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Construction of a Joint Newmark–Runout Model for Seismic Landslide Risk Identification: A Case Study in the Eastern Tibetan Plateau

Author

Listed:
  • Zhihua Yang

    (Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China)

  • Yuming Wu

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Changbao Guo

    (Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China)

  • Ximao Mai

    (Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China
    Faculty of Engineering, China University of Geosciences, Wuhan 430074, China)

Abstract

The key to seismic landslide risk identification resides in the accurate evaluation of seismic landslide hazards. The traditional evaluation models for seismic landslide hazard seldom consider the landslide dynamic runout process, leading to an underestimation of seismic landslide hazard. Therefore, a joint Newmark–Runout model based on landslide dynamic runout is proposed. According to the evaluation results of static seismic landslide hazard, the landslide source points can be extracted, and the landslide dynamic runout process is simulated to obtain the dynamic seismic landslide hazard. Finally, the static and dynamic seismic landslide hazards are fused to obtain an optimized seismic landslide hazard. In September 2022, a strong Ms6.8 earthquake occurred in the eastern Tibetan Plateau, triggering thousands of landslides. Taking the 2022 Luding earthquake-induced landslide as a sample, the function relationship between seismic slope displacement and landslide occurrence probability is statistically modeled, which partly improves the traditional Newmark model. The optimized seismic landslide hazard evaluation of the Luding earthquake area is conducted, and then, the seismic landslide risk identification is completed by taking roads and buildings as hazard-affected bodies. The results show that the length of the roads facing very high and high seismic landslide risks are 3.36 km and 15.66 km, respectively, and the buildings on the Moxi platform near the epicenter are less vulnerable to seismic landslides. The research findings can furnish critical scientific and technological support for swift earthquake relief operations.

Suggested Citation

  • Zhihua Yang & Yuming Wu & Changbao Guo & Ximao Mai, 2024. "Construction of a Joint Newmark–Runout Model for Seismic Landslide Risk Identification: A Case Study in the Eastern Tibetan Plateau," Land, MDPI, vol. 13(11), pages 1-20, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1832-:d:1513649
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    References listed on IDEAS

    as
    1. Israr Ullah & Bilal Aslam & Syed Hassan Iqbal Ahmad Shah & Aqil Tariq & Shujing Qin & Muhammad Majeed & Hans-Balder Havenith, 2022. "An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping," Land, MDPI, vol. 11(8), pages 1-20, August.
    2. Kai Ke & Yichen Zhang & Jiquan Zhang & Yanan Chen & Chenyang Wu & Zuoquan Nie & Junnan Wu, 2023. "Risk Assessment of Earthquake–Landslide Hazard Chain Based on CF-SVM and Newmark Model—Using Changbai Mountain as an Example," Land, MDPI, vol. 12(3), pages 1-20, March.
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