CR3.2

CR3 EDI
Snow avalanche formation and dynamics: from snow mechanics to mitigation strategies 

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. This session is also devoted to the dynamics of dense and powder snow avalanches and their accompanying transitional regimes. One focus is their interaction with, and impact on, vulnerable elements, such as buildings, protection dams, forests, and roads.
We welcome novel experimental and computational contributions including, but not limited to the topics of spatial variability, avalanche release mechanics, remote avalanche detection, avalanche forecasting, physical vulnerability of structures impacted by snow avalanches, avalanche hazard zoning and avalanche mitigation strategies.

Convener: Cristina Pérez-GuillénECSECS | Co-conveners: Thierry Faug, Betty Sovilla, Alec van Herwijnen, Pascal Hagenmuller, Johan Gaume
Presentations
| Wed, 25 May, 15:10–18:26 (CEST)
 
Room 1.15/16

Presentations: Wed, 25 May | Room 1.15/16

Chairpersons: Bobillier Gregoire, Cristina Pérez-Guillén, Martin Hendrick
15:10–15:11
15:11–15:18
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EGU22-2973
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ECS
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On-site presentation
Francis Meloche, Francis Gauthier, and Alexandre Langlois

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.

15:18–15:25
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EGU22-5242
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ECS
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Presentation form not yet defined
Michael Lombardo, Peter Lehmann, Anders Kaestner, Amelie Fees, Alec van Herwijnen, and Jürg Schweizer

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.

15:25–15:32
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EGU22-7074
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On-site presentation
Philipp Rosendahl, Valentin Adam, Florian Rheinschmidt, Bastian Bergfeld, Alec van Herwijnen, and Philipp Weißgraeber

The accurate measurement of elastic and fracture properties of snow is challenging but crucial for the modeling of avalanche events. We propose the combination of a closed-form model of the mechanical behavior of layered snowpacks with full-field displacement measurements of propagation saw tests (PSTs) for the identification of the elastic properties of all snow layers and of the fracture toughness of weak layers.

The analytical model provides snow cover deformations, weak-layer stresses and energy release rates of cracks within the weak-layer for arbitrarily layered snowpacks. It can be used for real-time analyses of skier-loaded slopes and for stability tests such as the propagation saw test. Its real-time evaluation is particularly important for the present application.

Full-field measurements of snow cover deformations during propagation saw tests can be obtained using digital image correlation. Recordings of a simple handheld camera suffice for the present application.

The elastic material properties of each layer can then be obtained by fitting the modeled displacement field to the recorded field. The solution of this optimization problem requires many evaluations of the model with varied material parameters of all layers but finally yields the elastic properties of each layer of the snow cover.

Finally, the model allows for the calculation of weak-layer fracture toughnesses using the above determined material properties and the critical crack length of the PST experiment. The results indicate an increasing slab stiffness and an increasing weak-layer fracture toughness throughout one winter season.

How to cite: Rosendahl, P., Adam, V., Rheinschmidt, F., Bergfeld, B., van Herwijnen, A., and Weißgraeber, P.: Model-based identification of snow properties from full-field measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7074, https://doi.org/10.5194/egusphere-egu22-7074, 2022.

15:32–15:39
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EGU22-13133
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ECS
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On-site presentation
Silke Griesser and Ingrid Reiweger

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.

15:39–15:46
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EGU22-6943
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ECS
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On-site presentation
Amelie Fees, Alec van Herwijnen, Michael Lombardo, and Jürg Schweizer

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.

15:46–15:47
15:47–15:54
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EGU22-13066
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Virtual presentation
Giacomo Ulivieri, Sandro Vezzosi, Walter Steinkogler, and Lisa Dreier

During the winter seasons 2019-2020 and 2020-2021 as well as during the current season 26 avalanche infrasound detection systems (IDA®) were used by Parks Canada, the Norwegian Road Administration and Swiss cantons as support for operational decision making to minimize avalanche risk. In this period, more than 800 controlled avalanches and 2700 natural avalanches were detected and delivered in near-real-time to avalanche teams.

Operational efficiency of the Trans-Canada Highway (BC) in the winter months is highly dependent on effective avalanche control. Here the goal of infrasound technology is to provide the Glacier National Park avalanche control team with information on avalanche activity in specific avalanche sectors and paths to reduce avalanche related road closure times. Infrasound technology has also been successfully applied in sparsely vegetated terrain at high altitudes and latitudes such as in Norway, where the Norwegian Public Roads Administration operates infrasound systems along remote road sections where visual observations are difficult to gather. Experiences from regional avalanche forecasting teams in Switzerland where multiple arrays are distributed over a larger area are also presented.

In this work we want to give an overview of results obtained from the use of infrasound technology with particular focus on advantages and limitations encountered in various operational contexts. Recent results of ongoing developments such as multi array processing are also presented.

How to cite: Ulivieri, G., Vezzosi, S., Steinkogler, W., and Dreier, L.: Infrasound detection of avalanches: operational experience from 26 systems in Canada, Switzerland and Norway - individual and multi-array based approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13066, https://doi.org/10.5194/egusphere-egu22-13066, 2022.

15:54–16:01
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EGU22-3749
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On-site presentation
Arnaud Caiserman, Roy C Sidle, Deo Raj Gurung, and Ben Jarihani

Snow avalanches are one of the most predominant natural hazards in mountain areas. Every year throughout the world, they are the cause of much material destruction and loss of life. It is therefore essential for local communities and public authorities to assess areas most vulnerable to avalanches. Here, we propose a new method for automatic avalanche detection from Landsat archives, using a snow index. This open-source and user-friendly model in Google Engine is the first to automatically inventory all the avalanches that have occurred each year across wide catchment areas, over a period of 32 years. The Snow Avalanche Frequency Estimation (SAFE) model was tested in the mountains of Afghanistan - Amu Panj Basin - one of the most remote regions in the world and one of the poorest in terms of avalanche monitoring. SAFE correctly detected 76% of the actual avalanches identified on Google Earth images and in the field. Since 1990, this region of Afghanistan has been impacted by 810,000 avalanches with an average frequency of 0.88 avalanches/km²yr-1. With SAFE, it is now possible to clearly identify villages, roads, and rivers that are frequently affected by avalanches and thus help decision-makers in their investments in avalanche protection infrastructure. It was also found that the frequency of avalanches has not changed over the last 32 years, but SAFE has identified a northeast shift of these hazards, notably due to a slight increase in temperatures in the south at the beginning of winter. SAFE is the first robust model that can be used worldwide and is capable of filling data voids on snow avalanche impacts in inaccessible regions.

How to cite: Caiserman, A., Sidle, R. C., Gurung, D. R., and Jarihani, B.: Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3749, https://doi.org/10.5194/egusphere-egu22-3749, 2022.

16:01–16:08
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EGU22-7403
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ECS
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Presentation form not yet defined
Martin Hendrick, Frank Techel, Michele Volpi, Olevski Tasko, Cristina Pérez-Guillén, Alec van Herwijnen, and Jürg Schweizer

Avalanche hazard forecasting is essential to reduce the risk for people and infrastructure in mountain areas. Among the different types of avalanches, wet-snow avalanches are particularly challenging to predict due to the poor understanding of their release mechanism. We therefore trained a random forest model to predict wet-snow avalanche activity based on weather and snow measurements and downstream SNOWPACK simulations provided by automated weather stations. The model was trained on a database covering 20 years of avalanche observations (avalanche type, size, location, slope aspect) in the context of operational avalanche forecasting in Switzerland. The prediction performance (F1-score: harmonic mean between recall and precision) for wet-snow avalanche active days is around 76% (recall: 73%, precision: 80%), and is 99% for days with no activity.  The model not only well reproduced the onset, but also the end of wet-snow avalanche periods. Operational testing during winter 2021-2022 allow to evaluate differences in model performance between nowcast derived from meteorological measurements and forecast from numerical weather prediction models. Overall, the results are promising and are an important step forward a more reliable forecast of wet-snow avalanche activity.

How to cite: Hendrick, M., Techel, F., Volpi, M., Tasko, O., Pérez-Guillén, C., van Herwijnen, A., and Schweizer, J.: Automated prediction of wet-snow avalanche activity in Switzerland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7403, https://doi.org/10.5194/egusphere-egu22-7403, 2022.

16:08–16:15
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EGU22-7909
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ECS
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On-site presentation
Benjamin Reuter, Pascal Hagenmuller, and Nicolas Eckert

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.

16:15–16:22
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EGU22-9519
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ECS
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Presentation form not yet defined
Christine Seupel, Cristina Pérez Guillén, and Alec van Herwijnen

Seismic and infrasound monitoring systems have been used for remote detection and characterization of avalanches and their dynamics. Recent studies have shown that seismic and infrasound methods are very complementary. Infrasound signals from avalanches are relatively easy to detect automatically but only contain partial information about the avalanche. Seismic signals, on the other hand, are more difficult to detect automatically but contain more information about the entire avalanche. To exploit the advantages of both wave types, we installed a combined seismo-acoustic array consisting of five seismometers and five infrasound sensors at our field site in Dischma valley above Davos. Additionally, we obtained ground-truth data on avalanches from several automatic cameras, field surveys and drone flights.

Results from data collected over two winter seasons show that both dry- and wet-snow avalanche were detected by our system, and highlight differences in seismic and infrasound wave characteristics depending on the avalanche type and size. Specifically, detection distance increased with avalanche size for both wave types. Furthermore, differences in seismic and infrasound signal characteristics were generally more pronounced for wet-snow avalanches than for dry-snow avalanches. Using array techniques we localized the avalanche paths and extracted seismic and infrasound waveform features. We also trained a machine learning model to automatically identify signals generated by avalanches, with promising results. Overall, our results indicate that combining seismic and infrasound wave characteristics can improve the remote detection and characterization of avalanches.

How to cite: Seupel, C., Pérez Guillén, C., and van Herwijnen, A.: Characterization and detection of avalanche signals using a seismo-acoustic array, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9519, https://doi.org/10.5194/egusphere-egu22-9519, 2022.

16:22–16:23
16:23–16:30
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EGU22-5723
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Presentation form not yet defined
Bertil Trottet, Ron Simenhois, Grégoire Bobillier, Alec van Herwijnen, and Johan Gaume

The release of snow slab avalanches starts with the failure of highly porous weak snow layer buried beneath a cohesive snow slab leading to mixed-mode crack propagation along the slope. The first modelling attempt of the process date back to 1979 with a pure shear weak layer fracture assumption proposed by McClung. Later, Heierli extended the concept of anticrack, to account for weak layer volumetric collapse and subsequent slab bending. Recent advances reconciled these different approaches and have shown the existence of a supershear crack propagation regime leading to intersonic crack propagation speeds in the up and down-slope directions.
    
    In this work, based on the Material Point Method, finite strain elastoplasticy and critical state theory, we report a transition from sub-Rayleigh anticrack to supershear crack propagation involving the Burridge--Andrews mechanism. The existence of this transition is further confirmed by full-scale avalanche analyses. By accounting for slab fracture, we highlight that soft slabs can prevent supershear transitions to occur. In addition, it is shown that crack branching in the slab can either occur from top to bottom in the case of slow propagating anticracks or from bottom to top for supershear cracks. Through a sensitivity analysis, we investigate the conditions for crack arrest or for the so-called 'en échelon' slab fracture mechanism. Finally, full 3D simulations reveal interesting propagation and release patterns related to the interplay between cross-slope and down/up-slope propagation as well as slab tensile failure. This enables to analyse slab fracture modes at crown, flanks and staunchwall of the avalanche. These new findings allow us to reach a next step in our understanding of the avalanche release mechanics in order to predict both avalanche release shapes and sizes.

How to cite: Trottet, B., Simenhois, R., Bobillier, G., van Herwijnen, A., and Gaume, J.: New insights on avalanche release mechanics based on large-scale elastoplastic simulations , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5723, https://doi.org/10.5194/egusphere-egu22-5723, 2022.

16:30–16:37
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EGU22-7747
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Presentation form not yet defined
Bobillier Gregoire, Bergfeld Bastian, Gaume Johan, van Herwijnen Alec, and Schweizer Jürg

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.

Coffee break
Chairpersons: Thierry Faug, Betty Sovilla
17:00–17:07
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EGU22-12376
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ECS
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On-site presentation
Jonas Ritter and Michael Zaiser

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.

17:07–17:14
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EGU22-5830
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ECS
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On-site presentation
Louis Guillet, Bertil Trottet, Lars Blatny, Denis Steffen, and Johan Gaume

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.

17:14–17:15
17:15–17:22
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EGU22-11328
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ECS
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Presentation form not yet defined
Rene Neurauter, Michael Neuhauser, Johannes Blobel, Robert Winkler, Falko Dressler, Jan-Thomas Fischer, and Johannes Gerstmayr

The dynamics of snow avalanches and in particular their rheology is of big importance to develop improved avalanche models and thus increase safety in mountainous areas. Existing measurement systems only allow a limited in situ view of the dynamics of snow avalanches and therefore demand the development of innovative measurement systems. Furthermore, due to the limited measurement capability of existing systems, comprehensible motion reconstruction is currently not possible. Therefore, the aim of this work is to present a measurement system that enables accurate in flow observations of snow avalanches and has the mechanical properties of a typical snow granule. A main objective of the measurement system is to allow a full motion reconstruction regarding translations and rotations with a high sampling rate and without exceeding sensor ranges.

The newly developed system, denoted as AvaNode, has the shape of a concave cube with a variable density to fit typical snow granules in flowing avalanches and their deposits. The AvaNode contains a strapdown inertial navigation sensor capable of measuring accelerations, angular velocities, and magnetic flux densities with up to 400Hz and allows for an estimation of the orientations, velocities, and positions of the AvaNode using state of the art motion reconstruction algorithms. The reconstruction is significantly improved due to precise calibration of all sensors using reference measurements with a 6R robot and onsite magnetic field calibration. In order to get a refined motion trajectory, the AvaNodes are also equipped with radio ranging modules. These modules allow performing time of flight (TOF) measurements, determining the distance between several nodes. A Global Navigation Satellite System (GNSS) module determines longitude, latitude, and altitude, as well as world time, however, with low frequency resolution and larger errors due to snow coverage. To measure the temperature evolution in avalanches, an infrared temperature sensor is attached.  Multiple recovery systems like Recco rescue reflector (passive), Pieps TX600 (active), and Lambda4 Smilla (active) are integrated to allow fast retrieval of the sensors.

As first results, we present the employed sensor calibration approaches for the inertial navigation with corresponding laboratory data signatures. The sensor calibration allows in-depth analysis of motion data, identifying typical data signatures observed in avalanches. Furthermore, we show first data acquired from in-flow snow avalanche measurements, which prove the functionality of the system and allow the first insights into trajectories of snow granules, regarding accelerations, angular velocities, rotations, and position.

How to cite: Neurauter, R., Neuhauser, M., Blobel, J., Winkler, R., Dressler, F., Fischer, J.-T., and Gerstmayr, J.: An in-flow sensor system for data acquisition in snow avalanches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11328, https://doi.org/10.5194/egusphere-egu22-11328, 2022.

17:22–17:29
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EGU22-7599
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Virtual presentation
Betty Sovilla, Michael Kyburz, Camille Ligneau, Cristina Pérez- Guillén, Pierre Huguenin, Michael Hohl, and Johan Gaume

Powder snow avalanches (PSAs) are a major threat to people and infrastructure in many mountainous regions of the world. Their force is enough to easily destroy mature forests and any infrastructure located along their path. This is to some extent related to the high velocities and flow heights they can develop, but the basic physical mechanisms controlling their destructiveness remain unclear. Part of this insecurity is related to the fact that the structure of a PSA is very complex. In fact, it can be visualized as the superposition of three distinct layers: (i) a dense granular basal layer, (ii) a transition layer in the form of a turbulent flow with strong density stratification, and (iii) a dilute turbulent suspension of finer particles covering the whole.

Experimental data collected at the Vallée de la Sionne avalanche test site (VdlS) in Switzerland suggests that the destructive capacity of PSAs is largely related to high-energy pulses within this complex stratification. This data indicates that there are at least three different physical processes at the origin of the pulses, specifically: (i) waves at the surface of the dense basal layer (e.g roll-waves, erosion-deposition waves), (ii) coherent vertical structures within the transition layer, and (iii) coherent turbulence structures in the suspension layer. All of these processes, which can also coexist, can be associated with maxima in the dynamic pressure measured over a fixed obstacle.

Although the origin of these pulses still remains largely speculative, with this contribution we aim to present experimental evidence for the existence of various mechanisms of pulsing in PSAs and discuss their relevance in terms of dynamic pressure calculations.

How to cite: Sovilla, B., Kyburz, M., Ligneau, C., Pérez- Guillén, C., Huguenin, P., Hohl, M., and Gaume, J.: On the origin of the pulsing activity in powder snow avalanches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7599, https://doi.org/10.5194/egusphere-egu22-7599, 2022.

17:29–17:36
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EGU22-6416
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ECS
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Presentation form not yet defined
Michael Neuhauser, Rene Neurauter, Steffen Tuermer, Johannes Gerstmayr, Marc Adams, Anselm Koehler, and Jan-Thomas Fischer

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.

17:36–17:43
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EGU22-8220
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ECS
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Virtual presentation
Ragnar Lárusson, Kristin Martha Hakonardottir, and Haukur Elvar Hafsteinsson

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.

17:43–17:44
17:44–17:51
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EGU22-4216
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ECS
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On-site presentation
Camille Ligneau, Betty Sovilla, and Johan Gaume

The effect of climate change becomes more and more perceptible in mountain areas. For instance, in low to medium elevations, the avalanche activity is already impacted because of a warmer snow cover. This warming has an important effect on the mechanical properties of snow, especially close to 0°C, where the temperature and the presence of liquid water greatly affects the cohesion and friction. In snow avalanches, the transition from cold (T < -2°C) to warm (T = 0°C) snow generates a variety of dense flow regimes which differ drastically in terms of velocity and shearing profiles. For a cold and loose snow, one can typically observe fast flows with Bagnold-shaped profiles of a few tens of meters per second, while a warm and wet snow exhibits low- or zero-sheared velocity profiles with a magnitude of a few meters per second, over the same topography.

The present work aims to investigate the rheology of flowing snow as a function of its physical properties, with a view to bring more physics into continuum avalanches models based on empirical coefficients. We use a 2D Discrete Element Modeling (DEM) to model snow as a cohesive granular material. The simulated distinct particles interact through a contact model that can be tuned in terms of cohesion and friction, in order to satisfy the four dense flow regimes: cold dense regime, sliding slab regime, warm shear regime and warm plug regime.

First, we calibrate the contact model to find the adequate ranges of cohesion and friction corresponding to the four flow regimes. We also highlight the particular boundary conditions that are required for specific flow regimes to occur, particularly the importance of the ground friction and the initial cohesion of the snow. Second, we extract rheological features such as the friction law μ(I) and the values of θstop for each flow regime and discuss their relevance regarding avalanche dynamics. Finally, the interaction of the flow regimes with an erodible snow cover is explored and discussed qualitatively.

How to cite: Ligneau, C., Sovilla, B., and Gaume, J.: On the rheology of dense flow regimes in snow avalanches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4216, https://doi.org/10.5194/egusphere-egu22-4216, 2022.

17:51–17:58
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EGU22-7745
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ECS
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Presentation form not yet defined
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer

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.

17:58–18:05
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EGU22-1764
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ECS
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On-site presentation
Michael Lukas Kyburz, Betty Sovilla, Johan Gaume, and Christophe Ancey

Calculating snow avalanche impact pressure is an essential task for safe construction and hazard mapping in mountainous regions. Although avalanche-obstacle interaction crucially depends on the flow regime, practitioners mostly assume that impact pressure is similar to the dynamic pressure in inviscid fluids, that is, it is proportional to the square velocity weighted by an empirical drag coefficient. When fitting this coefficient to field measurements, one does not end up with a unique value, but with a range of possible values that cover more than one order of magnitude. In the absence of a physics-based framework, setting the right drag coefficient requires good working knowledge and experience from practitioners. Indeed, even for trained engineers it may be unclear how the impact pressure depends on the expected flow regime, on obstacle width, or on terrain configuration. To address these questions, we simulate the avalanche impact pressure on obstacles of varying geometry for four distinct avalanche flow regimes using the Discrete Element Method and a cohesive contact model. The results allow us to quantify the influence of the obstacle width and shape on the average impact pressure, as well as the detailed pressure distribution on the obstacle surface. Furthermore, we propose a novel method for estimating the drag coefficient based on simple geometrical considerations and key characteristics of avalanche flow. Our results are validated using experimental data from the Vallée de La Sionne test site, and make a step forward in the derivation of a physics-based framework for computing snow avalanche impact pressures for varied flow regimes depending on obstacle shape and dimensions.

How to cite: Kyburz, M. L., Sovilla, B., Gaume, J., and Ancey, C.: How investigating the physics of avalanche-obstacle interaction with DEM can help to calculate the avalanche impact pressure on obstacles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1764, https://doi.org/10.5194/egusphere-egu22-1764, 2022.

18:05–18:12
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EGU22-7984
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Virtual presentation
Alexander H. Jarosch, Tómas Jóhannesson, Kristín Martha Hákonardóttir, and Hafþór Örn Pétursson

Protective measures against snow- and landslides are widely used to improve the safety of settlements in avalanche-prone areas. Modelling of granular flow against obstructions is important for the design of catching and deflecting dams and other protective measures in run-out zones and for hazard zoning both below protective measures and in general for avalanche paths with complex terrain geometry. We describe the implementation of a two-phase (granular material and air), incompressible granular-flow rheology for the OpenFOAM / interFoam computational fluid dynamics software system based on the recently developed μ(I) granular rheology. The model has been calibrated with observations from eight large Icelandic avalanches and shown to reproduce the observed shapes of the avalanche deposits in the run-out zones, and some available radar measurements of avalanche velocities, with observed and estimated values for avalanche volume and release depth in the starting areas. Several of the avalanches are from paths with complicated geometries, including deep gullies and ridges that split the avalanche in the run-out zone, which indirectly provides constraints on the simulated flow dynamics. The model represents an important improvement with respect to depth-averaged models for snow-avalanche flow in complicated terrain geometries as it is able to simulate the full three-dimensional flow at impact with obstacles such as catching and deflecting dams and braking mounds, including the formation and time-dependent development of hydraulic jumps. Thus, splashing and airborne jets formed at impact with obstacles and landing of granular material on the terrain below obstacles can be modelled, as well as the formation of wedges behind the upstream face of dams or mounds, variations of the flow direction with depth within the flow and thus shearing overflow of the upper part of avalanches at impact with deflecting dams that deflects the main avalanche flow, the effect of the steepness of the upper face of catching and deflecting dams, and the effect of the curvature of the axis of deflecting dams and many other aspects of the flow against obstructions. Some examples of simulations of the flow of design avalanches against protective dams in Iceland will be shown in the presentation.

How to cite: Jarosch, A. H., Jóhannesson, T., Hákonardóttir, K. M., and Pétursson, H. Ö.: Full three-dimensional simulations of snow-avalanche flow with two-phase, incompressible, granular μ(I) rheology using OpenFOAM / interFoam , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7984, https://doi.org/10.5194/egusphere-egu22-7984, 2022.

18:12–18:19
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EGU22-7278
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ECS
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Virtual presentation
Louis Védrine, Xingyue Li, and Johan Gaume

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.

18:19–18:26
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EGU22-10346
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On-site presentation
Christopher D'Amboise, Michael Neuhauser, Anne Hormes, Matthias Ploerer, Jan-Thomas Fischer, and Michaela Teich

Forests cover large parts of mountain areas. It is therefore necessary to include their effects in simulations at the regional scale to understand the key role forests have for risk mitigation. Process-based physical models can be used for such simulations, but they often require larger computational resources than statistical models. Flow-Py is a customizable, open-source simulation tool to predict the runout and intensity of gravitational mass flows (GMF). Flow-Py is based on data-driven empirical modeling ideas with automated path identification to solve the routing and stopping of GMFs in three-dimensional terrain, requiring fewer parameters than physical GMF runout models. Here we present the custom-built forest plug-in to the Flow-Py simulation tool which accounts for forest effects in the transit and runout zones of snow avalanches. 

Flow-Py employs the well-known runout angle (α) concept to determine the stopping of a GMF, and routing algorithm consisting of a terrain contribution and persistence contribution. The interaction between forest and avalanches, which can reduce their runout and decrease their intensity can be broken down into two main processes, 1) adding friction and 2) reducing flowing mass or the detrainment of snow. The forest plug-in has the capability to mimic these physical interactions by increasing the runout angle and adjusting the routing flux in forested areas. We present the framework of the forest plug-in for a test case and the results of a sensitivity study on parameters controlling the forest-avalanche interaction.  

The forest plug-in requires the spatial extent of the forest and an estimate of the kinetic energy of the avalanche to compute the forest’s effect on the avalanche movement. Additional information on the structure of the forest (e.g., forest type, stem density, canopy cover, basal area) can be used to amplify or dampen these effects. The forest information is summarized in the forest structure index (FSI), which indicates how developed a forest is with regards to its optimal protective effect against snow avalanches and ranges between 0 (no protection) and 1 (optimal protection), considering, e.g., dominant forest type, elevation band, or the forest development stage. 

Forests located in the starting zones of avalanches have long been used as an efficient mitigation measure to reduce avalanche risk. However, forests located in the transit and runout zones of avalanches also have mitigating properties, but the degree of protection is difficult to quantify without simulation tools and their integrated models. Including forest-avalanche interactions in regional-scale simulations with Flow-Py and its forest plug-in allows to estimate the degree to which forest protects human activity and infrastructure against potential avalanches. That is, by combining simulation results with and without forest it is possible to estimate the forest impact, i.e., how much the forest reduces the magnitude (runout and intensity) of the avalanche. Such regional overviews can be calculated fast with large-scale input data, which is important to, e.g., quantify changes in the protective effect of a forest area caused by disturbance agents such as wind, bark beetles or fire.  

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.