[1]王鑫盈a,马 超a,b,等.浅层黄土滑坡易发性评价:以晋西黄土区蔡家川农地小流域为例[J].山地学报,2023,(6):904-915.[doi:10.16089/j.cnki.1008-2786.000796]
　WANG Xinyinga,MA Chaoa,b,et al.Risk Assessment of Shallow Loess Landslides: Taking a Small Watershed of Caijiachuan Farmland in the Loess Region of Western Shanxi of China as an Example[J].Mountain Research,2023,(6):904-915.[doi:10.16089/j.cnki.1008-2786.000796]
点击复制

浅层黄土滑坡易发性评价:以晋西黄土区蔡家川农地小流域为例
分享到：

《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2023年第6期
页码:
904-915
栏目:
山地灾害
出版日期:
2024-02-05

文章信息/Info

Title:
Risk Assessment of Shallow Loess Landslides: Taking a Small Watershed of Caijiachuan Farmland in the Loess Region of Western Shanxi of China as an Example
文章编号:
1008-2786-(2023)6-904-12
作者:
王鑫盈a; 马 超a; b; 张 岩a; b*
(北京林业大学 a.水土保持学院,北京100083; b.山西吉县森林生态系统国家野外科学观测研究站,山西 临汾042200)
Author(s):
WANG Xinyinga;  MA Chaoa; b;  ZHANG Yana; b*
(a. School of Soil and Water Conservation, Beijing 100083; b. Shanxi Ji County Station of Chinese National Ecosystem Research Network, Linfen 042200, Shanxi, Beijing Forestry University, China)
关键词:
极端降雨;  浅层黄土滑坡;  易发性分区;  黄土高原
Keywords:
extreme rainstorm;  shallow loess landslide;  susceptibility zoning;  Loess Plateau
分类号:
P642.22
DOI:
10.16089/j.cnki.1008-2786.000796
文献标志码:
A
摘要:
黄土高原中部地区极端暴雨事件频发,引发大面积浅层滑坡和泥流灾害。随着全球变暖,降雨增加,中国西北黄土高原植被覆盖发生显著变化,不考虑植被因素的黄土滑坡易发性分区的评价方法需要改进。本文以晋西黄土区蔡家川流域农地小流域为研究对象,基于暴雨前后流域高分辨率图像、数字高程模型,野外滑坡调查和室内岩土测试,利用半定量的信息量模型、信息量-逻辑回归耦合模型和定量的物理模型,按有植被和无植被两种工况开展了浅层黄土滑坡易发性分区,并评估模型精度。结果表明:考虑植被时,半定量模型获取的易发性指数均下降,物理模型计算的稳定区面积显著增大,说明植被对浅层滑坡有抑制作用; 考虑植被时,各个模型的评价精度都有所提高,信息量-逻辑回归耦合模型的精度高于信息量模型,物理模型的精度整体高于两个半定量模型。研究结果可为以暴雨滑坡为主要类型的小流域水土流失预测预报提供参考。
Abstract:
Extreme heavy rainfall events are frequent in the central Loess Plateau region, triggering extensive shallow loess landslides and loess mudflow. With global warming and increased rainfall in Loess Plateau, vegetation cover in Northwest China has changed significantly, and the evaluation method of loess landslide susceptibility zoning without considering the vegetation needs to be improved.
In this paper, it took a small watershed of Caijiachuan farmland in the loess region of western Shanxi of China for a case study. After comparison of post-rainstorm high-resolution images of the watershed and pre-rainstorm ones, digital elevation model construction, field landslide investigation and indoor geotechnical testing, shallow loess landslide susceptibility zoning was completed for the Caijiachuan watershed in terms of two working cinnerio, vegetated or unvegetated loess slopes. Semi-quantitative informativeness model, informativeness-logistic regression coupling model, and quantitative physical model were utilized in the evaluation and then the model accuracy was evaluated by ROC curves and the F1 proxy respectively.
It finds that when vegetation was included in simulation, the vulnerability index obtained by the semi-quantitative model decreased, and the area of stable zone calculated by physical model increased significantly, indicating that vegetation had an restricted effect on shallow landslides. The evaluation accuracy of each model improved in the presence of vegetation, with the coupled informative-logistic regression model having a higher accuracy than the informative model, and the physical model having an overall higher accuracy than the two semi-quantitative models. The accuracies of the two semi-quantitative models with vegetation consideration are better than those without vegetation involved.
The results of this work can be supportive for rainfall-induced landslides prediction in vegetated landscape, Loess Plateau, China.

参考文献/References:

[1] WANG Genlong, LI Tonglu, XING Xianli, et al. Research on loess flow-slides induced by rainfall in July 2013 in Yan'an, NW China[J]. Environmental Earth Sciences, 2014, 73(12): 7933-7944. DOI: 10.1007/s12665-014-3951-9
[2] 韩勇, 郑粉莉, 徐锡蒙, 等.子午岭林区浅层滑坡侵蚀与植被的关系——以富县“7·21”特大暴雨为例[J]. 生态学报, 2016, 36(15): 4635-4643. [HAN Yong, ZHENG Fenli, XU Ximeng, et al. Relationship between shallow landslide erosion and vegetation in the Ziwuling forest area: A case study of the “7·21” disaster in Fuxiancounty[J]. Acta Ecologica Sinica, 2016, 36(15): 4635-4643] DOI: 10.5846/stxb201501140117
[3] DENG Jiayong, MA Chao, ZHANG Yan. Shallow landslide characteristics and its response to vegetation by example of July 2013, extreme rainstorm, Central Loess Plateau, China[J]. Bulletin of Engineering Geology and the Environment, 2022, 81(3): 100.DOI: 10.1007/s10064-022-02606-1
[4] LI Muyang, MA Chao, DU Cui, et al. Landslide response to vegetation by example of July 25-26, 2013, extreme rainstorm, Tianshui, Gansu province, China[J]. Bulletin of Engineering Geology and the Environment, 2021, 80(1): 751-764.DOI: 10.1007/s10064-020-02000-9
[5] 杨萌, 宋晓鹏, 张岩, 等. 黄土高原丘一区典型流域坡耕地分布及其侵蚀地形特征[J]. 中国水土保持科学, 2020, 18(6): 1-8. [YANG Meng, SONG Xiaopeng, ZHANG Yan, et al. Distribution of sloping cropland and correlative erosional landform in typical watersheds on the hilly Loess Plateau[J].Science of Soil and Water Conservation, 2020, 18(6): 1-8] DOI: 10.16843/j.sswc.2020.06.001
[6] 郭富赟, 孟兴民, 黎志恒, 等. 天水市“7·25”群发性地质灾害特征及成因[J]. 山地学报, 2015, 33(1): 100-107. [GUO Fuyun, MENG Xinmin, LI Zhiheng, et al. Characteristics and causes of assembled geo-hazards induced by the rainstorm on 25th July 2013 in Tianshui city, Gansu, China[J]. Mountain Research, 2015, 33(1): 100-107] DOI: 10.16089/j.cnki.1008-2786.000014
[7] 彭建兵, 王启耀, 门玉明, 等. 黄土高原滑坡灾害[M]. 北京: 科学出版社, 2019: 26-48. [PENG Jianbing, WANG Qiyao, MEN Yuming, et al. Loess Plateau landslide hazard[M]. Beijing: Science Press, 2019: 26-48]
[8] 司康平, 田原, 汪大明, 等. 滑坡灾害危险性评价的3种统计方法比较——以深圳市为例[J]. 北京大学学报(自然科学版), 2009, 45(4): 639-646.[SI Kangping, TIAN Yuan, WANG Daming, et al. Comparison of three statistical methods on landslide susceptibility analysis: A case study of Shenzhen city[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2009, 45(4): 639-646] DOI: 10.13209/j.0479-8023.2009.096
[9] 胡德勇, 李京, 陈云浩, 等. GIS支持下滑坡灾害空间预测方法研究[J]. 遥感学报, 2007, 11(6): 852-859.[HU Deyong, LI Jing, CHEN Yunhao, et al.GIS-based landslide spatial prediction methods: A case study in Cameron highland, Malaysia[J]. Journal of Remote Sensing, 2007, 11(6): 852-859]DOI: 10.3321/j.issn: 1007-4619.2007.06.012
[10] 王晓波, 鲁恒, 刘雪梅, 等. 基于SHALSTAB模型与面向对象遥感影像分析的地震滑坡信息快速检测[J]. 地震研究, 2019, 42(2): 273-279+306. [WANG Xiaobo, LU Heng, LIU Xuemei, et al. Rapid detection of seismic landslide information based on SHALSTAB model and object-oriented remote sensing image[J]. Journal of Seismological Research, 2019, 42(2): 273-279+306] DOI: 10.3969/j.issn.1000-0666.2019.02.016
[11] 崔阳阳. 基于不同评价单元的滑坡易发性评价方法研究——以陕西省洛南县为例[D]. 西安: 西安科技大学, 2021: 1-3 [CUI Yangyang. A comparative study on evaluation methods of landslide susceptibility based on different evaluation units: A case study of Luonan county, Shaanxi province[D]. Xi'an: Xi'an University of Science and Technology, 2021: 1-3]DOI: 10.27397/d.cnki.gxaku.2021.000039
[12] 庄建琦, 彭建兵, 张利勇.不同降雨条件下黄土高原浅层滑坡危险性预测评价[J]. 吉林大学学报(地球科学版), 2013, 43(3): 867-876.[ZHUANG Jianqi, PENG Jianbing, ZHANG Liyong.Risk assessment and prediction of the shallow landslide at different precipitation in Loess Plateau[J]. Journal of Jilin University(Earth Science Edition), 2013, 43(3): 867-876] DOI: 10.13278/j.cnki.jjuese.2013.03.039
[13] 孙何生, 邱海军, 朱亚茹, 等. 黄河上游典型流域滑坡稳定性预测及模型应用[J]. 西北大学学报(自然科学版), 2022, 52(3): 380-390. [SUN Hesheng, QIU Haijun, ZHU Yaru, et al. Landslide stability prediction and model application in typical watersheds of the upper Yellow River[J].Journal of Northwest University(Natural Science Edition), 2022, 52(3): 380-390] DOI: 10.16152/j.cnki.xdxbzr.2022-03-003
[14] 邬宁珊, 王佳希, 张岩, 等. 基于无人机可见光影像的树种和树冠信息提取——以晋西黄土区蔡家川流域为例[J]. 浙江农业学报, 2021, 33(8): 1505-1518. [WU Ningshan, WANG Jiaxi, ZHANG Yan, et al. Determining tree species and crown width from unmanned aerial vehicle imagery in hilly loess region of west Shanxi, China: A case study from Caijiachuan watershed [J]. Acta Agriculturae Zhejiangensis, 2021, 33(8): 1505-1518] DOI: 10.3969/j.issn.1004-1524.2021.08.18
[15] 李阳, 张建军, 魏广阔, 等. 晋西黄土区极端降雨后浅层滑坡调查及影响因素分析[J]. 水土保持学报, 2022, 36(5): 44-50. [LI Yang, ZHANG Jianjun, WEI Guangkuo, et al. Investigation of shallow landslide after extreme rainfall and analysis of its influencing factors in the west Shanxi Loess Region [J]. Journal of Soil and Water Conservation, 2022, 36(5): 44-50] DOI: 10.13870/j.cnki.stbcxb.2022.05.007
[16] 周萍, 邓辉, 张文江, 等. 基于信息量模型和机器学习方法的滑坡易发性评价研究——以四川理县为例[J]. 地理科学, 2022, 42(9): 1665-1675.[ZHOU Ping, DENG Hui, ZHANG Wenjiang, et al. Landslide susceptibility evaluation based on Information Value model and Machine Learning method: A case study of Lixian county, Sichuan province [J]. Scientia Geographica Sinica, 2022, 42(9): 1665-1675]DOI: 10.13249/j.cnki.sgs.2022.09.016
[17] 李信, 薛桂澄, 柳长柱, 等.基于信息量模型和信息量—逻辑回归模型的海南岛中部山区地质灾害易发性研究[J].地质力学学报, 2022, 28(2): 294-305.[LI Xin, XUE Guicheng, LIU Changzhu, et al. Evaluation of geohazard susceptibility based on information value model and information value-logistic regression model: A case study of the central mountainous area of Hainan Island[J]. Journal of Geomechanics, 2022, 28(2): 294-305] DOI: 10.12090/j.issn.1006-6616.2021111
[18] 李信, 薛桂澄, 夏南, 等. 基于CF、CF-LR和CF-AHP模型的国家热带雨林公园地质灾害易发性研究: 以海南保亭为例[J].现代地质, 2023, 37(4): 1033-1043.[LI Xin, XUE Guicheng, XIA Nan, et al. Geological hazard susceptibility evaluation based on CF, CF-LR and CF-AHP models in national tropical rain forest park: Taking Baoting county, Hainan province as an example [J]. Geoscience, 2023, 37(4): 1033-1043] DOI: 10.19657/j.geoscience.1000-8527.2023.002
[19] MONTGOMERY D R, DIETRICH W E. Channel initiation and the problem of landscape scale [J]. Science, 1992, 255(5046): 826-830. DOI: 10.1126/science.255.5046.826
[20] 康超, 谌文武, 张帆宇, 等. 确定性模型在黄土沟壑区斜坡稳定性预测中的应用[J]. 岩土力学, 2011, 32(1): 207-210+260. [KANG Chao, ZHAN Wenwu, ZHANG Fan, et al. Application of deterministic model to analyzing stability of hillslope of loess gully area[J].Rock and Soil Mechanics, 2011, 32(1): 207-210+260]DOI: 10.16285/j.rsm.2011.01.009
[21] CATANI F, SEGONI S, FALORNI G. An empirical geomorphology‐based approach to the spatial prediction of soil thickness at catchment scale[J]. Water Resources Research, 2010, 46(5): W05508. DOI: 10.1029/2008WR007450
[22] WANG Xinhao, MA Chao, WANG Yunqi, et al. Effect of root architecture on rainfall threshold for slope stability: Variabilities in saturated hydraulic conductivity and strength of root-soil composite[J]. Landslides, 2020, 17(11): 1965-1977. DOI: 10.1007/s10346-020-01422-6
[23] 余芹芹, 胡夏嵩, 李国荣, 等. 寒旱环境灌木植物根—土复合体强度模型试验研究[J]. 岩石力学与工程学报, 2013, 32(5): 1020-1031. [YU Qinqin, HU Xiasong, LI Guorong, et al. Research on strength model test of shrub root-soil composite system in cold and arid environments[J]. Chinese Journal of Rock Mechanics and Engineering, 2013, 32(5): 1020-1031] DOI: 10.3969/j.issn.1000-6915.2013.05.021
[24] 李佳, 汪霞, 贾海霞, 等. 浅层滑坡多发区典型灌木根系对边坡土体抗剪强度的影响[J]. 生态学报, 2019, 39(14): 5117-5126. [LI Jia, WANG Xia, JIA Haixia, et al. Ecological restoration with shrub roots for slope reinforcement in a shallow landslide-prone region[J]. Acta Ecologica Sinica, 2019, 39(14): 5117-5126] DOI: 10.5846 /stxb201809141986
[25] DAI Zhisheng, MA Chao, MIAO Lv, et al. Initiation conditions of shallow landslides in two man-made forests and back estimation of the possible rainfall threshold[J]. Landslides, 2022, 19(5): 1031-1044 DOI: 10.1007/s10346-021-01823-1
[26] HUANG J C, KAO S J, HSU M L, et al. Stochastic procedure to extract and to integrate landslide susceptibility maps: An example of mountainous watershed in Taiwan[J]. Natural Hazards and Earth System Sciences, 2006, 6(5): 803-815. DOI: 10.5194/nhess-6-803-2006

相似文献/References:

[1]谭红梅,贺中华*,陈莉会,等.贵州省极端降雨特征及其影响因子[J].山地学报,2023,(5):748.[doi:10.16089/j.cnki.1008-2786.000784]
　TAN Hongmei,HE Zhonghua,*,et al.Characteristics of Extreme Rainfall and Its Influencing Factors in Guizhou Province, China[J].Mountain Research,2023,(6):748.[doi:10.16089/j.cnki.1008-2786.000784]

备注/Memo

备注/Memo:
收稿日期(Received date): 2023- 03-22; 改回日期(Accepted date):2023-12- 05
基金项目(Foundation item): 国家自然科学基金(42177309)。[National Natural Science Foundation of China(42177309)]
作者简介(Biography): 王鑫盈(1998-),女,山东东营人,硕士研究生,主要研究方向:山地灾害预测预报。[WANG Xinying(1998-), female, born in Dongying, Shandong province, M. Sc, candidate, research on the mountain hazard prediction] E-mail: wxyxyy1123@163.com
*通讯作者(Corresponding author): 张岩(1970-),女,博士,教授,主要研究方向:土壤侵蚀与水土保持。[ZHANG Yan(1970-), female, Ph.D., professor, research on soil erosion and soil and water conservation] E-mail:zhangyan9@bjfu.edu.cn
更新日期/Last Update: 2023-11-30