Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (2): 271-286.doi: 10.1007/s11442-019-1596-z

• Research Articles • Previous Articles     Next Articles

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

Huan WANG1,2(), Jiangbo GAO1,*(), Wenjuan HOU1   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-08-19 Accepted:2018-10-20 Online:2019-02-25 Published:2019-03-20
  • Contact: Jiangbo GAO E-mail:wangh.16s@igsnrr.ac.cn;gaojiangbo@igsnrr.ac.cn
  • About author:

    Author: Huan Wang (1993-), Master Candidate, specialized in the research of karst ecosystem services. E-mail: wangh.16s@igsnrr.ac.cn

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

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.

Key words: soil erosion distribution, influencing factors, RUSLE model, geodetector, karst, Sancha River basin