[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]
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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
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《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:
2023-11-25

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.

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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