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The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) data have been widely used to monitor and analyze extreme hydrological events globally. However, their coarse spatial resolution limits their application in small- and medium-scale regions. In this study, we proposed a partitioned random forest downscaling (PRFD) strategy to improve the spatial resolution of GRACE/GRACE-FO data and quantitatively assessed the downscaling performance using a closed-loop simulation experiment. Our enhanced approach improved the spatial resolution of GRACE/GRACE-FO data from 1°to 0.1°, and the downscaled data were used to characterize the 2022 extreme drought in the Yangtze River basin (YRB), with particular on a smaller basin (i.e. the Wu River basin, WRB). Our findings show that the PRFD reduced the root mean square error by 39.29% compared to the traditional over RF downscaling (ORFD), and 27.8% of grid points showed significantly accuracy improvements. The downscaled results provided a more detailed depiction of the 2022 extreme drought in the YRB, allowing for precision identification of drought onset, extent and severity, and a more accurate assessment of the drought impacts in the WRB. The extreme drought originated in the northern WRB, gradually extending southward across the basin, with more severe drought conditions in the north than in the south. High temperatures and low precipitation were primary drives, while elevated high human water use also contributed. This study provides a valuable technique for downscaling GRACE/GRACE-FO data and understanding extreme drought in regional-scale areas. [ABSTRACT FROM AUTHOR]

Floods have always threatened the survival and development of human beings. To reduce the adverse effects of floods, it is very important to understand the influencing factors of floods and their formation mechanisms. In our study, we integrated the Gravity Recovery and Climate Experiment and its Follow-On and Swarm solutions to estimate an uninterrupted 19-year flood potential index (FPI) time series, discussed the spatiotemporal distribution characteristics of the FPI and monitored major floods in the Volga River basin (VRB) from 2003 to 2021. Finally, we analyzed the relationship between the FPI and hydrometeorological factors to comprehend the flood formation mechanism. The results show that data fusion has reduced the uncertainty of terrestrial water storage change (TWSC), and the TWSC from the combined satellite gravity observations has a good consistency with that from the Global Land Data Assimilation System model (correlation coefficient = 0.92). During the study period, two major floods (June 2005 and May 2018) occurred in the VRB. The FPI has a significant seasonal change characteristic, and shows a high flood risk in spring and a low one in autumn. With regards to spatial distribution, the flood risk is increasing in the north (increasing rate = 0.1) and decreasing in the south (decreasing rate = 0.39). Snow water equivalent (SWE, correlation coefficient = 0.75) has a stronger correlation with the FPI than precipitation (PPT, correlation coefficient = 0.46), which is attributed to the recharge of SWE on water resources greater than that of PPT. The rising surface temperature (ST) speeds up snow melt, resulting in excessive groundwater and soil moisture, and the flood risk greatly increases at this time. The process lasts about three months. Therefore, except for PPT, ST is also a climatic factor leading to the floods in the VRB. Our study provides a reference for flood research in high-latitude regions. [ABSTRACT FROM AUTHOR]