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

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

气候与水文

基于MGWR模型的黄河流域GPM卫星降水数据降尺度研究

  • 柏荷 ,
  • 明義森 ,
  • 刘启航 ,
  • 黄昌
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  • 1.陕西省地表系统与环境承载力重点实验室，陕西 西安 710127
    2.西北大学城市与环境学院，陕西 西安 710127
    3.西北大学城市与环境学院地表系统与灾害研究院，陕西 西安 710127
柏荷（1996-），女，硕士研究生，主要从事水文水资源遥感方面的研究. E-mail: baihhhe@163.com

收稿日期: 2022-09-21

  修回日期: 2022-11-17

  网络出版日期: 2023-08-03

基金资助

国家重点研发计划项目(2017YFC1502501);陕西省自然科学基金(2021JM314)

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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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摘要

黄河流域地域广阔，但气象站点分布较少，导致气象资料短缺。卫星降水可以作为气象站点观测的重要补充，但其空间分辨率有限，导致其在区域研究中作用有限。以黄河流域作为研究区域，针对全球降水观测计划（GPM）卫星降水产品，以2002、2012年和2020年降水数据作为干旱年、标准年以及湿润年3个典型气候年份，在综合考虑归一化植被指数（NDVI）、数字高程模型（DEM）、坡度（Slope）、地表温度（LST）和风速（WDS）多种反映降水量空间分布特征的影响因子及其空间非平稳性特征的基础上，采用地理加权回归（GWR）模型、混合地理加权回归（MGWR）模型2种降尺度方法，得到了黄河流域1 km空间分辨率的降尺度降水数据，并进一步通过地面气象站点数据对降尺度结果进行验证。结果表明：（1）GPM年降水数据与地面气象站点观测数据在2002、2012年和2020年的黄河流域地区具有较高的相关性。（2）经MGWR模型降尺度的降水数据空间分辨率得到了显著提高，且在降水变化的空间细节表达方面较GWR模型更优。（3）在3个典型气候年份中，MGWR模型在降水量标准年中相对于GWR模型具有更高的准确性。研究结果能够为相关区域范围的降水降尺度研究提供宏观参考与借鉴，促进区域气候水文研究。

关键词： 混合地理加权回归模型（MGWR）; 地理加权回归模型（GWR）; 全球降水观测计划（GPM）; 黄河流域

本文引用格式

柏荷 , 明義森 , 刘启航 , 黄昌 . 基于MGWR模型的黄河流域GPM卫星降水数据降尺度研究[J]. 干旱区地理, 2023 , 46(7) : 1052 -1062 . DOI: 10.12118/j.issn.1000-6060.2022.475

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

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