Advancing GRACE-Derived Groundwater Monitoring through multi-model Analysis across the Nubian Sandstone Aquifer System

Groundwater is indispensable for drinking water, agriculture, and ecosystem resilience, yet its subsurface nature and accelerating depletion under climate stress render it one of the most difficult resources to monitor. In arid regions, sparse and uneven in situ data further complicate basin-scale assessments, leaving critical knowledge gaps for sustainable management. Satellite remote sensing has transformed this challenge, with gravimetric missions such as the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO enabling unprecedented monitoring of terrestrial water storage anomalies (TWSA) at continental scales. However, isolating the groundwater signal (GWA) from total storage remains highly uncertain, due to limited validation data and inconsistencies in auxiliary datasets representing soil moisture, snow, canopy water, lakes, and rivers. This study introduces a plausibility-constrained multi-model framework designed to enhance the reliability of GRACE-derived groundwater estimates across the Nubian Sandstone Aquifer System (NSAS), a vast transboundary aquifer spanning ~2.2 million km² across Egypt, Libya, Sudan, and Chad. By integrating multiple GRACE solutions with diverse non-groundwater datasets, we reveal substantial variability in GWA estimates. To constrain structural uncertainty, we apply physically based plausibility criteria that systematically isolate hydrologically consistent realizations. The results demonstrate that this framework markedly enhances the reliability and interpretability of GRACE-derived groundwater estimates in arid regions. Beyond the NSAS, the approach offers a transferable methodology for advancing groundwater monitoring in data-scarce environments, underscoring the critical role of satellite remote sensing in supporting sustainable management of transboundary aquifers under mounting climatic and anthropogenic pressures.