Topographic effects on spatial pattern of surface air temperature in complex mountain environment

Abstract

Understanding topographic effects on surface air temperature (SAT) is essential in developing an accurate prediction model in complex mountain environment. A nonlinear topographic regression model is developed to predict the spatial SAT pattern using latitude, elevation, slope, and aspect. Monthly SAT data have been collected from 14 meteorological stations between 1960 and 2010 in the Daqing Mountains of China. Topographic data are acquired from a 30 m resolution digital elevation model of the study region. Results show that: (1) the SAT models are able to explain 89–95.8 % of the spatial variation in different months. Elevation has the strongest effect on the SAT variation in all months (average 84.78 %). (2) The combined contribution of slope and aspect to SAT variations is larger (average 9.14 %) than that of latitude (average 6.07 %). (3) The combined effect of slope and aspect on SAT variations is higher in winter than in summer. The introduction of slope and aspect optimizes the SAT modeling in different months. In further studies, the accuracy of SAT model might be improved by introducing alternative topographic factors to capture the vegetation and weather condition.