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

Bainbridge, Rupert, Lim, Michael, Dunning, Stuart, Winter, Mike G, Diaz-Moreno, Alejandro ORCID: 0000-0003-3288-3728, Martin, James, Torun, Hamdi, Sparkes, Bradley, Khan, Muhammad W and Jin, Nanlin (2022) Detection and forecasting of shallow landslides: lessons from a natural laboratory. GEOMATICS NATURAL HAZARDS & RISK, 13 (1). pp. 686-704.

Access the full-text of this item by clicking on the Open Access link.

Official URL: http://dx.doi.org/10.1080/19475705.2022.2041108

Abstract

Rapid shallow 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 5,350 m3 (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 (>62 mm) and intensity-duration (>10 h) 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
Uncontrolled Keywords:	Debris flow, detection, forecasting, thresholds, monitoring
Divisions:	Faculty of Science and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	08 Jun 2022 14:46
Last Modified:	18 Jan 2023 21:00
DOI:	10.1080/19475705.2022.2041108
Open Access URL:	https://www.tandfonline.com/doi/full/10.1080/19475...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3156069