Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions

International audience The application of GRACE satellite-derived Total Water Storage (TWS) data for local water management is constrained by its coarse spatial resolution (100-300 km). To address this limitation, a Random Forest-based model was employed to downscale GRACE TWS data from 100 km to 1 km resolution over Morocco, a drought-prone region, covering the period from 2002 to 2022. The input datasets included precipitation (GPM, 10 km), NDVI (MODIS, 1 km), land surface temperature (LST, MODIS, 1 km), evapotranspiration (MODIS, 500 m), elevation (SRTM, 30 m), and the Normalised Difference Snow Index (NDSI, MODIS, 500 m). While downscaling improves the spatial resolution of GRACE data, validating these higher-resolution outputs presents challenges. In this study, the downscaled data were validated using three complementary approaches: statistical validation, groundwater level in-situ data validation, and validation against known aquifer dynamics. Statistical validation demonstrated strong model performance, with a Nash-Sutcliffe Efficiency (NSE) of 0.80, a low RMSE of 0.82 cm, and MAE of 0.57 cm, along with an R² of 0.80 between original and downscaled data. Cross-validation confirmed the model's consistency, yielding mean, median, and maximum R² values of 0.56, 0.64, and 0.89 respectively. Error metrics remained consistently low throughout the study period, with MAE values ranging from 0.36 cm to 0.6 cm and RMSE values between 0.5 cm and 0.8 cm. Comparison with in-situ groundwater levels showed significant improvements, with correlation coefficients increasing for 63% of the 139 analysed wells. The 1 km TWS data revealed localised variations and clearer trends across different aquifers, with aquifer systems within the same structural domain exhibiting similar TWS patterns. These findings highlight the potential of the downscaling model to enhance local water management by capturing finer hydrological variations. The proposed approach effectively overcomes GRACE's spatial resolution limitations, as demonstrated through comprehensive validation. This methodology shows particular promise for water resource monitoring in drought-vulnerable regions such as Morocco, providing decision-makers with higher-resolution data for improved water management strategies.