This study presents an enhanced approach for estimating groundwater storage (GWS) dynamics using downscaled Gravity Recovery and Climate Experiment (GRACE) data combined with Global Land Data Assimilation System (GLDAS) outputs in the transboundary Bug River Basin. We applied three innovations to improve satellite-based GWS estimation. First, we applied the random forest (RF) algorithm to downscale GRACE terrestrial water storage (TWS) data to 0.1° × 0.1° resolution, using precipitation, evapotranspiration, runoff, and soil moisture as predictors. Second, we introduced a novel cumulative component to the GLDAS-based TWS change indicator, representing vadose-zone water equivalent, which depends on groundwater level (GWL) depth. This adaptation accounted for hydrodynamic conditions by extending the accumulation period with increasing GWL depth, effectively reducing phase shifts and temporal delays relative to the in-situ GWS observations common in prior studies. Third, satellite-based GWS estimates were calibrated using in-situ groundwater measurements combined with RF and kriging. The proposed approach significantly improved consistency between satellite-derived and in-situ GWS (correlation coefficients between 0.66 and 0.95), enhancing the reliability of groundwater monitoring. The GWS seasonal variability and amplitude were found to strongly depend on vadose zone properties and GWL depth. Despite an overall decline in total TWS, GWS in the Bug River Basin remained stable, reflecting system resilience to climatic fluctuations. Our methodology enhances groundwater monitoring and forecasting in transboundary catchments and enables the development of continuous changes in GWS in time and space, which is particularly important for regions with a sparse network of in-situ observations.
Solovey et al. (Mon,) studied this question.
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