Feng Qian

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. 

The Three Gorges Reservoir region suffers from severe soil erosion that leads to serious soil degradation and eutrophication. Interrill erosion models are commonly used in developing soil erosion control measures. Laboratory simulation experiments were conducted to investigate the relationship between interrill erosion rate and three commonly hydraulic parameters (flow velocity V, shear stress τ and stream power W). The slope gradients ranged from 17.6% to 36.4%, and the rainfall intensities varied from 0.6 to 2.54 mm·min⁻¹. The results showed that surface runoff volume and soil loss rates varied greatly with the change of slope and rainfall intensity. Surface runoff accounted for 67.2–85.4% of the precipitation on average. Soil loss rates increased with increases of rainfall intensity and slope gradient, Regression analysis showed that interrill erosion rate could be calculated by a linear function of V and W. Predictions based on V (R ² = 0.843, ME = 0.843) and W (R ² = 0.862, ME = 0.862) were powerful. τ (R ² = 0.721, ME = 0.721) did not seem to be a good predictor for interrill erosion rates. Five ordinarily interrill erosion models were analyzed, the accuracy of the models in predicting soil loss rate was: Model 3 (ME = 0.977) > Model 4 (ME = 0.966) > Model 5 (ME = 0.963) > Model 2 (ME = 0.923) > Model 1 (ME = 0.852). The interrill erodibility used in the model 3 (WEPP) was calculated as 0.332×10⁶ kg·s·m⁻⁴. The results can improve the precision of interrill erosion estimation on purple soil slopes in the Three Gorges Reservoir area.

Hydrological processes play important roles in soil erosion processes of the hillslopes. This study was conducted to investigate the hydrological processes and the associated erosional responses on the purple soil slope. Based on a comprehensive survey of the Wangjiaqiao watershed in the Three Gorges Reservoir, four typical slope gradients (5°, 10°, 15°and 20°) were applied to five rainfall intensities (0.6, 1.1, 1.61, 2.12 and 2.54 mm·min^-1). The results showed that both surface and subsurface runoff varied greatly depending on the rainfall intensity and slope gradient. Surface runoff volume was 48.1 to 280.1 times of that for subsurface runoff. The critical slope gradient was about 10°. The sediment yield rate increased with increases in both rainfall intensity and slope gradient, while the effect of rainfall intensity on the sediment yield rate was greater than slope gradient. There was a good linear relationship between sediment yield rate and Reynolds numbers, flow velocity and stream power, while Froude numbers, Darcy-Weisbach and Manning friction coefficients were not good hydraulic indicators of the sediment yield rate of purple soil erosion. Among the three good indicators (Re, v and w), stream power was the best predictor of sediment yield rate (R² = 0.884). Finally, based on the power regression relationship between sediment yield rate, runoff rate, slope gradient and rainfall intensity, an erosion model was proposed to predict the purple soil erosion (R² = 0.897). The results can help us to understand the relationship between flow hydraulics and sediment generation of slope erosion and offer useful data for the building of erosion model in purple soil.