EGU22-13151, updated on 09 Jan 2024
https://doi.org/10.5194/egusphere-egu22-13151
EGU General Assembly 2022
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Geophysical imaging for local landslide early warning systems

Jim Whiteley1,2, Arnaud Watlet1, John-Michael Kendall3, and Jonathan Chambers1
Jim Whiteley et al.
  • 1Shallow Geohazards and Earth Observation, British Geological Survey, Environmental Science Centre, Nicker Hill, Nottingham, NG12 5GG, United Kingdom
  • 2School of Earth Sciences, University of Bristol, Wills Memorial Building, Queens Road, Bristol, BS8 1RJ, United Kingdom
  • 3Department of Earth Sciences, University of Oxford, South Parks Road, Oxford, OX1 3AN, United Kingdom

A complete assessment of slope stability is achieved by identifying and monitoring the subsurface properties and processes leading to slope failure conditions. Monitoring technologies need to be applied at appropriate scales and resolution, and with sufficient coverage, to be able to assess these conditions in local landslide early warning systems. A holistic understanding of the subsurface at the slope-scale is not always captured by some landslide monitoring approaches, such as remote sensing observations with limited depth penetration or sparse resolution, or point sensor measurements with highly localised information. Geophysical techniques have demonstrable capacity to link between the different scales, resolutions and coverage of these established landslide monitoring techniques. Here, we present a novel framework identifying the benefits and limitations of including geophysical imaging and monitoring techniques at different stages of local landslide early warning system strategies. These include the use of geophysical inputs to aid the design of local landslide early warning systems, monitor slopes at risk of failure, inform forecasting, and support decision making for stakeholders.

How to cite: Whiteley, J., Watlet, A., Kendall, J.-M., and Chambers, J.: Geophysical imaging for local landslide early warning systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13151, https://doi.org/10.5194/egusphere-egu22-13151, 2022.

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