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

Cui, Lilu, Li, Yu, Zhong, Bo, An, Jiachuan, Meng, Jiacheng, Guo, Haoyang, and Xu, Chuang, 2025. Assessing the impact of 2022 extreme drought on the Yangtze River basin using downscaled GRACE/GRACE-FO data obtained by partitioned random forest algorithm. International Journal of Remote Sensing, 46(3):1219–1247, doi:10.1080/01431161.2024.2427915.

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@ARTICLE{2025IJRS...46.1219C,
       author = {{Cui}, Lilu and {Li}, Yu and {Zhong}, Bo and {An}, Jiachuan and {Meng}, Jiacheng and {Guo}, Haoyang and {Xu}, Chuang},
        title = "{Assessing the impact of 2022 extreme drought on the Yangtze River basin using downscaled GRACE/GRACE-FO data obtained by partitioned random forest algorithm}",
      journal = {International Journal of Remote Sensing},
     keywords = {Partitioned downscaling strategy, random forest, GRACE/GRACE-FO, Yangtze River basin, extreme drought, Wu River Basin},
         year = 2025,
        month = feb,
       volume = {46},
       number = {3},
        pages = {1219-1247},
     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{\textdegree}to
        0.1{\textdegree}, 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.}",
          doi = {10.1080/01431161.2024.2427915},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025IJRS...46.1219C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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