Integrating Spatial Weights into Random Forest to Overcome Aspatial Limitations in GRACE data Downscaling: Tracking Groundwater Depletion in the North China Plain

We present a novel downscaling methodology that addresses the critical challenge of spatial heterogeneity in coarse-scale Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data. Accurately capturing this heterogeneity is essential for local-scale hydrological applications. While machine learning approaches such as the global random forest (GRF) model have been used, the aspatial nature of the GRF model limits its ability to capture spatial heterogeneity when downscaling GRACE (-FO) data. To overcome this, we propose a Geographically Weighted Random Forest (GWRF) model, which integrates spatial weighting into the GRF algorithm to downscale groundwater storage anomalies (GWSAs) to 0.1° resolution over the North China Plain (2003-2025). The added value of this approach is rigorously quantified through benchmarking. We found that the GWRF model outperforms the GRF model, increasing R² from 0.957 (GRF: training) and 0.73 (GRF: testing) to 0.999 (GWRF: training) and 0.897 (GWRF: testing). The high-resolution GWSAs output exhibits a strong correlation (r = 80) with independent in-situ groundwater observational measurements, thereby enhancing its credibility. The downscaled GWSAs data provide a tangible application, revealing significant groundwater depletion in the Piedmont Plain (PP: -13.42 mm/yr), Yellow River Plain (YRP: -13.25 mm/yr), Hai River Plain (HRP: -12.68), and a moderate depletion in the Coastal Plain (CP: 5.98 mm/yr) sub-regions of NCP. Using a two-stage Generalized Additive Model (GAM), we quantitatively attribute 69-83% of the GWSAs decline to anthropogenic drivers (primarily cropland expansion, NDVI, and population growth) and 7-12% to climatic factors (downward shortwave radiation, precipitation, and sea surface temperature). This work advances downscaling techniques by demonstrating how geographically-aware machine learning can unlock finer-scale insights from GRACE (-FO) satellite data, providing a valuable tool for climate impact assessments and water resource management.