Monitoring snow depth in Antarctica is essential for understanding permafrost dynamics and soil thermal regimes. This study assesses the performance of low-cost, high-resolution, autocleaning ultrasonic sensors (MB7574-SCXL-Maxsonar-WRST7), powered by lithium D-type battery Geoprecision-Box dataloggers, in the South Shetland Islands. Traditional methods for estimating snow thickness, such as air temperature sensors in snow stakes, are economical but involve high maintenance costs and various complexities. To address these issues, we deployed ultrasonic sensors across 12 stations on Livingston and Deception islands from early 2023 to early 2024. Located at altitudes from 15 to 274 m above sea level and with varying wind exposures, these devices demonstrated notable durability and reliability, with only one sensor failure occurring due to structural damage. Data processing involved using an R script to filter out noise, and this process provided accurate hourly snow-depth measurements and revealed significant spatial and altitudinal variability, with depths ranging from 20 to 110 cm. Snow accumulation began in April and peaked in August and October, with major snowfall events contributing temporarily to snow depth but not to long-term accumulation. Our findings suggest that these sensors, as low-cost alternatives, could be integrated into networks such as the Global Terrestrial Network for Permafrost (GTN-P), supporting climate and permafrost studies.

Greenland’s peripheral glaciers and ice caps contribute disproportionately to sea-level rise relative to their small area. Winter snow accumulation directly influences glacier mass balance and downstream hydrology, but spatially extensive observations of this important mass balance component remain sparse. In this study, we present a unique multi-year (2008–2024) dataset of winter snow accumulation over A.P. Olsen Ice Cap, Northeast Greenland, from ground-penetrating radar surveys covering an average of 47 km per survey year. Our results reveal strong spatial heterogeneity that is likely influenced by wind redistribution and local topography, especially in the ablation zone. We compare our findings with automatic weather station data from three sites and outputs from the Copernicus Arctic Regional Reanalysis (CARRA). Governed by the high spatial variability, the automatic weather station point-based observations significantly underestimate regional accumulation by 40–45%. Despite the high spatial variability, the CARRA accumulated precipitation variable provides a reasonable overall mean winter snow accumulation (RMSE of 0.07 m w.e.); however, it fails to reproduce the complex non-linear relationship between snow depth and elevation observed in the radar data. Our findings emphasize the need for high-resolution, spatially extensive measurements to better understand snow accumulation on ice caps and glaciers and improve reanalysis assessments.

Quantifying snow water equivalent (SWE) with ground-penetrating radar (GPR) in a warming climate is complicated by the incidence and variability of liquid water in snow. Snow surveys conducted during the melt season serve as a valuable analog to conditions under future warming. Here, we determine the variability of wet snowpack properties (relative permittivity and density) to quantify their impact on SWE estimates using GPR. We collected spatially continuous snowpack measurements with 400 MHz GPR in 2012 and 2021 across repeat transects (∼150 km each year) along with spring and summer snow depth and density measurements from snow pits and snow cores. Snow relative permittivity values ranged from 2.06 to 2.62 in 2012 and 2.11 to 5.11 in 2021, resulting in calculated volumetric liquid water content (LWC) between 1.7% and 5.7% in 2012 and 2.1% and 16% in 2021. This variability in snow relative permittivity results in SWE uncertainties of 8% —33%, with more extreme cases reaching 13%—45%. We attribute this uncertainty to spatial and temporal variability in LWC when using GPR to estimate SWE. As snowpacks become wetter with rising atmospheric temperatures, GPR surveys should include in situ relative permittivity measurements to reduce depth and SWE interpretation uncertainties.

A key challenge in advancing slushflow management is the limited record of past incidents. Identifying their starting points and enhancing the quality of slushflow documentation are important in order to improve the regional early warning and develop slushflow numerical runout models and susceptibility maps. Here we investigate three major slushflow events at Kistrandfjellet, northern Norway and quantify the differences between registered slushflows in the national rapid mass movement database and the actual events. We use unique image datasets from the events in February 2021, January 2023 and January 2024, and identify slushflow starting points and flow paths. The curvature of the starting point locations is examined to assess how local topography influences slushflow release at the field site. Our mapping reveals 25 slushflows across the three events, whereas only five were registered in the database. For the 2021 event, we found six times as many slushflows as were officially registered. Comparison of our mapped slushflows to modeled drainage pathways and FKB-Vann (the official surface water dataset of Norway), yielded an average overlap of 35%. To improve slushflow management, we recommend establishing a standardized protocol for future data collection.

SnowModel-LG reconstructs snow depth and density over sea ice, explicitly resolving important snow sinks like blowing snow sublimation, static surface sublimation and melt, but not snow-ice formation. To examine snow sinks on level sea ice, we coupled SnowModel-LG with HIGHTSI, a 1-D thermodynamic sea-ice model, to create SMLG_HS. SMLG_HS simulations of snow depth and level ice thickness were evaluated against high-resolution airborne observations from the western Arctic, highlighting the importance of snow mass redistribution processes, i.e. snow’s tendency to leave level ice and accumulate over deformed ice due to wind-induced redistribution. Not accounting for snow mass redistribution, SMLG_HS overestimates snow depth on level ice, resulting in underestimation of level ice thickness and overestimation of snow-ice thickness. Our case study shows that snow depth on level ice needs to be reduced by 40% to simulate both snow depth and level ice thickness realistically in the western Arctic in April 2017. An independent analysis of snow volume distribution between level and deformed sea ice using airborne radar observations supported the model results and revealed a linear relationship that enables estimating the amount of snow remaining on level ice at the end of winter based on the amount of ice deformation.

In many technical and geomechanics applications, for example tire and ski design or avalanche prediction, the capability to model the mechanical behaviour of snow is of high importance. To this end, we propose in the present study to extend the 3-D H-model, a multi-scale constitutive law originally developed for granular materials, to densely packed snow. In the model, single ice grains are described by spherical particles bonded by brittle elasto-viscoplastic bridges. Snow is thus described explicitly through its ice skeleton microstructure. As a validation, confined compression test results from the litterature are used to assess the suitability of the model to correctly describe snow behaviour. Multiple parameter studies were conducted to demonstrate the capability of the model to capture the behaviour of different snow types over a significant range of temperatures and loading rates at small deformations. Notably, the initial bond radius emerges as an effective proxy for snow aging under isothermal conditions, with stress levels increasing directly with the initial bond radius. Additionally, low strain rates and elevated temperatures are shown to influence the viscous response of ice bonds, their failure rates and the overall stress within the snow material.

Brine migration from sea ice into the overlying snowpack is relatively understudied yet can significantly modify thermodynamic and electromagnetic properties of the snow. In this study, we investigate the impact of snow properties on brine wicking and runoff by producing samples of four distinctly different snow types (soft wind slab, hard wind slab, faceted grains and melt-freeze clusters) and monitor changes in snow properties after adding brine. The results illustrate that snow grain type and density have a pronounced effect on the height of brine wicking and runoff, snow compaction rates and salt concentrations. In all samples, we observed separation of the initial brine-saturated slush layer into two sublayers with distinctly different properties: solid saline snow-ice at the bottom and less saline brine-wetted snow above it. The maximum height of brine wicking ranged from 6.5 cm in faceted snow to 8.9 cm in hard wind slab samples, which was equivalent of 40 to 50% of total samples’ height. The volume and timing of brine runoff varied between samples, occurring later and in smaller volumes in hard wind slab compared to soft wind slab and coarse-grained samples.

Capillary suction across the soil–snow interface is a possible mechanism for the formation of wet basal snow layers, which are necessary for snow gliding and glide-snow avalanches. However, little is known about the conditions under which this process occurs. We investigated capillary suction across the soil–snow interface considering realistic snow and soil properties. Snow properties were determined from snow profiles and soil properties were determined from field measurements of liquid water content, matric potential, soil texture and bulk density for 40 alpine soils in Davos, Switzerland, as well as a field site in the region (Seewer Berg) with glide-snow avalanche activity. For the alpine soils investigated here, the results show that capillary flow from the soil to the snow is possible for realistic snow properties but requires a soil saturation of ∼90% or higher at the soil surface. When comparing the 90% saturation threshold to field measurements, the results suggest that capillary suction across the soil–snow interface is unlikely to contribute significantly to the formation of wet basal layers on Seewer Berg. These results are also relevant for soil and snow hydrology, where water transport across the soil–snow interface is important and understudied.

Diffuse optical spectroscopy (DOS) techniques characterize scattering media by examining their optical response to laser illumination. Time-domain DOS methods involve illuminating the medium with a laser pulse and using a fast photodetector to measure the time-dependent intensity of light that exits the medium after multiple scattering events. While DOS research traditionally focused on characterizing biological tissues, we demonstrate that time-domain diffuse optical measurements can also be used to characterize snow. We introduce a model that predicts the time-dependent reflectance of a dry snowpack as a function of its density, grain size, and black carbon content. We develop an algorithm that retrieves these properties from measurements at two wavelengths. To validate our approach, we assembled a two-wavelength lidar system to measure the time-dependent reflectance of snow samples with varying properties. Rather than measuring direct surface returns, our system captures photons that enter and exit the snow at different points, separated by a small distance (4–10 cm). We observe clear, linear correlations between our retrievals of density and black carbon concentration, and ground truth. For black carbon concentration the correlation is nearly one-to-one. We also find that our method is capable of distinguishing between small and large grain sizes.

Photosynthetically active radiation (PAR) at the ice-ocean interface is critical for primary production. The value of PAR is affected by the thickness of snow and sea ice, with additional absorbers (e.g. algae) further attenuating PAR. Sea-ice algae exhibit a substantial geo-temporal variance in column-integrated concentration (0–500 mg chl-a m−2) and are typically present within the bottom 0.01–0.2 m of sea ice. PAR transmittance is affected by algae concentrations and vertical thicknesses of ice algal layers. Small column-integrated concentrations of chl-a (~<10 mg chl-a m−2) have a negligible effect on the value of PAR transmittance, and large column-integrated concentrations of chl-a (~>10 mg chl-a m−2) can significantly reduce the value of PAR transmittance. Large column-integrated concentrations of chl-a need consideration when calculating PAR transmittance in areas of high sea-ice algae biomass (e.g. the ‘interior’ shelves of the Arctic Ocean, the Canadian Arctic and Antarctica).

Salt is often present in the snow overlying seasonal sea ice, and has profound thermodynamic and electromagnetic effects. However, its provenance and behaviour within the snow remain uncertain. We describe two investigations tracing upward brine movement in snow: one conducted in the laboratory and one in the field. The laboratory experiments involved the addition of dyed brine to the base of terrestrial snow samples, with subsequent wicking being measured. Our field experiment involved dye being added directly (without brine) to bare sea-ice and lake ice surfaces, with snow then accumulating on top over several days. On the sea ice, the dye migrated upwards into the snow by up to 5 cm as the snow's basal layer became more salty, whereas no migration occurred in our control experiment over non-saline lake ice. This occurred in relatively dry snowpacks where brine took up $< 6\%$ of the snow's calculated pore volume, suggesting pore saturation is not required for upward salt transport. Our results highlight the potential role of microstructural parameters beyond those currently retrievable with penetrometry, and the potential value of longitudinal, process-based field studies of young snowpacks.

Avalanche modeling is an essential tool to assess snow avalanche hazard. Today, most popular numerical approaches adopt depth-averaged equations. These methods are computationally efficient but limited in capturing processes occurring in the flow depth direction, e.g. erosion or deposition, which are often considered using ad hoc parameterizations or neglected completely. However, processes such as snow erosion, can crucially influence flow dynamics and run-out and are often not negligible. We address these issues by using a new three-dimensional (3-D) model, based on the material point method and finite strain elastoplasticity. To assess the possibilities and challenges associated with these highly detailed but computationally expensive calculations, we simulated the ‘Salezer’ snow avalanche that released in Davos, Switzerland in 2019. To reproduce the event in our simulations, we use the release areas mapped in a photogrammetric drone survey and estimate the snow conditions on the day of the event. We compare macroscopic features, such as flow outline and snow deposition of the simulated avalanche to field observations. An in-depth analysis of transient 3-D flow structures at the avalanche head not only demonstrates the degree of physical detail in the model, but also highlights challenges which still need to be addressed.

The objective of this research is to model snow's response to dynamic, impact loading. Two constitutive relationships are considered: elastic and Maxwell-viscoelastic. These material models are applied to laboratory experiments consisting of 1000 individual impacts across 22 snow column configurations. The columns are 60 cm tall with a 30 cm by 30 cm cross-section. The snow ranges in density from 135 to 428 kg m−3 and is loaded with both short-duration (~1 ms) and long-duration (~10 ms) impacts. The Maxwell-viscoelastic model more accurately describes snow's response because it contains a mechanism for energy dissipation, which the elastic model does not. Furthermore, the ascertained model parameters show a clear dependence on impact duration; shorter duration impacts resulted in higher wave speeds and greater damping coefficients. The stress wave's magnitude is amplified when it hits a stiffer material because of the positive interference between incident and reflected waves. This phenomenon is observed in the laboratory and modeled with the governing equations.

Spatial variability of snowpack properties adds uncertainty in the evaluation of avalanche hazard. We propose a combined mechanical–statistical approach to study how spatial variation of slab depth affects the skier-triggering probability and possible release size. First, we generate multiple slab depth maps on a plane fictional slope based on Gaussian Random Fields (GRF) for a specific set of mean, variance and correlation length. For each GRF, we derive analytically the Skier Propagation Index (SPI). We then simulate multiple skier tracks and computed the probability based on the number of skier hits where SPI is below 1. Finally, we use a depth-averaged material point method to evaluate the possible avalanche size for given slab depth variations. The results of this analysis show that large correlation lengths and small variances lead to a lower probability of skier-triggering as it reduces the size and the number of areas with low slab depth. Then, we show the effect of skiing style and skier group size on skier-triggering probability. Spatial variability also affects the possible avalanche size by adding stress fluctuation causing early or late tensile failure. Finally, we demonstrate with our models the well-known relationship between the probability and the size in avalanche forecasting.

The conductive heat flux through the snow and ice is a critical component of the mass and energy budgets in the Arctic sea ice system. We use high horizontal resolution (3–15 cm) measurements of snow topography to explore the impacts of sub-meter-scale snow surface roughness on heat flux as simulated by the Finite Element method. Simulating horizontal heat flux in a variable snow cover modestly increases the total simulated heat flux. With horizontal heat flux, as opposed to simple 1D-vertical heat flux modeling, the simulated heat flux is 10% greater than that for uniform snow with the same mean snow thickness for a 31.5 × 21 m region of sea ice (the largest region we studied). Vertical-only (1D) heat flux simulates just a 6% increase for the same region. However, this is highly dependent on observation resolution. Had we measured the snow cover at 1 m horizontal spacing or greater, simulating horizontal heat flux would not have changed the net heat flux from that simulated with vertical-only heat flux. These findings suggest that measuring and modeling snow roughness at sub-meter horizontal scales may be necessary to accurately represent horizontal heat flux on level Arctic sea ice.

The relationship between the behavior of water in snow and its microstructure is crucial to improve the prediction of wet snow disasters. X-ray computed tomography (X-ray CT) is frequently used to observe snow microscopically. However, distinguishing between ice and water in the X-ray images is difficult because ice exhibits an X-ray absorption coefficient similar to that of water. In contrast, magnetic resonance imaging (MRI) acquires nuclear magnetic resonance (NMR) signals of protons in a liquid and visualizes the NMR signal intensity, enabling discrimination between water and ice signals. However, snow grains and pore spaces cannot be distinguished in MRI images because they do not generate NMR signals.

To investigate the relationship between the microstructure of snow and the distribution of liquids in snow, we developed a novel method that combines X-ray CT and MRI images to compensate for the disadvantages associated with each method. Using this method, we successfully visualized where liquid (C12H24) occupied pore spaces. We also showed the possibility of using C12H24 instead of water to obtain water retention curve of snow cover, which is a fundamental aspect of hydraulic properties. Although there is room for improvement in the visualization of water in snow, such as shortening the imaging time to escape snow metamorphosis and image superimposition methods, this method is expected to effectively elucidate the behavior of water in snow and clarify the characteristics of wet snow.

We analyze the internal structure of two polythermal glaciers, Hurd and Johnsons, located on Livingston Island, Antarctica, using 200 and 750 MHz GPR data collected in 2003/04, 2008/09 and 2016/17 field campaigns. Based on the different permittivities of snow and ice, we determined the thickness distribution of the end-of winter snow cover and of the cold ice layer. Their knowledge is fundamental for mass balance and glacier dynamics studies due to the different densities and rheological properties of such media. The average measured thicknesses for the snow and cold ice layers (the latter including the snow layer) were of 1.44 ± 0.09 and 29.1 ± 1.5 m, and their corresponding maxima were of 2.45 ± 0.21 and 80.8 ± 2.5 m. GPR snow profiling allowed for extension of the coverage of the snow thickness survey, but added little information to that supplied by snow pits, stake readings and manual snow probing, because of the multiplicity of reflections within the seasonal snowpack caused by internal ice layers and lenses. The polythermal structure determined for Hurd Glacier fits into the so-called Scandinavian type, seldom reported for the Antarctic region.

Understanding the dynamics of snow avalanches is crucial for predicting their destructive potential and mobility. To gain insight into avalanche dynamics at a particle level, the AvaNode in-flow sensor system was developed. These synthetic particles, equipped with advanced and affordable sensors such as an inertial measurement unit (IMU) and global navigation satellite system (GNSS), travel with the avalanche flow. This study focuses on assessing the feasibility of the in-flow measurement systems. The experiments were conducted during the winter seasons of 2021–2023, both in static snow cover and dynamic avalanche conditions of medium-sized events. Radar measurements were used in conjunction with the particle trajectories and velocities to understand the behaviour of the entire avalanche flow. The dynamic avalanche experiments allowed to identify three distinct particle flow states: (I) initial rapid acceleration, (II) a steady state flow with the highest velocities (9–17 ms−1), and (III) a longer deceleration state accompanied by the largest measured rotation rates. The particles tend to travel towards the tail of the avalanche and reach lower velocities compared to the frontal approach velocities deduced from radar measurements (ranging between 23–28 ms−1). The presented data give a first insight in avalanche particle measurements.

Liquid water at the ground–snow interface is thought to play a crucial role in the release of glide-snow avalanches, which can be massive and threaten infrastructure in alpine regions. Several mechanisms have been postulated to explain the formation of this interfacial water. However, these mechanisms remain poorly understood, in part because suitable measurement techniques are lacking. Here, we demonstrate the use of neutron radiography for imaging water transport in soil–snow systems. Columns of sand, gravel and snow were used to simulate the capillary forces of the soil–vegetation–snow layering found in nature. The columns were connected to a water reservoir to maintain a constant-pressure boundary condition and placed in a climatic chamber within the neutron beam. We show that neutron radiography is capable of measuring changes in the optical density distribution (related to liquid water content) within all three layers of the model system. Results suggest that a porous interface between the sand and snow may induce the formation of a water layer in the basal snowpack. Improved understanding of the water transport in soil–snow systems should lead to better prediction of glide-snow avalanche release and could also benefit other fields such as snow hydrology.