Bias-Aware Machine Learning Spatial Downscaling of GRACE Signals: Application to the Bug River Basin

Abstract

GRACE and GRACE-FO satellite gravimetry provide unique observations of terrestrial water storage (TWS), but their coarse effective resolution and intermittent temporal gaps limit water-resource applications at subregional and basin scales. This study presents a framework to temporally reconstruct and spatially downscale GRACE TWS anomalies for the transboundary Bug River Basin (Poland–Ukraine–Belarus), a region where in situ monitoring is limited and further disrupted by the 2022 war in Ukraine. First, missing monthly GRACE TWS anomalies (2002–2024) are imputed using a Random Forest model driven only by lagged GRACE values (1–3 months) and seasonal timing, thereby avoiding potential information leakage. Second, the continuous GRACE signal is downscaled to 0.1° using an independent set of hydroclimatic predictors with lagged and rolling features, together with elevation, land type and lithology. Model performance is evaluated under strict spatiotemporal holdouts and cross-validation. The key methodological advance is a bias-aware, block-wise mass-conserving correction that reconciles downscaled fields with the original GRACE water mass at coarse resolution. After downscaling to 0.1°, systematic residual biases between aggregated high-resolution estimates and GRACE observations are quantified monthly and redistributed within spatial blocks using river-runoff-based weights. This procedure enforces exact mass closure while preserving physically meaningful sub-grid variability.