This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.
Detection and forecasting of shallow landslides: lessons from a natural laboratory
Downloads
Authors
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
Older Versions
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.
There are no comments or no comments have been made public for this article.