CR5.3

Snow avalanche formation: from snow mechanics to avalanche detection

Snow avalanches range among the most prominent natural hazards which threaten mountain communities worldwide. Snow avalanche formation is a complex critical phenomenon which starts with failure processes at the scale of snow crystals and ends with the release of a large volume of snow at a scale of up to several hundred meters. The practical application of avalanche formation is avalanche forecasting, requiring a thorough understanding of the physical and mechanical properties of snow as well as the influence of meteorological boundary conditions (e.g. precipitation, wind and radiation).

This session aims to improve our understanding of avalanche formation processes and to foster the application to avalanche forecasting. We therefore welcome contributions from novel field, laboratory and numerical studies on topics including, but not limited to, the mechanical properties of snow, snow cover simulations, snow instability assessment, meteorological driving factors including drifting and blowing snow, spatial variability, avalanche release mechanics, remote avalanche detection and avalanche forecasting. While the main focus of this session is on avalanche formation, detection and forecasting, it is closely linked to session ‘CR5.2 Snow avalanche dynamics: from basic physical knowledge to mitigation strategies’, which addresses avalanche dynamics, risk assessment and mitigation strategies.

Co-organized by NH1
Convener: Pascal Hagenmuller | Co-conveners: Johan Gaume, Cristina Pérez-Guillén, Alec van Herwijnen
vPICO presentations
| Tue, 27 Apr, 11:00–12:30 (CEST)

vPICO presentations: Tue, 27 Apr

Chairpersons: Pascal Hagenmuller, Cristina Pérez-Guillén
11:00–11:02
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EGU21-1341
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ECS
Léo Viallon-Galinier, Pascal Hagenmuller, Nicolas Eckert, and Benjamin Reuter

The use of numerical modeling of the snow cover in support of avalanche hazard forecasting has been increasing in the last decade. Besides field observations and numerical weather forecasting, these numerical tools provide information otherwise unavailable on the present and future state of the snow cover. In order to provide useful input for avalanche hazard assessment, different mechanical stability indicators are typically computed from simulated snow stratigraphy. Such indicators condense the wealth of information produced by snow cover models, especially when dealing with large data (e.g., large domains, high spatial resolution, ensemble forecasting). Here, we provide an overview of such indicators. Mechanical stability indicators can be classified in two types i.e., whether they are solely based on mechanical rules or whether they include additional expert rules. These indicators span different mechanical processes involved in avalanche release: failure initiation and crack propagation, for instance. The indicators rely on mechanical properties of each layer. We discuss parameterizations of mechanical properties and the associated technical implementation details. We show simplified examples of snow stratigraphy to illustrate the benefit of different stability indicators in typical situations. There is no perfect indicator to describe the instability for any situation. All indicators are sensitive to the snow cover modeling assumptions and the computation of mechanical properties and hence, require some tuning before operational use. In practice, a combination of indicators should be considered to capture the variety of avalanche situations.

How to cite: Viallon-Galinier, L., Hagenmuller, P., Eckert, N., and Reuter, B.: Mechanical stability indicators derived from detailed snow cover simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1341, https://doi.org/10.5194/egusphere-egu21-1341, 2021.

11:02–11:04
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EGU21-1578
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ECS
Benjamin Reuter, Léo Viallon-Galinier, Stephanie Mayer, Pascal Hagenmuller, and Samuel Morin

Snow cover models have mostly been developed to support avalanche forecasting. Recently developed snow instability metrics can help interpreting modeled snow cover data. However, presently snow cover models cannot forecast the relevant avalanche problem types – an essential element to describe avalanche danger. We present an approach to detect, track and assess weak layers in snow cover model output data to eventually assess the related avalanche problem type. We demonstrate the applicability of this approach with both, SNOWPACK and CROCUS snow cover model output for one winter season at Weissfluhjoch. We introduced a classification scheme for four commonly used avalanche problem types including new snow, wind slabs, persistent weak layers and wet snow, so different avalanche situations during a winter season can be classified based on weak layer type and meteorological conditions. According to the modeled avalanche problem types and snow instability metrics both models produced weaknesses in the modeled stratigraphy during similar periods. For instance, in late December 2014 the models picked up a non-persistent as well as a persistent weak layer that were both observed in the field and caused widespread instability in the area. Times when avalanches released naturally were recorded with two seismic avalanche detection systems, and coincided reasonably well with periods of low modeled stability. Moreover, the presented approach provides the avalanche problem types that relate to the observed natural instability which makes the interpretation of modeled snow instability metrics easier. As the presented approach is process-based, it is applicable to any model in any snow avalanche climate. It could be used to anticipate changes in avalanche problem type due to changing climate. Moreover, the presented approach is suited to support the interpretation of snow stratigraphy data for operational forecasting.

How to cite: Reuter, B., Viallon-Galinier, L., Mayer, S., Hagenmuller, P., and Morin, S.: A tracking algorithm to identify slab and weak layer combinations for assessing snow instability and avalanche problem type, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1578, https://doi.org/10.5194/egusphere-egu21-1578, 2021.

11:04–11:06
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EGU21-4056
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ECS
Gianmarco Vallero, Monica Barbero, Fabrizio Barpi, Mauro Borri-Brunetto, and Valerio De Biagi

The progressive failure of a snow layer deposited on a stiff substrate is at the base of the comprehension of several physical processes that can be found both in natural and artificial conditions. For instance, glide avalanches often originate from the reduction of the basal friction between the snowpack and the underlying ground due to the presence of liquid water film or depth hoar at the snow-ground interface. Moreover, the interaction between snow and construction materials relates to many other applications such as the study of new and more efficient snow removal techniques, the safety of travelers along snow covered roads, the snow redistribution from roofs and buildings, etc. 

Despite this large number of application fields, laboratory investigations are still limited. We performed cold room tests on artificially made snow-mortar interface specimens through a direct shear test device. The effects of confinement pressure, temperature and dry snow hardness (due to sintering times) were taken into account. The tests were carried out in displacement-controlled conditions in order to study the entire failure process at the interface and the following irreversible sliding. The results show some interesting and encouraging aspects for understanding the shear strength of the interface. From a micromechanical point of view we recorded the tests with a high-definition video camera and analyzed the data with the Particle Image Velocimetry technique to obtain the motion fields on the external side of the specimens. Here, we present and discuss some preliminary results of the experimental activity and suggest some future implementations and further developments of the studied topic.       

How to cite: Vallero, G., Barbero, M., Barpi, F., Borri-Brunetto, M., and De Biagi, V.: A new experimental set-up to study the shear strength of snow-mortar interfaces, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4056, https://doi.org/10.5194/egusphere-egu21-4056, 2021.

11:06–11:08
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EGU21-5498
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ECS
Anurag Kumar Singh, Puneet Mahajan, and Praveen Kumar Srivastava

An existing orthotropic elasto-plastic model, which takes the microstructure fabric of trabecular bone into consideration, is extended for snow and used to study the stress response in a layered snowpack. The mean intercept length, determined from X-ray tomography, represents the microstructure fabric in the macroscopic constitutive law. The yield surface for snow accounts for strength asymmetry of snow in tension and compression and shows isotropic hardening till ultimate strength is reached and then softens till complete failure. Tomographic image dataset of various snow types in conjunction with 3D μ- FE analysis of these snow types was used to evaluate the elastic and failure criteria constants in the model. The macroscopic law is implemented as a user subroutine FE code to predict the stress-strain response of snow samples and shows good agreement with the μ-FE based data.

The stress-strain law is used to study stresses in a snowpack of length 5m and thickness between 0.11 to 0.81m with a strong layer of round grain and a weak layer of faceted grains. A plane strain finite element analysis is performed. The density of the strong and weak layers is approximately 210kg/m3, and 118kg/m3, respectively. The snowpack was subjected to gravity, and a skier loads (80kg) and stresses were investigated for slope angles of 0o, 30o, and 90o. The variation of compressive stress normal to slope and shear stress along the snowpack's length for different thicknesses of the strong layer is computed. The maximum normal compressive stress and shear stresses are observed at the centre of the weak layer. The normal compressive stress pattern obtained is in agreement with the previous studies.

How to cite: Singh, A. K., Mahajan, P., and Srivastava, P. K.: Fabric based strength criterion and its application on a layered snowpack, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5498, https://doi.org/10.5194/egusphere-egu21-5498, 2021.

11:08–11:10
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EGU21-6108
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ECS
Lars Blatny, Henning Löwe, Stephanie Wang, and Johan Gaume

The effective mechanical behavior of snow can be deduced from microstructural homogenization through numerical simulations. Although such numerical upscaling of elasticity and strength of snow microstructures is standard (using FEM), numerical schemes to study generic features of the transition from small to large strain situations that involve yielding and failure are scarce. This prevents the development of accurate homogenized constitutive models valid for the post-failure and large deformation regimes. It has been shown that treating this transition is feasible using DEM under the assumption of particulate microstructures. However, this requires snow microstructures to be segmented into a granular collection of (usually spherical) cohesive elements. Here, we suggest generating random porous microstructures by level-cutting Gaussian random fields and using the material point method to numerically simulate them under mechanical loading. This allows investigating both small and large deformation characteristics of irregular porous media, such as snow, where a segmentation into grains and bonds can be ambiguous. We demonstrate our approach by examining elasticity and failure as a function of a wide range of solid volume fractions, from 20% (low-density snow) to 80% (high-density firn), as the most important control on the mechanical behavior. Observing that onset of failure can be well described through the second order work, we show that the failure strength follows a power law similar to that of the elastic moduli. Moreover, we propose that the failure envelope can be approximated by a porosity-dependent quadratic curve in the space of the two first stress invariants. Furthermore, we observe that plastic deformation appears to be governed by an associative plastic flow rule. Finally, these results combined with a viscoplastic Perzyna model and a sintering (hardening) model should allow us to develop a universal homogenized snow constitutive model.

How to cite: Blatny, L., Löwe, H., Wang, S., and Gaume, J.: A unified framework for computational microstructure-based snow mechanics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6108, https://doi.org/10.5194/egusphere-egu21-6108, 2021.

11:10–11:12
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EGU21-6154
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ECS
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Highlight
Cristina Pérez-Guillén, Martin Hendrick, Frank Techel, Alec van Herwijnen, Michele Volpi, Olevski Tasko, Fernando Pérez-Cruz, Guillaume Obozinski, and Jürg Schweizer

Avalanche forecasting implies predicting current and future snow instability in time and space. In Switzerland, avalanche bulletins are issued daily during the winter season to warn the public about the avalanche hazard, described by region with one of five danger levels. Assessing avalanche danger is by large a data-driven, yet experience-based decision-making process. It involves analysing a multitude of data diverse in scale – time and space, and concluding by expert judgment on the avalanche scenario. Numerous statistical models were developed in the past, but rarely applied due to limited usefulness in operational forecasting. Modern machine learning techniques open up new possibilities for developing support tools for operational avalanche forecasting. With this aim, we developed a data-driven approach based on the supervised Random Forest (RF) classifier to automatically predict the danger level for dry-snow avalanche conditions in the Swiss Alps. A large database of more than 20 years of meteorological data and modelled snow stratigraphy data obtained with the numerical snow cover model SNOWPACK were used to train the RF algorithm. We optimized the model and selected the best set of input features that combine meteorological variables and features extracted from the simulated profiles, resampled at the same daily resolution as the forecasts. Our target variable was the regional danger level forecast in the public bulletin. We evaluated the predictive performance of the RF model with an independent test set with data of two winter seasons (2018-2019 and 2019-2020). The test set accuracy was 72 %, which is slightly lower than the accuracy estimate of the public forecasts (about 76 %). Given this uncertainty in our target variable, we trained an optimized RF model on a subset containing so-called verified avalanche danger levels. The test set accuracy then increased to 80 %. During the winter season 2020-2021, both RF models were tested in operational setting and automatically predicted a ‘nowcast’ and a ‘forecast’ in real-time.  In parallel, we also tested a deep recurrent neural network model, which used a 7-days time series with 3-hours time resolution as input and also predicted the avalanche danger level. We present a comparison of the performance of the three models. This is one of the first times that a data-driven approach is tested in real-time as a feasible tool for operational avalanche forecasting.

How to cite: Pérez-Guillén, C., Hendrick, M., Techel, F., van Herwijnen, A., Volpi, M., Tasko, O., Pérez-Cruz, F., Obozinski, G., and Schweizer, J.: Data-driven automatic predictions of avalanche danger in Switzerland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6154, https://doi.org/10.5194/egusphere-egu21-6154, 2021.

11:12–11:14
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EGU21-6600
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ECS
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Highlight
Oscar Dick, Léo Viallon-Galinier, Pascal Hagenmuller, Mathieu Fructus, Matthieu Lafaysse, and Marie Dumont
Mineral dust and black carbon are potent drivers of the snow cover evolution. After their deposition on the snow surface, they can impact snow albedo and thus the snowpack evolution including the timing of snow-melt. While BC deposition is rather constant along the winter season, mineral dust deposition is more sporadic in the French Alps, subject to large dust outbreak events coming from Sahara. The dust deposition drastically changes the snow color, its absorption of solar energy and, as a consequence, modifies the internal temperature of the snow layers and their metamorphism. While mountain practitioners often report higher avalanche activities after dust deposition events, there is, up to now, no clear evidence neither from observations nor modelling that dust deposition enhances avalanche activity. Here, we investigate, using ensemble detailed snowpack simulations, the impact of dust outbreak on snow metamorphism, snow stratigraphy and mechanical stability by comparing simulations with and without dust deposition under several meteorological conditions. The results show that the dust deposition can impact the spatial and temporal distribution of the unstable slopes. The effect of the deposition largely depends on the timing of dust deposition with respect to subsequent snowfalls. It also depends on the elevation, the aspect and the time since deposition event. By using multiphysics simulations, we were able to assess the robustness of our conclusions with respect to snowpack modelling errors.

How to cite: Dick, O., Viallon-Galinier, L., Hagenmuller, P., Fructus, M., Lafaysse, M., and Dumont, M.: Can Saharan dust deposition impact snow stability in the French Alps?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6600, https://doi.org/10.5194/egusphere-egu21-6600, 2021.

11:14–11:16
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EGU21-7772
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ECS
Bastian Bergfeld, Alec van Herwijnen, Gregoire Bobillier, and Jürg Schweizer

For a slab avalanche to release, a weak layer buried below a cohesive snow slab is required, and the system of weak layer and slab must support crack propagation over large distances. This process, called “dynamic crack propagation”, is highly relevant for avalanche release, and computational models are nowadays able to model crack propagation over increasingly larger scales. Field measurements on dynamic crack propagation are however very scarce, although these are required to validate models. We therefore performed a series of flat field PST experiments up to ten meters long over a period of 10 weeks. During this time, PST results evolved from crack arrest to full propagation and back to crack arrest – reflecting the life cycle of the weak layer. All PST experiments were analyzed using digital image correlation to derive high-resolution displacement fields to compute dynamic crack propagation metrics, including crack length and speed as well as touchdown distance, the distance from the crack tip to the trailing point where the slab comes into contact with the substratum. Comparing the displacement fields during sawing to a mechanical model, we estimated the effective elastic modulus of slab and weak layer as well as the specific fracture energy of the weak layer. Our results show how dynamic crack propagation characteristics change over the life cycle of a weak layer and how these measures relate to snowpack properties such as load and effective elastic modulus of the slab. We found that crack speed was highest for PSTs resulting in full propagation and that the touchdown length increased with increasing elastic modulus of the slab. Our dataset provides unique insight into the dynamics of crack propagation, and provides valuable data to validate models used to study sustained crack propagation.

How to cite: Bergfeld, B., van Herwijnen, A., Bobillier, G., and Schweizer, J.: Characteristics of dynamic crack propagation in a weak snowpack layer over its entire life cycle, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7772, https://doi.org/10.5194/egusphere-egu21-7772, 2021.

11:16–11:18
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EGU21-8253
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ECS
Bertil Trottet, Ron Simenhois, Gregoire Bobillier, Alec van Herwijnen, Chenfanfu Jiang, and Johan Gaume

Snow slab avalanche release can be separated in four distinct phases : (i) failure initiation in a weak snow layer buried below a cohesive snow slab, (ii) the onset and, (iii) dynamic phase of crack propagation within the weak layer across the slope and (iv) the slab release. The highly porous character of the weak layer implies volumetric collapse during failure which leads to the closure of crack faces followed by the onset of frictional contact. To better understand the mechanisms of dynamic crack propagation, we performed numerical simulations, snow fracture experiments, and analyzed the release of full scale avalanches. Simulations of crack propagation are based on the Material Point Method (MPM) and finite strain elastoplasticity. Experiments consist of the so-called Propagation Saw Test (PST). Concerning full scale measurements, an algorithm is applied to detect changes in image pixel intensity induced by slab displacements. We report the existence of a transition from sub-Rayleigh anticrack to supershear crack propagation following the Burridge-Andrews mechanism. In detail, after reaching the critical crack length, self-propagation starts in a sub-Rayleigh regime and is driven by slab bending induced by weak layer collapse. If the slope angle is larger than a critical value, and if a so-called super critical crack length is reached, supershear crack propagation occurs. The corresponding critical angle may be lower than the weak layer friction angle due to the loss of frictional resistance during volumetric collapse. The sub-Rayleigh regime is driven by mixed mode anticrack propagation while the supershear regime corresponds to a pure mode II propagation with intersonic crack speeds (v: crack speed, cs: shear wave speed, cp: longitudinal wave speed, E: slab Young's modulus and ρ: slab density). This intersonic regime of crack propagation thus leads to pure tensile slab fractures initiating from the bottom of the slab as opposed to top initiations induced by slab bending in the sub-Rayleigh regime. Key ingredients for the existence of this transition are discussed such as the role played by friction angle, collapse height and slab secondary fractures. 

How to cite: Trottet, B., Simenhois, R., Bobillier, G., van Herwijnen, A., Jiang, C., and Gaume, J.: From sub-Rayleigh to intersonic crack propagation in snow slab avalanche release, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8253, https://doi.org/10.5194/egusphere-egu21-8253, 2021.

11:18–11:20
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EGU21-9475
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ECS
Pere Roig Lafon, Emma Suriñach, and Mar Tapia

Knowledge of the snow avalanche release area is key information in snow avalanche studies. However, it is not easy to obtain from a remote location. The study of the seismic vibrations produced in the initial stages of the snow avalanche, makes possible to identify their origin and to link them to the starting area of the snow avalanche. We developed a methodology for this purpose, applied to seismic data acquired from a 3D seismic station (2Hz eigenfrequency) placed at Cavern A in Vallée de la Sionne experimental site (VDLS, WSL-SLF), deployed in 2013 by UB-RISKNAT. This is the closest position to the snow avalanche release areas, at 700 m to the farthest point. We focus on spontaneous triggered snow avalanches to achieve better signal-to-noise ratio and to be more realistic on its application.

For the isolation of the Signal Onset (SON) section of seismic data, which corresponds to those vibrations produced by the initial stage of the snow avalanche, we use the STA/LTA ratios and seismic signal amplitude, common methodologies in seismology. The STA/LTA is used for the identification of the first vibrations produced by the movement of the snow mass and the seismic signal amplitude thresholds for the identification of the end of the SON section -when the snow avalanche front reaches the seismic sensor position-. The 3D seismic data [ZNE components] of the SON section were processed in time windows. The study of polarization of the particle motion to obtain the direction of the back-azimuth of the signal (Vidale, 1986; Jurckevicks, 1988) was carried out for each time window of the seismic signal. The accumulation of back-azimuth directions for the entire SON section is related to the origin of the vibrations and, by extension, to the snow avalanche release area.

The entire algorithm has been automated. In its application on all the trigger activations at VDLS since 2015 until 2020, it was achieved a success rate of 78% on snow avalanche release area identification. In addition, we defined an algorithm based on STA/LTA ratio to select the snow avalanches from other seismic events, used with a success rate of 95%.

We present the application of our method in a case study, a large spontaneous snow avalanche released on 16th February 2018 at VDLS. The snow avalanche had two main release areas, clearly identified in photos of the site. The two developed fronts can be recognized in the seismic data. The directions to the release areas from Cavern A position can be identified using the presented method. Also, more interpretations can be done on the downhill snow avalanche path.

How to cite: Roig Lafon, P., Suriñach, E., and Tapia, M.: The use of seismic ground particle motion for snow avalanche release area identification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9475, https://doi.org/10.5194/egusphere-egu21-9475, 2021.

11:20–11:22
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EGU21-11482
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ECS
Bobillier Gregoire, Bergfled Bastian, Gaume Johan, van Herwijnen Alec, and Schweizer Jürg

Dry-snow slab avalanche release is a multi-scale process starting with the formation of localized failure in a highly porous weak snow layer below a cohesive snow slab, which can be followed by rapid crack propagation within the weak layer. Finally, a tensile fracture through the slab leads to its detachment. About 15 years ago, the propagation saw test (PST) was developed. The PST is a fracture mechanical field test that provides information on crack propagation propensity in weak snowpack layers. It has become a valuable research tool to investigate the processes involved in crack propagation. While this has led to a better understanding of the onset of crack propagation, much less is known about the ensuing propagation dynamics. Here, we use the discrete element method to numerically simulate PSTs in 3D and analyze the fracture dynamics using a micro-mechanical approach. Our DEM model reproduced the observed PST behavior extracted from experimental analysis. We developed different indicators to define the crack tip that allowed deriving crack speed. Our results show that crack propagation in level terrain reaches a stationary speed if the snow column is long enough. Moreover, we define stress concentration sections. Their length evolution during crack propagation suggests the development of a steady-state stress regime. Slab and weak layer elastic modulus, as well as weak layer shear strength, are the key input parameters for modeling crack propagation; they affect stress concentrations, crack speed, and the critical length for the onset of crack propagation. The results of our sensitivity study highlight the effect of these mechanical parameters on the emergence of a steady-state propagation regime and consequences for dry-snow slab avalanche release. Our DEM approach opens the possibility for a comprehensive study on the influence of the snowpack mechanical properties on the fundamental processes for avalanche release.

How to cite: Gregoire, B., Bastian, B., Johan, G., Alec, V. H., and Jürg, S.: Micro-mechanical modeling using DEM to study the effect of mechanical properties on crack propagation for snow slab avalanches, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11482, https://doi.org/10.5194/egusphere-egu21-11482, 2021.

11:22–11:24
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EGU21-12259
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ECS
Stephanie Mayer, Alec van Herwijnen, and Jürg Schweizer

Numerical snow cover models enable simulating present or future snow stratigraphy based on meteorological input data from automatic weather stations, numerical weather prediction or climate models. To assess avalanche danger for short-term forecasts or with respect to long-term trends induced by a warming climate, modeled snow stratigraphy has to be interpreted in terms of mechanical instability. Several instability metrics describing the mechanical processes of avalanche release have been implemented into the detailed snow cover model SNOWPACK. However, there exists no readily available method that combines these metrics to predict snow instability.

To overcome this issue, we compared a comprehensive dataset of almost 600 manual snow profiles with SNOWPACK simulations. The manual profiles were observed in the region of Davos over 17 different winter seasons and include a Rutschblock stability test as well as a local assessment of avalanche danger. To simulate snow stratigraphy at the locations of the manual profiles, we interpolated meteorological input data from a network of automatic weather stations. For each simulated profile, we manually determined the layer corresponding to the weakest layer indicated by the Rutschblock test in the corresponding observed snow profile. We then used the subgroups of the most unstable and the most stable profiles to train a random forest (RF) classification model on the observed stability described by a binary target variable (unstable vs. stable).

As potential explanatory variables, we considered all implemented stability indices calculated for the manually picked weak layers in the simulated profiles as well as further weak layer and slab properties (e.g. weak layer grain size or slab density).  After selecting the six most decisive features and tuning the hyper-parameters of the RF, the model was able to distinguish between unstable and stable profiles with a five-fold cross-validated accuracy of 88%.

Our RF model provides the probability of instability (POI) for any simulated snow layer given the features of this layer and the overlying slab. Applying the RF model to each layer of a complete snow profile thus enables the detection of the most unstable layers by considering the local maxima of the POI among all layers of the profile. To analyze the evolution of snow instability over a complete winter season, the RF model can provide the daily maximal POI values for a time series of snow profiles. By comparing this series of POI values with observed avalanche activity, the RF model can be validated.

The resulting statistical model is an important step towards exploiting numerical snow cover models for snow instability assessment.

How to cite: Mayer, S., van Herwijnen, A., and Schweizer, J.: A random forest model to assess snow instability from simulated snow stratigraphy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12259, https://doi.org/10.5194/egusphere-egu21-12259, 2021.

11:24–11:26
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EGU21-13989
Agraj Upadhyay, Puneet Mahajan, and Rajneesh Sharma

Abstract

Fracture propagation in weak snow layers followed by the failure of overlying homogeneous snow slab leads to the formation of snow slab avalanches. The extent of fracture propagation in the weak layer and size of the avalanche release area depends on the mechanical behavior of overlying snow layers. To model the snow slab failure in slab avalanche formation process, in present work, mechanical behavior of natural snow is studied through high strain rate (1×10-4 s-1 or higher) uniaxial tension and compression experiments on natural snow layers. Uniaxial loading and unloading experiments are also carried out to understand the permanent strains at high strain rates. Elastic modulus of snow is derived from loading unloading test data and compared with the tangent modulus obtained from maximum slope of the stress-strain curve. Tensile and compressive strengths are derived from peak load at failure and fracture energy is derived from post peak stress-strain curve. For a density range of 100-400 Kg/m3 the range of obtained mechanical properties of natural snow are: Elastic modulus: 0.1-45 MPa, Tensile strength: 0.24-20 kPa, Compressive strength: 0.1-105 kPa, Fracture energy: 0.007-0.15 J/m2. For low density snow (<150 Kg/m3) tensile and compressive strength values are quite close but for higher densities compressive strength is significantly higher than the tensile strength. At low strain rates (<1×10-4 s-1) snow generally exhibit no failure and large permanent deformations whereas, at high strain rates (1×10-3 s-1 or higher) failure strains are generally in the range 0.05-1.5 %. In all cases a sharp decrease in load at failure suggests a near brittle failure. By fitting the experimental dataset with power law, density dependent expressions for elastic modulus, tensile and compressive strength are obtained. On the basis of the experimental observations, a continuum elastic-plastic-damage material model is considered to model mechanical behavior of snow layers. To model the asymmetry in tensile and compressive strengths, pressure dependent Drucker-Prager model is considered for yield criterion and model parameters (friction angle and cohesion) are obtained using density dependent expressions of tensile and compressive strength of snow. Effective plastic strain based damage initiation and evolution models are used to model quasi-brittle failure of snow. This model has been used for modeling the snow slab failure in two dimensional propagation saw tests and the obtained results on the influence of slab density, thickness and slope angle on slab failure have been presented.



How to cite: Upadhyay, A., Mahajan, P., and Sharma, R.: Modeling snow slab failure in propagation saw test using Drucker-Prager model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13989, https://doi.org/10.5194/egusphere-egu21-13989, 2021.

11:26–11:28
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EGU21-14534
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ECS
Antoine Bernard, Pascal Hagenmuller, Guillaume Chambon, and Maurine Montagnat

Once on the ground, the microstructure of snow, i.e. the three-dimensional arrangement of ice and pores, quickly evolves with metamorphism and deforms under the overburden of the overlaying snow. Understanding these concurrent processes is important to predict the evolution of the physical and mechanical properties of snow which are crucial for many applications, such as avalanche forecasting. To this end, we monitored oedometric creep tests of snow under isothermal conditions at -8.6°C for about one week with X-ray tomography. We investigated the evolution of recent snow under a constant load of around 4 kPa, where both ice matrix creep and metamorphism are active. Our time-series comprises one of the most highly-resolved images of snow microstructure evolution, with a temporal resolution of 3 h and spatial resolution of 8.5 microns and thousands of images. Interestingly, we observed distinct effects of the overburden and of the vapor transport on the microstructure evolution. In particular, the quantification of the ice bond network through the Euler characteristic and the min-cut surface shows that metamorphism progressively increases the bond size almost independently of the applied overburden, while the application of an overburden yields a rapid increase of the bond coordination number. These distinct impacts exhibit the difficulty to accurately reproduce the time evolution of recent snow by snow cover models, whose snow microstructure representation with density and snow type remains too coarse. 

How to cite: Bernard, A., Hagenmuller, P., Chambon, G., and Montagnat, M.: Tomography-based investigation of concurrent snow creep and isothermal metamorphism, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14534, https://doi.org/10.5194/egusphere-egu21-14534, 2021.

11:28–11:30
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EGU21-15260
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ECS
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Highlight
James Glover, Sebastian Althoff, Max Witek, Christine Seupel, Seraina Braun, and Imad Lifa

Gliding snow avalanches are of growing concern for the management of ski areas, transport corridors and spatial planning. With a warming climate there appear to be increasing reports of gliding snow hazards in alpine regions. The management of gliding snow avalanches can be achieved through either stabilization or artificially triggering a slide. Triggering sliding is attractive because it has the potential to remove the hazard entirely. In this research, we investigate the potential of managing gliding snow avalanches through the early release of snow accumulations using low friction geotextiles.

A series of geotextiles have been installed on slopes between 25 and 35° during the autumn months and the behavior of snow accumulations observed during the winter. Initial findings indicate that reducing the basal friction can be effective in inducing early release of gliding snow avalanches. However, the interaction of the flanking snow pack and stauchwall appear dominant in the behavior of the system. This contribution reports on the initial findings of these experiments and discusses the potential applications to managing gliding snow avalanches.  

How to cite: Glover, J., Althoff, S., Witek, M., Seupel, C., Braun, S., and Lifa, I.: Induced glide-snow avalanches with low friction geotextiles, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15260, https://doi.org/10.5194/egusphere-egu21-15260, 2021.

11:30–11:32
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EGU21-16565
David McClung

Field observations and measurements show that dry snow slab avalanches initiate by propagating shear fractures within a relatively thin weak layer sandwiched between a planar, stronger, thicker slab above and stronger material below. After initiation, the weak layer fracture can propagate up and down slope for distances which range from about 10 to 100’s of meters to cause tensile fracture through the body of the slab which results in avalanche release.  In this paper, dynamic fracture mechanics is applied to slab tensile fracture after which avalanche release is imminent. Two mechanisms for production of tensile stress are explored employing field measurements of slab properties and lab measurements. The first considers inertial effects related to quasi-brittle fracture near the tip of a propagating weak layer shear fracture. The second is concerned with the tensile stress generation from the stress drop behind the crack front as the fracture propagates in the weak layer. Analysis suggest that both mechanisms can contribute to produce the tensile fracture line which precedes avalanche release. Even though both mechanisms may operate together, they are analyzed separately in this paper. 

How to cite: McClung, D.: Dynamic fracture mechanics in dry snow slab avalanche release, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16565, https://doi.org/10.5194/egusphere-egu21-16565, 2021.

11:32–12:30