Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Remote Sensing

DOI: 10.3390/rs17183172

Authors: Musaab A. A. Mohammed, Norbert P. Szabó, Joseph O. Alao, Péter Szűcs

Institutions: University of Miskolc, University of Bahri, Air Force Institute of Technology Kaduna

Language: English

Topic: Geophysics and Gravity Measurements

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,258