地理研究 ›› 2020, Vol. 39 ›› Issue (7): 1680-1690.doi: 10.11821/dlyj020190545

• 论文 • 上一篇    下一篇

太行山区遥感卫星反演降雨产品降尺度研究

胡实1, 韩建2, 占车生1(), 刘梁美子1,3   

  1. 1. 中国科学院地理科学与资源研究所陆地水循环及地表过程重点实验室,北京 100101
    2. 中国电建集团西北勘测设计研究院有限公司,西安 710065
    3. 中国科学院大学,北京 100049
  • 收稿日期:2019-07-02 修回日期:2019-11-22 出版日期:2020-07-20 发布日期:2020-09-20
  • 通讯作者: 占车生
  • 作者简介:胡实(1982-),女,湖北咸宁人,博士,助理研究员,主要从事生态水文研究。E-mail:hus.08b@igsnrr.ac.cn
  • 基金资助:
    国家重点研发计划(2017YFA0603702);国家自然科学基金项目(41971232);国家自然科学基金项目(41571019,51779009);国家重点研发计划(2016YFC0402406)

Spatial downscaling of remotely sensed precipitation in Taihang Mountains

HU Shi1, HAN Jian2, ZHAN Chesheng1(), LIU Liangmeizi1,3   

  1. 1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Powerchina Northwest Engineering Corporation Limited, Xi'an 710065, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-07-02 Revised:2019-11-22 Online:2020-07-20 Published:2020-09-20
  • Contact: ZHAN Chesheng

摘要:

高时空分辨率降雨数据的获取对陆地水循环研究至关重要。遥感卫星反演降水产品虽然能有效再现降雨的空间格局,但存在空间分辨率较低的问题。以植被指数NDVI(Normalized Difference Vegetation Index)和海拔高度为自变量,通过构建太行山区GPM降水(Global Precipitation Measurement Mission)的时滞地理加权回归模型,得到了2014—2016年研究区1 km分辨率GPM降水数据。研究结果表明:利用植被指数和海拔高度构建的时滞地理加权回归模型能够有效地对太行山月尺度GPM降雨数据进行尺度下延,在提高GPM数据空间分辨率的同时保留了原始数据的观测精度。考虑NDVI的时滞性提高了地理加权回归模型的降尺度效果,相对于多元线性回归模型和不考虑NDVI时滞效应的地理加权回归模型,时滞地理加权回归模型的降尺度结果与站点实测数据的确定性系数更高,RMSE更低。冬季降雨与第二年春季植被NDVI的关系较为密切,虽然采用第二年春季的NDVI作为解释变量构建降尺度模型能有效地提高冬季降雨的降尺度效果,但基于植被指数和海拔高度构建的时滞地理加权回归模型更加适用于植被生长季GPM降雨数据的降尺度研究。

关键词: 时滞地理加权回归, GPM, 降尺度, 太行山

Abstract:

As a fundamental component in water circulation, the spatio-temporal pattern of precipitation is critical for terrestrial hydrological cycle simulation. The satellite-based precipitation can describe the spatial pattern of precipitation properly, but the relatively low spatial resolution of the product limited its application in terrestrial hydrological cycle simulation. By taking Taihang Mountains as an example, based on the relationship between precipitation, altitude and NDVI (Normalized Difference Vegetation Index), the monthly GPM (Global Precipitation Measurement Mission) data from 2014 to 2016 are disaggregated to 1-km resolution with a GWRDL model (Geographically Weighted Regression Model coupled with Distributed Lagging). The results showed that with the aid of the altitude and NDVI, the GWRDL model could effectively downscale monthly GPM data. The spatial resolution of downscaled GPM data was increased by the GWRDL model, and the accuracy of the original GPM data was retained at the same time. Compared with precipitation downscaled by the Geographically Weighted Regression Model and Multiple Linear Regression Model, the precipitation downscaled by GWRDL model has highest coefficients of determination (R2), lowest root mean square error (RMSE) and lowest mean absolute error (MAE) with the observed data, indicating that using NDVI in other months as explanatory variable is better than that using NDVI in current month, and this practice improved the downscaling algorithm and highlighted the accuracy of downscaled precipitation. Because the relationship between precipitation and NDVI was closer in the next spring than that in winter, using NDVI in the following 2-3 months (NDVI in next spring) as an explanatory variable in GWRDL model can improve precipitation downscaling precision in winter. Although the GWRDL model, which gives a consideration of time lagging of NDVI, has a better performance than GWR model in winter, it is more suitable for precipitation downscaling in vegetation growing stage than in winter. Compared with original GPM data, the coefficients of determination between downscaled GPM data and observed precipitation was averagely increased by 0.02 with GWRDL model in vegetation growing stage (April to October), which is higher than that in winter (0.002). Therefore, we suggest that the GWRDL model should be used in GPM downscaling in vegetation growing seasons.

Key words: Geographically Weighted Regression Model coupled with distributed lagging, Global Precipitation Measurement Mission, downscaling algorithm, Taihang Mountains