Rainfall erosivity estimation models for the Tibetan Plateau

Many models have been developed internationally to estimate rainfall erosivity. However, the effectiveness of these pre-existing models has not been verified for the Tibetan Plateau with large spatiotemporal heterogeneity of rainfall due to lack of high-temporal resolution rainfall data. The purpose of this study was to develop models to accurately estimate daily and semi-monthly rainfall erosivity values in the Tibetan Plateau. We used one -minute resolution rainfall data from 1,182 weather stations to develop rainfall erosivity models over the Ti-betan Plateau for the first time. The particle swarm optimization (PSO) method was performed to determine the newly developed model parameters. The newly developed models were compared with three internationally common existing models (exponentially seasonal1, cosine2, and exponentially semi-month3) to evaluate their effectiveness. Our results showed that (1) the three existing models all overestimated the values of mean annual rainfall erosivity in the Tibetan Plateau because limited rainfall data in the area were used to develop these models; (2) the new models greatly reduced computational error values of mean annual rainfall erosivity (the symmetric mean absolute percentage error (SMAPE) of <= 23%), relative to the three existing models (SMAPE of >= 40%); (3) the new semi-monthly models (SMAPE of 17.7 - 29.2%) generally outperformed the new daily models (SMAPE of 17.9 - 30.7%) because of their better ability to estimate the values of extreme rainfall erosivity. It is advisable to use the semi-monthly model rather than daily model to compute rainfall erosivity when using universal soil loss equation (USLE) and its derived models to calculate soil erosion. That will benefit to reduce the time-scale conversion errors between event-based and daily data when lacking high-temporal resolution data and using daily rainfall data to analyze the influence of rainfall on soil erosion.