Stuart A. Dunning

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.

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.

A deadly cascade: A catastrophic landslide in Uttarakhand state in India on February 2021 damaged two hydropower plants, and more than 200 people were killed or are missing. Shugar et al. describe the cascade of events that led to this disaster. A massive rock and ice avalanche roared down a Himalayan valley, turning into a deadly debris flow upstream from the first of the two hydropower plants. The sequence of events highlights the increasing risk in the Himalayas caused by increased warming and development. Science , abh4455, this issue p. 300

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.

How landscapes respond to, and evolve from, large jökulhlaups (glacial outburst floods) is poorly constrained due to limited observations and detailed monitoring. We investigate how melt of glacier ice transported and deposited by multiple jökulhlaups during the 2010 eruption of Eyjafjallajökull, Iceland, modified the volume and surface elevation of jökulhlaup deposits. Jökulhlaups generated by the eruption deposited large volumes of sediment and ice, causing significant geomorphic change in the Gígjökull proglacial basin over a 4-week period. Observation of these events enabled robust constraints on the physical properties of the floods which informs our understanding of the deposits. Using ground-based LiDAR, GPS observations and the satellite-image-derived ArcticDEMs, we quantify the post-depositional response of the 60 m-thick Gígjökull sediment package to the meltout of buried ice and other geomorphic processes. Between 2010 and 2016, total deposit volume reduced by −0.95 × 10⁶ m³ a⁻¹, with significant surface lowering of up to 1.88 m a⁻¹. Surface lowering and volumetric loss of the deposits is attributed to three factors: (i) meltout of ice deposited by the jökulhlaups; (ii) rapid melting of the buried Gígjökull glacier snout; and (iii) incision of the proglacial meltwater system into the jökulhlaup deposits.

Structural glaci‐geological processes can entrain basal sediment into ice, leading to its transportation and deposition downstream. Sediments potentially rich in essential nutrients, like silica and iron, can thus be transferred from continental sources to the ocean, where deposition could enhance marine primary productivity. However, a lack of data has limited our knowledge of sediment entrainment, transfer, and distribution in Antarctica, until now. We use ice‐penetrating radar to examine englacial sediments in the Weddell Sea sector of the West Antarctic Ice Sheet. Radargrams reveal englacial reflectors on the leeside of nunataks and subglacial highlands, where Mie scattering analysis of the reflectors suggests particle sizes consistent with surface moraine sediments. We hypothesize that these sediments are entrained at the thermal boundary between cold and warm‐based ice. Conservative estimates of >130 × 10⁹ kg of englacial sediment in Horseshoe Valley alone suggest that the ice sheet has significant entrainment potential unappreciated previously.

Tropical landscapes are changing rapidly due to changes in land use and land management. Being able to predict and monitor land use change impacts on species for conservation or food security concerns requires the use of habitat quality metrics, that are consistent, can be mapped using above-ground sensor data and are relevant for species performance. Here, we focus on ground surface temperature (Thermal_ground) and ground vegetation greenness (NDVI_down) as potentially suitable metrics of habitat quality. Both have been linked to species demography and community structure in the literature. We test whether they can be measured consistently from the ground and whether they can be up-scaled indirectly using canopy structure maps (Leaf Area Index, LAI, and Fractional vegetation cover, FCover) developed from Landsat remote sensing data. We measured Thermal_ground and NDVI_down across habitats differing in tree cover (natural grassland to forest edges to forests and tree plantations) in the human-modified coastal forested landscapes of Kwa-Zulua Natal, South Africa. We show that both metrics decline significantly with increasing canopy closure and leaf area, implying a potential pathway for upscaling both metrics using canopy structure maps derived using earth observation. Specifically, our findings suggest that opening forest canopies by 20% or decreasing forest canopy LAI by one unit would result in increases of Thermal_ground by 1.2 °C across the range of observations studied. NDVI_down appears to decline by 0.1 in response to an increase in canopy LAI by 1 unit and declines nonlinearly with canopy closure. Accounting for micro-scale variation in temperature and resources is seen as essential to improve biodiversity impact predictions. Our study suggests that mapping ground surface temperature and ground vegetation greenness utilising remotely sensed canopy cover maps could provide a useful tool for mapping habitat quality metrics that matter to species. However, this approach will be constrained by the predictive capacity of models used to map field-derived forest canopy attributes. Furthermore, sampling efforts are needed to capture spatial and temporal variation in Thermal_ground within and across days and seasons to validate the transferability of our findings. Finally, whilst our approach shows that surface temperature and ground vegetation greenness might be suitable habitat quality metric used in biodiversity monitoring, the next step requires that we map demographic traits of species of different threat status onto maps of these metrics in landscapes differing in disturbance and management histories. The derived understanding could then be exploited for targeted landscape restoration that benefits biodiversity conservation at the landscape scale.