As a typical fragile ecological plateau area, the risk of water erosion on the Tibetan Plateau (TP) in China continues to increase with climate change. Rainfall erosivity is one of the most widely used indicators to assess the potential impact of rainfall events on water erosion. However, limited by the scarcity of historical in situ precipitation observations with sufficient spatiotemporal resolution, the estimates of rainfall erosivity over the TP have much larger biases than those of other regions in China. To accurately investigate the spatiotemporal evolution of rainfall erosivity, empirical models were first established to estimate monthly rainfall erosivity using 1-minute in situ precipitation observations from 1711 meteorological stations on the TP. The independent assessment showed that the correlation correction values between the observed and estimated monthly values were greater than 0.81 for all months. The annual rainfall erosivity data were then produced with a 0.1 degrees spatial resolution for the 1979-2018 period based on the China Meteorological Forcing Dataset (CMFD) precipitation data using newly established estimation models. Our results show that the CMFD-based estimates successfully captured the decreasing spatial pattern of the multiyear average annual rainfall erosivity from the southeast to northwest of the TP. In addition, the CMFD-based annual rainfall erosivity had a good linear relationship with the observed values but with a certain overestimation. Therefore, standardized annual rainfall erosivity values were used to detect the changes in rainfall erosivity. For most regions on the TP, annual rainfall erosivity values have exhibited significant increasing trends over the last 40 years. This study provides a theoretical basis and reference for controlling water erosion and climate adaptation on the TP.
Chen, YL; Ding, MH; Zhang, G; Duan, XW; Wang, CX
The possible role of fused precipitation data in detection of the spatiotemporal pattern of rainfall erosivity over the Tibetan Plateau, China
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