Abstract
The erosivity factor in the universal soil loss equation (USLE) provides an effective means of evaluating the erosivity power of rainfall. The present study proposes three regression models for estimating the erosivity factor based on daily, monthly, and annual precipitation data of rainfall station network, respectively. The validity of the proposed models is investigated using a dataset consisting of 16,560 storm events monitored by 55 rainfall stations in southern Taiwan. The results show that, for 49 of the 55 stations, a strong positive correlation [Formula: see text] exists between the annual rainfall amount and the annual rainfall erosivity factor. In other words, the estimation model based on the annual precipitation data provides a reliable means of predicting the long-term annual rainfall erosivity in southern Taiwan. Furthermore, the root mean square error (RMSE) and mean absolute percentage error (MAPE) analysis results show that the estimation models based on annual and monthly precipitation data have a more accurate prediction performance than that based on daily precipitation data.
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