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

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 becom...

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Bibliographic Details
Published in:International journal of environmental research and public health 2022-10, Vol.19 (21), p.13866
Main Authors: Pan, Yi, Yuan, Qiqi, Ma, Jinsong, Wang, Lachun
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Agriculture
China
Climate change
Cold
Environmental Monitoring - methods
Humans
Hydrologic data
Hydrology
Interpolation
Lake basins
Lakes
Land cover
Land use
Precipitation
Rain
Rainfall
Satellites
Spatial discrimination
Spatial Regression
Spatial resolution
Surface roughness
Temporal distribution
Variables
Weather
Weighting methods
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph192113866