Detection and forecasting of shallow landslides: lessons from a natural laboratory

Bainbridge, Rupert, Lim, Michael, Dunning, Stuart, Winter, Mike G., Diaz-Moreno, Alejandro, Martin, James, Torun, Hamdi, Sparkes, Bradley, Khan, Muhammad Waqas and Jin, Nanlin (2022) Detection and forecasting of shallow landslides: lessons from a natural laboratory. Geomatics, Natural Hazards and Risk, 13 (1). pp. 686-704. ISSN 1947-5705

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Shallow-rapid landslides are a significant hillslope erosion mechanism and limited understanding of their initiation and development results in persistent risk to infrastructure. Here, we analyse the slope above the strategic A83 Rest and be Thankful road in the west of Scotland. An inventory of 70 landslides (2003-2020) shows three types of shallow landslide, debris flows, creep deformation and debris falls. Debris flows dominate and account for 5,350m3 (98 ) of shallow-landslide source volume across the site. We use novel time-lapse vector tracking to detect and quantify slope instabilities, whilst seismometers demonstrate the potential for live detection and location of debris flows. Using on-slope rainfall data, we show that shallow-landslides are typically triggered by abrupt changes in the rainfall trend, characterised by high-intensity, long duration rainstorms, sometimes part of larger seasonal rainfall changes. We derive empirical antecedent precipitation (>62mm) and intensity-duration (>10 hours) thresholds over which shallow-landslides occur. Analysis shows the new thresholds are more effective at raising hazard alerts than the current management plan.The low-cost sensors provide vital notification of increasing hazard, the initiation of movement, and final failure. This approach offers considerable advances to support operational decision-making for infrastructure threatened by complex slope hazards.

Item Type: Article
Additional Information: Funding information: We thank NERC (NE/P000010/1, NE/T00567X/1, NE/T005653/1), Research England ( “SlopeRIoT”), Transport Scotland and the Scottish Road Research Board (SRRB) for funding. We also thank BEAR Scotland, GeoRope, Jacobs, Forestry and Land Scotland, Glencroe Farm, and John Mather for research, access, and on-site support. We declare no conflicts of interest.
Uncontrolled Keywords: Debris flow, detection, forecasting, thresholds, monitoring
Subjects: F900 Others in Physical Sciences
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 08 Feb 2022 12:24
Last Modified: 04 Mar 2022 08:45

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