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Present_Water_Table_Level(PWTL)

This repository use the Water Table Model (WTM) to simulate the present-day water table levels across various regions. The simulations aim to provide insights into groundwater behavior using robust modeling techniques.

Repository Structure

The repository is structured into the following folders:

  • codes/: Contains all Python scripts for:
    • Preparing input data.
    • Generating individual layers.
    • Extracting and processing blocks of data at yearly, monthly, and seasonal scales.
    • Performing Bias Correction and Spatial Downscaling (BCSD).
    • Calculating variables such as Vapor Pressure Deficit (VPD).
    • Converting units for datasets (e.g., mm to m).

Example of Codes Included:

  • all_1year_block_extraction.py: Extracts one-year blocks from datasets.
  • all_monthly_block_extraction.py: Extracts monthly blocks from datasets.
  • Calculate_VPD.py: Script to calculate Vapor Pressure Deficit (VPD).
  • convert_mm_to_m.py: Converts data units from mm to m for standardization.

Simulations and Outputs

The simulations conducted in this repository aim to:

  • Reconstruct present-day water table levels using the WTM.
  • Integrate high-resolution datasets for better accuracy and representation.
  • Provide layered outputs for further analysis.

How to Use

  1. Clone the repository:

    git clone https://github.com/yourusername/Present_Water_Table_Level.git

  2. Navigate to the codes directory:

    cd Present_Water_Table_Level-(PWTL)/codes

  3. Execute scripts for specific data preparation or simulations. For example:

    python Calculate_VPD.py

  4. Review outputs stored in their respective directories.

License

This repository is licensed under MIT Licence.

Contributions

This repository is developed in collaboration with Professor Callaghan, whose expertise and guidance have been instrumental in using the Water Table Model (WTM) to simulate present-day water table levels. Contributions to this project include the development of scripts for data preparation, bias correction, spatial downscaling, and the integration of high-resolution datasets.

If you would like to contribute to this repository, feel free to submit issues or create pull requests. Suggestions and improvements are always welcome.