SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion

Abstract

The Revised Universal Soil Loss Equation (RUSLE) models are most widely used for quantitative prediction of soil erosion. However, these models have many shortcomings. For example, the annual total rainfall is often adopted, ignoring the inhomogeneity of seasonal rainfall. The adopted vegetation coverage indexes (VCIs) are usually the annual average vegetation coverage or VCIs obtained by monitoring on a specific day, ignoring the seasonal changes in VCIs during the year. In addition, the impact of slope on the conservation practices factor is not considered. To overcome these problem, this study aims to propose a seasonal and slope factor-based RUSLE (SUSLE) model that considers the seasonal changes in rainfall and VCIs and the effect of slope on the conservation practices factor. Based on GIS and remote sensing, the quantitative prediction of soil erosion in Ningdu County, Jiangxi Province, in 2017 is taken as a case study. The traditional RUSLE model and the proposed SUSLE model are analyzed and compared. Results show that the overall distribution characteristics of soil erosion in the two models are similar that the SUSLE model is more consistent than the RUSLE model in all erosion levels and that the prediction performances of the SUSLE model in the very low, moderate, and high erosion levels are better than those of the RUSLE model. The distribution characteristics of soil erosion in different periods and the relationships between soil erosion and environmental factors (e.g., slope and land use) under the SUSLE model are discussed. The results show that the maximum erosion area occurred in spring and the minimum area in autumn; the soil erosion amount on slopes of 8~25° reached 65.14% of the total amount; bare grassland and cultivated land are the main land cover types impacted by soil erosion in Ningdu County.

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Funding

This research is funded by the National Key Research and Development Program (NO. 2017YFC1502505); the National Natural Science Foundation of China (No.41807285, 41762020, 51879127, and 51769014); the National Science Foundation of Jiangxi Province, China (20192BAB216034, 20192ACB2102, 20192ACB20020); the Postdoctoral Science Foundation of China (NO.2019M652287); and the Jiangxi Provincial Postdoctoral Science Foundation (NO. 2019KY08).

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Correspondence to Chi Yao.

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Huang, F., Chen, J., Yao, C. et al. SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion. Bull Eng Geol Environ (2020). https://doi.org/10.1007/s10064-020-01886-9

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Keywords

  • Soil erosion
  • Revised Universal Soil Loss Equation
  • Seasonal and slope-based RUSLE model
  • Geographic information system
  • Remote sensing