Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China

Climate change and excessive exploitation of water resources exert pressure on groundwater supply and the ecosystem in drylands. Although The Gravity Recovery and Climate Experiment (GRACE) satellites has demonstrated the feasibility of quantifying global groundwater storage variations, monitoring regional-scale groundwater has been challenging due to the coarse resolution of GRACE. Previous GRACE downscaling studies focused on develop new algorithms based on the perspective of pixel spatial correlation to improve resolution, which cannot better capture the temporal evolution of GRACE data effectively. In this study, we employ the semi-supervised variational autoencoder (SSVAER) algorithm and the multi-scale geographically weighted regression (MGWR) model to establish two different downscaling schemes: pixel temporal continuity downscaling and pixel spatial correlation downscaling. These schemes achieve spatial resolution downscaling of GRACE-derived groundwater storage anomalies (GWSA) from 0.5° to 0.1°. Additionally, the applicability of the PCR-GLOBWB model in drylands is verified. Furtherly, the spatiotemporal distribution patterns of GWSA are analyzed. The results show that (1) Both the temporal and spatial downscaling methods produced consistent results, with data correlations ranged from 0.94 to 0.98 observed in over 80 % of the range before and after downscaling; (2) The groundwater storage change rate in the northern Tarim River Basin (TRB) is 25 times greater than the model results, while in other regions, the average deviation is 2.6 times; (3) The two schemes enhance the correlation (0.27) between GWSA and groundwater level anomaly (GWLA) to 0.59 and 0.52, respectively, with a three-month lag in GWSA relative to GWLA. The temporal downscaling approach exhibited higher CC and lower RMSE, outperforming the spatial downscaling approach. The high-resolution results in this study can well complement groundwater level prediction efforts in arid regions and provide quantitative information for local water resource management.