A novel downscaling-integration approach of satellite rainfall estimates for accurate river flow prediction

基于高时间分辨率（30分钟）、长期（2001-2020年）卫星降水产品绘制了全球年和月降雨侵蚀力图；全球降水综合多卫星反演（GPM-IMERG）和全球降雨侵蚀力数据库（GloREDa）站点（n = 3286）的年平均降雨侵蚀力。采用残差融合方案，利用地理加权回归将基于gpm - imerga的降雨侵蚀力与GloREDa进行融合。总体而言，仅使用gpm - imergs估算低估了降雨侵蚀力。与单独使用gpm - imerga（纳什-苏特克里夫效率= 0.83%，偏差百分比= -2.4%，均方根误差= 1122 MJ mm/ha/h/yr）相比，使用gpm - imerga（纳什-苏特克里夫效率= 0.51%，偏差百分比= 27.8%，均方根误差= 1730 MJ mm/ha/h/yr）估算降雨侵蚀力的准确性得到了显著提高（纳什-苏特克里夫效率= 0.83%，偏差百分比= -2.4%，均方根误差= 1122 MJ mm/ha/h/yr）。

Global annual and monthly rainfall erosivity was mapped based on a high-temporal-resolution (30-min), long-term (2001-2020) satellite-based precipitation product; the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG); and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). A residual-based merging scheme was applied to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = －2.4%, and root mean square error = 1122 MJ mm/ha/h/yr) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm/ha/h/yr).