Assessing the accuracy of large-scale rainfall erosivity estimation based on climate zones and rainfall patterns

Abstract

Rainfall erosivity is affected by the amount and intensity of rainfall in a certain period, which is an essential factor for soil erosion prediction. However, it is generally calculated by field measurements on a local scale. With a focus on global soil erosion assessment, some researchers have improved the estimation of global rainfall erosivity by using statistical models in some climate zones. However, the climate zones cannot represent actual erosive rainfall events. Therefore, such usage of models would lead to more uncertainties when estimating rainfall erosivity across the globe. Here, our study compared six common-used models of rainfall erosivity and then improved the accuracy of rainfall erosivity estimations based on global rainfall patterns, which are defined by the amount and distribution of rainfall in a year. Results showed that: (1) Compared with the climate zone classification, the model fitting under the rainfall pattern classification can improve the model accuracy and result in higher variation among the rainfall patterns. The average accuracy of all models was improved by 8%, and the accuracy of annual models was increased by 33%. (2) Models based on annual rainfall are more suitable for the drought and seasonal rainfall patterns, while most models based on monthly rainfall are suitable for the moderate rainfall pattern. However, most models based on monthly or annual rainfall have high uncertainties and low accuracies in regions with annual precipitation < 200 mm or > 850 mm. This study can provide helpful implications for model selection and parameter calibration associated with large-scale water erosion prediction.