scholarly journals Large landslides at the northeastern margin of the Bayan Har Block, Tibetan Plateau, China

2019 ◽  
Vol 6 (1) ◽  
pp. 180844 ◽  
Author(s):  
Bo Zhao ◽  
Yunsheng Wang ◽  
Yonghong Luo ◽  
Ruifeng Liang ◽  
Jia Li ◽  
...  

Large landslides (volume greater than or equal to 10 6 m 3 ) usually have disastrous consequences and clearly influence the evolution of the local landscape. In this study, a detailed investigation of large landslides, across 20 towns over an area of 5000 km 2 , was carried out on the northeastern margin of the Bayan Har Block, at the eastern margin of the Tibetan Plateau, China. The results show that there are 129 large landslides in this area. Among them, 79 landslides have volumes within 10 6 –10 7 m 3 , 52 landslides have volumes within 10 7 –10 8 m 3 and 2 landslides have volumes larger than 10 8 m 3 . Most of these landslides are distributed along rivers, and more than 32% are densely concentrated in three small regions. The landslides mainly occur in high slopes and exhibit obvious sturzstrom characteristics. Analysis of the factors controlling landslide occurrence shows that elevation, slope angle, slope aspect, lithology, faults and rivers (valley) clearly influence landslide occurrence, while rainfall has no obvious influence. Earthquakes are considered an important trigger of and contributor to landslide occurrence.

2014 ◽  
Vol 396 ◽  
pp. 88-96 ◽  
Author(s):  
Mong-Han Huang ◽  
Roland Bürgmann ◽  
Andrew M. Freed

2012 ◽  
Vol 335-336 ◽  
pp. 195-205 ◽  
Author(s):  
Zhiwei Li ◽  
Sidao Ni ◽  
Tianyao Hao ◽  
Yi Xu ◽  
Steven Roecker

2019 ◽  
Vol 169 ◽  
pp. 1-10 ◽  
Author(s):  
Chongjian Shao ◽  
Shao Liu ◽  
Yong Li ◽  
Rongjun Zhou ◽  
Shiyuan Wang ◽  
...  

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.


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