This session is 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 avalanche dynamics and related processes, physical vulnerability of structures impacted by snow avalanches, avalanche hazard zoning and avalanche mitigation strategies. These include field, laboratory and numerical studies that rely on new methods and techniques (radars, drone, satellite, etc.) as well as practical case studies.

Furthermore, we solicit novel contributions from the area of granular flows, viscoplastic flows, density currents, turbulent flows, as well as contributions from other gravitational mass flows communities, which can improve our understanding and modeling of snow avalanche propagation and their interaction with natural or man-made structures.

While the main focus of this session is on snow avalanche dynamics from basic knowledge to mitigation strategies, it is closely linked to session CR3.4 entitled "Snow avalanche formation: from snow mechanics to avalanche detection" which addresses avalanche formation, detection and forecasting.

Co-organized by NH3
Convener: Thierry Faug | Co-conveners: Jan-Thomas Fischer, Florence Naaim-Bouvet, Betty Sovilla
| Attendance Fri, 08 May, 16:15–18:00 (CEST)

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Chat time: Friday, 8 May 2020, 16:15–18:00

D2220 |
Xingyue Li, Betty Sovilla, Stephanie Wang, Chenfanfu Jiang, and Johan Gaume

Snow avalanches are one of the most dangerous and catastrophic hazards in mountainous regions, which cause fatalities and property losses. Understanding the dynamics of snow avalanches is essential for designing safe and optimised mitigation measures. This study presents numerical modeling of snow avalanche dynamics, based on the Material Point Method (MPM) and an elastoplastic constitutive model for porous cohesive materials. MPM is a hybrid Eulerian-Lagrangian numerical method, which can simulate processes with large deformation, collisions and fractures. The elastoplastic model consists of an ellipsoid yield surface, a hardening law, and an associative flow rule. It enables us to capture the mixed-mode failure of snow including tensile, shear and compressive failure. Both ideal and real terrains are modeled in our study. By varying the properties of snow on the ideal slope, the model can reproduce four typical reported flow regimes, namely, cold shear, warm shear, warm plug and slab sliding regimes. In addition, surges and roll-waves are observed especially for flows in the transition from cold shear to warm shear regimes. The evolution of the avalanche front, the free surface shape and the velocity vertical profile show distinct characteristics for the different flow regimes. In addition to the snow properties, slope angle and path length are changed to investigate their effects on the maximum velocity, the run-out distance and the avalanche deposit height. The relation between the maximum velocity and the run-out distance obtained from our MPM simulations is analyzed along with data collected from literature. Furthermore, we benchmark the MPM model by simulating snow avalanches on real terrain. The evolution of the avalanche front position and velocity from the MPM simulations are quantitatively compared with the measurement data from past studies.

How to cite: Li, X., Sovilla, B., Wang, S., Jiang, C., and Gaume, J.: Numerical modeling of snow avalanche dynamics based on the Material Point Method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2153, https://doi.org/10.5194/egusphere-egu2020-2153, 2020.

D2221 |
| Highlight
Guillaume Chambon, Thierry Faug, Mohamed Naaim, and Nicolas Eckert

Recent winters saw a striking increase in wet snow avalanche activity. Compared to dry avalanches, wet snow avalanches present uniquely distinctive features such as slower velocities, larger depths, unusual trajectories and deposit shapes, and a paste-like rheology that can result in large shear and normal stresses. In addition, the behavior of wet avalanches may strongly vary depending on the actual snow liquid water content. Complex transitions between dry (cold) and wet (hot) behaviors have also been observed during the propagation of single avalanche events. Current numerical models of avalanche dynamics are challenged when it comes to capturing the full spectrum of these different regimes, and the transitions in between. In this contribution, we critically review the various rheological models that have been proposed in the literature to simulate dry and wet snow avalanches in the frame of depth-averaged shallow-flow approaches. On this basis, a simplified parametric rheological law is proposed, with the objective of representing both dry-like and wet-like behaviors and allowing for smooth transitions between them. The law is implemented in a robust 2D shallow-flow simulation code, and systematic sensitivity studies are performed on synthetic and real topographies. Simulation outcomes are analysed in terms of propagation dynamics and deposition patterns, and the ability of the model to capture both dry and wet regimes is discussed. Lastly, a specific calibration methodology is proposed to infer the relevant mechanical parameters from documented avalanche events.

How to cite: Chambon, G., Faug, T., Naaim, M., and Eckert, N.: Simulating the propagation of wet snow avalanches: challenges and perspectives, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18607, https://doi.org/10.5194/egusphere-egu2020-18607, 2020.

D2222 |
| Highlight
Camille Ligneau, Betty Sovilla, and Johan Gaume

In the near future, climate change will impact the snow cover in Alpine regions. Higher precipitations and warmer temperatures are expected at lower altitude, leading to larger gradients of snow temperature, snow water content and snow depth between the top and the bottom of slopes. As a consequence, climate change will also indirectly influence the behavior of snow avalanches.

The present work aims to investigate how changes in snow cover properties will affect snow avalanches dynamics. Experimental studies allowed to characterize different avalanche flow regimes which result from particular combinations of snow physical and mechanical properties. In particular, expected variations of snow temperatures with elevation will cause more frequent and more extreme flow regime transitions inside the same avalanche. For example, a fast avalanche characterized by cold and low-cohesive snow in the upper part of the avalanche track will transform more frequently into a slow flow made of wet and heavy snow when the avalanche will entrain warm snow along the slope. A better understanding of these flow regime transitions, which have already been largely reported, is crucial, because it affects both daily danger assessment (e.g. forecasting services, road controls) and hazard mapping of avalanches.

To date, most avalanche modeling methods are not considering temperature effects and are then unable to simulate flow regime transitions and unprecedented scenarios. Our goal is then to develop a model capable of simulating reported flow regimes, flow transitions and the interactions between the snow cover and the flow (erosion, entrainment). Since these elements are not yet fully understood, we firstly model these mesoscopic processes using a 2D Discrete Element Model (DEM) in which varying particle cohesion and friction mimic the effect of changes in snow temperature. Flow regimes are simulated by granular assemblies put into motion by gravity on an inclined slope, which interact with a granular and erodible bed surface. Simulations are calibrated using experimental data coming from the avalanche test site located in Vallée de la Sionne, which record avalanches since more than 20 years. This modeling will then be used as an input to improve slope-scale models and make them more appropriate for avalanche risk management in the context of climate change.

How to cite: Ligneau, C., Sovilla, B., and Gaume, J.: Avalanche flow regime transitions in a changing climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18565, https://doi.org/10.5194/egusphere-egu2020-18565, 2020.

D2223 |
Betty Sovilla, Michael Kyburz, Camille Ligneau, Jan-Thomas Fischer, and Mark Schaer

Measurements of snow avalanche impact pressures are performed at the Vallée de la Sionne test site since winter 1999. In these years of operation, we recorded the impact pressure of around 60 avalanches characterized by different flow regimes and dimensions.

Pressure measurements were performed, simultaneously, on three different structures which are spatially distributed with a maximum distance of 30 m, in the run-out zone of the Vallée de la Sionne test site. The structure widths range from 0.25 to 1 m. On these structures pressure sensors ranging from small cells with 0.10 to 0.25 m in diameter to large pressure plates with area of 1m2 are mounted at different heights.

A systematic analysis of all 60 avalanche data sets shows that the pressure measured at the different obstacles varies considerably, even within the same avalanche, both in space and time. Part of these differences can be attributed to different drag coefficients and dependence on obstacle size, but a large part of these differences can only be explained by the spatial variability of the flow properties and the temporal variability of the physical processes governing the interaction of the avalanche and the structures.

In this contribution we show how spatial and temporal impact pressure variabilities correlate to avalanche dimension and flow regimes and we discuss the implication of such variations for structural design and hazard mapping.

How to cite: Sovilla, B., Kyburz, M., Ligneau, C., Fischer, J.-T., and Schaer, M.: Spatial and temporal variability of snow avalanche impact pressure and its importance for structural design, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8133, https://doi.org/10.5194/egusphere-egu2020-8133, 2020.

D2224 |
Thierry Faug

Recent well-documented measurements on full-scale snow avalanches impacting civil engineering structures have identified an impact force regime for which the pressure exerted on the obstacle is depth-dependent, rather than being controlled by the square of the avalanche speed. In addition, these measurements have shown that the depth-dependent force could be many times greater than the hydrostatic force associated with the thickness of the incoming avalanche-flow. The present paper proposes a general analytic form for the impact force of dense avalanches on any kind of structure, with the help of the depth-averaged hydrodynamics applied to a control-volume surrounding the influence zone of the obstacle. This form extends the recently established force models for wall-like and pylon-like obstacles impacted by flows of dry granular materials. A criterion to distinguish between the depth-dependent force regime and the velocity-square force regime is derived. It is demonstrated that the size of the influence zone of the obstacle, relative to the dimension of the obstacle and/or the avalanche thickness, is a key ingredient---in addition to the traditional Froude number---to demarcate the depth-dependent from velocity-square impact forces. There is still a need for further developments to unravel the size and shape of the influence zone of any kind obstacle for any type of flowing snow, and then being able to hone this criterion as well as to predict the force amplification in the depth-dependent regime. However the present study takes a step forward for a better understanding of granular avalanche impact force on civil engineering structures.

How to cite: Faug, T.: Impact of dense avalanches on civil engineering structures: demarcating depth-dependent from velocity-squared impact forces, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12725, https://doi.org/10.5194/egusphere-egu2020-12725, 2020.

D2225 |
Michael L. Kyburz, Betty Sovilla, Johan Gaume, and Christophe Ancey

In order to estimate avalanche loads on buildings and structures of various sizes and geometries,  practitioners are interested in recommendations or experimental data for a wide variety of obstacle geometries and sizes. Full-scale avalanche measurements are performed across the world since the late 1970s to increase knowledge about avalanche flow behaviour, including impact on structures. These structures are usually equipped with sensors to measure impact pressure, avalanche velocity and/or snow density. Modifying the structure profile is hardly possible because of high construction costs. To date, it has thus been possible to test and calibrate empirical relationships used in engineering only on a limited number of structures for which experimental data exist. We therefore aim to calibrate the drag coefficient and amplification factor for a broader range of obstacle shapes and sizes. In this context the drag coefficient generalizes the drag coefficient used in Newtonian fluid mechanics when computing the flow past an obstacle. The amplification factor reflects the snow load’s deviation from a hydrostatic-like pressure. To estimate these two parameters, we simulate how an avalanche interacts with differently sized and shaped obstacles using the Discrete Element Method (DEM). First, we test the DEM model’s capacity to reproduce full-scale pressure measurements performed on two different obstacles at the Vallée de la Sionne test site by comparing simulated and measured impact pressures. Second, we run new simulations involving other geometries and dimensions, for which no experimental data exist. Our results show that the pressure distribution depends not only on the obstacle geometry, but also on avalanche flow regime and snow properties. We eventually examine the pressure distribution for different generic geometries and avalanche scenarios. This analysis should ultimately help to improve extant engineering guidelines.

How to cite: Kyburz, M. L., Sovilla, B., Gaume, J., and Ancey, C.: How obstacle geometry and snow properties influence avalanche impact pressure , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18391, https://doi.org/10.5194/egusphere-egu2020-18391, 2020.

D2226 |
Hippolyte Kern, Vincent Jomelli, Nicolas Eckert, and Delphine Grancher

Avalanche deposits cause various types of damage to properties and infrastructures every winter, resulting in significant direct and indirect economic losses. However, the factors controlling the deposit volumes are still largely unknown. The main objective of this study is to analyze the geometric characteristics of avalanche deposits in order to understand their relationships with the avalanche corridors’ morphology in the French Alps. Our study focuses on the analysis of 1491 avalanche deposits spread out over 79 corridor in the upper part of the Haute-Maurienne valley, Savoie department, during the period 2003-2017. This work uses data from the Permanent Avalanche Survey (EPA) database, an inventory of avalanche events occurring at well-known, delineated and mapped corridors in France. A statistical method is used to study the relationships between corridor morphological variables and their associated deposit volumes. Our study area exhibits an mean deposit volume of 17 500m3 (q5% = 4 500 m3 and q95%= 84 000 m3).

Results show that the relationships between corridor morphology and deposit volumes are only significant ( > 0,3 and P < 0,001) for avalanches that occur in winter (November-February). The frequency of snow avalanches also influences the size of the deposits, with the largest deposits observed in corridors that show high annual avalanche frequency. However, avalanche deposit volumes occurring in corridors with a low annual frequency correlate more strongly with the corridor morphology. On the other hand, snow avalanche volumes deposited in spring (March-May) seem to be mostly driven by meteorological variables with almost no correlation with the corridor’s morphology. In more details, deposit volumes are primarily determined by the corridor maximum or mean altitude, which reflects the potential amount of snow that can be mobilized. Corridor slope also exhibit a significant relationship with deposit volumes, which is partially indirect through the effect of the slope on corridors mean annual avalanche frequency. Eventually, surprisingly enough, morphological variables that may intuitively appear as important for deposit volumes such as surface area or orientation are uncorrelated or only poorly correlated with avalanche deposit volumes.

How to cite: Kern, H., Jomelli, V., Eckert, N., and Grancher, D.: Relationships between corridor morphological variables and avalanche deposits volumes , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22006, https://doi.org/10.5194/egusphere-egu2020-22006, 2020.

D2227 |
| Highlight
Michael Neuhauser, Christopher D’Amboise, Michaela Teich, and Jan Thomas Fischer

Recently, strong wind storms have caused large-scale damages in Alpine mountain forests, leaving the underlying infrastructure exposed. These forests often provide protection against gravitational natural hazard processes such as avalanches, rockfall and soil slides. To manage these disturbed forests efficiently and effectively, it is important to know 1) which forest areas serve a protective function to the underlying infrastructure, 2) what is the actual protective effect of these forests on the hazard process, and 3) how one could improve this effect.

To define protective functions and to quantify the protective effects of forests, we created the Flow-Py model that identifies process areas of gravitational hazards, including avalanches, rockfall and debris slides. The model is written in Python to keep it easy adjustable. The run out routine of Flow-Py is based on the principles of energy conservation including frictional dissipation assuming simple coulomb friction, leading to constant travel-angle. Potential release areas and the corresponding travel angle have to be adapted for each type of gravitational mass movements. A important improvement, compared to similar models, is that it can handle mass movement in flat and uphill terrain. One major advantage of this model is its simplicity, resulting in a computationally inexpensive implementation, which allows for an application on a regional scale, covering large simulation areas. The adaptivity of the model further allows to consider existing infrastructure and to detect starting zones endangering the corresponding areas in a back-calculation step. Additionally, by adding forest cover to the simulations we can identify which forest area has a protective function and, based on information about forest structure, calculate the protective effect this forest provides to down slope infrastructure.

Flow-Py is a useful tool to identify forest areas that are important for hazard protection (protective function) and to quantify their protective effect. The model can be applied in protection forest management to prioritize measures in wind throw areas. Furthermore, it is possible to use this tool for analyzing the protective functions and effects of different forest extents and structural conditions, for example, caused by climate change or forest disturbances. In this work we elaborate the potential of Flow-Py by presenting an avalanche case study in the central alpine region of Austria (Gries/Vals, Tyrol, AT). For this case the simulation results indicate a process area affected by avalanches of ~65% with respect to the total area of ~ 195 km².

How to cite: Neuhauser, M., D’Amboise, C., Teich, M., and Fischer, J. T.: Flow-Py: Identifying protection forests and their effects on gravitational natural hazard processes on a regional scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21938, https://doi.org/10.5194/egusphere-egu2020-21938, 2020.

D2228 |
Taline Zgheib, Florie Giacona, Anne-Marie Granet-Abisset, Samuel Morin, and Nicolas Eckert

Land cover and particularly forests have significant impact on snow avalanche initiation and propagation. Mountain forests can prevent avalanche initiation by stabilizing the snow in release areas, and potentially decelerate an avalanche, thus reducing runout distances. Interaction between forests and avalanches is recognized in avalanche modelling mostly by increasing friction parameters. For instance, the dry –Coulomb friction μ of the Voellmy friction law is thought to summarize snow properties, whereas the velocity-dependent friction ξ aims at representing the roughness of the path potentially related to land cover properties. In this work, we hypothesize on the temporal variability of both friction factors, inherited from their dependability on land cover, particularly the forest fraction, namely the aerial percentage of the terrain covered by forests within the extension of the avalanche path. Specifically, we show how the evolution of the forest fraction within the avalanche path affects the return period of runout distances and further dynamical characteristics of simulated avalanches. First, a Bayesian statistical-dynamical model is used to model avalanche frequency and magnitude on the selected path. The two processes are independently modelled, and the joint posterior distribution is estimated using a sequential Metropolis-Hastings algorithm. The forest-avalanche interaction is represented by increasing the total basal friction within the Voellmy friction law (TBF). Accordingly, to increase TBF, the velocity-dependent friction (turbulent friction) ξ is gradually decreased, whereas the dry –Coulomb friction μ is increased. To that end, ξ is assumed to be exponentially decaying with the forest fraction and is modelled as such. The dry –Coulomb friction μ  is assumed to be normally distributed with parameters characterizing its dependency on the release abscissa, mean release depth and the forest fraction. Then, the return period for runout distances and the whole distribution of velocities, flow depths and pressures corresponding to any return periods is computed for different forest fractions representing the true forest evolution within the studied path. Results for a typical avalanche path of the French Alps notably show that, logically, the larger the forest fraction, the higher the return period, but only for runout distances exceeding a given threshold. Future work will include the explicit calibration of the forest cover dependency within the statistical-dynamical approach.


How to cite: Zgheib, T., Giacona, F., Granet-Abisset, A.-M., Morin, S., and Eckert, N.: Impact of land cover on avalanche hazard: how forest cover changes affect return periods and dynamical characteristics simulated by a statistical-numerical avalanche model., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-165, https://doi.org/10.5194/egusphere-egu2020-165, 2020.

D2229 |
Adrien Favillier, Robin Mainieri, Jérôme Lopez-Saez, Mélanie Saulnier, Nicolas Eckert, Jean-Luc Peiry, Markus Stoffel, and Christophe Corona

In the course of the 20th century, high-mountain regions, such as the Alps, have experienced a significant warming with temperature increase twice as much as the global average. Such warming strongly alters the cryosphere components. It induces, for example, a shift from solid to liquid precipitation, more frequent and more intense snowmelt phases or a strong decrease in the amount and duration of snow cover, especially at the location of the snow-rain transition. Such changes in snow cover characteristics are expected to induce changes in spontaneous avalanche activity.

On forested stands, dendrogeomorphic analyses provide long and continuous chronologies of snow avalanche events and can thus contribute to the detection of trends potentially related to climate change. However, the non-stationarities found in tree-ring based chronologies of snow avalanches may also be related to socio-environmental changes. In this context, based on the latest the latest developments in dendrogeomorphology, we reconstructed the snow avalanche activity for 6 contiguous paths located in the Grand Bois de Souliers slope (Queyras massif, French Alps) with the aim to :

  1. Detect and illustrate such confounding effects;
  2. Disentangle the trends inherent to tree-ring approaches from real fluctuations in avalanche activity.

The resulting reconstruction covers the period 1750-2016 and evidences two clearly different trends: on the three southern avalanche paths, a sharp increase in the frequency of reconstructed events is observed since the 1970s. The distribution of tree ages, in combination with old topographic maps, allows an attribution of this non-stationarity to the destruction of a large part of the forest stand in the 1910-20s, presumably related to a devastating avalanche event. This extreme event induced a sudden change in the capability of newly colonizing trees to yield dendrogeomorphic records as information on previous or subsequent events has been removed. By contrast, on the three northern paths, snow avalanche activity is truly characterized by a strong reduction since the 1930s related to the progressive afforestation of the paths since the mid-18th century and to the colonization of the release areas since World War 2. Even if we cannot rule out the possibility that global warming may have played a certain, yet likely minor, role in the evolution of these avalanche-forest ecosystem, we conclude that the contrasted evolutions observed between the avalanche paths can, above all, be explained by socio-environmental factors (e.g., forest and grazing management) during the 18th century that have gained in importance by the rural exodus and the abatement of pastoral practices during the 20th century. In that sense, our results evidence quite clearly the crucial need for future studies aimed at detecting changes in mass-movement activity from tree-ring analyses to systematically interpret trends in activity considering interrelations between forest evolution, global warming, social practices and process activity itself.

How to cite: Favillier, A., Mainieri, R., Lopez-Saez, J., Saulnier, M., Eckert, N., Peiry, J.-L., Stoffel, M., and Corona, C.: Impacts of land-cover changes on dendrogeomorphic reconstructions of snow avalanches: Insights from the Queyras massif (French Alps), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17597, https://doi.org/10.5194/egusphere-egu2020-17597, 2020.

D2230 |
Stephan Harvey, Günter Schmudlach, Yves Bühler, Dürr Lukas, Andreas Stoffel, and Marc Christen

Terrain characteristics are one of the main factors contributing to avalanche formation as well as affecting the runout. Hence, terrain assessment is crucial for planning and decision making when travelling in the backcountry. So far, terrain is mainly interpreted manually from topographic maps or by observations in the field. Recent support for interpreting avalanche terrain is given by slope angle layers derived from digital elevation models or the Avalanche Terrain Exposure Scale (ATES) for classifying avalanche terrain manually. While digital elevation models and numerical simulations are used as standard for mapping avalanche hazard threatening settlements and key infrastructure, this is hardly the case when planning tours in the backcountry. Thus, our scope was to classify and map terrain of maximum size class 3 avalanches, which typically threaten backcountry recreationists. We present a new methodology for a high-resolution automatic classification of the avalanche terrain specifically for recreational backcountry travel by taking into account: a) potential avalanche release areas, b) remote triggering of avalanches, c) possible runout zones of max. size 3 avalanches.

Potential release areas were specified by computing a density estimate based on terrain characteristics of observed avalanche starting zones in the Davos region. The potential of remote triggering was estimated with a least-cost path analyses depending on the triggering distance from remotely triggered avalanches. Avalanche runout zones were performed with the avalanche simulation model RAMMS::EXTENDED. Combining all these methods and out of many simulations a classified avalanche terrain map for the entire Swiss Alps and the Jura was created characterizing potential release areas and runout zones. A validation of 870 accidental avalanches in the backcountry of Switzerland shows that only 2% of the mapped avalanche perimeters do not overlap with the simulations. The distribution of the terrain characteristics within both the release areas of the training dataset and the validation data was almost identical. Thus, the extrapolation from the calculated density estimate to the whole of Switzerland is feasible and appropriate. The created map assists the interpretation of avalanche terrain for travelling in the backcountry considering release areas and runout zones. Although the focus is on Switzerland, the methods can also be applied to other mountain areas worldwide.

How to cite: Harvey, S., Schmudlach, G., Bühler, Y., Lukas, D., Stoffel, A., and Christen, M.: Automatic high-resolution mapping and classification of avalanche terrain regarding potential release, triggering and run-out zones, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11465, https://doi.org/10.5194/egusphere-egu2020-11465, 2020.

D2231 |
| Highlight
Gregor Ortner, Michael Bruendl, David N. Bresch, and Yves Bühler

Various studies show that changes in the climate system, such as temperature rise and extreme precipitation events strongly influence gravity driven hazards. In 2018, the research programme “Climate Change Impacts on Alpine Mass Movements” began at the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. Within this programme, we develop a framework to model risk caused by climate and socio-economic change. In a first approach, we model avalanche risk in central Switzerland. The changing hazard disposition is modelled with the RAMMS::LSHM Large Scale Hazard Mapping method and risks are assessed with the probabilistic, Python-based risk assessment platform CLIMADA developed at ETH Zurich. We use several hazard scenarios considering different 3-day increases in snow height, an algorithm for determining potential avalanche release areas, a high-resolution terrain model and a forest layer to model the spatial distribution of avalanche hazard for each of the chosen scenarios. The so-derived hazard indication maps are taken as input into CLIMADA to estimate the risk to buildings and infrastructure applying various functions to quantify the avalanche impact.
The result are risk maps which depict spatial and temporal changes of avalanche risk based on various hazard scenarios. The combination with exposure and damageability information, leading to spatio-temporally explicit risk maps provides a comprehensive basis and allows for the appraisal of appropriate risk management options. A risk based approach for lifelines and residential areas will contribute to decision support and highlight adaptations needed for climate change.

How to cite: Ortner, G., Bruendl, M., Bresch, D. N., and Bühler, Y.: From Large Scale Hazard Mapping to Risk Assessment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8490, https://doi.org/10.5194/egusphere-egu2020-8490, 2020.