School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China.
Int J Environ Res Public Health. 2022 Oct 25;19(21):13866. doi: 10.3390/ijerph192113866.
Accurately estimating the spatial and temporal distribution of precipitation is crucial for hydrological modeling. However, precipitation products based on a single source have their advantages and disadvantages. How to effectively combine the advantages of different precipitation datasets has become an important topic in developing high-quality precipitation products internationally in recent years. This paper uses the measured precipitation data of Multi-Source Weighted-Ensemble Precipitation (MSWEP) and in situ rainfall observation in the Taihu Lake Basin, as well as the longitude, latitude, elevation, slope, aspect, surface roughness, distance to the coastline, and land use and land cover data, and adopts a two-step method to achieve precipitation fusion: (1) downscaling the MSWEP source precipitation field using the bilinear interpolation method and (2) using the geographically weighted regression (GWR) method and tri-cube function weighting method to achieve fusion. Considering geographical and human activities factors, the spatial and temporal distribution of precipitation errors in MSWEP is detected. The fusion of MSWEP and gauge observation precipitation is realized. The results show that the method in this paper significantly improves the spatial resolution and accuracy of precipitation data in the Taihu Lake Basin.
准确估计降水的时空分布对于水文模型至关重要。然而，基于单一数据源的降水产品都有其优点和缺点。如何有效地结合不同降水数据集的优势，已成为近年来国际上开发高质量降水产品的一个重要课题。本文使用多源权重集合降水（MSWEP）的实测降水数据和太湖流域的局地降雨观测数据，以及经度、纬度、海拔、坡度、方位、地表粗糙度、到海岸线的距离、土地利用和土地覆盖数据，采用两步法实现降水融合：（1）使用双线性插值法对 MSWEP 源降水场进行降尺度处理，（2）使用地理加权回归（GWR）方法和三次方函数加权方法实现融合。考虑地理和人类活动因素，检测 MSWEP 中降水误差的时空分布。实现了 MSWEP 和雨量计观测降水的融合。结果表明，本文方法显著提高了太湖流域降水数据的空间分辨率和精度。
