中国地理科学 ›› 2019, Vol. 20 ›› Issue (3): 446-462.doi: 10.1007/s11769-019-1033-3

• 论文 • 上一篇    下一篇

Spatial Downscaling of the Tropical Rainfall Measuring Mission Pre-cipitation Using Geographically Weighted Regression Kriging over the Lancang River Basin, China

LI Yungang1,2, ZHANG Yueyuan1,2, HE Daming1,2, LUO Xian1,2, JI Xuan1,2   

  1. 1. Institute of International Rivers and Eco-security, Yunnan University, Kunming 650091, China;
    2. Yunnan Key Laboratory of Inter-national Rivers and Transboundary Eco-security, Yunnan University, Kunming 650091, China
  • 收稿日期:2018-03-06 出版日期:2019-06-27 发布日期:2019-05-06
  • 通讯作者: HE Daming. E-mail:dmhe@ynu.edu.cn E-mail:dmhe@ynu.edu.cn
  • 基金资助:

    Under the auspices of the National Natural Science Foundation of China (No. 41661099); the National Key Research and Development Program of China (No. Grant 2016YFA0601601)

Spatial Downscaling of the Tropical Rainfall Measuring Mission Pre-cipitation Using Geographically Weighted Regression Kriging over the Lancang River Basin, China

LI Yungang1,2, ZHANG Yueyuan1,2, HE Daming1,2, LUO Xian1,2, JI Xuan1,2   

  1. 1. Institute of International Rivers and Eco-security, Yunnan University, Kunming 650091, China;
    2. Yunnan Key Laboratory of Inter-national Rivers and Transboundary Eco-security, Yunnan University, Kunming 650091, China
  • Received:2018-03-06 Online:2019-06-27 Published:2019-05-06
  • Contact: HE Daming. E-mail:dmhe@ynu.edu.cn E-mail:dmhe@ynu.edu.cn
  • Supported by:

    Under the auspices of the National Natural Science Foundation of China (No. 41661099); the National Key Research and Development Program of China (No. Grant 2016YFA0601601)

摘要:

Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds. This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging (GWRK), to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 Version 7 over the Lancang River Basin (LRB) for 2001-2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index (NDVI), the Land Surface Temperature (LST), and the Digital Elevation Model (DEM). Geographical ratio analysis (GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001-2015. Results showed that:1) The TRMM 3B43 precipitation was highly accurate with slight overestimation at the basin scale (i.e., CC (correlation coefficient)=0.91, Bias=13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC=0.75, RMSE (root mean square error)=182 mm, MAE (mean absolute error)=142 mm, and Bias=0.78% for annual precipitation and CC=0.95, RMSE=25 mm, MAE=16 mm, and Bias=0.67% for monthly precipitation.

关键词: precipitation, Tropical Rainfall Measuring Mission (TRMM) 3B43, Geographically Weighted Regression Kriging (GWRK), spatial downscaling, the Lancang River Basin, China

Abstract:

Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds. This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging (GWRK), to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 Version 7 over the Lancang River Basin (LRB) for 2001-2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index (NDVI), the Land Surface Temperature (LST), and the Digital Elevation Model (DEM). Geographical ratio analysis (GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001-2015. Results showed that:1) The TRMM 3B43 precipitation was highly accurate with slight overestimation at the basin scale (i.e., CC (correlation coefficient)=0.91, Bias=13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC=0.75, RMSE (root mean square error)=182 mm, MAE (mean absolute error)=142 mm, and Bias=0.78% for annual precipitation and CC=0.95, RMSE=25 mm, MAE=16 mm, and Bias=0.67% for monthly precipitation.

Key words: precipitation, Tropical Rainfall Measuring Mission (TRMM) 3B43, Geographically Weighted Regression Kriging (GWRK), spatial downscaling, the Lancang River Basin, China