Abstract：Spatial interpolation for climate data in mountainous regions with complex topography has always been a hot issue in the geographical information science. In this study， we used three interpolation methods including Inverse Distance Weight (IDW)， Ordinary Kriging (OK) and Thin Plate Smoothing Spline (TPS) incorporated with elevation to interpolate monthly average temperature and precipitation data based on the meteorological stations in Kangdian region of China and digital elevation model at the spatial resolution of 1 km. Results showed that there was significant regional variations in the spatial distribution of temperature in Kangdian region while not in precipitation. TPS performed best for the spatial interpolation of temperature and precipitation in Kangdian region based on the statistics index of root mean squared error. It's concluded that TPS was more appropriately applied for mountainous regions with complex topography， compared to IDW and OK.
2018, Vol. 52 
