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

Rupert Bainbridge*, Michael Lim, Stuart Dunning, Mike G. Winter, Alejandro Diaz-Moreno, James Martin, Hamdi Torun, Bradley Sparkes, Muhammad W. Khan, Nanlin Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
17 Downloads (Pure)

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 m 3 (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.

Original languageEnglish
Pages (from-to)686-704
Number of pages19
JournalGeomatics, Natural Hazards and Risk
Volume13
Issue number1
Early online date23 Feb 2022
DOIs
Publication statusE-pub ahead of print - 23 Feb 2022

Fingerprint

Dive into the research topics of 'Detection and forecasting of shallow landslides: lessons from a natural laboratory'. Together they form a unique fingerprint.

Cite this