Quantifying Groundwater Depletion in an Agricultural Region Using Integrated In-Situ and Satellite-Based Approaches: Insights from the San Luis Valley, CO

Paper Link: https://doi.org/10.1016/j.jhydrol.2026.135438

Overview

This repository contains Python scripts and datasets for quantifying groundwater storage change in the San Luis Valley (SLV), Colorado, using an integrated approach combining in-situ measurements and satellite-based remote sensing. The study employs water-level observations, pumping records, InSAR-derived land subsidence data, and climate variables to characterize groundwater depletion across multiple subdistricts.

Key Features:

• Water balance modeling with time-varying recharge coefficients
• InSAR-based subsidence analysis for fine-grained aquifer compaction
• Multivariate regression for storativity and recharge estimation
• Integration of PRISM precipitation
• Comparative analysis across nine response areas in the SLV

Study Area

The San Luis Valley is a hydrologic basin in south-central Colorado covering approximately 8,000 km². The study focuses on groundwater systems within the Rio Grande Water Conservation District, encompassing:

• Alamosa/La Jara: Confined aquifer system
• Saguache: Northern portion with mixed confined/unconfined conditions
• Subdistrict 1 RA: Eastern region with significant subsidence
• Additional areas: Conejos, Costilla, Rio Grande Alluvium, San Luis, Trinchera, Closed Basin Project

Repository Structure

slv-groundwater-depletion/
│
├── scripts/                              # Python analysis scripts
│   ├── storage_change_calc.py           # Main storage change estimation
│   ├── storage_change_calc_from_subsidence.py  # InSAR-based analysis
│   ├── storativity_precip_coeff_inflow_determination.py  # Regression analysis
│   ├── percent_pumping_from_storage_change.py  # Pumping contribution analysis
│   └── diversion_plots.py               # Surface water diversion visualization
│
├── data/                                 # Input datasets (see DATA.md)
│   ├── shapefiles/                      # Response areas and watershed boundaries
│   ├── pumping/                         # Groundwater pumping records
│   ├── water_levels/                    # USGS monitoring well data
│   ├── climate/                         # PRISM precipitation data
│   └── subsidence/                      # InSAR displacement rasters
│
├── figures/                              # Output visualizations
│   ├── storage_change/                  # Storage change time series
│   ├── diversions/                      # Surface water plots
│   └── regression/                      # Statistical model outputs
│
├── docs/                                 # Documentation
│   ├── DATA.md                          # Data sources and processing
│   ├── METHODS.md                       # Detailed methodology
│   └── API.md                           # Script function reference
│
├── tests/                                # Unit tests
│   └── test_storativity_precip_coeff_inflow_determination.py
│
├── requirements.txt                      # Python dependencies
├── environment.yml                       # Conda environment file
├── .gitignore                           # Git ignore rules
├── LICENSE                              # MIT License
└── README.md                            # This file

Installation

Prerequisites

• Python 3.8 or higher
• Anaconda or Miniconda (recommended)
• Google Earth Engine account (for precipitation data extraction)

Setup

1. Clone the repository:

git clone https://github.com/yourusername/slv-groundwater-depletion.git
cd slv-groundwater-depletion

2. Create conda environment:

conda env create -f environment.yml
conda activate slv-groundwater

Or use pip:

pip install -r requirements.txt

3. Configure Google Earth Engine (for precipitation analysis):

earthengine authenticate

4. Update file paths: Edit the file paths in each script to match your local directory structure. All scripts use absolute paths that need to be modified.

Quick Start

1. Estimate Storage Change from Water Levels

python scripts/storage_change_calc.py

This script:

• Clips monitoring wells to response area boundaries
• Calculates annual mean hydraulic head changes
• Integrates pumping data to estimate storage change
• Produces storage change time series for each subdistrict

Key outputs:

• 20251110_final_storage_change_data.csv
• Storage change plots by subdistrict

2. Calculate Subsidence-Derived Storage Change

python scripts/storage_change_calc_from_subsidence.py

This script:

• Masks InSAR subsidence rasters using subdistrict polygons
• Converts vertical displacement to volumetric storage change
• Combines fine-grained and coarse-grained storage estimates

Key outputs:

• Percentage contribution of subsidence to total storage loss
• Cumulative storage change plots

3. Determine Aquifer Properties

python scripts/storativity_precip_coeff_inflow_determination.py

This script:

• Extracts PRISM precipitation data for each watershed
• Performs multivariate OLS regression: ΔS = S·A·Δh - α·P + Q
• Estimates storativity, precipitation coefficient, and net inflow
• Uses bootstrap resampling for uncertainty quantification

Key outputs:

• Storativity estimates for each subdistrict
• Precipitation recharge coefficients
• Regression diagnostics and confidence intervals

4. Analyze Pumping Contributions

python scripts/percent_pumping_from_storage_change.py

This script:

• Aggregates annual pumping volumes by subdistrict
• Compares pumping to independently estimated storage change
• Quantifies closure of the water budget

5. Visualize Diversions

python scripts/diversion_plots.py

Creates stacked bar plots of surface water diversions over time.

Data Requirements

The analysis requires the following datasets (see docs/DATA.md for details):

1. Shapefiles:

   • Response area boundaries (Response_Areas_2014_1_21.shp)
   • Watershed polygons for precipitation extraction
   • InSAR coverage boundaries

2. Groundwater Pumping:

   • Annual pumping records by well location (acre-feet)
   • Converted to cubic meters in scripts

3. Water Level Data:

   • USGS National Water Information System (NWIS) data
   • Monitoring well measurements (daily to monthly)

4. Climate Data:

   • PRISM monthly precipitation (water-year totals)
   • Extracted via Google Earth Engine

5. InSAR Subsidence:

   • GeoTIFF raster of mean vertical displacement (2015-2022)
   • Sentinel-1 derived displacement maps

6. Surface Water Diversions:

   • Monthly diversion records (cubic meters)
   • Used to adjust pumping in Subdistrict 1

Methodology

The analysis employs a water balance approach:

ΔS = S·A·Δh - α·P + Q - R

Where:

• ΔS: Change in groundwater storage (m³)
• S: Storativity (dimensionless)
• A: Aquifer area (m²)
• Δh: Change in hydraulic head (m)
• α: Precipitation recharge coefficient
• P: Precipitation volume (m³)
• Q: Net groundwater inflow (m³)
• R: Pumping (m³)

Key Steps:

1. Hydraulic Head Analysis: Calculate mean annual head change from kriged water-level surfaces
2. Precipitation Processing: Extract water-year precipitation totals for each watershed
3. Regression Analysis: Estimate S, α, and Q using multivariate OLS with bootstrap
4. Storage Estimation: Compute annual storage change incorporating all terms
5. Subsidence Integration: Add fine-grained compaction from InSAR analysis
6. Validation: Compare independent estimates and assess budget closure

See docs/METHODS.md for detailed methodology.

Results

Storage Change Estimates (2015-2022):

Subdistrict	Total Storage Loss (m³)	Subsidence Contribution (%)
Alamosa/La Jara	-XXX × 10⁶	XX%
Saguache	-XXX × 10⁶	XX%
Subdistrict 1 RA	-XXX × 10⁶	XX%

Estimated Aquifer Properties:

Subdistrict	Storativity	Recharge Coefficient (α)
Alamosa/La Jara	X.XX ± X.XX	X.XX ± X.XX
Saguache	X.XX ± X.XX	X.XX ± X.XX
Subdistrict 1 RA	X.XX ± X.XX	X.XX ± X.XX

Citation

If you use this code or data in your research, please cite:

@article{alfatta2025slv,
  title={Quantifying Groundwater Depletion in an Agricultural Region Using Integrated In-Situ and Satellite-Based Approaches: Insights from the San Luis Valley, Colorado},
  author={Al Fatta, Abdullah and [Co-authors]},
  journal={Journal of Hydrology},
  year={2025},
  volume={XXX},
  pages={XXX-XXX},
  doi={10.xxxx/xxxxx}
}

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Colorado State University - Department of Civil and Environmental Engineering
• Rio Grande Water Conservation District - Pumping and water-level data
• USGS - National Water Information System (NWIS) data
• NASA - InSAR data from ASF DAAC
• PRISM Climate Group - Precipitation data

Contact

Abdullah Al Fatta
Ph.D. Candidate, Civil Engineering
Colorado State University
Email: abdullah.al_fatta@colostate.edu
LinkedIn: https://www.linkedin.com/in/abdullahalfatta

Related Publications

1. Al Fatta, Abdullah and Smith, Ryan and Vajedian, Sanaz and Schreuder, Willem and Butler, James J., Quantifying Groundwater Depletion in an Agricultural Region Using Integrated In-Situ and Satellite-Based Approaches: Insights from the San Luis Valley, CO. Available at SSRN: http://dx.doi.org/10.2139/ssrn.5987334

Project Status

Active Development - This repository is associated with ongoing doctoral research. Updates are made regularly as analysis progresses.

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Keywords: groundwater depletion, InSAR, subsidence, water balance, San Luis Valley, aquifer storage, hydrogeology, remote sensing, Python