Deep-Learning-Based Probabilistic Forecasting of Groundwater Storage Dynamics in Sudan Using Multisource Remote Sensing and Geophysical Data

Highlights: What are the main findings? The integrated machine learning approach successfully reconstructed gaps in GRACE/GRACE-FO data and produced reliable forecasts of groundwater storage dynamics with quantified uncertainty. Groundwater storage anomalies in Sudan shows a consistent long-term recovery trend, with the strongest increases observed in semi-tropical regions. What are the implications of the main findings? The developed framework demonstrates how combining satellite observations with machine learning can guide sustainable groundwater monitoring and management in data-scarce and climate-stressed regions. The findings provide new evidence that groundwater is the dominant driver of water storage variability in Sudan, underscoring its critical role in sustaining water supply for domestic and agricultural purposes. Geophysical and remote sensing observations offer powerful means to monitor large-scale hydrological changes, particularly in regions where in situ data are scarce. In this study, we integrate satellite-derived water storage from the Gravity Recovery and Climate Experiment (GRACE) with land surface variables from the Global Land Data Assimilation System (GLDAS) to assess and forecast groundwater storage (GWS) dynamics across eight major regions in Sudan. Missing GRACE observations of terrestrial water storage (TWS) were first reconstructed using a Random Forest machine learning model, after which GWS anomalies were estimated by subtracting GLDAS-based surface and root-zone components from TWS. The resulting GWS time series was decomposed into trend, seasonal, and residual components, and the trend signals were used to train a bootstrapped Bidirectional Long Short-Term Memory (BiLSTM) model. This framework generated probabilistic forecasts accompanied by confidence intervals, which were generally narrow and consistent with the historical range. The forecasted GWS anomalies indicate positive recovery across all regions, with Sen’s slope values ranging from 0.014 to 0.051 per month. The strongest recoveries are evident in the southern and southwestern regions, while northern and eastern areas display more modest gains. This work represents one of the first applications of deep learning with uncertainty quantification for GRACE-based groundwater analysis in Sudan, demonstrating the potential of such an integrated approach to support informed and sustainable groundwater management in data-limited environments.