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Arid Land Geography >

2023 , Vol. 46 >Issue 7: 1052 - 1062

DOI: https://doi.org/10.12118/j.issn.1000-6060.2022.475

Climatology and Hydrology

Downscaling of GPM satellite precipitation data in the Yellow River Basin based on MGWR model

  • He BAI ,
  • Yisen MING ,
  • Qihang LIU ,
  • Chang HUANG
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  • 1. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi’an 710127, Shaanxi, China
    2. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, Shaanxi, China
    3. Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, Shaanxi, China

Received date: 2022-09-21

  Revised date: 2022-11-17

  Online published: 2023-08-03

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Abstract

Because the Yellow River Basin of China is a vast area with sparse meteorological stations, limited meteorological data are available. Satellite precipitation data are an alternative for precipitation observations. In this study, the precipitation data of the Yellow River Basin for 2002, 2012, and 2020 were considered representative of dry, standard, and wet years to downscale global precipitation measurement (GPM) precipitation data. The normalized difference vegetation index, digital elevation model, slope, land surface temperature, and wind speed that reflect the spatial distribution characteristics of precipitation and the characteristics of spatial nonstationarity were investigated and used in two downscaling methods, namely the geographically weighted regression model (GWR) and mixed geographically weighted regression model (MGWR) to obtain the downscaling precipitation data of 1-km spatial resolution in the Yellow River Basin. The downscaling results were verified by the ground meteorological station data. The results revealed that: (1) GPM annual precipitation data were highly correlated with ground meteorological station observation data in the Yellow River Basin in 2002, 2012, and 2020. (2) Downscaling with the MGWR model considerably improved the spatial resolution. In terms of the spatial details of precipitation, the downscaling results of the MGWR model were superior to those of the GWR model. (3) In the three typical climate years, the accuracy of MGWR downscaling data in the precipitation standard year was slightly higher than that of GWR downscaling data. The results of this study can provide a reference for precipitation downscaling research in related regions and promote regional climate and hydrological research.

Key words： mixed geographically weighted regression model (MGWR); geographically weighted regression model (GWR); global precipitation measurement (GPM); Yellow River Basin

Cite this article

He BAI , Yisen MING , Qihang LIU , Chang HUANG . Downscaling of GPM satellite precipitation data in the Yellow River Basin based on MGWR model[J]. Arid Land Geography, 2023 , 46(7) : 1052 -1062 . DOI: 10.12118/j.issn.1000-6060.2022.475

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