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

根据高时间分辨率（30分钟），长期（2001-2020年）基于卫星的降水产品，全球降雨测量（GPM-IMERG）的综合多卫星检索和全球降雨侵蚀力数据库（GloREDa）站（n = 3286）的平均年降雨侵蚀力绘制全球年降雨侵蚀力。基于残差的合并方案被应用到集成GPM-IMERG的降雨侵蚀力与GloREDa使用地理加权回归。总体而言，GPM-IMERG-only估计低估了降雨侵蚀力。GPM-IMERG与GloREDa合并的降雨侵蚀力估计的准确性大大提高（Nash-Sutcliffe效率= 0.83，偏差百分比=-2.4%，均方根误差= 1122 MJ mm/ha/h/yr）与仅使用GPM-IMERG的估计值相比（Nash-Sutcliffe效率= 0.51，偏差百分比= 27.8%，均方根误差= 1730 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).