Quantifying water resource changes across natural and managed landscapes in the Indus Basin drainage catchments using satellite-informed machine learning–based downscaled GRACE data

The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (G-FO) are promising tools as satellite-derived monitoring of terrestrial water storage (TWS) and groundwater storage (GWS) changes globally. However, the coarse spatial resolution of these datasets (ranging from 0.25° to 1°) limits their utility decision-relevant characterization of water resource changes. Herein, we used geospatial modeling approaches including machine learning (ML) (random forest (RF) and hybrid geographically weighted random forest (hybrid RFgw)) and spatial regression (geographically weighted regression (GWR)) in geospatial downscaling of three different GRACE datasets (JPL-SH, JPL-M, and CSR-M), to generate 1 km² downscaled-GRACE data for purpose of quantifying water resources changes. Specifically, we quantified TWS anomalies (TWSA) and GWS anomalies (GWSA) across natural and managed landscapes in different catchments of the Indus Basin from 2002 to 2020. We evaluated the effectiveness of downscaled results against in-situ GWSA over 1238 monitoring wells. Results indicate that: (1) the hybrid RFgw model outperformed GWR and global RF models in predicting high-resolution TWSA and GWSA; (2) downscaled-GWS anomalies from the hybrid RFgw model agreed well with in-situ observational data, with higher R² values for the downscaled JPL-SH (0.60 - 0.86), JPL-M (0.54 - 0.75), and CSR-M (0.54 - 0.85); (3) the highest TWS (−133.49 mm/year) and GWS (−150.38 mm/year) decline rates were detected in catchments dominated by managed landscapes; and (4) croplands experienced the largest storage losses (62-85 km³) followed by urban areas (8-15 km³) despite their much smaller spatial extent. The paper demonstrates the effectiveness of 1-km² downscaled GRACE/G-FO datasets to provide localized information for water resource management across landscapes facing data-scarcity and water-food security challenges.