资源环境科技发展态势分析平台

The spatial distribution of rainfall is paramount for water-related research such as hydrological modelling and watershed management. The use of different interpolation methods in the same area may cause large differences and deviations from the real spatial distribution of rainfall; these differences depend on the type of chosen model, its mode of geographical management and the resolution used. In this study, different algorithms of spatial interpolation of rainfall in a region of southern Italy (Calabria) were applied and the results of geostatistical and deterministic approaches were compared in order to choose the best method for reproducing the actual precipitation field surface. In particular, inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), ordinary cokriging (COK) and empirical Bayesian kriging (EBK) were applied to produce the monthly rainfall maps of Calabria. The maps were obtained from a rainfall data set of 129 monthly rainfall series (about one station per 117km(2)) collected in the period 1951-2006. Cross-validation and visual analysis of the precipitation maps were performed to examine the results of these different models. Results clearly indicate that geostatistical methods outperform inverse distance. Moreover, among these methods, the kriging with an external drift showed the smallest error of prediction.