This repository contains a Jupyter notebook script that automates the preprocessing of a large dataset of TIFF files for use in the HEC-RMS (Hydrologic Engineering Center's River Analysis System) software.

The script performs the following tasks:

1. Resampling TIFF files: The script iterates through all TIFF files in the input_folder directory and resamples them to a desired pixel size (in this case, 0.0001). The resampled TIFF files are saved in the output_folder directory with a "_resampled" suffix.

2. Extracting data using a shapefile: The script then iterates through the resampled TIFF files in the output_folder directory and extracts the data using a specified shapefile (shapefile_path). The extracted data is saved in the extracted folder with an "_extracted" suffix.

3. Converting TIFF to ASCII: Finally, the script converts the extracted TIFF files in the extracted folder to the ASCII format and saves them in the ascii folder with a ".asc" extension.

This preprocessing step is essential for preparing the data to be used as input for the HEC-RMS software, which requires the data to be in a specific format. By automating these tasks, the script helps to streamline the data preparation process and ensures that the input data is properly formatted for use in HEC-RMS.

1. Ensure you have the following dependencies installed:

   • Python
   • GDAL (Geospatial Data Abstraction Library)

2. Update the following variables in the script to match your specific setup:

   • input_folder: The directory containing the TIFF files to be processed.
   • output_folder: The directory where the resampled TIFF files will be saved.
   • shapefile_path: The file path to the shapefile used for data extraction.

3. Run the Jupyter notebook script to execute the data preprocessing steps.

This script has been tested and verified to work with HEC-RMS. While the core preprocessing logic should be transferable to HEC-RAS as well, you may need to make some adjustments to accommodate any differences in data format requirements between the two systems.

If you have any suggestions, improvements, or bug fixes, feel free to submit a pull request. I'm always happy to collaborate and enhance the functionality of this script.

This project is licensed under the MIT License.

https://doi.org/10.5281/zenodo.10959672