Comparison of Spatial Interpolation Methods for Precipitation in Ningxia, China

As basic data, the reliability of precipitation data makes a significant impact on many results of environmental applications. In order to obtain spatially distributed precipitation data, measured points are interpolated. There are many spatial interpolation schemes, but none of them can perform best in all cases. So criteria of precision evaluation are established. This study aims to find an optimal interpolation scheme for rainfall in Ningxia. The study area is located in northwest China. Meteorological stations distribute at a low density here. Six interpolation methods have been tested after exploring data. Cross-validation was used as the criterion to evaluate the accuracy of various methods. The best results were obtained by cokriging with elevation as the second variable, while the inverse distance weighting (IDW) preform worst. Three types of model in cokriging were compared, and Gaussian model is the best.