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

Abstract

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 steep slopes above a strategic road, the A83 Rest and be Thankful in the west of Scotland. An inventory of 70 landslides (2003-2020) shows the development of debris flows, creep deformation and debris falls. Debris flows dominate and account for 5,350 m3 (98 %) of landslide source volume. 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 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 debris flows occur. Our analysis shows the new thresholds are more effective at raising hazard alerts than the current management plan. The low-cost combination of sensors provides vital information to notify of increasing hazard, the initiation of movement, and then final failure. This approach offers considerable advances to support operational decision-making for infrastructure threatened by complex slope hazards.