EGU2020-2489, updated on 31 Dec 2021
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

A framework of abrupt changes and trends detection for rainfall erosivity

Feng Qian1,2, Bo Hu1, Honghu Liu1,3, and Jingjun Liu4
Feng Qian et al.
  • 1Changjiang River Scientific Research Institute, Department of Soil and Water Conservation, Wuhan,China (,
  • 2School of Water Resources and Hydropower Engineering of Wuhan University, Wuhan,China (
  • 3State Key Lab Soil Erosion Dryland Farming Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of sciences and Ministry of Water Resources,Bureau, Yangling, China (
  • 4Wuhan Hydrology and Water Resources Survey Bureau, Wuhan, China (

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. 

How to cite: Qian, F., Hu, B., Liu, H., and Liu, J.: A framework of abrupt changes and trends detection for rainfall erosivity , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2489,, 2020.