Title Bias-aware machine learning spatial downscaling of GRACE signals: application to the Bug River Basin
Authors Samalavičius, Vytautas ; Solovey, Tatiana ; Śliwińska-Bronowicz, Justyna ; Stradczuk, Anna ; Zaslavsky, Ilya
DOI 10.3390/rs18172909
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Is Part of Remote sensing.. Basel : MDPI. 2026, vol. 18, iss. 17, art. no. 2909, p. 1-45.. eISSN 2072-4292
Keywords [eng] machine learning ; remote sensing ; GRACE/GRACE-FO ; Bug River Basin ; spatial downscaling
Abstract [eng] 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.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2026
CC license CC license description