In water-scarce regions where climate variability limits surface water, groundwater is a vital resource for domestic, agricultural and industrial use. Traditional monitoring through boreholes is constrained by incomplete data, inaccessibility and data sparsity, making it difficult to track regional trends. Satellite tools such as the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On, GRACE-FO, provide valuable alternatives for observing large-scale changes in terrestrial water storage. This review evaluates GRACE (-FO) applications in groundwater research, with emphasis on water-scarce regions. GRACE (-FO) applications in hydrogeology have grown steadily, with most case studies concentrated in Asia and Africa, where groundwater stress and sparse monitoring increase reliance on satellite data. Data-rich regions are underrepresented, underscoring the need for benchmark studies to validate GRACE (-FO). Case studies show both the potential and limits of GRACE (-FO): depletion trends in Saudi Arabia and Iran are linked to agricultural abstraction, while replenishment in parts of Africa and South America reflects rainfall recharge or reservoirs recharge. These confirm GRACE (-FO)’s ability to diagnose depletion hotspots and compare how climate and management shape storage. Study area delineation is often guided by groundwater use, data availability, and monitoring networks, with performance further influenced by land use, aquifer type, and study area size. While best suited to large basins, downscaling has extended GRACE (-FO) to smaller regions. Machine learning supports this, with Random Forest widely used for its robustness, though newer approaches remain underexplored. Future work should refine downscaling, strengthen integration with regional models, and link storage with water quality, particularly in coastal aquifers vulnerable to depletion and salinisation. • Mascon solution has been the preferred over Spherical Harmonics since 2020. • Groundwater depletion is common worldwide, largely from agricultural abstraction. • Random Forest is the most common and effective method for downscaling GRACE data. • GRACE validation heavily relies on in-situ groundwater measurements. • A key gap is limited use of GRACE in operational groundwater models and management.
Nenweli et al. (Sun,) studied this question.