Jingjun Liu

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 R_m(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 R_m(t) varied with the methods. Compared with the single-test method, the proposed framework can effectively reduce uncertainty.

Rainfall erosivity (R factor), in the Universal Soil Loss Equation (USLE) , a climate index, is used worldwide to assess and predict the potential of rainfall to cause erosion. The temporal variation in rainfall erosivity, informs of abrupt change and trend, are critical for soil loss prediction. To find a simple and effective method for accurate detection of abrupt change and trend has implication for soil and water conservation planning. In this paper, a four-step framework is proposed to detect abrupt change and trend in rainfall erosivity time series, i.e., evaluate the significance of variation in rainfall erosivity time series at three levels: no, weak and strong, abrupt change and trend detection for rainfall erosivity,  estimation of correlation coefficient between the variation component and rainfall erosivity series, remove the variation component with the largest correlation coefficient from the rainfall erosivity series, repeat the above steps for the new series until variance coefficient was insignificance. The first step is based on an index of Hurst coefficient. The trend detection is implemented using both Spearman rank and Kendall rank correlation test. For abrupt change ,three kinds of methods (Mann-Kendall, Moving T and Bayesian test) are employed.  This framework is applied to the annual rainfall erosivity series of the Three Gorges Reservoir , China. There was a large uncertainty in detecting variability with a single test method. Application of the proposed framework can reduce uncertainty  associated with soil erosion assessment and achieve more accurate regional soil and water management.