Analysis Code for NWM-CNN

This repository contains the IPython notebooks used for the analysis presented in our paper on flood prediction using the NWM-CNN model. The analysis demonstrates the capability of the NWM-CNN model to predict surface water area across California, leveraging data from the U.S. National Water Model and a convolutional neural network.

Repository Structure

• Figure1.ipynb: Analysis code for Figure 1 in the paper.
• Table1.ipynb: Analysis code for Table 1 in the paper.
• Figure2: QGIS map (no code), which can be found in the Hydroshare resource described below.
• Figure3.ipynb: Analysis code for Figure 3 in the paper.
• environment.yml: Conda environment file to recreate the analysis environment.

Data Directory Structure

• The data used in this analysis is hosted on HydroShare (https://www.hydroshare.org/resource/8b76906c4b604c458fbcb5ea7c8c0be7) and has the following directory structure within the repository:
• NWM-CNN_predictions: Predictions from the NWM-CNN model.
• csv_files: Miscellaneous data files in CSV format.
• images_for_sacramento_stats: Images and statistics for Sacramento area analysis.
• map: QGIS map files.
• shapefile: Shapefiles used in the analysis.

Usage

To use the analysis notebooks:

1. Ensure you have Conda installed.
2. Clone this repository to your local machine.
3. Navigate to the repository directory
4. Download the data from the HyroShare link above.

• wget https://www.hydroshare.org/resource/8b76906c4b604c458fbcb5ea7c8c0be7/data/contents/data.zip
• Unzip the compressed file. unizp data.zip

5. create the Conda environment from environment.yml:

• conda env create -f environment.yml

6. Activate the environment:

• conda activate nwm-cnn

7. Open the Jupyter notebooks in Jupyter Lab or Notebook
8. Modify the paths to your locally downloaded data, replacing the existing path if needed:

• LOC_DATA_DIR = "./data/"

Floodbase

For more information please visit: (https://www.floodbase.com/about)