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.

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.

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 m³ (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.

Accurate topographic data acquired at appropriate spatio‐temporal resolution is often the cornerstone of geomorphic research. Recent decades have seen advances in our ability to generate highly accurate topographic data, primarily through the application of remote sensing techniques. Structure from Motion‐Multi View Stereo (SfM‐MVS) and lidar have revolutionised the spatial resolution of surveys across large spatial extents. Technological developments have led to commercialisation of small form factor (SFF) 3D lidar sensors that are suited to deployment on both mobile (e.g., uncrewed aerial systems), and in fixed semi‐permanent installations. Whilst the former has been adopted, the potential for the latter to generate data suitable for geomorphic investigations has yet to be assessed. We address this gap here in the context of a 3‐month deployment where channel change is assessed in an adjusting fluvial system. We find that SFF 3D lidar sensors generate change detection products comparable to those generated using a conventional lidar system. Areas of no geomorphic change are characterised as such (mean 3D change of 0.014 m compared with 0.0014 m for the Riegl VZ‐4000), with differences in median change in eroding sections of between 0.02 and 0.04 m. We illustrate that this data enables: (a) accurate characterisation of river channel adjustments through extraction of bank long‐profiles; (b) the assessment of bank retreat patterns which help elucidate failure mechanics; and (c) the extraction of water surface elevations. The deployment of this technology will enable a better understanding of processes across a variety of geomorphic systems, as data can be captured in 4D with near real‐time processing.

Shallow landslides are a major natural hazard in the UK, causing more than £10M of economic losses annually and posing clear threats to life. Rainfall is an essential control on shallow landslide risk, but network operators currently use generic ‘on-off’ warnings based on localised rainfall intensity and duration, giving no strategic information about where the hazard is highest or evolving the quickest for any particular rainfall event. A new procedure has been developed to automatically evaluate the stability of slopes across regional scales in response to spatially and temporally variable rainfall events, quantified by rainfall radar data updated every 5 minutes. A physically based programme, TRIGRS, was used to assess the factor of safety (FS) of slopes in response to spatially variable rainfall radar data. The dynamic FS maps produced provide early warning and informed decision-making for the management of regional to national-scale infrastructures. Sensitivity analyses were performed to investigate the effect of the soil thickness, shear strength parameters, hydraulic parameters, and antecedent rainfall on the FS during a known landslide-causing rainfall event in Glen Croe, western Scotland. The results specifically highlight the slope where the failure occurred as a high-hazard area 1.0 hour before the event. The dynamic FS maps were particularly sensitive to soil thickness, the saturated hydraulic diffusivity and Gardner’s unsaturated conductivity, suggesting that more effort should be put into improving network-scale datasets of these parameters. Ultimately, the ability developed here to account for near-real-time spatial variabilities in rainfall data and slope responses measured through the relative change in FS values provides a potentially transformative new tool for network operators to proactively mitigate the impacts of specific storms as they develop.