Tyt4tVngAQ09dmw6E8fLW57vEilHelVLy5fm06ko+/dGohcgu1/F3w3OLn7bVVn1WLcdX1OTj+pOdvxe6mK/GXfhtyPzShVrlYbHbFkmdfV+upJb/qXH3eJjLT1x/1vJAG2AKkM7Wd32KSH7F1cp2PkKPu+8U/1PJ0R22qjwdz5k/P98IP5ngeQ0bNb33PzNtRa2uYFHLMTF3b8eFLefjLxbE2vl4zq34DNmpZqT24oxhqp5DuO0Eru34Hq4K3wHz1u5snyxyq79NPu894lch9kazs5NveYgJkYO4qdqUG23Ow+tiUOV1Epz7HY51K8Db6XafqHtW0tj5EVXnqiyUmNvd9gES80Y/5ekx68kSV0ckTN8rTKdUTnzpBy7w+8p2/nKJ7Tyv/1KaamABpSM1KSWwfIJPJpOvmfQcpF301EgSPjx4rH4LXlI6t6rQUH/x2EO/9rM78ZY5vqnIezzU6vuqQGse7Ny2On6w8hG/qmLzX21tEPu2vit8e9FwH1MbTuWOEcbHqwj5AGlJzgFqxx+kXyccRFKT/BMhbSu9ZbcXsrl9GjfdKqEyqXoAECLpuBUrPLsQt7ZqipNyCuVsq+4O9+NeOaNEoXOPI/JOnWz1aHCtWmx2JG4+7vFa9/hu6YKfswyy4bE/DL3ZKJgpKdgdw259Msa2pjy1AfkbKbRR3zhaV47SIfjpGpeTJ681YH3rjbVJe1/sOXB7s0F7lYRKxc0f5QhAqH5vep8Bgi0r3QfPpITC3o4XLG6/U1YlZXEzys/WY733xfHUsrxT3Tkuu8bqev4R4y9PtZbH77G4ARz1gAuRHjHDrSi6SboEpF4aLzIKLnheqg7dxCiLGGKr7/cqW0Pu/HkRBqYhO/AoY/KX74Q38mbuP06zxY9juYvLUMdhdkrmtWl+7jYfzRF9c5UpaX/thj+QJbAUolyCpnXj5U6LHBEhnpKQwUjpBu3SaVbER09PEearPU6nR2Stmsw9N34z5W9UZMqCozP0I10p9HGKe/jMSo33ZqP40pq/kOIvqHmVdnH/N34n/1DLthhje7IeY4T/0zG4XcMnLfaj+ZcnouRATIA3JXYmeKSzDzpN1P60EAK//oM6swQAUuf0AqH/iiRl/ZMHWk7hz8nqvt5F94VKNSSVrxuH+damtODdOWC1peSPyukVNzIjcSt8C86Fu8HQsaDIZsodtuotZbBnoYbodoO7jTU+tJoO/TEHnd1f60Cqro53xkeYJUGJiImJjYxEREYG4uDhs3ry5zuWTk5MRFxeHiIgItG/fHrNnz3b5+4EDBzBo0CC0a9cOJpMJCQkJCkbvG7ctOD7kRNtPXBA1hs4GBTsa6pFaTyqsPnhO8W+H7h6Dl5NdAI7llSizclKFGke7EZ/+cZdQqbUbehqt2vElefWBcxpHoj1NE6ClS5filVdewVtvvYX09HT06dMHDzzwALKy3E+xkJmZiQcffBB9+vRBeno63nzzTYwaNQrLli1zLlNWVob27dvj448/RsuWLdXaFVKZEStgpfjeB8j1/88tSPUpHncyRCTmclPyGDHaLTCtefooAuF0VioJUrPoqreEG70e1jQBmjZtGoYNG4bhw4ejc+fOSEhIQExMDGbNmuV2+dmzZ6NNmzZISEhA586dMXz4cDz33HOYOnWqc5lbbrkFU6ZMwRNPPIHwcHGP2VZUVKC4uNjlRw1qVqF6+gbiL3Zn+9egYA6n//R9xOrqx9sby/fJ0ufDk4vmuvucycVoT4HJTZVWpmpbKbok//GzO7vQMBM2y83dMVzXOE1AZcJj8JzHhWYJkNlsRlpaGvr16+fyer9+/bBt2za370lJSamxfP/+/ZGamgqLxfuTY9KkSYiMjHT+xMTEeL0uf/TLnrPo/9kmyU8+iKFGJ2ihlt99pUWrhju+jgP03i8HXP5vF3z/Zufu7ecvmn1apxhixlsymo9W1N0vrDrP/W18iUYZYkJaImLy5bq3UXMrW44VYHSVua58Wr9WBSvjZt/5SdogpTo8lCTRLAEqKCiAzWZDVFSUy+tRUVHIzXU/N0lubq7b5a1WKwoKvB8fYty4cSgqKnL+ZGdne70uX0mZ7E+tZvhRi9Nx+FyJbBWFHIx+4umJu2/Wvg5I5+7ziQhVvrpxfdrRuKqe2VInE1alBUiGbeglEVuxL0fxbQiCWp+LhMFUvYxHJx+bLDTvBO3unmJdSUBt9yB9mSU4PDwcjRs3dvlRhQ5Hgq6LVmO5UN0EAbLXSodyfesI7e54C1KhuU9vFxm5nC28JHq7KowxKZnnPkDKB63pSNCKrvvK2u/4eD0mrzwk6n1lZqvh+/D4SrMEqHnz5ggODq7R2pOXl1ejlcehZcuWbpcPCQlBs2bNFIuVKl30MKaPmnydC8zo573S8Uu9leTm+Zqar1R5adCsbYr0vbDYlZ+5XYtjp/fH6zF19WFZ1iVH+It9vB0FeL79bbSO5nqoUs4WlWNWtSlHajMx6RD+75s0Seuvvo9Gr0c1S4DCwsIQFxeHNWvWuLy+Zs0a9O7d2+17evXqVWP51atXo2fPnggNDVUsVqX4eoJnXfC9s6oURj/YAeCblLo7+RlRZQOQvj4ct0lnlRjPFF7ChsN5sm/34yRx337dKSqz4MPfDuLA2St9u1bur3l7RI2SdpcczNwg7sKmRoSOOd3Ecjs/mZ/crhRFpzu4+mBgPwqv6WSoY8aMwZAhQ9CzZ0/06tULX375JbKyshAfHw+gsm/OmTNnsHDhQgBAfHw8ZsyYgTFjxuD5559HSkoK5s6di8WLFzvXaTabcfDgQefvZ86cwe7du9GwYUN06NBB/Z00uOpzNumnydS7OByztqs+ArWClPhM7D7eR3H3bru9+v/lj/vwuSu37qQmheN/O4Dlu864vBa/aFeN5QRB0HXrhKfD4c+LZmw4lIfIesb70ugLvX1JkIuWU2EYvUw1TYAGDx6M8+fPY8KECcjJyUG3bt2QlJSEtm3bAgBycnJcxgSKjY1FUlISRo8ejZkzZyI6OhrTp0/HoEGDnMucPXsWN910k/P/U6dOxdSpU9G3b19s3LhRtX3zFyv3u++QbiTVT9GC0gq/aM2qSu798TU3cZeUVa8s9fYRHDwr7qk+JVvcHp6xBbfFNkXHqxt5vQ5PkT351XbVJzz2ZhwgtS+uiRuPYfGOLPwQ3xtRjSNkXbdc+1JY5t2TlL70ka1OP1+CfadpAgQAI0eOxMiRI93+bcGCBTVe69u3L3btqvmtzKFdu3aG+YCkzOWllQseTjhBEPDLnrPoGh3p1fqzL6g/R1TPD9eqvk25Va1QlTjafX2c3N27DXJaeqTkfuw9XYS9p4vwyaDuim1D7eRHLwpKzPh595la//7Jyso+Vglrj2LSwBskr9/TcbFGhttNE5MOYcBNrbF81xn8I+4aNGsYLvv5b6lj4lx/OYcdNE+AqBqdZUAFJXU/+bVyfy5eXrJbnWCqkNYJ2thn7ZRV3vdr8ZYS4wB5s4zSMXi1Xt21Xbky4vHudlC+Kv31apu8V4pLFpuoukqJW7OCALy0OF2WdQ2dvxMHzhZjw6E8LP2/XqLeI2UMt7RTf9b5d5dx1Yx3qLnQ/DH4QKazXMetz9cdrfPvu08XqhOIxqasOox8D8mgUqp2fnU3F9imI/k4V1Iu6zbFXAPqKg+3fYB0VFvmFpVjyirvnqqaszkTVpvC++JD5aDLx+A9/P2zNTXrmaqTnN44YTUKFRgJWi1yfiSOTvp/ZF6oXLeI82piUobo9RvhuiQXtgAFCB1de2Shxe5MklCJKCWnqObtixe/k+ebZVV1JSu7sv5Ei4bh+Nt/t9S6jLtKWU8X5km/e/9Zeps4qcWILUCzkz0/4bZXpS9bnrrL1Fa8xit1YgKkoXlbpT1KSpXu+Hg9HureSvXtqjGVgycCKufr+Sm99r4Mcm2nNgMT3U9V44meWoDKzDatQ1CMfkr5CjGd4j2vQ65o6lZXArR4R5bLk4ZiqZ2Uvv/LAbz/cFevtl9Xh2lfp93RGyZAGrHa7Fh1wFhjMOjluD9TeAlfbjohenm5Tlg9XMAFQfp8Pd6YtfE4WvrwJIz7wSerPwWmfXnqVSDdhhBr2/HzWoeAccv3aR2CKAu2nXRJgKSoKwFcl5GHRdt9HwRTL9gHSCNqV/2BfLGRa9+rj4nk76pPklqXGlPUuCnz6sWnZSdov04wdHiY6v2pwKrDfSzeIf9ckEruqprFmFPk2tdQT5+hN5gA6YyeB1jTb2Tq0EMLkFHoffoRfx4BV0fFbBjxi6RNCeGWng5wHwRSPc8EiEQToI9maKlkuwUm/9RVfstdmaudQBq51bO2fhienua5WGFFqU7m7Ltt4lrnmDLuBzr0L1rtT12nleNJTSmxWSW0dBv5HAOYAGnGiF8WSiusWH9I/vmbjMJmxA9NI+5vgbH8fOWp79uczfp5sOJccQX6f7ap1r8H0uGgxb5OX3cUt3y0FnO3ZEpq1Xniy+2KxaQ3TIA0UlvmrNQcVYFU2SiFF3DxxNwCU7o0tx4zXmulr/70cqoEpZwouFj5i8FOHbtdwCWzDR+tOIjUkxe0Dscr09YcAQB88NtBxbZh9CqRCZBGajtweJGVn1xlqsQIsf5KD51el+6UvzOrWgKhH4aeb59sPJKHxI3H8NXmTDw2O0XrcEghTIB05otk8Y93S6HfqsY4eAtMAhF9gJQeG2Vthv92dDYad8mOnk+nnSf/xNFz4qePALTbHx0Xo0fJR/I13T7HAQoQRhwdVm9YhOKxD5A2lLqFHmhmbfQ8MjX5nnx9svIQ+nZqIUss3mACpAFBqLy/rCZZHvM0qO0n5LmHz+u3eO7KSotJc41KSiJTdMmCRxO34oFuLZULyEf+cO6kZ9U9SahmNCzcbu+t8un9Wh8XvAWmgSFzd+CmD9aous1zxcpM5Kn1AaymANpVn7krqyIDT2aptq+3nRS97KLtp3Ai/6LLpLlGYKRW6dyicjzqYQoYrfo0GacUa9I6diZAGthyrEDrEGRjtgbO4DhGqrC1plZZfbRCuSdctLTndJHoZY0wQvkaNwNP7j9TrEEk3jl1/qLWIfglretUJkDkE/Y5IHfUqta+0tG4N+TeqfMX8dqyvTVeX3kg183S+hQU5Lmi0/Mo/uQeEyDySSA1irATb+2qls03KSeRnlWoXTCkK6f/vKR1CD4Tkf/o+rF+co8JEBH5zDFJYn5JBd75WfwkquT/ft+fo3UIPqttapKqtLqlZ+TvZVrHzgRIZWcLjf9tKFCdPF+mdQi6J+XpRi0qv/G/HtC830GgWbQ9S+sQfMabW8rQutWMCZDKPlpR92SGRhNIt4W06PC9/4z4zrB6oPc+YfO3nkTKCf+dIoP9UJQRpOMDW+skwhdaXz6YAKnsolkfMzWTMYz/1X9vJ2lVcRdf4jlI0ug4/yEfMAFSmT+dR2Vmq+YZPOmLES4UgTwoKHln5X7jPLHmjl6raa3jYgKkMjGd6Yxi5objhm5+Jfn50/FN5HAi39jjAOn1rNS6Px4TIJXp9UD0FluAyOFwbonfHd9Gw/xTGXotV5tdEPX0mV6raa2vH0yAVKbXE8lbej2xSH1fbDouqbOo1pWfP8ovUWbKm0Cn13r7lo/Wah2CoTEBUp1OzyQvZfHRcHIQpF0o3vxxn3KxBJCqRf7LnrOaxUHqu3DRLGq5wjJ9zsOn9XcgzgavMr1+k/CWP81rRr5Znn4GFTbxQwWUWwJnHjkyNrZWKkPrPkBMgFTmZ/kPKeSS2Ybj+aWGq3hX7DX+qL9E1QXSeGdq0rpUmQCpzN9agEgZnd9dqXUIRHTZGY7g75fYB0hlHKmVPHniyxStQyCiKrSa58vfad2wxgRIZWwBIk+2n7igdQhERIrTehw5JkAqW33wnNYhEBERaY4tQAHGZte62xcREZH2mAAREZFkwxemYvPRfK3DIDIsJkBERAY1ZO4OrUMg8prW4wAxAVKR1h82ERGRXmh9RWQCpCLmP0T6pdfpAjzhk6VE3mECpCKOJkqkX7nF5VqHQBRQtL4kMgFSER8AIyK5WWysWMiYOA5QAGELEBHJ7fN1R7UOgcgrWl8SmQCpSOsPm4iISC8uVlg13T4TIBWxBYiIiKiSjY/BBw4mQERERJWsGvdfYwKkIqY/RERElawaPxmkeQKUmJiI2NhYREREIC4uDps3b65z+eTkZMTFxSEiIgLt27fH7NmzayyzbNkydOnSBeHh4ejSpQt+/PFHpcKXRLBrHQEREREBGidAS5cuxSuvvIK33noL6enp6NOnDx544AFkZWW5XT4zMxMPPvgg+vTpg/T0dLz55psYNWoUli1b5lwmJSUFgwcPxpAhQ7Bnzx4MGTIEjz/+OP744w+1dqtWvAVGRESkDyZBw/kZbrvtNtx8882YNWuW87XOnTtjwIABmDRpUo3lX3/9dfzyyy/IyMhwvhYfH489e/YgJSUFADB48GAUFxfj999/dy5z//33o0mTJli8eLGouIqLixEZGYmioiI0btzY292rYXd2IQbM3Crb+oiIiIwqOMiE4xMflHWdUq7fmrUAmc1mpKWloV+/fi6v9+vXD9u2bXP7npSUlBrL9+/fH6mpqbBYLHUuU9s6AaCiogLFxcUuP0rIvlCmyHqJiCgwyJ0waCmuTRNNt69ZAlRQUACbzYaoqCiX16OiopCbm+v2Pbm5uW6Xt1qtKCgoqHOZ2tYJAJMmTUJkZKTzJyYmxptd8ui+LlFo26y+IuuWWzuN42x9VT2Eh3g+PP95exvFY+nTsbmi64/vey2ui2qEv3Vvpeh2AOCjR7t5/d6hd7Rz/n5ru6Z49KbWmh8nYrRoFA4AaBQRgq7RjdHh6oYaR+TZzW2uQlhIkGHqC3ceuqH24/nRm1oruu0PB3TDf/pfh4jQmnXI9S0b4T/9r3P+v2F4COL7XgsA+Ffvds7Xx9zXCf+L76VonFKNua8TgoNM2D++v/O1+L7XIjwkCDFN6wEArr58vGsltnkDt6/fGHMVXrv/OpfXisu1nX9Ps1tgZ8+eRevWrbFt2zb06nXlIPvoo4/wzTff4NChQzXe06lTJwwdOhTjxo1zvrZ161bceeedyMnJQcuWLREWFoavv/4aTz75pHOZb7/9FsOGDUN5ufu5fioqKlBRUeH8f3FxMWJiYmS/BQYAtsu93u2CgNDgINjtAqx2AQIECAIQERoMq80OuwCYbXY0DA+BzS7AYrMjLDgIdkFAcJAJpRVWhIUEwW4HgoIqB1kMvfz3CqsdNpuA8NAgBAeZYLHZYbEJCAkyISwkCFabgNBgEyqsdtgFAQ3DQ1BusSM8JAhlFhsahocAqJy93lRtpkVH/EGmyqk97IIAm11AeEgQCsssiAgNRkRoEMw2O0KDglButSHIZMIlsw0CgODL62sQHuycGsR8ed9MJsBstSM0uDJu0+W/hYcEoeiSBZH1QlF0yYKG4SEw2+wIMpkQERqMcosN4SFBsNory9RstSPscvJktdkRUqWcK6yV+2e1C5fLzASbXYBNEFBusSOyXigstsoYSsotCA0OQkRoMITL5VphtQMCEH65Yg0OMjnLu0F4MIJMJgQFmWC9HF+51YbwkGBY7XaUm+3Oz0QQKt8bZIJLGdvtAsw2OyJCg2GzCwgyoXKbAMJDglBhtSMkyITgIBNKKqyw2QQEXf6MHZ+bo+zsl7dhsdkvf2aVn78gCLhksSEkKMjl83M8keH43WKzIyQoCFa7HeEhwQgOMsF+uaxCg2teWCw2O8xWOwTA+Xk6lrPZK7djMgEmVE7fIEBwHgMhQSaUW2wICQ5CWJX3hAabIFz+u+Mzc/zNLlTG4igPQahcTkDl47UCBGfc1dntleVWtcxNJuCS+crxb7EJqBdWeXyFBJlgttlhtQuICAlGmdmK+mEhKK2wIiI0CEEmU+X+BgUhKMgEs9XuLDcAuGSxoUFYMMotdtQLq3x/kKlyn0Iux2Eywbm8Yx8dZe5YZ1hIkLM+MJkqy9lxTJhtdphgggAB9UKDUVxuda7D8WXCUW6NIkJhttpRUm5B0wZhuGi2ISIkCAIq65Iys9V5nlhsdtQPC0G5xYZ6ocGwXl5HWHCQM36L3Y5gk8l5LFc9ru2XP/cKa+W5bLMLCAkOcp5nglB5bpqtdtgur9dRLwj2ynPNLggIMlWeq/XDrpRphcWOkGCTs550lGlYlf01ASiz2GC6fA7Uu/x+R1wW25XlHarWIVWPGavjmBQAi90OQag8X0oqrIgICXbWz45zqszsWmYWmx3Bl4/l0KAg1AsLht0uoMxiQ1hwZdlcstjQIDwYoUFBsNjtsNsrp4yo/ByvHB9V62fH51z9+DZb7QgywVlfhocEobTCikYRoc79dNS7DcJDcLHCWvnZhgQhPCQY5svXCMf5XFJhRWhQkLP8G0WEoKyiMt7KuqbyvWarHbbLn4Nw+TytGnu5xYbiS5XHXoibusQXUm6Bhci6ZQmaN2+O4ODgGi0zeXl5NVpwHFq2bOl2+ZCQEDRr1qzOZWpbJwCEh4cjPFydrNlxkAbjckURZEJYtQracUA4TsDgIBOCgyoPnqDL73McwDXWD1ONi1PN/7tuB4CzUnBU/gBqJD9V4weAYJNje5X/b9IgzPk3x8FeP6xyfRGhVw7+6qpWNNVjjbi831fVD3P5t2rsjnWHBptqrM+xnKOcHX9zLFu5jAkhVWJ2xFC1jE2Xky13++EuGXBs17H/wUHBLhVAbYKCTM59dpR11W1W/b1xLcdAdY5jx8FkMjnjco35yu+hwVeOo7AqDcVBQSbnMVhdaHCQ27KojMHkcuy4K4q6jhFHTGJ5WJUz+XH87ijzqp+R41dHXFWPubCQyuOwaciVY76qsJAgl3JznFeO88xd+VfnKC9HrO7qg9riA4DIenUfH2EhQWjWMNwlvit/c5xnV9bf4PIyVesrx+/hQbUXeFC14zjk8ufoOFZMJpPL8VZ9v2pTPywE9asUv2O9VeNzlGH1/asaV1hIzeOqevLjWN6xbpPJdZ+rn4uO0KuXmbt9CgoyucTnOEaAusu1av1cPcmvfsxUPTaq1mtX6sMgl3ir/92h6n464oysX/W8qFkHA6YaiUZtdanaNLsFFhYWhri4OKxZs8bl9TVr1qB3795u39OrV68ay69evRo9e/ZEaGhoncvUtk4iIiIKPJq1AAHAmDFjMGTIEPTs2RO9evXCl19+iaysLMTHxwMAxo0bhzNnzmDhwoUAKp/4mjFjBsaMGYPnn38eKSkpmDt3rsvTXS+//DL+8pe/YPLkyXjkkUfw888/Y+3atdiyZYsm+0hERET6o2kCNHjwYJw/fx4TJkxATk4OunXrhqSkJLRt2xYAkJOT4zImUGxsLJKSkjB69GjMnDkT0dHRmD59OgYNGuRcpnfv3liyZAnefvttvPPOO7j22muxdOlS3HbbbarvHxEREemTpuMA6ZVS4wARERGRcgwxDhARERGRVpgAERERUcBhAkREREQBhwkQERERBRwmQERERBRwmAARERFRwGECRERERAGHCRAREREFHCZAREREFHA0nQpDrxyDYxcXF2scCREREYnluG6LmeSCCZAbJSUlAICYmBiNIyEiIiKpSkpKEBkZWecynAvMDbvdjrNnz6JRo0YwmUxah6O54uJixMTEIDs7m3OjKYjlrA6WszpYzuphWV8hCAJKSkoQHR2NoKC6e/mwBciNoKAgXHPNNVqHoTuNGzcO+JNLDSxndbCc1cFyVg/LupKnlh8HdoImIiKigMMEiIiIiAIOEyDyKDw8HO+99x7Cw8O1DsWvsZzVwXJWB8tZPSxr77ATNBEREQUctgARERFRwGECRERERAGHCRAREREFHCZAREREFHCYABEREVHAYQJEREREAYcJEJEKysvLtQ4hYJw+fRo5OTkAxM0ITd6xWq3O31nO6mA5y4sJUICyWCyYP38+fvzxRxw6dEjrcPySIAgQBAEvvfQSHnroIVy4cEHrkPyaxWLB//3f/6F379745ptvAICTGSvAbDbjjTfewMiRI/Hee+/h0qVLLGcFmM1mTJ48GTNmzEBycjIAHs9yYwIUgL744gtERUVh3rx5eOWVVzBo0CB8//33AAC73a5xdP7DZDKhsLAQP/zwAzZs2OAsY5JfdnY27rjjDuzbtw//+9//8OSTTzoTUJLPTz/9hLZt22LHjh2IiIjAlClTMGLECJa1zH7//XdER0fjp59+wvz58/Hoo4/i7bffZkuyzJgABRCr1YqEhATMnDkTM2bMwObNm/Hrr7/innvuwSeffAK73Y6gIB4Scjpw4ADuuecevP/++3jrrbeQnZ2tdUh+afXq1YiMjMTWrVtx2223wWQywWq18huzjCoqKvDll1/iueeew/r16zF9+nQsXrwYy5cvR0VFBctaRvPnz8djjz2GlJQUrF27Fl9++SWmTp2K2bNn49KlS1qH5zd4tQsgZrMZJSUleOyxx/DEE08AALp3746uXbsiJCQE+fn5GkfoPxzfhkNDQ3Hs2DGMHj0ajRo1wqRJkzSOzL/Y7XYIgoDU1FTceOONKCwsxOOPP4777rsPt956K0aMGIHc3Fytw/QLe/fuxcaNG3HPPfc4X8vNzcWIESPYciyjzMxMbNu2DX369AEANGnSBI899hhGjBiBRYsWYcuWLRpH6D+YAPm51atXY8+ePQCA+vXrY8iQIXj33XcRFBTkvEg3adIEpaWluPrqq7UM1dAc5ey4EDi+DaempqJjx45o1KgRPvzwQ8ydOxepqamYPXs2jh8/rmXIhlW1rIOCgmAymbB//34AQEJCAgBgxowZiI+Px6+//or33nsPZ86cAcBOpFI4ytlmswEAbrnlFjRt2hQzZszA77//jv/85z8YOXIk1q9fj44dO2LWrFnOL1EsZ/GOHz/uUl5t27aFxWJBcXExADhbfN577z1cvHgRv//+O0pLSzWJ1e8I5Jfmz58vtGzZUrjhhhuERo0aCS+88IKQk5Pj/LvNZnP+PnToUOGf//ynIAiCYDabVY/VyKqX88iRI4WzZ886/z5jxgzhpZdecv6/U6dOgslkEu68807h2LFjWoRsWO7KOisrSxAEQZg2bZoQHBwsdOrUSdi5c6fLe7p27Sr8+uuvWoVtOO7KOTs7WxAEQdi4caMwcuRI4dZbbxU6dOggrFu3Tjh8+LDw4YcfCh07dhS+/vprjaM3jrlz5wpt2rQR4uLihNtuu0345ptvBKvVKgiCIIwYMULo3r27c1lHvfzxxx8LMTExwp9//qlFyH6HCZAfmjNnjtChQwdh8eLFQn5+vvDtt98KDRs2FHbv3u2ynNVqFSwWi3DzzTcLX3zxRY31VE2SqCYx5fziiy8Ks2bNEo4cOSJ07dpVaNKkiRAUFCQsWrRIw8iNx11ZN2jQwFnWu3btEm666SahXbt2wpkzZ1ze27p1a2HWrFlahG04nspZEATBYrEI/fr1q5HsdO3aVXjzzTfVDtmQEhISnOW8ZcsW4d133xVMJpOQmJgo2O124ddffxU6deokJCQkCIIgCOXl5YIgCEJ+fr5Qr149YfPmzVqG7zd4C8yPCIIAm82G9evXo1evXnjiiSfQvHlzPPXUU4iOjq6xfHBwMAoKCpCXl4e+ffsCAHbv3o1//etfAMAO0bXwVM6O219WqxXBwcF4+eWX0bVrV/zlL3/B0aNH8fzzz+O9997D+fPnNd4T/aurrFu3bu28ddCxY0c89dRTyM7OxtatW53vz8/PR9OmTdGoUSOtdsEQPJVzVfn5+di5cyfuuusuAIDNZkNRURHq1auHBg0aaBC9sZSVlWHFihV4+umn8cQTT6B3794YP3487rzzTkycOBGrV6/Gfffdh/79++PTTz/F2bNnER4eDgBIT09HixYteDzLhFc4P2IymRAcHIyDBw8iPDwc586dAwCMGjUKJpMJP/30E7Zv3+7yFMHatWsRGxuL6OhoDBs2DLfeeisKCwudnUupJk/l/OOPPyIlJQUhISHo3r07/vWvfyElJQWJiYlo1qwZ3n77bZw9exZ79+7VeE/0z1NZ//zzz0hJSUFERARefPFF/O1vf8PYsWPx/vvvY/fu3Rg3bhxCQkLw17/+VeM90TexdUdZWRmaNWuGmJgYxMfHY+/evTh9+jTGjh2Lixcv4pFHHtF4T/QvJCQEaWlpuO666wBUPl0HAFdffTXsdju+/fZbWCwWvPTSS2jTpg0eeughfPvttzh27BjmzZuHTp06oWPHjlrugv/QsvmJfPP9998Lw4cPFxISEoS9e/c6X1+yZInQtm1boV+/fkKzZs2E66+/XpgwYYJw9913CzfeeKPw0UcfOZcdPHiwEBwcLDRq1Ejo2bOnkJGRocWu6Jo35dytWzfh008/FQRBcN7Xr6qwsFC1+I3Em7Lu3r27MHHiREEQKvtKjBo1SoiLixOuu+46oW/fvuxr5Ya3dcfHH38sCIIgbNq0SWjRooXQqVMn4ZprrhHuvvtu4ejRo1rtjm7VVs5PPvmkcP311wunT58WBEEQFi1aJNx9993C8OHDhQ4dOgh79uwRBEEQcnNzhfvvv1/o3Lmz0Lp1a+GOO+4QMjMztdgVv8QEyIAKCgqExx57TGjZsqUQHx8v3HnnnUJ0dLQwf/585zJ5eXnClClThL59+wrFxcXO159//nnh0UcfFfLy8gRBEIQnnnhCaNeunbBixQq1d0P35CjngoICDSI3HjnK+ty5c87XSktLmfi44Ws5DxgwwHlMnzp1StixY4ewY8cOtXdD99yVc6tWrYSFCxcKgiAIR44cEdq3by+0b99eiI6OFurXry8sW7ZMEARBCAkJcamPy8vLhZycHGHfvn2a7Is/C9G6BYqk27BhA7KyspCamuq8Pz9gwABMmDABkZGRePTRR9GkSROkpaXhvvvuQ6NGjWA2mxEWFoZGjRph/fr1aNiwIQBgwoQJbE6thRzlXK9ePY33whjkPKYBoEGDBrj22mu12h3dkqOcIyIiAABt2rRBmzZttNwd3aqtnN999100atQIAwYMQHJyMg4ePIjc3Fw8+eSTCA0NRX5+Ptq0aYOysjLnusLDw9GyZUu0bNlSq93xW+wDZEDfffcdrrnmGrRu3do5HsSjjz6KkydPYubMmcjLy0NISAjOnz+P1NRUAEBYWBjOnTuHI0eO4IknnnBWYkx+aidHOdevX1/LXTAMlrU65ChndnT2rLZyPnXqFGbMmIH8/Hxcc801uPfee/HMM88gNDQUQGXiFBYWhjvvvFPL8AMGEyCd27RpE1atWuUy83LHjh1x4MABAHB+6z106BD++te/ory8HD/99BMAYNy4cVixYgXuuOMOjBw5Ej179kRxcTFGjBjBYeurUaqcqSaWtTpYzurwpZyDgoKQn5+PQ4cOYcaMGRg9ejQGDhyI5s2b8yEUNWh9D47cy8/PF5555hnBZDIJN954o0vHt+PHjwstWrQQ+vbtK0yePFno1auXEBsbK6xbt0648cYbhbffftu57I8//ii8/vrrwlNPPSV8//33GuyJvrGc1cOyVgfLWR2+lPM777zjXDYtLU0YMGCAEBsbK3zzzTca7EngYgKkQxaLRUhMTBT69+8vLFmyRKhfv74wadIk52BYgiAIW7ZsEZ5//nnh5ptvFl588UUhPz9fEARBGDJkiDBo0CCtQjcUlrN6WNbqYDmrQ+5y3rVrl6rxUyUmQDq1fft25/D948ePF1q0aCGkp6fXWK6iosL5+7lz54Ru3boJH374oSAIHMlZDJazeljW6mA5q0OOcrZYLKrESu4xAdIpu93u8v/o6GhhxIgRzsdSq/790qVLgtlsFhITE4WbbrrJZbwJqhvLWT0sa3WwnNXBcjY+JkA65/j28P333wshISHC6tWrXf5++vRpITExUejZs6fQtGlT4bvvvtMiTMNjOauHZa0OlrM6WM7GZRIEdjU3it69e6NBgwb49ttvcfXVVyM/Px8tWrTA4sWLcfbsWYwdO1brEP0Cy1k9LGt1sJzVwXI2FiZABmC1WhESEoIDBw7gxhtvxLRp03D8+HFs2bIFX3/9Nbp166Z1iH6B5awelrU6WM7qYDkblLYNUCTVLbfcIphMJqFt27bCypUrtQ7Hb7Gc1cOyVgfLWR0sZ+PgQIgGcfz4cdxwww04cOAAvvrqK5w8eRL9+/fXOiy/w3JWD8taHSxndbCcjYcJkEEEBwdj0KBBKCgowLBhw7QOx2+xnNXDslYHy1kdLGfjYR8gIiIiCjhsASIiIqKAwwSIiIiIAg4TICIiIgo4TICIiIgo4DABIiIiooDDBIiIiIgCDhMgIiIiCjhMgIjIb2zcuBEmkwmFhYVah0JEOseBEInIsO666y706NEDCQkJAACz2YwLFy4gKioKJpNJ2+CISNdCtA6AiEguYWFhaNmypdZhEJEB8BYYERnSv/71LyQnJ+Pzzz+HyWSCyWTCggULXG6BLViwAFdddRV+++03XHfddahfvz4ee+wxXLx4EV9//TXatWuHJk2a4KWXXoLNZnOu22w247XXXkPr1q3RoEED3Hbbbdi4caM2O0pEimALEBEZ0ueff44jR46gW7dumDBhAgDgwIEDNZYrKyvD9OnTsWTJEpSUlGDgwIEYOHAgrrrqKiQlJeHEiRMYNGgQ7rzzTgwePBgAMHToUJw8eRJLlixBdHQ0fvzxR9x///3Yt28fOnbsqOp+EpEymAARkSFFRkYiLCwM9evXd972OnToUI3lLBYLZs2ahWuvvRYA8Nhjj+Gbb77BuXPn0LBhQ3Tp0gV33303NmzYgMGDB+P48eNYvHgxTp8+jejoaADAq6++ipUrV2L+/PmYOHGiejtJRIphAkREfq1+/frO5AcAoqKi0K5dOzRs2NDltby8PADArl27IAgCOnXq5LKeiooKNGvWTJ2giUhxTICIyK+Fhoa6/N9kMrl9zW63AwDsdjuCg4ORlpaG4OBgl+WqJk1EZGxMgIjIsMLCwlw6L8vhpptugs1mQ15eHvr06SPruolIP/gUGBEZVrt27fDHH3/g5MmTKCgocLbi+KJTp054+umn8cwzz2D58uXIzMzEzp07MXnyZCQlJckQNRHpARMgIjKsV199FcHBwejSpQtatGiBrKwsWdY7f/58PPPMMxg7diyuu+46PPzww/jjjz8QExMjy/qJSHscCZqIiIgCDluAiIiIKOAwASIiIqKAwwSIiIiIAg4TICIiIgo4TICIiIgo4DABIiIiooDDBIiIiIgCDhMgIiIiCjhMgIiIiCjgMAEiIiKigMMEiIiIiALO/wOhHmg18M8+cAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The final step is to open the dataset with xarray by using the 'open_zarr()' function. The following block performs multiple operations to get the data that we want:\n",
+        "\n",
+        "- It opens the data using xarray\n",
+        "- It extracts only the times that we need for the reference/historical period\n",
+        "- It then subsets it spatially by getting only the points within the catchment boundaries. If your catchments is too small and this fails, try with a larger basin or apply a buffer around your boundaries.\n",
+        "- Since we are running a lumped model, it take the spatial average.\n",
+        "- It will then remove unnecessary coordinates that could cause problems later ('height', in this case)\n",
+        "- It will then rechunk the data into a format that makes it much faster to read and process\n",
+        "\n",
+        "Finally, we will display the output of this entire process.\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ERA5_pr.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Here we need to make sure that our units are all in the correct format. You can play around with the tools we've seen thus far to explore the units\n",
-    "# and make sure everything is consistent.\n",
-    "\n",
-    "# Let's start with precipitation:\n",
-    "ERA5_pr = xclim.core.units.convert_units_to(ERA5_pr, \"mm\", context=\"hydro\")\n",
-    "# The CMIP data is a rate rather than an absolute value, so let's get the absolute values:\n",
-    "historical_pr = xclim.core.units.rate2amount(historical_pr)\n",
-    "future_pr = xclim.core.units.rate2amount(future_pr)\n",
-    "\n",
-    "# Now we can actually convert units in absolute terms.\n",
-    "historical_pr = xclim.core.units.convert_units_to(historical_pr, \"mm\", context=\"hydro\")\n",
-    "future_pr = xclim.core.units.convert_units_to(future_pr, \"mm\", context=\"hydro\")\n",
-    "\n",
-    "# Now let's do temperature:\n",
-    "ERA5_tmin = xclim.core.units.convert_units_to(ERA5_tmin, \"degC\")\n",
-    "ERA5_tmax = xclim.core.units.convert_units_to(ERA5_tmax, \"degC\")\n",
-    "historical_tasmin = xclim.core.units.convert_units_to(historical_tasmin, \"degC\")\n",
-    "historical_tasmax = xclim.core.units.convert_units_to(historical_tasmax, \"degC\")\n",
-    "future_tasmin = xclim.core.units.convert_units_to(future_tasmin, \"degC\")\n",
-    "future_tasmax = xclim.core.units.convert_units_to(future_tasmax, \"degC\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model is now going to be trained to find correction factors between the reference dataset (observations) and historical dataset (climate model outputs for the same time period). The correction factors obtained are then applied to both reference and future climate outputs to correct them. This step is called the bias correction. In this test-case, we apply a method named `detrended quantile mapping`.\n",
-    "\n",
-    "Here we use the `xclim` utilities to bias-correct CMIP6 GCM data using ERA5 reanalysis data as the reference. See `xclim` documentation for more options! (https://xclim.readthedocs.io/en/stable/notebooks/sdba.html)\n",
-    "\n",
-    "> **Warning**\n",
-    "> This following block of code will take a while to run, and some warning messages will appear during the process (related to longitude wrapping and other information on calendar types). Unless an error message appears, the code should run just fine!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
-      "  warn(\n",
-      "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
-      "  warn(\n",
-      "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
-      "  warn(\n",
-      "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
-      "  warn(\n",
-      "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
-      "  warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Use xclim utilities (sbda) to give information on the type of window used for the bias correction.\n",
-    "group_month_window = sdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
-    "\n",
-    "# This is an adjusting function. It builds the tool that will perform the corrections.\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
-    "    ref=ERA5_pr, hist=historical_pr, nquantiles=50, kind=\"+\", group=group_month_window\n",
-    ")\n",
-    "\n",
-    "# Apply the correction factors on the reference period\n",
-    "corrected_ref_precip = Adjustment.adjust(historical_pr, interp=\"linear\")\n",
-    "\n",
-    "# Apply the correction factors on the future period\n",
-    "corrected_fut_precip = Adjustment.adjust(future_pr, interp=\"linear\")\n",
-    "\n",
-    "# Ensure that the precipitation is non-negative, which can happen with some climate models\n",
-    "corrected_ref_precip = corrected_ref_precip.where(corrected_ref_precip > 0, 0)\n",
-    "corrected_fut_precip = corrected_fut_precip.where(corrected_fut_precip > 0, 0)\n",
-    "\n",
-    "# Train the model to find the correction factors for the maximum temperature (tasmax) data\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
-    "    ref=ERA5_tmax,\n",
-    "    hist=historical_tasmax,\n",
-    "    nquantiles=50,\n",
-    "    kind=\"+\",\n",
-    "    group=group_month_window,\n",
-    ")\n",
-    "\n",
-    "# Apply the correction factors on the reference period\n",
-    "corrected_ref_tasmax = Adjustment.adjust(historical_tasmax, interp=\"linear\")\n",
-    "\n",
-    "# Apply the correction factors on the future period\n",
-    "corrected_fut_tasmax = Adjustment.adjust(future_tasmax, interp=\"linear\")\n",
-    "\n",
-    "# Train the model to find the correction factors for the minimum temperature (tasmin) data\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
-    "    ref=ERA5_tmin,\n",
-    "    hist=historical_tasmin,\n",
-    "    nquantiles=50,\n",
-    "    kind=\"+\",\n",
-    "    group=group_month_window,\n",
-    ")\n",
-    "\n",
-    "# Apply the correction factors on the reference period\n",
-    "corrected_ref_tasmin = Adjustment.adjust(historical_tasmin, interp=\"linear\")\n",
-    "\n",
-    "# Apply the correction factors on the future period\n",
-    "corrected_fut_tasmin = Adjustment.adjust(future_tasmin, interp=\"linear\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The corrected reference and future data are then converted to netCDF files. This will take a while to run (perhaps a minute or two), since it will need to write the datasets to disk after having processed everything via lazy loading."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Convert the reference corrected data into netCDF file. We will then apply a special code to remove a dimension in the dataset to make it applicable to the RAVEN models.\n",
-    "ref_dataset = xr.merge(\n",
-    "    [\n",
-    "        corrected_ref_precip.to_dataset(name=\"pr\"),\n",
-    "        corrected_ref_tasmax.to_dataset(name=\"tasmax\"),\n",
-    "        corrected_ref_tasmin.to_dataset(name=\"tasmin\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# Write to temporary folder\n",
-    "fn_ref = tmp / \"reference_dataset.nc\"\n",
-    "ref_dataset.to_netcdf(fn_ref)\n",
-    "\n",
-    "# Convert the future corrected data into netCDF file\n",
-    "fut_dataset = xr.merge(\n",
-    "    [\n",
-    "        corrected_fut_precip.to_dataset(name=\"pr\"),\n",
-    "        corrected_fut_tasmax.to_dataset(name=\"tasmax\"),\n",
-    "        corrected_fut_tasmin.to_dataset(name=\"tasmin\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "fn_fut = tmp / \"future_dataset.nc\"\n",
-    "fut_dataset.to_netcdf(fn_fut)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "hwloc/linux: Ignoring PCI device with non-16bit domain.\n",
+            "Pass --enable-32bits-pci-domain to configure to support such devices\n",
+            "(warning: it would break the library ABI, don't enable unless really needed).\n"
+          ]
+        },
+        {
+          "data": {
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+              "\n",
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+              ".xr-icon-file-text2,\n",
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+              "  stroke: currentColor;\n",
+              "  fill: currentColor;\n",
+              "}\n",
+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tasmin&#x27; (time: 3653, geom: 1)&gt;\n",
+              "dask.array&lt;rechunk-merge, shape=(3653, 1), dtype=float32, chunksize=(3653, 1), chunktype=numpy.ndarray&gt;\n",
+              "Coordinates: (12/19)\n",
+              "  * time       (time) datetime64[ns] 1980-12-31T12:00:00 ... 1990-12-31T12:00:00\n",
+              "    lon        (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    lat        (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "  * geom       (geom) int64 0\n",
+              "    COAST      (geom) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    DIST_MAIN  (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    ...         ...\n",
+              "    PFAF_ID    (geom) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    SIDE       (geom) object dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    SORT       (geom) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    SUB_AREA   (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    UP_AREA    (geom) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "    id         (geom) object dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+              "Attributes:\n",
+              "    cell_measures:  area: areacella\n",
+              "    cell_methods:   area: mean time: minimum\n",
+              "    comment:        minimum near-surface (usually, 2 meter) air temperature (...\n",
+              "    long_name:      Daily Minimum Near-Surface Air Temperature\n",
+              "    original_name:  T2\n",
+              "    standard_name:  air_temperature\n",
+              "    units:          K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tasmin'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3653</li><li><span class='xr-has-index'>geom</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-022f59d1-fb5f-4454-8d82-8b30b5fba09a' class='xr-array-in' type='checkbox' checked><label for='section-022f59d1-fb5f-4454-8d82-8b30b5fba09a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(3653, 1), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 14.27 kiB </td>\n",
+              "                        <td> 14.27 kiB </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (3653, 1) </td>\n",
+              "                        <td> (3653, 1) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
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+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">3653</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></div></li><li class='xr-section-item'><input id='section-6c6a2381-6107-4f4f-b424-ae784f78483b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c6a2381-6107-4f4f-b424-ae784f78483b' class='xr-section-summary' >Coordinates: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-12-31T12:00:00 ... 1990-12-...</div><input id='attrs-d6296ab6-c276-4640-a8c6-1babdafe8448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6296ab6-c276-4640-a8c6-1babdafe8448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-631c426d-af9f-4ed9-8a77-19e3ec04413c' class='xr-var-data-in' type='checkbox'><label for='data-631c426d-af9f-4ed9-8a77-19e3ec04413c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1980-12-31T12:00:00.000000000&#x27;, &#x27;1981-01-01T12:00:00.000000000&#x27;,\n",
+              "       &#x27;1981-01-02T12:00:00.000000000&#x27;, ..., &#x27;1990-12-29T12:00:00.000000000&#x27;,\n",
+              "       &#x27;1990-12-30T12:00:00.000000000&#x27;, &#x27;1990-12-31T12:00:00.000000000&#x27;],\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-1d32a6f7-da37-4d90-b289-01219a1fc622' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d32a6f7-da37-4d90-b289-01219a1fc622' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6b23a6b-1a71-49d8-8547-95fc91fe91c1' class='xr-var-data-in' type='checkbox'><label for='data-c6b23a6b-1a71-49d8-8547-95fc91fe91c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-0a5899aa-af31-4af3-8a72-12448aac7782' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a5899aa-af31-4af3-8a72-12448aac7782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06b09edb-c277-43d7-8369-ba8b53d6e741' class='xr-var-data-in' type='checkbox'><label for='data-06b09edb-c277-43d7-8369-ba8b53d6e741' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-dc9e14bc-68e8-461a-ace0-ed7375f2525a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc9e14bc-68e8-461a-ace0-ed7375f2525a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa1d9a10-3246-4dcb-b534-97c26375051b' class='xr-var-data-in' type='checkbox'><label for='data-aa1d9a10-3246-4dcb-b534-97c26375051b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>COAST</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-1a96ba39-bbd7-494e-8107-f005d91161f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a96ba39-bbd7-494e-8107-f005d91161f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fa5e3e8-5862-4182-b5fc-9e4997fab3a5' class='xr-var-data-in' type='checkbox'><label for='data-2fa5e3e8-5862-4182-b5fc-9e4997fab3a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIST_MAIN</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-4e67f7ed-5d3d-4c48-9d34-1cd653e60073' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e67f7ed-5d3d-4c48-9d34-1cd653e60073' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a822ca33-9696-4380-8071-c163b24ac4ea' class='xr-var-data-in' type='checkbox'><label for='data-a822ca33-9696-4380-8071-c163b24ac4ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIST_SINK</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-29dee96d-05f2-4d4c-abe4-f99c2b50b57b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29dee96d-05f2-4d4c-abe4-f99c2b50b57b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1eefa1f-0659-416b-9695-b4bb3c8ba7bd' class='xr-var-data-in' type='checkbox'><label for='data-a1eefa1f-0659-416b-9695-b4bb3c8ba7bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENDO</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-bb3c3c63-df14-435a-b326-d5dee05c4702' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb3c3c63-df14-435a-b326-d5dee05c4702' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2588b63-a479-4909-b4a2-1e1b2da6cb35' class='xr-var-data-in' type='checkbox'><label for='data-c2588b63-a479-4909-b4a2-1e1b2da6cb35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>HYBAS_ID</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d00a27e5-1543-4723-867e-5f76a7baaaf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d00a27e5-1543-4723-867e-5f76a7baaaf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2700eaf2-20fc-42cb-a686-9bfdf4ac06e1' class='xr-var-data-in' type='checkbox'><label for='data-2700eaf2-20fc-42cb-a686-9bfdf4ac06e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LAKE</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-8e86202a-c161-4287-8571-7bdf4f082bf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e86202a-c161-4287-8571-7bdf4f082bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b8f548b-1ddd-4323-8123-28b82b722f8b' class='xr-var-data-in' type='checkbox'><label for='data-2b8f548b-1ddd-4323-8123-28b82b722f8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NEXT_DOWN</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-b4bbbdf8-b4e9-4b79-93ed-bdaf13789cbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4bbbdf8-b4e9-4b79-93ed-bdaf13789cbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe548ef4-6f67-455e-b2ea-196b4726eb8e' class='xr-var-data-in' type='checkbox'><label for='data-fe548ef4-6f67-455e-b2ea-196b4726eb8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NEXT_SINK</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-873a1248-1e26-465e-a981-413c814aa20d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-873a1248-1e26-465e-a981-413c814aa20d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7add80c8-2fcd-45cb-b190-e268cbc62b25' class='xr-var-data-in' type='checkbox'><label for='data-7add80c8-2fcd-45cb-b190-e268cbc62b25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ORDER</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-f49331c9-e55b-499c-9308-7650e7092b4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f49331c9-e55b-499c-9308-7650e7092b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee4f56ae-5013-4693-a9ad-356fecf16a76' class='xr-var-data-in' type='checkbox'><label for='data-ee4f56ae-5013-4693-a9ad-356fecf16a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PFAF_ID</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-79942194-69ac-4782-9de5-c33e39bf4420' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79942194-69ac-4782-9de5-c33e39bf4420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d09cce2-272f-462c-a9d3-847932cd9f97' class='xr-var-data-in' type='checkbox'><label for='data-8d09cce2-272f-462c-a9d3-847932cd9f97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIDE</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-382cff4b-9b8a-4e10-a552-9bb4958cad5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-382cff4b-9b8a-4e10-a552-9bb4958cad5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b72b5f67-64bb-4c9c-9a3d-ab15207e7f9e' class='xr-var-data-in' type='checkbox'><label for='data-b72b5f67-64bb-4c9c-9a3d-ab15207e7f9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SORT</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-735c12ed-6bcc-4dbd-a4e8-4aaefa960ee8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-735c12ed-6bcc-4dbd-a4e8-4aaefa960ee8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5f50bbb-bbad-466b-81cb-feee4914f5c0' class='xr-var-data-in' type='checkbox'><label for='data-f5f50bbb-bbad-466b-81cb-feee4914f5c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SUB_AREA</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e0be4ed1-f463-4b70-acce-1c9b0307e699' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0be4ed1-f463-4b70-acce-1c9b0307e699' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c93420d1-4718-4316-af03-38ef8727c9fe' class='xr-var-data-in' type='checkbox'><label for='data-c93420d1-4718-4316-af03-38ef8727c9fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UP_AREA</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c71bb2a4-fa75-4fc1-a27d-926028fe4fbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c71bb2a4-fa75-4fc1-a27d-926028fe4fbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fef9e742-54f3-4a93-95a1-2cfe26c13724' class='xr-var-data-in' type='checkbox'><label for='data-fef9e742-54f3-4a93-95a1-2cfe26c13724' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-47b0622c-871f-4ba0-b9ab-3972b47833a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b0622c-871f-4ba0-b9ab-3972b47833a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-823713bc-9964-45f4-8b2b-d41f3281712f' class='xr-var-data-in' type='checkbox'><label for='data-823713bc-9964-45f4-8b2b-d41f3281712f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+              "    <tr>\n",
+              "        <td>\n",
+              "            <table style=\"border-collapse: collapse;\">\n",
+              "                <thead>\n",
+              "                    <tr>\n",
+              "                        <td> </td>\n",
+              "                        <th> Array </th>\n",
+              "                        <th> Chunk </th>\n",
+              "                    </tr>\n",
+              "                </thead>\n",
+              "                <tbody>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Bytes </th>\n",
+              "                        <td> 8 B </td>\n",
+              "                        <td> 8 B </td>\n",
+              "                    </tr>\n",
+              "                    \n",
+              "                    <tr>\n",
+              "                        <th> Shape </th>\n",
+              "                        <td> (1,) </td>\n",
+              "                        <td> (1,) </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Dask graph </th>\n",
+              "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+              "                    </tr>\n",
+              "                    <tr>\n",
+              "                        <th> Data type </th>\n",
+              "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
+              "                    </tr>\n",
+              "                </tbody>\n",
+              "            </table>\n",
+              "        </td>\n",
+              "        <td>\n",
+              "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+              "\n",
+              "  <!-- Horizontal lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Vertical lines -->\n",
+              "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+              "\n",
+              "  <!-- Colored Rectangle -->\n",
+              "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+              "\n",
+              "  <!-- Text -->\n",
+              "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+              "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+              "</svg>\n",
+              "        </td>\n",
+              "    </tr>\n",
+              "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2d205e1-d732-4911-a624-e54c630c0225' class='xr-section-summary-in' type='checkbox'  ><label for='section-e2d205e1-d732-4911-a624-e54c630c0225' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-002f6a54-f07c-4714-b86c-0c9cce7aab6b' class='xr-index-data-in' type='checkbox'/><label for='index-002f6a54-f07c-4714-b86c-0c9cce7aab6b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1980-12-31 12:00:00&#x27;, &#x27;1981-01-01 12:00:00&#x27;,\n",
+              "               &#x27;1981-01-02 12:00:00&#x27;, &#x27;1981-01-03 12:00:00&#x27;,\n",
+              "               &#x27;1981-01-04 12:00:00&#x27;, &#x27;1981-01-05 12:00:00&#x27;,\n",
+              "               &#x27;1981-01-06 12:00:00&#x27;, &#x27;1981-01-07 12:00:00&#x27;,\n",
+              "               &#x27;1981-01-08 12:00:00&#x27;, &#x27;1981-01-09 12:00:00&#x27;,\n",
+              "               ...\n",
+              "               &#x27;1990-12-22 12:00:00&#x27;, &#x27;1990-12-23 12:00:00&#x27;,\n",
+              "               &#x27;1990-12-24 12:00:00&#x27;, &#x27;1990-12-25 12:00:00&#x27;,\n",
+              "               &#x27;1990-12-26 12:00:00&#x27;, &#x27;1990-12-27 12:00:00&#x27;,\n",
+              "               &#x27;1990-12-28 12:00:00&#x27;, &#x27;1990-12-29 12:00:00&#x27;,\n",
+              "               &#x27;1990-12-30 12:00:00&#x27;, &#x27;1990-12-31 12:00:00&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3653, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>geom</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e6171c2-9a88-4333-a144-76fbbcdc41d0' class='xr-index-data-in' type='checkbox'/><label for='index-6e6171c2-9a88-4333-a144-76fbbcdc41d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=0, stop=1, step=1, name=&#x27;geom&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eda96ba8-1ff2-4571-9a27-09db99b2802e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eda96ba8-1ff2-4571-9a27-09db99b2802e' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: minimum</dd><dt><span>comment :</span></dt><dd>minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: min&#x27;)</dd><dt><span>long_name :</span></dt><dd>Daily Minimum Near-Surface Air Temperature</dd><dt><span>original_name :</span></dt><dd>T2</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.DataArray 'tasmin' (time: 3653, geom: 1)>\n",
+              "dask.array<rechunk-merge, shape=(3653, 1), dtype=float32, chunksize=(3653, 1), chunktype=numpy.ndarray>\n",
+              "Coordinates: (12/19)\n",
+              "  * time       (time) datetime64[ns] 1980-12-31T12:00:00 ... 1990-12-31T12:00:00\n",
+              "    lon        (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    lat        (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "  * geom       (geom) int64 0\n",
+              "    COAST      (geom) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    DIST_MAIN  (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    ...         ...\n",
+              "    PFAF_ID    (geom) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    SIDE       (geom) object dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    SORT       (geom) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    SUB_AREA   (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    UP_AREA    (geom) float64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "    id         (geom) object dask.array<chunksize=(1,), meta=np.ndarray>\n",
+              "Attributes:\n",
+              "    cell_measures:  area: areacella\n",
+              "    cell_methods:   area: mean time: minimum\n",
+              "    comment:        minimum near-surface (usually, 2 meter) air temperature (...\n",
+              "    long_name:      Daily Minimum Near-Surface Air Temperature\n",
+              "    original_name:  T2\n",
+              "    standard_name:  air_temperature\n",
+              "    units:          K"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Get the CMIP6 data from Google Cloud and read it in memory using xarray. This is done via \"lazy loading\" and is not actually reading the data in memory\n",
+        "# yet, but is keeping track of what it will need to get, eventually.\n",
+        "ds = xr.open_zarr(mapper, consolidated=True)\n",
+        "\n",
+        "# Convert to numpy.datetime64 object to be compatbile\n",
+        "if type(ds.time[0].values) is not type(np.datetime64(\"1980-01-01\")):\n",
+        "    ds = ds.convert_calendar(\"standard\")\n",
+        "\n",
+        "# Extract only the dates that we really want. Again, this is done via lazy loading, and is not actually using memory at this point.\n",
+        "ds = ds.sel(time=slice(reference_start_day, reference_end_day))\n",
+        "\n",
+        "# Use the clisops subsetting tools to extract the data for the watershed boundaries and take the spatial average\n",
+        "ds = average.average_shape(ds, basin_contour)\n",
+        "\n",
+        "# Correct the coordinates that are unnecessary for our variable\n",
+        "ds = ds.reset_coords(\"height\", drop=True)\n",
+        "\n",
+        "# Rechunk the data so it is much faster to read (single chunk rather than 1 chunk per day)\n",
+        "historical_tasmin = ds[\"tasmin\"].chunk(-1)\n",
+        "\n",
+        "# Show the end result!\n",
+        "display(historical_tasmin)"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f3b572c7430>]"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that we have a single chunk of 10 years of tasmin data, as expected! However, you might also have noticed that there is no metadata, such as units and variable properties left in the data array. We can fix that by wrapping the code in a block that forces xarray to keep the metadata.\n",
+        "\n",
+        "Also, since we will need to use this block of code for each variable, it might become tedious. Therefore, to simplify the code, we can combine everything into a function."
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "def extract_and_average(mapper, start, end, geometry):\n",
+        "    with xr.set_options(keep_attrs=True):\n",
+        "        ds = xr.open_zarr(mapper, consolidated=True)\n",
+        "\n",
+        "        # Convert to numpy.datetime64 object to be compatbile\n",
+        "        if type(ds.time[0].values) is not type(np.datetime64(\"1980-01-01\")):\n",
+        "            ds = ds.convert_calendar(\"standard\")\n",
+        "\n",
+        "        # Compute average over region\n",
+        "        out = average.average_shape(ds.sel(time=slice(start, end)), geometry)\n",
+        "\n",
+        "        # Convert geometry variables into attributes\n",
+        "        attrs = {\n",
+        "            key: out[key].values.item()\n",
+        "            for key in out.coords\n",
+        "            if key not in [\"time\", \"time_bnds\", \"lon\", \"lat\"]\n",
+        "        }\n",
+        "        out = out.isel(geom=0).reset_coords(attrs.keys(), drop=True)\n",
+        "        out.attrs.update(attrs)\n",
+        "\n",
+        "        return out.chunk(-1)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Show the corrected future precipitation.\n",
-    "corrected_fut_precip.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Much better! we have all the information we need. Let's repeat the process for the 3 variables and for the reference and future periods using ssp585. You probably don't have to change anything in this following block of code, but you can taylor it to your needs knowing how everything is built now."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "historical tasmin\n",
+            "historical tasmax\n",
+            "historical pr\n",
+            "ssp585 tasmin\n",
+            "ssp585 tasmax\n",
+            "ssp585 pr\n"
+          ]
+        }
+      ],
+      "source": [
+        "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    # Load the files from the PanGEO catalogs, for reference and future variables of temperature and precipitation.\n",
+        "    out = {}\n",
+        "    for exp in [\"historical\", \"ssp585\"]:\n",
+        "        if exp == \"historical\":\n",
+        "            period_start = reference_start_day\n",
+        "            period_end = reference_end_day\n",
+        "        else:\n",
+        "            period_start = future_start_day\n",
+        "            period_end = future_end_day\n",
+        "\n",
+        "        out[exp] = {}\n",
+        "        for variable in [\"tasmin\", \"tasmax\", \"pr\"]:\n",
+        "            print(exp, variable)\n",
+        "            query = dict(\n",
+        "                experiment_id=exp,\n",
+        "                table_id=\"day\",\n",
+        "                variable_id=variable,\n",
+        "                member_id=\"r1i1p1f1\",\n",
+        "                source_id=climate_model,\n",
+        "            )\n",
+        "            col_subset = col.search(require_all_on=[\"source_id\"], **query)\n",
+        "            mapper = fsCMIP.get_mapper(col_subset.df.zstore[0])\n",
+        "            ds = xr.open_zarr(mapper, consolidated=True)\n",
+        "\n",
+        "            out[exp][variable] = extract_and_average(\n",
+        "                mapper, period_start, period_end, basin_contour\n",
+        "            )[variable]\n",
+        "\n",
+        "# We can now extract the variables that we will need later:\n",
+        "historical_tasmax = out[\"historical\"][\"tasmax\"]\n",
+        "historical_tasmin = out[\"historical\"][\"tasmin\"]\n",
+        "historical_pr = out[\"historical\"][\"pr\"]\n",
+        "future_tasmax = out[\"ssp585\"][\"tasmax\"]\n",
+        "future_tasmin = out[\"ssp585\"][\"tasmin\"]\n",
+        "future_pr = out[\"ssp585\"][\"pr\"]"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f37efd13e80>]"
+      "cell_type": "markdown",
+      "metadata": {
+        "tags": []
+      },
+      "source": [
+        "### Reference data to prepare bias correction\n",
+        "We have extracted the historical period and future period data from the GCM. Now we need the reference data to use as the baseline for bias-correction. Here we will use ERA5 and we will gather it again, since we can't be sure that the dates we selected in the 3rd notebook are still valid.\n",
+        "\n"
       ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Regenerate the ERA5 data to be sure. In an operational context, you could combine everything onto one notebook and ensure that the\n",
+        "# dates and locations are constant!\n",
+        "\n",
+        "# Get the ERA5 data from the Wasabi/Amazon S3 server.\n",
+        "catalog_name = \"https://raw.githubusercontent.com/hydrocloudservices/catalogs/main/catalogs/atmosphere.yaml\"\n",
+        "cat = intake.open_catalog(catalog_name)\n",
+        "ds = cat.era5_reanalysis_single_levels.to_dask()\n",
+        "\n",
+        "\"\"\"\n",
+        "Get the ERA5 data. We will rechunk it to a single chunck to make it compatible with other codes on the platform, especially bias-correction.\n",
+        "We are also taking the daily min and max temperatures as well as the daily total precipitation.\n",
+        "\"\"\"\n",
+        "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    ERA5_reference = subset.subset_shape(\n",
+        "        ds.sel(time=slice(reference_start_day, reference_end_day)), basin_contour\n",
+        "    ).mean({\"latitude\", \"longitude\"})\n",
+        "    ERA5_tmin = ERA5_reference[\"t2m\"].resample(time=\"1D\").min().chunk(-1, -1, -1)\n",
+        "    ERA5_tmax = ERA5_reference[\"t2m\"].resample(time=\"1D\").max().chunk(-1, -1, -1)\n",
+        "    ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f39904c4730>]"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "ERA5_pr.plot()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Here we need to make sure that our units are all in the correct format. You can play around with the tools we've seen thus far to explore the units\n",
+        "# and make sure everything is consistent.\n",
+        "\n",
+        "# Let's start with precipitation:\n",
+        "ERA5_pr = xclim.core.units.convert_units_to(ERA5_pr, \"mm\", context=\"hydro\")\n",
+        "# The CMIP data is a rate rather than an absolute value, so let's get the absolute values:\n",
+        "historical_pr = xclim.core.units.rate2amount(historical_pr)\n",
+        "future_pr = xclim.core.units.rate2amount(future_pr)\n",
+        "\n",
+        "# Now we can actually convert units in absolute terms.\n",
+        "historical_pr = xclim.core.units.convert_units_to(historical_pr, \"mm\", context=\"hydro\")\n",
+        "future_pr = xclim.core.units.convert_units_to(future_pr, \"mm\", context=\"hydro\")\n",
+        "\n",
+        "# Now let's do temperature:\n",
+        "ERA5_tmin = xclim.core.units.convert_units_to(ERA5_tmin, \"degC\")\n",
+        "ERA5_tmax = xclim.core.units.convert_units_to(ERA5_tmax, \"degC\")\n",
+        "historical_tasmin = xclim.core.units.convert_units_to(historical_tasmin, \"degC\")\n",
+        "historical_tasmax = xclim.core.units.convert_units_to(historical_tasmax, \"degC\")\n",
+        "future_tasmin = xclim.core.units.convert_units_to(future_tasmin, \"degC\")\n",
+        "future_tasmax = xclim.core.units.convert_units_to(future_tasmax, \"degC\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The model is now going to be trained to find correction factors between the reference dataset (observations) and historical dataset (climate model outputs for the same time period). The correction factors obtained are then applied to both reference and future climate outputs to correct them. This step is called the bias correction. In this test-case, we apply a method named `detrended quantile mapping`.\n",
+        "\n",
+        "Here we use the `xclim` utilities to bias-correct CMIP6 GCM data using ERA5 reanalysis data as the reference. See `xclim` documentation for more options! (https://xclim.readthedocs.io/en/stable/notebooks/sdba.html)\n",
+        "\n",
+        "> **Warning**\n",
+        "> This following block of code will take a while to run, and some warning messages will appear during the process (related to longitude wrapping and other information on calendar types). Unless an error message appears, the code should run just fine!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
+            "  warn(\n",
+            "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
+            "  warn(\n",
+            "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
+            "  warn(\n",
+            "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
+            "  warn(\n",
+            "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/sdba/adjustment.py:117: UserWarning: Strange results could be returned when using dayofyear grouping on data defined in the proleptic_gregorian calendar \n",
+            "  warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Use xclim utilities (sbda) to give information on the type of window used for the bias correction.\n",
+        "group_month_window = sdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
+        "\n",
+        "# This is an adjusting function. It builds the tool that will perform the corrections.\n",
+        "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+        "    ref=ERA5_pr, hist=historical_pr, nquantiles=50, kind=\"+\", group=group_month_window\n",
+        ")\n",
+        "\n",
+        "# Apply the correction factors on the reference period\n",
+        "corrected_ref_precip = Adjustment.adjust(historical_pr, interp=\"linear\")\n",
+        "\n",
+        "# Apply the correction factors on the future period\n",
+        "corrected_fut_precip = Adjustment.adjust(future_pr, interp=\"linear\")\n",
+        "\n",
+        "# Ensure that the precipitation is non-negative, which can happen with some climate models\n",
+        "corrected_ref_precip = corrected_ref_precip.where(corrected_ref_precip > 0, 0)\n",
+        "corrected_fut_precip = corrected_fut_precip.where(corrected_fut_precip > 0, 0)\n",
+        "\n",
+        "# Train the model to find the correction factors for the maximum temperature (tasmax) data\n",
+        "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+        "    ref=ERA5_tmax,\n",
+        "    hist=historical_tasmax,\n",
+        "    nquantiles=50,\n",
+        "    kind=\"+\",\n",
+        "    group=group_month_window,\n",
+        ")\n",
+        "\n",
+        "# Apply the correction factors on the reference period\n",
+        "corrected_ref_tasmax = Adjustment.adjust(historical_tasmax, interp=\"linear\")\n",
+        "\n",
+        "# Apply the correction factors on the future period\n",
+        "corrected_fut_tasmax = Adjustment.adjust(future_tasmax, interp=\"linear\")\n",
+        "\n",
+        "# Train the model to find the correction factors for the minimum temperature (tasmin) data\n",
+        "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+        "    ref=ERA5_tmin,\n",
+        "    hist=historical_tasmin,\n",
+        "    nquantiles=50,\n",
+        "    kind=\"+\",\n",
+        "    group=group_month_window,\n",
+        ")\n",
+        "\n",
+        "# Apply the correction factors on the reference period\n",
+        "corrected_ref_tasmin = Adjustment.adjust(historical_tasmin, interp=\"linear\")\n",
+        "\n",
+        "# Apply the correction factors on the future period\n",
+        "corrected_fut_tasmin = Adjustment.adjust(future_tasmin, interp=\"linear\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The corrected reference and future data are then converted to netCDF files. This will take a while to run (perhaps a minute or two), since it will need to write the datasets to disk after having processed everything via lazy loading."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Convert the reference corrected data into netCDF file. We will then apply a special code to remove a dimension in the dataset to make it applicable to the RAVEN models.\n",
+        "ref_dataset = xr.merge(\n",
+        "    [\n",
+        "        corrected_ref_precip.to_dataset(name=\"pr\"),\n",
+        "        corrected_ref_tasmax.to_dataset(name=\"tasmax\"),\n",
+        "        corrected_ref_tasmin.to_dataset(name=\"tasmin\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "# Write to temporary folder\n",
+        "fn_ref = tmp / \"reference_dataset.nc\"\n",
+        "ref_dataset.to_netcdf(fn_ref)\n",
+        "\n",
+        "# Convert the future corrected data into netCDF file\n",
+        "fut_dataset = xr.merge(\n",
+        "    [\n",
+        "        corrected_fut_precip.to_dataset(name=\"pr\"),\n",
+        "        corrected_fut_tasmax.to_dataset(name=\"tasmax\"),\n",
+        "        corrected_fut_tasmin.to_dataset(name=\"tasmin\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "fn_fut = tmp / \"future_dataset.nc\"\n",
+        "fut_dataset.to_netcdf(fn_fut)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f3b572c7430>]"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Show the corrected future precipitation.\n",
+        "corrected_fut_precip.plot()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f37efd13e80>]"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Compare it to the future precipitation without bias-correction.\n",
+        "future_pr.plot()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
     }
-   ],
-   "source": [
-    "# Compare it to the future precipitation without bias-correction.\n",
-    "future_pr.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-"kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb b/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb
index 15fbd397..e465ebde 100644
--- a/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb
+++ b/docs/notebooks/09_Hydrological_impacts_of_climate_change.ipynb
@@ -1,221 +1,221 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 09 - Hydrological impacts of climate change"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 09 - Hydrological impacts of climate change"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Performing bias correction on climate model data to perform climate change impact studies on hydrology\n",
+        "\n",
+        "This notebook will allow evaluating the impacts of climate change on the hydrology of a catchment. We will use the data we previously generated in notebook \"08 - Getting and bias-correcting CMIP6 data\", where we produced both reference and future forcing datasets.\n",
+        "\n",
+        "You can apply this notebook to other models, climate datasets, and generally pick and choose parts of various notebooks to build your own complete workflow."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\"\"\"\n",
+        "Import the required packages\n",
+        "\"\"\"\n",
+        "import datetime as dt\n",
+        "import warnings\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "\n",
+        "from ravenpy import Emulator\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities.testdata import get_file\n",
+        "\n",
+        "warnings.filterwarnings(\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Simulate the flows on the reference period\n",
+        "\n",
+        "In this step, we will take the reference period climate data and run the GR4J-CN hydrological model with it. We will then plot a graph to see the streamflow representative of the reference period."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "# Define the hydrological response unit. We can use the information from the tutorial notebook #02! Here we are using\n",
+        "# arbitrary data for a test catchment.\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Define the start and end dates of the reference period:\n",
+        "start_date = dt.datetime(1981, 1, 1)\n",
+        "end_date = dt.datetime(1990, 12, 31)\n",
+        "\n",
+        "# We get the netCDF for testing on a server. You can replace the getfile method by a string containing the path\n",
+        "# to your own netCDF\n",
+        "\n",
+        "reference_ds = get_file(\"notebook_inputs/reference_dataset.nc\")\n",
+        "\n",
+        "# Alternate names for the data in the climate data NetCDF files\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tasmin\",\n",
+        "    \"TEMP_MAX\": \"tasmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Types of data required by the Raven GR4JCN instance\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"latitude\": hru[\"latitude\"],\n",
+        "        \"longitude\": hru[\"longitude\"],\n",
+        "    }\n",
+        "}\n",
+        "# Start a model instance, in this case a GR4JCN model emulator.\n",
+        "m = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            reference_ds,  # path to the reference period dataset.\n",
+        "            data_type=data_type,\n",
+        "            alt_names=alt_names,\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"test\",\n",
+        ")\n",
+        "\n",
+        "# Prepare the emulator by writing files on disk\n",
+        "e = Emulator(config=m)\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "outputs_reference = e.run()\n",
+        "\n",
+        "outputs_reference.hydrograph.q_sim.plot(label=\"Reference\", color=\"blue\", lw=0.5)\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Now do the same but for the future period!\n",
+        "We will copy the block of code from above, changing only the file path (from reference dataset to future dataset) as well as the start and end dates."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Define the start and end dates of the reference period:\n",
+        "start_date = dt.datetime(2081, 1, 1)\n",
+        "end_date = dt.datetime(2090, 12, 31)\n",
+        "\n",
+        "# Get the future period dataset (path)\n",
+        "future_ds = get_file(\"notebook_inputs/future_dataset.nc\")\n",
+        "\n",
+        "# Start a new model instance, again in this case a GR4JCN model emulator.\n",
+        "m = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            # name of the future period dataset.\n",
+        "            future_ds,\n",
+        "            data_type=data_type,\n",
+        "            alt_names=alt_names,\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"test\",\n",
+        ")\n",
+        "\n",
+        "# Prepare the emulator by writing files on disk\n",
+        "e = Emulator(config=m)\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "outputs_future = e.run()\n",
+        "\n",
+        "outputs_future.hydrograph.q_sim.plot(label=\"Future\", color=\"orange\", lw=0.5)\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## You have just generated streamflows for the reference and future periods!\n",
+        "We can analyze these hydrographs with many tools in PAVICS-Hydro, or export them to use elsewhere, or use them as inputs to another process!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the path to the hydrograph file:\n",
+        "outputs_future.files[\"hydrograph\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Work with the hydrograph data directly:\n",
+        "outputs_future.hydrograph"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Performing bias correction on climate model data to perform climate change impact studies on hydrology\n",
-    "\n",
-    "This notebook will allow evaluating the impacts of climate change on the hydrology of a catchment. We will use the data we previously generated in notebook \"08 - Getting and bias-correcting CMIP6 data\", where we produced both reference and future forcing datasets.\n",
-    "\n",
-    "You can apply this notebook to other models, climate datasets, and generally pick and choose parts of various notebooks to build your own complete workflow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "Import the required packages\n",
-    "\"\"\"\n",
-    "import datetime as dt\n",
-    "import warnings\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from ravenpy import Emulator\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities.testdata import get_file\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulate the flows on the reference period\n",
-    "\n",
-    "In this step, we will take the reference period climate data and run the GR4J-CN hydrological model with it. We will then plot a graph to see the streamflow representative of the reference period."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Define the hydrological response unit. We can use the information from the tutorial notebook #02! Here we are using\n",
-    "# arbitrary data for a test catchment.\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Define the start and end dates of the reference period:\n",
-    "start_date = dt.datetime(1981, 1, 1)\n",
-    "end_date = dt.datetime(1990, 12, 31)\n",
-    "\n",
-    "# We get the netCDF for testing on a server. You can replace the getfile method by a string containing the path\n",
-    "# to your own netCDF\n",
-    "\n",
-    "reference_ds = get_file(\"notebook_inputs/reference_dataset.nc\")\n",
-    "\n",
-    "# Alternate names for the data in the climate data NetCDF files\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tasmin\",\n",
-    "    \"TEMP_MAX\": \"tasmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Types of data required by the Raven GR4JCN instance\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"latitude\": hru[\"latitude\"],\n",
-    "        \"longitude\": hru[\"longitude\"],\n",
-    "    }\n",
-    "}\n",
-    "# Start a model instance, in this case a GR4JCN model emulator.\n",
-    "m = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            reference_ds,  # path to the reference period dataset.\n",
-    "            data_type=data_type,\n",
-    "            alt_names=alt_names,\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"test\",\n",
-    ")\n",
-    "\n",
-    "# Prepare the emulator by writing files on disk\n",
-    "e = Emulator(config=m)\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "outputs_reference = e.run()\n",
-    "\n",
-    "outputs_reference.hydrograph.q_sim.plot(label=\"Reference\", color=\"blue\", lw=0.5)\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Now do the same but for the future period!\n",
-    "We will copy the block of code from above, changing only the file path (from reference dataset to future dataset) as well as the start and end dates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the start and end dates of the reference period:\n",
-    "start_date = dt.datetime(2081, 1, 1)\n",
-    "end_date = dt.datetime(2090, 12, 31)\n",
-    "\n",
-    "# Get the future period dataset (path)\n",
-    "future_ds = get_file(\"notebook_inputs/future_dataset.nc\")\n",
-    "\n",
-    "# Start a new model instance, again in this case a GR4JCN model emulator.\n",
-    "m = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            # name of the future period dataset.\n",
-    "            future_ds,\n",
-    "            data_type=data_type,\n",
-    "            alt_names=alt_names,\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"test\",\n",
-    ")\n",
-    "\n",
-    "# Prepare the emulator by writing files on disk\n",
-    "e = Emulator(config=m)\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "outputs_future = e.run()\n",
-    "\n",
-    "outputs_future.hydrograph.q_sim.plot(label=\"Future\", color=\"orange\", lw=0.5)\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## You have just generated streamflows for the reference and future periods!\n",
-    "We can analyze these hydrographs with many tools in PAVICS-Hydro, or export them to use elsewhere, or use them as inputs to another process!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the path to the hydrograph file:\n",
-    "outputs_future.files[\"hydrograph\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Work with the hydrograph data directly:\n",
-    "outputs_future.hydrograph"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/10_Data_assimilation.ipynb b/docs/notebooks/10_Data_assimilation.ipynb
index ee839e3f..c8740cee 100644
--- a/docs/notebooks/10_Data_assimilation.ipynb
+++ b/docs/notebooks/10_Data_assimilation.ipynb
@@ -1,524 +1,524 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10 - Data Assimilation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using ravenpy to perform data assimilation of streamflow to prepare the model states for a forecast.\n",
-    "\n",
-    "Here we apply the Ensemble Kalman Filter (EnKF) data assimilation method to the initial states of a `Raven` hydrological model, which will allow improving the estimation of the initial states to reduce the initial model bias. This also helps improve the forecast skill for shorter-term forecasts (up to a few days lead-time), and in some instances, can also improve longer-term forecasts.\n",
-    "\n",
-    "We will first start by importing important packages, gathering important datasets and configuration settings as we have seen previously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "from numba.core.errors import NumbaDeprecationWarning\n",
-    "\n",
-    "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import packages\n",
-    "import datetime as dt\n",
-    "import tempfile\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "\n",
-    "from ravenpy import Emulator, EnsembleReader\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config import options as o\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities.testdata import get_file\n",
-    "\n",
-    "# Import hydrometeorological data\n",
-    "salmon_meteo = get_file(\n",
-    "    \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n",
-    ")\n",
-    "\n",
-    "# Define HRU\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Alternative names for variables in meteo forcing file\n",
-    "alt_names = {\n",
-    "    \"RAINFALL\": \"rain\",\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"SNOWFALL\": \"snow\",\n",
-    "}\n",
-    "\n",
-    "# The types of meteorological data available in the file\n",
-    "data_type = [\"RAINFALL\", \"TEMP_MIN\", \"TEMP_MAX\", \"SNOWFALL\"]\n",
-    "\n",
-    "# Additional information about the weather station gauge required by Raven\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"latitude\": hru[\"latitude\"],\n",
-    "        \"longitude\": hru[\"longitude\"],\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Force a test path.\n",
-    "tmp_path = Path(tempfile.mkdtemp())\n",
-    "\n",
-    "# Generate the meteorological gauge data required by raven\n",
-    "gauge = [\n",
-    "    rc.Gauge.from_nc(\n",
-    "        salmon_meteo,\n",
-    "        data_type=data_type,\n",
-    "        alt_names=alt_names,\n",
-    "        data_kwds=data_kwds,\n",
-    "    ),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We will now start the assimilation with a spinup period\n",
-    "\n",
-    "Data assimilation is best performed on a series of initial states that are already somewhat reasonable. Starting a model from empty states and applying assimilation will work but will take more time to converge, and might in some instances create numerical instability. In this example, we perform a 1-year simulation to generate reasonable model states, and at the last time step, Raven will apply the Ensemble Kalman Filter (EnKF) to assimilate the states for the next step (forecasting or closed-loop assimilation)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Spin up the model. This period will be used to do an initial spinup, at the end of which the model states\n",
-    "# will be assimilated to better represent the observed streamflow and thus setting up parameters for the next\n",
-    "# steps. We first need to specify the spinup dates:\n",
-    "start_date = dt.datetime(1996, 9, 1)\n",
-    "end_date = dt.datetime(1997, 8, 31)\n",
-    "\n",
-    "# Prepare the configuration for the spinup. Since we have added information about Ensemble Kalman Filter data\n",
-    "# assimilation, a \".rve\" file will also be written to disk and Raven will use this to perform the assimilation.\n",
-    "conf_spinup = GR4JCN(\n",
-    "    # Model parameters\n",
-    "    params=[0.14, -0.005, 576, 7.0, 1.1, 0.92],\n",
-    "    # Meteorological gauge data from the Salmon river\n",
-    "    Gauge=gauge,\n",
-    "    # Streamflow observations. Very important for data assimilation, or else there is no target to attain.\n",
-    "    ObservationData=[rc.ObservationData.from_nc(salmon_meteo, alt_names=\"qobs\")],\n",
-    "    # Sepcify the HRUs composing the watershed. Here we are using a lumped model, so there is a single HRU.\n",
-    "    HRUs=[hru],\n",
-    "    # Start and end dates of the simulation. EnKF will be applied at the last date (EndDate)\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    # Specify which mode of EnKF we want to use. We want the spinup for now, but later we will use other\n",
-    "    # options. We are also using 25 members in the ensemble, but this can be changed according to your needs.\n",
-    "    EnsembleMode=rc.EnsembleMode(n=25),\n",
-    "    EnKFMode=o.EnKFMode.SPINUP,\n",
-    "    # Run name of the spinup period. This is important because it will be required in the next step.\n",
-    "    RunName=\"spinup\",\n",
-    "    # Let's specify some metrics to assess the model performance.\n",
-    "    EvaluationMetrics=(\"NASH_SUTCLIFFE\",),\n",
-    "    # The folder where the ensemble runs will be generated. By default, the runs are called ens_1... ens_N.\n",
-    "    OutputDirectoryFormat=\"./ens_*\",\n",
-    "    # We need to tell Raven which inputs to perturb. the perturbation is applied following a distribution\n",
-    "    # that should realistically represent the uncertainty of the observations of these variables. Here we\n",
-    "    # use precipitation, but we could also add temperature for example.\n",
-    "    ForcingPerturbation=[\n",
-    "        rc.ForcingPerturbation(\n",
-    "            forcing=\"PRECIP\",\n",
-    "            dist=\"DIST_NORMAL\",\n",
-    "            p1=1.0,\n",
-    "            p2=0.5,\n",
-    "            adj=\"MULTIPLICATIVE\",\n",
-    "        ),\n",
-    "        rc.ForcingPerturbation(\n",
-    "            forcing=\"TEMP_MAX\",\n",
-    "            dist=\"DIST_NORMAL\",\n",
-    "            p1=0.0,\n",
-    "            p2=2.0,\n",
-    "            adj=\"ADDITIVE\",\n",
-    "        ),\n",
-    "        rc.ForcingPerturbation(\n",
-    "            forcing=\"TEMP_MIN\",\n",
-    "            dist=\"DIST_NORMAL\",\n",
-    "            p1=0.0,\n",
-    "            p2=2.0,\n",
-    "            adj=\"ADDITIVE\",\n",
-    "        ),\n",
-    "    ],\n",
-    "    # Define the HRU Groups the assimilation will be applied on. Here we apply to all HRUs (single HRU)\n",
-    "    DefineHRUGroups=[\"All\"],\n",
-    "    HRUGroup=[{\"name\": \"All\", \"groups\": [\"1\"]}],\n",
-    "    # Define which variables we want to assimilate.\n",
-    "    # Here we only adjust the water content of the 2 first layers of soil (SOIL[0] and SOIL[1])\n",
-    "    AssimilatedState=[\n",
-    "        rc.AssimilatedState(state=\"SOIL[0]\", group=\"All\"),\n",
-    "        rc.AssimilatedState(state=\"SOIL[1]\", group=\"All\"),\n",
-    "    ],\n",
-    "    # Define which subbasin id the streamflow is associated with\n",
-    "    AssimilateStreamflow=[rc.AssimilateStreamflow(sb_id=1)],\n",
-    "    # Define the error model for the observed streamflow. We will have a STD equal to 7% of the streamflow\n",
-    "    # value for each day, following a normal distribution.\n",
-    "    ObservationErrorModel=[\n",
-    "        rc.ObservationErrorModel(\n",
-    "            state=\"STREAMFLOW\",\n",
-    "            dist=\"DIST_NORMAL\",\n",
-    "            p1=1,\n",
-    "            p2=0.07,\n",
-    "            adj=\"MULTIPLICATIVE\",\n",
-    "        )\n",
-    "    ],\n",
-    "    # Set to true for more details (verbosity)\n",
-    "    DebugMode=False,\n",
-    "    NoisyMode=False,\n",
-    ")\n",
-    "\n",
-    "# Now that the configuration is completed, we can actually launch Raven to do the assimilation\n",
-    "spinup = Emulator(config=conf_spinup, workdir=tmp_path, overwrite=True).run(\n",
-    "    overwrite=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have now run the model and obtained an ensemble of simulations that each have perturbed meteorological data and new initial states. We can read-in the generated hydrographs and see what our spinup period flows look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 10 - Data Assimilation"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Get the paths to all the ens_1...ens_N folders, one per member\n",
-    "paths_spinup = list(tmp_path.glob(\"ens_*\"))\n",
-    "\n",
-    "# Read those into memory in an EnsembleReader object\n",
-    "ens_spinup = EnsembleReader(run_name=conf_spinup.run_name, paths=paths_spinup)\n",
-    "\n",
-    "# We can now plot the results\n",
-    "ens_spinup.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False, lw=0.5)\n",
-    "ens_spinup.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecasts\", lw=0.5)\n",
-    "ens_spinup.hydrograph.q_obs[1, :, 0].plot.line(\n",
-    "    x=\"time\", color=\"black\", label=\"Observation\"\n",
-    ")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.ylabel(\"Streamlfow (m³/s)\")\n",
-    "plt.title(\"Spinup period\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Start converging model states by closed-loop assimilation\n",
-    "We have completed the spinup period, which has left us with a set of 25 initial states that can be used to sample initial state uncertainty. However, we need to do a few more assimilation passes before the model starts to converge to appropriate values. From the assimilated states of the spinup period, let's now do a single 3-day simulation and see what happens:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Using ravenpy to perform data assimilation of streamflow to prepare the model states for a forecast.\n",
+        "\n",
+        "Here we apply the Ensemble Kalman Filter (EnKF) data assimilation method to the initial states of a `Raven` hydrological model, which will allow improving the estimation of the initial states to reduce the initial model bias. This also helps improve the forecast skill for shorter-term forecasts (up to a few days lead-time), and in some instances, can also improve longer-term forecasts.\n",
+        "\n",
+        "We will first start by importing important packages, gathering important datasets and configuration settings as we have seen previously."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set the start date equal to the assimilated date of the prior run, as we want to start from the assimilated\n",
-    "# states. The end date is set 3 days later, after which assimilation will be automatically performed.\n",
-    "start_date = end_date\n",
-    "end_date = end_date + dt.timedelta(days=3)\n",
-    "\n",
-    "# Closed-Loop assimilation. From the previous configuration, we can make a copy and only change the required\n",
-    "# parameters, such as the run name, start and end dates, and the type of EnKF (switch from spinup to closed-loop).\n",
-    "conf_loop = conf_spinup.duplicate(\n",
-    "    EnKFMode=o.EnKFMode.CLOSED_LOOP,\n",
-    "    # This will be the name of the output files in the closed-loop run.\n",
-    "    RunName=\"loop\",\n",
-    "    # This is the name of the run we will start from, i.e. the assimilated spinup states from earlier!\n",
-    "    SolutionRunName=\"spinup\",\n",
-    "    # We need to tell the model not to set the default initial conditions (it will use the assimilated states)\n",
-    "    UniformInitialConditions=None,\n",
-    "    # Set the new dates\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    ")\n",
-    "\n",
-    "# Now that the configuration is ready, launch the assimilation run. Raven will run 25 times: Once for each member\n",
-    "# With the same perturbed meteorological and hydrometric data and parameters as defined previously, but for this\n",
-    "# new 3-day period.\n",
-    "loop = Emulator(config=conf_loop, workdir=tmp_path, overwrite=True).run(overwrite=True)\n",
-    "\n",
-    "# Get the paths to all the ens_1...ens_N folders, one per member\n",
-    "paths_loop = list(tmp_path.glob(\"ens_*\"))\n",
-    "\n",
-    "# Repeat the same process as the spinup to look at model results:\n",
-    "ens_loop = EnsembleReader(run_name=conf_loop.run_name, paths=paths_loop)\n",
-    "\n",
-    "# We can now plot the results\n",
-    "ens_loop.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False, lw=0.5)\n",
-    "ens_loop.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecasts\", lw=0.5)\n",
-    "ens_loop.hydrograph.q_obs[1, :, 0].plot.line(\n",
-    "    x=\"time\", color=\"black\", label=\"Observation\"\n",
-    ")\n",
-    "plt.legend(loc=\"lower left\")\n",
-    "plt.ylabel(\"Streamlfow (m³/s)\")\n",
-    "plt.title(\"First closed-loop period\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the assimilation has not converged very well. This is expected, as there have only been 2 assimilation steps performed as of yet: One after the spinup period, and this one that happens 3 days later. We will iterate the assimilation loop to help the model converge after multiple assimilation steps. Here we will loop over 30 steps of 3 days each."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "\n",
+        "from numba.core.errors import NumbaDeprecationWarning\n",
+        "\n",
+        "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Let's store the hydrograph from the previous 3-day run in a variable that we will append to at each time step.\n",
-    "total_hydrograph = ens_loop.hydrograph\n",
-    "\n",
-    "# Here is where the assimilation loop is performed. We will apply the assimilation 30 successive times, advancing\n",
-    "# in time by 3 days each iteration.\n",
-    "for i in range(0, 30):\n",
-    "    # Set the new start_date and end_dates\n",
-    "    start_date = end_date\n",
-    "    end_date = end_date + dt.timedelta(days=3)\n",
-    "\n",
-    "    # Again, copy the configuration object and change some elements\n",
-    "    conf_loop = conf_loop.duplicate(\n",
-    "        # Here we will set RunName and SolutionRunName to the same values such that the model will read the \"loop\"\n",
-    "        # run, perform the assimilation, and save the results to \"loop\" again, making them available for the\n",
-    "        # next run, effectively overwriting the results at each step. We could preserve each run's result by changing\n",
-    "        # these run names dynamically, but in our case it is not important nor required to do so.\n",
-    "        RunName=\"loop\",\n",
-    "        SolutionRunName=\"loop\",\n",
-    "        # Again, set the initial conditions to None to preserve the assimilated ones.\n",
-    "        UniformInitialConditions=None,\n",
-    "        # Set the start and end date of the simulation period, with the assimilation being performed on the final date.\n",
-    "        StartDate=start_date,\n",
-    "        EndDate=end_date,\n",
-    "    )\n",
-    "\n",
-    "    # Perform the actual simulation and assimilation for this 3-day step.\n",
-    "    new_loop = Emulator(config=conf_loop, workdir=tmp_path, overwrite=True).run(\n",
-    "        overwrite=True\n",
-    "    )\n",
-    "\n",
-    "    # Extract the results for this 3-day hydrograph and store it into our \"total_hydrograph\" which keeps track\n",
-    "    # of the flows for each of the 3-day periods.\n",
-    "    ens_loop = EnsembleReader(run_name=conf_loop.run_name, paths=paths_loop)\n",
-    "    total_hydrograph = xr.concat([total_hydrograph, ens_loop.hydrograph], dim=\"time\")\n",
-    "\n",
-    "\n",
-    "# Once the loop is complete, plot the results:\n",
-    "total_hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False, lw=0.5)\n",
-    "total_hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecasts\", lw=0.5)\n",
-    "total_hydrograph.q_obs[1, :, 0].plot.line(x=\"time\", color=\"black\", label=\"Observation\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.ylabel(\"Streamlfow (m³/s)\")\n",
-    "plt.title(\"All closed-loop periods\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before going any further, let's compare the assimilated results to those obtained using a simple non-assimilated run (open-loop):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import packages\n",
+        "import datetime as dt\n",
+        "import tempfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import xarray as xr\n",
+        "\n",
+        "from ravenpy import Emulator, EnsembleReader\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config import options as o\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities.testdata import get_file\n",
+        "\n",
+        "# Import hydrometeorological data\n",
+        "salmon_meteo = get_file(\n",
+        "    \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n",
+        ")\n",
+        "\n",
+        "# Define HRU\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Alternative names for variables in meteo forcing file\n",
+        "alt_names = {\n",
+        "    \"RAINFALL\": \"rain\",\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"SNOWFALL\": \"snow\",\n",
+        "}\n",
+        "\n",
+        "# The types of meteorological data available in the file\n",
+        "data_type = [\"RAINFALL\", \"TEMP_MIN\", \"TEMP_MAX\", \"SNOWFALL\"]\n",
+        "\n",
+        "# Additional information about the weather station gauge required by Raven\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"latitude\": hru[\"latitude\"],\n",
+        "        \"longitude\": hru[\"longitude\"],\n",
+        "    }\n",
+        "}\n",
+        "\n",
+        "# Force a test path.\n",
+        "tmp_path = Path(tempfile.mkdtemp())\n",
+        "\n",
+        "# Generate the meteorological gauge data required by raven\n",
+        "gauge = [\n",
+        "    rc.Gauge.from_nc(\n",
+        "        salmon_meteo,\n",
+        "        data_type=data_type,\n",
+        "        alt_names=alt_names,\n",
+        "        data_kwds=data_kwds,\n",
+        "    ),\n",
+        "]"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Reset the start and end-dates to cover the entire period (spinup + 30 3-day steps)\n",
-    "start_date = dt.datetime(1996, 9, 1)\n",
-    "end_date = dt.datetime(1997, 8, 31) + dt.timedelta(days=30 * 3)\n",
-    "\n",
-    "# Setup a standard GR4JCN model\n",
-    "conf_openloop = GR4JCN(\n",
-    "    params=[0.14, -0.005, 576, 7.0, 1.1, 0.92],\n",
-    "    Gauge=gauge,\n",
-    "    ObservationData=[rc.ObservationData.from_nc(salmon_meteo, alt_names=\"qobs\")],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"OPEN_LOOP\",\n",
-    "    EvaluationMetrics=(\"NASH_SUTCLIFFE\",),\n",
-    ")\n",
-    "\n",
-    "openloop = Emulator(config=conf_openloop, workdir=tmp_path, overwrite=True).run(\n",
-    "    overwrite=True\n",
-    ")\n",
-    "\n",
-    "openloop.hydrograph.q_sim.plot.line(\"r\", x=\"time\", label=\"Open-loop simulation\")\n",
-    "total_hydrograph.q_sim[:, :, 0].mean(dim=\"member\").plot.line(\n",
-    "    \"b\", x=\"time\", label=\"Closed-loop assimilation\"\n",
-    ")\n",
-    "openloop.hydrograph.q_obs.plot.line(x=\"time\", color=\"black\", label=\"Observations\")\n",
-    "\n",
-    "plt.xlim([dt.date(1997, 9, 1), dt.date(1997, 12, 1)])\n",
-    "plt.ylim([0, 50])\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.ylabel(\"Streamlfow (m³/s)\")\n",
-    "plt.title(\"Closed-loop vs. Open-loop simulations\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the data assimilation as vastly improved most of the hydrograph. Making the assimilation more frequent, changing other state variables, or adjusting the error model hyperparameters could also lead to better simulations.\n",
-    "\n",
-    "Once we are satisfied with the initial states, our model would now be ready for forecasting, using the ensemble initial states as initial conditions for generating the forecasts. This can be done using the EnKF forecating method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### We will now start the assimilation with a spinup period\n",
+        "\n",
+        "Data assimilation is best performed on a series of initial states that are already somewhat reasonable. Starting a model from empty states and applying assimilation will work but will take more time to converge, and might in some instances create numerical instability. In this example, we perform a 1-year simulation to generate reasonable model states, and at the last time step, Raven will apply the Ensemble Kalman Filter (EnKF) to assimilate the states for the next step (forecasting or closed-loop assimilation)."
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Spin up the model. This period will be used to do an initial spinup, at the end of which the model states\n",
+        "# will be assimilated to better represent the observed streamflow and thus setting up parameters for the next\n",
+        "# steps. We first need to specify the spinup dates:\n",
+        "start_date = dt.datetime(1996, 9, 1)\n",
+        "end_date = dt.datetime(1997, 8, 31)\n",
+        "\n",
+        "# Prepare the configuration for the spinup. Since we have added information about Ensemble Kalman Filter data\n",
+        "# assimilation, a \".rve\" file will also be written to disk and Raven will use this to perform the assimilation.\n",
+        "conf_spinup = GR4JCN(\n",
+        "    # Model parameters\n",
+        "    params=[0.14, -0.005, 576, 7.0, 1.1, 0.92],\n",
+        "    # Meteorological gauge data from the Salmon river\n",
+        "    Gauge=gauge,\n",
+        "    # Streamflow observations. Very important for data assimilation, or else there is no target to attain.\n",
+        "    ObservationData=[rc.ObservationData.from_nc(salmon_meteo, alt_names=\"qobs\")],\n",
+        "    # Sepcify the HRUs composing the watershed. Here we are using a lumped model, so there is a single HRU.\n",
+        "    HRUs=[hru],\n",
+        "    # Start and end dates of the simulation. EnKF will be applied at the last date (EndDate)\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    # Specify which mode of EnKF we want to use. We want the spinup for now, but later we will use other\n",
+        "    # options. We are also using 25 members in the ensemble, but this can be changed according to your needs.\n",
+        "    EnsembleMode=rc.EnsembleMode(n=25),\n",
+        "    EnKFMode=o.EnKFMode.SPINUP,\n",
+        "    # Run name of the spinup period. This is important because it will be required in the next step.\n",
+        "    RunName=\"spinup\",\n",
+        "    # Let's specify some metrics to assess the model performance.\n",
+        "    EvaluationMetrics=(\"NASH_SUTCLIFFE\",),\n",
+        "    # The folder where the ensemble runs will be generated. By default, the runs are called ens_1... ens_N.\n",
+        "    OutputDirectoryFormat=\"./ens_*\",\n",
+        "    # We need to tell Raven which inputs to perturb. the perturbation is applied following a distribution\n",
+        "    # that should realistically represent the uncertainty of the observations of these variables. Here we\n",
+        "    # use precipitation, but we could also add temperature for example.\n",
+        "    ForcingPerturbation=[\n",
+        "        rc.ForcingPerturbation(\n",
+        "            forcing=\"PRECIP\",\n",
+        "            dist=\"DIST_NORMAL\",\n",
+        "            p1=1.0,\n",
+        "            p2=0.5,\n",
+        "            adj=\"MULTIPLICATIVE\",\n",
+        "        ),\n",
+        "        rc.ForcingPerturbation(\n",
+        "            forcing=\"TEMP_MAX\",\n",
+        "            dist=\"DIST_NORMAL\",\n",
+        "            p1=0.0,\n",
+        "            p2=2.0,\n",
+        "            adj=\"ADDITIVE\",\n",
+        "        ),\n",
+        "        rc.ForcingPerturbation(\n",
+        "            forcing=\"TEMP_MIN\",\n",
+        "            dist=\"DIST_NORMAL\",\n",
+        "            p1=0.0,\n",
+        "            p2=2.0,\n",
+        "            adj=\"ADDITIVE\",\n",
+        "        ),\n",
+        "    ],\n",
+        "    # Define the HRU Groups the assimilation will be applied on. Here we apply to all HRUs (single HRU)\n",
+        "    DefineHRUGroups=[\"All\"],\n",
+        "    HRUGroup=[{\"name\": \"All\", \"groups\": [\"1\"]}],\n",
+        "    # Define which variables we want to assimilate.\n",
+        "    # Here we only adjust the water content of the 2 first layers of soil (SOIL[0] and SOIL[1])\n",
+        "    AssimilatedState=[\n",
+        "        rc.AssimilatedState(state=\"SOIL[0]\", group=\"All\"),\n",
+        "        rc.AssimilatedState(state=\"SOIL[1]\", group=\"All\"),\n",
+        "    ],\n",
+        "    # Define which subbasin id the streamflow is associated with\n",
+        "    AssimilateStreamflow=[rc.AssimilateStreamflow(sb_id=1)],\n",
+        "    # Define the error model for the observed streamflow. We will have a STD equal to 7% of the streamflow\n",
+        "    # value for each day, following a normal distribution.\n",
+        "    ObservationErrorModel=[\n",
+        "        rc.ObservationErrorModel(\n",
+        "            state=\"STREAMFLOW\",\n",
+        "            dist=\"DIST_NORMAL\",\n",
+        "            p1=1,\n",
+        "            p2=0.07,\n",
+        "            adj=\"MULTIPLICATIVE\",\n",
+        "        )\n",
+        "    ],\n",
+        "    # Set to true for more details (verbosity)\n",
+        "    DebugMode=False,\n",
+        "    NoisyMode=False,\n",
+        ")\n",
+        "\n",
+        "# Now that the configuration is completed, we can actually launch Raven to do the assimilation\n",
+        "spinup = Emulator(config=conf_spinup, workdir=tmp_path, overwrite=True).run(\n",
+        "    overwrite=True\n",
+        ")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have now run the model and obtained an ensemble of simulations that each have perturbed meteorological data and new initial states. We can read-in the generated hydrographs and see what our spinup period flows look like."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Get the paths to all the ens_1...ens_N folders, one per member\n",
+        "paths_spinup = list(tmp_path.glob(\"ens_*\"))\n",
+        "\n",
+        "# Read those into memory in an EnsembleReader object\n",
+        "ens_spinup = EnsembleReader(run_name=conf_spinup.run_name, paths=paths_spinup)\n",
+        "\n",
+        "# We can now plot the results\n",
+        "ens_spinup.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False, lw=0.5)\n",
+        "ens_spinup.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecasts\", lw=0.5)\n",
+        "ens_spinup.hydrograph.q_obs[1, :, 0].plot.line(\n",
+        "    x=\"time\", color=\"black\", label=\"Observation\"\n",
+        ")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.ylabel(\"Streamlfow (m³/s)\")\n",
+        "plt.title(\"Spinup period\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Start converging model states by closed-loop assimilation\n",
+        "We have completed the spinup period, which has left us with a set of 25 initial states that can be used to sample initial state uncertainty. However, we need to do a few more assimilation passes before the model starts to converge to appropriate values. From the assimilated states of the spinup period, let's now do a single 3-day simulation and see what happens:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Set the start date equal to the assimilated date of the prior run, as we want to start from the assimilated\n",
+        "# states. The end date is set 3 days later, after which assimilation will be automatically performed.\n",
+        "start_date = end_date\n",
+        "end_date = end_date + dt.timedelta(days=3)\n",
+        "\n",
+        "# Closed-Loop assimilation. From the previous configuration, we can make a copy and only change the required\n",
+        "# parameters, such as the run name, start and end dates, and the type of EnKF (switch from spinup to closed-loop).\n",
+        "conf_loop = conf_spinup.duplicate(\n",
+        "    EnKFMode=o.EnKFMode.CLOSED_LOOP,\n",
+        "    # This will be the name of the output files in the closed-loop run.\n",
+        "    RunName=\"loop\",\n",
+        "    # This is the name of the run we will start from, i.e. the assimilated spinup states from earlier!\n",
+        "    SolutionRunName=\"spinup\",\n",
+        "    # We need to tell the model not to set the default initial conditions (it will use the assimilated states)\n",
+        "    UniformInitialConditions=None,\n",
+        "    # Set the new dates\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        ")\n",
+        "\n",
+        "# Now that the configuration is ready, launch the assimilation run. Raven will run 25 times: Once for each member\n",
+        "# With the same perturbed meteorological and hydrometric data and parameters as defined previously, but for this\n",
+        "# new 3-day period.\n",
+        "loop = Emulator(config=conf_loop, workdir=tmp_path, overwrite=True).run(overwrite=True)\n",
+        "\n",
+        "# Get the paths to all the ens_1...ens_N folders, one per member\n",
+        "paths_loop = list(tmp_path.glob(\"ens_*\"))\n",
+        "\n",
+        "# Repeat the same process as the spinup to look at model results:\n",
+        "ens_loop = EnsembleReader(run_name=conf_loop.run_name, paths=paths_loop)\n",
+        "\n",
+        "# We can now plot the results\n",
+        "ens_loop.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False, lw=0.5)\n",
+        "ens_loop.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecasts\", lw=0.5)\n",
+        "ens_loop.hydrograph.q_obs[1, :, 0].plot.line(\n",
+        "    x=\"time\", color=\"black\", label=\"Observation\"\n",
+        ")\n",
+        "plt.legend(loc=\"lower left\")\n",
+        "plt.ylabel(\"Streamlfow (m³/s)\")\n",
+        "plt.title(\"First closed-loop period\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the assimilation has not converged very well. This is expected, as there have only been 2 assimilation steps performed as of yet: One after the spinup period, and this one that happens 3 days later. We will iterate the assimilation loop to help the model converge after multiple assimilation steps. Here we will loop over 30 steps of 3 days each."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Let's store the hydrograph from the previous 3-day run in a variable that we will append to at each time step.\n",
+        "total_hydrograph = ens_loop.hydrograph\n",
+        "\n",
+        "# Here is where the assimilation loop is performed. We will apply the assimilation 30 successive times, advancing\n",
+        "# in time by 3 days each iteration.\n",
+        "for i in range(0, 30):\n",
+        "    # Set the new start_date and end_dates\n",
+        "    start_date = end_date\n",
+        "    end_date = end_date + dt.timedelta(days=3)\n",
+        "\n",
+        "    # Again, copy the configuration object and change some elements\n",
+        "    conf_loop = conf_loop.duplicate(\n",
+        "        # Here we will set RunName and SolutionRunName to the same values such that the model will read the \"loop\"\n",
+        "        # run, perform the assimilation, and save the results to \"loop\" again, making them available for the\n",
+        "        # next run, effectively overwriting the results at each step. We could preserve each run's result by changing\n",
+        "        # these run names dynamically, but in our case it is not important nor required to do so.\n",
+        "        RunName=\"loop\",\n",
+        "        SolutionRunName=\"loop\",\n",
+        "        # Again, set the initial conditions to None to preserve the assimilated ones.\n",
+        "        UniformInitialConditions=None,\n",
+        "        # Set the start and end date of the simulation period, with the assimilation being performed on the final date.\n",
+        "        StartDate=start_date,\n",
+        "        EndDate=end_date,\n",
+        "    )\n",
+        "\n",
+        "    # Perform the actual simulation and assimilation for this 3-day step.\n",
+        "    new_loop = Emulator(config=conf_loop, workdir=tmp_path, overwrite=True).run(\n",
+        "        overwrite=True\n",
+        "    )\n",
+        "\n",
+        "    # Extract the results for this 3-day hydrograph and store it into our \"total_hydrograph\" which keeps track\n",
+        "    # of the flows for each of the 3-day periods.\n",
+        "    ens_loop = EnsembleReader(run_name=conf_loop.run_name, paths=paths_loop)\n",
+        "    total_hydrograph = xr.concat([total_hydrograph, ens_loop.hydrograph], dim=\"time\")\n",
+        "\n",
+        "\n",
+        "# Once the loop is complete, plot the results:\n",
+        "total_hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False, lw=0.5)\n",
+        "total_hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecasts\", lw=0.5)\n",
+        "total_hydrograph.q_obs[1, :, 0].plot.line(x=\"time\", color=\"black\", label=\"Observation\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.ylabel(\"Streamlfow (m³/s)\")\n",
+        "plt.title(\"All closed-loop periods\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Before going any further, let's compare the assimilated results to those obtained using a simple non-assimilated run (open-loop):"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Reset the start and end-dates to cover the entire period (spinup + 30 3-day steps)\n",
+        "start_date = dt.datetime(1996, 9, 1)\n",
+        "end_date = dt.datetime(1997, 8, 31) + dt.timedelta(days=30 * 3)\n",
+        "\n",
+        "# Setup a standard GR4JCN model\n",
+        "conf_openloop = GR4JCN(\n",
+        "    params=[0.14, -0.005, 576, 7.0, 1.1, 0.92],\n",
+        "    Gauge=gauge,\n",
+        "    ObservationData=[rc.ObservationData.from_nc(salmon_meteo, alt_names=\"qobs\")],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"OPEN_LOOP\",\n",
+        "    EvaluationMetrics=(\"NASH_SUTCLIFFE\",),\n",
+        ")\n",
+        "\n",
+        "openloop = Emulator(config=conf_openloop, workdir=tmp_path, overwrite=True).run(\n",
+        "    overwrite=True\n",
+        ")\n",
+        "\n",
+        "openloop.hydrograph.q_sim.plot.line(\"r\", x=\"time\", label=\"Open-loop simulation\")\n",
+        "total_hydrograph.q_sim[:, :, 0].mean(dim=\"member\").plot.line(\n",
+        "    \"b\", x=\"time\", label=\"Closed-loop assimilation\"\n",
+        ")\n",
+        "openloop.hydrograph.q_obs.plot.line(x=\"time\", color=\"black\", label=\"Observations\")\n",
+        "\n",
+        "plt.xlim([dt.date(1997, 9, 1), dt.date(1997, 12, 1)])\n",
+        "plt.ylim([0, 50])\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.ylabel(\"Streamlfow (m³/s)\")\n",
+        "plt.title(\"Closed-loop vs. Open-loop simulations\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the data assimilation as vastly improved most of the hydrograph. Making the assimilation more frequent, changing other state variables, or adjusting the error model hyperparameters could also lead to better simulations.\n",
+        "\n",
+        "Once we are satisfied with the initial states, our model would now be ready for forecasting, using the ensemble initial states as initial conditions for generating the forecasts. This can be done using the EnKF forecating method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Set up the forecast configuration, basing it on the previous (final) assimilation step.\n",
+        "conf_forecast = conf_loop.duplicate(\n",
+        "    EnKFMode=o.EnKFMode.FORECAST,\n",
+        "    RunName=\"forecast\",\n",
+        "    SolutionRunName=\"loop\",\n",
+        "    UniformInitialConditions=None,\n",
+        "    # Set the start date equal to the end date of the last assimilation run.\n",
+        "    StartDate=end_date,\n",
+        "    # Here we will do a 30-day forecast using the observed meteorological data as forecast data. However it is\n",
+        "    # possible to replace the Gauge forcing data with that of a forecast, as we have done before.\n",
+        "    EndDate=end_date + dt.timedelta(days=30),\n",
+        ")\n",
+        "\n",
+        "forecast = Emulator(config=conf_forecast, workdir=tmp_path, overwrite=True).run(\n",
+        "    overwrite=True\n",
+        ")\n",
+        "\n",
+        "# We will plot the resulting forecast. Note that since we have 25 members, we also have 25 forecasts, i.e. one\n",
+        "# per possible initial state. We could take the mean hydrograph to get the best estimator of the forecasted flow.\n",
+        "ens = EnsembleReader(run_name=conf_forecast.run_name, paths=paths_loop)\n",
+        "ens.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", label=None, lw=0.5)\n",
+        "ens.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecast\", lw=0.5)\n",
+        "ens.hydrograph.q_obs[1, :, 0].plot.line(\"black\", x=\"time\", label=\"Observation\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.ylabel(\"Streamlfow (m³/s)\")\n",
+        "plt.title(\"Forecast after assimilation\")\n",
+        "plt.xlim([dt.date(1997, 11, 29), dt.date(1997, 12, 29)])\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
     }
-   ],
-   "source": [
-    "# Set up the forecast configuration, basing it on the previous (final) assimilation step.\n",
-    "conf_forecast = conf_loop.duplicate(\n",
-    "    EnKFMode=o.EnKFMode.FORECAST,\n",
-    "    RunName=\"forecast\",\n",
-    "    SolutionRunName=\"loop\",\n",
-    "    UniformInitialConditions=None,\n",
-    "    # Set the start date equal to the end date of the last assimilation run.\n",
-    "    StartDate=end_date,\n",
-    "    # Here we will do a 30-day forecast using the observed meteorological data as forecast data. However it is\n",
-    "    # possible to replace the Gauge forcing data with that of a forecast, as we have done before.\n",
-    "    EndDate=end_date + dt.timedelta(days=30),\n",
-    ")\n",
-    "\n",
-    "forecast = Emulator(config=conf_forecast, workdir=tmp_path, overwrite=True).run(\n",
-    "    overwrite=True\n",
-    ")\n",
-    "\n",
-    "# We will plot the resulting forecast. Note that since we have 25 members, we also have 25 forecasts, i.e. one\n",
-    "# per possible initial state. We could take the mean hydrograph to get the best estimator of the forecasted flow.\n",
-    "ens = EnsembleReader(run_name=conf_forecast.run_name, paths=paths_loop)\n",
-    "ens.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", label=None, lw=0.5)\n",
-    "ens.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"Forecast\", lw=0.5)\n",
-    "ens.hydrograph.q_obs[1, :, 0].plot.line(\"black\", x=\"time\", label=\"Observation\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.ylabel(\"Streamlfow (m³/s)\")\n",
-    "plt.title(\"Forecast after assimilation\")\n",
-    "plt.xlim([dt.date(1997, 11, 29), dt.date(1997, 12, 29)])\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-"kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/11_Climatological_ESP_forecasting.ipynb b/docs/notebooks/11_Climatological_ESP_forecasting.ipynb
index c6611b3c..b2d59fb7 100644
--- a/docs/notebooks/11_Climatological_ESP_forecasting.ipynb
+++ b/docs/notebooks/11_Climatological_ESP_forecasting.ipynb
@@ -1,299 +1,299 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 11 - Climatological ESP forecasting"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# 11 - Climatological ESP forecasting"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Extended Streamflow Prediction (ESP) forecasts from climatological time series\n",
+        "\n",
+        "This notebook shows how to perform a climatological Extended Streamflow Prediction (ESP) forecast, using historical weather as a proxy for future weather.\n",
+        "\n",
+        "The general idea is to initialize the state of the hydrological model to represent current conditions, but instead of using weather forecasts to predict future flows, we run the model with observed, historical weather series from past years. So for example if we have 30 years of weather observations, we get 30 different forecasts. The accuracy of this forecast ensemble can then be evaluated by different probabilistic metrics."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import datetime as dt\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities import forecasting\n",
+        "from ravenpy.utilities.testdata import get_file"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Run the model simulations\n",
+        "\n",
+        "Here we set model parameters somewhat arbitrarily, but you can set the parameters to the calibrated parameters as seen in the \"06_Raven_calibration\" notebook we previously encountered.\n",
+        "\n",
+        "We also need to choose the forecast issue date. Each forecast will start with the same day and month. For example, jun-06-1980 will compare the climatology using all jun-06's from the dataset. Finally, we can provide the forecast duration (in number of days) as well as the historical meteorological years we want to use to generate the ESP forecast. This allows selecting years that we want to include in the forecast. For example, perhaps we only want to generate a forecast using wet or dry years."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get the selected watershed's time series. You can use your own time-series for your catchment by replacing\n",
+        "# this line with the name / path of your input file.\n",
+        "ts = get_file(\"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\")\n",
+        "\n",
+        "# This is the forecast start date, on which the forecasts will be launched.\n",
+        "start_date = dt.datetime(1980, 6, 1)\n",
+        "\n",
+        "# Provide the length of the forecast, in days:\n",
+        "forecast_duration = 100\n",
+        "\n",
+        "# Define HRU to build the hydrological model\n",
+        "hru = {}\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"RAINFALL\": \"rain\",\n",
+        "    \"SNOWFALL\": \"snow\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"RAINFALL\", \"SNOWFALL\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\n",
+        "            \"elevation\"\n",
+        "        ],  # No need for lat/lon as they are included in the netcdf file already\n",
+        "    }\n",
+        "}\n",
+        "# Model configuration\n",
+        "model_config = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    Duration=forecast_duration,\n",
+        "    RunName=\"full\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Issuing the ESP forecast\n",
+        "\n",
+        "Here we launch the code that will perform the ESP forecast. Depending on the number of years in the historical dataset and the forecast duration, it might take a while to return a forecast result."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Simulate the climatological ESP:\n",
+        "ESP_sims = forecasting.climatology_esp(\n",
+        "    config=model_config,\n",
+        "    years=[\n",
+        "        1982,\n",
+        "        1998,\n",
+        "        2003,\n",
+        "        2004,\n",
+        "    ],  # List of years to use in the forecast. Optional. Will use all years by default.\n",
+        ")\n",
+        "\n",
+        "# Show the results in an xarray dataset, ready to use:\n",
+        "ESP_sims.hydrograph"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now inspect and graph the resulting climatological ESP:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(x=\"time\")\n",
+        "plt.title(\"GR4JCN climatological ESP for 1980-06-01\")\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.grid(\"on\")\n",
+        "plt.xlabel(\"Time [days]\")\n",
+        "plt.ylabel(\"Streamflow $[m^3s^{-1}]$\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compute the forecast scores\n",
+        "\n",
+        "There are different metric to evaluate the performance of forecasts. As an example, here we are computing the CRPS metric, using the [xskillscore](https://xskillscore.readthedocs.io/en/stable/) library included in PAVICS-Hydro."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import xarray as xr\n",
+        "import xskillscore as xs\n",
+        "\n",
+        "# Align time axes to get the observed streamflow time series for the same time frame as the ESP forecast ensemble\n",
+        "q_obs, q_sims = xr.align(xr.open_dataset(ts).qobs, ESP_sims.hydrograph, join=\"inner\")\n",
+        "\n",
+        "# Adjust the streamflow to convert missing data from -1.2345 format to NaN. Set all negative values to NaN.\n",
+        "q_obs = q_obs.where(q_obs > 0, np.nan)\n",
+        "\n",
+        "# Compute the Continuous Ranked Probability Score using xskillscore\n",
+        "xs.crps_ensemble(q_obs, q_sims, dim=\"time\").q_sim.values[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Performing a climatology ESP hindcast\n",
+        "In this section, we make the hindcasts for each initialization date that we desire. Here we will extract ESP forecasts for a given calendar date for the years in \"hindcast_years\" as hindcast dates. Each ESP hindcast uses all available data in the `ts` dataset, so in this case we will have 56/57 members for each hindcast initialization depending on the date that we start on, UNLESS we specify a list of years manually. The \"hindcasts\" dataset generated contains all of the flow data from the ESP hindcasts for the initialization dates. The `q_obs` dataset contains all q_obs in the timeseries: Climpred will sort it all out during its processing. Note that the format of these datasets is tailor-made to be used in climpred, and thus has specific dimension names.\n",
+        "\n",
+        "This is a slimmed down example of how we would run an ESP forecast over multiple years to assess the skill of such a forecast."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hindcasts = forecasting.hindcast_climatology_esp(\n",
+        "    config=model_config,  # Note that the forecast duration is already set-up in the model_config above.\n",
+        "    warm_up_duration=365,  # number of days for the warm-up\n",
+        "    years=[1985, 1986, 1987, 1988, 1989, 1990],\n",
+        "    hindcast_years=[2001, 2002, 2003, 2004, 2005, 2006, 2007],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluate the forecast using different metrics\n",
+        "Once we have the correctly formatted datasets, Make the hindcast object for climpred\n",
+        "\n",
+        "These three functions respectively compute the rank histogram, the CRPS and the reliability for the set of initialized dates (i.e. forecast issue dates, here 1 day per year at the same calendar day)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Once we have the correctly formatted datasets, Make the hindcast object for climpred\n",
+        "\n",
+        "# We first need to get the observed streamflow:\n",
+        "q_obs = xr.open_dataset(ts)\n",
+        "\n",
+        "# However, our simulated streamflow is named \"q_sim\" and climpred requires the observation to be named the same thing\n",
+        "# so let's rename it. While we're at it, we need to make sure that the identifier is the same. In our observation\n",
+        "# dataset, it is called \"nstations\" but in our simulated streamflow it's called \"nbasins\". Here we standardize.\n",
+        "q_obs = q_obs.rename({\"qobs\": \"q_sim\", \"nstations\": \"nbasins\"})\n",
+        "\n",
+        "# Make the hindcasting object we can use to compute statistics and metrics\n",
+        "hindcast_object = forecasting.to_climpred_hindcast_ensemble(hindcasts, q_obs)\n",
+        "\n",
+        "\n",
+        "# This function is used to convert to binary to see if yes/no forecast is larger than observations\n",
+        "def pos(x):\n",
+        "    return x > 0  # Check for binary outcome\n",
+        "\n",
+        "\n",
+        "# Rank histogram verification metric\n",
+        "rank_histo_verif = hindcast_object.verify(\n",
+        "    metric=\"rank_histogram\",\n",
+        "    comparison=\"m2o\",\n",
+        "    dim=[\"member\", \"init\"],\n",
+        "    alignment=\"same_inits\",\n",
+        ")\n",
+        "# CRPS verification metric\n",
+        "crps_verif = hindcast_object.verify(\n",
+        "    metric=\"crps\",\n",
+        "    comparison=\"m2o\",\n",
+        "    dim=[\"member\", \"init\"],\n",
+        "    alignment=\"same_inits\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We can explore and plot the CRPS as a function of lead-time, for example. Results are stored as a dataset and\n",
+        "# can thus be integrated into any simulation or processes.\n",
+        "plt.plot(crps_verif.q_sim)\n",
+        "plt.xlabel(\"Lead time [days]\")\n",
+        "plt.ylabel(\"CRPS $[m^3s^{-1}]$\")\n",
+        "plt.grid(\"on\")\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extended Streamflow Prediction (ESP) forecasts from climatological time series\n",
-    "\n",
-    "This notebook shows how to perform a climatological Extended Streamflow Prediction (ESP) forecast, using historical weather as a proxy for future weather.\n",
-    "\n",
-    "The general idea is to initialize the state of the hydrological model to represent current conditions, but instead of using weather forecasts to predict future flows, we run the model with observed, historical weather series from past years. So for example if we have 30 years of weather observations, we get 30 different forecasts. The accuracy of this forecast ensemble can then be evaluated by different probabilistic metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities import forecasting\n",
-    "from ravenpy.utilities.testdata import get_file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run the model simulations\n",
-    "\n",
-    "Here we set model parameters somewhat arbitrarily, but you can set the parameters to the calibrated parameters as seen in the \"06_Raven_calibration\" notebook we previously encountered.\n",
-    "\n",
-    "We also need to choose the forecast issue date. Each forecast will start with the same day and month. For example, jun-06-1980 will compare the climatology using all jun-06's from the dataset. Finally, we can provide the forecast duration (in number of days) as well as the historical meteorological years we want to use to generate the ESP forecast. This allows selecting years that we want to include in the forecast. For example, perhaps we only want to generate a forecast using wet or dry years."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the selected watershed's time series. You can use your own time-series for your catchment by replacing\n",
-    "# this line with the name / path of your input file.\n",
-    "ts = get_file(\"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\")\n",
-    "\n",
-    "# This is the forecast start date, on which the forecasts will be launched.\n",
-    "start_date = dt.datetime(1980, 6, 1)\n",
-    "\n",
-    "# Provide the length of the forecast, in days:\n",
-    "forecast_duration = 100\n",
-    "\n",
-    "# Define HRU to build the hydrological model\n",
-    "hru = {}\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"RAINFALL\": \"rain\",\n",
-    "    \"SNOWFALL\": \"snow\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"RAINFALL\", \"SNOWFALL\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\n",
-    "            \"elevation\"\n",
-    "        ],  # No need for lat/lon as they are included in the netcdf file already\n",
-    "    }\n",
-    "}\n",
-    "# Model configuration\n",
-    "model_config = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    Duration=forecast_duration,\n",
-    "    RunName=\"full\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Issuing the ESP forecast\n",
-    "\n",
-    "Here we launch the code that will perform the ESP forecast. Depending on the number of years in the historical dataset and the forecast duration, it might take a while to return a forecast result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Simulate the climatological ESP:\n",
-    "ESP_sims = forecasting.climatology_esp(\n",
-    "    config=model_config,\n",
-    "    years=[\n",
-    "        1982,\n",
-    "        1998,\n",
-    "        2003,\n",
-    "        2004,\n",
-    "    ],  # List of years to use in the forecast. Optional. Will use all years by default.\n",
-    ")\n",
-    "\n",
-    "# Show the results in an xarray dataset, ready to use:\n",
-    "ESP_sims.hydrograph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now inspect and graph the resulting climatological ESP:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(x=\"time\")\n",
-    "plt.title(\"GR4JCN climatological ESP for 1980-06-01\")\n",
-    "plt.xticks(rotation=90)\n",
-    "plt.grid(\"on\")\n",
-    "plt.xlabel(\"Time [days]\")\n",
-    "plt.ylabel(\"Streamflow $[m^3s^{-1}]$\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Compute the forecast scores\n",
-    "\n",
-    "There are different metric to evaluate the performance of forecasts. As an example, here we are computing the CRPS metric, using the [xskillscore](https://xskillscore.readthedocs.io/en/stable/) library included in PAVICS-Hydro."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import xskillscore as xs\n",
-    "\n",
-    "# Align time axes to get the observed streamflow time series for the same time frame as the ESP forecast ensemble\n",
-    "q_obs, q_sims = xr.align(xr.open_dataset(ts).qobs, ESP_sims.hydrograph, join=\"inner\")\n",
-    "\n",
-    "# Adjust the streamflow to convert missing data from -1.2345 format to NaN. Set all negative values to NaN.\n",
-    "q_obs = q_obs.where(q_obs > 0, np.nan)\n",
-    "\n",
-    "# Compute the Continuous Ranked Probability Score using xskillscore\n",
-    "xs.crps_ensemble(q_obs, q_sims, dim=\"time\").q_sim.values[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Performing a climatology ESP hindcast\n",
-    "In this section, we make the hindcasts for each initialization date that we desire. Here we will extract ESP forecasts for a given calendar date for the years in \"hindcast_years\" as hindcast dates. Each ESP hindcast uses all available data in the `ts` dataset, so in this case we will have 56/57 members for each hindcast initialization depending on the date that we start on, UNLESS we specify a list of years manually. The \"hindcasts\" dataset generated contains all of the flow data from the ESP hindcasts for the initialization dates. The `q_obs` dataset contains all q_obs in the timeseries: Climpred will sort it all out during its processing. Note that the format of these datasets is tailor-made to be used in climpred, and thus has specific dimension names.\n",
-    "\n",
-    "This is a slimmed down example of how we would run an ESP forecast over multiple years to assess the skill of such a forecast."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hindcasts = forecasting.hindcast_climatology_esp(\n",
-    "    config=model_config,  # Note that the forecast duration is already set-up in the model_config above.\n",
-    "    warm_up_duration=365,  # number of days for the warm-up\n",
-    "    years=[1985, 1986, 1987, 1988, 1989, 1990],\n",
-    "    hindcast_years=[2001, 2002, 2003, 2004, 2005, 2006, 2007],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluate the forecast using different metrics\n",
-    "Once we have the correctly formatted datasets, Make the hindcast object for climpred\n",
-    "\n",
-    "These three functions respectively compute the rank histogram, the CRPS and the reliability for the set of initialized dates (i.e. forecast issue dates, here 1 day per year at the same calendar day)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Once we have the correctly formatted datasets, Make the hindcast object for climpred\n",
-    "\n",
-    "# We first need to get the observed streamflow:\n",
-    "q_obs = xr.open_dataset(ts)\n",
-    "\n",
-    "# However, our simulated streamflow is named \"q_sim\" and climpred requires the observation to be named the same thing\n",
-    "# so let's rename it. While we're at it, we need to make sure that the identifier is the same. In our observation\n",
-    "# dataset, it is called \"nstations\" but in our simulated streamflow it's called \"nbasins\". Here we standardize.\n",
-    "q_obs = q_obs.rename({\"qobs\": \"q_sim\", \"nstations\": \"nbasins\"})\n",
-    "\n",
-    "# Make the hindcasting object we can use to compute statistics and metrics\n",
-    "hindcast_object = forecasting.to_climpred_hindcast_ensemble(hindcasts, q_obs)\n",
-    "\n",
-    "\n",
-    "# This function is used to convert to binary to see if yes/no forecast is larger than observations\n",
-    "def pos(x):\n",
-    "    return x > 0  # Check for binary outcome\n",
-    "\n",
-    "\n",
-    "# Rank histogram verification metric\n",
-    "rank_histo_verif = hindcast_object.verify(\n",
-    "    metric=\"rank_histogram\",\n",
-    "    comparison=\"m2o\",\n",
-    "    dim=[\"member\", \"init\"],\n",
-    "    alignment=\"same_inits\",\n",
-    ")\n",
-    "# CRPS verification metric\n",
-    "crps_verif = hindcast_object.verify(\n",
-    "    metric=\"crps\",\n",
-    "    comparison=\"m2o\",\n",
-    "    dim=[\"member\", \"init\"],\n",
-    "    alignment=\"same_inits\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We can explore and plot the CRPS as a function of lead-time, for example. Results are stored as a dataset and\n",
-    "# can thus be integrated into any simulation or processes.\n",
-    "plt.plot(crps_verif.q_sim)\n",
-    "plt.xlabel(\"Lead time [days]\")\n",
-    "plt.ylabel(\"CRPS $[m^3s^{-1}]$\")\n",
-    "plt.grid(\"on\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/12_Performing_hindcasting_experiments.ipynb b/docs/notebooks/12_Performing_hindcasting_experiments.ipynb
index 970d8598..960c001e 100644
--- a/docs/notebooks/12_Performing_hindcasting_experiments.ipynb
+++ b/docs/notebooks/12_Performing_hindcasting_experiments.ipynb
@@ -1,327 +1,327 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hindcasting with CaSPAr-Archived ECCC forecasts\n",
-    "\n",
-    "This notebook shows how to perform a streamflow hindcast, using CaSPar archived weather forecasts. It generates the hindcasts and plots them.\n",
-    "\n",
-    "CaSPAr (Canadian Surface Prediction Archive) is an archive of historical ECCC forecasts developed by Juliane Mai at the University of Waterloo, Canada. More details on CaSPAr can be found here https://caspar-data.ca/.\n",
-    "\n",
-    "\n",
-    "Mai, J., Kornelsen, K.C., Tolson, B.A., Fortin, V., Gasset, N., Bouhemhem, D., Schäfer, D., Leahy, M., Anctil, F. and Coulibaly, P., 2020. The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin of the American Meteorological Society, 101(3), pp.E341-E356.\n"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Hindcasting with CaSPAr-Archived ECCC forecasts\n",
+        "\n",
+        "This notebook shows how to perform a streamflow hindcast, using CaSPar archived weather forecasts. It generates the hindcasts and plots them.\n",
+        "\n",
+        "CaSPAr (Canadian Surface Prediction Archive) is an archive of historical ECCC forecasts developed by Juliane Mai at the University of Waterloo, Canada. More details on CaSPAr can be found here https://caspar-data.ca/.\n",
+        "\n",
+        "\n",
+        "Mai, J., Kornelsen, K.C., Tolson, B.A., Fortin, V., Gasset, N., Bouhemhem, D., Schäfer, D., Leahy, M., Anctil, F. and Coulibaly, P., 2020. The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin of the American Meteorological Society, 101(3), pp.E341-E356.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# This entire section is cookie-cutter template to import required packages and prepare the temporary writing space.\n",
+        "import datetime as dt\n",
+        "import tempfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import xarray as xr\n",
+        "from clisops.core import average, subset\n",
+        "\n",
+        "from ravenpy import Emulator, RavenWarning\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.extractors.forecasts import get_CASPAR_dataset\n",
+        "from ravenpy.utilities import forecasting\n",
+        "from ravenpy.utilities.testdata import get_file\n",
+        "\n",
+        "tmp = Path(tempfile.mkdtemp())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Run the model simulations\n",
+        "\n",
+        "Here we set model parameters somewhat arbitrarily, but you can set the parameters to the calibrated parameters as seen in the \"06_Raven_calibration\" notebook we previously encountered. We can then specify the start date for the hindcast ESP simulations and run the simulations.This means we need to choose the forecast (hindcast) date. Available data include May 2017 onwards."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "# Date of the hindcast\n",
+        "hdate = dt.datetime(2018, 6, 1)\n",
+        "\n",
+        "# Get the Forecast data from GEPS via CASPAR\n",
+        "ts_hindcast, _ = get_CASPAR_dataset(\"GEPS\", hdate)\n",
+        "\n",
+        "# Get basin contour\n",
+        "basin_contour = get_file(\"notebook_inputs/salmon_river.geojson\")\n",
+        "\n",
+        "# Subset the data for the region of interest and take the mean to get a single vector\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    ts_subset = subset.subset_shape(ts_hindcast, basin_contour).mean(\n",
+        "        dim=(\"rlat\", \"rlon\")\n",
+        "    )\n",
+        "ts_subset = ts_subset.resample(time=\"6H\").nearest(\n",
+        "    tolerance=\"1H\"\n",
+        ")  # To make the timesteps identical accross the entire duration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# See how many members we have available\n",
+        "len(ts_subset.members)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have the correct weather forecasts, we can setup the hydrological model for a warm-up run:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Prepare a RAVEN model run using historical data, GR4JCN in this case.\n",
+        "# This is a dummy run to get initial states. In a real forecast situation,\n",
+        "# this run would end on the day before the forecast, but process is the same.\n",
+        "\n",
+        "# Here we need a file of observation data to run a simulation to generate initial conditions for our forecast.\n",
+        "# ts = str(\n",
+        "#    get_file(\"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\")\n",
+        "# )\n",
+        "\n",
+        "# TODO: We will use ERA5 data for Salmon River because it covers the correct period.\n",
+        "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
+        "\n",
+        "# This is the model start date, on which the simulation will be launched for a certain duration\n",
+        "# to set up the initial states. We will then save the final states as a launching point for the\n",
+        "# forecasts.\n",
+        "\n",
+        "start_date = dt.datetime(2000, 1, 1)\n",
+        "end_date = dt.datetime(2018, 6, 2)\n",
+        "\n",
+        "# Define HRU to build the hydrological model\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "}\n",
+        "# Model configuration\n",
+        "model_config_warmup = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=\"NB12_warmup_run\",\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "out1 = Emulator(config=model_config_warmup).run()\n",
+        "\n",
+        "\n",
+        "# Extract the path to the final states file that will be used as the next initial states\n",
+        "hotstart = out1.files[\"solution\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now have the initial states ready for the next step, which is to launch the forecasts in hindcasting mode:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Explore the forecast data to see which variables we have:\n",
+        "display(ts_subset)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "# Configure and run a new model by setting the initial states (equal to the previous run's final states) and prepare\n",
+        "# the configuration for the forecasts (including forecast start date, which should be equal to the final simulation\n",
+        "# date + 1, as well as the forecast duration.)\n",
+        "\n",
+        "# We need to write the hindcast data as a file for Raven to be able to access it.\n",
+        "fname = tmp / \"hindcast.nc\"\n",
+        "ts_subset.to_netcdf(fname)\n",
+        "\n",
+        "# We need to adjust the data_type and alt_names according to the data in the forecast:\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_AVE\": \"tas\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_AVE\", \"PRECIP\"]\n",
+        "\n",
+        "\n",
+        "# We will need to reuse this for GR4J. Update according to your needs. For example, here we will also pass\n",
+        "# the catchment latitude and longitude as our CaSPAr data has been averaged at the catchment scale.\n",
+        "# We also need to tell the model to deaccumulate the precipitation and shift it in time by 6 hours for our\n",
+        "# catchment (UTC timezones):\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "    \"PRECIP\": {\n",
+        "        \"Deaccumulate\": True,\n",
+        "        \"TimeShift\": -0.25,\n",
+        "        \"LinearTransform\": {\n",
+        "            \"scale\": 1000.0\n",
+        "        },  # Since we are deaccumulating, we need to manually specify scale.\n",
+        "    },  # Converting meters to mm (multiply by 1000).\n",
+        "    \"TEMP_AVE\": {\n",
+        "        \"TimeShift\": -0.25,\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "\n",
+        "# Model configuration for forecasting, including correct start date and forecast duration\n",
+        "model_config_fcst = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            fname, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=end_date + dt.timedelta(days=1),\n",
+        "    Duration=7,\n",
+        "    RunName=\"NB12_forecast_run\",\n",
+        ")\n",
+        "\n",
+        "# Update the initial states\n",
+        "model_config_fcst = model_config_fcst.set_solution(hotstart)\n",
+        "\n",
+        "# Generate the hindcast by providing all necessary information to generate virtual stations representing\n",
+        "# the forecast members\n",
+        "hindcast = forecasting.hindcast_from_meteo_forecast(\n",
+        "    model_config_fcst,\n",
+        "    forecast=fname,\n",
+        "    # We also need to provide the necessary information to create gauges inside the forecasting model:\n",
+        "    data_kwds=data_kwds,\n",
+        "    data_type=data_type,\n",
+        "    alt_names=alt_names,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Explore the hindcast data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hindcast.hydrograph"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "And, for visual representation of the forecasts:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Simulate an observed streamflow timeseries: Here we take a member from the ensemble, but you should use your own\n",
+        "# observed timeseries:\n",
+        "qq = hindcast.hydrograph.q_sim[0, :, 0]\n",
+        "\n",
+        "hindcast.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
+        "hindcast.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"forecasts\")\n",
+        "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This entire section is cookie-cutter template to import required packages and prepare the temporary writing space.\n",
-    "import datetime as dt\n",
-    "import tempfile\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import xarray as xr\n",
-    "from clisops.core import average, subset\n",
-    "\n",
-    "from ravenpy import Emulator, RavenWarning\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.extractors.forecasts import get_CASPAR_dataset\n",
-    "from ravenpy.utilities import forecasting\n",
-    "from ravenpy.utilities.testdata import get_file\n",
-    "\n",
-    "tmp = Path(tempfile.mkdtemp())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run the model simulations\n",
-    "\n",
-    "Here we set model parameters somewhat arbitrarily, but you can set the parameters to the calibrated parameters as seen in the \"06_Raven_calibration\" notebook we previously encountered. We can then specify the start date for the hindcast ESP simulations and run the simulations.This means we need to choose the forecast (hindcast) date. Available data include May 2017 onwards."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Date of the hindcast\n",
-    "hdate = dt.datetime(2018, 6, 1)\n",
-    "\n",
-    "# Get the Forecast data from GEPS via CASPAR\n",
-    "ts_hindcast, _ = get_CASPAR_dataset(\"GEPS\", hdate)\n",
-    "\n",
-    "# Get basin contour\n",
-    "basin_contour = get_file(\"notebook_inputs/salmon_river.geojson\")\n",
-    "\n",
-    "# Subset the data for the region of interest and take the mean to get a single vector\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    ts_subset = subset.subset_shape(ts_hindcast, basin_contour).mean(\n",
-    "        dim=(\"rlat\", \"rlon\")\n",
-    "    )\n",
-    "ts_subset = ts_subset.resample(time=\"6H\").nearest(\n",
-    "    tolerance=\"1H\"\n",
-    ")  # To make the timesteps identical accross the entire duration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# See how many members we have available\n",
-    "len(ts_subset.members)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have the correct weather forecasts, we can setup the hydrological model for a warm-up run:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Prepare a RAVEN model run using historical data, GR4JCN in this case.\n",
-    "# This is a dummy run to get initial states. In a real forecast situation,\n",
-    "# this run would end on the day before the forecast, but process is the same.\n",
-    "\n",
-    "# Here we need a file of observation data to run a simulation to generate initial conditions for our forecast.\n",
-    "# ts = str(\n",
-    "#    get_file(\"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\")\n",
-    "# )\n",
-    "\n",
-    "# TODO: We will use ERA5 data for Salmon River because it covers the correct period.\n",
-    "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
-    "\n",
-    "# This is the model start date, on which the simulation will be launched for a certain duration\n",
-    "# to set up the initial states. We will then save the final states as a launching point for the\n",
-    "# forecasts.\n",
-    "\n",
-    "start_date = dt.datetime(2000, 1, 1)\n",
-    "end_date = dt.datetime(2018, 6, 2)\n",
-    "\n",
-    "# Define HRU to build the hydrological model\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "}\n",
-    "# Model configuration\n",
-    "model_config_warmup = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=\"NB12_warmup_run\",\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "out1 = Emulator(config=model_config_warmup).run()\n",
-    "\n",
-    "\n",
-    "# Extract the path to the final states file that will be used as the next initial states\n",
-    "hotstart = out1.files[\"solution\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have the initial states ready for the next step, which is to launch the forecasts in hindcasting mode:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Explore the forecast data to see which variables we have:\n",
-    "display(ts_subset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Configure and run a new model by setting the initial states (equal to the previous run's final states) and prepare\n",
-    "# the configuration for the forecasts (including forecast start date, which should be equal to the final simulation\n",
-    "# date + 1, as well as the forecast duration.)\n",
-    "\n",
-    "# We need to write the hindcast data as a file for Raven to be able to access it.\n",
-    "fname = tmp / \"hindcast.nc\"\n",
-    "ts_subset.to_netcdf(fname)\n",
-    "\n",
-    "# We need to adjust the data_type and alt_names according to the data in the forecast:\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_AVE\": \"tas\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_AVE\", \"PRECIP\"]\n",
-    "\n",
-    "\n",
-    "# We will need to reuse this for GR4J. Update according to your needs. For example, here we will also pass\n",
-    "# the catchment latitude and longitude as our CaSPAr data has been averaged at the catchment scale.\n",
-    "# We also need to tell the model to deaccumulate the precipitation and shift it in time by 6 hours for our\n",
-    "# catchment (UTC timezones):\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "    \"PRECIP\": {\n",
-    "        \"Deaccumulate\": True,\n",
-    "        \"TimeShift\": -0.25,\n",
-    "        \"LinearTransform\": {\n",
-    "            \"scale\": 1000.0\n",
-    "        },  # Since we are deaccumulating, we need to manually specify scale.\n",
-    "    },  # Converting meters to mm (multiply by 1000).\n",
-    "    \"TEMP_AVE\": {\n",
-    "        \"TimeShift\": -0.25,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# Model configuration for forecasting, including correct start date and forecast duration\n",
-    "model_config_fcst = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            fname, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=end_date + dt.timedelta(days=1),\n",
-    "    Duration=7,\n",
-    "    RunName=\"NB12_forecast_run\",\n",
-    ")\n",
-    "\n",
-    "# Update the initial states\n",
-    "model_config_fcst = model_config_fcst.set_solution(hotstart)\n",
-    "\n",
-    "# Generate the hindcast by providing all necessary information to generate virtual stations representing\n",
-    "# the forecast members\n",
-    "hindcast = forecasting.hindcast_from_meteo_forecast(\n",
-    "    model_config_fcst,\n",
-    "    forecast=fname,\n",
-    "    # We also need to provide the necessary information to create gauges inside the forecasting model:\n",
-    "    data_kwds=data_kwds,\n",
-    "    data_type=data_type,\n",
-    "    alt_names=alt_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Explore the hindcast data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hindcast.hydrograph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "And, for visual representation of the forecasts:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Simulate an observed streamflow timeseries: Here we take a member from the ensemble, but you should use your own\n",
-    "# observed timeseries:\n",
-    "qq = hindcast.hydrograph.q_sim[0, :, 0]\n",
-    "\n",
-    "hindcast.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
-    "hindcast.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"forecasts\")\n",
-    "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/Assess_probabilistic_flood_risk.ipynb b/docs/notebooks/Assess_probabilistic_flood_risk.ipynb
index 0db93331..e13fc0f1 100644
--- a/docs/notebooks/Assess_probabilistic_flood_risk.ipynb
+++ b/docs/notebooks/Assess_probabilistic_flood_risk.ipynb
@@ -1,1331 +1,1331 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "chinese-dealer",
-   "metadata": {},
-   "source": [
-    "# Probabilistic flood risk assessment\n",
-    "\n",
-    "In this notebook, we combine the forecasting abilities and the time series analysis capabilities in a single seamless process to estimate the flood risk of a probabilistic forecast. As an example, we first perform a frequency analysis on an observed time series, then estimate the streamflow associated to a 2-year return period. We then perform a climatological ESP forecast (to ensure repeatability, but a realtime forecast would work too!) and estimate the probability of flooding (exceeding the threshold) given the ensemble of members in the probabilistic forecast."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "79d923bd-4ce5-41f1-a441-f439b23fc388",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "from numba.core.errors import NumbaDeprecationWarning\n",
-    "\n",
-    "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "descending-bedroom",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import datetime as dt\n",
-    "\n",
-    "import xclim\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from ravenpy.utilities.testdata import get_file, open_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "genuine-dodge",
-   "metadata": {},
-   "source": [
-    "Perform the time series analysis on observed data for the catchment using the frequency analysis WPS capabilities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "quiet-queens",
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "chinese-dealer",
+      "metadata": {},
+      "source": [
+        "# Probabilistic flood risk assessment\n",
+        "\n",
+        "In this notebook, we combine the forecasting abilities and the time series analysis capabilities in a single seamless process to estimate the flood risk of a probabilistic forecast. As an example, we first perform a frequency analysis on an observed time series, then estimate the streamflow associated to a 2-year return period. We then perform a climatological ESP forecast (to ensure repeatability, but a realtime forecast would work too!) and estimate the probability of flooding (exceeding the threshold) given the ensemble of members in the probabilistic forecast."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "79d923bd-4ce5-41f1-a441-f439b23fc388",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "\n",
+        "from numba.core.errors import NumbaDeprecationWarning\n",
+        "\n",
+        "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
+      ]
+    },
     {
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-       "    estimator:           Maximum likelihood\n",
-       "    scipy_dist:          gumbel_r\n",
-       "    history:             [2023-05-31 13:22:25] fa_1maxannual: xclim.core.indi...\n",
-       "    cell_methods:        \n",
-       "    mode:                max</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'fa_1maxannual'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>return_period</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-60400ebc-0ce8-4cf6-a2a1-ce776dde804f' class='xr-array-in' type='checkbox' checked><label for='section-60400ebc-0ce8-4cf6-a2a1-ce776dde804f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>186.4 283.0 347.0 427.8 487.8 547.3</span></div><div class='xr-array-data'><pre>array([186.42526386, 283.04234564, 347.01126071, 427.83615447,\n",
-       "       487.79667785, 547.31446213])</pre></div></div></li><li class='xr-section-item'><input id='section-db3bc32a-e8d1-4093-bcbd-7b17f4b70bba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db3bc32a-e8d1-4093-bcbd-7b17f4b70bba' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>return_period</span></div><div class='xr-var-dims'>(return_period)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 5 10 25 50 100</div><input id='attrs-756f00b1-4d50-4450-ab90-144c7cb09daa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-756f00b1-4d50-4450-ab90-144c7cb09daa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71e7d61c-1405-484f-9c28-771bd9ab57f2' class='xr-var-data-in' type='checkbox'><label for='data-71e7d61c-1405-484f-9c28-771bd9ab57f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  2,   5,  10,  25,  50, 100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a0b4663-6523-429d-b1bf-8a9c20a5f0c2' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a0b4663-6523-429d-b1bf-8a9c20a5f0c2' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>return_period</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a1caf39-f22f-4afe-bf74-9162612b8963' class='xr-index-data-in' type='checkbox'/><label for='index-1a1caf39-f22f-4afe-bf74-9162612b8963' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([2, 5, 10, 25, 50, 100], dtype=&#x27;int64&#x27;, name=&#x27;return_period&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98a312a7-6577-441f-b522-adf5f7d2a4b4' class='xr-section-summary-in' type='checkbox'  ><label for='section-98a312a7-6577-441f-b522-adf5f7d2a4b4' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>original_long_name :</span></dt><dd>discharge observation</dd><dt><span>long_name :</span></dt><dd>N-year return level</dd><dt><span>description :</span></dt><dd>Frequency analysis for the maximal annual 1-day value estimated using the gumbel_r distribution.</dd><dt><span>method :</span></dt><dd>ML</dd><dt><span>estimator :</span></dt><dd>Maximum likelihood</dd><dt><span>scipy_dist :</span></dt><dd>gumbel_r</dd><dt><span>history :</span></dt><dd>[2023-05-31 13:22:25] fa_1maxannual: xclim.core.indicator.RETURN_LEVEL(da=qobs, mode=&#x27;max&#x27;, t=(2, 5, 10, 25, 50, 100), dist=&#x27;gumbel_r&#x27;, window=1, freq=None) with options check_missing=skip - xclim version: 0.43.0</dd><dt><span>cell_methods :</span></dt><dd></dd><dt><span>mode :</span></dt><dd>max</dd></dl></div></li></ul></div></div>"
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "descending-bedroom",
+      "metadata": {
+        "scrolled": true
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "import datetime as dt\n",
+        "\n",
+        "import xclim\n",
+        "from matplotlib import pyplot as plt\n",
+        "\n",
+        "from ravenpy.utilities.testdata import get_file, open_dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "genuine-dodge",
+      "metadata": {},
+      "source": [
+        "Perform the time series analysis on observed data for the catchment using the frequency analysis WPS capabilities."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "quiet-queens",
+      "metadata": {},
+      "outputs": [
+        {
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+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;fa_1maxannual&#x27; (return_period: 6)&gt;\n",
+              "array([186.42526386, 283.04234564, 347.01126071, 427.83615447,\n",
+              "       487.79667785, 547.31446213])\n",
+              "Coordinates:\n",
+              "  * return_period  (return_period) int64 2 5 10 25 50 100\n",
+              "Attributes:\n",
+              "    units:               m**3 s**-1\n",
+              "    original_long_name:  discharge observation\n",
+              "    long_name:           N-year return level\n",
+              "    description:         Frequency analysis for the maximal annual 1-day valu...\n",
+              "    method:              ML\n",
+              "    estimator:           Maximum likelihood\n",
+              "    scipy_dist:          gumbel_r\n",
+              "    history:             [2023-05-31 13:22:25] fa_1maxannual: xclim.core.indi...\n",
+              "    cell_methods:        \n",
+              "    mode:                max</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'fa_1maxannual'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>return_period</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-60400ebc-0ce8-4cf6-a2a1-ce776dde804f' class='xr-array-in' type='checkbox' checked><label for='section-60400ebc-0ce8-4cf6-a2a1-ce776dde804f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>186.4 283.0 347.0 427.8 487.8 547.3</span></div><div class='xr-array-data'><pre>array([186.42526386, 283.04234564, 347.01126071, 427.83615447,\n",
+              "       487.79667785, 547.31446213])</pre></div></div></li><li class='xr-section-item'><input id='section-db3bc32a-e8d1-4093-bcbd-7b17f4b70bba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db3bc32a-e8d1-4093-bcbd-7b17f4b70bba' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>return_period</span></div><div class='xr-var-dims'>(return_period)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 5 10 25 50 100</div><input id='attrs-756f00b1-4d50-4450-ab90-144c7cb09daa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-756f00b1-4d50-4450-ab90-144c7cb09daa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71e7d61c-1405-484f-9c28-771bd9ab57f2' class='xr-var-data-in' type='checkbox'><label for='data-71e7d61c-1405-484f-9c28-771bd9ab57f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  2,   5,  10,  25,  50, 100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a0b4663-6523-429d-b1bf-8a9c20a5f0c2' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a0b4663-6523-429d-b1bf-8a9c20a5f0c2' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>return_period</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a1caf39-f22f-4afe-bf74-9162612b8963' class='xr-index-data-in' type='checkbox'/><label for='index-1a1caf39-f22f-4afe-bf74-9162612b8963' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([2, 5, 10, 25, 50, 100], dtype=&#x27;int64&#x27;, name=&#x27;return_period&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98a312a7-6577-441f-b522-adf5f7d2a4b4' class='xr-section-summary-in' type='checkbox'  ><label for='section-98a312a7-6577-441f-b522-adf5f7d2a4b4' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>original_long_name :</span></dt><dd>discharge observation</dd><dt><span>long_name :</span></dt><dd>N-year return level</dd><dt><span>description :</span></dt><dd>Frequency analysis for the maximal annual 1-day value estimated using the gumbel_r distribution.</dd><dt><span>method :</span></dt><dd>ML</dd><dt><span>estimator :</span></dt><dd>Maximum likelihood</dd><dt><span>scipy_dist :</span></dt><dd>gumbel_r</dd><dt><span>history :</span></dt><dd>[2023-05-31 13:22:25] fa_1maxannual: xclim.core.indicator.RETURN_LEVEL(da=qobs, mode=&#x27;max&#x27;, t=(2, 5, 10, 25, 50, 100), dist=&#x27;gumbel_r&#x27;, window=1, freq=None) with options check_missing=skip - xclim version: 0.43.0</dd><dt><span>cell_methods :</span></dt><dd></dd><dt><span>mode :</span></dt><dd>max</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.DataArray 'fa_1maxannual' (return_period: 6)>\n",
+              "array([186.42526386, 283.04234564, 347.01126071, 427.83615447,\n",
+              "       487.79667785, 547.31446213])\n",
+              "Coordinates:\n",
+              "  * return_period  (return_period) int64 2 5 10 25 50 100\n",
+              "Attributes:\n",
+              "    units:               m**3 s**-1\n",
+              "    original_long_name:  discharge observation\n",
+              "    long_name:           N-year return level\n",
+              "    description:         Frequency analysis for the maximal annual 1-day valu...\n",
+              "    method:              ML\n",
+              "    estimator:           Maximum likelihood\n",
+              "    scipy_dist:          gumbel_r\n",
+              "    history:             [2023-05-31 13:22:25] fa_1maxannual: xclim.core.indi...\n",
+              "    cell_methods:        \n",
+              "    mode:                max"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "<xarray.DataArray 'fa_1maxannual' (return_period: 6)>\n",
-       "array([186.42526386, 283.04234564, 347.01126071, 427.83615447,\n",
-       "       487.79667785, 547.31446213])\n",
-       "Coordinates:\n",
-       "  * return_period  (return_period) int64 2 5 10 25 50 100\n",
-       "Attributes:\n",
-       "    units:               m**3 s**-1\n",
-       "    original_long_name:  discharge observation\n",
-       "    long_name:           N-year return level\n",
-       "    description:         Frequency analysis for the maximal annual 1-day valu...\n",
-       "    method:              ML\n",
-       "    estimator:           Maximum likelihood\n",
-       "    scipy_dist:          gumbel_r\n",
-       "    history:             [2023-05-31 13:22:25] fa_1maxannual: xclim.core.indi...\n",
-       "    cell_methods:        \n",
-       "    mode:                max"
+      "source": [
+        "# Get the data that we will be using for the demonstration.\n",
+        "file = \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n",
+        "ts = open_dataset(file).qobs\n",
+        "\n",
+        "# Perform the frequency analysis for various return periods. We compute 2, 5, 10, 25, 50 and 100 year return\n",
+        "# periods, but later on we will only compare the forecasts to the 2 year return period.\n",
+        "out = xclim.generic.return_level(\n",
+        "    ts, mode=\"max\", t=(2, 5, 10, 25, 50, 100), dist=\"gumbel_r\"\n",
+        ")\n",
+        "out"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the data that we will be using for the demonstration.\n",
-    "file = \"raven-gr4j-cemaneige/Salmon-River-Near-Prince-George_meteo_daily.nc\"\n",
-    "ts = open_dataset(file).qobs\n",
-    "\n",
-    "# Perform the frequency analysis for various return periods. We compute 2, 5, 10, 25, 50 and 100 year return\n",
-    "# periods, but later on we will only compare the forecasts to the 2 year return period.\n",
-    "out = xclim.generic.return_level(\n",
-    "    ts, mode=\"max\", t=(2, 5, 10, 25, 50, 100), dist=\"gumbel_r\"\n",
-    ")\n",
-    "out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "appointed-toner",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Threshold: 186.4\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "appointed-toner",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Threshold: 186.4\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Text(25, 10, 'Flow threshold, set at 2-year return period')"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot the results of the flows as a function of return period.\n",
+        "fig, ax = plt.subplots(1)\n",
+        "lines = out.plot(ax=ax)\n",
+        "\n",
+        "# Get 2-year return period from the frequency analysis\n",
+        "threshold = out.sel(return_period=2).values\n",
+        "print(f\"Threshold: {threshold:.1f}\")\n",
+        "\n",
+        "pt = ax.plot([2], [threshold], \"ro\")\n",
+        "\n",
+        "ax.annotate(\n",
+        "    \"Flow threshold, set at 2-year return period\",\n",
+        "    (2, threshold),\n",
+        "    xytext=(25, 10),\n",
+        "    textcoords=\"offset points\",\n",
+        "    arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"),\n",
+        ")"
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "Text(25, 10, 'Flow threshold, set at 2-year return period')"
+      "cell_type": "markdown",
+      "id": "explicit-accent",
+      "metadata": {},
+      "source": [
+        "## Probabilistic forecast\n",
+        "\n",
+        "In this example, we will perform an ensemble hydrological forecast and will then compute the probability of flooding given a flooding threshold. Start by building the model configuration as in the Tutorial Notebook 11:"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "excessive-apparatus",
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        }
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "hwloc/linux: Ignoring PCI device with non-16bit domain.\n",
+            "Pass --enable-32bits-pci-domain to configure to support such devices\n",
+            "(warning: it would break the library ABI, don't enable unless really needed).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities.forecasting import climatology_esp, compute_forecast_flood_risk\n",
+        "\n",
+        "# Choose the forecast date. Each forecast will start with the same day and month.\n",
+        "# For example, jan-05-2001 will compare the climatology using all jan-05ths from the dataset)\n",
+        "fdate = dt.datetime(2003, 4, 13)\n",
+        "\n",
+        "# The dataset to use to get the forecast timeseries:\n",
+        "duration = 30  # Length in days of the climatological ESP forecast\n",
+        "\n",
+        "# Define HRU to build the hydrological model\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"RAINFALL\": \"rain\",\n",
+        "    \"SNOWFALL\": \"snow\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"RAINFALL\", \"SNOWFALL\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\n",
+        "            \"elevation\"\n",
+        "        ],  # No need for lat/lon as they are included in the netcdf file already\n",
+        "    }\n",
+        "}\n",
+        "# Model configuration\n",
+        "model_config = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            get_file(file),\n",
+        "            data_type=data_type,\n",
+        "            alt_names=alt_names,\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=fdate,\n",
+        "    Duration=duration,\n",
+        "    RunName=\"Probabilistic_flood_risk_NB\",\n",
+        ")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the results of the flows as a function of return period.\n",
-    "fig, ax = plt.subplots(1)\n",
-    "lines = out.plot(ax=ax)\n",
-    "\n",
-    "# Get 2-year return period from the frequency analysis\n",
-    "threshold = out.sel(return_period=2).values\n",
-    "print(f\"Threshold: {threshold:.1f}\")\n",
-    "\n",
-    "pt = ax.plot([2], [threshold], \"ro\")\n",
-    "\n",
-    "ax.annotate(\n",
-    "    \"Flow threshold, set at 2-year return period\",\n",
-    "    (2, threshold),\n",
-    "    xytext=(25, 10),\n",
-    "    textcoords=\"offset points\",\n",
-    "    arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "explicit-accent",
-   "metadata": {},
-   "source": [
-    "## Probabilistic forecast\n",
-    "\n",
-    "In this example, we will perform an ensemble hydrological forecast and will then compute the probability of flooding given a flooding threshold. Start by building the model configuration as in the Tutorial Notebook 11:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "excessive-apparatus",
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hwloc/linux: Ignoring PCI device with non-16bit domain.\n",
-      "Pass --enable-32bits-pci-domain to configure to support such devices\n",
-      "(warning: it would break the library ABI, don't enable unless really needed).\n"
-     ]
-    }
-   ],
-   "source": [
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities.forecasting import climatology_esp, compute_forecast_flood_risk\n",
-    "\n",
-    "# Choose the forecast date. Each forecast will start with the same day and month.\n",
-    "# For example, jan-05-2001 will compare the climatology using all jan-05ths from the dataset)\n",
-    "fdate = dt.datetime(2003, 4, 13)\n",
-    "\n",
-    "# The dataset to use to get the forecast timeseries:\n",
-    "duration = 30  # Length in days of the climatological ESP forecast\n",
-    "\n",
-    "# Define HRU to build the hydrological model\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"RAINFALL\": \"rain\",\n",
-    "    \"SNOWFALL\": \"snow\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"RAINFALL\", \"SNOWFALL\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\n",
-    "            \"elevation\"\n",
-    "        ],  # No need for lat/lon as they are included in the netcdf file already\n",
-    "    }\n",
-    "}\n",
-    "# Model configuration\n",
-    "model_config = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            get_file(file),\n",
-    "            data_type=data_type,\n",
-    "            alt_names=alt_names,\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=fdate,\n",
-    "    Duration=duration,\n",
-    "    RunName=\"Probabilistic_flood_risk_NB\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8308cde3",
-   "metadata": {},
-   "source": [
-    "Now that the configuration is ready, launch the ESP forecasting tool to generate an ensemble hydrological forecast:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0c0b126a",
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "8308cde3",
+      "metadata": {},
+      "source": [
+        "Now that the configuration is ready, launch the ESP forecasting tool to generate an ensemble hydrological forecast:"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:     (member: 57, time: 31, nbasins: 1)\n",
-       "Coordinates:\n",
-       "  * member      (member) int64 1954 1955 1956 1957 1958 ... 2007 2008 2009 2010\n",
-       "  * time        (time) datetime64[ns] 2003-04-13 2003-04-14 ... 2003-05-13\n",
-       "    basin_name  (nbasins) object &#x27;sub_001&#x27;\n",
-       "Dimensions without coordinates: nbasins\n",
-       "Data variables:\n",
-       "    precip      (member, time) float64 nan 0.2054 0.0 4.304 ... 0.0 0.0 0.0 0.0\n",
-       "    q_sim       (member, time, nbasins) float64 0.0 0.1644 ... 0.5947 0.5763\n",
-       "    q_obs       (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
-       "    q_in        (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    featureType:  timeSeries\n",
-       "    history:      Created on 2023-05-31T13:22:36 by Raven 3.7\n",
-       "    description:  Standard Output\n",
-       "    references:   Craig J.R. and the Raven Development Team Raven user&#x27;s and ...\n",
-       "    model_id:     GR4JCN</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8fbbad0e-6644-42f6-b4b0-d7b69fe04b17' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8fbbad0e-6644-42f6-b4b0-d7b69fe04b17' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>member</span>: 57</li><li><span class='xr-has-index'>time</span>: 31</li><li><span>nbasins</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1b3b7455-e541-4371-ab35-3878e8fe7b44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b3b7455-e541-4371-ab35-3878e8fe7b44' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member</span></div><div class='xr-var-dims'>(member)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1954 1955 1956 ... 2008 2009 2010</div><input id='attrs-d027e6b8-fa59-48df-8ceb-68ce3a795df3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d027e6b8-fa59-48df-8ceb-68ce3a795df3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28f508d5-81b6-4ed8-ad3f-fa58bd0638a4' class='xr-var-data-in' type='checkbox'><label for='data-28f508d5-81b6-4ed8-ad3f-fa58bd0638a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,\n",
-       "       1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977,\n",
-       "       1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989,\n",
-       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,\n",
-       "       2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2003-04-13 ... 2003-05-13</div><input id='attrs-96768260-b0f1-4299-b1b5-da0b166babde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96768260-b0f1-4299-b1b5-da0b166babde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75d6fac0-2a73-47f2-87a7-fce1f6925164' class='xr-var-data-in' type='checkbox'><label for='data-75d6fac0-2a73-47f2-87a7-fce1f6925164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2003-04-13T00:00:00.000000000&#x27;, &#x27;2003-04-14T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-15T00:00:00.000000000&#x27;, &#x27;2003-04-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-17T00:00:00.000000000&#x27;, &#x27;2003-04-18T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-19T00:00:00.000000000&#x27;, &#x27;2003-04-20T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-21T00:00:00.000000000&#x27;, &#x27;2003-04-22T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-23T00:00:00.000000000&#x27;, &#x27;2003-04-24T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-25T00:00:00.000000000&#x27;, &#x27;2003-04-26T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-27T00:00:00.000000000&#x27;, &#x27;2003-04-28T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-04-29T00:00:00.000000000&#x27;, &#x27;2003-04-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-01T00:00:00.000000000&#x27;, &#x27;2003-05-02T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-03T00:00:00.000000000&#x27;, &#x27;2003-05-04T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-05T00:00:00.000000000&#x27;, &#x27;2003-05-06T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-07T00:00:00.000000000&#x27;, &#x27;2003-05-08T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-09T00:00:00.000000000&#x27;, &#x27;2003-05-10T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-11T00:00:00.000000000&#x27;, &#x27;2003-05-12T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2003-05-13T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;sub_001&#x27;</div><input id='attrs-520a0322-007a-4929-a2e9-f0795354d61a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-520a0322-007a-4929-a2e9-f0795354d61a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19373258-662b-49d5-9450-872809f3c23f' class='xr-var-data-in' type='checkbox'><label for='data-19373258-662b-49d5-9450-872809f3c23f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;sub_001&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a7a0157-91dd-48f1-95fa-65c334ca62fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a7a0157-91dd-48f1-95fa-65c334ca62fc' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>precip</span></div><div class='xr-var-dims'>(member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.2054 0.0 ... 0.0 0.0 0.0</div><input id='attrs-29216bb7-c50f-49e1-a572-07bfe62447a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29216bb7-c50f-49e1-a572-07bfe62447a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75e861e2-8823-4391-b2db-176caace9ba4' class='xr-var-data-in' type='checkbox'><label for='data-75e861e2-8823-4391-b2db-176caace9ba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm d**-1</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd></dl></div><div class='xr-var-data'><pre>array([[     nan, 0.205412, 0.      , ..., 2.475529, 5.122706, 8.276706],\n",
-       "       [     nan, 2.758588, 0.386235, ..., 0.021882, 0.      , 2.231294],\n",
-       "       [     nan, 0.      , 0.      , ..., 0.908824, 0.      , 0.      ],\n",
-       "       ...,\n",
-       "       [     nan, 0.      , 0.      , ..., 0.827176, 0.874118, 1.871765],\n",
-       "       [     nan, 2.160471, 0.085412, ..., 0.      , 4.861647, 3.018   ],\n",
-       "       [     nan, 0.      , 0.      , ..., 0.      , 0.      , 0.      ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_sim</span></div><div class='xr-var-dims'>(member, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1644 0.5532 ... 0.5947 0.5763</div><input id='attrs-7b887d0f-943d-4cd4-9c15-bdab3f77b15f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b887d0f-943d-4cd4-9c15-bdab3f77b15f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-936bcd9a-2443-4d16-9cab-1e62105b958f' class='xr-var-data-in' type='checkbox'><label for='data-936bcd9a-2443-4d16-9cab-1e62105b958f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div><div class='xr-var-data'><pre>array([[[0.        ],\n",
-       "        [0.16438672],\n",
-       "        [0.55323675],\n",
-       "        ...,\n",
-       "        [0.89759061],\n",
-       "        [2.05164214],\n",
-       "        [4.77510839]],\n",
-       "\n",
-       "       [[0.        ],\n",
-       "        [0.44183276],\n",
-       "        [1.21557097],\n",
-       "        ...,\n",
-       "        [0.61640215],\n",
-       "        [0.60126026],\n",
-       "        [0.59153068]],\n",
-       "\n",
-       "       [[0.        ],\n",
-       "        [0.16250213],\n",
-       "        [0.54491656],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [0.62064597],\n",
-       "        [0.60488002],\n",
-       "        [0.59731236]],\n",
-       "\n",
-       "       [[0.        ],\n",
-       "        [0.37405043],\n",
-       "        [1.05346711],\n",
-       "        ...,\n",
-       "        [0.62502891],\n",
-       "        [1.0587842 ],\n",
-       "        [1.90355708]],\n",
-       "\n",
-       "       [[0.        ],\n",
-       "        [0.16368434],\n",
-       "        [0.54971477],\n",
-       "        ...,\n",
-       "        [0.61085499],\n",
-       "        [0.59465142],\n",
-       "        [0.57628941]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_obs</span></div><div class='xr-var-dims'>(member, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-51e4dba2-5c33-4916-831d-b398beedb7f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51e4dba2-5c33-4916-831d-b398beedb7f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c99c66f8-40fb-4a32-9ab6-e75d710d0dfe' class='xr-var-data-in' type='checkbox'><label for='data-c99c66f8-40fb-4a32-9ab6-e75d710d0dfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed outflows</dd></dl></div><div class='xr-var-data'><pre>array([[[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_in</span></div><div class='xr-var-dims'>(member, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-a63d5740-4b29-42c4-bb12-e25b3c0bafa9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a63d5740-4b29-42c4-bb12-e25b3c0bafa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf0de24-32d9-4367-ba59-095fabae0365' class='xr-var-data-in' type='checkbox'><label for='data-dbf0de24-32d9-4367-ba59-095fabae0365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed inflows</dd></dl></div><div class='xr-var-data'><pre>array([[[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]],\n",
-       "\n",
-       "       [[nan],\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        ...,\n",
-       "        [nan],\n",
-       "        [nan],\n",
-       "        [nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f56fb599-ad4b-4d65-a2b0-31ee5284c6e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-f56fb599-ad4b-4d65-a2b0-31ee5284c6e7' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0c6bb9e-188c-4840-bcf6-933428bfe594' class='xr-index-data-in' type='checkbox'/><label for='index-a0c6bb9e-188c-4840-bcf6-933428bfe594' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964,\n",
-       "            1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975,\n",
-       "            1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986,\n",
-       "            1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
-       "            1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
-       "            2009, 2010],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;member&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad915f5f-a75c-4cab-a179-76113c0137b1' class='xr-index-data-in' type='checkbox'/><label for='index-ad915f5f-a75c-4cab-a179-76113c0137b1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2003-04-13&#x27;, &#x27;2003-04-14&#x27;, &#x27;2003-04-15&#x27;, &#x27;2003-04-16&#x27;,\n",
-       "               &#x27;2003-04-17&#x27;, &#x27;2003-04-18&#x27;, &#x27;2003-04-19&#x27;, &#x27;2003-04-20&#x27;,\n",
-       "               &#x27;2003-04-21&#x27;, &#x27;2003-04-22&#x27;, &#x27;2003-04-23&#x27;, &#x27;2003-04-24&#x27;,\n",
-       "               &#x27;2003-04-25&#x27;, &#x27;2003-04-26&#x27;, &#x27;2003-04-27&#x27;, &#x27;2003-04-28&#x27;,\n",
-       "               &#x27;2003-04-29&#x27;, &#x27;2003-04-30&#x27;, &#x27;2003-05-01&#x27;, &#x27;2003-05-02&#x27;,\n",
-       "               &#x27;2003-05-03&#x27;, &#x27;2003-05-04&#x27;, &#x27;2003-05-05&#x27;, &#x27;2003-05-06&#x27;,\n",
-       "               &#x27;2003-05-07&#x27;, &#x27;2003-05-08&#x27;, &#x27;2003-05-09&#x27;, &#x27;2003-05-10&#x27;,\n",
-       "               &#x27;2003-05-11&#x27;, &#x27;2003-05-12&#x27;, &#x27;2003-05-13&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1886d70f-d65e-46ad-9092-d7991c557646' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1886d70f-d65e-46ad-9092-d7991c557646' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>history :</span></dt><dd>Created on 2023-05-31T13:22:36 by Raven 3.7</dd><dt><span>description :</span></dt><dd>Standard Output</dd><dt><span>references :</span></dt><dd>Craig J.R. and the Raven Development Team Raven user&#x27;s and developer&#x27;s manual (Version 3.7) URL: http://raven.uwaterloo.ca/ (2023).</dd><dt><span>model_id :</span></dt><dd>GR4JCN</dd></dl></div></li></ul></div></div>"
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "0c0b126a",
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+              "<defs>\n",
+              "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+              "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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+              "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+              "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+              "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+              "</symbol>\n",
+              "</defs>\n",
+              "</svg>\n",
+              "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+              " *\n",
+              " */\n",
+              "\n",
+              ":root {\n",
+              "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+              "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+              "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+              "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+              "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+              "  --xr-background-color: var(--jp-layout-color0, white);\n",
+              "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+              "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+              "}\n",
+              "\n",
+              "html[theme=dark],\n",
+              "body[data-theme=dark],\n",
+              "body.vscode-dark {\n",
+              "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+              "  --xr-border-color: #1F1F1F;\n",
+              "  --xr-disabled-color: #515151;\n",
+              "  --xr-background-color: #111111;\n",
+              "  --xr-background-color-row-even: #111111;\n",
+              "  --xr-background-color-row-odd: #313131;\n",
+              "}\n",
+              "\n",
+              ".xr-wrap {\n",
+              "  display: block !important;\n",
+              "  min-width: 300px;\n",
+              "  max-width: 700px;\n",
+              "}\n",
+              "\n",
+              ".xr-text-repr-fallback {\n",
+              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-header {\n",
+              "  padding-top: 6px;\n",
+              "  padding-bottom: 6px;\n",
+              "  margin-bottom: 4px;\n",
+              "  border-bottom: solid 1px var(--xr-border-color);\n",
+              "}\n",
+              "\n",
+              ".xr-header > div,\n",
+              ".xr-header > ul {\n",
+              "  display: inline;\n",
+              "  margin-top: 0;\n",
+              "  margin-bottom: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-obj-type,\n",
+              ".xr-array-name {\n",
+              "  margin-left: 2px;\n",
+              "  margin-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-obj-type {\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-sections {\n",
+              "  padding-left: 0 !important;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item {\n",
+              "  display: contents;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input + label {\n",
+              "  color: var(--xr-disabled-color);\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input:enabled + label {\n",
+              "  cursor: pointer;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-section-item input:enabled + label:hover {\n",
+              "  color: var(--xr-font-color0);\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary {\n",
+              "  grid-column: 1;\n",
+              "  color: var(--xr-font-color2);\n",
+              "  font-weight: 500;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary > span {\n",
+              "  display: inline-block;\n",
+              "  padding-left: 0.5em;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:disabled + label {\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in + label:before {\n",
+              "  display: inline-block;\n",
+              "  content: '►';\n",
+              "  font-size: 11px;\n",
+              "  width: 15px;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:disabled + label:before {\n",
+              "  color: var(--xr-disabled-color);\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:checked + label:before {\n",
+              "  content: '▼';\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:checked + label > span {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary,\n",
+              ".xr-section-inline-details {\n",
+              "  padding-top: 4px;\n",
+              "  padding-bottom: 4px;\n",
+              "}\n",
+              "\n",
+              ".xr-section-inline-details {\n",
+              "  grid-column: 2 / -1;\n",
+              "}\n",
+              "\n",
+              ".xr-section-details {\n",
+              "  display: none;\n",
+              "  grid-column: 1 / -1;\n",
+              "  margin-bottom: 5px;\n",
+              "}\n",
+              "\n",
+              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+              "  display: contents;\n",
+              "}\n",
+              "\n",
+              ".xr-array-wrap {\n",
+              "  grid-column: 1 / -1;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 20px auto;\n",
+              "}\n",
+              "\n",
+              ".xr-array-wrap > label {\n",
+              "  grid-column: 1;\n",
+              "  vertical-align: top;\n",
+              "}\n",
+              "\n",
+              ".xr-preview {\n",
+              "  color: var(--xr-font-color3);\n",
+              "}\n",
+              "\n",
+              ".xr-array-preview,\n",
+              ".xr-array-data {\n",
+              "  padding: 0 5px !important;\n",
+              "  grid-column: 2;\n",
+              "}\n",
+              "\n",
+              ".xr-array-data,\n",
+              ".xr-array-in:checked ~ .xr-array-preview {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              ".xr-array-in:checked ~ .xr-array-data,\n",
+              ".xr-array-preview {\n",
+              "  display: inline-block;\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list {\n",
+              "  display: inline-block !important;\n",
+              "  list-style: none;\n",
+              "  padding: 0 !important;\n",
+              "  margin: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list li {\n",
+              "  display: inline-block;\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list:before {\n",
+              "  content: '(';\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list:after {\n",
+              "  content: ')';\n",
+              "}\n",
+              "\n",
+              ".xr-dim-list li:not(:last-child):after {\n",
+              "  content: ',';\n",
+              "  padding-right: 5px;\n",
+              "}\n",
+              "\n",
+              ".xr-has-index {\n",
+              "  font-weight: bold;\n",
+              "}\n",
+              "\n",
+              ".xr-var-list,\n",
+              ".xr-var-item {\n",
+              "  display: contents;\n",
+              "}\n",
+              "\n",
+              ".xr-var-item > div,\n",
+              ".xr-var-item label,\n",
+              ".xr-var-item > .xr-var-name span {\n",
+              "  background-color: var(--xr-background-color-row-even);\n",
+              "  margin-bottom: 0;\n",
+              "}\n",
+              "\n",
+              ".xr-var-item > .xr-var-name:hover span {\n",
+              "  padding-right: 5px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-list > li:nth-child(odd) > div,\n",
+              ".xr-var-list > li:nth-child(odd) > label,\n",
+              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+              "  background-color: var(--xr-background-color-row-odd);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name {\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-dims {\n",
+              "  grid-column: 2;\n",
+              "}\n",
+              "\n",
+              ".xr-var-dtype {\n",
+              "  grid-column: 3;\n",
+              "  text-align: right;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-preview {\n",
+              "  grid-column: 4;\n",
+              "}\n",
+              "\n",
+              ".xr-index-preview {\n",
+              "  grid-column: 2 / 5;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name,\n",
+              ".xr-var-dims,\n",
+              ".xr-var-dtype,\n",
+              ".xr-preview,\n",
+              ".xr-attrs dt {\n",
+              "  white-space: nowrap;\n",
+              "  overflow: hidden;\n",
+              "  text-overflow: ellipsis;\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name:hover,\n",
+              ".xr-var-dims:hover,\n",
+              ".xr-var-dtype:hover,\n",
+              ".xr-attrs dt:hover {\n",
+              "  overflow: visible;\n",
+              "  width: auto;\n",
+              "  z-index: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  display: none;\n",
+              "  background-color: var(--xr-background-color) !important;\n",
+              "  padding-bottom: 5px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+              ".xr-var-data-in:checked ~ .xr-var-data,\n",
+              ".xr-index-data-in:checked ~ .xr-index-data {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              ".xr-var-data > table {\n",
+              "  float: right;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name span,\n",
+              ".xr-var-data,\n",
+              ".xr-index-name div,\n",
+              ".xr-index-data,\n",
+              ".xr-attrs {\n",
+              "  padding-left: 25px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs,\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  grid-column: 1 / -1;\n",
+              "}\n",
+              "\n",
+              "dl.xr-attrs {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 125px auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt,\n",
+              ".xr-attrs dd {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  float: left;\n",
+              "  padding-right: 10px;\n",
+              "  width: auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt {\n",
+              "  font-weight: normal;\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt:hover span {\n",
+              "  display: inline-block;\n",
+              "  background: var(--xr-background-color);\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dd {\n",
+              "  grid-column: 2;\n",
+              "  white-space: pre-wrap;\n",
+              "  word-break: break-all;\n",
+              "}\n",
+              "\n",
+              ".xr-icon-database,\n",
+              ".xr-icon-file-text2,\n",
+              ".xr-no-icon {\n",
+              "  display: inline-block;\n",
+              "  vertical-align: middle;\n",
+              "  width: 1em;\n",
+              "  height: 1.5em !important;\n",
+              "  stroke-width: 0;\n",
+              "  stroke: currentColor;\n",
+              "  fill: currentColor;\n",
+              "}\n",
+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+              "Dimensions:     (member: 57, time: 31, nbasins: 1)\n",
+              "Coordinates:\n",
+              "  * member      (member) int64 1954 1955 1956 1957 1958 ... 2007 2008 2009 2010\n",
+              "  * time        (time) datetime64[ns] 2003-04-13 2003-04-14 ... 2003-05-13\n",
+              "    basin_name  (nbasins) object &#x27;sub_001&#x27;\n",
+              "Dimensions without coordinates: nbasins\n",
+              "Data variables:\n",
+              "    precip      (member, time) float64 nan 0.2054 0.0 4.304 ... 0.0 0.0 0.0 0.0\n",
+              "    q_sim       (member, time, nbasins) float64 0.0 0.1644 ... 0.5947 0.5763\n",
+              "    q_obs       (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
+              "    q_in        (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
+              "Attributes:\n",
+              "    Conventions:  CF-1.6\n",
+              "    featureType:  timeSeries\n",
+              "    history:      Created on 2023-05-31T13:22:36 by Raven 3.7\n",
+              "    description:  Standard Output\n",
+              "    references:   Craig J.R. and the Raven Development Team Raven user&#x27;s and ...\n",
+              "    model_id:     GR4JCN</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8fbbad0e-6644-42f6-b4b0-d7b69fe04b17' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8fbbad0e-6644-42f6-b4b0-d7b69fe04b17' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>member</span>: 57</li><li><span class='xr-has-index'>time</span>: 31</li><li><span>nbasins</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1b3b7455-e541-4371-ab35-3878e8fe7b44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b3b7455-e541-4371-ab35-3878e8fe7b44' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member</span></div><div class='xr-var-dims'>(member)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1954 1955 1956 ... 2008 2009 2010</div><input id='attrs-d027e6b8-fa59-48df-8ceb-68ce3a795df3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d027e6b8-fa59-48df-8ceb-68ce3a795df3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28f508d5-81b6-4ed8-ad3f-fa58bd0638a4' class='xr-var-data-in' type='checkbox'><label for='data-28f508d5-81b6-4ed8-ad3f-fa58bd0638a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,\n",
+              "       1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977,\n",
+              "       1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989,\n",
+              "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,\n",
+              "       2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2003-04-13 ... 2003-05-13</div><input id='attrs-96768260-b0f1-4299-b1b5-da0b166babde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96768260-b0f1-4299-b1b5-da0b166babde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75d6fac0-2a73-47f2-87a7-fce1f6925164' class='xr-var-data-in' type='checkbox'><label for='data-75d6fac0-2a73-47f2-87a7-fce1f6925164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2003-04-13T00:00:00.000000000&#x27;, &#x27;2003-04-14T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-15T00:00:00.000000000&#x27;, &#x27;2003-04-16T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-17T00:00:00.000000000&#x27;, &#x27;2003-04-18T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-19T00:00:00.000000000&#x27;, &#x27;2003-04-20T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-21T00:00:00.000000000&#x27;, &#x27;2003-04-22T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-23T00:00:00.000000000&#x27;, &#x27;2003-04-24T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-25T00:00:00.000000000&#x27;, &#x27;2003-04-26T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-27T00:00:00.000000000&#x27;, &#x27;2003-04-28T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-04-29T00:00:00.000000000&#x27;, &#x27;2003-04-30T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-05-01T00:00:00.000000000&#x27;, &#x27;2003-05-02T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-05-03T00:00:00.000000000&#x27;, &#x27;2003-05-04T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-05-05T00:00:00.000000000&#x27;, &#x27;2003-05-06T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-05-07T00:00:00.000000000&#x27;, &#x27;2003-05-08T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-05-09T00:00:00.000000000&#x27;, &#x27;2003-05-10T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-05-11T00:00:00.000000000&#x27;, &#x27;2003-05-12T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2003-05-13T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;sub_001&#x27;</div><input id='attrs-520a0322-007a-4929-a2e9-f0795354d61a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-520a0322-007a-4929-a2e9-f0795354d61a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19373258-662b-49d5-9450-872809f3c23f' class='xr-var-data-in' type='checkbox'><label for='data-19373258-662b-49d5-9450-872809f3c23f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;sub_001&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a7a0157-91dd-48f1-95fa-65c334ca62fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a7a0157-91dd-48f1-95fa-65c334ca62fc' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>precip</span></div><div class='xr-var-dims'>(member, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.2054 0.0 ... 0.0 0.0 0.0</div><input id='attrs-29216bb7-c50f-49e1-a572-07bfe62447a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29216bb7-c50f-49e1-a572-07bfe62447a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75e861e2-8823-4391-b2db-176caace9ba4' class='xr-var-data-in' type='checkbox'><label for='data-75e861e2-8823-4391-b2db-176caace9ba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm d**-1</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd></dl></div><div class='xr-var-data'><pre>array([[     nan, 0.205412, 0.      , ..., 2.475529, 5.122706, 8.276706],\n",
+              "       [     nan, 2.758588, 0.386235, ..., 0.021882, 0.      , 2.231294],\n",
+              "       [     nan, 0.      , 0.      , ..., 0.908824, 0.      , 0.      ],\n",
+              "       ...,\n",
+              "       [     nan, 0.      , 0.      , ..., 0.827176, 0.874118, 1.871765],\n",
+              "       [     nan, 2.160471, 0.085412, ..., 0.      , 4.861647, 3.018   ],\n",
+              "       [     nan, 0.      , 0.      , ..., 0.      , 0.      , 0.      ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_sim</span></div><div class='xr-var-dims'>(member, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1644 0.5532 ... 0.5947 0.5763</div><input id='attrs-7b887d0f-943d-4cd4-9c15-bdab3f77b15f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b887d0f-943d-4cd4-9c15-bdab3f77b15f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-936bcd9a-2443-4d16-9cab-1e62105b958f' class='xr-var-data-in' type='checkbox'><label for='data-936bcd9a-2443-4d16-9cab-1e62105b958f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div><div class='xr-var-data'><pre>array([[[0.        ],\n",
+              "        [0.16438672],\n",
+              "        [0.55323675],\n",
+              "        ...,\n",
+              "        [0.89759061],\n",
+              "        [2.05164214],\n",
+              "        [4.77510839]],\n",
+              "\n",
+              "       [[0.        ],\n",
+              "        [0.44183276],\n",
+              "        [1.21557097],\n",
+              "        ...,\n",
+              "        [0.61640215],\n",
+              "        [0.60126026],\n",
+              "        [0.59153068]],\n",
+              "\n",
+              "       [[0.        ],\n",
+              "        [0.16250213],\n",
+              "        [0.54491656],\n",
+              "        ...,\n",
+              "...\n",
+              "        ...,\n",
+              "        [0.62064597],\n",
+              "        [0.60488002],\n",
+              "        [0.59731236]],\n",
+              "\n",
+              "       [[0.        ],\n",
+              "        [0.37405043],\n",
+              "        [1.05346711],\n",
+              "        ...,\n",
+              "        [0.62502891],\n",
+              "        [1.0587842 ],\n",
+              "        [1.90355708]],\n",
+              "\n",
+              "       [[0.        ],\n",
+              "        [0.16368434],\n",
+              "        [0.54971477],\n",
+              "        ...,\n",
+              "        [0.61085499],\n",
+              "        [0.59465142],\n",
+              "        [0.57628941]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_obs</span></div><div class='xr-var-dims'>(member, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-51e4dba2-5c33-4916-831d-b398beedb7f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51e4dba2-5c33-4916-831d-b398beedb7f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c99c66f8-40fb-4a32-9ab6-e75d710d0dfe' class='xr-var-data-in' type='checkbox'><label for='data-c99c66f8-40fb-4a32-9ab6-e75d710d0dfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed outflows</dd></dl></div><div class='xr-var-data'><pre>array([[[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "...\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_in</span></div><div class='xr-var-dims'>(member, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-a63d5740-4b29-42c4-bb12-e25b3c0bafa9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a63d5740-4b29-42c4-bb12-e25b3c0bafa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf0de24-32d9-4367-ba59-095fabae0365' class='xr-var-data-in' type='checkbox'><label for='data-dbf0de24-32d9-4367-ba59-095fabae0365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed inflows</dd></dl></div><div class='xr-var-data'><pre>array([[[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "...\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]],\n",
+              "\n",
+              "       [[nan],\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        ...,\n",
+              "        [nan],\n",
+              "        [nan],\n",
+              "        [nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f56fb599-ad4b-4d65-a2b0-31ee5284c6e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-f56fb599-ad4b-4d65-a2b0-31ee5284c6e7' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0c6bb9e-188c-4840-bcf6-933428bfe594' class='xr-index-data-in' type='checkbox'/><label for='index-a0c6bb9e-188c-4840-bcf6-933428bfe594' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964,\n",
+              "            1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975,\n",
+              "            1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986,\n",
+              "            1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
+              "            1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
+              "            2009, 2010],\n",
+              "           dtype=&#x27;int64&#x27;, name=&#x27;member&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad915f5f-a75c-4cab-a179-76113c0137b1' class='xr-index-data-in' type='checkbox'/><label for='index-ad915f5f-a75c-4cab-a179-76113c0137b1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2003-04-13&#x27;, &#x27;2003-04-14&#x27;, &#x27;2003-04-15&#x27;, &#x27;2003-04-16&#x27;,\n",
+              "               &#x27;2003-04-17&#x27;, &#x27;2003-04-18&#x27;, &#x27;2003-04-19&#x27;, &#x27;2003-04-20&#x27;,\n",
+              "               &#x27;2003-04-21&#x27;, &#x27;2003-04-22&#x27;, &#x27;2003-04-23&#x27;, &#x27;2003-04-24&#x27;,\n",
+              "               &#x27;2003-04-25&#x27;, &#x27;2003-04-26&#x27;, &#x27;2003-04-27&#x27;, &#x27;2003-04-28&#x27;,\n",
+              "               &#x27;2003-04-29&#x27;, &#x27;2003-04-30&#x27;, &#x27;2003-05-01&#x27;, &#x27;2003-05-02&#x27;,\n",
+              "               &#x27;2003-05-03&#x27;, &#x27;2003-05-04&#x27;, &#x27;2003-05-05&#x27;, &#x27;2003-05-06&#x27;,\n",
+              "               &#x27;2003-05-07&#x27;, &#x27;2003-05-08&#x27;, &#x27;2003-05-09&#x27;, &#x27;2003-05-10&#x27;,\n",
+              "               &#x27;2003-05-11&#x27;, &#x27;2003-05-12&#x27;, &#x27;2003-05-13&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1886d70f-d65e-46ad-9092-d7991c557646' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1886d70f-d65e-46ad-9092-d7991c557646' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>history :</span></dt><dd>Created on 2023-05-31T13:22:36 by Raven 3.7</dd><dt><span>description :</span></dt><dd>Standard Output</dd><dt><span>references :</span></dt><dd>Craig J.R. and the Raven Development Team Raven user&#x27;s and developer&#x27;s manual (Version 3.7) URL: http://raven.uwaterloo.ca/ (2023).</dd><dt><span>model_id :</span></dt><dd>GR4JCN</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.Dataset>\n",
+              "Dimensions:     (member: 57, time: 31, nbasins: 1)\n",
+              "Coordinates:\n",
+              "  * member      (member) int64 1954 1955 1956 1957 1958 ... 2007 2008 2009 2010\n",
+              "  * time        (time) datetime64[ns] 2003-04-13 2003-04-14 ... 2003-05-13\n",
+              "    basin_name  (nbasins) object 'sub_001'\n",
+              "Dimensions without coordinates: nbasins\n",
+              "Data variables:\n",
+              "    precip      (member, time) float64 nan 0.2054 0.0 4.304 ... 0.0 0.0 0.0 0.0\n",
+              "    q_sim       (member, time, nbasins) float64 0.0 0.1644 ... 0.5947 0.5763\n",
+              "    q_obs       (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
+              "    q_in        (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
+              "Attributes:\n",
+              "    Conventions:  CF-1.6\n",
+              "    featureType:  timeSeries\n",
+              "    history:      Created on 2023-05-31T13:22:36 by Raven 3.7\n",
+              "    description:  Standard Output\n",
+              "    references:   Craig J.R. and the Raven Development Team Raven user's and ...\n",
+              "    model_id:     GR4JCN"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:     (member: 57, time: 31, nbasins: 1)\n",
-       "Coordinates:\n",
-       "  * member      (member) int64 1954 1955 1956 1957 1958 ... 2007 2008 2009 2010\n",
-       "  * time        (time) datetime64[ns] 2003-04-13 2003-04-14 ... 2003-05-13\n",
-       "    basin_name  (nbasins) object 'sub_001'\n",
-       "Dimensions without coordinates: nbasins\n",
-       "Data variables:\n",
-       "    precip      (member, time) float64 nan 0.2054 0.0 4.304 ... 0.0 0.0 0.0 0.0\n",
-       "    q_sim       (member, time, nbasins) float64 0.0 0.1644 ... 0.5947 0.5763\n",
-       "    q_obs       (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
-       "    q_in        (member, time, nbasins) float64 nan nan nan nan ... nan nan nan\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    featureType:  timeSeries\n",
-       "    history:      Created on 2023-05-31T13:22:36 by Raven 3.7\n",
-       "    description:  Standard Output\n",
-       "    references:   Craig J.R. and the Raven Development Team Raven user's and ...\n",
-       "    model_id:     GR4JCN"
+      "source": [
+        "# Launch the ESP forecasting method\n",
+        "ESP_sims = climatology_esp(\n",
+        "    config=model_config,\n",
+        ")\n",
+        "\n",
+        "# Show the results in an xarray dataset, ready to use:\n",
+        "ESP_sims.hydrograph"
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Launch the ESP forecasting method\n",
-    "ESP_sims = climatology_esp(\n",
-    "    config=model_config,\n",
-    ")\n",
-    "\n",
-    "# Show the results in an xarray dataset, ready to use:\n",
-    "ESP_sims.hydrograph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "embedded-patrol",
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "embedded-patrol",
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot the forecasts and the 2-year threshold previously estimated\n",
+        "fig, ax = plt.subplots(1)\n",
+        "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(\n",
+        "    ax=ax, hue=\"member\", add_legend=False, color=\"gray\", lw=0.5\n",
+        ")\n",
+        "t = ax.axhline(threshold, color=\"red\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the forecasts and the 2-year threshold previously estimated\n",
-    "fig, ax = plt.subplots(1)\n",
-    "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(\n",
-    "    ax=ax, hue=\"member\", add_legend=False, color=\"gray\", lw=0.5\n",
-    ")\n",
-    "t = ax.axhline(threshold, color=\"red\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "british-bunch",
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flood risk')"
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "british-bunch",
+      "metadata": {
+        "pycharm": {
+          "is_executing": true
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Flood risk')"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Now compute the flood risk given the probabilistic forecast and the threshold associated to the 2-year return\n",
+        "# period.\n",
+        "\n",
+        "threshold = out.sel(return_period=2).values\n",
+        "\n",
+        "# Run the flood forecast risk tool to extract the probability of exceedance in netcdf format and xarray Dataset format\n",
+        "flood_risk_data = compute_forecast_flood_risk(\n",
+        "    forecast=ESP_sims.hydrograph.q_sim,\n",
+        "    flood_level=threshold,\n",
+        ")\n",
+        "\n",
+        "# Extract the data and plot\n",
+        "fig, ax = plt.subplots(1)\n",
+        "l = flood_risk_data.exceedance_probability.plot()\n",
+        "ax.set_ylabel(\"Flood risk\")"
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "surface-constitutional",
+      "metadata": {},
+      "source": [
+        "### Results analysis\n",
+        "We can see from the above figure that there is no risk of exceeding the 2-year return period for the selected dates of the forecast.\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "# Now compute the flood risk given the probabilistic forecast and the threshold associated to the 2-year return\n",
-    "# period.\n",
-    "\n",
-    "threshold = out.sel(return_period=2).values\n",
-    "\n",
-    "# Run the flood forecast risk tool to extract the probability of exceedance in netcdf format and xarray Dataset format\n",
-    "flood_risk_data = compute_forecast_flood_risk(\n",
-    "    forecast=ESP_sims.hydrograph.q_sim,\n",
-    "    flood_level=threshold,\n",
-    ")\n",
-    "\n",
-    "# Extract the data and plot\n",
-    "fig, ax = plt.subplots(1)\n",
-    "l = flood_risk_data.exceedance_probability.plot()\n",
-    "ax.set_ylabel(\"Flood risk\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "surface-constitutional",
-   "metadata": {},
-   "source": [
-    "### Results analysis\n",
-    "We can see from the above figure that there is no risk of exceeding the 2-year return period for the selected dates of the forecast.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
diff --git a/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb b/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb
index 2821714a..7dfbea82 100644
--- a/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb
+++ b/docs/notebooks/Comparing_hindcasts_and_ESP_forecasts.ipynb
@@ -1,378 +1,378 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Comparing hindcasts to a climatological ensemble streamflow prediction (ESP)\n",
-    "\n",
-    "This notebook shows how to use climatological weather to perform a Climatology-based Extended Streamflow Prediction (ESP) forecast. Then using the same initial states, uses the CaSPar archived weather forecasts to generate streamflow hindcasts over the same period. It is thus possible to compare both approaches.\n",
-    "\n",
-    "CaSPAr (Canadian Surface Prediction Archive) is an archive of historical ECCC forecasts developed by Juliane Mai at the University of Waterloo, Canada. More details on CaSPAr can be found here https://caspar-data.ca/.\n",
-    "\n",
-    "Mai, J., Kornelsen, K.C., Tolson, B.A., Fortin, V., Gasset, N., Bouhemhem, D., Schäfer, D., Leahy, M., Anctil, F. and Coulibaly, P., 2020. The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin of the American Meteorological Society, 101(3), pp.E341-E356."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:34:43.522681Z",
-     "iopub.status.busy": "2021-09-08T20:34:43.521515Z",
-     "iopub.status.idle": "2021-09-08T20:34:46.587872Z",
-     "shell.execute_reply": "2021-09-08T20:34:46.587489Z"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Comparing hindcasts to a climatological ensemble streamflow prediction (ESP)\n",
+        "\n",
+        "This notebook shows how to use climatological weather to perform a Climatology-based Extended Streamflow Prediction (ESP) forecast. Then using the same initial states, uses the CaSPar archived weather forecasts to generate streamflow hindcasts over the same period. It is thus possible to compare both approaches.\n",
+        "\n",
+        "CaSPAr (Canadian Surface Prediction Archive) is an archive of historical ECCC forecasts developed by Juliane Mai at the University of Waterloo, Canada. More details on CaSPAr can be found here https://caspar-data.ca/.\n",
+        "\n",
+        "Mai, J., Kornelsen, K.C., Tolson, B.A., Fortin, V., Gasset, N., Bouhemhem, D., Schäfer, D., Leahy, M., Anctil, F. and Coulibaly, P., 2020. The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin of the American Meteorological Society, 101(3), pp.E341-E356."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:34:43.522681Z",
+          "iopub.status.busy": "2021-09-08T20:34:43.521515Z",
+          "iopub.status.idle": "2021-09-08T20:34:46.587872Z",
+          "shell.execute_reply": "2021-09-08T20:34:46.587489Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "# This entire section is cookie-cutter template to allow calling the servers and instantiating the connection\n",
+        "# to the WPS server. Do not modify this block.\n",
+        "import datetime as dt\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import xarray as xr\n",
+        "from clisops.core import average, subset\n",
+        "\n",
+        "from ravenpy import Emulator\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.extractors.forecasts import get_CASPAR_dataset\n",
+        "from ravenpy.utilities import forecasting\n",
+        "from ravenpy.utilities.testdata import get_file, open_dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setting up the warm-up file\n",
+        "\n",
+        "Here we tell the model that we want to forecast over the Salmon River catchment and provide its properties (area, lat/long, elevation). We will run it using the GR4JCN hydrological model and have provided some parameters. Other information on the forecast conditions is provided. Thr first step is to generate a hotstart file to prepare the model to generate forecasts."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:34:46.593357Z",
+          "iopub.status.busy": "2021-09-08T20:34:46.592812Z",
+          "iopub.status.idle": "2021-09-08T20:34:54.155192Z",
+          "shell.execute_reply": "2021-09-08T20:34:54.155550Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# Define the warmup period dates\n",
+        "start_date_wu = dt.datetime(2010, 1, 1)\n",
+        "end_date_wu = dt.datetime(2018, 6, 30)\n",
+        "\n",
+        "# Define the catchment contour. Here we use the Salmon River file we previously generated using the Delineator\n",
+        "# in Tutorial Notebook 01.\n",
+        "basin_contour = get_file(\"notebook_inputs/salmon_river.geojson\")\n",
+        "\n",
+        "# Define some of the catchment properties. Could also be replaced by a call to the properties WPS as in\n",
+        "# the Tutorial Notebook 02.\n",
+        "hru = {}\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Observed weather data for the Salmon river. We extracted this using Tutorial Notebook 03 and the\n",
+        "# salmon_river.geojson file as the contour.\n",
+        "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
+        "\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "# Model configuration\n",
+        "model_config_warmup = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date_wu,\n",
+        "    EndDate=end_date_wu,\n",
+        "    RunName=\"ESP_vs_NWP_warmup\",\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "out1 = Emulator(config=model_config_warmup).run()\n",
+        "\n",
+        "# Extract the path to the final states file that will be used as the next initial states\n",
+        "hotstart = out1.files[\"solution\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dss = open_dataset(ts)\n",
+        "dss"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Hindcasting using Climatological Ensemble Streamflow Prediction (ESP)\n",
+        "Now that we have the hotstart file ready to go, we can configure our model for forecasting in climatology ESP mode:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Date of the hindcast\n",
+        "hdate = dt.datetime(2018, 7, 1)\n",
+        "\n",
+        "# Duration of the hindcast, in days\n",
+        "duration = 7\n",
+        "\n",
+        "# Build a new model config:\n",
+        "# Model configuration\n",
+        "model_config_ESP = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=hdate,\n",
+        "    Duration=duration,\n",
+        "    RunName=\"ESP_vs_NWP_ESPfcst\",\n",
+        ")\n",
+        "\n",
+        "# Set the initial states of this new config to the correct values, i.e. the end of the previous forecast.\n",
+        "model_config_ESP = model_config_ESP.set_solution(hotstart)\n",
+        "\n",
+        "# Simulate the climatological ESP:\n",
+        "ESP_sims = forecasting.climatology_esp(config=model_config_ESP)\n",
+        "\n",
+        "# Show the results in an xarray dataset, ready to use:\n",
+        "ESP_sims.hydrograph"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have now run the hindcast using Climatological ESP and retrieved the results. Let's take a look at the resulting forecast."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:34:54.332948Z",
+          "iopub.status.busy": "2021-09-08T20:34:54.332560Z",
+          "iopub.status.idle": "2021-09-08T20:34:54.882568Z",
+          "shell.execute_reply": "2021-09-08T20:34:54.882869Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# Invent an observation so we can compute metrics later, and display as Qobs here. TODO: Add real streamflow data.\n",
+        "qq = ESP_sims.hydrograph.q_sim[0, :, 0]\n",
+        "\n",
+        "# This is to be replaced with a call to the forecast graphing WPS as soon as merged.\n",
+        "# model.q_sim.plot.line(\"b\", x=\"time\")\n",
+        "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
+        "ESP_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"ESP forecasts\")\n",
+        "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Hindcasting using archived weather forecasts from a weather forecast model\n",
+        "\n",
+        "In this next part, we will use the CaSPAr dataset (archived weather forecasts from Environment and Climate Change Canada) to forecast flows on the same period using the same hotstart file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:34:54.977578Z",
+          "iopub.status.busy": "2021-09-08T20:34:54.976799Z",
+          "iopub.status.idle": "2021-09-08T20:34:55.346922Z",
+          "shell.execute_reply": "2021-09-08T20:34:55.346595Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# Get the Forecast data from GEPS via CASPAR.\n",
+        "# Take an extra day to ensure time-shift doesn't remove a part of our day\n",
+        "ts_hindcast, _ = get_CASPAR_dataset(\"GEPS\", hdate - dt.timedelta(days=1))\n",
+        "\n",
+        "# Subset the data for the region of interest and take the mean to get a single vector\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    ts_subset = subset.subset_shape(ts_hindcast, basin_contour).mean(\n",
+        "        dim=(\"rlat\", \"rlon\")\n",
+        "    )\n",
+        "\n",
+        "ts_subset = ts_subset.resample(time=\"6H\").nearest(\n",
+        "    tolerance=\"1H\"\n",
+        ")  # To make the timesteps identical accross the entire duration\n",
+        "\n",
+        "# We need to write the hindcast data as a file for Raven to be able to access it.\n",
+        "fname = \"/tmp/hindcast.nc\"\n",
+        "ts_subset.to_netcdf(fname)\n",
+        "\n",
+        "# We need to adjust the data_type and alt_names according to the data in the forecast:\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_AVE\": \"tas\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_AVE\", \"PRECIP\"]\n",
+        "\n",
+        "# We will need to reuse this for GR4J. Update according to your needs. For example, here we will also pass\n",
+        "# the catchment latitude and longitude as our CaSPAr data has been averaged at the catchment scale.\n",
+        "# We also need to tell the model to deaccumulate the precipitation and shift it in time by 6 hours for our\n",
+        "# catchment (UTC timezones):\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "    \"PRECIP\": {\n",
+        "        \"Deaccumulate\": True,\n",
+        "        \"TimeShift\": -0.25,\n",
+        "        \"LinearTransform\": {\n",
+        "            \"scale\": 1000.0\n",
+        "        },  # Since we are deaccumulating, we need to manually specify scale.\n",
+        "    },  # Converting meters to mm (multiply by 1000).\n",
+        "    \"TEMP_AVE\": {\n",
+        "        \"TimeShift\": -0.25,\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "# Model configuration for forecasting, including correct start date and forecast duration\n",
+        "model_config_fcst = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            fname, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=hdate,\n",
+        "    Duration=duration,\n",
+        "    RunName=\"NB12_forecast_run\",\n",
+        ")\n",
+        "\n",
+        "# Update the initial states\n",
+        "model_config_fcst = model_config_fcst.set_solution(hotstart)\n",
+        "\n",
+        "# Generate the hindcast by providing all necessary information to generate virtual stations representing\n",
+        "# the forecast members\n",
+        "hindcast_sims = forecasting.hindcast_from_meteo_forecast(\n",
+        "    model_config_fcst,\n",
+        "    forecast=fname,\n",
+        "    overwrite=True,\n",
+        "    # We also need to provide the necessary information to create gauges inside the forecasting model:\n",
+        "    data_kwds=data_kwds,\n",
+        "    data_type=data_type,\n",
+        "    alt_names=alt_names,\n",
+        ")\n",
+        "\n",
+        "# Display the hydrographs\n",
+        "display(hindcast_sims.hydrograph)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hindcast_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
+        "hindcast_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"hindcasts\")\n",
+        "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The model has run in forecast mode and we can now easily compare results:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hindcast_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
+        "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(\"g\", x=\"time\", add_legend=False)\n",
+        "ESP_sims.hydrograph.q_sim[1, :, 0].plot.line(\"g\", x=\"time\", label=\"ESP forecasts\")\n",
+        "hindcast_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"hindcasts\")\n",
+        "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
     }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "# This entire section is cookie-cutter template to allow calling the servers and instantiating the connection\n",
-    "# to the WPS server. Do not modify this block.\n",
-    "import datetime as dt\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "from clisops.core import average, subset\n",
-    "\n",
-    "from ravenpy import Emulator\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.extractors.forecasts import get_CASPAR_dataset\n",
-    "from ravenpy.utilities import forecasting\n",
-    "from ravenpy.utilities.testdata import get_file, open_dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up the warm-up file\n",
-    "\n",
-    "Here we tell the model that we want to forecast over the Salmon River catchment and provide its properties (area, lat/long, elevation). We will run it using the GR4JCN hydrological model and have provided some parameters. Other information on the forecast conditions is provided. Thr first step is to generate a hotstart file to prepare the model to generate forecasts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:34:46.593357Z",
-     "iopub.status.busy": "2021-09-08T20:34:46.592812Z",
-     "iopub.status.idle": "2021-09-08T20:34:54.155192Z",
-     "shell.execute_reply": "2021-09-08T20:34:54.155550Z"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
     }
-   },
-   "outputs": [],
-   "source": [
-    "# Define the warmup period dates\n",
-    "start_date_wu = dt.datetime(2010, 1, 1)\n",
-    "end_date_wu = dt.datetime(2018, 6, 30)\n",
-    "\n",
-    "# Define the catchment contour. Here we use the Salmon River file we previously generated using the Delineator\n",
-    "# in Tutorial Notebook 01.\n",
-    "basin_contour = get_file(\"notebook_inputs/salmon_river.geojson\")\n",
-    "\n",
-    "# Define some of the catchment properties. Could also be replaced by a call to the properties WPS as in\n",
-    "# the Tutorial Notebook 02.\n",
-    "hru = {}\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Observed weather data for the Salmon river. We extracted this using Tutorial Notebook 03 and the\n",
-    "# salmon_river.geojson file as the contour.\n",
-    "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
-    "\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "# Model configuration\n",
-    "model_config_warmup = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date_wu,\n",
-    "    EndDate=end_date_wu,\n",
-    "    RunName=\"ESP_vs_NWP_warmup\",\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "out1 = Emulator(config=model_config_warmup).run()\n",
-    "\n",
-    "# Extract the path to the final states file that will be used as the next initial states\n",
-    "hotstart = out1.files[\"solution\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dss = open_dataset(ts)\n",
-    "dss"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hindcasting using Climatological Ensemble Streamflow Prediction (ESP)\n",
-    "Now that we have the hotstart file ready to go, we can configure our model for forecasting in climatology ESP mode:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Date of the hindcast\n",
-    "hdate = dt.datetime(2018, 7, 1)\n",
-    "\n",
-    "# Duration of the hindcast, in days\n",
-    "duration = 7\n",
-    "\n",
-    "# Build a new model config:\n",
-    "# Model configuration\n",
-    "model_config_ESP = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=hdate,\n",
-    "    Duration=duration,\n",
-    "    RunName=\"ESP_vs_NWP_ESPfcst\",\n",
-    ")\n",
-    "\n",
-    "# Set the initial states of this new config to the correct values, i.e. the end of the previous forecast.\n",
-    "model_config_ESP = model_config_ESP.set_solution(hotstart)\n",
-    "\n",
-    "# Simulate the climatological ESP:\n",
-    "ESP_sims = forecasting.climatology_esp(config=model_config_ESP)\n",
-    "\n",
-    "# Show the results in an xarray dataset, ready to use:\n",
-    "ESP_sims.hydrograph"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have now run the hindcast using Climatological ESP and retrieved the results. Let's take a look at the resulting forecast."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:34:54.332948Z",
-     "iopub.status.busy": "2021-09-08T20:34:54.332560Z",
-     "iopub.status.idle": "2021-09-08T20:34:54.882568Z",
-     "shell.execute_reply": "2021-09-08T20:34:54.882869Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Invent an observation so we can compute metrics later, and display as Qobs here. TODO: Add real streamflow data.\n",
-    "qq = ESP_sims.hydrograph.q_sim[0, :, 0]\n",
-    "\n",
-    "# This is to be replaced with a call to the forecast graphing WPS as soon as merged.\n",
-    "# model.q_sim.plot.line(\"b\", x=\"time\")\n",
-    "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
-    "ESP_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"ESP forecasts\")\n",
-    "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hindcasting using archived weather forecasts from a weather forecast model\n",
-    "\n",
-    "In this next part, we will use the CaSPAr dataset (archived weather forecasts from Environment and Climate Change Canada) to forecast flows on the same period using the same hotstart file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:34:54.977578Z",
-     "iopub.status.busy": "2021-09-08T20:34:54.976799Z",
-     "iopub.status.idle": "2021-09-08T20:34:55.346922Z",
-     "shell.execute_reply": "2021-09-08T20:34:55.346595Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Get the Forecast data from GEPS via CASPAR.\n",
-    "# Take an extra day to ensure time-shift doesn't remove a part of our day\n",
-    "ts_hindcast, _ = get_CASPAR_dataset(\"GEPS\", hdate - dt.timedelta(days=1))\n",
-    "\n",
-    "# Subset the data for the region of interest and take the mean to get a single vector\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    ts_subset = subset.subset_shape(ts_hindcast, basin_contour).mean(\n",
-    "        dim=(\"rlat\", \"rlon\")\n",
-    "    )\n",
-    "\n",
-    "ts_subset = ts_subset.resample(time=\"6H\").nearest(\n",
-    "    tolerance=\"1H\"\n",
-    ")  # To make the timesteps identical accross the entire duration\n",
-    "\n",
-    "# We need to write the hindcast data as a file for Raven to be able to access it.\n",
-    "fname = \"/tmp/hindcast.nc\"\n",
-    "ts_subset.to_netcdf(fname)\n",
-    "\n",
-    "# We need to adjust the data_type and alt_names according to the data in the forecast:\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_AVE\": \"tas\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_AVE\", \"PRECIP\"]\n",
-    "\n",
-    "# We will need to reuse this for GR4J. Update according to your needs. For example, here we will also pass\n",
-    "# the catchment latitude and longitude as our CaSPAr data has been averaged at the catchment scale.\n",
-    "# We also need to tell the model to deaccumulate the precipitation and shift it in time by 6 hours for our\n",
-    "# catchment (UTC timezones):\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "    \"PRECIP\": {\n",
-    "        \"Deaccumulate\": True,\n",
-    "        \"TimeShift\": -0.25,\n",
-    "        \"LinearTransform\": {\n",
-    "            \"scale\": 1000.0\n",
-    "        },  # Since we are deaccumulating, we need to manually specify scale.\n",
-    "    },  # Converting meters to mm (multiply by 1000).\n",
-    "    \"TEMP_AVE\": {\n",
-    "        \"TimeShift\": -0.25,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "# Model configuration for forecasting, including correct start date and forecast duration\n",
-    "model_config_fcst = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            fname, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=hdate,\n",
-    "    Duration=duration,\n",
-    "    RunName=\"NB12_forecast_run\",\n",
-    ")\n",
-    "\n",
-    "# Update the initial states\n",
-    "model_config_fcst = model_config_fcst.set_solution(hotstart)\n",
-    "\n",
-    "# Generate the hindcast by providing all necessary information to generate virtual stations representing\n",
-    "# the forecast members\n",
-    "hindcast_sims = forecasting.hindcast_from_meteo_forecast(\n",
-    "    model_config_fcst,\n",
-    "    forecast=fname,\n",
-    "    overwrite=True,\n",
-    "    # We also need to provide the necessary information to create gauges inside the forecasting model:\n",
-    "    data_kwds=data_kwds,\n",
-    "    data_type=data_type,\n",
-    "    alt_names=alt_names,\n",
-    ")\n",
-    "\n",
-    "# Display the hydrographs\n",
-    "display(hindcast_sims.hydrograph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hindcast_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
-    "hindcast_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"hindcasts\")\n",
-    "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model has run in forecast mode and we can now easily compare results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hindcast_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
-    "ESP_sims.hydrograph.q_sim[:, :, 0].plot.line(\"g\", x=\"time\", add_legend=False)\n",
-    "ESP_sims.hydrograph.q_sim[1, :, 0].plot.line(\"g\", x=\"time\", label=\"ESP forecasts\")\n",
-    "hindcast_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"hindcasts\")\n",
-    "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
+  "nbformat": 4,
+  "nbformat_minor": 1
 }
diff --git a/docs/notebooks/Distributed_hydrological_modelling.ipynb b/docs/notebooks/Distributed_hydrological_modelling.ipynb
index ab884bb6..9e68faac 100644
--- a/docs/notebooks/Distributed_hydrological_modelling.ipynb
+++ b/docs/notebooks/Distributed_hydrological_modelling.ipynb
@@ -1,239 +1,239 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Distributed hydrological modelling\n",
-    "\n",
-    "## Using Ravenpy to build a distributed hydrological model\n",
-    "\n",
-    "In this notebook, we will demonstrate how to build a distributed hydrological model using Raven as well as \"Routing product\" (Generated by BasinMaker), a database of subbasins and how they link to one another in a river network. Currently, Routing product is only available for North American catchments. However, if in time it becomes available on a larger scale, it would be trivial to change the setup apply it to other supported regions."
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Distributed hydrological modelling\n",
+        "\n",
+        "## Using Ravenpy to build a distributed hydrological model\n",
+        "\n",
+        "In this notebook, we will demonstrate how to build a distributed hydrological model using Raven as well as \"Routing product\" (Generated by BasinMaker), a database of subbasins and how they link to one another in a river network. Currently, Routing product is only available for North American catchments. However, if in time it becomes available on a larger scale, it would be trivial to change the setup apply it to other supported regions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import the list of possible model templates for distributed hydrological modelling\n",
+        "from ravenpy.config.emulators import (\n",
+        "    GR4JCN,\n",
+        "    HBVEC,\n",
+        "    HMETS,\n",
+        "    HYPR,\n",
+        "    SACSMA,\n",
+        "    Blended,\n",
+        "    CanadianShield,\n",
+        "    Mohyse,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import datetime as dt\n",
+        "import tempfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import xarray as xr\n",
+        "\n",
+        "from ravenpy import Emulator\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.extractors.routing_product import (\n",
+        "    BasinMakerExtractor,\n",
+        "    GridWeightExtractor,\n",
+        "    open_shapefile,\n",
+        "    upstream_from_coords,\n",
+        ")\n",
+        "from ravenpy.utilities.testdata import get_file, open_dataset\n",
+        "\n",
+        "tmp_path = Path(tempfile.mkdtemp())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In the next step, we will get the Routing product file for our catchment. These can be downloaded here: http://hydrology.uwaterloo.ca/basinmaker/download_regional.html"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Get path to pre-downloaded BasinMaker Routing product database for our catchment\n",
+        "shp_path = get_file(\"basinmaker/drainage_region_0175_v2-1/finalcat_info_v2-1.zip\")\n",
+        "\n",
+        "# Note that for this to work, the coordinates must be in the small\n",
+        "# BasinMaker example (drainage_region_0175)\n",
+        "df = open_shapefile(shp_path)\n",
+        "\n",
+        "# Gauge station for observations at Matapedia\n",
+        "# SubId: 175000128\n",
+        "# -67.12542 48.10417\n",
+        "sub = upstream_from_coords(-67.12542, 48.10417, df)\n",
+        "\n",
+        "# Extract the subbasins and HRUs (one HRU per sub-basin)\n",
+        "bm = BasinMakerExtractor(\n",
+        "    df=sub,\n",
+        "    hru_aspect_convention=\"ArcGIS\",\n",
+        ")\n",
+        "\n",
+        "# Get the .rvh file that we will provide to the config and that links HRUs/subbasins to the river network\n",
+        "rvh = bm.extract(hru_from_sb=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have the HRUs and river network all setup, let's get the hydrometeorological data. We first get the database of streamflows and then do the same for weather. You can provide your own for your own catchments, here we are using our datasets to keep things tidy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Streamflow observations file\n",
+        "qobs_fn = get_file(\"matapedia/Qobs_Matapedia_01BD009.nc\")\n",
+        "\n",
+        "# Make an obervation gauge from the observed streamflow\n",
+        "qobs = rc.ObservationData.from_nc(qobs_fn, alt_names=(\"discharge\",))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now prepare the meteorological data using the Gauge format. Note that this dataset of stations is a combination of stations that we iterate on, making a Gauge object for each station in our dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Meteo observations file\n",
+        "meteo_grid_fn = get_file(\"matapedia/Matapedia_meteo_data_stations.nc\")\n",
+        "\n",
+        "# Alternate names for variables in the files\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Make virtual Gauges\n",
+        "meteo_forcing_stations = [\n",
+        "    rc.Gauge.from_nc(\n",
+        "        meteo_grid_fn,\n",
+        "        data_type=alt_names.keys(),\n",
+        "        station_idx=i + 1,\n",
+        "        alt_names=alt_names,\n",
+        "    )\n",
+        "    for i in range(6)  # Since we have 6 stations\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have the data, we can run the distributed model as usual. Note that we must provide the AVG_ANNUAL_RUNOFF parameter to initialize the catchment's hydrological states for distributed models:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "# Prepare the model configuration\n",
+        "model_config = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    StartDate=dt.datetime(1998, 1, 1),\n",
+        "    EndDate=dt.datetime(2020, 12, 31),\n",
+        "    ObservationData=[qobs],\n",
+        "    Gauge=meteo_forcing_stations,\n",
+        "    GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 40.65},\n",
+        "    **rvh,\n",
+        ")\n",
+        "\n",
+        "# Run the model with the configuration we just built\n",
+        "distributed_outputs = Emulator(model_config).run(overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Explore the results, just like for any other model. However, this time we have a few gauges because the Routing Product integrates some gauges already. We want data for the first gauge:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "# Show the hydrographs object\n",
+        "display(distributed_outputs.hydrograph)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the resulting streamflow\n",
+        "distributed_outputs.hydrograph.q_sim.isel(nbasins=0).plot.line(\n",
+        "    x=\"time\", label=\"Distributed model\", color=\"blue\", lw=1.5\n",
+        ")\n",
+        "\n",
+        "# Plot the observed streamflow\n",
+        "qobs_data = open_dataset(qobs_fn)\n",
+        "qobs_data.discharge.plot.line(x=\"time\", label=\"Observations\", color=\"red\", lw=1.5)\n",
+        "\n",
+        "plt.legend()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the list of possible model templates for distributed hydrological modelling\n",
-    "from ravenpy.config.emulators import (\n",
-    "    GR4JCN,\n",
-    "    HBVEC,\n",
-    "    HMETS,\n",
-    "    HYPR,\n",
-    "    SACSMA,\n",
-    "    Blended,\n",
-    "    CanadianShield,\n",
-    "    Mohyse,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "import tempfile\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "\n",
-    "from ravenpy import Emulator\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.extractors.routing_product import (\n",
-    "    BasinMakerExtractor,\n",
-    "    GridWeightExtractor,\n",
-    "    open_shapefile,\n",
-    "    upstream_from_coords,\n",
-    ")\n",
-    "from ravenpy.utilities.testdata import get_file, open_dataset\n",
-    "\n",
-    "tmp_path = Path(tempfile.mkdtemp())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next step, we will get the Routing product file for our catchment. These can be downloaded here: http://hydrology.uwaterloo.ca/basinmaker/download_regional.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get path to pre-downloaded BasinMaker Routing product database for our catchment\n",
-    "shp_path = get_file(\"basinmaker/drainage_region_0175_v2-1/finalcat_info_v2-1.zip\")\n",
-    "\n",
-    "# Note that for this to work, the coordinates must be in the small\n",
-    "# BasinMaker example (drainage_region_0175)\n",
-    "df = open_shapefile(shp_path)\n",
-    "\n",
-    "# Gauge station for observations at Matapedia\n",
-    "# SubId: 175000128\n",
-    "# -67.12542 48.10417\n",
-    "sub = upstream_from_coords(-67.12542, 48.10417, df)\n",
-    "\n",
-    "# Extract the subbasins and HRUs (one HRU per sub-basin)\n",
-    "bm = BasinMakerExtractor(\n",
-    "    df=sub,\n",
-    "    hru_aspect_convention=\"ArcGIS\",\n",
-    ")\n",
-    "\n",
-    "# Get the .rvh file that we will provide to the config and that links HRUs/subbasins to the river network\n",
-    "rvh = bm.extract(hru_from_sb=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have the HRUs and river network all setup, let's get the hydrometeorological data. We first get the database of streamflows and then do the same for weather. You can provide your own for your own catchments, here we are using our datasets to keep things tidy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Streamflow observations file\n",
-    "qobs_fn = get_file(\"matapedia/Qobs_Matapedia_01BD009.nc\")\n",
-    "\n",
-    "# Make an obervation gauge from the observed streamflow\n",
-    "qobs = rc.ObservationData.from_nc(qobs_fn, alt_names=(\"discharge\",))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now prepare the meteorological data using the Gauge format. Note that this dataset of stations is a combination of stations that we iterate on, making a Gauge object for each station in our dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Meteo observations file\n",
-    "meteo_grid_fn = get_file(\"matapedia/Matapedia_meteo_data_stations.nc\")\n",
-    "\n",
-    "# Alternate names for variables in the files\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Make virtual Gauges\n",
-    "meteo_forcing_stations = [\n",
-    "    rc.Gauge.from_nc(\n",
-    "        meteo_grid_fn,\n",
-    "        data_type=alt_names.keys(),\n",
-    "        station_idx=i + 1,\n",
-    "        alt_names=alt_names,\n",
-    "    )\n",
-    "    for i in range(6)  # Since we have 6 stations\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have the data, we can run the distributed model as usual. Note that we must provide the AVG_ANNUAL_RUNOFF parameter to initialize the catchment's hydrological states for distributed models:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Prepare the model configuration\n",
-    "model_config = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    StartDate=dt.datetime(1998, 1, 1),\n",
-    "    EndDate=dt.datetime(2020, 12, 31),\n",
-    "    ObservationData=[qobs],\n",
-    "    Gauge=meteo_forcing_stations,\n",
-    "    GlobalParameter={\"AVG_ANNUAL_RUNOFF\": 40.65},\n",
-    "    **rvh,\n",
-    ")\n",
-    "\n",
-    "# Run the model with the configuration we just built\n",
-    "distributed_outputs = Emulator(model_config).run(overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Explore the results, just like for any other model. However, this time we have a few gauges because the Routing Product integrates some gauges already. We want data for the first gauge:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Show the hydrographs object\n",
-    "display(distributed_outputs.hydrograph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot the resulting streamflow\n",
-    "distributed_outputs.hydrograph.q_sim.isel(nbasins=0).plot.line(\n",
-    "    x=\"time\", label=\"Distributed model\", color=\"blue\", lw=1.5\n",
-    ")\n",
-    "\n",
-    "# Plot the observed streamflow\n",
-    "qobs_data = open_dataset(qobs_fn)\n",
-    "qobs_data.discharge.plot.line(x=\"time\", label=\"Observations\", color=\"red\", lw=1.5)\n",
-    "\n",
-    "plt.legend()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/HydroShare_integration.ipynb b/docs/notebooks/HydroShare_integration.ipynb
index 2fa10331..93977d6e 100644
--- a/docs/notebooks/HydroShare_integration.ipynb
+++ b/docs/notebooks/HydroShare_integration.ipynb
@@ -1,224 +1,224 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HHsuQMMJyms4"
-   },
-   "source": [
-    "# Accessing HydroShare content\n",
-    "\n",
-    "The following code snippets show examples for how to use the HydroShare Python Client for search and acquire data. See the [documentation](https://hydroshare.github.io/hsclient/) to explore further."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CZNOazcn9-23"
-   },
-   "source": [
-    "## Authenticating with HydroShare\n",
-    "\n",
-    "Before you start interacting with resources in HydroShare you will need to authenticate. Just call `hsclient.Hydroshare()` to be prompted for your username and password. You may also pass your credentials programatically. For this public notebook, we use a token and client_id to authenticate. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "from hsclient import HydroShare, Token\n",
-    "\n",
-    "# Authentication method using username and password\n",
-    "\"\"\"\n",
-    "username = 'XXXXX'\n",
-    "password = 'XXXXX'\n",
-    "hs = HydroShare(username=username, password=password)\n",
-    "\"\"\"\n",
-    "\n",
-    "client_id = os.environ.get(\"HYDROSHARE_AUTH_CLIENT_ID\", \"<your_client_id>\")\n",
-    "access_token = os.environ.get(\"HYDROSHARE_AUTH_TOKEN\", \"<your_auth_token>\")\n",
-    "\n",
-    "token = Token(access_token=access_token, token_type=\"bearer\")\n",
-    "hs = HydroShare(client_id=client_id, token=token)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we're authenticated, let's search for data from the 2017 Harvey flood. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "test harvey netcdf file :  75d17f265dba4c8396725cf98f652302\n",
-      "RAPID: Archiving and Enabling Community Access to Data from Recent US Hurricanes :  564b6d73040142579ad3236e1aeb4712\n",
-      "Hurricane Harvey 2017 Collection :  2836494ee75e43a9bfb647b37260e461\n",
-      "USGS - Harvey Gaged Streamflow Timeseries :  51d1539bf6e94b15ac33f7631228118c\n",
-      "Harvey Flood Data Collections :  12e69ee668124fdf833b29b5167e03c3\n",
-      "NOAA NHC - Harvey 2017 Storm Track :  6168b9969c984b658952a896710b65ef\n",
-      "USGS - Harvey High Water Marks :  615d426f70cc4346875c725b4b8fdc59\n",
-      "Harvey Basemap Data Collections :  7661752c688a4f3ebcf58f8657773530\n",
-      "Texas-Harvey Basemap - Addresses and Boundaries :  d2bab32e7c1d4d55b8cba7221e51b02d\n",
-      "Preserving a Flood of Data: Hurricane Harvey 2017 Data Archive :  64f6af3dcea4475688cac0d6b4917d8d\n",
-      "Hurricane Harvey Streamflow Preview Notebook :  4c089adbbad74aeda3932d8ff283b2b5\n",
-      "Harvey Basemap - Hydrology Map Data :  adb14c9c073e4eee8be82fadb21a0a93\n",
-      "Civil Air Patrol - Harvey Oblique Aerial Photos :  85c5f592e347452a84f552f17a9a05c1\n",
-      "CDC Social Vulnerability Index 2014 :  c2df2a80b9d6490788704a24854f4879\n",
-      "HUC 120200 :  a29b1b3c4889429bb23072c214e432e8\n",
-      "Hurricane Harvey NWM Subsetting Exercise :  3db192783bcb4599bab36d43fc3413db\n",
-      "NOAA NHC - Irma Storm Track - Best Track + Advisories :  aa5c9982a4694a19be2fa9299b78e5ca\n",
-      "Hurricane Harvey 2017 Story Map :  8161a96a08474d12bba219852409be61\n",
-      "Perspectives on research gaps from the differences amongst Hurricane Harvey, Irma and Maria :  7be94dcca60c428e81a6846e97122579\n",
-      "Hurricane Harvey NWIS Data :  2f469f714ea541dc86b6578066e7815f\n",
-      "Data for \"A Computationally Efficient and Physically Based Approach for Urban Flood Modeling Using a Flexible Spatiotemporal Structure\" :  e314ed52a83a46dd9e575304c299bd83\n",
-      "Test for versioning published resource :  c4037062b89d4e989b66a60b6ef176e4\n",
-      "Test Collection Inner :  c34cacb091074999aba351698eba3861\n",
-      "FEMA - Harvey Damage Assessments and Claims :  a52d209d46eb42578be0a7472c48e2d5\n",
-      "FEMA - Harvey Flood Depths Grid :  e8768f4cb4d5478a96d2b1cbd00d9e85\n",
-      "ECMWF GloFAS - Harvey+Irma Flood Area Grids :  9ff2b9ad3eb74b06a5af8491c399ee57\n",
-      "NOAA NWC - Harvey National Water Model Streamflow Forecasts :  35d4502200764c2985c24ae5c8836ab9\n",
-      "Hurricane Harvey flood inundation simulation :  fae24734d6fc47be8bf0b54d6a175d86\n",
-      "Coupling coastal and hydrologic models through BMI and Nextgen National Water Model Framework in low gradient coastal regions of Galveston Bay, Texas, USA Results :  379b4c8c663c460d87c246641dc5cea2\n",
-      "IGUIDE Shapefile Testing Resource :  9d413b9d57824a79b8239a5f7c4fdf51\n"
-     ]
-    }
-   ],
-   "source": [
-    "results = hs.search(subject=[\"Harvey\"])\n",
-    "for r in results:\n",
-    "    print(r.resource_title, \": \", r.resource_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "HydroShare resources are identified uniquely by their `resource_id`. Here we use the ID for the `USGS - Harvey Gaged Streamflow Timeseries` to see which files are stored. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HHsuQMMJyms4"
+      },
+      "source": [
+        "# Accessing HydroShare content\n",
+        "\n",
+        "The following code snippets show examples for how to use the HydroShare Python Client for search and acquire data. See the [documentation](https://hydroshare.github.io/hsclient/) to explore further."
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "['USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.dbf',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile.zip',\n",
-       " 'download_usgs_gage_height_inst.R',\n",
-       " 'USGS_gage_discharge_timeseries.zip',\n",
-       " 'USGS_Harvey_gages_TxLaMsAr.csv',\n",
-       " 'README.md',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.prj',\n",
-       " 'USGS gage timeseries example.png',\n",
-       " 'USGS-NWS gages in Harvey study area.png',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shp',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shx',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shp.xml',\n",
-       " 'USGS_gage_height_timeseries.zip',\n",
-       " 'README-USGS Gaged Streamflow Timeseries.pdf',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.cpg',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.sbx',\n",
-       " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.sbn',\n",
-       " 'download_usgs_gage_discharge_inst.R']"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CZNOazcn9-23"
+      },
+      "source": [
+        "## Authenticating with HydroShare\n",
+        "\n",
+        "Before you start interacting with resources in HydroShare you will need to authenticate. Just call `hsclient.Hydroshare()` to be prompted for your username and password. You may also pass your credentials programatically. For this public notebook, we use a token and client_id to authenticate. "
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = hs.resource(\"51d1539bf6e94b15ac33f7631228118c\", validate=False)\n",
-    "res.files()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can simply use the `file_download` method to save a copy locally. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "from hsclient import HydroShare, Token\n",
+        "\n",
+        "# Authentication method using username and password\n",
+        "\"\"\"\n",
+        "username = 'XXXXX'\n",
+        "password = 'XXXXX'\n",
+        "hs = HydroShare(username=username, password=password)\n",
+        "\"\"\"\n",
+        "\n",
+        "client_id = os.environ.get(\"HYDROSHARE_AUTH_CLIENT_ID\", \"<your_client_id>\")\n",
+        "access_token = os.environ.get(\"HYDROSHARE_AUTH_TOKEN\", \"<your_auth_token>\")\n",
+        "\n",
+        "token = Token(access_token=access_token, token_type=\"bearer\")\n",
+        "hs = HydroShare(client_id=client_id, token=token)"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "'/tmp/USGS_Harvey_gages_TxLaMsAr.csv'"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we're authenticated, let's search for data from the 2017 Harvey flood. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "test harvey netcdf file :  75d17f265dba4c8396725cf98f652302\n",
+            "RAPID: Archiving and Enabling Community Access to Data from Recent US Hurricanes :  564b6d73040142579ad3236e1aeb4712\n",
+            "Hurricane Harvey 2017 Collection :  2836494ee75e43a9bfb647b37260e461\n",
+            "USGS - Harvey Gaged Streamflow Timeseries :  51d1539bf6e94b15ac33f7631228118c\n",
+            "Harvey Flood Data Collections :  12e69ee668124fdf833b29b5167e03c3\n",
+            "NOAA NHC - Harvey 2017 Storm Track :  6168b9969c984b658952a896710b65ef\n",
+            "USGS - Harvey High Water Marks :  615d426f70cc4346875c725b4b8fdc59\n",
+            "Harvey Basemap Data Collections :  7661752c688a4f3ebcf58f8657773530\n",
+            "Texas-Harvey Basemap - Addresses and Boundaries :  d2bab32e7c1d4d55b8cba7221e51b02d\n",
+            "Preserving a Flood of Data: Hurricane Harvey 2017 Data Archive :  64f6af3dcea4475688cac0d6b4917d8d\n",
+            "Hurricane Harvey Streamflow Preview Notebook :  4c089adbbad74aeda3932d8ff283b2b5\n",
+            "Harvey Basemap - Hydrology Map Data :  adb14c9c073e4eee8be82fadb21a0a93\n",
+            "Civil Air Patrol - Harvey Oblique Aerial Photos :  85c5f592e347452a84f552f17a9a05c1\n",
+            "CDC Social Vulnerability Index 2014 :  c2df2a80b9d6490788704a24854f4879\n",
+            "HUC 120200 :  a29b1b3c4889429bb23072c214e432e8\n",
+            "Hurricane Harvey NWM Subsetting Exercise :  3db192783bcb4599bab36d43fc3413db\n",
+            "NOAA NHC - Irma Storm Track - Best Track + Advisories :  aa5c9982a4694a19be2fa9299b78e5ca\n",
+            "Hurricane Harvey 2017 Story Map :  8161a96a08474d12bba219852409be61\n",
+            "Perspectives on research gaps from the differences amongst Hurricane Harvey, Irma and Maria :  7be94dcca60c428e81a6846e97122579\n",
+            "Hurricane Harvey NWIS Data :  2f469f714ea541dc86b6578066e7815f\n",
+            "Data for \"A Computationally Efficient and Physically Based Approach for Urban Flood Modeling Using a Flexible Spatiotemporal Structure\" :  e314ed52a83a46dd9e575304c299bd83\n",
+            "Test for versioning published resource :  c4037062b89d4e989b66a60b6ef176e4\n",
+            "Test Collection Inner :  c34cacb091074999aba351698eba3861\n",
+            "FEMA - Harvey Damage Assessments and Claims :  a52d209d46eb42578be0a7472c48e2d5\n",
+            "FEMA - Harvey Flood Depths Grid :  e8768f4cb4d5478a96d2b1cbd00d9e85\n",
+            "ECMWF GloFAS - Harvey+Irma Flood Area Grids :  9ff2b9ad3eb74b06a5af8491c399ee57\n",
+            "NOAA NWC - Harvey National Water Model Streamflow Forecasts :  35d4502200764c2985c24ae5c8836ab9\n",
+            "Hurricane Harvey flood inundation simulation :  fae24734d6fc47be8bf0b54d6a175d86\n",
+            "Coupling coastal and hydrologic models through BMI and Nextgen National Water Model Framework in low gradient coastal regions of Galveston Bay, Texas, USA Results :  379b4c8c663c460d87c246641dc5cea2\n",
+            "IGUIDE Shapefile Testing Resource :  9d413b9d57824a79b8239a5f7c4fdf51\n"
+          ]
+        }
+      ],
+      "source": [
+        "results = hs.search(subject=[\"Harvey\"])\n",
+        "for r in results:\n",
+        "    print(r.resource_title, \": \", r.resource_id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "HydroShare resources are identified uniquely by their `resource_id`. Here we use the ID for the `USGS - Harvey Gaged Streamflow Timeseries` to see which files are stored. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.dbf',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile.zip',\n",
+              " 'download_usgs_gage_height_inst.R',\n",
+              " 'USGS_gage_discharge_timeseries.zip',\n",
+              " 'USGS_Harvey_gages_TxLaMsAr.csv',\n",
+              " 'README.md',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.prj',\n",
+              " 'USGS gage timeseries example.png',\n",
+              " 'USGS-NWS gages in Harvey study area.png',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shp',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shx',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.shp.xml',\n",
+              " 'USGS_gage_height_timeseries.zip',\n",
+              " 'README-USGS Gaged Streamflow Timeseries.pdf',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.cpg',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.sbx',\n",
+              " 'USGS_Gages_TxLaMsAr_shapefile/USGS_Gages_TxLaMsAr.sbn',\n",
+              " 'download_usgs_gage_discharge_inst.R']"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "res = hs.resource(\"51d1539bf6e94b15ac33f7631228118c\", validate=False)\n",
+        "res.files()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can simply use the `file_download` method to save a copy locally. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'/tmp/USGS_Harvey_gages_TxLaMsAr.csv'"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "res.file_download(\"USGS_Harvey_gages_TxLaMsAr.csv\", save_path=\"/tmp\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "From here, the data are stored locally and can be integrated into workflows.\n"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "res.file_download(\"USGS_Harvey_gages_TxLaMsAr.csv\", save_path=\"/tmp\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From here, the data are stored locally and can be integrated into workflows.\n"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "HS_RDF_Examples.ipynb",
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "HS_RDF_Examples.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/Hydrological_realtime_forecasting.ipynb b/docs/notebooks/Hydrological_realtime_forecasting.ipynb
index 3fc7e182..5a880246 100644
--- a/docs/notebooks/Hydrological_realtime_forecasting.ipynb
+++ b/docs/notebooks/Hydrological_realtime_forecasting.ipynb
@@ -1,271 +1,271 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Real-time flow forecasts with ECCC weather forecasts\n",
-    "\n",
-    "This notebook shows how to perform a streamflow forecast, using ECCC weather forecasts. Generates the forecasts and plots them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:35:47.455423Z",
-     "iopub.status.busy": "2021-09-08T20:35:47.455017Z",
-     "iopub.status.idle": "2021-09-08T20:35:49.547045Z",
-     "shell.execute_reply": "2021-09-08T20:35:49.546636Z"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Real-time flow forecasts with ECCC weather forecasts\n",
+        "\n",
+        "This notebook shows how to perform a streamflow forecast, using ECCC weather forecasts. Generates the forecasts and plots them."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:35:47.455423Z",
+          "iopub.status.busy": "2021-09-08T20:35:47.455017Z",
+          "iopub.status.idle": "2021-09-08T20:35:49.547045Z",
+          "shell.execute_reply": "2021-09-08T20:35:49.546636Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "# Import the required packages\n",
+        "\n",
+        "import datetime as dt\n",
+        "\n",
+        "import fiona\n",
+        "import matplotlib.pyplot as plt\n",
+        "import xarray as xr\n",
+        "from clisops.core import average, subset\n",
+        "\n",
+        "from ravenpy import Emulator\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.extractors.forecasts import get_recent_ECCC_forecast\n",
+        "from ravenpy.utilities import forecasting\n",
+        "from ravenpy.utilities.testdata import get_file, open_dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Define the catchment contour. Here we use the Salmon River file we previously generated using the Delineator\n",
+        "# in Tutorial Notebook 01.\n",
+        "basin_contour = get_file(\"notebook_inputs/salmon_river.geojson\")\n",
+        "\n",
+        "# Get the most recent ECCC forecast data from the Geomet extraction tool:\n",
+        "forecast_data = get_recent_ECCC_forecast(\n",
+        "    fiona.open(basin_contour), climate_model=\"GEPS\"\n",
+        ")\n",
+        "display(forecast_data)\n",
+        "\n",
+        "# We need to write the forecast data as a file for Raven to be able to access it.\n",
+        "fname = \"/tmp/forecast.nc\"\n",
+        "forecast_data.to_netcdf(fname)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:35:49.551680Z",
+          "iopub.status.busy": "2021-09-08T20:35:49.551277Z",
+          "iopub.status.idle": "2021-09-08T20:35:58.407199Z",
+          "shell.execute_reply": "2021-09-08T20:35:58.406725Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# Define the warmup period dates. Our weather file ends before the forecast date so our states will not be as\n",
+        "# good as those of a model run operationally.\n",
+        "start_date_wu = dt.datetime(2010, 1, 1)\n",
+        "end_date_wu = dt.datetime(2020, 3, 30)\n",
+        "\n",
+        "# Define some catchment properties. Could also be replaced by a call to the properties WPS as in\n",
+        "# the Tutorial Notebook 02.\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Observed weather data for the Salmon river. We extracted this using Tutorial Notebook 03 and the\n",
+        "# salmon_river.geojson file as the contour. Used for the model warm-up.\n",
+        "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
+        "\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "# Model configuration\n",
+        "model_config_warmup = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date_wu,\n",
+        "    EndDate=end_date_wu,\n",
+        "    RunName=\"ESP_vs_NWP_warmup\",\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs.\n",
+        "out1 = Emulator(config=model_config_warmup).run()\n",
+        "\n",
+        "# Extract the path to the final states file that will be used as the next initial states\n",
+        "hotstart = out1.files[\"solution\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Length of the desired forecast, in days\n",
+        "duration = 7\n",
+        "\n",
+        "# We need to adjust the data_type and alt_names according to the data in the forecast:\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_AVE\": \"tas\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_AVE\", \"PRECIP\"]\n",
+        "\n",
+        "# We will need to reuse this for GR4J. Update according to your needs. For example, here we will also pass\n",
+        "# the catchment latitude and longitude as our CaSPAr data has been averaged at the catchment scale.\n",
+        "# We also need to tell the model to deaccumulate the precipitation and shift it in time by 6 hours for our\n",
+        "# catchment (UTC timezones):\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "    \"PRECIP\": {\n",
+        "        \"Deaccumulate\": True,\n",
+        "        \"TimeShift\": -0.25,\n",
+        "        \"LinearTransform\": {\n",
+        "            \"scale\": 1.0\n",
+        "        },  # Since we are deaccumulating, we need to manually specify scale.\n",
+        "    },  # We are already in mm, so leave it like so (scale = 1.0).\n",
+        "    \"TEMP_AVE\": {\n",
+        "        \"TimeShift\": -0.25,\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "# ECCC forecast time format is a bit complex to work with, so we will use cftime to make it more manageable.\n",
+        "fcst_tmp = open_dataset(fname, use_cftime=True)\n",
+        "\n",
+        "# Get the first timestep that will be used for the model simulation\n",
+        "start_date = fcst_tmp.time.data[0] + dt.timedelta(days=1)\n",
+        "\n",
+        "# Model configuration for forecasting, including correct start date and forecast duration and initial state\n",
+        "model_config_fcst = GR4JCN(\n",
+        "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            fname, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    Duration=duration,\n",
+        "    RunName=\"Realtime_forecast_NB\",\n",
+        ").set_solution(hotstart, timestamp=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "scrolled": false
+      },
+      "outputs": [],
+      "source": [
+        "# Generate the forecast by providing all necessary information to generate virtual stations representing\n",
+        "# the forecast members. Note that we are using the hindcasting tools, becasue there is effectively no difference\n",
+        "# between operational hindcasting and operational forecasting except for the forecast issue time and data\n",
+        "# availability, which we solved by using the most recent ECCC forecasts with a warmed-up model and hotstart file.\n",
+        "\n",
+        "forecast_sims = forecasting.hindcast_from_meteo_forecast(\n",
+        "    model_config_fcst,\n",
+        "    forecast=fname,\n",
+        "    ens_dim=\"members\",\n",
+        "    # We also need to provide the necessary information to create gauges inside the forecasting model:\n",
+        "    data_kwds=data_kwds,\n",
+        "    data_type=data_type,\n",
+        "    alt_names=alt_names,\n",
+        ")\n",
+        "\n",
+        "display(forecast_sims.hydrograph)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## And, for visual representation of the forecasts:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Simulate an observed streamflow timeseries: Here we take a member from the ensemble, but you should use your own\n",
+        "# observed timeseries:\n",
+        "qq = forecast_sims.hydrograph.q_sim[0, :, 0]\n",
+        "\n",
+        "# This is to be replaced with a call to the forecast graphing WPS as soon as merged.\n",
+        "# model.q_sim.plot.line(\"b\", x=\"time\")\n",
+        "forecast_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
+        "forecast_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"forecasts\")\n",
+        "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
+        "plt.legend(loc=\"upper left\")\n",
+        "plt.show()"
+      ]
     }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "# Import the required packages\n",
-    "\n",
-    "import datetime as dt\n",
-    "\n",
-    "import fiona\n",
-    "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "from clisops.core import average, subset\n",
-    "\n",
-    "from ravenpy import Emulator\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.extractors.forecasts import get_recent_ECCC_forecast\n",
-    "from ravenpy.utilities import forecasting\n",
-    "from ravenpy.utilities.testdata import get_file, open_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the catchment contour. Here we use the Salmon River file we previously generated using the Delineator\n",
-    "# in Tutorial Notebook 01.\n",
-    "basin_contour = get_file(\"notebook_inputs/salmon_river.geojson\")\n",
-    "\n",
-    "# Get the most recent ECCC forecast data from the Geomet extraction tool:\n",
-    "forecast_data = get_recent_ECCC_forecast(\n",
-    "    fiona.open(basin_contour), climate_model=\"GEPS\"\n",
-    ")\n",
-    "display(forecast_data)\n",
-    "\n",
-    "# We need to write the forecast data as a file for Raven to be able to access it.\n",
-    "fname = \"/tmp/forecast.nc\"\n",
-    "forecast_data.to_netcdf(fname)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:35:49.551680Z",
-     "iopub.status.busy": "2021-09-08T20:35:49.551277Z",
-     "iopub.status.idle": "2021-09-08T20:35:58.407199Z",
-     "shell.execute_reply": "2021-09-08T20:35:58.406725Z"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
     }
-   },
-   "outputs": [],
-   "source": [
-    "# Define the warmup period dates. Our weather file ends before the forecast date so our states will not be as\n",
-    "# good as those of a model run operationally.\n",
-    "start_date_wu = dt.datetime(2010, 1, 1)\n",
-    "end_date_wu = dt.datetime(2020, 3, 30)\n",
-    "\n",
-    "# Define some catchment properties. Could also be replaced by a call to the properties WPS as in\n",
-    "# the Tutorial Notebook 02.\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Observed weather data for the Salmon river. We extracted this using Tutorial Notebook 03 and the\n",
-    "# salmon_river.geojson file as the contour. Used for the model warm-up.\n",
-    "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
-    "\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "# Model configuration\n",
-    "model_config_warmup = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date_wu,\n",
-    "    EndDate=end_date_wu,\n",
-    "    RunName=\"ESP_vs_NWP_warmup\",\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs.\n",
-    "out1 = Emulator(config=model_config_warmup).run()\n",
-    "\n",
-    "# Extract the path to the final states file that will be used as the next initial states\n",
-    "hotstart = out1.files[\"solution\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Length of the desired forecast, in days\n",
-    "duration = 7\n",
-    "\n",
-    "# We need to adjust the data_type and alt_names according to the data in the forecast:\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_AVE\": \"tas\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_AVE\", \"PRECIP\"]\n",
-    "\n",
-    "# We will need to reuse this for GR4J. Update according to your needs. For example, here we will also pass\n",
-    "# the catchment latitude and longitude as our CaSPAr data has been averaged at the catchment scale.\n",
-    "# We also need to tell the model to deaccumulate the precipitation and shift it in time by 6 hours for our\n",
-    "# catchment (UTC timezones):\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "    \"PRECIP\": {\n",
-    "        \"Deaccumulate\": True,\n",
-    "        \"TimeShift\": -0.25,\n",
-    "        \"LinearTransform\": {\n",
-    "            \"scale\": 1.0\n",
-    "        },  # Since we are deaccumulating, we need to manually specify scale.\n",
-    "    },  # We are already in mm, so leave it like so (scale = 1.0).\n",
-    "    \"TEMP_AVE\": {\n",
-    "        \"TimeShift\": -0.25,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "# ECCC forecast time format is a bit complex to work with, so we will use cftime to make it more manageable.\n",
-    "fcst_tmp = open_dataset(fname, use_cftime=True)\n",
-    "\n",
-    "# Get the first timestep that will be used for the model simulation\n",
-    "start_date = fcst_tmp.time.data[0] + dt.timedelta(days=1)\n",
-    "\n",
-    "# Model configuration for forecasting, including correct start date and forecast duration and initial state\n",
-    "model_config_fcst = GR4JCN(\n",
-    "    params=[0.529, -3.396, 407.29, 1.072, 16.9, 0.947],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            fname, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    Duration=duration,\n",
-    "    RunName=\"Realtime_forecast_NB\",\n",
-    ").set_solution(hotstart, timestamp=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Generate the forecast by providing all necessary information to generate virtual stations representing\n",
-    "# the forecast members. Note that we are using the hindcasting tools, becasue there is effectively no difference\n",
-    "# between operational hindcasting and operational forecasting except for the forecast issue time and data\n",
-    "# availability, which we solved by using the most recent ECCC forecasts with a warmed-up model and hotstart file.\n",
-    "\n",
-    "forecast_sims = forecasting.hindcast_from_meteo_forecast(\n",
-    "    model_config_fcst,\n",
-    "    forecast=fname,\n",
-    "    ens_dim=\"members\",\n",
-    "    # We also need to provide the necessary information to create gauges inside the forecasting model:\n",
-    "    data_kwds=data_kwds,\n",
-    "    data_type=data_type,\n",
-    "    alt_names=alt_names,\n",
-    ")\n",
-    "\n",
-    "display(forecast_sims.hydrograph)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## And, for visual representation of the forecasts:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Simulate an observed streamflow timeseries: Here we take a member from the ensemble, but you should use your own\n",
-    "# observed timeseries:\n",
-    "qq = forecast_sims.hydrograph.q_sim[0, :, 0]\n",
-    "\n",
-    "# This is to be replaced with a call to the forecast graphing WPS as soon as merged.\n",
-    "# model.q_sim.plot.line(\"b\", x=\"time\")\n",
-    "forecast_sims.hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
-    "forecast_sims.hydrograph.q_sim[1, :, 0].plot.line(\"b\", x=\"time\", label=\"forecasts\")\n",
-    "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
+  "nbformat": 4,
+  "nbformat_minor": 1
 }
diff --git a/docs/notebooks/Managing_Jupyter_Environments.ipynb b/docs/notebooks/Managing_Jupyter_Environments.ipynb
index 1822ce55..8233b74a 100644
--- a/docs/notebooks/Managing_Jupyter_Environments.ipynb
+++ b/docs/notebooks/Managing_Jupyter_Environments.ipynb
@@ -1,142 +1,142 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4f561d83-4d79-4896-a4d5-723dadf9dceb",
-   "metadata": {},
-   "source": [
-    "# Managing Jupyter Environments\n",
-    "\n",
-    "This Notebook shows how to customize your Jupyter environment to install packages, reset the environment to defaults, and exporting the environment for reproducibility. We also provide some information on general guidelines on using the PAVICS-Hydro JupyterLab instance.\n",
-    "\n",
-    "## Installing packages\n",
-    "It is possible to install packages to the environment if they are not currently installed. To do so, we should prioritize \"mamba\" which can be seen as a faster/more efficient conda, and use pip if mamba fails. We can install packages by issuing the command in a notebook cell. Here we will try importing the \"seaborn\" package, which is not installed by default on PAVICS."
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "4f561d83-4d79-4896-a4d5-723dadf9dceb",
+      "metadata": {},
+      "source": [
+        "# Managing Jupyter Environments\n",
+        "\n",
+        "This Notebook shows how to customize your Jupyter environment to install packages, reset the environment to defaults, and exporting the environment for reproducibility. We also provide some information on general guidelines on using the PAVICS-Hydro JupyterLab instance.\n",
+        "\n",
+        "## Installing packages\n",
+        "It is possible to install packages to the environment if they are not currently installed. To do so, we should prioritize \"mamba\" which can be seen as a faster/more efficient conda, and use pip if mamba fails. We can install packages by issuing the command in a notebook cell. Here we will try importing the \"seaborn\" package, which is not installed by default on PAVICS."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "df83766d-036e-4685-b90b-7c2887967eba",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Attempt to install seaborn. This will fail when run for the first time!\n",
+        "\n",
+        "# UNCOMMENT THE FOLLOWING LINE TO TEST THE EXISTENCE OF THE SEABORN PACKAGE. It is currently commented to ensure the automatic notebook checks do not fail for an obvious reason.\n",
+        "# import seaborn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "121d9b5e-23b0-4948-8acb-616602c3df02",
+      "metadata": {},
+      "source": [
+        "This has failed because the package is not currently installed. Let's install it using mamba. The same command can be used with pip, simply replace \"mamba\" with \"pip\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a29b12d8-4e9e-4aeb-abf9-9096dd6e24a5",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Install using mamba, and provide the \"--yes\" option to pre-confirm installation\n",
+        "!mamba install seaborn --yes\n",
+        "\n",
+        "# This will take a few seconds to download, install and confirm installation."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b5b07347-c7e2-48ee-b525-6cd3bd0bff51",
+      "metadata": {},
+      "source": [
+        "We can now import the newly installed package:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5a82d19e-79e6-4529-9b3b-a6fc5d2a4b60",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# This will now work.\n",
+        "import seaborn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "88cc7291-d5b4-4df7-9746-d0183380be3a",
+      "metadata": {},
+      "source": [
+        "## Resetting the environment\n",
+        "If a package is installed that causes conflicts or causes code to break, it is possible to reset the environment by closing the server and respawning a new one, that will have the default packages installed. To do so, simply go to:\n",
+        "--> File\n",
+        "        --> Hub Control Panel\n",
+        "                             --> Stop my server.\n",
+        "\n",
+        "\n",
+        "Doing so will kill the server, but it will nonetheless keep all of your files. Respawning the server will open a fresh default environment. You can test this now! When you try and re-run the notebook, the first cell will fail again because 'seaborn' will have not been installed yet on this server instance."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e67814b2-d84a-47dd-8c34-a02d6b7903c3",
+      "metadata": {},
+      "source": [
+        "## Exporting your environment\n",
+        "\n",
+        "To export your environment to replicate it elsewhere (such as a local installation, or to make a backup in case of future updates), you need to export two elements:\n",
+        " - The data\n",
+        " - The installed packages\n",
+        "\n",
+        "The data can be exported using the explorer on the left. You can select the files you want to download directly, or you can select \"Download current folder as an archive\". This will allow you to keep a copy of your data on your personal computer. However, note that data stored on this server is not removed or purged. Users are encouraged to use storage on an as-needed basis and to remove data that is not required to free-up resources for other users. PAVICS developers will contact users that use unreasonable amounts of storage space in order to find an alternative solution. The same reasoning also applies to computing power. Users can run multiple kernels/notebooks in parallel, but users are encouraged to use resources on an as-needed basis, with power users potentially being contacted to find alternative solutions.\n",
+        "\n",
+        "The environment can be exported using the following commands:\n",
+        "\n",
+        "**Export it to text in this Notebook:**\n",
+        "\n",
+        "```shell\n",
+        "conda env export\n",
+        "```\n",
+        "\n",
+        "### Other methods to export environments\n",
+        "You can also export the environment to files, using these commands:\n",
+        "\n",
+        "**Export it to file with explicit packages and channels:**\n",
+        "\n",
+        "```shell\n",
+        "conda list --explicit>ENV.txt\n",
+        "```\n",
+        "**Export it cross-platform:**\n",
+        "\n",
+        "```shell\n",
+        "conda env export --from-history>ENV.yml\n",
+        "```\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.16"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "df83766d-036e-4685-b90b-7c2887967eba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Attempt to install seaborn. This will fail when run for the first time!\n",
-    "\n",
-    "# UNCOMMENT THE FOLLOWING LINE TO TEST THE EXISTENCE OF THE SEABORN PACKAGE. It is currently commented to ensure the automatic notebook checks do not fail for an obvious reason.\n",
-    "# import seaborn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "121d9b5e-23b0-4948-8acb-616602c3df02",
-   "metadata": {},
-   "source": [
-    "This has failed because the package is not currently installed. Let's install it using mamba. The same command can be used with pip, simply replace \"mamba\" with \"pip\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a29b12d8-4e9e-4aeb-abf9-9096dd6e24a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install using mamba, and provide the \"--yes\" option to pre-confirm installation\n",
-    "!mamba install seaborn --yes\n",
-    "\n",
-    "# This will take a few seconds to download, install and confirm installation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5b07347-c7e2-48ee-b525-6cd3bd0bff51",
-   "metadata": {},
-   "source": [
-    "We can now import the newly installed package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a82d19e-79e6-4529-9b3b-a6fc5d2a4b60",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This will now work.\n",
-    "import seaborn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88cc7291-d5b4-4df7-9746-d0183380be3a",
-   "metadata": {},
-   "source": [
-    "## Resetting the environment\n",
-    "If a package is installed that causes conflicts or causes code to break, it is possible to reset the environment by closing the server and respawning a new one, that will have the default packages installed. To do so, simply go to:\n",
-    "--> File\n",
-    "        --> Hub Control Panel\n",
-    "                             --> Stop my server.\n",
-    "\n",
-    "\n",
-    "Doing so will kill the server, but it will nonetheless keep all of your files. Respawning the server will open a fresh default environment. You can test this now! When you try and re-run the notebook, the first cell will fail again because 'seaborn' will have not been installed yet on this server instance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e67814b2-d84a-47dd-8c34-a02d6b7903c3",
-   "metadata": {},
-   "source": [
-    "## Exporting your environment\n",
-    "\n",
-    "To export your environment to replicate it elsewhere (such as a local installation, or to make a backup in case of future updates), you need to export two elements:\n",
-    " - The data\n",
-    " - The installed packages\n",
-    "\n",
-    "The data can be exported using the explorer on the left. You can select the files you want to download directly, or you can select \"Download current folder as an archive\". This will allow you to keep a copy of your data on your personal computer. However, note that data stored on this server is not removed or purged. Users are encouraged to use storage on an as-needed basis and to remove data that is not required to free-up resources for other users. PAVICS developers will contact users that use unreasonable amounts of storage space in order to find an alternative solution. The same reasoning also applies to computing power. Users can run multiple kernels/notebooks in parallel, but users are encouraged to use resources on an as-needed basis, with power users potentially being contacted to find alternative solutions.\n",
-    "\n",
-    "The environment can be exported using the following commands:\n",
-    "\n",
-    "**Export it to text in this Notebook:**\n",
-    "\n",
-    "```shell\n",
-    "conda env export\n",
-    "```\n",
-    "\n",
-    "### Other methods to export environments\n",
-    "You can also export the environment to files, using these commands:\n",
-    "\n",
-    "**Export it to file with explicit packages and channels:**\n",
-    "\n",
-    "```shell\n",
-    "conda list --explicit>ENV.txt\n",
-    "```\n",
-    "**Export it cross-platform:**\n",
-    "\n",
-    "```shell\n",
-    "conda env export --from-history>ENV.yml\n",
-    "```\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
diff --git a/docs/notebooks/Perform_Regionalization.ipynb b/docs/notebooks/Perform_Regionalization.ipynb
index 8baf90a9..b39de3ba 100644
--- a/docs/notebooks/Perform_Regionalization.ipynb
+++ b/docs/notebooks/Perform_Regionalization.ipynb
@@ -1,295 +1,295 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regionalization of model parameters\n",
-    "\n",
-    "Here we call the Regionalization WPS service to provide estimated streamflow (best estimate and ensemble) at an ungauged site using three pre-calibrated hydrological models and a large hydrometeorological database with catchment attributes (Extended CANOPEX). Multiple regionalization strategies are allowed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:36:25.297511Z",
-     "iopub.status.busy": "2021-09-08T20:36:25.291877Z",
-     "iopub.status.idle": "2021-09-08T20:36:27.317135Z",
-     "shell.execute_reply": "2021-09-08T20:36:27.315889Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities.regionalization import (\n",
-    "    read_gauged_params,\n",
-    "    read_gauged_properties,\n",
-    "    regionalize,\n",
-    ")\n",
-    "from ravenpy.utilities.testdata import get_file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can first start by setting up our model. This model will be setup on our ungauged basin, for which we want to generate streamflow. We still need to provide meteorological forcings and other descriptors (HRUs), however we do not provide a parameter set. This will be done by regionalization later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:36:27.375133Z",
-     "iopub.status.busy": "2021-09-08T20:36:27.374674Z",
-     "iopub.status.idle": "2021-09-08T20:36:27.378052Z",
-     "shell.execute_reply": "2021-09-08T20:36:27.377685Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Get the forcing dataset for the ungauged watershed\n",
-    "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
-    "\n",
-    "# Get HRUs of ungauged watershed\n",
-    "hru = dict(\n",
-    "    area=4250.6,\n",
-    "    elevation=843.0,\n",
-    "    latitude=54.4848,\n",
-    "    longitude=-123.3659,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Set alternative names for netCDF variables\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# Data types to extract from netCDF\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"Latitude\": hru[\"latitude\"],\n",
-    "        \"Longitude\": hru[\"longitude\"],\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "# Model configuration for the ungauged watershed. Notice we are not providing parameters, because,\n",
-    "# by definition, we do not have the optimal parameters for an ungauged basin.\n",
-    "# Also note that, for now, only the GR4JCN, HMETS and MOHYSE models are supported, as they are the only ones\n",
-    "# for which we have a pre-computed database of parameters to use to estimate relationships between descriptors\n",
-    "# and model parameters.\n",
-    "model_config = GR4JCN(\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=dt.datetime(1990, 1, 1),\n",
-    "    EndDate=dt.datetime(2010, 12, 31),\n",
-    "    RunName=\"regionalization\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now start working on the regionalization method and the required information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We need to provide the name of the model structure we are using. Can be \"GR4JCN\", \"HMETS\" or \"MOHYSE\"\n",
-    "model_structure = \"GR4JCN\"\n",
-    "\n",
-    "# Read the table of model parameters and calibrated NSE values for all the basins in the donors dataset\n",
-    "nash, params = read_gauged_params(model_structure)\n",
-    "\n",
-    "# Which variables do we want to use to estimate the parameter relationships?\n",
-    "# Possible values and their description are provided here:\n",
-    "\"\"\"\n",
-    "latitude  (catchment centroid latitude, degrees)\n",
-    "longitude (catchment centroid longitude, degrees)\n",
-    "area      (drainage area, km²)\n",
-    "gravelius (Gravelius index)\n",
-    "perimeter (catchment perimeter, m)\n",
-    "elevation (mean catchment elevation, m)\n",
-    "slope     (mean catchment slope, %)\n",
-    "aspect    (catchment orientation vs. North, degrees)\n",
-    "forest    (Land-use percentage as forest (%))\n",
-    "grass     (Land-use percentage as grass (%))\n",
-    "wetland   (Land-use percentage as wetlands (%))\n",
-    "urban     (Land-use percentage as urban areas (%))\n",
-    "shrubs    (Land-use percentage as shrubs (%))\n",
-    "crops     (Land-use percentage as crops (%))\n",
-    "snowIce   (Land-use percentage as permanent snow/ice (%))\n",
-    "\"\"\"\n",
-    "variables = [\"latitude\", \"longitude\", \"area\", \"forest\"]\n",
-    "\n",
-    "# Read the desired properties from the donors table\n",
-    "props = read_gauged_properties(variables)\n",
-    "\n",
-    "# Provide the values for the desired variables for the ungauged basin (used to estimate relationships)\n",
-    "ungauged_props = {\n",
-    "    \"latitude\": 40.4848,\n",
-    "    \"longitude\": -103.3659,\n",
-    "    \"area\": 4250.6,\n",
-    "    \"forest\": 0.4,\n",
-    "}\n",
-    "\n",
-    "# Choice of the regionalization method. You can choose between the following methods (with their description):\n",
-    "\"\"\"\n",
-    "SP   (Spatial Proximity: Uses the latitude and longitude only by default, returns the nearest donors)\n",
-    "PS   (Physical Similarity: Finds the most similar donor catchments according to your desired variables)\n",
-    "MLR  (Multiple Linear Regression: Build a linear regression between the desired variables and the model\n",
-    "     parameters from the donor database. Then estimate parameters from the linear regression using\n",
-    "     the ungauged basin's properties.)\n",
-    "SP-IDW (Spatial Proximity but average the results of multiple donors using the inverse distance weighting\n",
-    "        based on distance)\n",
-    "PS-IDW (Physical Similarity but average the results of multiple donors using the inverse distance weighting\n",
-    "        of degree of similarity)\n",
-    "SP-IDW-RA (SP-IDW while adding regression-based parameters to the donor parameter dataset\n",
-    "          [Arsenault and Brissette, 2014])\n",
-    "PS-IDW-RA (PS-IDW while adding regression-based parameters to the donor parameter dataset\n",
-    "          [Arsenault and Brissette, 2014])\n",
-    "---\n",
-    "Arsenault, R., and Brissette, F. P. (2014), Continuous streamflow prediction in ungauged basins:\n",
-    "The effects of equifinality and parameter set selection on uncertainty in regionalization approaches,\n",
-    "Water Resour. Res., 50, 6135–6153, doi:10.1002/2013WR014898.\n",
-    "\"\"\"\n",
-    "regionalization_method = \"SP-IDW-RA\"\n",
-    "\n",
-    "# Here we provide a threshold to exclude donor catchments. Basically, any donors whose calibration NSE is lower\n",
-    "# than this threshold is considered unreliable and is removed from the database prior to processing. 0.6-0.7 are\n",
-    "# generally well-accepted values in the literature. The higher the threshold, the fewer donors remain so an\n",
-    "# equilibrium must be found.\n",
-    "minimum_donor_NSE = 0.7\n",
-    "\n",
-    "# Finally, we can choose how many donors we want to use. The value is only used for SP- and PS-based methods.\n",
-    "# The hydrographs generated by running the model using the parameters of multiple donors are averaged (either\n",
-    "# using a simple mean, or using IDW if we used the IDW tag) which results in generally better hydrographs than\n",
-    "# any of the single hydrographs.\n",
-    "number_donors = 5\n",
-    "\n",
-    "# Launch the regionalization method and get\n",
-    "#  - hydrograph: the mean hydrograph, and\n",
-    "#  - ensemble_hydrograph: the hydrographs of each of the individual donors before averaging\n",
-    "hydrograph, ensemble_hydrograph = regionalize(\n",
-    "    config=model_config,\n",
-    "    method=regionalization_method,\n",
-    "    nash=nash,\n",
-    "    params=params,\n",
-    "    props=props,\n",
-    "    target_props=ungauged_props,\n",
-    "    min_NSE=minimum_donor_NSE,\n",
-    "    size=number_donors,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `hydrograph` and `ensemble` outputs are netCDF files storing the time series. These files are opened by default using `xarray`, which provides convenient and powerful time series analysis and plotting tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:36:29.574816Z",
-     "iopub.status.busy": "2021-09-08T20:36:29.573713Z",
-     "iopub.status.idle": "2021-09-08T20:36:29.578724Z",
-     "shell.execute_reply": "2021-09-08T20:36:29.578262Z"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Regionalization of model parameters\n",
+        "\n",
+        "Here we call the Regionalization WPS service to provide estimated streamflow (best estimate and ensemble) at an ungauged site using three pre-calibrated hydrological models and a large hydrometeorological database with catchment attributes (Extended CANOPEX). Multiple regionalization strategies are allowed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:36:25.297511Z",
+          "iopub.status.busy": "2021-09-08T20:36:25.291877Z",
+          "iopub.status.idle": "2021-09-08T20:36:27.317135Z",
+          "shell.execute_reply": "2021-09-08T20:36:27.315889Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import datetime as dt\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities.regionalization import (\n",
+        "    read_gauged_params,\n",
+        "    read_gauged_properties,\n",
+        "    regionalize,\n",
+        ")\n",
+        "from ravenpy.utilities.testdata import get_file"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can first start by setting up our model. This model will be setup on our ungauged basin, for which we want to generate streamflow. We still need to provide meteorological forcings and other descriptors (HRUs), however we do not provide a parameter set. This will be done by regionalization later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:36:27.375133Z",
+          "iopub.status.busy": "2021-09-08T20:36:27.374674Z",
+          "iopub.status.idle": "2021-09-08T20:36:27.378052Z",
+          "shell.execute_reply": "2021-09-08T20:36:27.377685Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# Get the forcing dataset for the ungauged watershed\n",
+        "ts = get_file(\"notebook_inputs/ERA5_weather_data_Salmon.nc\")\n",
+        "\n",
+        "# Get HRUs of ungauged watershed\n",
+        "hru = dict(\n",
+        "    area=4250.6,\n",
+        "    elevation=843.0,\n",
+        "    latitude=54.4848,\n",
+        "    longitude=-123.3659,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Set alternative names for netCDF variables\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# Data types to extract from netCDF\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"Latitude\": hru[\"latitude\"],\n",
+        "        \"Longitude\": hru[\"longitude\"],\n",
+        "    },\n",
+        "}\n",
+        "\n",
+        "# Model configuration for the ungauged watershed. Notice we are not providing parameters, because,\n",
+        "# by definition, we do not have the optimal parameters for an ungauged basin.\n",
+        "# Also note that, for now, only the GR4JCN, HMETS and MOHYSE models are supported, as they are the only ones\n",
+        "# for which we have a pre-computed database of parameters to use to estimate relationships between descriptors\n",
+        "# and model parameters.\n",
+        "model_config = GR4JCN(\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            ts, data_type=data_type, alt_names=alt_names, data_kwds=data_kwds\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=dt.datetime(1990, 1, 1),\n",
+        "    EndDate=dt.datetime(2010, 12, 31),\n",
+        "    RunName=\"regionalization\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can now start working on the regionalization method and the required information."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We need to provide the name of the model structure we are using. Can be \"GR4JCN\", \"HMETS\" or \"MOHYSE\"\n",
+        "model_structure = \"GR4JCN\"\n",
+        "\n",
+        "# Read the table of model parameters and calibrated NSE values for all the basins in the donors dataset\n",
+        "nash, params = read_gauged_params(model_structure)\n",
+        "\n",
+        "# Which variables do we want to use to estimate the parameter relationships?\n",
+        "# Possible values and their description are provided here:\n",
+        "\"\"\"\n",
+        "latitude  (catchment centroid latitude, degrees)\n",
+        "longitude (catchment centroid longitude, degrees)\n",
+        "area      (drainage area, km²)\n",
+        "gravelius (Gravelius index)\n",
+        "perimeter (catchment perimeter, m)\n",
+        "elevation (mean catchment elevation, m)\n",
+        "slope     (mean catchment slope, %)\n",
+        "aspect    (catchment orientation vs. North, degrees)\n",
+        "forest    (Land-use percentage as forest (%))\n",
+        "grass     (Land-use percentage as grass (%))\n",
+        "wetland   (Land-use percentage as wetlands (%))\n",
+        "urban     (Land-use percentage as urban areas (%))\n",
+        "shrubs    (Land-use percentage as shrubs (%))\n",
+        "crops     (Land-use percentage as crops (%))\n",
+        "snowIce   (Land-use percentage as permanent snow/ice (%))\n",
+        "\"\"\"\n",
+        "variables = [\"latitude\", \"longitude\", \"area\", \"forest\"]\n",
+        "\n",
+        "# Read the desired properties from the donors table\n",
+        "props = read_gauged_properties(variables)\n",
+        "\n",
+        "# Provide the values for the desired variables for the ungauged basin (used to estimate relationships)\n",
+        "ungauged_props = {\n",
+        "    \"latitude\": 40.4848,\n",
+        "    \"longitude\": -103.3659,\n",
+        "    \"area\": 4250.6,\n",
+        "    \"forest\": 0.4,\n",
+        "}\n",
+        "\n",
+        "# Choice of the regionalization method. You can choose between the following methods (with their description):\n",
+        "\"\"\"\n",
+        "SP   (Spatial Proximity: Uses the latitude and longitude only by default, returns the nearest donors)\n",
+        "PS   (Physical Similarity: Finds the most similar donor catchments according to your desired variables)\n",
+        "MLR  (Multiple Linear Regression: Build a linear regression between the desired variables and the model\n",
+        "     parameters from the donor database. Then estimate parameters from the linear regression using\n",
+        "     the ungauged basin's properties.)\n",
+        "SP-IDW (Spatial Proximity but average the results of multiple donors using the inverse distance weighting\n",
+        "        based on distance)\n",
+        "PS-IDW (Physical Similarity but average the results of multiple donors using the inverse distance weighting\n",
+        "        of degree of similarity)\n",
+        "SP-IDW-RA (SP-IDW while adding regression-based parameters to the donor parameter dataset\n",
+        "          [Arsenault and Brissette, 2014])\n",
+        "PS-IDW-RA (PS-IDW while adding regression-based parameters to the donor parameter dataset\n",
+        "          [Arsenault and Brissette, 2014])\n",
+        "---\n",
+        "Arsenault, R., and Brissette, F. P. (2014), Continuous streamflow prediction in ungauged basins:\n",
+        "The effects of equifinality and parameter set selection on uncertainty in regionalization approaches,\n",
+        "Water Resour. Res., 50, 6135–6153, doi:10.1002/2013WR014898.\n",
+        "\"\"\"\n",
+        "regionalization_method = \"SP-IDW-RA\"\n",
+        "\n",
+        "# Here we provide a threshold to exclude donor catchments. Basically, any donors whose calibration NSE is lower\n",
+        "# than this threshold is considered unreliable and is removed from the database prior to processing. 0.6-0.7 are\n",
+        "# generally well-accepted values in the literature. The higher the threshold, the fewer donors remain so an\n",
+        "# equilibrium must be found.\n",
+        "minimum_donor_NSE = 0.7\n",
+        "\n",
+        "# Finally, we can choose how many donors we want to use. The value is only used for SP- and PS-based methods.\n",
+        "# The hydrographs generated by running the model using the parameters of multiple donors are averaged (either\n",
+        "# using a simple mean, or using IDW if we used the IDW tag) which results in generally better hydrographs than\n",
+        "# any of the single hydrographs.\n",
+        "number_donors = 5\n",
+        "\n",
+        "# Launch the regionalization method and get\n",
+        "#  - hydrograph: the mean hydrograph, and\n",
+        "#  - ensemble_hydrograph: the hydrographs of each of the individual donors before averaging\n",
+        "hydrograph, ensemble_hydrograph = regionalize(\n",
+        "    config=model_config,\n",
+        "    method=regionalization_method,\n",
+        "    nash=nash,\n",
+        "    params=params,\n",
+        "    props=props,\n",
+        "    target_props=ungauged_props,\n",
+        "    min_NSE=minimum_donor_NSE,\n",
+        "    size=number_donors,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `hydrograph` and `ensemble` outputs are netCDF files storing the time series. These files are opened by default using `xarray`, which provides convenient and powerful time series analysis and plotting tools."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:36:29.574816Z",
+          "iopub.status.busy": "2021-09-08T20:36:29.573713Z",
+          "iopub.status.idle": "2021-09-08T20:36:29.578724Z",
+          "shell.execute_reply": "2021-09-08T20:36:29.578262Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "display(hydrograph)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "display(ensemble_hydrograph)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "qq = ensemble_hydrograph.q_sim[0, :, 0]\n",
+        "\n",
+        "ensemble_hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
+        "ensemble_hydrograph.q_sim[1, :, 0].plot.line(\n",
+        "    \"b\", x=\"time\", label=\"Regionalized hydrographs\"\n",
+        ")\n",
+        "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
+        "plt.legend(loc=\"upper right\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:36:29.759579Z",
+          "iopub.status.busy": "2021-09-08T20:36:29.751986Z",
+          "iopub.status.idle": "2021-09-08T20:36:29.761276Z",
+          "shell.execute_reply": "2021-09-08T20:36:29.761561Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Max: \", hydrograph.max())\n",
+        "print(\"Mean: \", hydrograph.mean())\n",
+        "print(\"Monthly means: \", hydrograph.groupby(\"time.month\").mean(dim=\"time\"))"
+      ]
     }
-   },
-   "outputs": [],
-   "source": [
-    "display(hydrograph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(ensemble_hydrograph)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "qq = ensemble_hydrograph.q_sim[0, :, 0]\n",
-    "\n",
-    "ensemble_hydrograph.q_sim[:, :, 0].plot.line(\"b\", x=\"time\", add_legend=False)\n",
-    "ensemble_hydrograph.q_sim[1, :, 0].plot.line(\n",
-    "    \"b\", x=\"time\", label=\"Regionalized hydrographs\"\n",
-    ")\n",
-    "qq.plot.line(\"r\", x=\"time\", label=\"observations\")\n",
-    "plt.legend(loc=\"upper right\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:36:29.759579Z",
-     "iopub.status.busy": "2021-09-08T20:36:29.751986Z",
-     "iopub.status.idle": "2021-09-08T20:36:29.761276Z",
-     "shell.execute_reply": "2021-09-08T20:36:29.761561Z"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
     }
-   },
-   "outputs": [],
-   "source": [
-    "print(\"Max: \", hydrograph.max())\n",
-    "print(\"Mean: \", hydrograph.mean())\n",
-    "print(\"Monthly means: \", hydrograph.groupby(\"time.month\").mean(dim=\"time\"))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
+  "nbformat": 4,
+  "nbformat_minor": 1
 }
diff --git a/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb b/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb
index 3ebf2d97..fde67322 100644
--- a/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb
+++ b/docs/notebooks/Running_HMETS_with_CANOPEX_dataset.ipynb
@@ -1,1013 +1,1013 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Forcing HMETS with the extended CANOPEX dataset\n",
-    "\n",
-    "Here we use ravenpy to launch the HMETS hydrological model and analyze the output. We also prepare and gather data directly from the CANOPEX dataset made available freely for all users."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "from numba.core.errors import NumbaDeprecationWarning\n",
-    "\n",
-    "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cookie-cutter template necessary to provide the tools, packages and paths for the project. All notebooks\n",
-    "# need this template (or a slightly adjusted one depending on the required packages)\n",
-    "import datetime as dt\n",
-    "import tempfile\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import pandas as pd\n",
-    "import spotpy\n",
-    "import xarray as xr\n",
-    "\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import HMETS\n",
-    "from ravenpy.utilities.calibration import SpotSetup\n",
-    "from ravenpy.utilities.testdata import get_file\n",
-    "\n",
-    "# Make a temporary folder\n",
-    "tmp = Path(tempfile.mkdtemp())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Forcing HMETS with the extended CANOPEX dataset\n",
+        "\n",
+        "Here we use ravenpy to launch the HMETS hydrological model and analyze the output. We also prepare and gather data directly from the CANOPEX dataset made available freely for all users."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "\n",
+        "from numba.core.errors import NumbaDeprecationWarning\n",
+        "\n",
+        "warnings.simplefilter(\"ignore\", category=NumbaDeprecationWarning)"
+      ]
+    },
     {
-     "data": {
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-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:        (time: 22280, watershed: 5797)\n",
-       "Coordinates:\n",
-       "  * time           (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n",
-       "  * watershed      (watershed) |S64 b&#x27;St. John River at Ninemile Bridge, Main...\n",
-       "Data variables:\n",
-       "    drainage_area  (watershed) float64 ...\n",
-       "    pr             (watershed, time) float64 ...\n",
-       "    tasmax         (watershed, time) float64 ...\n",
-       "    tasmin         (watershed, time) float64 ...\n",
-       "    discharge      (watershed, time) float64 ...\n",
-       "Attributes: (12/15)\n",
-       "    title:          Hydrometeorological data for lumped hydrological modellin...\n",
-       "    institute_id:   ETS\n",
-       "    contact:        Richard Arsenault: richard.arsenault@etsmtl.ca\n",
-       "    date_created:   2020-08-01\n",
-       "    source:         Hydrometric data from USGS National Water Information Ser...\n",
-       "    featureType:    timeSeries\n",
-       "    ...             ...\n",
-       "    activity:       PAVICS_Hydro\n",
-       "    Conventions:    CF-1.6, ACDD-1.3\n",
-       "    summary:        Hydrometeorological database for the PAVICS-Hydro platfor...\n",
-       "    institution:    ETS (École de technologie supérieure)\n",
-       "    DODS.strlen:    72\n",
-       "    DODS.dimName:   string72</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dd666bb4-6992-4c40-b7de-16ad08d8a577' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd666bb4-6992-4c40-b7de-16ad08d8a577' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 22280</li><li><span class='xr-has-index'>watershed</span>: 5797</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-69b25d7e-852d-41fe-8859-0f4c3555d7fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69b25d7e-852d-41fe-8859-0f4c3555d7fb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2010-12-31</div><input id='attrs-db14a257-37bb-42d5-add0-964f5ddd3bb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db14a257-37bb-42d5-add0-964f5ddd3bb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da347b42-712b-4d94-9515-5da0991962d8' class='xr-var-data-in' type='checkbox'><label for='data-da347b42-712b-4d94-9515-5da0991962d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>_ChunkSizes :</span></dt><dd>22280</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1950-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>watershed</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>b&#x27;St. John River at Ninemile Bri...</div><input id='attrs-c20af9a5-f90e-44ac-9dbc-3804ad8e0b68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c20af9a5-f90e-44ac-9dbc-3804ad8e0b68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa6339de-05dd-4aa5-9f70-78347a879e0a' class='xr-var-data-in' type='checkbox'><label for='data-aa6339de-05dd-4aa5-9f70-78347a879e0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name of watershed</dd><dt><span>encoding :</span></dt><dd>utf-8</dd><dt><span>standard_name :</span></dt><dd>watershed_name</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;St. John River at Ninemile Bridge, Maine&#x27;,\n",
-       "       b&#x27;St. John River at Dickey, Maine&#x27;, b&#x27;Fish River near Fort Kent, Maine&#x27;,\n",
-       "       ..., b&#x27;MIDDLE THAMES RIVER AT THAMESFORD&#x27;,\n",
-       "       b&#x27;BIG OTTER CREEK AT TILLSONBURG&#x27;, b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;],\n",
-       "      dtype=&#x27;|S64&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-311b32f9-46d3-4266-a328-6c8595166721' class='xr-section-summary-in' type='checkbox'  checked><label for='section-311b32f9-46d3-4266-a328-6c8595166721' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>drainage_area</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac6e4cf5-08ac-48af-949d-bde75fb5a263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac6e4cf5-08ac-48af-949d-bde75fb5a263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f129eb9e-7fb2-4103-bf69-63018bf83dbd' class='xr-var-data-in' type='checkbox'><label for='data-f129eb9e-7fb2-4103-bf69-63018bf83dbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>drainage_area_at_river_stretch_outlet</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd><dt><span>long_name :</span></dt><dd>drainage_area</dd><dt><span>units :</span></dt><dd>km2</dd><dt><span>_ChunkSizes :</span></dt><dd>5797</dd></dl></div><div class='xr-var-data'><pre>[5797 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-63ea2dc3-e5bb-46e9-8c39-b9188784c7e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63ea2dc3-e5bb-46e9-8c39-b9188784c7e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7449d254-bb51-4400-84af-537fdf5ccd1b' class='xr-var-data-in' type='checkbox'><label for='data-7449d254-bb51-4400-84af-537fdf5ccd1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c3c5117e-8933-4eac-ab61-222ffc61466f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3c5117e-8933-4eac-ab61-222ffc61466f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5228b5b4-2eb4-4c78-8fc5-e5eba835c56b' class='xr-var-data-in' type='checkbox'><label for='data-5228b5b4-2eb4-4c78-8fc5-e5eba835c56b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a872ec5a-a203-4177-b577-9d13490b3a75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a872ec5a-a203-4177-b577-9d13490b3a75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed6441d1-c217-4fdc-942e-90985906cc6f' class='xr-var-data-in' type='checkbox'><label for='data-ed6441d1-c217-4fdc-942e-90985906cc6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Minimum Near-Surface Air Temperature\t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>discharge</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1eea166-0d2f-4cc8-8eff-b7f29f82f534' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1eea166-0d2f-4cc8-8eff-b7f29f82f534' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff82218-673c-46ff-8d08-edcdc5fa9b26' class='xr-var-data-in' type='checkbox'><label for='data-dff82218-673c-46ff-8d08-edcdc5fa9b26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>discharge</dd><dt><span>standard_name :</span></dt><dd>water_volume_transport_in_river_channel</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21af813c-b84c-4426-bf4c-14b5dd82b699' class='xr-section-summary-in' type='checkbox'  ><label for='section-21af813c-b84c-4426-bf4c-14b5dd82b699' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e557621e-7e8c-4bf4-967b-01896f510ef4' class='xr-index-data-in' type='checkbox'/><label for='index-e557621e-7e8c-4bf4-967b-01896f510ef4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1950-01-01&#x27;, &#x27;1950-01-02&#x27;, &#x27;1950-01-03&#x27;, &#x27;1950-01-04&#x27;,\n",
-       "               &#x27;1950-01-05&#x27;, &#x27;1950-01-06&#x27;, &#x27;1950-01-07&#x27;, &#x27;1950-01-08&#x27;,\n",
-       "               &#x27;1950-01-09&#x27;, &#x27;1950-01-10&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2010-12-22&#x27;, &#x27;2010-12-23&#x27;, &#x27;2010-12-24&#x27;, &#x27;2010-12-25&#x27;,\n",
-       "               &#x27;2010-12-26&#x27;, &#x27;2010-12-27&#x27;, &#x27;2010-12-28&#x27;, &#x27;2010-12-29&#x27;,\n",
-       "               &#x27;2010-12-30&#x27;, &#x27;2010-12-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=22280, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>watershed</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6573299-728c-4e82-acae-b53ab965027a' class='xr-index-data-in' type='checkbox'/><label for='index-e6573299-728c-4e82-acae-b53ab965027a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([         b&#x27;St. John River at Ninemile Bridge, Maine&#x27;,\n",
-       "                         b&#x27;St. John River at Dickey, Maine&#x27;,\n",
-       "                        b&#x27;Fish River near Fort Kent, Maine&#x27;,\n",
-       "        b&#x27;St. John River below Fish R, nr Fort Kent, Maine&#x27;,\n",
-       "                    b&#x27;Aroostook River near Masardis, Maine&#x27;,\n",
-       "                          b&#x27;MACHIAS RIVER NEAR ASHLAND, ME&#x27;,\n",
-       "                      b&#x27;Aroostook River at Washburn, Maine&#x27;,\n",
-       "                b&#x27;Little Madawaska River at Caribou, Maine&#x27;,\n",
-       "       b&#x27;Meduxnekeag River at Lowery Rd. nr Houlton, Maine&#x27;,\n",
-       "                     b&#x27;St. Croix River at Vanceboro, Maine&#x27;,\n",
-       "       ...\n",
-       "                         b&#x27;NOTTAWASAGA RIVER NEAR ALLISTON&#x27;,\n",
-       "                             b&#x27;ROSEWAY RIVER AT LOWER OHIO&#x27;,\n",
-       "                              b&#x27;CONESTOGO RIVER AT DRAYTON&#x27;,\n",
-       "                      b&#x27;SOUTH MAITLAND RIVER AT SUMMERHILL&#x27;,\n",
-       "                       b&#x27;MIDDLE MAITLAND RIVER ABOVE ETHEL&#x27;,\n",
-       "                               b&#x27;NITH RIVER ABOVE NITHBURG&#x27;,\n",
-       "                          b&#x27;FAIRCHILD CREEK NEAR BRANTFORD&#x27;,\n",
-       "                       b&#x27;MIDDLE THAMES RIVER AT THAMESFORD&#x27;,\n",
-       "                          b&#x27;BIG OTTER CREEK AT TILLSONBURG&#x27;,\n",
-       "                              b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;watershed&#x27;, length=5797))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-001944b0-3a94-4373-9115-7f9576e842fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-001944b0-3a94-4373-9115-7f9576e842fc' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Hydrometeorological data for lumped hydrological modelling of 5797 catchments in North America</dd><dt><span>institute_id :</span></dt><dd>ETS</dd><dt><span>contact :</span></dt><dd>Richard Arsenault: richard.arsenault@etsmtl.ca</dd><dt><span>date_created :</span></dt><dd>2020-08-01</dd><dt><span>source :</span></dt><dd>Hydrometric data from USGS National Water Information Service and ECCC Water Survey Canada. Meteorological data from ECCC stations, NRCan 10km gridded interpolated dataset and Livneh 2014 database. Catchment areas from ECCC HYDAT, HydroSheds and USGS.</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>cdm_data_type :</span></dt><dd>station</dd><dt><span>license :</span></dt><dd>ODC-BY</dd><dt><span>keywords :</span></dt><dd>hydrology, North America, streamflow, hydrometeorological, PAVICS, PAVICS-Hydro, modelling</dd><dt><span>activity :</span></dt><dd>PAVICS_Hydro</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>summary :</span></dt><dd>Hydrometeorological database for the PAVICS-Hydro platform, including precipitation, temperature, discharge and catchment area to drive the RAVEN hydrological modelling framework. Provided by the HC3 Laboratory at École de technologie supérieure, Montréal, Canada.</dd><dt><span>institution :</span></dt><dd>ETS (École de technologie supérieure)</dd><dt><span>DODS.strlen :</span></dt><dd>72</dd><dt><span>DODS.dimName :</span></dt><dd>string72</dd></dl></div></li></ul></div></div>"
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Cookie-cutter template necessary to provide the tools, packages and paths for the project. All notebooks\n",
+        "# need this template (or a slightly adjusted one depending on the required packages)\n",
+        "import datetime as dt\n",
+        "import tempfile\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import pandas as pd\n",
+        "import spotpy\n",
+        "import xarray as xr\n",
+        "\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import HMETS\n",
+        "from ravenpy.utilities.calibration import SpotSetup\n",
+        "from ravenpy.utilities.testdata import get_file\n",
+        "\n",
+        "# Make a temporary folder\n",
+        "tmp = Path(tempfile.mkdtemp())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
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+              "  text-align: right;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-preview {\n",
+              "  grid-column: 4;\n",
+              "}\n",
+              "\n",
+              ".xr-index-preview {\n",
+              "  grid-column: 2 / 5;\n",
+              "  color: var(--xr-font-color2);\n",
+              "}\n",
+              "\n",
+              ".xr-var-name,\n",
+              ".xr-var-dims,\n",
+              ".xr-var-dtype,\n",
+              ".xr-preview,\n",
+              ".xr-attrs dt {\n",
+              "  white-space: nowrap;\n",
+              "  overflow: hidden;\n",
+              "  text-overflow: ellipsis;\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name:hover,\n",
+              ".xr-var-dims:hover,\n",
+              ".xr-var-dtype:hover,\n",
+              ".xr-attrs dt:hover {\n",
+              "  overflow: visible;\n",
+              "  width: auto;\n",
+              "  z-index: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  display: none;\n",
+              "  background-color: var(--xr-background-color) !important;\n",
+              "  padding-bottom: 5px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+              ".xr-var-data-in:checked ~ .xr-var-data,\n",
+              ".xr-index-data-in:checked ~ .xr-index-data {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              ".xr-var-data > table {\n",
+              "  float: right;\n",
+              "}\n",
+              "\n",
+              ".xr-var-name span,\n",
+              ".xr-var-data,\n",
+              ".xr-index-name div,\n",
+              ".xr-index-data,\n",
+              ".xr-attrs {\n",
+              "  padding-left: 25px !important;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs,\n",
+              ".xr-var-attrs,\n",
+              ".xr-var-data,\n",
+              ".xr-index-data {\n",
+              "  grid-column: 1 / -1;\n",
+              "}\n",
+              "\n",
+              "dl.xr-attrs {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  display: grid;\n",
+              "  grid-template-columns: 125px auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt,\n",
+              ".xr-attrs dd {\n",
+              "  padding: 0;\n",
+              "  margin: 0;\n",
+              "  float: left;\n",
+              "  padding-right: 10px;\n",
+              "  width: auto;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt {\n",
+              "  font-weight: normal;\n",
+              "  grid-column: 1;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dt:hover span {\n",
+              "  display: inline-block;\n",
+              "  background: var(--xr-background-color);\n",
+              "  padding-right: 10px;\n",
+              "}\n",
+              "\n",
+              ".xr-attrs dd {\n",
+              "  grid-column: 2;\n",
+              "  white-space: pre-wrap;\n",
+              "  word-break: break-all;\n",
+              "}\n",
+              "\n",
+              ".xr-icon-database,\n",
+              ".xr-icon-file-text2,\n",
+              ".xr-no-icon {\n",
+              "  display: inline-block;\n",
+              "  vertical-align: middle;\n",
+              "  width: 1em;\n",
+              "  height: 1.5em !important;\n",
+              "  stroke-width: 0;\n",
+              "  stroke: currentColor;\n",
+              "  fill: currentColor;\n",
+              "}\n",
+              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+              "Dimensions:        (time: 22280, watershed: 5797)\n",
+              "Coordinates:\n",
+              "  * time           (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n",
+              "  * watershed      (watershed) |S64 b&#x27;St. John River at Ninemile Bridge, Main...\n",
+              "Data variables:\n",
+              "    drainage_area  (watershed) float64 ...\n",
+              "    pr             (watershed, time) float64 ...\n",
+              "    tasmax         (watershed, time) float64 ...\n",
+              "    tasmin         (watershed, time) float64 ...\n",
+              "    discharge      (watershed, time) float64 ...\n",
+              "Attributes: (12/15)\n",
+              "    title:          Hydrometeorological data for lumped hydrological modellin...\n",
+              "    institute_id:   ETS\n",
+              "    contact:        Richard Arsenault: richard.arsenault@etsmtl.ca\n",
+              "    date_created:   2020-08-01\n",
+              "    source:         Hydrometric data from USGS National Water Information Ser...\n",
+              "    featureType:    timeSeries\n",
+              "    ...             ...\n",
+              "    activity:       PAVICS_Hydro\n",
+              "    Conventions:    CF-1.6, ACDD-1.3\n",
+              "    summary:        Hydrometeorological database for the PAVICS-Hydro platfor...\n",
+              "    institution:    ETS (École de technologie supérieure)\n",
+              "    DODS.strlen:    72\n",
+              "    DODS.dimName:   string72</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dd666bb4-6992-4c40-b7de-16ad08d8a577' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd666bb4-6992-4c40-b7de-16ad08d8a577' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 22280</li><li><span class='xr-has-index'>watershed</span>: 5797</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-69b25d7e-852d-41fe-8859-0f4c3555d7fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69b25d7e-852d-41fe-8859-0f4c3555d7fb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2010-12-31</div><input id='attrs-db14a257-37bb-42d5-add0-964f5ddd3bb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db14a257-37bb-42d5-add0-964f5ddd3bb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da347b42-712b-4d94-9515-5da0991962d8' class='xr-var-data-in' type='checkbox'><label for='data-da347b42-712b-4d94-9515-5da0991962d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>_ChunkSizes :</span></dt><dd>22280</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
+              "       &#x27;1950-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
+              "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
+              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>watershed</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>b&#x27;St. John River at Ninemile Bri...</div><input id='attrs-c20af9a5-f90e-44ac-9dbc-3804ad8e0b68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c20af9a5-f90e-44ac-9dbc-3804ad8e0b68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa6339de-05dd-4aa5-9f70-78347a879e0a' class='xr-var-data-in' type='checkbox'><label for='data-aa6339de-05dd-4aa5-9f70-78347a879e0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name of watershed</dd><dt><span>encoding :</span></dt><dd>utf-8</dd><dt><span>standard_name :</span></dt><dd>watershed_name</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;St. John River at Ninemile Bridge, Maine&#x27;,\n",
+              "       b&#x27;St. John River at Dickey, Maine&#x27;, b&#x27;Fish River near Fort Kent, Maine&#x27;,\n",
+              "       ..., b&#x27;MIDDLE THAMES RIVER AT THAMESFORD&#x27;,\n",
+              "       b&#x27;BIG OTTER CREEK AT TILLSONBURG&#x27;, b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;],\n",
+              "      dtype=&#x27;|S64&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-311b32f9-46d3-4266-a328-6c8595166721' class='xr-section-summary-in' type='checkbox'  checked><label for='section-311b32f9-46d3-4266-a328-6c8595166721' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>drainage_area</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac6e4cf5-08ac-48af-949d-bde75fb5a263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac6e4cf5-08ac-48af-949d-bde75fb5a263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f129eb9e-7fb2-4103-bf69-63018bf83dbd' class='xr-var-data-in' type='checkbox'><label for='data-f129eb9e-7fb2-4103-bf69-63018bf83dbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>drainage_area_at_river_stretch_outlet</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd><dt><span>long_name :</span></dt><dd>drainage_area</dd><dt><span>units :</span></dt><dd>km2</dd><dt><span>_ChunkSizes :</span></dt><dd>5797</dd></dl></div><div class='xr-var-data'><pre>[5797 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-63ea2dc3-e5bb-46e9-8c39-b9188784c7e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63ea2dc3-e5bb-46e9-8c39-b9188784c7e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7449d254-bb51-4400-84af-537fdf5ccd1b' class='xr-var-data-in' type='checkbox'><label for='data-7449d254-bb51-4400-84af-537fdf5ccd1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c3c5117e-8933-4eac-ab61-222ffc61466f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3c5117e-8933-4eac-ab61-222ffc61466f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5228b5b4-2eb4-4c78-8fc5-e5eba835c56b' class='xr-var-data-in' type='checkbox'><label for='data-5228b5b4-2eb4-4c78-8fc5-e5eba835c56b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a872ec5a-a203-4177-b577-9d13490b3a75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a872ec5a-a203-4177-b577-9d13490b3a75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed6441d1-c217-4fdc-942e-90985906cc6f' class='xr-var-data-in' type='checkbox'><label for='data-ed6441d1-c217-4fdc-942e-90985906cc6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Minimum Near-Surface Air Temperature\t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>discharge</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1eea166-0d2f-4cc8-8eff-b7f29f82f534' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1eea166-0d2f-4cc8-8eff-b7f29f82f534' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff82218-673c-46ff-8d08-edcdc5fa9b26' class='xr-var-data-in' type='checkbox'><label for='data-dff82218-673c-46ff-8d08-edcdc5fa9b26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>discharge</dd><dt><span>standard_name :</span></dt><dd>water_volume_transport_in_river_channel</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><div class='xr-var-data'><pre>[129157160 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21af813c-b84c-4426-bf4c-14b5dd82b699' class='xr-section-summary-in' type='checkbox'  ><label for='section-21af813c-b84c-4426-bf4c-14b5dd82b699' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e557621e-7e8c-4bf4-967b-01896f510ef4' class='xr-index-data-in' type='checkbox'/><label for='index-e557621e-7e8c-4bf4-967b-01896f510ef4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1950-01-01&#x27;, &#x27;1950-01-02&#x27;, &#x27;1950-01-03&#x27;, &#x27;1950-01-04&#x27;,\n",
+              "               &#x27;1950-01-05&#x27;, &#x27;1950-01-06&#x27;, &#x27;1950-01-07&#x27;, &#x27;1950-01-08&#x27;,\n",
+              "               &#x27;1950-01-09&#x27;, &#x27;1950-01-10&#x27;,\n",
+              "               ...\n",
+              "               &#x27;2010-12-22&#x27;, &#x27;2010-12-23&#x27;, &#x27;2010-12-24&#x27;, &#x27;2010-12-25&#x27;,\n",
+              "               &#x27;2010-12-26&#x27;, &#x27;2010-12-27&#x27;, &#x27;2010-12-28&#x27;, &#x27;2010-12-29&#x27;,\n",
+              "               &#x27;2010-12-30&#x27;, &#x27;2010-12-31&#x27;],\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=22280, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>watershed</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6573299-728c-4e82-acae-b53ab965027a' class='xr-index-data-in' type='checkbox'/><label for='index-e6573299-728c-4e82-acae-b53ab965027a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([         b&#x27;St. John River at Ninemile Bridge, Maine&#x27;,\n",
+              "                         b&#x27;St. John River at Dickey, Maine&#x27;,\n",
+              "                        b&#x27;Fish River near Fort Kent, Maine&#x27;,\n",
+              "        b&#x27;St. John River below Fish R, nr Fort Kent, Maine&#x27;,\n",
+              "                    b&#x27;Aroostook River near Masardis, Maine&#x27;,\n",
+              "                          b&#x27;MACHIAS RIVER NEAR ASHLAND, ME&#x27;,\n",
+              "                      b&#x27;Aroostook River at Washburn, Maine&#x27;,\n",
+              "                b&#x27;Little Madawaska River at Caribou, Maine&#x27;,\n",
+              "       b&#x27;Meduxnekeag River at Lowery Rd. nr Houlton, Maine&#x27;,\n",
+              "                     b&#x27;St. Croix River at Vanceboro, Maine&#x27;,\n",
+              "       ...\n",
+              "                         b&#x27;NOTTAWASAGA RIVER NEAR ALLISTON&#x27;,\n",
+              "                             b&#x27;ROSEWAY RIVER AT LOWER OHIO&#x27;,\n",
+              "                              b&#x27;CONESTOGO RIVER AT DRAYTON&#x27;,\n",
+              "                      b&#x27;SOUTH MAITLAND RIVER AT SUMMERHILL&#x27;,\n",
+              "                       b&#x27;MIDDLE MAITLAND RIVER ABOVE ETHEL&#x27;,\n",
+              "                               b&#x27;NITH RIVER ABOVE NITHBURG&#x27;,\n",
+              "                          b&#x27;FAIRCHILD CREEK NEAR BRANTFORD&#x27;,\n",
+              "                       b&#x27;MIDDLE THAMES RIVER AT THAMESFORD&#x27;,\n",
+              "                          b&#x27;BIG OTTER CREEK AT TILLSONBURG&#x27;,\n",
+              "                              b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;],\n",
+              "      dtype=&#x27;object&#x27;, name=&#x27;watershed&#x27;, length=5797))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-001944b0-3a94-4373-9115-7f9576e842fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-001944b0-3a94-4373-9115-7f9576e842fc' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Hydrometeorological data for lumped hydrological modelling of 5797 catchments in North America</dd><dt><span>institute_id :</span></dt><dd>ETS</dd><dt><span>contact :</span></dt><dd>Richard Arsenault: richard.arsenault@etsmtl.ca</dd><dt><span>date_created :</span></dt><dd>2020-08-01</dd><dt><span>source :</span></dt><dd>Hydrometric data from USGS National Water Information Service and ECCC Water Survey Canada. Meteorological data from ECCC stations, NRCan 10km gridded interpolated dataset and Livneh 2014 database. Catchment areas from ECCC HYDAT, HydroSheds and USGS.</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>cdm_data_type :</span></dt><dd>station</dd><dt><span>license :</span></dt><dd>ODC-BY</dd><dt><span>keywords :</span></dt><dd>hydrology, North America, streamflow, hydrometeorological, PAVICS, PAVICS-Hydro, modelling</dd><dt><span>activity :</span></dt><dd>PAVICS_Hydro</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>summary :</span></dt><dd>Hydrometeorological database for the PAVICS-Hydro platform, including precipitation, temperature, discharge and catchment area to drive the RAVEN hydrological modelling framework. Provided by the HC3 Laboratory at École de technologie supérieure, Montréal, Canada.</dd><dt><span>institution :</span></dt><dd>ETS (École de technologie supérieure)</dd><dt><span>DODS.strlen :</span></dt><dd>72</dd><dt><span>DODS.dimName :</span></dt><dd>string72</dd></dl></div></li></ul></div></div>"
+            ],
+            "text/plain": [
+              "<xarray.Dataset>\n",
+              "Dimensions:        (time: 22280, watershed: 5797)\n",
+              "Coordinates:\n",
+              "  * time           (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n",
+              "  * watershed      (watershed) |S64 b'St. John River at Ninemile Bridge, Main...\n",
+              "Data variables:\n",
+              "    drainage_area  (watershed) float64 ...\n",
+              "    pr             (watershed, time) float64 ...\n",
+              "    tasmax         (watershed, time) float64 ...\n",
+              "    tasmin         (watershed, time) float64 ...\n",
+              "    discharge      (watershed, time) float64 ...\n",
+              "Attributes: (12/15)\n",
+              "    title:          Hydrometeorological data for lumped hydrological modellin...\n",
+              "    institute_id:   ETS\n",
+              "    contact:        Richard Arsenault: richard.arsenault@etsmtl.ca\n",
+              "    date_created:   2020-08-01\n",
+              "    source:         Hydrometric data from USGS National Water Information Ser...\n",
+              "    featureType:    timeSeries\n",
+              "    ...             ...\n",
+              "    activity:       PAVICS_Hydro\n",
+              "    Conventions:    CF-1.6, ACDD-1.3\n",
+              "    summary:        Hydrometeorological database for the PAVICS-Hydro platfor...\n",
+              "    institution:    ETS (École de technologie supérieure)\n",
+              "    DODS.strlen:    72\n",
+              "    DODS.dimName:   string72"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:        (time: 22280, watershed: 5797)\n",
-       "Coordinates:\n",
-       "  * time           (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n",
-       "  * watershed      (watershed) |S64 b'St. John River at Ninemile Bridge, Main...\n",
-       "Data variables:\n",
-       "    drainage_area  (watershed) float64 ...\n",
-       "    pr             (watershed, time) float64 ...\n",
-       "    tasmax         (watershed, time) float64 ...\n",
-       "    tasmin         (watershed, time) float64 ...\n",
-       "    discharge      (watershed, time) float64 ...\n",
-       "Attributes: (12/15)\n",
-       "    title:          Hydrometeorological data for lumped hydrological modellin...\n",
-       "    institute_id:   ETS\n",
-       "    contact:        Richard Arsenault: richard.arsenault@etsmtl.ca\n",
-       "    date_created:   2020-08-01\n",
-       "    source:         Hydrometric data from USGS National Water Information Ser...\n",
-       "    featureType:    timeSeries\n",
-       "    ...             ...\n",
-       "    activity:       PAVICS_Hydro\n",
-       "    Conventions:    CF-1.6, ACDD-1.3\n",
-       "    summary:        Hydrometeorological database for the PAVICS-Hydro platfor...\n",
-       "    institution:    ETS (École de technologie supérieure)\n",
-       "    DODS.strlen:    72\n",
-       "    DODS.dimName:   string72"
+      "source": [
+        "# DATA MAIN SOURCE - DAP link to CANOPEX dataset.\n",
+        "CANOPEX_DAP = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ets/Watersheds_5797_cfcompliant.nc\"\n",
+        "ds = xr.open_dataset(CANOPEX_DAP)\n",
+        "\n",
+        "# Explore the dataset:\n",
+        "display(ds)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# DATA MAIN SOURCE - DAP link to CANOPEX dataset.\n",
-    "CANOPEX_DAP = \"https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ets/Watersheds_5797_cfcompliant.nc\"\n",
-    "ds = xr.open_dataset(CANOPEX_DAP)\n",
-    "\n",
-    "# Explore the dataset:\n",
-    "display(ds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We could explore the dataset and find a watershed of interest, but for now, let's pick one at random\n",
-    "# from the dataset:\n",
-    "watershedID = 5600\n",
-    "\n",
-    "# And show what it includes:\n",
-    "ts = ds.isel({\"watershed\": watershedID})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Let's write the file to disk to make it more efficient to retrieve:\n",
-    "fname = tmp / \"CANOPEX_extracted.nc\"\n",
-    "ts.to_netcdf(fname)\n",
-    "ds.close()\n",
-    "ts.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Basin name:  [b'St. John River at Ninemile Bridge, Maine'\n",
-      " b'St. John River at Dickey, Maine' b'Fish River near Fort Kent, Maine'\n",
-      " ... b'MIDDLE THAMES RIVER AT THAMESFORD'\n",
-      " b'BIG OTTER CREEK AT TILLSONBURG' b'KETTLE CREEK AT ST. THOMAS']\n",
-      "Latitude:  49.51119663557124  °N\n",
-      "Area:  3650.476384548832  km^2\n"
-     ]
-    }
-   ],
-   "source": [
-    "# With this info, we can gather some properties from the CANOPEX database. This same database is used for\n",
-    "# regionalization, so let's query it there where more information is available:\n",
-    "tmp = pd.read_csv(get_file(\"regionalisation_data/gauged_catchment_properties.csv\"))\n",
-    "\n",
-    "basin_area = float(tmp[\"area\"][watershedID])\n",
-    "basin_latitude = float(tmp[\"latitude\"][watershedID])\n",
-    "basin_longitude = float(tmp[\"longitude\"][watershedID])\n",
-    "basin_elevation = float(tmp[\"elevation\"][watershedID])\n",
-    "basin_name = ds.watershed.data\n",
-    "\n",
-    "print(\"Basin name: \", basin_name)\n",
-    "print(\"Latitude: \", basin_latitude, \" °N\")\n",
-    "print(\"Area: \", basin_area, \" km^2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we might have the model and data, but we don't have model parameters! We need to calibrate. This next snippets show how to configure the model and the calibration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We could explore the dataset and find a watershed of interest, but for now, let's pick one at random\n",
+        "# from the dataset:\n",
+        "watershedID = 5600\n",
+        "\n",
+        "# And show what it includes:\n",
+        "ts = ds.isel({\"watershed\": watershedID})"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: registry._helper_single_adder(): Redefining 'percent' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining '%' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'year' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'yr' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'C' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'd' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'h' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'degrees_north' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'degrees_east' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining '[speed]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining '[radiation]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# We will also calibrate on only a subset of the years for now to keep the computations faster in this notebook.\n",
-    "start_calib = dt.datetime(1998, 1, 1)\n",
-    "end_calib = dt.datetime(1999, 12, 31)\n",
-    "\n",
-    "# General parameters depending on the data source. We can find them by exploring the CANOPEX dataset in the\n",
-    "# cells above.\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tasmin\",\n",
-    "    \"TEMP_MAX\": \"tasmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "hru = {}\n",
-    "hru = dict(\n",
-    "    area=basin_area,\n",
-    "    elevation=basin_elevation,\n",
-    "    latitude=basin_latitude,\n",
-    "    longitude=basin_longitude,\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"latitude\": hru[\"latitude\"],\n",
-    "        \"longitude\": hru[\"longitude\"],\n",
-    "    }\n",
-    "}\n",
-    "# Set the evaluation metrics to be calculated by Raven\n",
-    "eval_metrics = (\"NASH_SUTCLIFFE\",)\n",
-    "\n",
-    "model_config = HMETS(\n",
-    "    ObservationData=[\n",
-    "        rc.ObservationData.from_nc(fname, alt_names=\"discharge\", station_idx=1)\n",
-    "    ],\n",
-    "    Gauge=[\n",
-    "        rc.Gauge.from_nc(\n",
-    "            fname,\n",
-    "            station_idx=1,\n",
-    "            data_type=data_type,  # Note that this is the list of all the variables\n",
-    "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
-    "            data_kwds=data_kwds,\n",
-    "        )\n",
-    "    ],\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_calib,\n",
-    "    EndDate=end_calib,\n",
-    "    RunName=\"CANOPEX_test\",\n",
-    "    EvaluationMetrics=eval_metrics,\n",
-    "    RainSnowFraction=\"RAINSNOW_DINGMAN\",\n",
-    "    SuppressOutput=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The model parameters bounds can either be set independently or we can use the defaults.\n",
-    "low_params = (\n",
-    "    0.3,\n",
-    "    0.01,\n",
-    "    0.5,\n",
-    "    0.15,\n",
-    "    0.0,\n",
-    "    0.0,\n",
-    "    -2.0,\n",
-    "    0.01,\n",
-    "    0.0,\n",
-    "    0.01,\n",
-    "    0.005,\n",
-    "    -5.0,\n",
-    "    0.0,\n",
-    "    0.0,\n",
-    "    0.0,\n",
-    "    0.0,\n",
-    "    0.00001,\n",
-    "    0.0,\n",
-    "    0.00001,\n",
-    "    0.0,\n",
-    "    0.0,\n",
-    ")\n",
-    "high_params = (\n",
-    "    20.0,\n",
-    "    5.0,\n",
-    "    13.0,\n",
-    "    1.5,\n",
-    "    20.0,\n",
-    "    20.0,\n",
-    "    3.0,\n",
-    "    0.2,\n",
-    "    0.1,\n",
-    "    0.3,\n",
-    "    0.1,\n",
-    "    2.0,\n",
-    "    5.0,\n",
-    "    1.0,\n",
-    "    3.0,\n",
-    "    1.0,\n",
-    "    0.02,\n",
-    "    0.1,\n",
-    "    0.01,\n",
-    "    0.5,\n",
-    "    2.0,\n",
-    ")\n",
-    "\n",
-    "# Setup the spotpy optimizer\n",
-    "spot_setup = SpotSetup(\n",
-    "    config=model_config,\n",
-    "    low=low_params,\n",
-    "    high=high_params,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can run the optimizer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Let's write the file to disk to make it more efficient to retrieve:\n",
+        "fname = tmp / \"CANOPEX_extracted.nc\"\n",
+        "ts.to_netcdf(fname)\n",
+        "ds.close()\n",
+        "ts.close()"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing the  Dynamically Dimensioned Search (DDS) algorithm  with  50  repetitions\n",
-      "The objective function will be maximized\n",
-      "Starting the DDS algotrithm with 50 repetitions...\n",
-      "Finding best starting point for trial 1 using 5 random samples.\n",
-      "Initialize database...\n",
-      "['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']\n",
-      "40 of 50, maximal objective function=-24.7202, time remaining: 00:00:00\n",
-      "Best solution found has obj function value of -24.6817 at 5\n",
-      "\n",
-      "\n",
-      "\n",
-      "*** Final SPOTPY summary ***\n",
-      "Total Duration: 2.61 seconds\n",
-      "Total Repetitions: 50\n",
-      "Maximal objective value: -24.6817\n",
-      "Corresponding parameter setting:\n",
-      "GAMMA_SHAPE: 7.63524\n",
-      "GAMMA_SCALE: 0.769725\n",
-      "GAMMA_SHAPE2: 11.4388\n",
-      "GAMMA_SCALE2: 1.0265\n",
-      "MIN_MELT_FACTOR: 17.5457\n",
-      "MAX_MELT_FACTOR: 0.0603\n",
-      "DD_MELT_TEMP: -0.420604\n",
-      "DD_AGGRADATION: 0.01893\n",
-      "SNOW_SWI_MIN: 0.0308324\n",
-      "SNOW_SWI_MAX: 0.0106\n",
-      "SWI_REDUCT_COEFF: 0.1\n",
-      "DD_REFREEZE_TEMP: -0.0332151\n",
-      "REFREEZE_FACTOR: 0.664621\n",
-      "REFREEZE_EXP: 0.958967\n",
-      "PET_CORRECTION: 1.10548\n",
-      "HMETS_RUNOFF_COEFF: 0.214056\n",
-      "PERC_COEFF: 0.0117801\n",
-      "BASEFLOW_COEFF_1: 0.0155179\n",
-      "BASEFLOW_COEFF_2: 0.00345655\n",
-      "TOPSOIL: 0.494935\n",
-      "PHREATIC: 0.145286\n",
-      "******************************\n",
-      "\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Basin name:  [b'St. John River at Ninemile Bridge, Maine'\n",
+            " b'St. John River at Dickey, Maine' b'Fish River near Fort Kent, Maine'\n",
+            " ... b'MIDDLE THAMES RIVER AT THAMESFORD'\n",
+            " b'BIG OTTER CREEK AT TILLSONBURG' b'KETTLE CREEK AT ST. THOMAS']\n",
+            "Latitude:  49.51119663557124  °N\n",
+            "Area:  3650.476384548832  km^2\n"
+          ]
+        }
+      ],
+      "source": [
+        "# With this info, we can gather some properties from the CANOPEX database. This same database is used for\n",
+        "# regionalization, so let's query it there where more information is available:\n",
+        "tmp = pd.read_csv(get_file(\"regionalisation_data/gauged_catchment_properties.csv\"))\n",
+        "\n",
+        "basin_area = float(tmp[\"area\"][watershedID])\n",
+        "basin_latitude = float(tmp[\"latitude\"][watershedID])\n",
+        "basin_longitude = float(tmp[\"longitude\"][watershedID])\n",
+        "basin_elevation = float(tmp[\"elevation\"][watershedID])\n",
+        "basin_name = ds.watershed.data\n",
+        "\n",
+        "print(\"Basin name: \", basin_name)\n",
+        "print(\"Latitude: \", basin_latitude, \" °N\")\n",
+        "print(\"Area: \", basin_area, \" km^2\")"
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "[{'sbest': spotpy.parameter.ParameterSet(),\n",
-       "  'trial_initial': [2.4446232183696073,\n",
-       "   3.648993786086633,\n",
-       "   6.808285937892062,\n",
-       "   0.9190984550605087,\n",
-       "   18.936595179011256,\n",
-       "   6.460481436876682,\n",
-       "   -0.48087709565992487,\n",
-       "   0.07545129002831279,\n",
-       "   0.09286020935352743,\n",
-       "   0.03369469595949492,\n",
-       "   0.09584518083667387,\n",
-       "   1.375621144444363,\n",
-       "   0.7970479176500463,\n",
-       "   0.6446325223885151,\n",
-       "   1.9186703071461322,\n",
-       "   0.06648882787110477,\n",
-       "   0.0029416036552497686,\n",
-       "   0.06919911158176603,\n",
-       "   0.0024456422338723837,\n",
-       "   0.44961927013891523,\n",
-       "   0.30659009606737986],\n",
-       "  'objfunc_val': -24.6817}]"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, we might have the model and data, but we don't have model parameters! We need to calibrate. This next snippets show how to configure the model and the calibration."
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We'll definitely want to adjust the random seed and number of model evaluations:\n",
-    "model_evaluations = (\n",
-    "    50  # This is to keep computing time fast for the demo, increase as necessary\n",
-    ")\n",
-    "\n",
-    "# Setup the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer to\n",
-    "# the spotpy documentation for more options. We recommend sticking to this format for efficiency of most applications.\n",
-    "sampler = spotpy.algorithms.dds(\n",
-    "    spot_setup,\n",
-    "    dbname=\"CANOPEX_test\",\n",
-    "    dbformat=\"ram\",\n",
-    "    save_sim=False,\n",
-    ")\n",
-    "\n",
-    "# Launch the actual optimization. Multiple trials can be launched, where the entire process is repeated and\n",
-    "# the best overall value from all trials is returned.\n",
-    "sampler.sample(model_evaluations, trials=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING: registry._helper_single_adder(): Redefining 'percent' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining '%' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'year' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'yr' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'C' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'd' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'h' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'degrees_north' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'degrees_east' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining '[speed]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining '[radiation]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# We will also calibrate on only a subset of the years for now to keep the computations faster in this notebook.\n",
+        "start_calib = dt.datetime(1998, 1, 1)\n",
+        "end_calib = dt.datetime(1999, 12, 31)\n",
+        "\n",
+        "# General parameters depending on the data source. We can find them by exploring the CANOPEX dataset in the\n",
+        "# cells above.\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tasmin\",\n",
+        "    \"TEMP_MAX\": \"tasmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "hru = {}\n",
+        "hru = dict(\n",
+        "    area=basin_area,\n",
+        "    elevation=basin_elevation,\n",
+        "    latitude=basin_latitude,\n",
+        "    longitude=basin_longitude,\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"latitude\": hru[\"latitude\"],\n",
+        "        \"longitude\": hru[\"longitude\"],\n",
+        "    }\n",
+        "}\n",
+        "# Set the evaluation metrics to be calculated by Raven\n",
+        "eval_metrics = (\"NASH_SUTCLIFFE\",)\n",
+        "\n",
+        "model_config = HMETS(\n",
+        "    ObservationData=[\n",
+        "        rc.ObservationData.from_nc(fname, alt_names=\"discharge\", station_idx=1)\n",
+        "    ],\n",
+        "    Gauge=[\n",
+        "        rc.Gauge.from_nc(\n",
+        "            fname,\n",
+        "            station_idx=1,\n",
+        "            data_type=data_type,  # Note that this is the list of all the variables\n",
+        "            alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
+        "            data_kwds=data_kwds,\n",
+        "        )\n",
+        "    ],\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_calib,\n",
+        "    EndDate=end_calib,\n",
+        "    RunName=\"CANOPEX_test\",\n",
+        "    EvaluationMetrics=eval_metrics,\n",
+        "    RainSnowFraction=\"RAINSNOW_DINGMAN\",\n",
+        "    SuppressOutput=True,\n",
+        ")"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nash-Sutcliffe value is: [-24.6817]\n",
-      "Best parameter set:\n",
-      "GAMMA_SHAPE=7.635242457794534, GAMMA_SCALE=0.7697246308702375, GAMMA_SHAPE2=11.438816553712229, GAMMA_SCALE2=1.026504937776411, MIN_MELT_FACTOR=17.545677044445164, MAX_MELT_FACTOR=0.0603, DD_MELT_TEMP=-0.42060403078707786, DD_AGGRADATION=0.018930000290900063, SNOW_SWI_MIN=0.030832364134558664, SNOW_SWI_MAX=0.0106, SWI_REDUCT_COEFF=0.1, DD_REFREEZE_TEMP=-0.03321510587203654, REFREEZE_FACTOR=0.6646210627773466, REFREEZE_EXP=0.9589666833784665, PET_CORRECTION=1.1054800200214736, HMETS_RUNOFF_COEFF=0.2140562916769115, PERC_COEFF=0.011780138728707873, BASEFLOW_COEFF_1=0.015517857213688549, BASEFLOW_COEFF_2=0.0034565506868557516, TOPSOIL=0.49493522146275265, PHREATIC=0.14528642601470373\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# The model parameters bounds can either be set independently or we can use the defaults.\n",
+        "low_params = (\n",
+        "    0.3,\n",
+        "    0.01,\n",
+        "    0.5,\n",
+        "    0.15,\n",
+        "    0.0,\n",
+        "    0.0,\n",
+        "    -2.0,\n",
+        "    0.01,\n",
+        "    0.0,\n",
+        "    0.01,\n",
+        "    0.005,\n",
+        "    -5.0,\n",
+        "    0.0,\n",
+        "    0.0,\n",
+        "    0.0,\n",
+        "    0.0,\n",
+        "    0.00001,\n",
+        "    0.0,\n",
+        "    0.00001,\n",
+        "    0.0,\n",
+        "    0.0,\n",
+        ")\n",
+        "high_params = (\n",
+        "    20.0,\n",
+        "    5.0,\n",
+        "    13.0,\n",
+        "    1.5,\n",
+        "    20.0,\n",
+        "    20.0,\n",
+        "    3.0,\n",
+        "    0.2,\n",
+        "    0.1,\n",
+        "    0.3,\n",
+        "    0.1,\n",
+        "    2.0,\n",
+        "    5.0,\n",
+        "    1.0,\n",
+        "    3.0,\n",
+        "    1.0,\n",
+        "    0.02,\n",
+        "    0.1,\n",
+        "    0.01,\n",
+        "    0.5,\n",
+        "    2.0,\n",
+        ")\n",
+        "\n",
+        "# Setup the spotpy optimizer\n",
+        "spot_setup = SpotSetup(\n",
+        "    config=model_config,\n",
+        "    low=low_params,\n",
+        "    high=high_params,\n",
+        ")"
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "(7.63524246, 0.76972463, 11.43881655, 1.02650494, 17.54567704, 0.0603, -0.42060403, 0.01893, 0.03083236, 0.0106, 0.1, -0.03321511, 0.66462106, 0.95896668, 1.10548002, 0.21405629, 0.01178014, 0.01551786, 0.00345655, 0.49493522, 0.14528643)"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can run the optimizer:"
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the model diagnostics\n",
-    "diag = spot_setup.diagnostics\n",
-    "\n",
-    "# Print the NSE and the parameter set in 2 different ways:\n",
-    "print(\"Nash-Sutcliffe value is: \" + str(diag[\"DIAG_NASH_SUTCLIFFE\"]))\n",
-    "\n",
-    "# Get all the values of each iteration\n",
-    "results = sampler.getdata()\n",
-    "\n",
-    "# Get the raw resutlts directly in an array\n",
-    "params = spotpy.analyser.get_best_parameterset(results)[0]\n",
-    "params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this stage, we have calibrated the model on the observations for the desired dates. Now, let's run the model on a longer time period and look at the hydrograph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ravenpy import Emulator\n",
-    "\n",
-    "conf = model_config.set_params(params)\n",
-    "conf.suppress_output = False\n",
-    "out = Emulator(conf).run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `hydrograph` and `storage` outputs are netCDF files storing the time series. These files are opened by default using `xarray`, which provides convenient and powerful time series analysis and plotting tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "q = out.hydrograph.q_sim"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max:  1522.001921641669\n",
-      "Mean:  54.27677750325202\n",
-      "Monthly means:  [[6.93032482e-02]\n",
-      " [7.86399173e+00]\n",
-      " [2.87806769e+00]\n",
-      " [2.16481233e+01]\n",
-      " [1.84055794e+02]\n",
-      " [1.31270602e+02]\n",
-      " [8.73476499e+01]\n",
-      " [9.45753187e+01]\n",
-      " [5.70102246e+01]\n",
-      " [5.79744837e+01]\n",
-      " [1.88086182e+00]\n",
-      " [8.44691306e-02]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# You can also get statistics from the data directly.\n",
-    "print(\"Max: \", q.max().values)\n",
-    "print(\"Mean: \", q.mean().values)\n",
-    "print(\n",
-    "    \"Monthly means: \",\n",
-    "    q.groupby(\"time.month\").mean(dim=\"time\").values,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Initializing the  Dynamically Dimensioned Search (DDS) algorithm  with  50  repetitions\n",
+            "The objective function will be maximized\n",
+            "Starting the DDS algotrithm with 50 repetitions...\n",
+            "Finding best starting point for trial 1 using 5 random samples.\n",
+            "Initialize database...\n",
+            "['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']\n",
+            "40 of 50, maximal objective function=-24.7202, time remaining: 00:00:00\n",
+            "Best solution found has obj function value of -24.6817 at 5\n",
+            "\n",
+            "\n",
+            "\n",
+            "*** Final SPOTPY summary ***\n",
+            "Total Duration: 2.61 seconds\n",
+            "Total Repetitions: 50\n",
+            "Maximal objective value: -24.6817\n",
+            "Corresponding parameter setting:\n",
+            "GAMMA_SHAPE: 7.63524\n",
+            "GAMMA_SCALE: 0.769725\n",
+            "GAMMA_SHAPE2: 11.4388\n",
+            "GAMMA_SCALE2: 1.0265\n",
+            "MIN_MELT_FACTOR: 17.5457\n",
+            "MAX_MELT_FACTOR: 0.0603\n",
+            "DD_MELT_TEMP: -0.420604\n",
+            "DD_AGGRADATION: 0.01893\n",
+            "SNOW_SWI_MIN: 0.0308324\n",
+            "SNOW_SWI_MAX: 0.0106\n",
+            "SWI_REDUCT_COEFF: 0.1\n",
+            "DD_REFREEZE_TEMP: -0.0332151\n",
+            "REFREEZE_FACTOR: 0.664621\n",
+            "REFREEZE_EXP: 0.958967\n",
+            "PET_CORRECTION: 1.10548\n",
+            "HMETS_RUNOFF_COEFF: 0.214056\n",
+            "PERC_COEFF: 0.0117801\n",
+            "BASEFLOW_COEFF_1: 0.0155179\n",
+            "BASEFLOW_COEFF_2: 0.00345655\n",
+            "TOPSOIL: 0.494935\n",
+            "PHREATIC: 0.145286\n",
+            "******************************\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "[{'sbest': spotpy.parameter.ParameterSet(),\n",
+              "  'trial_initial': [2.4446232183696073,\n",
+              "   3.648993786086633,\n",
+              "   6.808285937892062,\n",
+              "   0.9190984550605087,\n",
+              "   18.936595179011256,\n",
+              "   6.460481436876682,\n",
+              "   -0.48087709565992487,\n",
+              "   0.07545129002831279,\n",
+              "   0.09286020935352743,\n",
+              "   0.03369469595949492,\n",
+              "   0.09584518083667387,\n",
+              "   1.375621144444363,\n",
+              "   0.7970479176500463,\n",
+              "   0.6446325223885151,\n",
+              "   1.9186703071461322,\n",
+              "   0.06648882787110477,\n",
+              "   0.0029416036552497686,\n",
+              "   0.06919911158176603,\n",
+              "   0.0024456422338723837,\n",
+              "   0.44961927013891523,\n",
+              "   0.30659009606737986],\n",
+              "  'objfunc_val': -24.6817}]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# We'll definitely want to adjust the random seed and number of model evaluations:\n",
+        "model_evaluations = (\n",
+        "    50  # This is to keep computing time fast for the demo, increase as necessary\n",
+        ")\n",
+        "\n",
+        "# Setup the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer to\n",
+        "# the spotpy documentation for more options. We recommend sticking to this format for efficiency of most applications.\n",
+        "sampler = spotpy.algorithms.dds(\n",
+        "    spot_setup,\n",
+        "    dbname=\"CANOPEX_test\",\n",
+        "    dbformat=\"ram\",\n",
+        "    save_sim=False,\n",
+        ")\n",
+        "\n",
+        "# Launch the actual optimization. Multiple trials can be launched, where the entire process is repeated and\n",
+        "# the best overall value from all trials is returned.\n",
+        "sampler.sample(model_evaluations, trials=1)"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7efd839c51f0>]"
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Nash-Sutcliffe value is: [-24.6817]\n",
+            "Best parameter set:\n",
+            "GAMMA_SHAPE=7.635242457794534, GAMMA_SCALE=0.7697246308702375, GAMMA_SHAPE2=11.438816553712229, GAMMA_SCALE2=1.026504937776411, MIN_MELT_FACTOR=17.545677044445164, MAX_MELT_FACTOR=0.0603, DD_MELT_TEMP=-0.42060403078707786, DD_AGGRADATION=0.018930000290900063, SNOW_SWI_MIN=0.030832364134558664, SNOW_SWI_MAX=0.0106, SWI_REDUCT_COEFF=0.1, DD_REFREEZE_TEMP=-0.03321510587203654, REFREEZE_FACTOR=0.6646210627773466, REFREEZE_EXP=0.9589666833784665, PET_CORRECTION=1.1054800200214736, HMETS_RUNOFF_COEFF=0.2140562916769115, PERC_COEFF=0.011780138728707873, BASEFLOW_COEFF_1=0.015517857213688549, BASEFLOW_COEFF_2=0.0034565506868557516, TOPSOIL=0.49493522146275265, PHREATIC=0.14528642601470373\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "(7.63524246, 0.76972463, 11.43881655, 1.02650494, 17.54567704, 0.0603, -0.42060403, 0.01893, 0.03083236, 0.0106, 0.1, -0.03321511, 0.66462106, 0.95896668, 1.10548002, 0.21405629, 0.01178014, 0.01551786, 0.00345655, 0.49493522, 0.14528643)"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Get the model diagnostics\n",
+        "diag = spot_setup.diagnostics\n",
+        "\n",
+        "# Print the NSE and the parameter set in 2 different ways:\n",
+        "print(\"Nash-Sutcliffe value is: \" + str(diag[\"DIAG_NASH_SUTCLIFFE\"]))\n",
+        "\n",
+        "# Get all the values of each iteration\n",
+        "results = sampler.getdata()\n",
+        "\n",
+        "# Get the raw resutlts directly in an array\n",
+        "params = spotpy.analyser.get_best_parameterset(results)[0]\n",
+        "params"
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "At this stage, we have calibrated the model on the observations for the desired dates. Now, let's run the model on a longer time period and look at the hydrograph"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from ravenpy import Emulator\n",
+        "\n",
+        "conf = model_config.set_params(params)\n",
+        "conf.suppress_output = False\n",
+        "out = Emulator(conf).run()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The `hydrograph` and `storage` outputs are netCDF files storing the time series. These files are opened by default using `xarray`, which provides convenient and powerful time series analysis and plotting tools."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "q = out.hydrograph.q_sim"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Max:  1522.001921641669\n",
+            "Mean:  54.27677750325202\n",
+            "Monthly means:  [[6.93032482e-02]\n",
+            " [7.86399173e+00]\n",
+            " [2.87806769e+00]\n",
+            " [2.16481233e+01]\n",
+            " [1.84055794e+02]\n",
+            " [1.31270602e+02]\n",
+            " [8.73476499e+01]\n",
+            " [9.45753187e+01]\n",
+            " [5.70102246e+01]\n",
+            " [5.79744837e+01]\n",
+            " [1.88086182e+00]\n",
+            " [8.44691306e-02]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# You can also get statistics from the data directly.\n",
+        "print(\"Max: \", q.max().values)\n",
+        "print(\"Mean: \", q.mean().values)\n",
+        "print(\n",
+        "    \"Monthly means: \",\n",
+        "    q.groupby(\"time.month\").mean(dim=\"time\").values,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7efd839c51f0>]"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot the simulated hydrograph\n",
+        "from pandas.plotting import register_matplotlib_converters\n",
+        "\n",
+        "register_matplotlib_converters()\n",
+        "q.plot()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
     }
-   ],
-   "source": [
-    "# Plot the simulated hydrograph\n",
-    "from pandas.plotting import register_matplotlib_converters\n",
-    "\n",
-    "register_matplotlib_converters()\n",
-    "q.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/paper/Perform_a_climate_change_impact_study_on_a_watershed.ipynb b/docs/notebooks/paper/Perform_a_climate_change_impact_study_on_a_watershed.ipynb
index 715ffa4d..59f8ca72 100644
--- a/docs/notebooks/paper/Perform_a_climate_change_impact_study_on_a_watershed.ipynb
+++ b/docs/notebooks/paper/Perform_a_climate_change_impact_study_on_a_watershed.ipynb
@@ -1,1379 +1,1379 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Perform a climate change impact study on the hydrology of a watershed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hydrological models typically need geographical information about watersheds being simulated: latitude and longitude, area, mean altitude, land-use, etc. This notebook shows how to obtain this information using remote services that are made available for users in PAVICS-Hydro. These services connect to a digital elevation model (DEM) and a land-use data set to extract relevant information.\n",
-    "\n",
-    "The DEM used in the following is the [EarthEnv-DEM90](https://www.earthenv.org/DEM), while the land-use dataset is the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/). Other data sources could be used, given their availability through the Web Coverage Service (WCS) protocol.\n",
-    "\n",
-    "Since these computations happen on a specific Geoserver hosted in PAVICS, we need to establish a connection to that service. While the steps are a bit more complex, the good news is that you only need to change a few items in this notebook to taylor results to your needs.\n",
-    "\n",
-    "We will also setup a hydrological model, calibrate it, and use it in evaluating the impacts of climate change on the hydrology of a catchment. We will be using the Mistassini river as the test-case for this example, but you can substitute the data for any catchment of your liking. We provide:\n",
-    "\n",
-    "1- Streamflow observations (Water Survey Canada station 02RD003)\n",
-    "\n",
-    "2- Watershed boundaries in the form of shapefiles (all shape files .shp, .shx, .prj, .dbf, etc. zipped into a single file. The platform will detect and unzip the file to extract the required data)\n",
-    "\n",
-    "\n",
-    "The rest will be done by PAVICS-Hydro, including getting meteorological information from our ERA5 reanalysis database and climate change model data from CMIP hosted by PanGEO.\n",
-    "\n",
-    "## Software setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Import the required packages for this notebook.\n",
-    "Note that since the notebook includes the entire process from data collection to climate change impact studies, there are a lot more packages required than usual."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: registry._helper_single_adder(): Redefining 'percent' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining '%' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'year' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'yr' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'C' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'd' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'h' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'degrees_north' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining 'degrees_east' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining '[speed]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n",
-      "WARNING: registry._helper_single_adder(): Redefining '[radiation]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n",
-      "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/indices/fire/_cffwis.py:207: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
-      "  def _day_length(lat: int | float, mth: int):  # pragma: no cover\n",
-      "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/indices/fire/_cffwis.py:227: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
-      "  def _day_length_factor(lat: float, mth: int):  # pragma: no cover\n"
-     ]
-    }
-   ],
-   "source": [
-    "# We need to import a few packages required to do the work:\n",
-    "\n",
-    "import datetime as dt\n",
-    "\n",
-    "# Basic system packages\n",
-    "import os\n",
-    "import tempfile\n",
-    "import warnings\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# Packages for data extraction on remote servers/filesystems\n",
-    "import fsspec\n",
-    "import gcsfs\n",
-    "import geopandas as gpd\n",
-    "\n",
-    "# Packages for geographic processing\n",
-    "import intake\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import rasterio\n",
-    "import rioxarray as rio\n",
-    "import s3fs\n",
-    "\n",
-    "# Packages related to ravenpy and hydrological modelling:\n",
-    "import spotpy\n",
-    "import xarray as xr\n",
-    "\n",
-    "# Packages required for data processing\n",
-    "import xclim\n",
-    "import xclim.sdba as sdba\n",
-    "from birdy import WPSClient\n",
-    "from clisops.core import average, subset\n",
-    "\n",
-    "from ravenpy import Emulator\n",
-    "from ravenpy.config import commands as rc\n",
-    "from ravenpy.config.emulators import GR4JCN\n",
-    "from ravenpy.utilities.calibration import SpotSetup\n",
-    "from ravenpy.utilities.testdata import get_file"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Prepare some boilerplate items that will be required later on"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# The platform provides lots of user warnings and information points. We will disable them for now.\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "# This is the URL of the Geoserver that will perform the computations for us.\n",
-    "url = os.environ.get(\n",
-    "    \"WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\"\n",
-    ")\n",
-    "\n",
-    "# Connect to the PAVICS-Hydro Raven WPS server to get the geospatial data from GeoServer\n",
-    "wps = WPSClient(url)\n",
-    "\n",
-    "# Make a temporary path where the data will be stored and used by Raven\n",
-    "tmp = Path(tempfile.mkdtemp())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Prepare datasets that will be required\n",
-    "This includes observed streamflow for the catchment of interest as well as the polygon/contour of that watershed. These could be gathered from CANOPEX, HYSETS or other databases, but we provide an example for user convenience here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Name of the watershed boundaries file that is uploaded to the server. Note that this file contains the\n",
-    "# .shx, .shp and other associated files for shapefiles, all zipped into one file. It will also be used later for\n",
-    "# extracting meteorological data.\n",
-    "basin_contour = get_file(\"paper/shapefile_basin_574_HYSETS.zip\")\n",
-    "\n",
-    "# This file is an extraction of streamflow for catchment 574 in HYSETS. Weather data will be gathered later from\n",
-    "# the ERA5 database, but could also be taken directly from HYSETS. This is to show how the process could be linked\n",
-    "# together for your own applications using ERA5 data.\n",
-    "streamflow_file = get_file(\"paper/Qobs_574_HYSETS.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Other user inputs of interest\n",
-    "We can also specify some information such as periods of interest for reference and future periods\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Reference period that will be used for ERA5 and climate model data for the reference period.\n",
-    "# Here let's focus on a 10-year period to keep running times lower.\n",
-    "reference_start_day = dt.datetime(1980, 12, 31)\n",
-    "reference_end_day = dt.datetime(1991, 1, 1)\n",
-    "# Notice we are using one day before and one day after the desired period of 1981-01-01 to 1990-12-31.\n",
-    "# This is to account for any UTC shifts that might require getting data in a previous or later time.\n",
-    "\n",
-    "# Same process for the future period, 100 years later\n",
-    "future_start_day = dt.datetime(2080, 12, 31)\n",
-    "future_end_day = dt.datetime(2091, 1, 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## <span style=\"color:blue\"> Geographic processing of watershed attributes</span>\n",
-    "\n",
-    "Here we will use a set of tools to extract watershed properties that can be used for various applications. Not all variables we extract here are required for the hydrological modelling, but could be used for other applications."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Perform a climate change impact study on the hydrology of a watershed"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Hydrological models typically need geographical information about watersheds being simulated: latitude and longitude, area, mean altitude, land-use, etc. This notebook shows how to obtain this information using remote services that are made available for users in PAVICS-Hydro. These services connect to a digital elevation model (DEM) and a land-use data set to extract relevant information.\n",
+        "\n",
+        "The DEM used in the following is the [EarthEnv-DEM90](https://www.earthenv.org/DEM), while the land-use dataset is the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/). Other data sources could be used, given their availability through the Web Coverage Service (WCS) protocol.\n",
+        "\n",
+        "Since these computations happen on a specific Geoserver hosted in PAVICS, we need to establish a connection to that service. While the steps are a bit more complex, the good news is that you only need to change a few items in this notebook to taylor results to your needs.\n",
+        "\n",
+        "We will also setup a hydrological model, calibrate it, and use it in evaluating the impacts of climate change on the hydrology of a catchment. We will be using the Mistassini river as the test-case for this example, but you can substitute the data for any catchment of your liking. We provide:\n",
+        "\n",
+        "1- Streamflow observations (Water Survey Canada station 02RD003)\n",
+        "\n",
+        "2- Watershed boundaries in the form of shapefiles (all shape files .shp, .shx, .prj, .dbf, etc. zipped into a single file. The platform will detect and unzip the file to extract the required data)\n",
+        "\n",
+        "\n",
+        "The rest will be done by PAVICS-Hydro, including getting meteorological information from our ERA5 reanalysis database and climate change model data from CMIP hosted by PanGEO.\n",
+        "\n",
+        "## Software setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Import the required packages for this notebook.\n",
+        "Note that since the notebook includes the entire process from data collection to climate change impact studies, there are a lot more packages required than usual."
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>features</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>OfficialID</th>\n",
-       "      <th>FlagPAVICS</th>\n",
-       "      <th>Source</th>\n",
-       "      <th>Area</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>MISTASSINI (RIVIERE) EN AMONT DE LA RIVIERE MI...</td>\n",
-       "      <td>02RD003</td>\n",
-       "      <td>1</td>\n",
-       "      <td>HYDAT</td>\n",
-       "      <td>9870</td>\n",
-       "      <td>POLYGON ((-72.26250 48.87917, -72.27720 48.881...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING: registry._helper_single_adder(): Redefining 'percent' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining '%' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'year' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'yr' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'C' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'd' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'h' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'degrees_north' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining 'degrees_east' (<class 'pint.delegates.txt_defparser.plain.UnitDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining '[speed]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n",
+            "WARNING: registry._helper_single_adder(): Redefining '[radiation]' (<class 'pint.delegates.txt_defparser.plain.DerivedDimensionDefinition'>)\n",
+            "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/indices/fire/_cffwis.py:207: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
+            "  def _day_length(lat: int | float, mth: int):  # pragma: no cover\n",
+            "/opt/conda/envs/birdy/lib/python3.9/site-packages/xclim/indices/fire/_cffwis.py:227: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
+            "  def _day_length_factor(lat: float, mth: int):  # pragma: no cover\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "   features                                               Name OfficialID  \\\n",
-       "0         1  MISTASSINI (RIVIERE) EN AMONT DE LA RIVIERE MI...    02RD003   \n",
-       "\n",
-       "   FlagPAVICS Source  Area                                           geometry  \n",
-       "0           1  HYDAT  9870  POLYGON ((-72.26250 48.87917, -72.27720 48.881...  "
+      "source": [
+        "# We need to import a few packages required to do the work:\n",
+        "\n",
+        "import datetime as dt\n",
+        "\n",
+        "# Basic system packages\n",
+        "import os\n",
+        "import tempfile\n",
+        "import warnings\n",
+        "from pathlib import Path\n",
+        "\n",
+        "# Packages for data extraction on remote servers/filesystems\n",
+        "import fsspec\n",
+        "import gcsfs\n",
+        "import geopandas as gpd\n",
+        "\n",
+        "# Packages for geographic processing\n",
+        "import intake\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import rasterio\n",
+        "import rioxarray as rio\n",
+        "import s3fs\n",
+        "\n",
+        "# Packages related to ravenpy and hydrological modelling:\n",
+        "import spotpy\n",
+        "import xarray as xr\n",
+        "\n",
+        "# Packages required for data processing\n",
+        "import xclim\n",
+        "import xclim.sdba as sdba\n",
+        "from birdy import WPSClient\n",
+        "from clisops.core import average, subset\n",
+        "\n",
+        "from ravenpy import Emulator\n",
+        "from ravenpy.config import commands as rc\n",
+        "from ravenpy.config.emulators import GR4JCN\n",
+        "from ravenpy.utilities.calibration import SpotSetup\n",
+        "from ravenpy.utilities.testdata import get_file"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare some boilerplate items that will be required later on"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# The platform provides lots of user warnings and information points. We will disable them for now.\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "# This is the URL of the Geoserver that will perform the computations for us.\n",
+        "url = os.environ.get(\n",
+        "    \"WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\"\n",
+        ")\n",
+        "\n",
+        "# Connect to the PAVICS-Hydro Raven WPS server to get the geospatial data from GeoServer\n",
+        "wps = WPSClient(url)\n",
+        "\n",
+        "# Make a temporary path where the data will be stored and used by Raven\n",
+        "tmp = Path(tempfile.mkdtemp())"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Prepare a plot of the catchment to see what we are working with.\n",
-    "df = gpd.read_file(basin_contour)\n",
-    "display(df)\n",
-    "df.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generic watershed properties\n",
-    "\n",
-    "Now that we have delineated a watershed, lets find the zonal statistics and other properties using the `shape_properties` process. This process requires a `shape` argument defining the watershed contour, the exterior polygon.\n",
-    "\n",
-    "Once the process has completed, we extract the data from the response, as follows. Note that you do not need to change anything here. The code will work and return the desired results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'id': '0',\n",
-       " 'features': 1,\n",
-       " 'Name': 'MISTASSINI (RIVIERE) EN AMONT DE LA RIVIERE MISTASSIBI',\n",
-       " 'OfficialID': '02RD003',\n",
-       " 'FlagPAVICS': 1,\n",
-       " 'Source': 'HYDAT',\n",
-       " 'Area': 9870,\n",
-       " 'area': 9569368968.087273,\n",
-       " 'centroid': [-72.7431067594341, 49.848278236356585],\n",
-       " 'perimeter': 727186.9587075961,\n",
-       " 'gravelius': 2.097005162538472}"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare datasets that will be required\n",
+        "This includes observed streamflow for the catchment of interest as well as the polygon/contour of that watershed. These could be gathered from CANOPEX, HYSETS or other databases, but we provide an example for user convenience here."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "{'area': 9569.368968087272,\n",
-       " 'longitude': -72.7431067594341,\n",
-       " 'latitude': 49.848278236356585,\n",
-       " 'gravelius': 2.097005162538472,\n",
-       " 'perimeter': 727186.9587075961}"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Name of the watershed boundaries file that is uploaded to the server. Note that this file contains the\n",
+        "# .shx, .shp and other associated files for shapefiles, all zipped into one file. It will also be used later for\n",
+        "# extracting meteorological data.\n",
+        "basin_contour = get_file(\"paper/shapefile_basin_574_HYSETS.zip\")\n",
+        "\n",
+        "# This file is an extraction of streamflow for catchment 574 in HYSETS. Weather data will be gathered later from\n",
+        "# the ERA5 database, but could also be taken directly from HYSETS. This is to show how the process could be linked\n",
+        "# together for your own applications using ERA5 data.\n",
+        "streamflow_file = get_file(\"paper/Qobs_574_HYSETS.nc\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "shape_resp = wps.shape_properties(shape=basin_contour)\n",
-    "\n",
-    "[\n",
-    "    properties,\n",
-    "] = shape_resp.get(asobj=True)\n",
-    "prop = properties[0]\n",
-    "display(prop)\n",
-    "\n",
-    "area = prop[\"area\"] / 1000000.0\n",
-    "longitude = prop[\"centroid\"][0]\n",
-    "latitude = prop[\"centroid\"][1]\n",
-    "gravelius = prop[\"gravelius\"]\n",
-    "perimeter = prop[\"perimeter\"]\n",
-    "\n",
-    "shape_info = {\n",
-    "    \"area\": area,\n",
-    "    \"longitude\": longitude,\n",
-    "    \"latitude\": latitude,\n",
-    "    \"gravelius\": gravelius,\n",
-    "    \"perimeter\": perimeter,\n",
-    "}\n",
-    "display(shape_info)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that these properties are a mix of the properties of the original file where the shape is stored, and properties computed by the process (area, centroid, perimeter and gravelius). Note also that the computed area is in m², while the \"SUB_AREA\" property is in km², and that there are slight differences between the two values due to the precision of HydroSHEDS and the delineation algorithm.\n",
-    "\n",
-    "### Land-use information\n",
-    "\n",
-    "Now we extract the land-use properties of the watershed using the `nalcms_zonal_stats` process. As mentioned, it uses a dataset from the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas), and retrieve properties over the given region.\n",
-    "\n",
-    "With the `nalcms_zonal_stats_raster` process, we also return the grid with variable accessors (`gdal`, `rasterio`, or `rioxarray`) depending on what libraries are available in our runtime environment (The following examples show `rioxarray`-like access)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "stats_resp = wps.nalcms_zonal_stats_raster(\n",
-    "    shape=basin_contour, select_all_touching=True, band=1, simple_categories=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we will get the raster data and compute statistics on it. It is also possible to download the extracted raseter offline (please see the tutorial for the steps on how to do this)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading to /tmp/tmpv9zzg043/subset_1.tiff.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Other user inputs of interest\n",
+        "We can also specify some information such as periods of interest for reference and future periods\n"
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "'Land use ratios'"
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Reference period that will be used for ERA5 and climate model data for the reference period.\n",
+        "# Here let's focus on a 10-year period to keep running times lower.\n",
+        "reference_start_day = dt.datetime(1980, 12, 31)\n",
+        "reference_end_day = dt.datetime(1991, 1, 1)\n",
+        "# Notice we are using one day before and one day after the desired period of 1981-01-01 to 1990-12-31.\n",
+        "# This is to account for any UTC shifts that might require getting data in a previous or later time.\n",
+        "\n",
+        "# Same process for the future period, 100 years later\n",
+        "future_start_day = dt.datetime(2080, 12, 31)\n",
+        "future_end_day = dt.datetime(2091, 1, 1)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "{'Ocean': 0.0,\n",
-       " 'Forest': 0.7246596208414477,\n",
-       " 'Shrubs': 0.14616312094792794,\n",
-       " 'Grass': 0.04322426804857576,\n",
-       " 'Wetland': 0.013300924493021603,\n",
-       " 'Crops': 0.00395034960218003,\n",
-       " 'Urban': 0.0035571063310866975,\n",
-       " 'Water': 0.06514460973576021,\n",
-       " 'SnowIce': 0.0}"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## <span style=\"color:blue\"> Geographic processing of watershed attributes</span>\n",
+        "\n",
+        "Here we will use a set of tools to extract watershed properties that can be used for various applications. Not all variables we extract here are required for the hydrological modelling, but could be used for other applications."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "'Land use percentages'"
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>features</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>OfficialID</th>\n",
+              "      <th>FlagPAVICS</th>\n",
+              "      <th>Source</th>\n",
+              "      <th>Area</th>\n",
+              "      <th>geometry</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>MISTASSINI (RIVIERE) EN AMONT DE LA RIVIERE MI...</td>\n",
+              "      <td>02RD003</td>\n",
+              "      <td>1</td>\n",
+              "      <td>HYDAT</td>\n",
+              "      <td>9870</td>\n",
+              "      <td>POLYGON ((-72.26250 48.87917, -72.27720 48.881...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   features                                               Name OfficialID  \\\n",
+              "0         1  MISTASSINI (RIVIERE) EN AMONT DE LA RIVIERE MI...    02RD003   \n",
+              "\n",
+              "   FlagPAVICS Source  Area                                           geometry  \n",
+              "0           1  HYDAT  9870  POLYGON ((-72.26250 48.87917, -72.27720 48.881...  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Prepare a plot of the catchment to see what we are working with.\n",
+        "df = gpd.read_file(basin_contour)\n",
+        "display(df)\n",
+        "df.plot()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "{'Ocean': '0.0 %',\n",
-       " 'Forest': '72.47 %',\n",
-       " 'Shrubs': '14.62 %',\n",
-       " 'Grass': '4.32 %',\n",
-       " 'Wetland': '1.33 %',\n",
-       " 'Crops': '0.4 %',\n",
-       " 'Urban': '0.36 %',\n",
-       " 'Water': '6.51 %',\n",
-       " 'SnowIce': '0.0 %'}"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generic watershed properties\n",
+        "\n",
+        "Now that we have delineated a watershed, lets find the zonal statistics and other properties using the `shape_properties` process. This process requires a `shape` argument defining the watershed contour, the exterior polygon.\n",
+        "\n",
+        "Once the process has completed, we extract the data from the response, as follows. Note that you do not need to change anything here. The code will work and return the desired results."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "features, statistics, grid0 = stats_resp.get(asobj=True)\n",
-    "lu = statistics[0]\n",
-    "total = sum(lu.values())\n",
-    "\n",
-    "land_use = {k: (v / total) for (k, v) in lu.items()}\n",
-    "display(\"Land use ratios\", land_use)\n",
-    "\n",
-    "land_use_pct = {k: f\"{np.round(v/total*100, 2)} %\" for (k, v) in lu.items()}\n",
-    "display(\"Land use percentages\", land_use_pct)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Terrain information from the DEM\n",
-    "\n",
-    "Here we collect terrain data, such as elevation, slope and aspect, from the DEM. We will do this using the `terrain_analysis` WPS service, which by default uses DEM data from [EarthEnv-DEM90](https://www.earthenv.org/DEM).\n",
-    "\n",
-    "Note here that while the feature outline is defined above in terms of geographic coordinates (latitude, longitude), the DEM is projected onto a 2D cartesian coordinate system (here NAD83, the Canada Atlas Lambert projection). This is necessary to perform slope calculations. For more information on this, see: https://en.wikipedia.org/wiki/Map_projection\n",
-    "\n",
-    "The DEM data returned in the process response here shows `rioxarray`-like access but using the URLs we can open the files however we like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'elevation': 423.6657935442332,\n",
-       " 'slope': 3.949426174669343,\n",
-       " 'aspect': 148.55915312059147}"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'id': '0',\n",
+              " 'features': 1,\n",
+              " 'Name': 'MISTASSINI (RIVIERE) EN AMONT DE LA RIVIERE MISTASSIBI',\n",
+              " 'OfficialID': '02RD003',\n",
+              " 'FlagPAVICS': 1,\n",
+              " 'Source': 'HYDAT',\n",
+              " 'Area': 9870,\n",
+              " 'area': 9569368968.087273,\n",
+              " 'centroid': [-72.7431067594341, 49.848278236356585],\n",
+              " 'perimeter': 727186.9587075961,\n",
+              " 'gravelius': 2.097005162538472}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'area': 9569.368968087272,\n",
+              " 'longitude': -72.7431067594341,\n",
+              " 'latitude': 49.848278236356585,\n",
+              " 'gravelius': 2.097005162538472,\n",
+              " 'perimeter': 727186.9587075961}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "shape_resp = wps.shape_properties(shape=basin_contour)\n",
+        "\n",
+        "[\n",
+        "    properties,\n",
+        "] = shape_resp.get(asobj=True)\n",
+        "prop = properties[0]\n",
+        "display(prop)\n",
+        "\n",
+        "area = prop[\"area\"] / 1000000.0\n",
+        "longitude = prop[\"centroid\"][0]\n",
+        "latitude = prop[\"centroid\"][1]\n",
+        "gravelius = prop[\"gravelius\"]\n",
+        "perimeter = prop[\"perimeter\"]\n",
+        "\n",
+        "shape_info = {\n",
+        "    \"area\": area,\n",
+        "    \"longitude\": longitude,\n",
+        "    \"latitude\": latitude,\n",
+        "    \"gravelius\": gravelius,\n",
+        "    \"perimeter\": perimeter,\n",
+        "}\n",
+        "display(shape_info)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "terrain_resp = wps.terrain_analysis(\n",
-    "    shape=basin_contour, select_all_touching=True, projected_crs=3978\n",
-    ")\n",
-    "\n",
-    "properties, dem0 = terrain_resp.get(asobj=True)\n",
-    "\n",
-    "elevation = properties[0][\"elevation\"]\n",
-    "slope = properties[0][\"slope\"]\n",
-    "aspect = properties[0][\"aspect\"]\n",
-    "\n",
-    "terrain = {\"elevation\": elevation, \"slope\": slope, \"aspect\": aspect}\n",
-    "display(terrain)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overview\n",
-    "\n",
-    "A synthesis of all watershed properties can be created by merging the various dictionaries created. This allows users to easily access any of these values, and to provide them to a Raven model as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'area': 9569.368968087272,\n",
-       " 'longitude': -72.7431067594341,\n",
-       " 'latitude': 49.848278236356585,\n",
-       " 'gravelius': 2.097005162538472,\n",
-       " 'perimeter': 727186.9587075961,\n",
-       " 'Ocean': 0.0,\n",
-       " 'Forest': 0.7246596208414477,\n",
-       " 'Shrubs': 0.14616312094792794,\n",
-       " 'Grass': 0.04322426804857576,\n",
-       " 'Wetland': 0.013300924493021603,\n",
-       " 'Crops': 0.00395034960218003,\n",
-       " 'Urban': 0.0035571063310866975,\n",
-       " 'Water': 0.06514460973576021,\n",
-       " 'SnowIce': 0.0,\n",
-       " 'elevation': 423.6657935442332,\n",
-       " 'slope': 3.949426174669343,\n",
-       " 'aspect': 148.55915312059147}"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note that these properties are a mix of the properties of the original file where the shape is stored, and properties computed by the process (area, centroid, perimeter and gravelius). Note also that the computed area is in m², while the \"SUB_AREA\" property is in km², and that there are slight differences between the two values due to the precision of HydroSHEDS and the delineation algorithm.\n",
+        "\n",
+        "### Land-use information\n",
+        "\n",
+        "Now we extract the land-use properties of the watershed using the `nalcms_zonal_stats` process. As mentioned, it uses a dataset from the [North American Land Change Monitoring System](http://www.cec.org/north-american-environmental-atlas), and retrieve properties over the given region.\n",
+        "\n",
+        "With the `nalcms_zonal_stats_raster` process, we also return the grid with variable accessors (`gdal`, `rasterio`, or `rioxarray`) depending on what libraries are available in our runtime environment (The following examples show `rioxarray`-like access)."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "all_properties = {**shape_info, **land_use, **terrain}\n",
-    "display(all_properties)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## <span style=\"color:blue\">Getting meteorological and climate data</span>\n",
-    "\n",
-    "Now that we have all the geographic information for our watershed, we can get the input meteorological data required to calibrate and run the model, as well as climate model data that will be used to perform a climate change impact study.\n",
-    "\n",
-    "We start by using an in-house solution that keeps updated ERA5 reanalysis datasets available with little to no wait."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Get the ERA5 data from the Wasabi/Amazon S3 server.\n",
-    "catalog_name = \"https://raw.githubusercontent.com/hydrocloudservices/catalogs/main/catalogs/atmosphere.yaml\"\n",
-    "cat = intake.open_catalog(catalog_name)\n",
-    "ds = cat.era5_reanalysis_single_levels.to_dask()\n",
-    "\n",
-    "\"\"\"\n",
-    "Get the ERA5 data. We will rechunk it to a single chunck to make it compatible with other codes on the platform,\n",
-    "especially bias-correction. We are also taking the daily min and max temperatures as well as the daily total\n",
-    "precipitation.\n",
-    "\"\"\"\n",
-    "# We will add a wrapper to ensure that the following operations will preserve the original data attributes,\n",
-    "# such as units and variable names.\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    ERA5_reference = subset.subset_shape(\n",
-    "        ds.sel(time=slice(reference_start_day, reference_end_day)), basin_contour\n",
-    "    )\n",
-    "    ERA5_tmin = ERA5_reference[\"t2m\"].resample(time=\"1D\").min().chunk(-1, -1, -1)\n",
-    "    ERA5_tmax = ERA5_reference[\"t2m\"].resample(time=\"1D\").max().chunk(-1, -1, -1)\n",
-    "    ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)\n",
-    "\n",
-    "    # Change the units\n",
-    "    ERA5_tmin = ERA5_tmin - 273.15  # K to °C\n",
-    "    ERA5_tmin.attrs[\"units\"] = \"degC\"\n",
-    "\n",
-    "    ERA5_tmax = ERA5_tmax - 273.15  # K to °C\n",
-    "    ERA5_tmax.attrs[\"units\"] = \"degC\"\n",
-    "\n",
-    "    ERA5_pr = ERA5_pr * 1000  # m to mm\n",
-    "    ERA5_pr.attrs[\"units\"] = \"mm\"\n",
-    "\n",
-    "    # Average the variables spatially\n",
-    "    ERA5_tmin = ERA5_tmin.mean({\"latitude\", \"longitude\"})\n",
-    "    ERA5_tmax = ERA5_tmax.mean({\"latitude\", \"longitude\"})\n",
-    "    ERA5_pr = ERA5_pr.mean({\"latitude\", \"longitude\"})\n",
-    "\n",
-    "    # Ensure that the precipitation is non-negative, which can happen with some reanalysis models.\n",
-    "    ERA5_pr[ERA5_pr < 0] = 0\n",
-    "\n",
-    "    # Transform them to a dataset such that they can be written with attributes to netcdf\n",
-    "    ERA5_tmin = ERA5_tmin.to_dataset(name=\"tmin\", promote_attrs=True)\n",
-    "    ERA5_tmax = ERA5_tmax.to_dataset(name=\"tmax\", promote_attrs=True)\n",
-    "    ERA5_pr = ERA5_pr.to_dataset(name=\"pr\", promote_attrs=True)\n",
-    "\n",
-    "    # Write to disk. Here is where we write to disk and where the notebook will fail if running it from the\n",
-    "    # original location on the server (which is read-only). Please move the notebooks to your writable-workspace.\n",
-    "    ERA5_weather = xr.merge([ERA5_tmin, ERA5_tmax, ERA5_pr])\n",
-    "    ERA5_weather.to_netcdf(tmp / \"ERA5_meteo_data.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We can now also get the climate model data\n",
-    "\n",
-    "Use the connection to PanGEO to gather the CMIP6 model data for the MIROC6 model. Other models are available, as described in the tutorial Notebook \"08 - Getting and Bias-Correcting CMIP6 data\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "historical tasmin\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "stats_resp = wps.nalcms_zonal_stats_raster(\n",
+        "    shape=basin_contour, select_all_touching=True, band=1, simple_categories=True\n",
+        ")"
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hwloc/linux: Ignoring PCI device with non-16bit domain.\n",
-      "Pass --enable-32bits-pci-domain to configure to support such devices\n",
-      "(warning: it would break the library ABI, don't enable unless really needed).\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we will get the raster data and compute statistics on it. It is also possible to download the extracted raseter offline (please see the tutorial for the steps on how to do this)."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "historical tasmax\n",
-      "historical pr\n",
-      "ssp585 tasmin\n",
-      "ssp585 tasmax\n",
-      "ssp585 pr\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Climate model to use\n",
-    "climate_model = \"MIROC6\"\n",
-    "\n",
-    "# Get the catalog info from the pangeo dataset, which basically is a list of links to the various products.\n",
-    "fsCMIP = gcsfs.GCSFileSystem(token=\"anon\", access=\"read_only\")\n",
-    "col = intake.open_esm_datastore(\n",
-    "    \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
-    ")\n",
-    "\n",
-    "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
-    "with xr.set_options(keep_attrs=True):\n",
-    "    # Load the files from the PanGEO catalogs, for reference and future variables of temperature and precipitation.\n",
-    "    out = {}\n",
-    "    for exp in [\"historical\", \"ssp585\"]:\n",
-    "        if exp == \"historical\":\n",
-    "            period_start = reference_start_day\n",
-    "            period_end = reference_end_day\n",
-    "        else:\n",
-    "            period_start = future_start_day\n",
-    "            period_end = future_end_day\n",
-    "\n",
-    "        out[exp] = {}\n",
-    "        for variable in [\"tasmin\", \"tasmax\", \"pr\"]:\n",
-    "            print(exp, variable)\n",
-    "            query = dict(\n",
-    "                experiment_id=exp,\n",
-    "                table_id=\"day\",\n",
-    "                variable_id=variable,\n",
-    "                member_id=\"r1i1p1f1\",\n",
-    "                source_id=climate_model,\n",
-    "            )\n",
-    "            col_subset = col.search(require_all_on=[\"source_id\"], **query)\n",
-    "            mapper = fsCMIP.get_mapper(col_subset.df.zstore[0])\n",
-    "\n",
-    "            # special case for precipitation, which does not have the \"height\" variable that we need to discard as for tasmax and tasmin.\n",
-    "            if variable == \"pr\":\n",
-    "                out[exp][variable] = average.average_shape(\n",
-    "                    xr.open_zarr(mapper, consolidated=True).sel(\n",
-    "                        time=slice(period_start, period_end)\n",
-    "                    )[variable],\n",
-    "                    basin_contour,\n",
-    "                ).chunk(-1)\n",
-    "            else:\n",
-    "                out[exp][variable] = average.average_shape(\n",
-    "                    xr.open_zarr(mapper, consolidated=True)\n",
-    "                    .sel(time=slice(period_start, period_end))\n",
-    "                    .reset_coords(\"height\", drop=True)[variable],\n",
-    "                    basin_contour,\n",
-    "                ).chunk(-1)\n",
-    "\n",
-    "# We can now extract the variables that we will need later:\n",
-    "historical_tasmax = out[\"historical\"][\"tasmax\"]\n",
-    "historical_tasmin = out[\"historical\"][\"tasmin\"]\n",
-    "historical_pr = out[\"historical\"][\"pr\"]\n",
-    "future_tasmax = out[\"ssp585\"][\"tasmax\"]\n",
-    "future_tasmin = out[\"ssp585\"][\"tasmin\"]\n",
-    "future_pr = out[\"ssp585\"][\"pr\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Change units\n",
-    "\n",
-    "Climate models and reanalysis datasets have often differing units to those expected by Raven. Here we update units to make them compatible"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Here we need to make sure that our units are all in the correct format. You can play around with the tools we've seen thus far to explore the units\n",
-    "# and make sure everything is consistent.\n",
-    "\n",
-    "# Let's start with precipitation:\n",
-    "# The CMIP data is a rate rather than an absolute value, so let's get the absolute values:\n",
-    "historical_pr = xclim.core.units.rate2amount(historical_pr)\n",
-    "future_pr = xclim.core.units.rate2amount(future_pr)\n",
-    "\n",
-    "# Now we can actually convert units in absolute terms.\n",
-    "historical_pr = xclim.core.units.convert_units_to(historical_pr, \"mm\", context=\"hydro\")\n",
-    "future_pr = xclim.core.units.convert_units_to(future_pr, \"mm\", context=\"hydro\")\n",
-    "\n",
-    "# Now let's do temperature:\n",
-    "historical_tasmin = xclim.core.units.convert_units_to(historical_tasmin, \"degC\")\n",
-    "historical_tasmax = xclim.core.units.convert_units_to(historical_tasmax, \"degC\")\n",
-    "future_tasmin = xclim.core.units.convert_units_to(future_tasmin, \"degC\")\n",
-    "future_tasmax = xclim.core.units.convert_units_to(future_tasmax, \"degC\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Apply bias-correction to the climate model data\n",
-    "\n",
-    "Here is where we perform the bias-correction to the reference and future climate data in order to remove biases as seen between the reference and historical data. The future dataset is then corrected with the same adjustment factors as those in the reference period. Feel free to modify the bias-correction method, quantiles, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Use xclim utilities (sbda) to give information on the type of window used for the bias correction.\n",
-    "group_month_window = sdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
-    "\n",
-    "# This is an adjusting function. It builds the tool that will perform the corrections.\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
-    "    ref=ERA5_weather.pr,\n",
-    "    hist=historical_pr,\n",
-    "    nquantiles=50,\n",
-    "    kind=\"+\",\n",
-    "    group=group_month_window,\n",
-    ")\n",
-    "\n",
-    "# Apply the correction factors on the reference period\n",
-    "corrected_ref_precip = Adjustment.adjust(historical_pr, interp=\"linear\")\n",
-    "\n",
-    "# Apply the correction factors on the future period\n",
-    "corrected_fut_precip = Adjustment.adjust(future_pr, interp=\"linear\")\n",
-    "\n",
-    "# Ensure that the precipitation is non-negative, which can happen with some climate models\n",
-    "corrected_ref_precip = corrected_ref_precip.where(corrected_ref_precip > 0, 0)\n",
-    "corrected_fut_precip = corrected_fut_precip.where(corrected_fut_precip > 0, 0)\n",
-    "\n",
-    "# Train the model to find the correction factors for the maximum temperature (tasmax) data\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
-    "    ref=ERA5_weather.tmax,\n",
-    "    hist=historical_tasmax,\n",
-    "    nquantiles=50,\n",
-    "    kind=\"+\",\n",
-    "    group=group_month_window,\n",
-    ")\n",
-    "\n",
-    "# Apply the correction factors on the reference period\n",
-    "corrected_ref_tasmax = Adjustment.adjust(historical_tasmax, interp=\"linear\")\n",
-    "\n",
-    "# Apply the correction factors on the future period\n",
-    "corrected_fut_tasmax = Adjustment.adjust(future_tasmax, interp=\"linear\")\n",
-    "\n",
-    "# Train the model to find the correction factors for the minimum temperature (tasmin) data\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
-    "    ref=ERA5_weather.tmin,\n",
-    "    hist=historical_tasmin,\n",
-    "    nquantiles=50,\n",
-    "    kind=\"+\",\n",
-    "    group=group_month_window,\n",
-    ")\n",
-    "\n",
-    "# Apply the correction factors on the reference period\n",
-    "corrected_ref_tasmin = Adjustment.adjust(historical_tasmin, interp=\"linear\")\n",
-    "\n",
-    "# Apply the correction factors on the future period\n",
-    "corrected_fut_tasmin = Adjustment.adjust(future_tasmin, interp=\"linear\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generate the NetCDF files\n",
-    "\n",
-    "Now that the datasets are created, we can generate files so that Raven can access them. This might take a bit of time since everything up until now has been done in a \"lazy\" framework by Python. Data processing is actually just now really starting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Convert the reference corrected data into netCDF file. We will then apply a special code to remove a dimension in the dataset to make it applicable to the RAVEN models.\n",
-    "ref_dataset = xr.merge(\n",
-    "    [\n",
-    "        corrected_ref_precip.to_dataset(name=\"pr\"),\n",
-    "        corrected_ref_tasmax.to_dataset(name=\"tasmax\"),\n",
-    "        corrected_ref_tasmin.to_dataset(name=\"tasmin\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# Write to temporary folder\n",
-    "fn_tmp_ref = tmp / \"reference_dataset_tmp.nc\"\n",
-    "ref_dataset.to_netcdf(fn_tmp_ref)\n",
-    "\n",
-    "# Convert the future corrected data into netCDF file\n",
-    "fut_dataset = xr.merge(\n",
-    "    [\n",
-    "        corrected_fut_precip.to_dataset(name=\"pr\"),\n",
-    "        corrected_fut_tasmax.to_dataset(name=\"tasmax\"),\n",
-    "        corrected_fut_tasmin.to_dataset(name=\"tasmin\"),\n",
-    "    ]\n",
-    ")\n",
-    "# Write to temporary folder\n",
-    "fn_tmp_fut = tmp / \"future_dataset_tmp.nc\"\n",
-    "fut_dataset.to_netcdf(fn_tmp_fut)\n",
-    "\n",
-    "# Write the data to disk to a temporary location for future use.\n",
-    "ref_dataset = xr.open_dataset(fn_tmp_ref)\n",
-    "ref_dataset.isel(geom=0).squeeze().to_netcdf(tmp / \"reference_dataset.nc\")\n",
-    "\n",
-    "fut_dataset = xr.open_dataset(fn_tmp_fut)\n",
-    "fut_dataset.isel(geom=0).squeeze().to_netcdf(tmp / \"future_dataset.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## <span style=\"color:blue\"> Set-up the hydrological model </span>\n",
-    "\n",
-    "Now that we have geographic and meteorological input data available, we can setup a Raven hydrological model and calibrate it. Many more details can be found in the documentation and tutorial notebooks.\n",
-    "\n",
-    "Start by setting up the configuration for the GR4JCN hydrological model we will use in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Define the hydrological response unit. We can use the geographic information we gathered previously to\n",
-    "# populate the fields for the HRU.\n",
-    "hru = {}\n",
-    "hru = dict(\n",
-    "    area=all_properties[\"area\"],\n",
-    "    elevation=all_properties[\"elevation\"],\n",
-    "    latitude=all_properties[\"latitude\"],\n",
-    "    longitude=all_properties[\"longitude\"],\n",
-    "    hru_type=\"land\",\n",
-    ")\n",
-    "\n",
-    "# Establish the start date for the calibration. This is set in the model configuration, so the calibrator\n",
-    "# will simply execute the model which has been pre-configured to run on this period.\n",
-    "start_date = dt.datetime(1981, 1, 1)\n",
-    "end_date = dt.datetime(1985, 12, 31)\n",
-    "\n",
-    "# The data types available in the forcing netcdf file from ERA5, as per the tutorials.\n",
-    "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
-    "\n",
-    "# Alternative variable names as described in the tutorial.\n",
-    "alt_names = {\n",
-    "    \"TEMP_MIN\": \"tmin\",\n",
-    "    \"TEMP_MAX\": \"tmax\",\n",
-    "    \"PRECIP\": \"pr\",\n",
-    "}\n",
-    "\n",
-    "# The data keywords necessary to indicate the elevation, latitude and longitude of the ERA5 forcing data. Here\n",
-    "# we use the information for the basin average as the ERA5 data is averaged on the watershed.\n",
-    "data_kwds = {\n",
-    "    \"ALL\": {\n",
-    "        \"elevation\": hru[\"elevation\"],\n",
-    "        \"latitude\": hru[\"latitude\"],\n",
-    "        \"longitude\": hru[\"longitude\"],\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Give a name to the simulation\n",
-    "run_name = \"Paper_example_simulation\"\n",
-    "\n",
-    "# Setup the gauge object that includes meteorological data from ERA5\n",
-    "gauge = [\n",
-    "    rc.Gauge.from_nc(\n",
-    "        tmp\n",
-    "        / \"ERA5_meteo_data.nc\",  # Path to the ERA5 file containing all three meteorological variables\n",
-    "        data_type=data_type,  # Note that this is the list of all the variables\n",
-    "        alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
-    "        data_kwds=data_kwds,\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "# Read the streamflow from the HYSETS catchment data for this basin\n",
-    "discharge_data = [rc.ObservationData.from_nc(streamflow_file, alt_names=\"discharge\")]\n",
-    "\n",
-    "# Which evaluation metric do we want to use for calibration. Raven will return this by default after each run,\n",
-    "# and the optimizer will read it directly to calibrate.\n",
-    "eval_metrics = (\"NASH_SUTCLIFFE\",)\n",
-    "\n",
-    "# Build the model configuration according to user preferences and inputs\n",
-    "model_config = GR4JCN(\n",
-    "    ObservationData=discharge_data,\n",
-    "    Gauge=gauge,\n",
-    "    HRUs=[hru],\n",
-    "    StartDate=start_date,\n",
-    "    EndDate=end_date,\n",
-    "    RunName=run_name,\n",
-    "    EvaluationMetrics=eval_metrics,  # We add this code to tell Raven which objective function we want to pass.\n",
-    "    SuppressOutput=True,  # This stops Raven from generating the output .nc files at each iteration.\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hydrological model calibration\n",
-    "\n",
-    "We have finished building the model configuration. We can now focus on the optimizer itself!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading to /tmp/tmpv9zzg043/subset_1.tiff.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "'Land use ratios'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'Ocean': 0.0,\n",
+              " 'Forest': 0.7246596208414477,\n",
+              " 'Shrubs': 0.14616312094792794,\n",
+              " 'Grass': 0.04322426804857576,\n",
+              " 'Wetland': 0.013300924493021603,\n",
+              " 'Crops': 0.00395034960218003,\n",
+              " 'Urban': 0.0035571063310866975,\n",
+              " 'Water': 0.06514460973576021,\n",
+              " 'SnowIce': 0.0}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "'Land use percentages'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'Ocean': '0.0 %',\n",
+              " 'Forest': '72.47 %',\n",
+              " 'Shrubs': '14.62 %',\n",
+              " 'Grass': '4.32 %',\n",
+              " 'Wetland': '1.33 %',\n",
+              " 'Crops': '0.4 %',\n",
+              " 'Urban': '0.36 %',\n",
+              " 'Water': '6.51 %',\n",
+              " 'SnowIce': '0.0 %'}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "features, statistics, grid0 = stats_resp.get(asobj=True)\n",
+        "lu = statistics[0]\n",
+        "total = sum(lu.values())\n",
+        "\n",
+        "land_use = {k: (v / total) for (k, v) in lu.items()}\n",
+        "display(\"Land use ratios\", land_use)\n",
+        "\n",
+        "land_use_pct = {k: f\"{np.round(v/total*100, 2)} %\" for (k, v) in lu.items()}\n",
+        "display(\"Land use percentages\", land_use_pct)"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing the  Dynamically Dimensioned Search (DDS) algorithm  with  200  repetitions\n",
-      "The objective function will be maximized\n",
-      "Starting the DDS algotrithm with 200 repetitions...\n",
-      "Finding best starting point for trial 1 using 5 random samples.\n",
-      "Initialize database...\n",
-      "['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']\n",
-      "8 of 200, maximal objective function=0.42431, time remaining: 00:00:47\n",
-      "16 of 200, maximal objective function=0.45263, time remaining: 00:00:48\n",
-      "24 of 200, maximal objective function=0.468736, time remaining: 00:00:46\n",
-      "32 of 200, maximal objective function=0.499368, time remaining: 00:00:44\n",
-      "40 of 200, maximal objective function=0.537301, time remaining: 00:00:43\n",
-      "47 of 200, maximal objective function=0.56329, time remaining: 00:00:41\n",
-      "55 of 200, maximal objective function=0.56329, time remaining: 00:00:39\n",
-      "62 of 200, maximal objective function=0.573199, time remaining: 00:00:37\n",
-      "70 of 200, maximal objective function=0.590347, time remaining: 00:00:35\n",
-      "78 of 200, maximal objective function=0.590347, time remaining: 00:00:33\n",
-      "86 of 200, maximal objective function=0.590347, time remaining: 00:00:31\n",
-      "94 of 200, maximal objective function=0.590347, time remaining: 00:00:29\n",
-      "102 of 200, maximal objective function=0.630814, time remaining: 00:00:27\n",
-      "110 of 200, maximal objective function=0.631958, time remaining: 00:00:25\n",
-      "118 of 200, maximal objective function=0.632488, time remaining: 00:00:22\n",
-      "126 of 200, maximal objective function=0.63296, time remaining: 00:00:20\n",
-      "134 of 200, maximal objective function=0.63296, time remaining: 00:00:18\n",
-      "142 of 200, maximal objective function=0.636451, time remaining: 00:00:16\n",
-      "150 of 200, maximal objective function=0.636451, time remaining: 00:00:14\n",
-      "158 of 200, maximal objective function=0.640698, time remaining: 00:00:11\n",
-      "166 of 200, maximal objective function=0.640809, time remaining: 00:00:09\n",
-      "174 of 200, maximal objective function=0.640809, time remaining: 00:00:07\n",
-      "182 of 200, maximal objective function=0.652205, time remaining: 00:00:05\n",
-      "189 of 200, maximal objective function=0.653347, time remaining: 00:00:03\n",
-      "197 of 200, maximal objective function=0.653347, time remaining: 00:00:01\n",
-      "Best solution found has obj function value of 0.653347 at 5\n",
-      "\n",
-      "\n",
-      "\n",
-      "*** Final SPOTPY summary ***\n",
-      "Total Duration: 55.49 seconds\n",
-      "Total Repetitions: 200\n",
-      "Maximal objective value: 0.653347\n",
-      "Corresponding parameter setting:\n",
-      "GR4J_X1: 0.707116\n",
-      "GR4J_X2: 5.57681\n",
-      "GR4J_X3: 229.656\n",
-      "GR4J_X4: 5.43001\n",
-      "CEMANEIGE_X1: 23.0596\n",
-      "CEMANEIGE_X2: 0.869073\n",
-      "******************************\n",
-      "\n",
-      "Run number 185 has the highest objectivefunction with: 0.6533\n",
-      "[0.7071157109958173, 5.5768060708382325, 229.65582184142454, 5.4300108886558025, 23.059584540418495, 0.8690732021805047]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# In order to calibrate your model, you need to give the lower and higher bounds of the model. In this case,\n",
-    "# we are passing the boundaries for a GR4JCN, but it's important to change them, if you are using another model.\n",
-    "low = (0.01, -15.0, 10.0, 0.0, 1.0, 0.0)\n",
-    "high = (2.5, 10.0, 700.0, 7.0, 30.0, 1.0)\n",
-    "\n",
-    "# Random seed. We will provide one for consistency purposes, but operationnaly this should not be provided.\n",
-    "random_seed = 42\n",
-    "np.random.seed(random_seed)\n",
-    "\n",
-    "# Build the optimizer object\n",
-    "spot_setup = SpotSetup(\n",
-    "    config=model_config,\n",
-    "    low=low,\n",
-    "    high=high,\n",
-    ")\n",
-    "\n",
-    "# Maximum number of model evaluations. We only use 200 here to keep the computation time as low as possible,\n",
-    "# but you will want to increase this for operational use, perhaps to 2000-5000 depending on the model.\n",
-    "max_iterations = 200\n",
-    "\n",
-    "# Setup the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer\n",
-    "# to the spotpy documentation for more options. We recommend sticking to this format for efficiency of most\n",
-    "# applications. Here we use DDS as the optimization algorithm. More are available: see the Spotpy documentation\n",
-    "# for more information. Here, DDS is used as it is powerful and particularly useful for optimizations with small\n",
-    "# evaluation budgets. For more details on DDS, see:\n",
-    "#\n",
-    "# Tolson, B.A. and Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water\n",
-    "# Resources Research, 43(1)\n",
-    "sampler = spotpy.algorithms.dds(\n",
-    "    spot_setup, dbname=\"RAVEN_model_run\", dbformat=\"ram\", save_sim=False\n",
-    ")\n",
-    "\n",
-    "# Launch the actual optimization. Multiple trials can be launched, where the entire process is repeated and\n",
-    "# the best overall value from all trials is returned.\n",
-    "sampler.sample(max_iterations, trials=1)\n",
-    "\n",
-    "# Get the model diagnostics\n",
-    "diag = spot_setup.diagnostics\n",
-    "\n",
-    "# Get all the values of each iteration\n",
-    "results = sampler.getdata()\n",
-    "\n",
-    "# Get the raw resutlts directly in an array\n",
-    "bestindex, bestobjfun = spotpy.analyser.get_maxlikeindex(\n",
-    "    results\n",
-    ")  # Want to get the MAX NSE (change for min for RMSE)\n",
-    "best_model_run = list(\n",
-    "    results[bestindex][0]\n",
-    ")  # Get the parameter set returning the best NSE\n",
-    "optimized_parameters = best_model_run[\n",
-    "    1:-1\n",
-    "]  # Remove the NSE value (position 0) and the ID at the last position to get the actual parameter set.\n",
-    "\n",
-    "# Display the parameter set ready to use in a future run:\n",
-    "print(optimized_parameters)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run the calibrated hydrological model on a validation period\n",
-    "\n",
-    "Now that the hydrological model has been calibrated, we can use these parameters to run the model on an independent period for validation, using ERA5 as the observation weather dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Terrain information from the DEM\n",
+        "\n",
+        "Here we collect terrain data, such as elevation, slope and aspect, from the DEM. We will do this using the `terrain_analysis` WPS service, which by default uses DEM data from [EarthEnv-DEM90](https://www.earthenv.org/DEM).\n",
+        "\n",
+        "Note here that while the feature outline is defined above in terms of geographic coordinates (latitude, longitude), the DEM is projected onto a 2D cartesian coordinate system (here NAD83, the Canada Atlas Lambert projection). This is necessary to perform slope calculations. For more information on this, see: https://en.wikipedia.org/wiki/Map_projection\n",
+        "\n",
+        "The DEM data returned in the process response here shows `rioxarray`-like access but using the URLs we can open the files however we like."
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'elevation': 423.6657935442332,\n",
+              " 'slope': 3.949426174669343,\n",
+              " 'aspect': 148.55915312059147}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "terrain_resp = wps.terrain_analysis(\n",
+        "    shape=basin_contour, select_all_touching=True, projected_crs=3978\n",
+        ")\n",
+        "\n",
+        "properties, dem0 = terrain_resp.get(asobj=True)\n",
+        "\n",
+        "elevation = properties[0][\"elevation\"]\n",
+        "slope = properties[0][\"slope\"]\n",
+        "aspect = properties[0][\"aspect\"]\n",
+        "\n",
+        "terrain = {\"elevation\": elevation, \"slope\": slope, \"aspect\": aspect}\n",
+        "display(terrain)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Copy the configuration of the previous model that we will modify for our validation:\n",
-    "model_validation = model_config.duplicate(\n",
-    "    params=optimized_parameters,\n",
-    "    StartDate=dt.datetime(1986, 1, 1),\n",
-    "    EndDate=dt.datetime(1990, 12, 31),\n",
-    "    SuppressOutput=False,\n",
-    ")\n",
-    "\n",
-    "sim_output = Emulator(config=model_validation).run()\n",
-    "\n",
-    "# Get validation NSE (note we are counting the first year without warm-up)\n",
-    "NSE = sim_output.diagnostics[\"DIAG_NASH_SUTCLIFFE\"]\n",
-    "\n",
-    "# Plot the model output\n",
-    "sim_output.hydrograph.q_sim.plot(color=\"blue\", label=\"Simulation\")\n",
-    "sim_output.hydrograph.q_obs.plot(color=\"black\", label=\"Observation\")\n",
-    "plt.legend()\n",
-    "plt.title(\"Validation period - NSE=\" + str(NSE[0]))\n",
-    "plt.ylabel(\"Streamflow (m³/s)\")\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## <span style=\"color:blue\">Climate change impacts on hydrology</span>\n",
-    "\n",
-    "We can now run GR4JCN to obtain streamflow using the climate model data. We will run the calibrated hydrological model with reference and future data and compare results.\n",
-    "\n",
-    "### Reference period simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Overview\n",
+        "\n",
+        "A synthesis of all watershed properties can be created by merging the various dictionaries created. This allows users to easily access any of these values, and to provide them to a Raven model as needed."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Setup a gauge for Raven to read-in the reference climate data, just like for ERA5\n",
-    "gauge_ref = [\n",
-    "    rc.Gauge.from_nc(\n",
-    "        tmp\n",
-    "        / \"reference_dataset.nc\",  # Path to the CMIP6 model reference data netcdf file\n",
-    "        data_type=data_type,\n",
-    "        alt_names=alt_names,\n",
-    "        data_kwds=data_kwds,\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "# Copy the configuration of the previous model that we will modify for our simulation on the reference period.\n",
-    "model_config_reference = model_validation.duplicate(\n",
-    "    Gauge=gauge_ref,\n",
-    "    StartDate=reference_start_day\n",
-    "    + dt.timedelta(days=1),  # Add a day here to account for the UTC lag in ERA5\n",
-    "    EndDate=reference_end_day,\n",
-    ")\n",
-    "\n",
-    "# Run the model from the configuration and get the outputs.\n",
-    "ref_output = Emulator(config=model_config_reference).run()\n",
-    "\n",
-    "# Plot the model output. Note that both simulations should have similar hydrological\n",
-    "# regime but day-to-day variability is not expected to match.\n",
-    "ref_output.hydrograph.q_sim.plot(color=\"blue\", label=\"Reference period simulation\")\n",
-    "ref_output.hydrograph.q_obs.plot(color=\"black\", label=\"Observation\")\n",
-    "plt.legend()\n",
-    "plt.title(\"Reference period\")\n",
-    "plt.ylabel(\"Streamflow (m³/s)\")\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Future period simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'area': 9569.368968087272,\n",
+              " 'longitude': -72.7431067594341,\n",
+              " 'latitude': 49.848278236356585,\n",
+              " 'gravelius': 2.097005162538472,\n",
+              " 'perimeter': 727186.9587075961,\n",
+              " 'Ocean': 0.0,\n",
+              " 'Forest': 0.7246596208414477,\n",
+              " 'Shrubs': 0.14616312094792794,\n",
+              " 'Grass': 0.04322426804857576,\n",
+              " 'Wetland': 0.013300924493021603,\n",
+              " 'Crops': 0.00395034960218003,\n",
+              " 'Urban': 0.0035571063310866975,\n",
+              " 'Water': 0.06514460973576021,\n",
+              " 'SnowIce': 0.0,\n",
+              " 'elevation': 423.6657935442332,\n",
+              " 'slope': 3.949426174669343,\n",
+              " 'aspect': 148.55915312059147}"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "all_properties = {**shape_info, **land_use, **terrain}\n",
+        "display(all_properties)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Setup a gauge for Raven to read-in the future climate data, just like for the reference data\n",
-    "gauge_fut = [\n",
-    "    rc.Gauge.from_nc(\n",
-    "        tmp / \"future_dataset.nc\",  # Path to the CMIP6 model reference data netcdf file\n",
-    "        data_type=data_type,\n",
-    "        alt_names=alt_names,\n",
-    "        data_kwds=data_kwds,\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "# Copy the configuration of the previous model that we will modify for our simulation on the reference period.\n",
-    "model_config_future = model_validation.duplicate(\n",
-    "    Gauge=gauge_fut,\n",
-    "    StartDate=future_start_day + dt.timedelta(days=1),\n",
-    "    EndDate=future_end_day,\n",
-    "    ObservationData=None,  # There are no observations for the future period.\n",
-    ")\n",
-    "\n",
-    "# Run the model and get the outputs and hydrographs.\n",
-    "fut_output = Emulator(config=model_config_future).run()\n",
-    "\n",
-    "# Plot the model output\n",
-    "fut_output.hydrograph.q_sim.plot(color=\"blue\", label=\"Future simulation\")\n",
-    "plt.legend()\n",
-    "plt.title(\"Future period\")\n",
-    "plt.ylabel(\"Streamflow (m³/s)\")\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compare results\n",
-    "We can now compare the results between:\n",
-    "- The observed flows;\n",
-    "- The simulation flows on the validation period;;\n",
-    "- The reference period flows;\n",
-    "- The future period flows.\n",
-    "\n",
-    "Results cannot be compared on a day-to-day basis because climate models do not reflect actual weather data. Therefore, we will compare the mean annual hydrographs to see changes in long-term flow patterns. Note that this test only uses 10 years (5 for the validation period) which is insufficient. Operational tests should include more years (ideally 30 or more) to reflect the climatology of the various periods.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## <span style=\"color:blue\">Getting meteorological and climate data</span>\n",
+        "\n",
+        "Now that we have all the geographic information for our watershed, we can get the input meteorological data required to calibrate and run the model, as well as climate model data that will be used to perform a climate change impact study.\n",
+        "\n",
+        "We start by using an in-house solution that keeps updated ERA5 reanalysis datasets available with little to no wait."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Get the ERA5 data from the Wasabi/Amazon S3 server.\n",
+        "catalog_name = \"https://raw.githubusercontent.com/hydrocloudservices/catalogs/main/catalogs/atmosphere.yaml\"\n",
+        "cat = intake.open_catalog(catalog_name)\n",
+        "ds = cat.era5_reanalysis_single_levels.to_dask()\n",
+        "\n",
+        "\"\"\"\n",
+        "Get the ERA5 data. We will rechunk it to a single chunck to make it compatible with other codes on the platform,\n",
+        "especially bias-correction. We are also taking the daily min and max temperatures as well as the daily total\n",
+        "precipitation.\n",
+        "\"\"\"\n",
+        "# We will add a wrapper to ensure that the following operations will preserve the original data attributes,\n",
+        "# such as units and variable names.\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    ERA5_reference = subset.subset_shape(\n",
+        "        ds.sel(time=slice(reference_start_day, reference_end_day)), basin_contour\n",
+        "    )\n",
+        "    ERA5_tmin = ERA5_reference[\"t2m\"].resample(time=\"1D\").min().chunk(-1, -1, -1)\n",
+        "    ERA5_tmax = ERA5_reference[\"t2m\"].resample(time=\"1D\").max().chunk(-1, -1, -1)\n",
+        "    ERA5_pr = ERA5_reference[\"tp\"].resample(time=\"1D\").sum().chunk(-1, -1, -1)\n",
+        "\n",
+        "    # Change the units\n",
+        "    ERA5_tmin = ERA5_tmin - 273.15  # K to °C\n",
+        "    ERA5_tmin.attrs[\"units\"] = \"degC\"\n",
+        "\n",
+        "    ERA5_tmax = ERA5_tmax - 273.15  # K to °C\n",
+        "    ERA5_tmax.attrs[\"units\"] = \"degC\"\n",
+        "\n",
+        "    ERA5_pr = ERA5_pr * 1000  # m to mm\n",
+        "    ERA5_pr.attrs[\"units\"] = \"mm\"\n",
+        "\n",
+        "    # Average the variables spatially\n",
+        "    ERA5_tmin = ERA5_tmin.mean({\"latitude\", \"longitude\"})\n",
+        "    ERA5_tmax = ERA5_tmax.mean({\"latitude\", \"longitude\"})\n",
+        "    ERA5_pr = ERA5_pr.mean({\"latitude\", \"longitude\"})\n",
+        "\n",
+        "    # Ensure that the precipitation is non-negative, which can happen with some reanalysis models.\n",
+        "    ERA5_pr[ERA5_pr < 0] = 0\n",
+        "\n",
+        "    # Transform them to a dataset such that they can be written with attributes to netcdf\n",
+        "    ERA5_tmin = ERA5_tmin.to_dataset(name=\"tmin\", promote_attrs=True)\n",
+        "    ERA5_tmax = ERA5_tmax.to_dataset(name=\"tmax\", promote_attrs=True)\n",
+        "    ERA5_pr = ERA5_pr.to_dataset(name=\"pr\", promote_attrs=True)\n",
+        "\n",
+        "    # Write to disk. Here is where we write to disk and where the notebook will fail if running it from the\n",
+        "    # original location on the server (which is read-only). Please move the notebooks to your writable-workspace.\n",
+        "    ERA5_weather = xr.merge([ERA5_tmin, ERA5_tmax, ERA5_pr])\n",
+        "    ERA5_weather.to_netcdf(tmp / \"ERA5_meteo_data.nc\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### We can now also get the climate model data\n",
+        "\n",
+        "Use the connection to PanGEO to gather the CMIP6 model data for the MIROC6 model. Other models are available, as described in the tutorial Notebook \"08 - Getting and Bias-Correcting CMIP6 data\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "historical tasmin\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "hwloc/linux: Ignoring PCI device with non-16bit domain.\n",
+            "Pass --enable-32bits-pci-domain to configure to support such devices\n",
+            "(warning: it would break the library ABI, don't enable unless really needed).\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "historical tasmax\n",
+            "historical pr\n",
+            "ssp585 tasmin\n",
+            "ssp585 tasmax\n",
+            "ssp585 pr\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Climate model to use\n",
+        "climate_model = \"MIROC6\"\n",
+        "\n",
+        "# Get the catalog info from the pangeo dataset, which basically is a list of links to the various products.\n",
+        "fsCMIP = gcsfs.GCSFileSystem(token=\"anon\", access=\"read_only\")\n",
+        "col = intake.open_esm_datastore(\n",
+        "    \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
+        ")\n",
+        "\n",
+        "# We will add a wrapper to ensure that the following operations will preserve the original data attributes, such as units and variable names.\n",
+        "with xr.set_options(keep_attrs=True):\n",
+        "    # Load the files from the PanGEO catalogs, for reference and future variables of temperature and precipitation.\n",
+        "    out = {}\n",
+        "    for exp in [\"historical\", \"ssp585\"]:\n",
+        "        if exp == \"historical\":\n",
+        "            period_start = reference_start_day\n",
+        "            period_end = reference_end_day\n",
+        "        else:\n",
+        "            period_start = future_start_day\n",
+        "            period_end = future_end_day\n",
+        "\n",
+        "        out[exp] = {}\n",
+        "        for variable in [\"tasmin\", \"tasmax\", \"pr\"]:\n",
+        "            print(exp, variable)\n",
+        "            query = dict(\n",
+        "                experiment_id=exp,\n",
+        "                table_id=\"day\",\n",
+        "                variable_id=variable,\n",
+        "                member_id=\"r1i1p1f1\",\n",
+        "                source_id=climate_model,\n",
+        "            )\n",
+        "            col_subset = col.search(require_all_on=[\"source_id\"], **query)\n",
+        "            mapper = fsCMIP.get_mapper(col_subset.df.zstore[0])\n",
+        "\n",
+        "            # special case for precipitation, which does not have the \"height\" variable that we need to discard as for tasmax and tasmin.\n",
+        "            if variable == \"pr\":\n",
+        "                out[exp][variable] = average.average_shape(\n",
+        "                    xr.open_zarr(mapper, consolidated=True).sel(\n",
+        "                        time=slice(period_start, period_end)\n",
+        "                    )[variable],\n",
+        "                    basin_contour,\n",
+        "                ).chunk(-1)\n",
+        "            else:\n",
+        "                out[exp][variable] = average.average_shape(\n",
+        "                    xr.open_zarr(mapper, consolidated=True)\n",
+        "                    .sel(time=slice(period_start, period_end))\n",
+        "                    .reset_coords(\"height\", drop=True)[variable],\n",
+        "                    basin_contour,\n",
+        "                ).chunk(-1)\n",
+        "\n",
+        "# We can now extract the variables that we will need later:\n",
+        "historical_tasmax = out[\"historical\"][\"tasmax\"]\n",
+        "historical_tasmin = out[\"historical\"][\"tasmin\"]\n",
+        "historical_pr = out[\"historical\"][\"pr\"]\n",
+        "future_tasmax = out[\"ssp585\"][\"tasmax\"]\n",
+        "future_tasmin = out[\"ssp585\"][\"tasmin\"]\n",
+        "future_pr = out[\"ssp585\"][\"pr\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Change units\n",
+        "\n",
+        "Climate models and reanalysis datasets have often differing units to those expected by Raven. Here we update units to make them compatible"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Here we need to make sure that our units are all in the correct format. You can play around with the tools we've seen thus far to explore the units\n",
+        "# and make sure everything is consistent.\n",
+        "\n",
+        "# Let's start with precipitation:\n",
+        "# The CMIP data is a rate rather than an absolute value, so let's get the absolute values:\n",
+        "historical_pr = xclim.core.units.rate2amount(historical_pr)\n",
+        "future_pr = xclim.core.units.rate2amount(future_pr)\n",
+        "\n",
+        "# Now we can actually convert units in absolute terms.\n",
+        "historical_pr = xclim.core.units.convert_units_to(historical_pr, \"mm\", context=\"hydro\")\n",
+        "future_pr = xclim.core.units.convert_units_to(future_pr, \"mm\", context=\"hydro\")\n",
+        "\n",
+        "# Now let's do temperature:\n",
+        "historical_tasmin = xclim.core.units.convert_units_to(historical_tasmin, \"degC\")\n",
+        "historical_tasmax = xclim.core.units.convert_units_to(historical_tasmax, \"degC\")\n",
+        "future_tasmin = xclim.core.units.convert_units_to(future_tasmin, \"degC\")\n",
+        "future_tasmax = xclim.core.units.convert_units_to(future_tasmax, \"degC\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Apply bias-correction to the climate model data\n",
+        "\n",
+        "Here is where we perform the bias-correction to the reference and future climate data in order to remove biases as seen between the reference and historical data. The future dataset is then corrected with the same adjustment factors as those in the reference period. Feel free to modify the bias-correction method, quantiles, etc."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Use xclim utilities (sbda) to give information on the type of window used for the bias correction.\n",
+        "group_month_window = sdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
+        "\n",
+        "# This is an adjusting function. It builds the tool that will perform the corrections.\n",
+        "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+        "    ref=ERA5_weather.pr,\n",
+        "    hist=historical_pr,\n",
+        "    nquantiles=50,\n",
+        "    kind=\"+\",\n",
+        "    group=group_month_window,\n",
+        ")\n",
+        "\n",
+        "# Apply the correction factors on the reference period\n",
+        "corrected_ref_precip = Adjustment.adjust(historical_pr, interp=\"linear\")\n",
+        "\n",
+        "# Apply the correction factors on the future period\n",
+        "corrected_fut_precip = Adjustment.adjust(future_pr, interp=\"linear\")\n",
+        "\n",
+        "# Ensure that the precipitation is non-negative, which can happen with some climate models\n",
+        "corrected_ref_precip = corrected_ref_precip.where(corrected_ref_precip > 0, 0)\n",
+        "corrected_fut_precip = corrected_fut_precip.where(corrected_fut_precip > 0, 0)\n",
+        "\n",
+        "# Train the model to find the correction factors for the maximum temperature (tasmax) data\n",
+        "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+        "    ref=ERA5_weather.tmax,\n",
+        "    hist=historical_tasmax,\n",
+        "    nquantiles=50,\n",
+        "    kind=\"+\",\n",
+        "    group=group_month_window,\n",
+        ")\n",
+        "\n",
+        "# Apply the correction factors on the reference period\n",
+        "corrected_ref_tasmax = Adjustment.adjust(historical_tasmax, interp=\"linear\")\n",
+        "\n",
+        "# Apply the correction factors on the future period\n",
+        "corrected_fut_tasmax = Adjustment.adjust(future_tasmax, interp=\"linear\")\n",
+        "\n",
+        "# Train the model to find the correction factors for the minimum temperature (tasmin) data\n",
+        "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+        "    ref=ERA5_weather.tmin,\n",
+        "    hist=historical_tasmin,\n",
+        "    nquantiles=50,\n",
+        "    kind=\"+\",\n",
+        "    group=group_month_window,\n",
+        ")\n",
+        "\n",
+        "# Apply the correction factors on the reference period\n",
+        "corrected_ref_tasmin = Adjustment.adjust(historical_tasmin, interp=\"linear\")\n",
+        "\n",
+        "# Apply the correction factors on the future period\n",
+        "corrected_fut_tasmin = Adjustment.adjust(future_tasmin, interp=\"linear\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate the NetCDF files\n",
+        "\n",
+        "Now that the datasets are created, we can generate files so that Raven can access them. This might take a bit of time since everything up until now has been done in a \"lazy\" framework by Python. Data processing is actually just now really starting."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Convert the reference corrected data into netCDF file. We will then apply a special code to remove a dimension in the dataset to make it applicable to the RAVEN models.\n",
+        "ref_dataset = xr.merge(\n",
+        "    [\n",
+        "        corrected_ref_precip.to_dataset(name=\"pr\"),\n",
+        "        corrected_ref_tasmax.to_dataset(name=\"tasmax\"),\n",
+        "        corrected_ref_tasmin.to_dataset(name=\"tasmin\"),\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "# Write to temporary folder\n",
+        "fn_tmp_ref = tmp / \"reference_dataset_tmp.nc\"\n",
+        "ref_dataset.to_netcdf(fn_tmp_ref)\n",
+        "\n",
+        "# Convert the future corrected data into netCDF file\n",
+        "fut_dataset = xr.merge(\n",
+        "    [\n",
+        "        corrected_fut_precip.to_dataset(name=\"pr\"),\n",
+        "        corrected_fut_tasmax.to_dataset(name=\"tasmax\"),\n",
+        "        corrected_fut_tasmin.to_dataset(name=\"tasmin\"),\n",
+        "    ]\n",
+        ")\n",
+        "# Write to temporary folder\n",
+        "fn_tmp_fut = tmp / \"future_dataset_tmp.nc\"\n",
+        "fut_dataset.to_netcdf(fn_tmp_fut)\n",
+        "\n",
+        "# Write the data to disk to a temporary location for future use.\n",
+        "ref_dataset = xr.open_dataset(fn_tmp_ref)\n",
+        "ref_dataset.isel(geom=0).squeeze().to_netcdf(tmp / \"reference_dataset.nc\")\n",
+        "\n",
+        "fut_dataset = xr.open_dataset(fn_tmp_fut)\n",
+        "fut_dataset.isel(geom=0).squeeze().to_netcdf(tmp / \"future_dataset.nc\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## <span style=\"color:blue\"> Set-up the hydrological model </span>\n",
+        "\n",
+        "Now that we have geographic and meteorological input data available, we can setup a Raven hydrological model and calibrate it. Many more details can be found in the documentation and tutorial notebooks.\n",
+        "\n",
+        "Start by setting up the configuration for the GR4JCN hydrological model we will use in this example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "# Define the hydrological response unit. We can use the geographic information we gathered previously to\n",
+        "# populate the fields for the HRU.\n",
+        "hru = {}\n",
+        "hru = dict(\n",
+        "    area=all_properties[\"area\"],\n",
+        "    elevation=all_properties[\"elevation\"],\n",
+        "    latitude=all_properties[\"latitude\"],\n",
+        "    longitude=all_properties[\"longitude\"],\n",
+        "    hru_type=\"land\",\n",
+        ")\n",
+        "\n",
+        "# Establish the start date for the calibration. This is set in the model configuration, so the calibrator\n",
+        "# will simply execute the model which has been pre-configured to run on this period.\n",
+        "start_date = dt.datetime(1981, 1, 1)\n",
+        "end_date = dt.datetime(1985, 12, 31)\n",
+        "\n",
+        "# The data types available in the forcing netcdf file from ERA5, as per the tutorials.\n",
+        "data_type = [\"TEMP_MAX\", \"TEMP_MIN\", \"PRECIP\"]\n",
+        "\n",
+        "# Alternative variable names as described in the tutorial.\n",
+        "alt_names = {\n",
+        "    \"TEMP_MIN\": \"tmin\",\n",
+        "    \"TEMP_MAX\": \"tmax\",\n",
+        "    \"PRECIP\": \"pr\",\n",
+        "}\n",
+        "\n",
+        "# The data keywords necessary to indicate the elevation, latitude and longitude of the ERA5 forcing data. Here\n",
+        "# we use the information for the basin average as the ERA5 data is averaged on the watershed.\n",
+        "data_kwds = {\n",
+        "    \"ALL\": {\n",
+        "        \"elevation\": hru[\"elevation\"],\n",
+        "        \"latitude\": hru[\"latitude\"],\n",
+        "        \"longitude\": hru[\"longitude\"],\n",
+        "    }\n",
+        "}\n",
+        "\n",
+        "# Give a name to the simulation\n",
+        "run_name = \"Paper_example_simulation\"\n",
+        "\n",
+        "# Setup the gauge object that includes meteorological data from ERA5\n",
+        "gauge = [\n",
+        "    rc.Gauge.from_nc(\n",
+        "        tmp\n",
+        "        / \"ERA5_meteo_data.nc\",  # Path to the ERA5 file containing all three meteorological variables\n",
+        "        data_type=data_type,  # Note that this is the list of all the variables\n",
+        "        alt_names=alt_names,  # Note that all variables here are mapped to their names in the netcdf file.\n",
+        "        data_kwds=data_kwds,\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "# Read the streamflow from the HYSETS catchment data for this basin\n",
+        "discharge_data = [rc.ObservationData.from_nc(streamflow_file, alt_names=\"discharge\")]\n",
+        "\n",
+        "# Which evaluation metric do we want to use for calibration. Raven will return this by default after each run,\n",
+        "# and the optimizer will read it directly to calibrate.\n",
+        "eval_metrics = (\"NASH_SUTCLIFFE\",)\n",
+        "\n",
+        "# Build the model configuration according to user preferences and inputs\n",
+        "model_config = GR4JCN(\n",
+        "    ObservationData=discharge_data,\n",
+        "    Gauge=gauge,\n",
+        "    HRUs=[hru],\n",
+        "    StartDate=start_date,\n",
+        "    EndDate=end_date,\n",
+        "    RunName=run_name,\n",
+        "    EvaluationMetrics=eval_metrics,  # We add this code to tell Raven which objective function we want to pass.\n",
+        "    SuppressOutput=True,  # This stops Raven from generating the output .nc files at each iteration.\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Hydrological model calibration\n",
+        "\n",
+        "We have finished building the model configuration. We can now focus on the optimizer itself!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Initializing the  Dynamically Dimensioned Search (DDS) algorithm  with  200  repetitions\n",
+            "The objective function will be maximized\n",
+            "Starting the DDS algotrithm with 200 repetitions...\n",
+            "Finding best starting point for trial 1 using 5 random samples.\n",
+            "Initialize database...\n",
+            "['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']\n",
+            "8 of 200, maximal objective function=0.42431, time remaining: 00:00:47\n",
+            "16 of 200, maximal objective function=0.45263, time remaining: 00:00:48\n",
+            "24 of 200, maximal objective function=0.468736, time remaining: 00:00:46\n",
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+            "182 of 200, maximal objective function=0.652205, time remaining: 00:00:05\n",
+            "189 of 200, maximal objective function=0.653347, time remaining: 00:00:03\n",
+            "197 of 200, maximal objective function=0.653347, time remaining: 00:00:01\n",
+            "Best solution found has obj function value of 0.653347 at 5\n",
+            "\n",
+            "\n",
+            "\n",
+            "*** Final SPOTPY summary ***\n",
+            "Total Duration: 55.49 seconds\n",
+            "Total Repetitions: 200\n",
+            "Maximal objective value: 0.653347\n",
+            "Corresponding parameter setting:\n",
+            "GR4J_X1: 0.707116\n",
+            "GR4J_X2: 5.57681\n",
+            "GR4J_X3: 229.656\n",
+            "GR4J_X4: 5.43001\n",
+            "CEMANEIGE_X1: 23.0596\n",
+            "CEMANEIGE_X2: 0.869073\n",
+            "******************************\n",
+            "\n",
+            "Run number 185 has the highest objectivefunction with: 0.6533\n",
+            "[0.7071157109958173, 5.5768060708382325, 229.65582184142454, 5.4300108886558025, 23.059584540418495, 0.8690732021805047]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# In order to calibrate your model, you need to give the lower and higher bounds of the model. In this case,\n",
+        "# we are passing the boundaries for a GR4JCN, but it's important to change them, if you are using another model.\n",
+        "low = (0.01, -15.0, 10.0, 0.0, 1.0, 0.0)\n",
+        "high = (2.5, 10.0, 700.0, 7.0, 30.0, 1.0)\n",
+        "\n",
+        "# Random seed. We will provide one for consistency purposes, but operationnaly this should not be provided.\n",
+        "random_seed = 42\n",
+        "np.random.seed(random_seed)\n",
+        "\n",
+        "# Build the optimizer object\n",
+        "spot_setup = SpotSetup(\n",
+        "    config=model_config,\n",
+        "    low=low,\n",
+        "    high=high,\n",
+        ")\n",
+        "\n",
+        "# Maximum number of model evaluations. We only use 200 here to keep the computation time as low as possible,\n",
+        "# but you will want to increase this for operational use, perhaps to 2000-5000 depending on the model.\n",
+        "max_iterations = 200\n",
+        "\n",
+        "# Setup the spotpy sampler with the method, the setup configuration, a run name and other options. Please refer\n",
+        "# to the spotpy documentation for more options. We recommend sticking to this format for efficiency of most\n",
+        "# applications. Here we use DDS as the optimization algorithm. More are available: see the Spotpy documentation\n",
+        "# for more information. Here, DDS is used as it is powerful and particularly useful for optimizations with small\n",
+        "# evaluation budgets. For more details on DDS, see:\n",
+        "#\n",
+        "# Tolson, B.A. and Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water\n",
+        "# Resources Research, 43(1)\n",
+        "sampler = spotpy.algorithms.dds(\n",
+        "    spot_setup, dbname=\"RAVEN_model_run\", dbformat=\"ram\", save_sim=False\n",
+        ")\n",
+        "\n",
+        "# Launch the actual optimization. Multiple trials can be launched, where the entire process is repeated and\n",
+        "# the best overall value from all trials is returned.\n",
+        "sampler.sample(max_iterations, trials=1)\n",
+        "\n",
+        "# Get the model diagnostics\n",
+        "diag = spot_setup.diagnostics\n",
+        "\n",
+        "# Get all the values of each iteration\n",
+        "results = sampler.getdata()\n",
+        "\n",
+        "# Get the raw resutlts directly in an array\n",
+        "bestindex, bestobjfun = spotpy.analyser.get_maxlikeindex(\n",
+        "    results\n",
+        ")  # Want to get the MAX NSE (change for min for RMSE)\n",
+        "best_model_run = list(\n",
+        "    results[bestindex][0]\n",
+        ")  # Get the parameter set returning the best NSE\n",
+        "optimized_parameters = best_model_run[\n",
+        "    1:-1\n",
+        "]  # Remove the NSE value (position 0) and the ID at the last position to get the actual parameter set.\n",
+        "\n",
+        "# Display the parameter set ready to use in a future run:\n",
+        "print(optimized_parameters)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Run the calibrated hydrological model on a validation period\n",
+        "\n",
+        "Now that the hydrological model has been calibrated, we can use these parameters to run the model on an independent period for validation, using ERA5 as the observation weather dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Copy the configuration of the previous model that we will modify for our validation:\n",
+        "model_validation = model_config.duplicate(\n",
+        "    params=optimized_parameters,\n",
+        "    StartDate=dt.datetime(1986, 1, 1),\n",
+        "    EndDate=dt.datetime(1990, 12, 31),\n",
+        "    SuppressOutput=False,\n",
+        ")\n",
+        "\n",
+        "sim_output = Emulator(config=model_validation).run()\n",
+        "\n",
+        "# Get validation NSE (note we are counting the first year without warm-up)\n",
+        "NSE = sim_output.diagnostics[\"DIAG_NASH_SUTCLIFFE\"]\n",
+        "\n",
+        "# Plot the model output\n",
+        "sim_output.hydrograph.q_sim.plot(color=\"blue\", label=\"Simulation\")\n",
+        "sim_output.hydrograph.q_obs.plot(color=\"black\", label=\"Observation\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Validation period - NSE=\" + str(NSE[0]))\n",
+        "plt.ylabel(\"Streamflow (m³/s)\")\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## <span style=\"color:blue\">Climate change impacts on hydrology</span>\n",
+        "\n",
+        "We can now run GR4JCN to obtain streamflow using the climate model data. We will run the calibrated hydrological model with reference and future data and compare results.\n",
+        "\n",
+        "### Reference period simulation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Setup a gauge for Raven to read-in the reference climate data, just like for ERA5\n",
+        "gauge_ref = [\n",
+        "    rc.Gauge.from_nc(\n",
+        "        tmp\n",
+        "        / \"reference_dataset.nc\",  # Path to the CMIP6 model reference data netcdf file\n",
+        "        data_type=data_type,\n",
+        "        alt_names=alt_names,\n",
+        "        data_kwds=data_kwds,\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "# Copy the configuration of the previous model that we will modify for our simulation on the reference period.\n",
+        "model_config_reference = model_validation.duplicate(\n",
+        "    Gauge=gauge_ref,\n",
+        "    StartDate=reference_start_day\n",
+        "    + dt.timedelta(days=1),  # Add a day here to account for the UTC lag in ERA5\n",
+        "    EndDate=reference_end_day,\n",
+        ")\n",
+        "\n",
+        "# Run the model from the configuration and get the outputs.\n",
+        "ref_output = Emulator(config=model_config_reference).run()\n",
+        "\n",
+        "# Plot the model output. Note that both simulations should have similar hydrological\n",
+        "# regime but day-to-day variability is not expected to match.\n",
+        "ref_output.hydrograph.q_sim.plot(color=\"blue\", label=\"Reference period simulation\")\n",
+        "ref_output.hydrograph.q_obs.plot(color=\"black\", label=\"Observation\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Reference period\")\n",
+        "plt.ylabel(\"Streamflow (m³/s)\")\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Future period simulation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Setup a gauge for Raven to read-in the future climate data, just like for the reference data\n",
+        "gauge_fut = [\n",
+        "    rc.Gauge.from_nc(\n",
+        "        tmp / \"future_dataset.nc\",  # Path to the CMIP6 model reference data netcdf file\n",
+        "        data_type=data_type,\n",
+        "        alt_names=alt_names,\n",
+        "        data_kwds=data_kwds,\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "# Copy the configuration of the previous model that we will modify for our simulation on the reference period.\n",
+        "model_config_future = model_validation.duplicate(\n",
+        "    Gauge=gauge_fut,\n",
+        "    StartDate=future_start_day + dt.timedelta(days=1),\n",
+        "    EndDate=future_end_day,\n",
+        "    ObservationData=None,  # There are no observations for the future period.\n",
+        ")\n",
+        "\n",
+        "# Run the model and get the outputs and hydrographs.\n",
+        "fut_output = Emulator(config=model_config_future).run()\n",
+        "\n",
+        "# Plot the model output\n",
+        "fut_output.hydrograph.q_sim.plot(color=\"blue\", label=\"Future simulation\")\n",
+        "plt.legend()\n",
+        "plt.title(\"Future period\")\n",
+        "plt.ylabel(\"Streamflow (m³/s)\")\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Compare results\n",
+        "We can now compare the results between:\n",
+        "- The observed flows;\n",
+        "- The simulation flows on the validation period;;\n",
+        "- The reference period flows;\n",
+        "- The future period flows.\n",
+        "\n",
+        "Results cannot be compared on a day-to-day basis because climate models do not reflect actual weather data. Therefore, we will compare the mean annual hydrographs to see changes in long-term flow patterns. Note that this test only uses 10 years (5 for the validation period) which is insufficient. Operational tests should include more years (ideally 30 or more) to reflect the climatology of the various periods.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Extract the mean annual hydrograph for each simulation.\n",
+        "observed_flows = ref_output.hydrograph.q_obs.groupby(\"time.dayofyear\").mean()\n",
+        "simulated_flows = sim_output.hydrograph.q_obs.groupby(\"time.dayofyear\").mean()\n",
+        "reference_flows = ref_output.hydrograph.q_sim.groupby(\"time.dayofyear\").mean()\n",
+        "future_flows = fut_output.hydrograph.q_sim.groupby(\"time.dayofyear\").mean()\n",
+        "\n",
+        "# Plot the model output\n",
+        "observed_flows.plot(color=\"black\", label=\"Observation\", x=\"dayofyear\")\n",
+        "simulated_flows.plot(color=\"green\", label=\"Simulation\", x=\"dayofyear\")\n",
+        "reference_flows.plot(color=\"blue\", label=\"Reference\", x=\"dayofyear\")\n",
+        "future_flows.plot(color=\"red\", label=\"Future\", x=\"dayofyear\")\n",
+        "plt.legend()\n",
+        "plt.ylabel(\"Streamflow (m³/s)\")\n",
+        "plt.xlabel(\"Day of year\")\n",
+        "plt.xlim([0, 365])\n",
+        "plt.title(\"Comparison of mean annual hydrographs\")\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can see that the simulation and reference period flows are similar to the observations, whereas the future flows show a hastening of the springmelt along with higher winter flows.\n",
+        "\n",
+        "Hydrographs could also be analysed using the tools shown in the \"Time-series analysis\" notebook.\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "# Extract the mean annual hydrograph for each simulation.\n",
-    "observed_flows = ref_output.hydrograph.q_obs.groupby(\"time.dayofyear\").mean()\n",
-    "simulated_flows = sim_output.hydrograph.q_obs.groupby(\"time.dayofyear\").mean()\n",
-    "reference_flows = ref_output.hydrograph.q_sim.groupby(\"time.dayofyear\").mean()\n",
-    "future_flows = fut_output.hydrograph.q_sim.groupby(\"time.dayofyear\").mean()\n",
-    "\n",
-    "# Plot the model output\n",
-    "observed_flows.plot(color=\"black\", label=\"Observation\", x=\"dayofyear\")\n",
-    "simulated_flows.plot(color=\"green\", label=\"Simulation\", x=\"dayofyear\")\n",
-    "reference_flows.plot(color=\"blue\", label=\"Reference\", x=\"dayofyear\")\n",
-    "future_flows.plot(color=\"red\", label=\"Future\", x=\"dayofyear\")\n",
-    "plt.legend()\n",
-    "plt.ylabel(\"Streamflow (m³/s)\")\n",
-    "plt.xlabel(\"Day of year\")\n",
-    "plt.xlim([0, 365])\n",
-    "plt.title(\"Comparison of mean annual hydrographs\")\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the simulation and reference period flows are similar to the observations, whereas the future flows show a hastening of the springmelt along with higher winter flows.\n",
-    "\n",
-    "Hydrographs could also be analysed using the tools shown in the \"Time-series analysis\" notebook.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/notebooks/time_series_analysis.ipynb b/docs/notebooks/time_series_analysis.ipynb
index a6084a75..5392fb1d 100644
--- a/docs/notebooks/time_series_analysis.ipynb
+++ b/docs/notebooks/time_series_analysis.ipynb
@@ -1,286 +1,286 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Analyzing time series\n",
-    "\n",
-    "We will use the 'xclim' package and it's powerful time-series analysis tools to analyze the streamflow observations of the Salmon River basin. We will compute a few indicators, but you can refer to the xclim documentation to see how you can best make use of it for your specific needs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:04.442336Z",
-     "iopub.status.busy": "2021-09-08T20:38:04.436943Z",
-     "iopub.status.idle": "2021-09-08T20:38:06.534178Z",
-     "shell.execute_reply": "2021-09-08T20:38:06.533771Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import xarray as xr\n",
-    "import xclim\n",
-    "from pandas.plotting import register_matplotlib_converters\n",
-    "\n",
-    "from ravenpy.utilities.testdata import get_file, open_dataset\n",
-    "\n",
-    "register_matplotlib_converters()\n",
-    "\n",
-    "# Get the file we will use to analyze flows\n",
-    "file = \"hydro_simulations/raven-gr4j-cemaneige-sim_hmets-0_Hydrographs.nc\"\n",
-    "ds = open_dataset(file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Base flow index\n",
-    "\n",
-    "The base flow index is the minimum 7-day average flow divided by the mean flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:06.537520Z",
-     "iopub.status.busy": "2021-09-08T20:38:06.536991Z",
-     "iopub.status.idle": "2021-09-08T20:38:06.539490Z",
-     "shell.execute_reply": "2021-09-08T20:38:06.539139Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "help(xclim.land.base_flow_index)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The base flow index needs as input arguments the link to a NetCDF file storing the stream flow time series, the name of the stream flow variable, and the frequency at which the index is computed (`YS`: yearly, `QS-DEC`: seasonally)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:06.543861Z",
-     "iopub.status.busy": "2021-09-08T20:38:06.543316Z",
-     "iopub.status.idle": "2021-09-08T20:38:11.302558Z",
-     "shell.execute_reply": "2021-09-08T20:38:11.301888Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "out = xclim.land.base_flow_index(ds.q_sim)\n",
-    "out.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To compute generic statistics of a time series, use the `stats` process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:11.305836Z",
-     "iopub.status.busy": "2021-09-08T20:38:11.305496Z",
-     "iopub.status.idle": "2021-09-08T20:38:11.307491Z",
-     "shell.execute_reply": "2021-09-08T20:38:11.307208Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "help(xclim.generic.stats)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:11.312062Z",
-     "iopub.status.busy": "2021-09-08T20:38:11.310083Z",
-     "iopub.status.idle": "2021-09-08T20:38:15.824630Z",
-     "shell.execute_reply": "2021-09-08T20:38:15.824926Z"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Analyzing time series\n",
+        "\n",
+        "We will use the 'xclim' package and it's powerful time-series analysis tools to analyze the streamflow observations of the Salmon River basin. We will compute a few indicators, but you can refer to the xclim documentation to see how you can best make use of it for your specific needs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:04.442336Z",
+          "iopub.status.busy": "2021-09-08T20:38:04.436943Z",
+          "iopub.status.idle": "2021-09-08T20:38:06.534178Z",
+          "shell.execute_reply": "2021-09-08T20:38:06.533771Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "import xarray as xr\n",
+        "import xclim\n",
+        "from pandas.plotting import register_matplotlib_converters\n",
+        "\n",
+        "from ravenpy.utilities.testdata import get_file, open_dataset\n",
+        "\n",
+        "register_matplotlib_converters()\n",
+        "\n",
+        "# Get the file we will use to analyze flows\n",
+        "file = \"hydro_simulations/raven-gr4j-cemaneige-sim_hmets-0_Hydrographs.nc\"\n",
+        "ds = open_dataset(file)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Base flow index\n",
+        "\n",
+        "The base flow index is the minimum 7-day average flow divided by the mean flow."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "outputs": [],
+      "source": [],
+      "metadata": {
+        "collapsed": false
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:06.537520Z",
+          "iopub.status.busy": "2021-09-08T20:38:06.536991Z",
+          "iopub.status.idle": "2021-09-08T20:38:06.539490Z",
+          "shell.execute_reply": "2021-09-08T20:38:06.539139Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "help(xclim.land.base_flow_index)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The base flow index needs as input arguments the link to a NetCDF file storing the stream flow time series, the name of the stream flow variable, and the frequency at which the index is computed (`YS`: yearly, `QS-DEC`: seasonally)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:06.543861Z",
+          "iopub.status.busy": "2021-09-08T20:38:06.543316Z",
+          "iopub.status.idle": "2021-09-08T20:38:11.302558Z",
+          "shell.execute_reply": "2021-09-08T20:38:11.301888Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "out = xclim.land.base_flow_index(ds.q_sim)\n",
+        "out.plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To compute generic statistics of a time series, use the `stats` process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:11.305836Z",
+          "iopub.status.busy": "2021-09-08T20:38:11.305496Z",
+          "iopub.status.idle": "2021-09-08T20:38:11.307491Z",
+          "shell.execute_reply": "2021-09-08T20:38:11.307208Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "help(xclim.generic.stats)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:11.312062Z",
+          "iopub.status.busy": "2021-09-08T20:38:11.310083Z",
+          "iopub.status.idle": "2021-09-08T20:38:15.824630Z",
+          "shell.execute_reply": "2021-09-08T20:38:15.824926Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "# Here we compute the annual summer (JJA) minimum\n",
+        "out = xclim.generic.stats(ds.q_sim, op=\"min\", season=\"JJA\")\n",
+        "out.plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Frequency analysis\n",
+        "\n",
+        "The process `freq_analysis` is similar to the previous stat in that it fits a series of annual maxima or minima to a statistical distribution, and returns the values corresponding to different return periods."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:15.828462Z",
+          "iopub.status.busy": "2021-09-08T20:38:15.828064Z",
+          "iopub.status.idle": "2021-09-08T20:38:15.830128Z",
+          "shell.execute_reply": "2021-09-08T20:38:15.829767Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "help(xclim.generic.return_level)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For example, computing the Q(2,7), the minimum 7-days streamflow with a two-year reoccurrence, can be done using the following."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:15.834847Z",
+          "iopub.status.busy": "2021-09-08T20:38:15.832982Z",
+          "iopub.status.idle": "2021-09-08T20:38:20.147443Z",
+          "shell.execute_reply": "2021-09-08T20:38:20.146100Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "out = xclim.generic.return_level(ds.q_sim, mode=\"min\", t=2, dist=\"gumbel_r\", window=7)\n",
+        "out"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "An array of return periods can be passed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:20.242000Z",
+          "iopub.status.busy": "2021-09-08T20:38:20.241553Z",
+          "iopub.status.idle": "2021-09-08T20:38:24.688611Z",
+          "shell.execute_reply": "2021-09-08T20:38:24.688203Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "out = xclim.generic.return_level(\n",
+        "    ds.q_sim, mode=\"max\", t=(2, 5, 10, 25, 50, 100), dist=\"gumbel_r\"\n",
+        ")\n",
+        "out.plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Getting the parameters of the distribution and comparing the fit\n",
+        "\n",
+        "It's sometimes more useful to store the fitted parameters of the distribution rather than storing only the quantiles. In the example below, we're first computing the annual maxima of the simulated time series, then fitting them to a gumbel distribution using the `fit` process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:24.771961Z",
+          "iopub.status.busy": "2021-09-08T20:38:24.771558Z",
+          "iopub.status.idle": "2021-09-08T20:38:29.098370Z",
+          "shell.execute_reply": "2021-09-08T20:38:29.097714Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "with xclim.set_options(\n",
+        "    check_missing=\"pct\", missing_options={\"pct\": {\"tolerance\": 0.05}}\n",
+        "):\n",
+        "    ts = xclim.generic.stats(ds.q_sim, op=\"max\")\n",
+        "\n",
+        "ts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2021-09-08T20:38:29.149166Z",
+          "iopub.status.busy": "2021-09-08T20:38:29.148807Z",
+          "iopub.status.idle": "2021-09-08T20:38:33.460798Z",
+          "shell.execute_reply": "2021-09-08T20:38:33.461069Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "with xclim.set_options(check_missing=\"skip\"):\n",
+        "    pa = xclim.generic.fit(ts.isel(nbasins=0), dist=\"gumbel_r\")\n",
+        "pa"
+      ]
     }
-   },
-   "outputs": [],
-   "source": [
-    "# Here we compute the annual summer (JJA) minimum\n",
-    "out = xclim.generic.stats(ds.q_sim, op=\"min\", season=\"JJA\")\n",
-    "out.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Frequency analysis\n",
-    "\n",
-    "The process `freq_analysis` is similar to the previous stat in that it fits a series of annual maxima or minima to a statistical distribution, and returns the values corresponding to different return periods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:15.828462Z",
-     "iopub.status.busy": "2021-09-08T20:38:15.828064Z",
-     "iopub.status.idle": "2021-09-08T20:38:15.830128Z",
-     "shell.execute_reply": "2021-09-08T20:38:15.829767Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "help(xclim.generic.return_level)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For example, computing the Q(2,7), the minimum 7-days streamflow with a two-year reoccurrence, can be done using the following."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:15.834847Z",
-     "iopub.status.busy": "2021-09-08T20:38:15.832982Z",
-     "iopub.status.idle": "2021-09-08T20:38:20.147443Z",
-     "shell.execute_reply": "2021-09-08T20:38:20.146100Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "out = xclim.generic.return_level(ds.q_sim, mode=\"min\", t=2, dist=\"gumbel_r\", window=7)\n",
-    "out"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An array of return periods can be passed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:20.242000Z",
-     "iopub.status.busy": "2021-09-08T20:38:20.241553Z",
-     "iopub.status.idle": "2021-09-08T20:38:24.688611Z",
-     "shell.execute_reply": "2021-09-08T20:38:24.688203Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "out = xclim.generic.return_level(\n",
-    "    ds.q_sim, mode=\"max\", t=(2, 5, 10, 25, 50, 100), dist=\"gumbel_r\"\n",
-    ")\n",
-    "out.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Getting the parameters of the distribution and comparing the fit\n",
-    "\n",
-    "It's sometimes more useful to store the fitted parameters of the distribution rather than storing only the quantiles. In the example below, we're first computing the annual maxima of the simulated time series, then fitting them to a gumbel distribution using the `fit` process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:24.771961Z",
-     "iopub.status.busy": "2021-09-08T20:38:24.771558Z",
-     "iopub.status.idle": "2021-09-08T20:38:29.098370Z",
-     "shell.execute_reply": "2021-09-08T20:38:29.097714Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with xclim.set_options(\n",
-    "    check_missing=\"pct\", missing_options={\"pct\": {\"tolerance\": 0.05}}\n",
-    "):\n",
-    "    ts = xclim.generic.stats(ds.q_sim, op=\"max\")\n",
-    "\n",
-    "ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2021-09-08T20:38:29.149166Z",
-     "iopub.status.busy": "2021-09-08T20:38:29.148807Z",
-     "iopub.status.idle": "2021-09-08T20:38:33.460798Z",
-     "shell.execute_reply": "2021-09-08T20:38:33.461069Z"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.9"
     }
-   },
-   "outputs": [],
-   "source": [
-    "with xclim.set_options(check_missing=\"skip\"):\n",
-    "    pa = xclim.generic.fit(ts.isel(nbasins=0), dist=\"gumbel_r\")\n",
-    "pa"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 2
 }