Development of high-resolution annual climate surfaces for Turkey using ANUSPLIN and comparison with other methods

Abstract

Many climate models have been developed because of the importance of climatic factors' effects on the physical and biological environment, e.g., rock weathering, species distribution, and growth patterns of plants. Accurate, reliable climate surfaces are necessary, especially for countries such as Turkey, which has a complex terrain and limited monitoring stations. The accuracy of these models mainly depends on the spatial modeling methods used. In this study, Australian National University spline (ANUSPLIN) model was used to develop climate surfaces and was compared with other methods such as inverse distance weighting, Co-Kriging, lapse rate, and multilinear regression. The results from the developed climate surfaces were validated using three methods: (1) diagnostic statistics from the surface fitting model, such as signal, mean, root mean square predictive error, root mean square error estimate, root mean square residual of the spline, and estimate of the standard deviation of the noise in the spline; (2) a comparison of error statistics between interpolated surfaces with and the withheld climate data from 81 stations; and (3) a comparison with other interpolation methods using model performance metrics, such as mean absolute error, mean error, root mean square error, and R2adj. The most accurate results were obtained by the ANUSPLIN model. It explained 95%, 88%, 92%, and 71% of the variance in annual mean, minimum and maximum temperature, and total precipitation, respectively. The mean absolute error of these models was 0.63 °C, 1.16 °C, 0.72 °C, and 54.82 mm. The generated climate surfaces, having a spatial resolution of 0.005º x 0.005º could contribute to the fields of forestry, agriculture, and hydrology.