Spatial Interpolation of Regional Precipitation Based on Mixed Geographical Weighted Regression Combined with Kriging Interpolation

doi:10.3969/j.issn.1000-6362.2018.10.006

Abstract

Abstract: Based on the precipitation data of 1981?2010 from 144 meteorological stations in Sichuan province, using mixed geographical weighted regression Kriging interpolation (MGWRK) model, and considering the impact of topographic factors, the spatial distribution of the average annual precipitation was obtained in this paper. The effect of interpolation value was compared with those values from OK, GRK, and GWRK methods. The result showed that the optimal influencing factors combination was longitude, latitude and slope, determined by using the stepwise regression method, could decrease the multi-collinearity among the explanatory variables significantly. The types of spatial variability of the explanatory variables were analyzed quantitatively based on the index ΔAICc, which was the difference between the value of AICc (Corrected Akaike Information Criterion) of the same variable calculated by GWR model and by GR model. Then set the slope variable as global variable, and the longitude and latitude variables as local variables, the interpolation of the average annual precipitation in Sichuan province was conducted by the MGWRK model. The MGWRK method presented in this paper showed higher accuracy than those of the ordinary Kriging (OK) and global regression Kriging (GRK), because the method has taken into consideration of various influence factors of the spatial position and topography, and the variability of the relationship between these factors and precipitation.

Key words: Precipitation, Mix geographically weighted regression, Kriging interpolation, Spatial Interpolation

LI Hao, LIU Tao, XU Jing-wen. Spatial Interpolation of Regional Precipitation Based on Mixed Geographical Weighted Regression Combined with Kriging Interpolation[J]. Chinese Journal of Agrometeorology, 2018, 39(10): 674-684.

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