Assessing the effects of vegetation and precipitation on soil erosion in the Three-River Headwaters Region of the Qinghai-Tibet Plateau, China

Abstract

Soil erosion in the Three-River Headwaters Region (TRHR) of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment. Vegetation and precipitation are considered to be the main factors for the variation in soil erosion. However, it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale. To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015, we employed the Revised Universal Soil Loss Equation (RUSLE) model to evaluate soil erosion in the TRHR, and then developed a method using the Logarithmic Mean Divisia Index model (LMDI) which can exponentially decompose the influencing factors, to calculate the contribution values of the vegetation cover factor (C factor) and the rainfall erosivity factor (R factor) to the variation of soil erosion from the pixel scale. In general, soil erosion in the TRHR was alleviated from 2005 to 2015, of which about 54.95% of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor, and 41.31% was caused by the change in the R factor. There were relatively few areas with increased soil erosion modulus, of which 64.10% of the area where soil erosion increased was caused by the change in the C factor, and 23.88% was caused by the combined effects of the C factor and the R factor. Therefore, the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion, while the C factor was the dominant factor for the increase of soil erosion. The area with decreased soil erosion caused by the C factor (12.10×103 km2) was larger than the area with increased soil erosion caused by the C factor (8.30×103 km2), which indicated that vegetation had a positive effect on soil erosion. This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion, and also provided a scientific basis for the regional control of soil erosion.

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References

  1. Cai C F, Ding S W, Shi Z H, et al. 2000. Study of applying USLE and geographical information system IDRISI to predict soil erosion in small watershed. Bulletin of Soil and Water Conservation, 14(2): 19–24. (in Chinese)

    Google Scholar 

  2. Dissanayake D, Morimoto T, Ranagalage M. 2019. Accessing the soil erosion rate based on RUSLE model for sustainable land use management: A case study of the Kotmale watershed, Sri Lanka. Modeling Earth Systems and Environment, 5(1): 291–306.

    Article  Google Scholar 

  3. Fayas C M, Abeysingha N S, Nirmanee K G S, et al. 2019. Soil loss estimation using RUSLE model to prioritize erosion control in KELANI river basin in Sri Lanka. International Soil and Water Conservation Research, 7(2): 130–137.

    Article  Google Scholar 

  4. Fu B J, Liu Y, Lü Y H, et al. 2011. Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China. Ecological Complexity, 8(4): 284–293.

    Article  Google Scholar 

  5. Fu S, Zha X. 2008. Study on predicting soil erosion in Dongzhen watershed based on GIS and USLE. Geo-information Science, 10(3): 390–395. (in Chinese)

    Google Scholar 

  6. Fu S H, Liu B Y, Zhou G Y, et al. 2015. Calculation tool of topographic factors. Science of Soil and Water Conservation, 13(5): 105–110. (in Chinese)

    Google Scholar 

  7. Ganasri B P, Ramesh H. 2016. Assessment of soil erosion by RUSLE model using remote sensing and GIS: A case study of Nethravathi Basin. Geoscience Frontiers, 7(6): 953–961.

    Article  Google Scholar 

  8. Kang L, Zhou T, Gan Y, et al. 2018. Spatial and temporal patterns of soil erosion in the Tibetan Plateau from 1984 to 2013. Chinese Journal of Applied and Environmental Biology, 24(2): 245–253. (in Chinese)

    Google Scholar 

  9. Li T H, Zheng L N. 2012. Soil erosion changes in the Yanhe watershed from 2001 to 2010 based on RUSLE model. Journal of Natural Resources, 27(7): 1164–1175.

    Google Scholar 

  10. Sharpley A N, Williams J R. 1990. EPIC-erosion/productivity impact calculator: I. Model documentation. II. User manual. Technical Bulletin-United States Department of Agriculture, 4(4): 206–207.

    Google Scholar 

  11. Wischmeier W H, Smith D D. 1978. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning. Hyattsville: USDA, Science and Education Administration, 57.

    Google Scholar 

  12. Zhao L, Yuan G L, Zhang Y, et al. 2007. The amount of soil erosion in Baoxiang Watershed of Dianchi Lake based on GIS and USLE. Bulletin of Soil and Water Conservation, 27(3): 42–46. (in Chinese)

    Google Scholar 

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Correspondence to Xiao’ai Dai.

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He, Q., Dai, X. & Chen, S. Assessing the effects of vegetation and precipitation on soil erosion in the Three-River Headwaters Region of the Qinghai-Tibet Plateau, China. J. Arid Land 12, 865–886 (2020). https://doi.org/10.1007/s40333-020-0075-9

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Keywords

  • soil erosion
  • vegetation cover
  • rainfall erosivity
  • Logarithmic Mean Divisia Index (LMDI)
  • quantitative assessment
  • Three-River Headwaters Region (TRHR)