This paper evaluates the applicability of a simple kriging with local means(SKLM) for highresolution spatial mapping of monthly mean temperature and rainfall in South Korea by using AWS observations in 2013 and elevation data. For an evaluation purpose, an inverse distance weighting(IDW) which has been widely applied in GIS and cokriging are also applied. From explanatory data analysis prior to spatial interpolation, negative correlations between elevation and temperature and positive correlation between elevation and rainfall were observed. Bias and root mean square errors are computed to compare prediction performance quantitatively. From the quantitative evaluation, SKLM showed the best prediction performance in all months. IDW generated abrupt changes in spatial patterns, whereas cokriging and SKLM ref lected not only the topographic effects but also the smoothing effects. In particular, local characteristics were better mapped by SKLM than by cokriging. Despite the potential of SKLM, more extensive comparative studies for data sets observed during the much longer time-period are required, since annual, seasonal, and local variations of temperature and rainfall are very severe in South Korea.

• 장동호(공주대학교 지리학과) | Dong-Ho Jang
• 위눈솔(공주대학교 지리학과) | Nun-Sol Wi
• 박노욱(인하대학교 공간정보공학과) | No-Wook Park Correspondence