Abstract: Rainfall erosivity is an essential parameter in the model for predicting soil erosion and also the base to make soil erosion quantification. The simple model of estimating rainfall erosivity was established based on the weather station data of Heber Province. And the annual average rainfall erosivity were calculated with this model. The object of this paper is to evaluate various spatial interpolation methods, including ordinary Kriging (OK), simple Kriging (SK), lognormal Kriging (LNK), universal Kriging (UK), disjunctive Kriging (DK) and inverse distiance weighting interpolation (IDW) for estimating rainfall erosivity with lognormal distribution, the second-order polynomial trend, and to determine the optimal interpolation method. The predicted errors, statistical feature values and prediction maps obtained by different interpolation methods were compared. The result indicated that second-order OK method performed better than both zero and first-order OK methods. Within the method of second-order trend DK with Gaussian semi-variogram model performed better than other interpolation methods with the spherical or exponential models. So the data set of rainfall erosivity of Hebei Province was built with the high spatial resolution of 250 m × 250 m. And the spatial distribution pattern was analyzed and discussed.

Key words: Rainfall erosivity, Geostatistics, Soil erosion, Interpolation methods