Improved Daily Spatial Precipitation Estimation by Merging Multi-Source Precipitation Data Based on the Geographically Weighted Regression Method: A Case Study of Taihu Lake Basin, China

Yi Pan, Qiqi Yuan, Jinsong Ma and Lachun Wang ()
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Yi Pan: School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China
Qiqi Yuan: School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China
Jinsong Ma: School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China
Lachun Wang: School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China

IJERPH, 2022, vol. 19, issue 21, 1-18

Abstract: 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.

Keywords: MSWEP; gauge observation; precipitation fusion; geographically weighted regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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