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

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.