diff --git a/docs/notebooks/bmi_MODFLOW_coupling.ipynb b/docs/notebooks/bmi_MODFLOW_coupling.ipynb new file mode 100644 index 00000000..5568aeee --- /dev/null +++ b/docs/notebooks/bmi_MODFLOW_coupling.ipynb @@ -0,0 +1,6839 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# STEMMUS_SCOPE & MODFLOW 6 BMI coupling\n", + "\n", + "This Python notebook shows a simple example of BMI coupling between the STEMMUS_SCOPE model and the MODFLOW 6 model. To run this notebook, the following are required:\n", + "- Install the related packages\n", + " - pyStemmusScope package (https://pystemmusscope.readthedocs.io/en/v0.2.0/installation_instructions/).\n", + " - modflowapi Python package (https://github.com/MODFLOW-USGS/modflowapi?tab=readme-ov-file#installation).\n", + "- The executable files of both models. \n", + " - STEMMUS_SCOPE executable file can be retrieved from the link (https://github.com/EcoExtreML/STEMMUS_SCOPE/tree/main. Note: the STEMMUS_SCOPE executable file is Linux based only.\n", + " - MODFLOW 6 executable files can be retrieved from the link (https://github.com/MODFLOW-USGS/modflow6). \n", + "- The input files of both models are already prepared in advance and not part of this notebook. The input data files can be accessed via this link (https://zenodo.org/records/13354850). The link include also a version of the executbale files needed to run the models.\n", + "\n", + "\n", + "Notes\n", + "- Units in STEMMUS_SCOPE are centimetres and seconds. The model runs every 30 mins.\n", + "- Units in MODFLOW 6 are meter and day. The model runs every day.\n", + "- STEMMUS_SCOPE runs per time steps.\n", + "- MODFLOW 6 runs per stress periods and time steps. A stress period is the period of time when the model forcing is fixed. Each stress period can be divided into a number of time steps. In each time step, model variables will be updated (but model forcing is still fixed).\n", + "- This example runs for 10 days. STEMMUS_SCOPE has 480 time steps (length of each time step = 30 mins). MODFLOW 6 has 10 stress periods (length of each period = 1 day and each period has 48 time steps), so in total MODFLOW 6 runs 480 times." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import shutil\n", + "import numpy as np\n", + "import xarray as xr\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from PyStemmusScope.config_io import read_config\n", + "from PyStemmusScope.bmi.implementation import StemmusScopeBmi\n", + "from modflowapi import ModflowApi\n", + "from modflowapi.extensions import ApiSimulation\n", + "from cftime import num2pydate\n", + "from rich import print" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "startTime = datetime.now()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Set up the workspace of both models" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "ws = os.path.join('.', 'SSM_examples', 'ex1')\n", + "exe_ws = os.path.join('.', 'SSM_examples', 'executables')\n", + "output_figures_ws = os.path.join(ws, 'output_figures')\n", + "if not os.path.exists(output_figures_ws):\n", + " os.makedirs(output_figures_ws)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1.1. Set up STEMMUS_SCOPE folder and config file" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "stsc_ws = os.path.join(ws, 'stsc_input', 'NL-Loo_2024-08-12-1341')\n", + "stsc_config_file = os.path.join(stsc_ws, \"NL-Loo_2024-08-12-1341_config.txt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we are using the local executable file we first have to add the Matlab runtime compiler locations to PATH:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
/opt/matlab/MATLAB_Runtime/R2023a/runtime/glnxa64:/opt/matlab/MATLAB_Runtime/R2023a/bin/glnxa64:/opt/matlab/MATLAB_\n",
+       "Runtime/R2023a/sys/os/glnxa64:/opt/matlab/MATLAB_Runtime/R2023a/extern/bin/glnxa64:/opt/matlab/MATLAB_Runtime/R2023\n",
+       "a/sys/opengl/lib/glnxa64\n",
+       "
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Set up MODFLOW 6 workspace and dll/so file" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "modflow_base_ws = os.path.join(ws, 'mf6_model_base')\n", + "modflow_ws = os.path.join(ws, 'mf6_model')\n", + "\n", + "if os.path.exists(modflow_ws):\n", + " shutil.rmtree(modflow_ws)\n", + "shutil.copytree(modflow_base_ws, modflow_ws)\n", + "\n", + "if sys.platform == \"win32\": # for Windows\n", + " dll = \"libmf6.dll\"\n", + "else:\n", + " dll = \"libmf6.so\" # for Linux\n", + "mf6_dll = os.path.join(exe_ws, dll)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Load and initialize models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.1. Initialize the STEMMUS_SCOPE model " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "stsc = StemmusScopeBmi()\n", + "stsc.initialize(stsc_config_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Note: STEMMUS_SCOPE needs to be updated by one timestep before the BMI is accessible" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "stsc.update()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Define arrays to store the needed STEMMUS_SCOPE variables" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "n_timesteps = int((stsc.get_end_time() - stsc.get_current_time())/stsc.get_time_step())\n", + "n_soil_layers = stsc.get_grid_size(stsc.get_var_grid(\"soil_moisture\"))\n", + "\n", + "soil_moisture = np.zeros((n_timesteps, n_soil_layers))\n", + "soil_temperature = np.zeros((n_timesteps, n_soil_layers))\n", + "gw_recharge = np.zeros(1)\n", + "gw_recharge_nstp = np.array([])\n", + "stsc_time = []\n", + "i = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.2. Initialize the MODFLOW 6 model and get other settings" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "mf6 = ModflowApi(mf6_dll, working_directory = modflow_ws)\n", + "mf6_config_file = os.path.join(modflow_ws, 'mfsim.nam')\n", + "mf6.initialize(mf6_config_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Load the MODFLOW 6 simulation and models" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('gwf', 'gwe')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim = ApiSimulation.load(mf6)\n", + "gwf_name = sim.model_names[0] # groundwater flow model \n", + "gwe_name = sim.model_names[1] # groundwater energy (heat) model\n", + "gwf_name, gwe_name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Get number of stress periods (days) and time steps of MODFLOW" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10, 48)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nper, nstp = sim.nper, sim.nstp\n", + "nper, nstp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Get pointers of MODFLOW API data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "top_tag = mf6.get_var_address(\"TOP\", gwf_name.upper(), \"DIS\") # tag of land surface (top) elevation\n", + "gwhead_tag = mf6.get_var_address(\"X\", gwf_name) # tag of groundwater heads\n", + "gwtemp_tag = mf6.get_var_address(\"X\", gwe_name) # tag of groundwater temperature\n", + "rch_tag = mf6.get_var_address(\"RECHARGE\", gwf_name.upper(), \"RCH_0\") # tag of groundwater recharge" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Get Initial head and temperature of MODFLOW" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2350.0, 10.0, 2600.0)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_elev = mf6.get_value(top_tag)[0] * 100 # multiply by 100 to convert from m to cm\n", + "gwhead_init = mf6.get_value(gwhead_tag)[0] * 100 # multiply by 100 to convert from m to cm\n", + "gwtemp_init = mf6.get_value(gwtemp_tag)[0]\n", + "\n", + "gwhead_init, gwtemp_init, top_elev" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Update MODFLOW 6 by one time step to match with the time step of STEMMUS_SCOPE" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "mf6.update()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Start STEMMUS_SCOPE & MODFLOW coupling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Enable groundwater coupling option in STEMMUS_SCOPE" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "stsc.set_value(\"groundwater_coupling_enabled\", np.array([True]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Set the MODFLOW 6 initial variables [Land surface elevation, groundwater initial head, and groundwater initial temperature (optional)] in STEMMUS_SCOPE" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "stsc.set_value(\"groundwater_elevation_top_aquifer\", top_elev)\n", + "stsc.set_value(\"groundwater_head_bottom_layer\", gwhead_init)\n", + "stsc.set_value(\"groundwater_temperature\", gwtemp_init) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Iterate through the time steps of both models and exchange variables.\n", + "\n", + "For every time step of STEMMUS_SCOPE:\n", + "- MODFLOW 6 gives groundwater head and groundwater temperature to STEMMUS_SCOPE\n", + "\n", + "For every stress period of MODFLOW 6 (which has 48 time steps of MODFLOW 6):\n", + "- STEMMUS_SCOPE gives groundwater recharge to MODFLOW 6 (recharge_stress_period = sum of 48 recharge_time_step)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n" + ], + "text/plain": [ + "Updating MODFLOW Stress period \u001b[1;36m10\u001b[0m, time step \u001b[1;36m47\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "while mf6.get_current_time() < mf6.get_end_time():\n", + " while stsc.get_current_time() < stsc.get_end_time(): \n", + " \n", + " # Get STEMMUS_SCOPE variables (soil moisture, soil temperature, groundwater recharge) \n", + " stsc.get_value(\"soil_moisture\", soil_moisture[i])\n", + " stsc.get_value(\"soil_temperature\", soil_temperature[i])\n", + " stsc.get_value(\"groundwater_recharge\", gw_recharge)\n", + " gw_recharge_nstp = np.append(gw_recharge_nstp, gw_recharge)\n", + "\n", + " # Store the current time as a datetime\n", + " stsc_time.append(num2pydate(stsc.get_current_time(), stsc.get_time_units()))\n", + " i+= 1\n", + "\n", + " # Get MODFLOW 6 variables (groundwater head, groundwater temperature)\n", + " gwhead = mf6.get_value(gwhead_tag)[0] * 100\n", + " gwtemp = mf6.get_value(gwtemp_tag)[0]\n", + "\n", + " # Set STEMMUS_SCOPE variables (groundwater head, groundwater temperature)\n", + " stsc.set_value(\"groundwater_head_bottom_layer\", gwhead)\n", + " stsc.set_value(\"groundwater_temperature\", gwtemp)\n", + " \n", + " # Set MODFLOW 6 variables (groundwater recharge)\n", + " kstp, kper = sim.kstp, sim.kper \n", + " if kstp == nstp - 1:\n", + " # groundwater recharge per stress period = sum of groundwater recharge per time step \n", + " gw_recharge_nper = np.array([-1 * np.sum(gw_recharge_nstp) / 100]) # multiply by -1 due to different signs of both models\n", + " mf6.set_value(rch_tag, gw_recharge_nper)\n", + " gw_recharge_nstp = np.array([]) \n", + " \n", + " # Track the simulation time of MODFLOW 6 \n", + " print('Updating MODFLOW Stress period ' + str(kper + 1) + ', time step ' + str(kstp + 1))\n", + " # print(kper + 1, kstp + 1, gwhead, gwtemp)\n", + " \n", + " # Update models\n", + " stsc.update()\n", + " mf6.update()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Finalize models" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " mf6.finalize()\n", + " stsc.finalize()\n", + "except:\n", + " raise RuntimeError" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Export results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Analyze the STEMMUS_SCOPE variables (put the data into xarray DataArray objects)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "gs = stsc.get_grid_size(1)\n", + "depths = np.ones(gs)\n", + "stsc.get_grid_z(1, depths)\n", + "\n", + "da_sm = xr.DataArray(\n", + " data=soil_moisture,\n", + " dims=(\"time\", \"depth\"),\n", + " coords={\"time\": np.array(stsc_time), \"depth\": depths},)\n", + "\n", + "da_t = xr.DataArray(\n", + " data=soil_temperature,\n", + " dims=(\"time\", \"depth\"),\n", + " coords={\"time\": np.array(stsc_time), \"depth\": depths},)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Show the STEMMUS_SCOPE soil moisture and soil temperature results" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(14,5))\n", + "ax1.set_title(\"Soil Moisture\")\n", + "ax2.set_title(\"Soil Temperature\")\n", + "ax1.set_ylabel(\"depth (m)\")\n", + "ax2.set_ylabel(\"depth (m)\")\n", + "da_sm.plot(y=\"depth\", ax=ax1, cbar_kwargs={'label': \"volumetric moisture content (m3 m-3)\"})\n", + "da_t.plot(y=\"depth\", ax=ax2, cbar_kwargs={'label': \"temperature (deg C)\"})\n", + "fig.savefig(os.path.join(output_figures_ws, 'soil_variables.png'), dpi = 600, bbox_inches = 'tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
the script took 0:05:38.029818 to run\n",
+       "
\n" + ], + "text/plain": [ + "the script took \u001b[1;92m0:05:38\u001b[0m.\u001b[1;36m029818\u001b[0m to run\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print ('the script took', datetime.now() - startTime, 'to run')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.9.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}