Mapping Monthly Precipitation in New Zealand by Using Different Interpolation Methods

Abstract

Spatial interpolation of rainfall data is an issue particularly significant for hydrological modelling and watershed management. The success of spatial interpolation varies according to the type of model chosen, its mode of geographical management and the resolution used. The aim of this paper was to develop different algorithms of spatial interpolation of rainfall in New Zealand and to compare the results of geostatistical and deterministic approaches in order to choose the method that best reproduces the actual surface. In particular, inverse distance weighting, ordinary kriging, kriging with an external drift and ordinary cokriging were applied to produce the monthly rainfall maps of New Zealand. The prediction performance of each method was evaluated through cross-validation and visual examination of the precipitation maps produced. Results clearly indicate that geostatistical methods outperform inverse distance. Moreover, among these methods, the ordinary cokriging and the kriging with an external drift showed the smallest error of prediction.