[Interpolation of daily mean temperature by using geographically weighted regression-Kriging]

Air temperature is the input variable of numerous models in agriculture, hydrology, climate, and ecology. Currently, in study areas where the terrain is complex, methods taking into account correlation between temperature and environment variables and autocorrelation of regression residual (e.g., regression Kriging, RK) are mainly adopted to interpolate the temperature. However, such methods are based on the global ordinary least squares (OLS) regression technique, without taking into account the spatial nonstationary relationship of environment variables. Geographically weighted regression-Kriging (GWRK) is a kind of method that takes into account spatial nonstationarity relationship of environment variables and spatial autocorrelation of regression residuals of environment variables. In this study, according to the results of correlation and stepwise regression analysis, RK1 (covariates only included altitude), GWRK1 (covariates only included altitude), RK2 (covariates included latitude, altitude and closest distance to the seaside) and GWRK2 (co-variates included altitude and closest distance to the seaside) were compared to predict the spatial distribution of mean daily air temperature on Hainan Island on December 18, 2013. The prediction accuracy was assessed using the maximum positive error, maximum negative error, mean absolute error and root mean squared error based on the 80 validation sites. The results showed that GWRK1's four assessment indices were all closest to 0. The fact that RK2 and GWRK2 were worse than RK1 and GWRK1 implied that correlation among covariates reduced model performance.