Evaluation of Landslide Susceptibility in Middle and Lower Reaches of Yangtze River—Taking Jiangxi Province as a Case Study

Tang Xinggang; Wang Huiyong; Huang Dou; Zhang Jinchi

doi:10.13961/j.cnki.stbctb.2021.03.023

您当前的位置：

首页 >

文章列表页 >

Evaluation of Landslide Susceptibility in Middle and Lower Reaches of Yangtze River—Taking Jiangxi Province as a Case Study

- Evaluation of Landslide Susceptibility in Middle and Lower Reaches of Yangtze River—Taking Jiangxi Province as a Case Study
- Bulletin of Soiland Water Conservation Vol. 41, Issue 3, Pages: 166-172(2021)
- 作者机构：
  
  1. 南京林业大学南方现代林业协同创新中心江苏省水土保持与生态修复重点实验室,江苏,南京,210037
  2. 南京林业大学林学院,江苏,南京,210037
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2021.03.023
  CLC： P642.22
- Published：2021
- 稿件说明：
移动端阅览
Tang Xinggang, Wang Huiyong, Huang Dou, et al. Evaluation of Landslide Susceptibility in Middle and Lower Reaches of Yangtze River—Taking Jiangxi Province as a Case Study[J]. Bulletin of Soiland Water Conservation, 2021, 41(3): 166-172.
DOI：

Tang Xinggang, Wang Huiyong, Huang Dou, et al. Evaluation of Landslide Susceptibility in Middle and Lower Reaches of Yangtze River—Taking Jiangxi Province as a Case Study[J]. Bulletin of Soiland Water Conservation, 2021, 41(3): 166-172. DOI： 10.13961/j.cnki.stbctb.2021.03.023.

摘要

[目的] 预测长江流域滑坡灾害分布情况，旨在开展隐患排查和危险评估，提升灾害的应对能力。[方法] 基于1 211个滑坡分布点数据和15个环境变量，通过MaxEnt模型对江西省的滑坡易发区分布进行预测，同时采用Jackknife检验评估15个环境变量对预测结果的重要程度，最终确定不同程度滑坡易发区的面积和分布以及影响滑坡灾害发生的主要环境变量。[结果] 江西省内极高、高和中易发区分别占全省总面积的29.6%，36.5%和23%。滑坡发生的概率以鄱阳湖平原为中心向四周呈现逐渐增加的趋势，集中分布在西部和南部的山地丘陵地带。海拔、坡度、植被归一化指数NDVI、年均降雨量和距离水系和道路的距离是影响滑坡发生和分布的主要环境变量，累积贡献率在83%以上。[结论] 江西省滑坡易发区的分布具有明显的空间差异性，主要分布在海拔较高、地质复杂和岩层节理裂隙发育的地区，降雨是滑坡发生的直接诱导因子。

Abstract

[Objective] The distribution predicting of landslide disasters in the Yangtze River basin were conducted to strengthen hidden danger investigation and risk assessment

and to improve disaster response capabilities. [Methods] Based on 1 211 landslide distribution points and 15 environmental variables

the MaxEnt model was used to predict the distribution of landslide-prone areas in Jiangxi Province. Then the Jackknife test was used to evaluate the importance of 15 environmental variables to the prediction results. Finally

the area and distribution of landslide prone areas with different risk levels and the main environmental variables affecting the occurrence of landslide disasters were determined. [Results] The extremely high

high

and medium landslide-prone areas in Jiangxi Province accounted for 29.6%

36.5%

and 23% of the total area of the province

respectively. The probability of landslide occurrence gradually increased from the Poyang Lake plain (as the center) to the surrounding areas

and was concentrated in the western and southern mountainous and hilly areas of Jiangxi Province. Altitude

slope

normalized difference vegetation index (NDVI)

average annual rainfall

and distance from rivers and roads were the main environmental variables affecting the occurrence and distribution of landslides

and the cumulative contribution rate was more than 83%. [Conclusion] The distribution of landslide-prone areas in Jiangxi Province has obvious spatial differences

mainly associated with areas of high altitude

complex geology

and developed joints and cracks in rock formations. Rainfall is the direct inducing factor for landslide occurrence.

关键词

Keywords

references

赵冬梅,角媛梅,邱应美,等.基于MaxEnt模型的哈尼梯田核心区滑坡易发性评价[J].水土保持研究,2020,27(4):392-399.

李晴.基于统计学的川渝地区滑坡影响因子及其危险性评价[D].北京:中国地质大学,2019.

张娇艳,李扬,王玥彤,等.未来气候变化对西南地区地质灾害的可能影响[J].高原山地气象研究,2020,40(2):70-77.

周莉,兰明才,蔡荣辉,等.21世纪前期长江中下游流域极端降水预估及不确定性分析[J].气象学报,2018,76(1):47-61.

李沁汶,王玉宽,徐佩,等.未来气候变化情景下横断山北部灾害易发区极端降水时空特征[J].山地学报,2019,37(3):400-408.

Allan R P, Soden B J. Atmospheric Warming and the Amplification of Precipitation Extremes[J]. Science, 2008,321(5895):1123-1138.

陈活泼. CMIP5模式对21世纪末中国极端降水事件变化的预估[J].科学通报,2013,58(8):743-752.

张天宇,范莉,程炳岩,等.1961-2007年三峡库区极端降水指数R5 d的变化规律及其未来情景预估[J].中国农业气象,2010,31(2):176-182.

熊伟,居辉,许吟隆,等.气候变化对中国农业温度阈值影响研究及其不确定性分析[J].地球科学进展,2006,21(1):70-76.

李博,周天军.基于IPCC A1 B情景的中国未来气候变化预估:多模式集合结果及其不确定性[J].气候变化研究进展,2010,6(4):270-276.

高宁,高彩云.基于时序AR补偿RBF模型的滑坡位移预测[J].人民长江,2020,51(3):119-122.

王念秦,郭有金,刘铁铭,等.基于支持向量机模型的滑坡危险性评价[J].科学技术与工程,2019,19(35):70-78.

何原荣,傅文杰.模糊支持向量机在滑坡危险性评价中的应用[J].自然灾害学报,2009,18(5):107-112.

赵晓萌,卫星君,王娜,等.降雨型滑坡灾害的特征聚合决策树预测模型[J].灾害学,2020,35(1):27-31.

Dickson M E, Perry G. Identifying the controls on coastal cliff landslides using machine-learning approaches[J]. Environmental Modelling and Software, 2016,76(4):117-127.

张路. MAXENT最大熵模型在预测物种潜在分布范围方面的应用[J].生物学通报,2015,50(11):9-12.

朱耿平,乔慧捷. MaxEnt模型复杂度对物种潜在分布区预测的影响[J].生物多样性,2016,24(10):1189-1196.

Elith J, Phillips S J, Hastie T, et al. A statistical explanation of MaxEnt for ecologists[J]. Diversity and Distributions, 2011,17(1):43-57.

Felicísimo Á, Cuartero A, Remondo J, et al. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods:A comparative study[J]. Landslides, 2013,10(2):175-189.

Fell R, Corominas J, Bonnard C, et al. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning[J]. Engineering Geology, 2008,102(3):99-111.

张福浩,朱月月,赵习枝,等.地理因子支持下的滑坡隐患点空间分布特征及识别研究[J].武汉大学学报(信息科学版),2020,45(8):1233-1244.

王国峥,耿其芳,肖孟阳,等.基于4种生态位模型的金钱松潜在适生区预测[J].生态学报,2020,40(17):6096-6104.

孔维尧,李欣海,邹红菲.最大熵模型在物种分布预测中的优化[J].应用生态学报,2019,30(6):2116-2128.

朱耿平,原雪姣,范靖宇,等. MaxEnt模型参数设置对其所模拟物种地理分布和生态位的影响:以茶翅蝽为例[J].生物安全学报,2018,27(2):118-123.

蔡波,黄勇,陈跃英,等.中国蛇类物种丰富度地理格局及其与生态因子的关系[J].动物学研究,2012,33(4):343-353.

贾翔,王超,金慧,等.基于优化的MaxEnt模型评价红松适宜分布区[J].生态学杂志,2019,38(8):2570-2576.

Phillips S J, Dudik M, Elith J, et al. Sample selection bias and presence-only distribution models:implications for background and pseudo-absence data[J]. Ecological Applications, 2009,19(1):181-197.

吴中海,周春景,谭成轩,等.长江经济带地区活动构造与区域地壳稳定性基本特征[J].地质力学学报,2016,22(3):379-411.

徐杰,马宗晋,陈国光,等.中国大陆东部新构造期北西向断裂带的初步探讨[J].地学前缘,2003,10(S1):193-198.

Kornejady A, Ownegh M, Bahremand A. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods[J]. Catena, 2017,152(1):112-125.

孙艳萍,张苏平,陈文凯,等.汶川地震滑坡危险性评价:以武都区和文县为例[J].地震工程学报,2018,40(5):1084-1091.

张福浩,朱月月,赵习枝,等.地理因子支持下的滑坡隐患点空间分布特征及识别研究[J].武汉大学学报(信息科学版),2020,45(8):1233-1244.

吴常润,赵冬梅,刘澄静,等.基于GIS的华宁县滑坡灾害影响因子分析及易发性评价[J].水土保持研究,2019,26(6):212-218.

成永刚,程谦恭.斜坡海拔高度对滑坡贡献率的研究[J].路基工程,2014,32(5):57-60.

倪树斌,马超,杨海龙,等.北京山区崩塌、滑坡、泥石流灾害空间分布及其敏感性分析[J].北京林业大学学报,2018,40(6):81-91.

杨坪坪,李瑞,盘礼东,等.地表粗糙度及植被盖度对坡面流曼宁阻力系数的影响[J].农业工程学报,2020,36(6):106-114.

郭京平.河流侵蚀对滑坡稳定性影响研究[J].甘肃水利水电技术,2019,55(11):38-41.

张娇艳,李扬,王玥彤,等.未来气候变化对西南地区地质灾害的可能影响[J].高原山地气象研究,2020,40(2):70-77.

魏丽,单九生,朱星球.植被覆盖对暴雨型滑坡影响的初步分析[J].气象与减灾研究,2006,29(1):29-33.

刘凤丽,黄国兵.旱涝急转形成机理及城市防涝减灾体系研究进展[J].长江科学院院报,2012,29(11):28-32.

Views

831

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Methods of Determining Rainfall Thresholds of Landslides and Debris Flows in Typical Region in Sichuan Province

Influence of Different Environmental Factor Connection Methods on Benggang Susceptibility Assessment

Achievement of Mass Monitoring and Preventing on Landslide and Debris Flow in Experimental Counties of South Gansu and Shaanxi Provinces

Research status and hotspots of plant diversity in desertified areas of China based on CiteSpace

Changes and its driving factors of ecological environment quality in Yellow River Source Park from 2000 to 2024

Related Author

LIU Haizhi

MA Zhenfeng

FAN Guangzhou

Guo Fei

Lai Peng

Chen Yang

Wang Xiujuan

Xia Dong

Related Institution

National Meteorological Centre

Sichuan Climat Center

Atmospheric Sciences College, Chengdu University of Information Technology

National Field Observation and Research Station of Landslides in Three Gorges Reservoir Area of Yangtze River

College of Civil Engineering & Architecture, China Three Gorges University

⁰