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.

Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey

“…The other variable to be correlated to the SDH was the normalized difference vegetation index (NDVI) obtained from NASA using the Google Earth Engine platform (Didan, 2015). The NDVI, calculated by Equation ( 2), serves as a measure of the quantity, health, and spread of green plants within a region, achieved by assessing the spectral reflectance disparity between the red (Red) and near-infrared (NIR) bands of an image [48]: The climate variables such as the mean annual temperature (MAT), mean annual minimum temperature (MAMINT), mean annual maximum temperature (MAMAXT), and total precipitation (MATP) were extracted from the spatial climate surfaces developed by Yener [49] at a spatial resolution of 0.005 • × 0.005 • (approximately 600 m). The soil variables, such as the bulk density (BD), OC, field capacity (FC), and pH, were extracted from the maps provided by Hengl and Wheeler [26] and Hengl [27,28].…”

“…The climatic variables used in this study were extracted from the climate surfaces developed by Yener [49]. According to this dataset, the study area's climate is characterized by an average of 10.6 • C mean, 7.0 • C mean minimum, and 14.8 • C mean maximum temperatures with minimum-maximum values of 6.8-14.1 • C, 2.5-11.3 • C, and 11.5-17.5 • C, respectively (Table 1, Figure 3a-c).…”