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

被引:39
|
作者
Wang, Hong [1 ]
Zang, Fei [1 ]
Zhao, Chuanyan [1 ]
Liu, Chenli [1 ]
机构
[1] Lanzhou Univ, Coll Pastoral Agr Sci & Technol, State Key Lab Grassland Agroecosyst, Key Lab Grassland Livestock Ind Innovat,Minist Ag, Lanzhou 730020, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Downscaling; GSMap products; Precipitation; Geographically weighted regression; Qilian Mountains; GPM IMERG PRODUCTS; PASSIVE MICROWAVE; TMPA; 3B42; NDVI; REGRESSION; PERFORMANCE; REFINEMENT; ALGORITHM; PROVINCE; PLATEAU;
D O I
10.1016/j.scitotenv.2021.152066
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 degrees x 0.1 degrees) to obtain high-resolution (1 km x 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.
引用
收藏
页数:15
相关论文
共 18 条
  • [1] A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China
    Wang, Hong
    Zang, Fei
    Zhao, Chuanyan
    Liu, Chenli
    Science of the Total Environment, 2022, 810
  • [2] Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China
    Xue, Dongping
    Gui, Dongwei
    Ci, Mengtao
    Liu, Qi
    Wei, Guanghui
    Liu, Yunfei
    SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 907
  • [3] Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China
    Xue, Dongping
    Gui, Dongwei
    Ci, Mengtao
    Liu, Qi
    Wei, Guanghui
    Liu, Yunfei
    Science of the Total Environment, 2024, 907
  • [4] High-resolution precipitation data derived from dynamical downscaling using the WRF model for the Heihe River Basin, northwest China
    Xuezhen Zhang
    Zhe Xiong
    Jingyun Zheng
    Quansheng Ge
    Theoretical and Applied Climatology, 2018, 131 : 1249 - 1259
  • [5] High-resolution precipitation data derived from dynamical downscaling using the WRF model for the Heihe River Basin, northwest China
    Zhang, Xuezhen
    Xiong, Zhe
    Zheng, Jingyun
    Ge, Quansheng
    THEORETICAL AND APPLIED CLIMATOLOGY, 2018, 131 (3-4) : 1249 - 1259
  • [6] An Improved Spatial-Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China's Qilian Mountains
    Wang, Lei
    Chen, Rensheng
    Han, Chuntan
    Yang, Yong
    Liu, Junfeng
    Liu, Zhangwen
    Wang, Xiqiang
    Liu, Guohua
    Guo, Shuhai
    REMOTE SENSING, 2019, 11 (07)
  • [7] Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China
    Duan, Limin
    Fan, Keke
    Li, Wei
    Liu, Tingxi
    THEORETICAL AND APPLIED CLIMATOLOGY, 2019, 135 (1-2) : 45 - 59
  • [8] Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China
    Limin Duan
    Keke Fan
    Wei Li
    Tingxi Liu
    Theoretical and Applied Climatology, 2019, 135 : 45 - 59
  • [9] Reconstructing high-resolution gridded precipitation data in the southwest China highland canyon area using an improved (MGWR) downscaling method
    Wang, Lihong
    Li, Yuechen
    Gan, Yushi
    Zhao, Long
    Fan, Lei
    Qin, Wei
    Ding, Lin
    SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 948
  • [10] A comparison between high-resolution satellite precipitation estimates and gauge measured data: case study of Gorganrood basin, Iran
    Dezfooli, Donya
    Abdollahi, Banafsheh
    Hosseini-Moghari, Seyed-Mohammad
    Ebrahimi, Kumars
    JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA, 2018, 67 (03): : 236 - 251