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

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Authors

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

Abstract

Shallow landslides are a significant hillslope erosion mechanism and limited understanding of controls on initiation and development results in persistent risk on linear infrastructure. We present an inventory of 63 landslides (2007-2019) from the west of Scotland and show the patterns and development of debris flows, accounting for 58% of landslide source volume. Using rainfall data, we show that landslides are often triggered during abrupt changes in the rainfall trend. We derive empirical antecedent precipitation (>62mm) and intensity-duration (>10 hours) thresholds over which debris flows occur. Analysis shows the thresholds are more effective at raising landslide alert levels than the current management plan. We use novel time-lapse vector tracking to detect slope instabilities, quantify deformation rates and indicate imminent failure. Seismometers are used to detect a debris flow and locate the source area. The suite of sensors provides vital information to support operational decision-making for infrastructure with complex slope hazards.

DOI

https://doi.org/10.31223/X52W2R

Subjects

Physical Sciences and Mathematics

Keywords

Risk, Debris-flow, Infrastructure, Hazard

Dates

Published: 2020-11-23 22:50

Last Updated: 2021-03-24 06:00

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Intended to be available via the BGS landslide database in the near future.

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