We evaluate the performance of the low-cost seismic sensor Raspberry Shake (RS) to identify and monitor icequakes (which occur when glacial ice experiences brittle deformation) in extreme environments. In January 2020, three RS3D sensors were installed on a katabatic wind-scoured blue ice area (BIA) close to the Princess Elisabeth Antarctica research station in Dronning Maud Land, East Antarctica. The sensors were configured for Antarctic deployment and placed in insulated enclosures to protect them from harsh weather systems. The RS network (installed in a triangular array) performed well in the cold and with rapid air temperature change, as diurnal temperatures fluctuated from a high of 0.0°C to a minimum temperature of −15.0°C. Although battery connectivity issues in one unit limit full triangulation of seismic signals, and high background noise may mask some seismic signals, data from the RS2 unit reveals that 2936 icequakes were detected over a 10-day period. The temporal occurrence of these icequakes, combined with satellite-derived surface temperature measurements and automatic weather station data, suggest that diurnal fluctuations in solar radiation control ice surface temperature changes, driving thermal contraction of the ice. Seismic investigations like these can therefore provide information on the thermal state and ice fracture mechanics of ablation zones such as BIAs. Our work highlights the potential application of the RS (after minimal modification) in glaciated environments where equipment often needs to be portable, temporary and lightweight, and able to perform in extreme weather conditions.

Shallow landslides are a significant hillslope erosion mechanism and can transform into destructive debris-flows. Limited understanding of the controls on debris-flow initiation, development and deposition results in persistent risk and high impacts where linear infrastructure is affected. Here, we analyse steep slopes above a key road, the A83 Rest and be Thankful, Scotland, where near-real-time rain gauge data, time-lapse camera deformation tracking and seismics allow us to define thresholds for increased debris-flow risk, examine long-term slope creep and, detect debris-flow occurrence. We show the patterns and development of channelized and hillslope debris-flows that act as a key geomorphic agent, accounting for 58% of landslide source volume over 13-years. On-slope rainfall data allow us to quantify the effect of antecedent rainfall and storm intensity-duration on landslide triggering and develop new local thresholds over which landslides are likely to occur. To better equip asset managers, we use time-lapse imagery vector tracking to detect slope instabilities, and deformation rates to calculate inverse-velocity values to indicate if This is a non-peer reviewed EarthArXiv preprint 3 failure is imminent. Low-cost seismometers are used to detect when a debris-flow has occurred and locate the source area. The suite of sensors has provided vital information both prior to failure, and during debris-flows to support operational decision-making for authorities dealing with complex slope hazards.

Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. This approach is applied to the Rest and Be Thankful slope, Argyle, Scotland, where past debris flows have blocked the A83 or forced preemptive closure. TLI of the Rest and Be Thankful are taken from a fixed station, 28 mm lens, time lapse camera every 15 min. Imagery is filtered to counter the effects of misalignment from wind induced vibration of the camera, asymmetric lighting, and fog. Particle image velocimetry (PIV) algorithms are then run to produce slope movement velocity vectors. PIV generated vectors are automatically post-processed to separate vectors generated by slope movement from false positives generated by harsh environmental conditions. Results for images over a 20-day period indicated precursor slope movement initiated by a rainfall event, a period of quiescence for 10 days, followed by a large landslide failure during proceeding rainfall where over 3000 tons of sediment reached the road. Results suggest low cost, live streamed TLI and this novel PIV approach correctly detect and, importantly, report precursor slope movement, allowing early warning, effective management and landslide impact mitigation. Future applications of this technique will allow the development of an effective decision-making tool for asset management of the A83, reducing the risk to life of motorists. The technique can also be applied to other critical infrastructure sites, allowing hazard risk reduction.

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