Abstract Runoff is an important factor in maintaining the normal operation of wetland ecosystems. An effective runoff simulation can provide an important decision basis for wetland protection and ecological restoration. Aiming to make the spatial resolution of remotely sensed precipitation products match the existing high-precision digital elevation model (DEM), this study used multiple linear regression (MLR) and geographic weighted regression (GWR) methods to build a downscaling correction method for precipitation products in the Qixing Wetland Watershed. The TRMM 3B42V7 data were downscaled from 0.25° × 0.25° to 0.05° × 0.05°. Compared with the downscaling correction of the nearest neighbor method, the GWR correction performed better and could noticeably reduce the average relative deviation between the corrected precipitation series and the measured precipitation series and increase the correlation coefficient of the two series. For the MLR correction, the two indicators (the average relative deviation and the correlation coefficient) improved slightly. The downscaled precipitation data were input into the semidistributed hydrological model. The flood simulation results revealed that the GWR-corrected rainfall data could improve the accuracy of flood simulation better than the MLR.

中文翻译：

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通过降尺度七星湿地流域遥感降水数据提高洪水模拟精度

摘要 径流是维持湿地生态系统正常运行的重要因素。有效的径流模拟可为湿地保护和生态恢复提供重要的决策依据。为了使遥感降水产品的空间分辨率与现有的高精度数字高程模型（DEM）相匹配，本研究采用多元线性回归（MLR）和地理加权回归（GWR）方法构建降水产品的降尺度校正方法。在七星湿地流域。TRMM 3B42V7 数据从 0.25° × 0.25° 缩小到 0.05° × 0.05°。与最近邻法的降尺度校正相比，GWR 校正效果较好，可以显着降低校正降水序列与实测降水序列的平均相对偏差，提高两个序列的相关系数。对于MLR修正，两个指标（平均相对偏差和相关系数）略有改善。降尺度的降水数据被输入到半分布式水文模型中。洪水模拟结果表明，GWR校正的降雨数据比MLR能更好地提高洪水模拟的精度。降尺度的降水数据被输入到半分布式水文模型中。洪水模拟结果表明，GWR校正的降雨数据比MLR能更好地提高洪水模拟的精度。降尺度的降水数据被输入到半分布式水文模型中。洪水模拟结果表明，GWR校正的降雨数据比MLR能更好地提高洪水模拟的精度。