Assessing the impact of 2022 extreme drought on the Yangtze River basin using downscaled GRACE/GRACE-FO data obtained by partitioned random forest algorithm

Abstract

ABSTRACT 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.