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

Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China
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摘要 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 pre-cipitation 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. 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 pre-cipitation 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.
出处 《Research in Cold and Arid Regions》 CSCD 2016年第6期485-494,共10页 寒旱区科学(英文版)
基金 conducted within the National Major Scientific Research Project (No. 2013CBA01806) the National Natural Science Foundation of China (No. 41271085) the National Scientific and Technological Support Project (No. 2013BAB05B03)
关键词 Kriging interpolation method sampling methods spatial sandwich sampling PRECIPITATION northwestern China Kriging interpolation method sampling methods spatial sandwich sampling precipitation northwestern China
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