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地理学报    2018, Vol. 73 Issue (9): 1674-1686     DOI: 10.11821/dlxb201809005
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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
王欢1,2(),高江波1(),侯文娟1
1. 中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室,北京 100101
2. 中国科学院大学,北京 100049
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
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摘要 

土壤侵蚀形成机制与影响因素识别是当前研究的核心与前沿议题,然而从多因素综合作用的角度进行定量归因仍需加强。以喀斯特典型峰丛洼地流域为研究区,基于GIS手段和RUSLE模型模拟土壤侵蚀,综合土地利用、坡度、降雨、高程、岩性、植被覆盖度等影响因子,应用地理探测器方法针对喀斯特不同地貌形态类型区进行土壤侵蚀的定量归因研究。结果表明,各影响因子对土壤侵蚀的解释力及因子间耦合作用程度在不同地貌形态类型区差异显著,其中土地利用和坡度是决定土壤侵蚀空间异质的主导因子,但在山地丘陵区,随着地形起伏度的升高,坡度的控制作用下降,即地理探测器q值表现为中海拔丘陵>小起伏中山>中起伏中山;生态探测器显示土地利用对土壤侵蚀的影响相比于其他因子有显著差异;双因子交互作用有助于增强对土壤侵蚀的解释力,土地利用与坡度的协同作用对土壤侵蚀的解释力达到70%以上;对于土壤侵蚀空间分布的差异性检验,风险探测器显示在小起伏中山、中起伏中山等地貌形态类型中,具有显著差异的影响因子分层组合数占比至少55%。因而,喀斯特地区土壤侵蚀的治理应综合考虑不同地貌形态类型区土壤侵蚀影响机制的空间异质性。

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王欢
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关键词 土壤侵蚀定量归因RUSLE模型地理探测器三岔河流域 
Abstract

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 wordssoil erosion distribution    influencing factor    RUSLE model    geographical detector    Sancha River Basin
收稿日期: 2017-10-10      出版日期: 2018-09-19
基金资助:国家重点基础研究发展计划(2015CB452702);国家自然科学基金项目(41671098, 41530749)
引用本文:   
王欢, 高江波, 侯文娟 . 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.
WANG Huan, GAO Jiangbo, HOU Wenjuan . 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.
链接本文:  
http://www.geog.com.cn/CN/10.11821/dlxb201809005      或      http://www.geog.com.cn/CN/Y2018/V73/I9/1674
Fig. 1  研究区地理位置
Fig. 2  三岔河流域地貌形态类型及土壤侵蚀影响因子(岩性、土地利用类型、坡度、降雨、植被覆盖度)
土地利用类型 水田 旱地 有林地 疏林地 灌木林 草地 水域 建设用地 裸岩
C 0.1 0.22 0.006 0.01 0.01 0.04 0 0 0
P 0.15 0.4 1 1 1 1 0 0 0
Tab. 1  C值和P值的赋值标准
判据 交互作用
q(X1∩X2) < Min(q(X1), q(X2)) 非线性减弱
Min(q(X1), q(X2))< q(X1∩X2) < Max(q(X1), q(X2)) 单因子非线性减弱
q(X1∩X2) > Max(q(X1), q(X2)) 双因子增强
q(X1∩X2) = q(X1)+ q(X2) 独立
q(X1∩X2) > q(X1)+ q(X2) 非线性增强
Tab. 2  自变量对因变量的交互作用方式
Fig. 3  2010年三岔河流域土壤侵蚀空间分布
土壤侵蚀量
(t ha-1 a-1
坡度
(°)
降雨
(mm)
海拔
(m)
耕地面积
(km2)
陡坡耕地面积
(km2)
区域总面积
(km2)
中海拔平原 8.32 4.80 1233.82 1256.51 17.54 0.06 98.30
中海拔台地 6.49 3.97 1172.98 1445.74 19.92 0.02 99.64
中海拔丘陵 12.62 11.00 1199.28 1383.46 159.60 3.41 717.72
小起伏中山 11.87 14.91 1136.88 1482.15 656.15 51.67 3016.14
中起伏中山 10.22 16.34 1096.33 1775.38 189.62 23.70 928.89
Tab. 3  不同地貌形态类型土壤侵蚀量及地理环境因子统计
土地利用类型 坡度 降雨 岩性 植被覆盖度 海拔
中海拔平原 0.622 0.082 0.091 - - 0.028
中海拔台地 0.685 0.086 0.037 - - -
中海拔丘陵 0.513 0.126 0.010 0.031 0.059 0.010
小起伏中山 0.567 0.071 0.051 0.01 0.005 0.013
中起伏中山 0.620 0.062 0.089 0.037 0.005 0.193
Tab. 4  不同地貌形态类型影响因子q值统计
地貌形态类型 中海拔平原 中海拔台地 中海拔丘陵 小起伏中山 中起伏中山
主导交互作用1 土地利用类
型∩降雨
土地利用类
型∩坡度
土地利用类
型∩坡度
土地利用类
型∩坡度
土地利用类
型∩坡度
q 0.710 0.764 0.726 0.707 0.742
主导交互作用2 土地利用类
型∩植被覆盖度
土地利用类
型∩植被覆盖度
土地利用类
型∩海拔
土地利用类
型∩降雨
土地利用∩降雨
q 0.695 0.720 0.567 0.648 0.679
主导交互作用3 土地利用类
型∩岩性
土地利用类
型∩降雨
土地利用类
型∩降雨
土地利用类
型∩岩性
土地利用∩海拔
q 0.682 0.708 0.566 0.586 0.665
Tab. 5  不同地貌形态类型区土壤侵蚀影响因子交互作用探测
中海拔平原 中海拔台地 中海拔丘陵 小起伏中山 中起伏中山
坡度(°) 20~25 15~20 30~35 >35 25~30
平均值 19.9 19.02 24.38 22.28 14.41
土地利用类型 旱地 旱地 旱地 旱地 旱地
平均值 22.6 15.36 23.43 24.36 21.9
植被覆盖度 0.5~0.6 < 0.3 0.8~0.9 0.9~1 0.5~0.6
平均值 9.35 10.02 14.5 14.17 10.7
岩性 灰岩与碎屑岩互层 灰岩夹层 碎屑岩 白云岩夹层 白云岩
平均值 13.93 9.49 19.52 15.59 20.07
海拔(m) 1087~1235 1531~1679 1383~1531 1383~1531 1235~1383
平均值 11.1 13.13 12.15 12.44 15.84
Tab. 6  不同地貌形态类型土壤侵蚀高风险区域及其平均值(t ha-1 a-1)
中海拔平原 中海拔台地 中海拔丘陵 小起伏中山 中起伏中山
土地利用 90.91 100.00 92.86 100.00 93.33
坡度 40.00 16.67 78.57 92.86 85.71
降雨 100.00 33.33 33.33 91.67 78.57
海拔 100.00 0.00 42.86 69.44 75.00
岩性 0.00 0.00 60.00 64.44 55.56
植被覆盖度 33.33 35.71 75.00 60.71 60.71
Tab. 7  各影响因子中有显著差异的分层组合数的百分比(%)
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