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Quantitative attribution analysis of soil erosion in different geomorphological types in karst areas: Based on the geodetector method

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Abstract

The formation mechanism and influencing factors identification of soil erosion are the core and frontier issues of current research. However, studies on the multi-factor synthesis are still relatively lacked. In this study, the simulation of soil erosion and its quantitative attribution analysis have been conducted in different geomorphological types in a typical karst basin based on the RUSLE model and the geodetector method. The influencing factors, such as land use type, slope, rainfall, elevation, lithology and vegetation cover, have been taken into consideration. Results show that the strength of association between the six influencing factors and soil erosion was notably different in diverse geomorphological types. Land use type and slope were the dominant factors of soil erosion in the Sancha River Basin, especially for land use type whose power of determinant (q value) for soil erosion was much higher than other factors. The q value of slope declined with the increase of relief in mountainous areas, namely it was ranked as follows: middle elevation hill> small relief mountain> middle relief mountain. Multi-factors interactions were proven to significantly strengthen soil erosion, particularly for the combination of land use type with slope, which can explain 70% of soil erosion distribution. It can be found that soil erosion in the same land use type with different slopes (such as dry land with slopes of 5° and above 25°) or in the diverse land use types with the same slope (such as dry land and forest with a slope of 5°), varied much. These indicate that prohibiting steep slope cultivation and Grain for Green Project are reasonable measures to control soil erosion in karst areas. Based on statistics of soil erosion difference between diverse stratifications of each influencing factor, results of risk detector suggest that the amount of stratification combinations with significant difference accounted for 55% at least in small relief mountain and middle relief mountainous areas. Therefore, the spatial heterogeneity of soil erosion and its influencing factors in different geomorphological types should be investigated to control karst soil loss more effectively.

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References

  • Arnoldus H M J, 1980. An approximation of the rainfall factor in the universal soil loss equation. In: De Boodt M, Gabriels D. Assessment of Erosion. Chichester UK: Wiley, 127–132.

    Google Scholar 

  • Bai X, Zhang X, Long Y et al., 2013. Use of 137Cs and 210Pbex measurements on deposits in a karst depression to study the erosional response of a small karst catchment in southwest China to land-use change. Hydrological Processes, 27(6): 822–829.

    Article  Google Scholar 

  • Cai Y, Wan J, Wang Y et al., 2015. Study on Land Change in Guizhou Karst Plateau Mountain Area. Beijing: Science Press, 61–62. (in Chinese)

    Google Scholar 

  • Chen S, Yang X, Xiao L et al., 2014. Study of soil erosion in the southern hillside area of China based on RUSLE model. Resources Science, 36(6): 1288–1297. (in Chinese)

    Google Scholar 

  • Dai Q, Peng X, Wang P et al., 2018. Surface erosion and underground leakage of yellow soil on slopes in karst regions of southwest China. Land Degradation & Development, 29(8): 2438–2448.

    Article  Google Scholar 

  • Febles-Gonzalez J M, Vega-Carreno M B, Tolon-Becerra A et al., 2012. Assessment of soil erosion in karst regions of Havana, Cuba. Land Degradation & Development, 23(5): 465–474.

    Article  Google Scholar 

  • Feng T, Chen H, Polyakov V O et al., 2016. Soil erosion rates in two karst peak-cluster depression basins of northwest Guangxi, China: Comparison of the RUSLE model with 137Cs measurements. Geomorphology, 253: 217–224.

    Article  Google Scholar 

  • Gutierrez F, Parise M, De Waele J et al., 2014. A review on natural and human-induced geohazards and impacts in karst. Earth-Science Reviews, 138: 61–88.

    Article  Google Scholar 

  • Hou W, Gao J, Peng T et al., 2016. Review of ecosystem vulnerability studies in the karst region of southwest China based on a structure-function-habitat framework. Progress in Geography, 35(3): 320–330. (in Chinese)

    Article  Google Scholar 

  • Hu Y, Wang J, Li X et al., 2011. Geographical detector-based risk assessment of the under-five mortality in the 2008 Wenchuan earthquake, China. Plos One, 6(6): e21427.

    Article  Google Scholar 

  • Hutchinson M F, 2002. Anusplin Version 4.2 User Guide. Centre for resource and environment studies. Canberra: Austrilian National University.

    Google Scholar 

  • Li J, Lu D, Xu C et al., 2017. Spatial heterogeneity and its changes of population on the two sides of Hu Line. Acta Geographica Sinica, 72(1): 148–160. (in Chinese)

    Google Scholar 

  • Li Z, Cao W, Liu B et al., 2008. Current status and developing trend of soil erosion in China. Science of Soil & Water Conservation, 6(1): 57–62. (in Chinese)

    Google Scholar 

  • Luo W, Jasiewicz J, Stepinski T et al., 2016. Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters, 43(2): 692–700.

    Article  Google Scholar 

  • McCool D K, Brown L C, Foster G R et al., 1987. Revised slope steepness factor for the universal soil loss equation. Transactions of the Asae, 30(5): 1387–1396.

    Article  Google Scholar 

  • McCool D K, Foster G R, Mutchler C K et al., 1989. Revised slope length factor for the universal soil loss equation. Transactions of the ASAE, 32(5): 1571–1576.

    Article  Google Scholar 

  • Peng T, Wang S, 2012. Effects of land use, land cover and rainfall regimes on the surface runoff and soil loss on karst slopes in southwest China. Catena, 90(1): 53–62.

    Article  Google Scholar 

  • Peng X, Dai Q, Li C et al., 2018. Role of underground fissure flow in near-surface rainfall-runoff process on a rock mantled slope in the karst rocky desertification area. Engineering Geology, 243: 10–17.

    Article  Google Scholar 

  • Renard K G, Foster G R, Weesies G A et al., 1997. Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agriculture handbook. USDA, Washington, DC.

    Google Scholar 

  • Tong L, Xu X, Fu Y et al., 2014. Impact of environmental factors on snail distribution using geographical detector model. Progress in Geography, 33(5): 625–635. (in Chinese)

    Google Scholar 

  • Wang J, Li X, Christakos G et al., 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 24(1): 107–127.

    Article  Google Scholar 

  • Wang J, Xu C, 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116–134. (in Chinese)

    Google Scholar 

  • Wang S, Liu Q, Zhang D, 2004. Karst rocky desertification in southwestern China: Geomorphology, landuse, impact and rehabilitation. Land Degradation & Development, 15(2): 115–121.

    Article  Google Scholar 

  • Wang Y, Cai Y, Pan M, 2013. Analysis on the relationship between soil erosion and land use in Wujiang River Basin in Guizhou province. Research of Soil and Water Conservation, 20(3): 11–18. (in Chinese)

    Google Scholar 

  • Williams J, Jones C A, Kiniry J R et al., 1989. The EPIC crop growth-model. Transactions of the ASAE, 32(2): 497–511.

    Article  Google Scholar 

  • Xiong K, Li J, Long M, 2012. Features of soil and water loss and key issues in demonstration areas for combating karst rocky desertification. Acta Geographica Sinica, 67(7): 878–888. (in Chinese)

    Google Scholar 

  • Xu Y, Shao X, 2006. Estimation of soil erosion supported by GIS and RUSLE: A case study of Maotiaohe watershed, Guizhou province. Journal of Beijing Forestry University, 28(4): 67–71. (in Chinese)

    Google Scholar 

  • Yan Y, Dai Q, Yuan Y et al., 2018. Effects of rainfall intensity on runoff and sediment yields on bare slopes in a karst area, SW China. Geoderma, 330: 30–40.

    Article  Google Scholar 

  • Zeng C, Wang S, Bai X et al., 2017. Soil erosion evolution and spatial correlation analysis in a typical karst geomorphology using RUSLE with GIS. Solid Earth, 8(4): 1–26.

    Article  Google Scholar 

  • Zhan D, Zhang W, Yu J et al., 2015. Analysis of influencing mechanism of residents’ livability satisfaction in Beijing using geographical detector. Progress in Geography, 34(8): 966–975. (in Chinese)

    Article  Google Scholar 

  • Zhang H, Yang Q, Li R et al., 2013a. Extension of a GIS procedure for calculating the RUSLE equation LS factor. Computers & Geosciences, 52: 177–188.

    Article  Google Scholar 

  • Zhang X, Wang S, Bai X et al., 2013b. Relationships between the spatial distribution of karst land desertification and geomorphology, lithology, precipitation, and population density in Guizhou province. Earth & Environment, 41(1): 1–6. (in Chinese)

    Google Scholar 

  • Zhang X, Wang S, He X et al., 2007. Soil creeping in weathering crusts of carbonate rocks and underground soil losses on karst slopes. Earth & Environment, 35(3): 202–206. (in Chinese)

    Google Scholar 

  • Zhou C, Cheng W, Qian J et al., 2009. Research on the classification system of digital land geomorphology of 1:1000000 in China. Journal of Geo-information Science, 11(6): 707–724. (in Chinese)

    Article  Google Scholar 

Download references

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Correspondence to Jiangbo Gao.

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Foundation: National Basic Research Program of China, No.2015CB452702; National Natural Science Foundation of China, No.41671098, No.41530749

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Wang, H., Gao, J. & Hou, W. Quantitative attribution analysis of soil erosion in different geomorphological types in karst areas: Based on the geodetector method. J. Geogr. Sci. 29, 271–286 (2019). https://doi.org/10.1007/s11442-019-1596-z

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  • DOI: https://doi.org/10.1007/s11442-019-1596-z

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