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. 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
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.
. 基于地理探测器的喀斯特不同地貌形态类型区土壤侵蚀定量归因[J]. 地理学报,
2018, 73(9): 1674-1686.
. Quantitative attribution analysis of soil erosion in different morphological types of geomorphology in karst areas: Based on the geographical detector method[J]. Acta Geographica Sinica,
2018, 73(9): 1674-1686.
ZhangXinbao, WangShijie, BaiXiaoyong, et al.Relationships between the spatial distribution of karst land desertification and geomorphology, lithology, precipitation, and population density in Guizhou Province. , 2013, 41(1): 1-6.
Bai XY, Zhang XB, LongY, et al.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. , 2013, 27(6): 822-829.http://doi.wiley.com/10.1002/hyp.v27.6
FengT, Chen HS, Polyakov VO, et al.Soil erosion rates in two karst peak-cluster depression basins of northwest Guangxi, China: Comparison of the RUSLE model with 137Cs measurements. , 2016, 253: 217-224.https://linkinghub.elsevier.com/retrieve/pii/S0169555X15301781
PengT, Wang SJ.Effects of land use, land cover and rainfall regimes on the surface runoff and soil loss on karst slopes in southwest China. , 2012, 90: 53-62.http://linkinghub.elsevier.com/retrieve/pii/S0341816211001937
HuY, Wang JF, Li XH, et al.Geographical detector-based risk assessment of the under-five mortality in the 2008 Wenchuan Earthquake, China. , 2011, 6(6): e21427.http://dx.plos.org/10.1371/journal.pone.0021427
ZengC, Wang SJ, Bai XY, et al.Soil erosion evolution and spatial correlation analysis in a typical karst geomorphology using RUSLE with GIS. , 2017, 8(4): 721-736.https://www.solid-earth.net/8/721/2017/
Renard KG, Foster GR, Weesies GA, et al.Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation (RUSLE). , 1997.
Arnoldus H MJ. An approximation of the rainfall factor in the universal soil loss equation//De Boodt M, Gabriels D. Assessment of Erosion. , 1980: 127-132.http://www.cabdirect.org/abstracts/19831974087.html
The SIGMA 12p2/p-index, in which p is monthly rainfall and P is annual rainfall, provides a rapid way of approximating the rainfall factor value, although a minimum number of stations still need to be treated in greater detail.
Williams, JR, Jones, CA, Kiniry J R, et al. The EPIC crop growth-model. , 1989, 32: 497-511.http://elibrary.asabe.org/abstract.asp??JID=3&AID=31032&CID=t1989&v=32&i=2&T=1
ABSTRACT The EPIC plant growth model was developed to estimate soil productivity as affected by erosion throughout the U.S. Since soil productivity is expressed in terms of crop yield, the model must be capable of simulating crop yields realistically for soils with a wide range of erosion damage. Also, simulation of many crops is required because of the wide variety grown in the U.S. EPIC simulates all crops with one crop growth model using unique parameter values for each crop. The processes simualted include leaf interception of solar radiation; conversion to biomass; division of biomass into roots, above ground mass, and economic yield; root growth; water use; and nutrient uptake. The model has been tested throughout the U.S. and in several foreign countries.
McCool DK, Brown LC, Foster GR, et al. Revised slope steepness factor for the universal soil loss equation. , 1987, 30(5): 1387-1396.http://elibrary.asabe.org/abstract.asp??JID=3&AID=30576&CID=t1987&v=30&i=5&T=1
ABSTRACT Areanalysis of historical and recent data from both natural and simulated rainfall soil erosion plots has resulted in new slope steepness relationships for the Universal Soil Loss Equation. For long slopes on which both interrill and rill erosion occur, the relationships consist of two linear segments with a breakpoint at 9% slope. These relationships predict less erosion than current relationships on slopes steeper than 9% and slopes flatter than about 1%. A separate equation is proposed for the slope effect on short slopes where only interrill erosion is present. For conditions where surface flow over thaw-weakened soil dominates the erosion process, two relationships with a breakpoint at 9% slope are presented.
McCool DK, Foster GR, Mutchler CK, et al. Revised slope length factor for the universal soil loss equation. , 1989, 32: 1571-1576.http://elibrary.asabe.org/abstract.asp??JID=3&AID=31192&CID=t1989&v=32&i=5&T=1
ABSTRACT An analysis based on theoretical considerations and data interpretation was used in developing revised relationships for the slope length exponent for the Universal Soil Loss Equation. The analysis was based on the ratio of rill to interrill erosion and resulted in a general relationship between the slope length exponent and slope steepness. Parameters in the relationship are changed depending upon whether the ratio of rill to interrill erosion is expected to be low, moderate or high. Such conditions might be representative of rangeland or no-till seeding, normal seedbed conditions or highly disturbed conditions, respectively. For thawing soil conditions where rill erosion is dominant, an exponent value of 0.5 is recommended..
Zhang HM, Yang QK, LiR, et al.Extension of a GIS procedure for calculating the RUSLE equation LS factor. , 2013, 52: 177-188.http://dl.acm.org/citation.cfm?id=2432855
78 We presented an improved algorithm that can automatically calculate LS factors. 78 Using slope steepness and channel networks breaks in calculating slope length. 78 Calculate L factor under consideration of convergence flow. 78 Our method corresponds more closely with the reality of the example catchment. 78 We developed LS-TOOL application using C73 with a user-friendly interface.
Wang JF, Li XH, ChristakosG, et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. , 2010, 24(1): 107-127.http://www.tandfonline.com/doi/abs/10.1080/13658810802443457
Febles-Gonzalez JM, Vega-Carreno MB, Tolon-BecerraA, et al. Assessment of soil erosion in karst regions of Havana, Cuba. , 2012, 23(5): 465-474.http://onlinelibrary.wiley.com/doi/10.1002/ldr.1089/pdf
ABSTRACT Only recently have erosion models begun to be used in research work in Cuba, specifically the USLE and the thematic cartography of factors in a GIS framework without using a specific model. It therefore becomes necessary to include simulation models for karst regions that make possible an integral assessment of the specific types of soil erosion in those environments and take into consideration the effects of climate change in soil management systems. Morphometric analysis of karst doline absorption forms in regions of La Habana Province in 1986, 1997, and 2009 allowed the characterisation and application of the Morgan Morgan Finney (MMF) conceptual empirical erosion model in the Country for the first time. The results showed previously unreported losses of 12·3–13·765t of soil ha65 611 65y 611 , which surpasses the permissible erosion threshold. Furthermore, it clearly shows the unsustainable trend of Red Ferralitic and Ferrasol Rhodic (World Reference Base) soils use. The model applied considered the effects of extreme rainfall events associated with climate change in recent years. The results found have led to strategies for coping with future climate change in each scenario and have made it possible to evaluate the consequences. Copyright 08 2011 John Wiley & Sons, Ltd.