Detection Framework of Abrupt Changes and Trends in Rainfall Erosivity in Three Gorges Reservoir, China

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Abstract
Rainfall erosivity is commonly used to estimate the probability of soil erosion caused by rainfall. The accurate detection of temporal changes in rainfall erosivity and the identification of abrupt changes and trends are important for understanding the physical causes of variation. In this study, a detection framework is introduced to identify temporal changes in rainfall erosivity time series as follows: (i) The significance of time series variation of rainfall erosivity is assessed based on the Hurst coefficient and divided into three levels: None, medium, and high. (ii) The detection of abrupt changes (Mann–Kendall, Moving T, and Bayesian tests) and trends (Spearman and Kendall rank correlation tests) of variate series and the correlation coefficient between the variation component and the original series is calculated. (iii) The modified series is obtained by preferentially eliminating the variation component (trend or change point) with larger correlation coefficients. (iv) We substituted the modified series into steps i to iii until the correlation coefficient was not significant. This framework is used to analyze the variation of rainfall erosivity in the Three Gorges Reservoir, China. The results showed that by using traditional methods, both an increasing trend and an upward change point were observed in Zigui station. However, after the upward change point was deducted from the annual rainfall erosivity series R(t), the resultant Rm(t) showed no statistically significant trend. Trend analysis should be performed considering the existence of an abrupt change to assess the long-term changes in rainfall erosivity series; otherwise, it would result in the wrong conclusion. In addition, the change points detected in the Rm(t) varied with the methods. Compared with the single-test method, the proposed framework can effectively reduce uncertainty.
Funding Information
  • National Key Research and Development Program of China (2021YFE0111900, 51909011, 2021BAA186, CKSF2019410TB)
  • National Natural Science Foundation of China (2021YFE0111900, 51909011, 2021BAA186, CKSF2019410TB)
  • Key R&D projects of Hubei Province (2021YFE0111900, 51909011, 2021BAA186, CKSF2019410TB)
  • the Fundamental Research Funds for Central Public Welfare Research Institutes (2021YFE0111900, 51909011, 2021BAA186, CKSF2019410TB)

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