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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yi Pan & Qiqi Yuan & Jinsong Ma & Lachun Wang, 2022. "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," IJERPH, MDPI, vol. 19(21), pages 1-18, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13866-:d:952539
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    References listed on IDEAS

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    1. Zice Ma & Rui Yao & Peng Sun & Zhen Zhuang & Chenhao Ge & Yifan Zou & Yinfeng Lv, 2023. "Quantitative Evaluation of Runoff Simulation and Its Driving Forces Based on Hydrological Model and Multisource Precipitation Fusion," Land, MDPI, vol. 12(3), pages 1-23, March.

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