Abstract

Understanding spatial and temporal variations in rainfall erosivity is essential for estimating the variability in soil erosion. However, rainfall erosivity in the middle Yellow River Basin is usually calculated based on daily rainfall data, which fails to consider the impact of the changing rainfall intensity at the subdaily scale. In this study, we collected hourly rainfall data from 355 meteorological stations within and around the region to calculate rainfall erosivity during 1960–2017. We found that the rainfall erosivity calculated based on hourly data had much larger variability than that calculated based on daily data, and the calculated spatial and temporal variability were substantially different in periods when the rainfall intensity was highly variable. Based on our new estimation, we found that the spatial distribution of rainfall erosivity was highly heterogeneous, with high erosivity values in the Loess Regions and Rocky Mountain Regions with high soil erodibility and low erosivity values in the Aeolian Regions and Soft Sandstone Regions with low soil erodibility. This heterogeneity was mainly due to the distinct spatial distribution patterns of rainfall amount and rainfall intensity. Although the long-term increasing trend of rainfall erosivity was statistically insignificant from 1960 to 2017, its rate of increase was higher than that of the annual rainfall amount. Decadal changes showed a slight decrease from the 1960 s to the 1980 s and an increase since the 1990 s, which was substantially different from the decadal changes in the annual rainfall amount. These differences in temporal variability were mainly caused by the contribution of changing rainfall intensity, which was greater in the Aeolian Regions and Soft Sandstone Regions than in the other subregions. Our study highlighted the importance of considering the changing rainfall intensity at a subdaily scale in semiarid and semihumid climate zones and provided the first estimation of the long-term variability in rainfall erosivity based on hourly rainfall data in this region.

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