Acta Geographica Sinica ›› 2018, Vol. 73 ›› Issue (9): 1674-1686.doi: 10.11821/dlxb201809005

• Earth Surface Process • Previous Articles     Next Articles

Quantitative attribution analysis of soil erosion in different morphological types of geomorphology in karst areas: Based on the geographical detector method

WANG Huan1,2(),GAO Jiangbo1(),HOU Wenjuan1   

  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:2017-10-10 Online:2018-09-25 Published:2018-09-19
  • Supported by:
    National Basic Research Program of China, No.2015CB452702;National Natural Science Foundation of China, No.41671098, No.41530749


The formation mechanism and influencing factors identification of soil erosion are the core and frontier issues of current research. However, studies on the multifactor synthesis are still insufficient. In this study, the simulation of soil erosion and its quantitative attribution analysis have been conducted in different morphological types of geomorphology in a typical karst basin based on the RUSLE model and the geographical detector 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 various morphological types of geomorphology. 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 that of 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-factor 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 a slope of 5°and dry land with slopes above 25°) or in the diverse land use types with the same slopes (such as dry land with a slope of 5° and forest with a slope of 5°), varied greatly. This indicates that prohibiting steep slope cultivation and the Grain for Green Project are reasonable measures to harness 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 and middle relief mountains. Therefore, the spatial heterogeneity of soil erosion and its influencing factors in different morphological types of geomorphology should be investigated to control karst soil loss more effectively.

Key words: soil erosion distribution, influencing factor, RUSLE model, geographical detector, Sancha River Basin