Spatiotemporal Variation Analysis of Groundwater Storage in Huaihe River Basin Based on GRACE and Interpretable Machine Learning

Groundwater represents an essential water resource across the Huaihe River Basin, and its sustainable use is important for maintaining regional water resource security. This study systematically examined the long-term evolution, seasonal variability, and major drivers of groundwater storage anomalies (GWSA) throughout the Huaihe River Basin over the period 2002–2024. GRACE satellite observations were combined with Seasonal-Trend decomposition (STL) and the XGBoost-SHAP interpretable machine learning framework to conduct the analysis. Results indicated that GWSA exhibited a significant declining trend (−4.08 mm/a), characterized by marked seasonal fluctuations with peaks in June and troughs in August. Spatially, groundwater storage depletion was predominantly observed in the northern and northwestern regions, whereas slight recovery occurred in parts of the central-southern plains. Feature attribution analysis identified population density (mean |SHAP| = 0.386) as the most important predictor of GWSA variability among the factors considered, followed by cropland area, temperature, precipitation, and NDVI. SHAP interaction analysis further revealed model-based interactions among anthropogenic and hydroclimatic factors. Spatial correlation analysis showed distinct spatial associations of GWSA with human-related and natural factors. And the temporal shift in SHAP values from negative to positive suggests changes in the associations between human-related factors and GWSA, which may be related to groundwater management and regional water-supply adjustments. These findings provide quantitative insights into the complex interplay between anthropogenic and climatic drivers on GWS, providing a scientific basis for groundwater management in the HRB.