Soil erosion assessment by RUSLE with improved P factor and its validation: Case study on mountainous and hilly areas of Hubei Province, China

The Revised Universal Soil Loss Equation (RUSLE) is widely used to estimate regional soil erosion. However, quantitative impacts of soil and water conservation (SWC) measures on conservation practice factor (P) of the RUSLE remain largely unclear, especially for the mountainous and hilly areas. In this study, we improved the RUSLE by considering quantitative impacts of different SWC measures on the P factor value. The improved RUSLE was validated against the long-term (2000-2015) soil erosion monitoring data obtained from 96 runoff plots (15-35 degrees) in mountainous and hilly areas of Hubei Province, China; the result presented a high accuracy with the determination coefficient of 0.89. Based on the erosion monitoring data of 2018 and 2019, the Root Mean Square Error of the result by the improved RUSLE was 28.0% smaller than that by the original RUSLE with decrement of 19.6%-24.0% in the average P factor values, indicating that the soil erosion modelling accuracy was significantly enhanced by the improved RUSLE. Relatively low P factor values appeared for farmlands with tillage measures (P < 0.53), grasslands with engineering measures (P < 0.23), woodlands with biological measures (P < 0.28), and other land use types with biological measures (P < 0.51). The soil erosion modulus showed a downward trend with the corresponding values of 1681.21, 1673.14, 1594.70, 1482.40 and 1437.50 t km(-2) a(-1) in 2000, 2005, 2010, 2015 and 2019, respectively. The applicability of the improved RUSLE was verified by the measurements in typical mountainous and hilly areas of Hubei Province, China, and arrangements of SWC measures of this area were proposed. (C) 2021 International Research and Training Center on Erosion and Sedimentation, China Water & Power Press. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Automated summary

The study improved the RUSLE model by considering the quantitative impacts of different SWC measures on the P factor value, resulting in significantly enhanced accuracy in soil erosion modeling. The improved RUSLE showed a high accuracy when validated against long-term soil erosion monitoring data, with a decreased average P factor value and improved soil erosion modeling accuracy.