Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century

Study region: Saudi Arabia. Study focus: The major goal of this study is to downscale GRACE (Gravity Recovery and Climate Experiment) groundwater storage (GWS) anomalies to assess the local-scale vulnerabilities of groundwater changes across western regions of Saudi Arabia (Al Jumum, Makkah, Jeddah, and Bahrah). This was accomplished by using multi-model ensemble machine learning (ML) approach leveraging Random Forest, CART, and Gradient Tree Boosting algorithms within Google Earth Engine (GEE). Additionally, we used the downscaled GWS and CMIP6 climate data with the Generalized Additive Model (GAM) to project the future GWS changes under climate change. New hydrological insights for the region: The ensemble results demonstrated robust performance (R² = 0.92 and RMSE = 20 mm) compared to the individual model (R² = 0.84–0.88 and RMSE = 25–28 mm). The areas of higher groundwater depletion were predominantly observed in Jeddah and Makkah, with average annual rates of − 165 mm/year and − 150 mm/year, respectively, from 2002 to 2023. The total volumetric losses range from 11.38 km³ to 15.31 km³ across different sub-regions. Seasonally, the peak GWS drop (-90 to − 125 mm) was detected during the summer months (April–July), aligning with periods of maximum water demand. Several key drivers that control the GWS changes were also identified, including anthropogenic effects, local climate anomalies, and large-scale climate oscillations. Projections for GWS reveal an irreversible decline throughout the 21st Century with potential reductions surpassing − 216 mm/year in high-emission scenarios (SSP5-8.5). The developed approach is transferable to other regions for quantitative assessment of local groundwater changes.