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Regional landslide susceptibility: spatiotemporal variations under dynamic soil moisture conditions

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  • Ram Ray
  • Jennifer Jacobs
  • Thomas Ballestero

Abstract

Quantification of landslide susceptibility variability in space and time in response to static and dynamic conditions is a fundamental research challenge. Here, we identify and apply new modeling and remote sensing observation techniques to statistically characterize susceptibility distributions under dynamic moisture conditions. The methods are applied at two study regions: Cleveland Corral, California, US and Dhading, Nepal. The results show that the temporal variability of safety factors is lower during the wet season than the dry season, but this variability, when scaled by mean seasonal stability, is constant annually. Relative variability differs by region with lower variability in Nepal, the highly susceptible region. L-Moment evaluations indicate that Nepal has a consistent, regional probability distribution, but that California has two distinct distributions. The variability in time is not normally distributed for either region. For both regions, transitional characteristic of safety factors show a strong power law relationship between the average duration and number of periods during which sites are highly susceptible. Because the mapped landslide locations typically had frequent crossings with brief unstable conditions, a consistent physical mechanism is pointed to as a possible cause of slope failure. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Ram Ray & Jennifer Jacobs & Thomas Ballestero, 2011. "Regional landslide susceptibility: spatiotemporal variations under dynamic soil moisture conditions," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(3), pages 1317-1337, December.
  • Handle: RePEc:spr:nathaz:v:59:y:2011:i:3:p:1317-1337
    DOI: 10.1007/s11069-011-9834-4
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    References listed on IDEAS

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    1. Changjiang Li & Tuhua Ma & Xinsheng Zhu, 2010. "aiNet- and GIS-based regional prediction system for the spatial and temporal probability of rainfall-triggered landslides," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 52(1), pages 57-78, January.
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    Cited by:

    1. Qing Yang & Zhanqiang Chang & Chou Xie & Chaoyong Shen & Bangsen Tian & Haoran Fang & Yihong Guo & Yu Zhu & Daoqin Zhou & Xin Yao & Guanwen Chen & Tao Xie, 2023. "Combining Soil Moisture and MT-InSAR Data to Evaluate Regional Landslide Susceptibility in Weining, China," Land, MDPI, vol. 12(7), pages 1-34, July.

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