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Assessing groundwater drought in Iran using GRACE data and machine learning
1Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.
Scientific Reports
|April 26, 2025
Summary
Groundwater depletion in Iran averages 29 cm annually, with the Caspian Sea basin most affected. Satellite data and machine learning offer reliable groundwater drought monitoring for better resource management.
Area of Science:
- Hydrology and Remote Sensing
- Environmental Science and Water Resource Management
- Climate Change Impact Assessment
Background:
- Groundwater is a vital freshwater resource, crucial for ecosystems and human needs, but vulnerable to drought impacts like reduced recharge and land subsidence.
- Traditional groundwater monitoring methods are costly and lack comprehensive spatial and temporal coverage.
- Satellite missions like GRACE provide large-scale water storage data, but localized studies face challenges due to resolution and data gaps.
Purpose of the Study:
- To investigate groundwater status and trends in six major Iranian river basins from 2002 to 2023.
- To assess groundwater depletion rates and drought propagation dynamics.
- To evaluate the effectiveness of satellite data and machine learning for groundwater drought monitoring in data-scarce regions.
Main Methods:
- Utilized GRACE and GLDAS data for terrestrial water storage anomaly (TWSA) analysis.
- Employed Extreme Gradient Boosting (XGBoost) for downscaling TWSA to 0.25° resolution.
- Applied Seasonal Autoregressive Integrated Moving Average (SARIMA) to fill data gaps and forecast groundwater storage anomalies (GWSA) using SSP2 scenario projections.
Main Results:
- Achieved high accuracy in downscaling TWSA (R=0.99) and estimating GWSA (R=0.93) compared to observational data.
- Revealed an average groundwater depletion of 29 cm/year across Iran, with the Caspian Sea basin showing the most significant decline.
- Identified an 8-month lag in drought propagation from meteorological (SPI) to groundwater (GGDI) and highlighted the influence of teleconnection indices on drought.
Conclusions:
- Satellite-derived data, enhanced by machine learning, provides a reliable method for monitoring groundwater drought, especially in regions with limited ground-based observations.
- The study underscores significant groundwater depletion in Iran, necessitating strategic water resource management and planning.
- Findings support the use of integrated satellite and modeling approaches for effective groundwater resource assessment and policy development.
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