Sciences in Cold and Arid Regions ›› 2016, Vol. 8 ›› Issue (6): 485-494.doi: 10.3724/SP.J.1226.2016.00485

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Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China

JinKui Wu1,2, ShiWei Liu2, LePing Ma3, Jia Qin2, JiaXin Zhou2, Hong Wei2   

  1. 1. Laboratory of Watershed Hydrology and Ecology, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
    2. State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
    3. Shule River Basin Water Resources Administration Bureau of Gansu Province, Yumen, Gansu 735200, China
  • Received:2016-03-18 Revised:2016-07-21 Published:2018-11-23
  • Contact: JinKui Wu, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. No. 320, West Donggang Road, Lanzhou, Gansu 730000, China. E-mail:
  • Supported by:
    This research was conducted within the National Major Scientific Research Project (No. 2013CBA01806), the National Natural Science Foundation of China (No. 41271085), and the National Scientific and Technological Support Project (No. 2013BAB05B03). The precipitation data were downloaded from the China Meteorological Data Sharing Service System ( We thank the National Climate Center of China for its data support.

Abstract: The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual precipitation interpolation based on spatial sandwich sampling, including ME(0.1513), RMSE(95.91), ASE(101.84), MSE(-0.0036), and RMSSE(1.0397), were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.

Key words: Kriging interpolation method, sampling methods, spatial sandwich sampling, precipitation, northwestern China

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