Landslides along the Engineering Corridors in the Northeastern Margin of the Qinghai-Tibet Plateau of China: Comprehensive Inventory and Mechanism Analysis

Climate change, earthquakes, and human activities are accelerating the degradation of permafrost, leading to loess failures and slope instability. Some engineering corridors (ECs)/infrastructures located on the northeastern margin of the Qinghai-Tibet Plateau (NE-QTP) of China are heavily influenced...

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Veröffentlicht in:Landslides 2024-12, Vol.21 (12), p.3049-3067
Hauptverfasser: Zhang, Jing, Chen, Jie, Li, Chengqiu, Lu, Wei, Hao, Junming, Niu, Pengfei, Li, Kechang, Ma, Siyuan, Yuan, Ren-mao
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Sprache:eng
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Zusammenfassung:Climate change, earthquakes, and human activities are accelerating the degradation of permafrost, leading to loess failures and slope instability. Some engineering corridors (ECs)/infrastructures located on the northeastern margin of the Qinghai-Tibet Plateau (NE-QTP) of China are heavily influenced by landslide phenomena due to being built on permafrost, loess, and seasonally frozen ground. However, few systematic investigations have been carried out in this area. To compile a comprehensive landslide inventory, we visually interpreted 11,914 landslides in GaoFen-6 images taken from 2021 to 2022. We observe that approximately 44.85% of the infrastructures are affected by landslides. Then, based on the ground types and triggering factors, landslides are classified into three types: freeze‒thaw landslides (FTLs), loess landslides (LLs), and general landslides (GLs). More specifically, FTLs are mainly distributed in the boundary regions between permafrost and seasonally frozen ground. The LLs exhibit high-density clustered distribution characteristics. GLs have significant transitional characteristics and commonalities between FTLs and LLs. Furthermore, we apply the geographical detector to determine the controlling factors of the landslides that occurred. We find that the temperature change is the primary controller on the FTLs. The water exhibits a certain correlation with LLs. And the earthquake is the most important factor on the GLs. Our study provides a significant dataset for quantifying the analysis of landslides in NE-QTP.
ISSN:1612-510X
1612-5118
DOI:10.1007/s10346-024-02341-6