Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

Abstract

Snow depth is a general input variable in many models of agriculture, hydrology, climate and ecology. This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging (GWRK) and regression kriging (RK) in a spatial interpolation of regional snow depth. The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor (VIF). Three variables, Height, topographic ruggedness index (TRI), and land surface temperature (LST) , are used as explanatory variables to establish a regression model for snow depth. The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained. The results indicate that 1) the result of GWRK's accuracy is slightly higher than that of RK (R2 = 0.55 vs. R2 = 0.50, RMSE (root mean square error) = 0.102 m vs. RMSE = 0.077 m); 2) for the subareas, GWRK and RK exhibit similar estimation results of snow depth. Areas in the Bayanbulak Basin with a snow depth greater than 0.15 m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin. However, the GWRK resulted in more detailed information on snow depth distribution than the RK. The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.