CR3.2

Snow avalanches represent a natural hazard for infrastructures and backcountry recreationists. Risk assessment of avalanche danger is difficult due to sparse nature of available observations informing on snowpack mechanical and geophysical properties. Spatial variability of these properties also add complexity to the decision-making and route finding in avalanche terrain for backcountry recreationists. Snow cover models simulate snow mechanical properties at fairly good resolution (around 100 m). However, small-scale variability such at the slope scale (5-50 m) remains critical to monitor given that slope stability and the possible size of an avalanche are governed by such scale. In order to better understand and predict the spatial variability at the slope scale, this work explores linkages between snow mechanical properties and microtopographic indicators. First, we compare their covariance models and scaling properties. Then, we predict snow mechanical properties, including point snow stability, using GAM spatial models (Generalized additives models) with microtopographic indicators as covariates. Snow mechanical properties such as snow density, elastic modulus, shear modulus and snow microstructural strength were measured at multiple locations over several studied slopes (20-40 m) using a high-resolution penetrometer (SMP), in Rogers Pass, British-Columbia, and Mt Albert, Québec. Point snow stability such as the skier crack length, critical propagation crack length and a skier stability index were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators such as the topographic position index (TPI), vegetation height and proximity, Winstral index (wind-exposed/sheltered area) and potential radiation index were derived from UAV surveys with sub-meter resolution. We computed the variogram and log-log variogram of snow mechanical properties and microtopographic indicators. The comparison shows some similarities in autocorrelation distances for snow depth, snow density, snow microstructural strength, TPI, vegetation height and the Winstral index. GAM models suggest several significant covariates such as snow depth and snow surface slope, but also TPI, Winstral index, vegetation height and distance to vegetation. The percentage of variance explained is around 50% ranging from 20% to 80%. Models predictions were better for the slab depth and slab density with higher variance explained (around 60/70%) with lower RMSE than point snow stability indicator (around 40%) with higher RMSE. At the slope scale, snow surface slope and snow depth remain the most important spatial indicators of point snow stability for backcountry recreationists in their route-finding decision making. The point snow stability map generated represents a good teaching material in avalanche skill training and awareness course. In future work, assuming that snow cover models simulate the mean snow mechanical properties of a simulation cell, the covariance function of microtropographic indicators could be used to infer the covariance function of snow mechanical properties using a gaussian process/Bayesian framework as a sub-grid parametrization scheme.

How to cite: Meloche, F., Gauthier, F., and Langlois, A.: Snow mechanical properties variation at slope scale, implication for snowpack stability assessment and snow cover models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2973, https://doi.org/10.5194/egusphere-egu22-2973, 2022.

The soil-snow interface plays a critical role in the release of glide-snow avalanches, which threaten life and infrastructure such as houses, roads, and chair lift masts in alpine areas. One of the important factors controlling glide-snow avalanche release is the presence of interfacial water between the ground and snowpack. Several mechanisms, such as percolation of water from the snow surface and geothermal melting of basal snow layers, have been postulated to explain the formation of this interfacial water. These mechanisms remain, however, poorly understood to poor predictability of glide-snow avalanche release. Here, we demonstrate the use of neutron transmission radiography for investigating the transport of water across the soil-snow interface at laboratory scales. We show that neutron transmission radiography is capable of capturing changes in water content during snow melt processes in both the snow and soil phases. Neutron imaging also revealed that the hydraulic properties of the porous interface between the soil and snow (mimicking a vegetation layer) affect the formation of an interfacial water layer. Improved understanding of the water transport across the soil-snow interface should lead to better prediction of glide-snow avalanche releases in the future.

How to cite: Lombardo, M., Lehmann, P., Kaestner, A., Fees, A., van Herwijnen, A., and Schweizer, J.: Imaging water transport across the soil-snow interface using neutron transmission radiography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5242, https://doi.org/10.5194/egusphere-egu22-5242, 2022.

A very powerful and commonly used method to assess the danger of avalanche release on a slope is the performance of snow stability tests. The present work aims to contribute towards a better understanding of snow stability test results by conducting force measurements during a snow stability test, namely the Compression Test (CT). We were particularly interested in the variability of the force applied to a potential weak layer during the test by different persons and for different snow covers. We therefore focused on the stress levels for the single taps and loading steps of a CT, and how they were influenced by different snow properties (effective depth, compaction depth, and snow hardness) as well as other factors, such as test subjects’ body weight and arm length. We used two capacitive pressure sensors to conduct force measurements during the performance of CTs at two different depths with eleven different people and at seven different locations. The evaluation and analysis of these measurements were conducted with Python. Our results showed that the penetration depth and compaction of the snow above the force sensor significantly influenced the transmission of stress. The stress levels of shoulder taps were in the range of stress levels below a standing skier and decreased non-linearly with penetration depth. Furthermore, we found that stress levels were rising also within distinct loading steps. Moreover, it was possible to confirm the influence of a person’s weight and arm length on stress levels, and consequently, statistically significant differences between different test persons. In terms of avalanche safety, our results indicate a non-linear decrease of the probability of fracture initiation with increasing tap number. Most importantly, we discovered that regardless of what was analysed, the data’s scattering decreased with increasing depth, which means that the significance of a CT result increased considerably with increasing fracture depth.

How to cite: Griesser, S. and Reiweger, I.: Force measurements during snow stability tests, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13133, https://doi.org/10.5194/egusphere-egu22-13133, 2022.

Glide-snow avalanches release due to a loss of friction at the interface between the snowpack and the ground. As a result, these avalanches can involve large snow volumes and can have a high damage potential. It is hypothesized that glide-snow avalanche release is linked to the presence of liquid water at the snow-soil interface, but the driving physical processes are poorly understood. We therefore monitored soil and snow properties at our Dorfberg field site above Davos, Switzerland. The field site is a south-east facing slope with frequent snow gliding and glide-snow avalanches. Our soil monitoring setup consisted of two parts. First, we installed a grid of 22 combined, water content and temperature sensors just below the soil surface. The grid covered the entire field site to increase the likelihood of avalanche release above a sensor. Second, we installed two vertical sensor profiles in the soil, each measuring matric potential, water content, and temperature at three depths down to -20 cm. These profiles were continued in the snow with water content and temperature sensors at three heights up to 20 cm. This allowed us to detect gradients and the direction of water flow across the snow-soil interface. Initial results from glide-snow avalanche events showed an increase in water content with increasing snow depth in the days preceding the event. In addition, the soil was close to saturation (high matric potential) and the soil temperature across the entire slope was constant and above 0 °C before release. Although more data are required to confirm our findings, these initial data provide a valuable step towards identifying the driving physical processes at the snow-soil interface. This will help to better understand the source of interfacial water and to improve glide-snow avalanche forecasting.

How to cite: Fees, A., van Herwijnen, A., Lombardo, M., and Schweizer, J.: Glide-snow avalanche formation: First insights from soil and snow monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6943, https://doi.org/10.5194/egusphere-egu22-6943, 2022.

Snow avalanches result from a complex interaction of weather and terrain, where past weather as well as internal snow cover processes play an important role. Snow cover models account for these processes and simulate the snow cover at a level of detail that allows to describe snow instability. That information on snow instability is required to assess avalanche climates rather than snow climates – which represents a classification based on weather observations. Running the model SURFEX/Crocus with long-term meteorological data (S2M reanalysis) covering the winter seasons between 1958 and 2020, we eventually derived the avalanche problem types for the 23 massifs representing different regions of the French Alps at daily resolution. This allows us to create a climatology based on avalanche problem types and study differences between mountain regions in the French Alps.

In a first step, we applied a commonly used snow climate classification to understand how in the different Alpine regions seasonal characteristics fluctuate under continental or coastal influence. In some regions the majority of the winter seasons had coastal characteristics, whereas in other regions almost half of the seasons were classified continental. In a second step, we add snow instability information by simulating avalanche problem types. This allows us to explore snow instability patterns that result from the meteorological forcing that the snow climate classification summarizes but which the snow climate classification cannot fully capture. Across the regions persistent weak layers and wet snow were the most common avalanche problems types on days when the model expected natural release. Moreover, we applied a k-means clustering to the frequencies of new snow, persistent and wet snow avalanche problem types to identify similarities between in the Alpine regions. We assessed the clustering performance and found that 4 clusters separate well our data which includes information on duration and frequency of avalanche problem types. The clusters coincide with the geographic location of the regions, i.e. Northern, Southern, inner-alpine or front-range regions have specific characteristics that manifest in frequency of avalanche problem types.

We showed how avalanche problem types can be used as an additional descriptor to the snow climate classification to include snow instability specific information. As avalanche problem types identified spatial differences between different snow climate regions, we are now ready to analyze how those changes evolve with time in the different regions.

How to cite: Reuter, B., Hagenmuller, P., and Eckert, N.: Simulating avalanche problem types to assess avalanche climate zones in the French Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7909, https://doi.org/10.5194/egusphere-egu22-7909, 2022.

Dry-snow slab avalanches are the main cause of avalanche fatalities in mountainous regions. Their release is a multi-scale process which starts with the formation of a localized failure in a highly porous weak snow layer underlying a cohesive snow slab, followed by rapid crack propagation within the weak layer. Finally, a tensile fracture through the slab leads to its detachment. The dynamic process of crack propagation, which affects the size of avalanche release zones, is still rather poorly understood. To shed more light on this crucial process, we performed a series of flat field fracture mechanical experiments, up to ten meters long, over a period of 10 weeks from January to March 2019. These experiments were analyzed using digital image correlation to derive high-resolution displacement fields to compute dynamic crack propagation metrics. We then used a 3D discrete element method (DEM) to numerically simulate these experiments to investigate the micro-mechanics. Both in the experiments and in the simulations, we observed a stationary regime after several meters of crack propagation. The DEM simulations showed that in this regime crack propagation is driven by compressive stresses. A parametric DEM study showed that the elastic moduli of the slab and weak layer, as well as weak layer shear strength, are key variables affecting crack propagation. Our results also highlight that these mechanical parameters influence the propagation distance required to attain the steady-state regime. Finally, DEM simulations on steep slopes showed the emergence of a so-called supershear crack propagation regime, driven by shear stresses, in which crack propagation velocity becomes intersonic. These simulations were confirmed by preliminary experimental results obtained on a steep slope. Our experimental and numerical datasets provide unique insight into the dynamics of crack propagation and lay the foundation for comprehensive studies on the influence of snowpack mechanical properties on the fundamental processes of slab avalanche release.

How to cite: Gregoire, B., Bastian, B., Johan, G., Alec, V. H., and Jürg, S.: Numerical and experimental investigation of crack propagation regimes in large-scale snow fracture experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7747, https://doi.org/10.5194/egusphere-egu22-7747, 2022.

Highly-porous cohesive granular materials such as snow possess complex modes of failure. Apart from classical failure modes, they show microstructural failure and fragmentation associated with densification within a local, narrow zone. Therefore cracks may form and propagate even under compressive load ('anticracks', 'compaction bands'). Such failure modes are of great importance in a range of geophysical contexts. For instance, they may control the release of snow slab avalanches and influence fracturing of porous rock formations. In the snow context, specific failure mechanisms of the ice matrix and their interplay with the microstructure geometry of snow are still poorly understood. Recently, X-ray computed tomography images have provided insights into snow microstructure and capability of directly simulating its elastic response by the finite element method (FEM). However, apart from thermodynamically driven healing processes the inelastic post-peak behaviour of the microstructure is controlled by localized damage, large deformations and internal contacts.  As a result of the well-known limitations of FEM to capture these processes we use Peridynamics (PD) as a non-local continuum method to approach the problem. Due to its formulation, (micro)cracks and damage are emergent features of the problem solution that do not need to be known or located in advance. In this contribution we perform unconfined displacement controlled high strain-rate uniaxial compression simulations of snow microstructures within a peridynamic framework. Computed tomography images of snow specimen serve as a simulation data base. The obtained results show a novel insight into local failure of snow and allow a better comprehension of the underlying failure mechanisms. This study contributes to improve non-local macroscopic constitutive models for snow for future applications.

How to cite: Ritter, J. and Zaiser, M.: High Strain Rate Compressive Failure of Porous Brittle Snow Microstructures Simulated by Peridynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12376, https://doi.org/10.5194/egusphere-egu22-12376, 2022.

Snow slab avalanches release due to crack propagation within a weak snow layer buried below a cohesive snow slab. In 1979, McClung [1] described this process assuming an interfacial and quasi-brittle shear failure for the weak layer. This model fails to explain observations of propagation on low angle terrain and remote avalanche triggering. To address this shortcoming, Heierli et al. [2] adapted in 2008 the anticrack concept developed for porous rocks to weak snow layers. In 2018, Gaume et al. [3] showed that mixed mode shear-compression failure and subsequent volumetric collapse (anticrack) of the weak layer were necessary ingredients to accurately model propagation mechanisms, thus reconciling apparently conflicting theories. More recently, large scale simulations based on the Material Point Method (MPM) and field observations revealed a transition from slow anticrack to fast supershear crack propagation [4]. This transition, which occurs after a few meters suggests that a pure shear model should be sufficient to estimate the release sizes of large avalanche release zones.

Motivated by this new understanding, we developed a depth-averaged MPM for the simulation of snow slab avalanches release. Here, the weak layer is treated as an external shear force acting at the base of the slab and is modeled as an elastic quasi-brittle material with residual friction. We first validate the model based on simulations of the so-called Propagation Saw Test (PST) and comparing numerical results to analytical solutions and 3D simulations. Second, we perform large scale simulations and analyse the shape and size of avalanche release zones. Finally we apply the model to a complex real topography. Due to the low computational cost compared to 3D MPM, we expect our work to have important operational applications for the evaluation of avalanche release sizes required as input in hazard mapping model chains. Finally, the model can be easily adapted to simulate both the initiation and dynamics of shallow landslides.

References

[1] McClung, D.M. Shear fracture precipitated by strain softening as a mechanism of dry slab avalanche release. Journal of Geophysical Research: Solid Earth (1979) 84 3519--3526
[2] Heierli, J., Gumbsch, P. and Zaiser, M. Anticrack nucleation as triggering mechanism for snow slab avalanches. Science (2008) 321(5886):240-3
[3] Gaume, J., Gast, T. and Teran, J. and van Herwijnen, A and Jiang, C. Dynamic anticrack propagation in snow. Nature Communications (2018) 9 3047
[4] Trottet, B., Simenhois, R., Bobillier, G., van Herwijnen, A., Jiang, C. and Gaume, J. Transition from sub-Rayleigh anticrack to supershear crack propagation in snow avalanches. (2021). doi:10.21203/rs.3.rs-963978/v1

How to cite: Guillet, L., Trottet, B., Blatny, L., Steffen, D., and Gaume, J.: A Depth-Averaged Material Point Method for the Simulation of Snow Slab Avalanche Release, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5830, https://doi.org/10.5194/egusphere-egu22-5830, 2022.

A detailed knowledge of avalanche dynamics is crucial to optimize flow models that allow avalanche simulation tools to be effectively used for dimensioning mitigation measures or identifying endangered terrain. There are different ways to observe the dynamics in an avalanche during the flow. It can be achieved with remote sensing approaches or fixed sensor systems that interact with the flow. In this Abstract we introduce an inflow sensor system, the so called AvaNodes that are equipped with a variety of sensors, investigating the potential of Global Navigation Satellite System (GNSS) modules.

The AvaNode is a cube with 16 cm side length. It is designed to flow in the avalanche and obtain GNSS position and velocity, inertial measurement unit (IMU) based accelerations, angular velocities and the magnetic flux densities, and temperature by means of an infrared thermometer.

The utilized GNSS modules are from the ublox CAM-M8 series, that have a position accuracy of 2 m and velocity accuracy of 0.05 m/s, according to the datasheet.

To estimate the position accuracy of the AvaNode while covered with snow, experiments were performed with the AvaNode buried in snow at different depths at a known location. Results show that the position accuracy is highly dependent on the number of satellites that the module currently tracks, ranging between 2 and 10 meters.  To estimate the GNSS velocity accuracy while the AvaNode is covered with snow, a dynamic experiment with moving sensors was performed. The AvaNode was transported on a sledge while it was buried in 10 and 20 cm of snow. An accuracy in the range of 0.5 m/s was observed, allowing to potentially investigate the dynamics in real avalanches. The influence of burial or snow cover depth did not show conclusive influence on the results and requires further investigation. In 2021 this inflow sensor system was used in two avalanche experiments, on March 15 and 16, obtaining start and end positions, as well as promising GNSS velocities. On March 15 one AvaNode was transported by an avalanche, where the GNSS velocity shows a maximum of 15 m/s and a duration of 50 seconds of the avalanche. On March 16 two AvaNodes were picked up by an avalanche, both showing similar velocity distributions, with a maximum velocity of 17 and 13 m/s.

How to cite: Neuhauser, M., Neurauter, R., Tuermer, S., Gerstmayr, J., Adams, M., Koehler, A., and Fischer, J.-T.: Investigating the potential of GNSS-modules for inflow avalanche measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6416, https://doi.org/10.5194/egusphere-egu22-6416, 2022.

Two large dry slab avalanches from the 700 m high mountain above the village of Flateyri, NW Iceland, partly overflowed two deflecting dams on the eve of the 14th of January 2020. While most of the avalanche snow was deflected from the village by two 15-20 m high deflecting dams, approximately 10 % of the total mass overflowed the dams, injuring one person and causing damage to one house, three vehicles and breaking a steel mast, a timber shed and shrub/bush. Radar measurements of the speed of the avalanche, the density of the avalanche deposit, damage to structures, and witness accounts suggest that the avalanche was a transitional avalanche with a 400 m long fluidized head, followed by a denser core, upstream of the dam and the overflow belonged to a fluidized region of intermittent density. It is believed that the sonic speed in such fluidized regions can be one order of magnitude lower than that of air. In such a case the speed of the fluidized region can be comparable to the sonic speed within it, giving rise to the possibility of supersonic shock wave formation.

The aim of the current study is to analyze the dynamics of the part of the avalanches that overflowed the deflecting dams, based on the in-situ damage that the overflow caused, and attempt to distinguish between damage that a shockwave would cause and damage that the kinetic energy in the overflow may cause. The focus is on the three vehicles that were hit by the avalanche overflow and transported 13 to 20 m horizontally, two of them over a three to four meters high pile of snow. We find that aerodynamic forces caused by the overflow could have been large enough and lasted long enough to transport the cars the observed distances. The calculations are simplified and effects of an uneven lateral density distribution are omitted. Supersonic conditions in the overflow are considered unlikely.

A moving supersonic shockwave would not have lasted long enough (order of 10-100 ms) to transport the cars. Weak compression shocks in subsonic avalanche flow may form upstream of stationary obstacles, due to the compressibility of the avalanche front. Those would also be too short-lived to contribute to the transport of the vehicles. Damage to brittle objects (with a resonance period in the same range as the pressure wave period, 2 to 100 ms), such as window glass, doors, and non-reinforced concrete walls, can however be contributed to such short-lived impacts and interactions with moving pressure waves.

Shockwave-turbulence interactions are generally known to cause high-intensity noise radiation of various characteristics. Standing shocks, due to supersonic flow upstream of stationary obstacles (e.g. buildings or high cliffs) could be a cause for impulsive sounds observed prior to the arrival of the avalanche.

How to cite: Lárusson, R., Hakonardottir, K. M., and Hafsteinsson, H. E.: Vehicle damage and transport and possible shock wave formation in the Flateyri avalanche in January 2020., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8220, https://doi.org/10.5194/egusphere-egu22-8220, 2022.

At the core of many avalanche simulation tools, numerical kernels are utilized to solve flow model equations. Aside from trying to fit the models as best as possible to the current understanding of actual flow mechanisms, these kernels have to fulfill general mathematical requirements, such as convergence, stability and consistency. The precision of numerical solutions is limited and needs to be determined by appropriate uncertainty quantification approaches. It is also necessary to assess the impact of input variability propagating through the numerical kernel.

To allow kernel testing and uncertainty quantification, the AvaFrame framework provides a suite of test cases as well as analysis tools. This includes tests with known solutions usable to determine the kernel errors (ana1Tests) and idealized/real world topographies to estimate effects of varying simulation setups. By changing numerical settings, flow model setup or input data it is possible to show their effects on simulation results in a quantitative manner. It therefore allows us to relate input variations to the uncertainty in simulation results. Error and uncertainty quantification is done using modules for computing statistical measures (ana4Stats), indicators along an avalanche path (ana3AIMEC) and various visualization routines.

We showcase this for our com1DFA dynamical dense flow avalanche (DFA) module. The kernel of com1DFA is based on depth integrated governing equations (shallow water) and solved numerically using the smoothed particle hydrodynamics (SPH) method. Applying our analysis tools, we evaluate the convergence of the DFA kernel with regard to the numerical parameters time step, SPH kernel size and particles size. We investigate the accuracy and precision of the numerical solution using the similarity solution test, a test with a semi-analytic solution for depth integrated equations. It allows us to establish a suitable relation between time step, SPH kernel size and particles size for the com1DFA kernel.

Using the same approach for an avalanche setup, we can also vary selected input parameters like friction coefficients and/or release thickness and quantify the resulting uncertainties on simulation results, e.g. runout and peak flow variables.

How to cite: Tonnel, M., Wirbel, A., Oesterle, F., and Fischer, J.-T.: Are avalanche models correct? An uncertain view on convergence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7745, https://doi.org/10.5194/egusphere-egu22-7745, 2022.

Mountain forests provide natural protection against avalanches. They can both prevent avalanche formation in release zones and reduce avalanche mobility in runout areas. Although the braking effect of forests has been previously explored through global statistical analyses on documented avalanches, little is known about the mechanism of snow detrainment in forests for small and medium avalanches. This study investigates the detrainment and braking of snow avalanches in forested terrain, by performing three-dimensional simulations using the Material Point Method (MPM) and a large strain elastoplastic snow constitutive model based on Critical State Soil Mechanics. First, the snow internal friction is evaluated using existing field measurements based on the detrainment mass, showing the feasibility of the numerical framework and offering a reference case for further exploration of different snow types. Then, we systematically investigate the influence of snow properties and forest parameters on avalanche characteristics. Our results suggest that, for both dry and wet avalanches, the detrainment mass decreases with the square of the avalanche front velocity before it reaches a plateau value. Furthermore, the detrainment mass significantly depends on snow properties. It can be as much as ten times larger for wet snow compared to dry snow. By examining the effect of forest configurations, it is found that forest density and tree diameter have cubic and square relations with the detrainment mass, respectively. Finally, through an energetic and mass study, our results suggest that compared to a regular aligned arrangement, forests with random and regular staggered arrangements have better protective effect. The outcomes of this study may contribute to the development of improved formulations of avalanche-forest interaction models in popular operational simulation tools and thus improve hazard assessment for alpine geophysical mass flows in forested terrain.

How to cite: Védrine, L., Li, X., and Gaume, J.: Detrainment and braking of small to medium snow avalanches interacting with forests., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7278, https://doi.org/10.5194/egusphere-egu22-7278, 2022.

How to cite: D'Amboise, C., Neuhauser, M., Hormes, A., Ploerer, M., Fischer, J.-T., and Teich, M.: Modelling forest effects on snow avalanche runout with the Flow-Py simulation tool, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10346, https://doi.org/10.5194/egusphere-egu22-10346, 2022.