A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China

Hong Wang; Fei Zang; Chuanyan Zhao; Chenli Liu

doi:10.1016/j.scitotenv.2021.152066

A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China

Sci Total Environ. 2022 Mar 1:810:152066. doi: 10.1016/j.scitotenv.2021.152066. Epub 2021 Dec 2.

Authors

Hong Wang¹, Fei Zang², Chuanyan Zhao³, Chenli Liu²

Affiliations

¹ State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China. Electronic address: wangh18@lzu.edu.cn.
² State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.
³ State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China. Electronic address: zhaochy@lzu.edu.cn.

PMID: 34863760
DOI: 10.1016/j.scitotenv.2021.152066

Abstract

Accurate precipitation data are crucial for hydrological, meteorological, and ecological research. However, it is difficult to obtain high-precision and high-resolution spatiotemporal distributions of precipitation in remote mountain regions with complex topography and sparse rain gauges. In addition, the spatial resolutions of existing satellite precipitation products are too coarse to apply them in the mountain regions with great spatiotemporal heterogeneity. To overcome the disadvantage, downscaling satellite precipitation products has been an effectively method to develop high-resolution precipitation data. In this study, a geographically weighted regression (GWR) model, coupling with topographical and water vapor source variables filtered by stepwise regression analysis (SRA), is applied to downscale the GSMaP-Gauge precipitation products (0.1° × 0.1°) to obtain high-resolution (1 km × 1 km) precipitation from 2000 to 2020 at annual, seasonal, and monthly scales over the Qilian Mountains. Besides, the accuracy of the downscaled precipitation based on all meteorological stations and the stations at high altitude (i.e., over 3000 m) are validated. Furtherly, the spatiotemporal variations of precipitation are analyzed. The results show that: (1) the accuracy after downscaling has been improved comparing with that of the original data. The accuracy of precipitation simulated at high-altitude stations is lower than that at all stations; (2) The trend of precipitation before and after downscaling is consistent in space. The spatial distributions of precipitation at annual, spring, summer, autumn, and months from March to November are decreased from the southeast to the northwest; (3) The spatial variations of precipitation show an increasing trend in most areas (>50%) at different time scales, except for March and September. Along with the time, the annual precipitation shows an increasing trend with a slope of 3.83 over the last 20 years. These findings suggest that the GWR method can be applied effectively to downscale annual, seasonal, and monthly precipitation of GSMaP-Gauge products in the Qilian Mountains.

Keywords: Downscaling; GSMap products; Geographically weighted regression; Precipitation; Qilian Mountains.

MeSH terms

China
Hydrology
Rain*
Seasons
Spatial Regression*