Gridded rainfall erosivity (2014–2022) in mainland China using 1 min precipitation data from densely distributed weather stations

Abstract. The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for understanding the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the limited spatiotemporal resolution of historical precipitation data, there are significant biases in the estimation of rainfall erosivity in China, particularly in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously strengthened its ground-based meteorological observation capabilities, establishing a dense network of observation stations. These high-precision precipitation data provide a reliable basis for quantifying rainfall erosivity pattern in China. In this study, rigorous quality control was performed on the 1 min ground observation precipitation data from nearly 70 000 stations nationwide between 2014 and 2022, ultimately selecting data from 60 129 stations. Using the data from these stations, event rainfall erosivity was calculated, producing a national dataset of mean annual rainfall erosivity with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ mm ha−1 h−1 yr−1, with values exceeding 4000 MJ mm ha−1 h−1 yr−1 primarily concentrated in southern China and the southern Tibetan Plateau. The mean annual rainfall erosivity in mainland China derived from previously released datasets was found to be 31 % to 65 % higher than the value calculated in this study, and basin-level discrepancies between our findings and other studies also vary significantly. In summary, the release of this dataset enables a more accurate assessment of the current intensity of water erosion in China. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.301206; Chen, 2024).