Articles | Volume 17, issue 6
https://doi.org/10.5194/tc-17-2245-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-2245-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jun 2023
Research article |
| 05 Jun 2023
| 05 Jun 2023
Combining modelled snowpack stability with machine learning to predict avalanche activity
Léo Viallon-Galinier
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM,Centre d’Études de la Neige, Grenoble, France
Univ. Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
École des Ponts, Champs-sur-Marne, France
Pascal Hagenmuller
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM,Centre d’Études de la Neige, Grenoble, France
Nicolas Eckert
Univ. Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Related authors
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
Short summary
Short summary
Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Louise Dallons Thanneur, Florie Giacona, Nicolas Eckert, and Philippe Frey
EGUsphere, https://doi.org/10.5194/egusphere-2025-761, https://doi.org/10.5194/egusphere-2025-761, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This paper proposes a methodology to develop a long-range multirisk database. Combining scattered pre-existing records and intensive research in historical archives provides a 1600–2020 record of past events in a valley of the French Alps. It goes far beyond any inventory existing in terms of number of events, temporal coverage and detailed description of events characteristics in a mountain context. Spatio-temporal patterns are analysed, opening perspective for multirisk assessment.
Clémence Herny, Pascal Hagenmuller, Guillaume Chambon, Isabel Peinke, and Jacques Roulle
The Cryosphere, 18, 3787–3805, https://doi.org/10.5194/tc-18-3787-2024, https://doi.org/10.5194/tc-18-3787-2024, 2024
Short summary
Short summary
This paper presents the evaluation of a numerical discrete element method (DEM) by simulating cone penetration tests in different snow samples. The DEM model demonstrated a good ability to reproduce the measured mechanical behaviour of the snow, namely the force evolution on the cone and the grain displacement field. Systematic sensitivity tests showed that the mechanical response depends not only on the microstructure of the sample but also on the mechanical parameters of grain contacts.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
Short summary
Short summary
Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Erwan Le Roux, Guillaume Evin, Raphaëlle Samacoïts, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 17, 4691–4704, https://doi.org/10.5194/tc-17-4691-2023, https://doi.org/10.5194/tc-17-4691-2023, 2023
Short summary
Short summary
We assess projected changes in snowfall extremes in the French Alps as a function of elevation and global warming level for a high-emission scenario. On average, heavy snowfall is projected to decrease below 3000 m and increase above 3600 m, while extreme snowfall is projected to decrease below 2400 m and increase above 3300 m. At elevations in between, an increase is projected until +3 °C of global warming and then a decrease. These results have implications for the management of risks.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
Short summary
Short summary
Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Cécile Duvillier, Nicolas Eckert, Guillaume Evin, and Michael Deschâtres
Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, https://doi.org/10.5194/nhess-23-1383-2023, 2023
Short summary
Short summary
This study develops a method that identifies individual potential release areas (PRAs) of snow avalanches based on terrain analysis and watershed delineation and demonstrates its efficiency in the French Alps context using an extensive cadastre of past avalanche limits. Results may contribute to better understanding local avalanche hazard. The work may also foster the development of more efficient PRA detection methods based on a rigorous evaluation scheme.
Pyei Phyo Lin, Isabel Peinke, Pascal Hagenmuller, Matthias Wächter, M. Reza Rahimi Tabar, and Joachim Peinke
The Cryosphere, 16, 4811–4822, https://doi.org/10.5194/tc-16-4811-2022, https://doi.org/10.5194/tc-16-4811-2022, 2022
Short summary
Short summary
Characterization of layers of snowpack with highly resolved micro-cone penetration tests leads to detailed fluctuating signals. We used advanced stochastic analysis to differentiate snow types by interpreting the signals as a mixture of continuous and discontinuous random fluctuations. These two types of fluctuation seem to correspond to different mechanisms of drag force generation during the experiments. The proposed methodology provides new insights into the characterization of snow layers.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
Earth Syst. Dynam., 13, 1059–1075, https://doi.org/10.5194/esd-13-1059-2022, https://doi.org/10.5194/esd-13-1059-2022, 2022
Short summary
Short summary
Anticipating risks related to climate extremes is critical for societal adaptation to climate change. In this study, we propose a statistical method in order to estimate future climate extremes from past observations and an ensemble of climate change simulations. We apply this approach to snow load data available in the French Alps at 1500 m elevation and find that extreme snow load is projected to decrease by −2.9 kN m−2 (−50 %) between 1986–2005 and 2080–2099 for a high-emission scenario.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
Short summary
Short summary
This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Luuk Dorren, Frédéric Berger, Franck Bourrier, Nicolas Eckert, Charalampos Saroglou, Massimiliano Schwarz, Markus Stoffel, Daniel Trappmann, Hans-Heini Utelli, and Christine Moos
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-32, https://doi.org/10.5194/nhess-2022-32, 2022
Publication in NHESS not foreseen
Short summary
Short summary
In the daily practice of rockfall hazard analysis, trajectory simulations are used to delimit runout zones. To do so, the expert needs to separate "realistic" from "unrealistic" simulated groups of trajectories. This is often done on the basis of reach probability values. This paper provides a basis for choosing a reach probability threshold value for delimiting the rockfall runout zone, based on recordings and simulations of recent rockfall events at 18 active rockfall sites in Europe.
Hippolyte Kern, Nicolas Eckert, Vincent Jomelli, Delphine Grancher, Michael Deschatres, and Gilles Arnaud-Fassetta
The Cryosphere, 15, 4845–4852, https://doi.org/10.5194/tc-15-4845-2021, https://doi.org/10.5194/tc-15-4845-2021, 2021
Short summary
Short summary
Snow avalanches are a major component of the mountain cryosphere that often put people, settlements, and infrastructures at risk. This study investigated avalanche path morphological factors controlling snow deposit volumes, a critical aspect of snow avalanche dynamics that remains poorly known. Different statistical techniques show a slight but significant link between deposit volumes and avalanche path morphology.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 15, 4335–4356, https://doi.org/10.5194/tc-15-4335-2021, https://doi.org/10.5194/tc-15-4335-2021, 2021
Short summary
Short summary
Extreme snowfall can cause major natural hazards (avalanches, winter storms) that can generate casualties and economic damage. In the French Alps, we show that between 1959 and 2019 extreme snowfall mainly decreased below 2000 m of elevation and increased above 2000 m. At 2500 m, we find a contrasting pattern: extreme snowfall decreased in the north, while it increased in the south. This pattern might be related to increasing trends in extreme snowfall observed near the Mediterranean Sea.
Marie Dumont, Frederic Flin, Aleksey Malinka, Olivier Brissaud, Pascal Hagenmuller, Philippe Lapalus, Bernard Lesaffre, Anne Dufour, Neige Calonne, Sabine Rolland du Roscoat, and Edward Ando
The Cryosphere, 15, 3921–3948, https://doi.org/10.5194/tc-15-3921-2021, https://doi.org/10.5194/tc-15-3921-2021, 2021
Short summary
Short summary
The role of snow microstructure in snow optical properties is only partially understood despite the importance of snow optical properties for the Earth system. We present a dataset combining bidirectional reflectance measurements and 3D images of snow. We show that the snow reflectance is adequately simulated using the distribution of the ice chord lengths in the snow microstructure and that the impact of the morphological type of snow is especially important when ice is highly absorptive.
Kévin Fourteau, Florent Domine, and Pascal Hagenmuller
The Cryosphere, 15, 2739–2755, https://doi.org/10.5194/tc-15-2739-2021, https://doi.org/10.5194/tc-15-2739-2021, 2021
Short summary
Short summary
The thermal conductivity of snow is an important physical property governing the thermal regime of a snowpack and its substrate. We show that it strongly depends on the kinetics of water vapor sublimation and that previous experimental data suggest a rather fast kinetics. In such a case, neglecting water vapor leads to an underestimation of thermal conductivity by up to 50 % for light snow. Moreover, the diffusivity of water vapor in snow is then directly related to the thermal conductivity.
Kévin Fourteau, Florent Domine, and Pascal Hagenmuller
The Cryosphere, 15, 389–406, https://doi.org/10.5194/tc-15-389-2021, https://doi.org/10.5194/tc-15-389-2021, 2021
Short summary
Short summary
There has been a long controversy to determine whether the effective diffusion coefficient of water vapor in snow is superior to that in free air. Using theory and numerical modeling, we show that while water vapor diffuses more than inert gases thanks to its interaction with the ice, the effective diffusion coefficient of water vapor in snow remains inferior to that in free air. This suggests that other transport mechanisms are responsible for the large vapor fluxes observed in some snowpacks.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
Nat. Hazards Earth Syst. Sci., 20, 2961–2977, https://doi.org/10.5194/nhess-20-2961-2020, https://doi.org/10.5194/nhess-20-2961-2020, 2020
Short summary
Short summary
To minimize the risk of structure collapse due to extreme snow loads, structure standards rely on 50-year return levels of ground snow load (GSL), i.e. levels exceeded once every 50 years on average, that do not account for climate change. We study GSL data in the French Alps massifs from 1959 and 2019 and find that these 50-year return levels are decreasing with time between 900 and 4800 m of altitude, but they still exceed return levels of structure standards for half of the massifs at 1800 m.
Pascal Hagenmuller, Frederic Flin, Marie Dumont, François Tuzet, Isabel Peinke, Philippe Lapalus, Anne Dufour, Jacques Roulle, Laurent Pézard, Didier Voisin, Edward Ando, Sabine Rolland du Roscoat, and Pascal Charrier
The Cryosphere, 13, 2345–2359, https://doi.org/10.5194/tc-13-2345-2019, https://doi.org/10.5194/tc-13-2345-2019, 2019
Short summary
Short summary
Light–absorbing particles (LAPs, e.g. dust or black carbon) in snow are a potent climate forcing agent. Their presence darkens the snow surface and leads to higher solar energy absorption. Several studies have quantified this radiative impact by assuming that LAPs were motionless in dry snow, without any clear evidence of this assumption. Using time–lapse X–ray tomography, we show that temperature gradient metamorphism of snow induces downward motion of LAPs, leading to self–cleaning of snow.
Pascal Hagenmuller, Margret Matzl, Guillaume Chambon, and Martin Schneebeli
The Cryosphere, 10, 1039–1054, https://doi.org/10.5194/tc-10-1039-2016, https://doi.org/10.5194/tc-10-1039-2016, 2016
Short summary
Short summary
The paper focuses on the characterization of snow microstructure with X-ray microtomography, a technique that is progressively becoming the standard for snow characterization. In particular, it rigorously investigates how the image processing algorithms affect the subsequent microstructure characterization in terms of density and specific surface area. From this analysis, practical recommendations concerning the processing X-ray tomographic images of snow are provided.
P. Hagenmuller, G. Chambon, and M. Naaim
The Cryosphere, 9, 1969–1982, https://doi.org/10.5194/tc-9-1969-2015, https://doi.org/10.5194/tc-9-1969-2015, 2015
Short summary
Short summary
This paper deals with a mechanical model that exploits a granular description of the snow microstructure. Its originality is that the geometry of the snow grains and of the inter-granular bonding system are explicitly defined from microtomographic data. It enables to model large deformations controlled by grain-rearrangements, which is of particular interest to study the collapse of weak layers or the characterization of the snowpack with an indenter.
Related subject area
Discipline: Snow | Subject: Natural Hazards
Impact of climate change on snow avalanche activity in the Swiss Alps
The source, quantity, and spatial distribution of interfacial water during glide-snow avalanche release: experimental evidence from field monitoring
Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations
Snow mechanical property variability at the slope scale – implication for snow mechanical modelling
Can Saharan dust deposition impact snowpack stability in the French Alps?
A closed-form model for layered snow slabs
A random forest model to assess snow instability from simulated snow stratigraphy
Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting
Snow Avalanche Frequency Estimation (SAFE): 32 years of monitoring remote avalanche depositional zones in high mountains of Afghanistan
Brief communication: Weak control of snow avalanche deposit volumes by avalanche path morphology
Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019
Dynamic crack propagation in weak snowpack layers: insights from high-resolution, high-speed photography
Avalanche danger level characteristics from field observations of snow instability
Using avalanche problems to examine the effect of large-scale atmosphere–ocean oscillations on avalanche hazard in western Canada
On the importance of snowpack stability, the frequency distribution of snowpack stability, and avalanche size in assessing the avalanche danger level
The mechanical origin of snow avalanche dynamics and flow regime transitions
On the relation between avalanche occurrence and avalanche danger level
Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK
Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps
Cold-to-warm flow regime transition in snow avalanches
Stephanie Mayer, Martin Hendrick, Adrien Michel, Bettina Richter, Jürg Schweizer, Heini Wernli, and Alec van Herwijnen
The Cryosphere, 18, 5495–5517, https://doi.org/10.5194/tc-18-5495-2024, https://doi.org/10.5194/tc-18-5495-2024, 2024
Short summary
Short summary
Understanding the impact of climate change on snow avalanche activity is crucial for safeguarding lives and infrastructure. Here, we project changes in avalanche activity in the Swiss Alps throughout the 21st century. Our findings reveal elevation-dependent patterns of change, indicating a decrease in dry-snow avalanches alongside an increase in wet-snow avalanches at elevations above the current treeline. These results underscore the necessity to revisit measures for avalanche risk mitigation.
Amelie Fees, Michael Lombardo, Alec van Herwijnen, Peter Lehmann, and Jürg Schweizer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2485, https://doi.org/10.5194/egusphere-2024-2485, 2024
Short summary
Short summary
Glide-snow avalanches release at the soil-snow interface due to a loss of friction which is suspected to be linked to interfacial water. The importance of the interfacial water was investigated with a spatio-temporal soil and local snow monitoring setup in an avalanche-prone slope. Seven glide-snow avalanches released on the monitoring grid (season 2021/22 to 2023/24) and provided insights into the source, quantity, and spatial distribution of interfacial water before avalanche release.
Elisabeth D. Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 18, 3807–3823, https://doi.org/10.5194/tc-18-3807-2024, https://doi.org/10.5194/tc-18-3807-2024, 2024
Short summary
Short summary
For many safety-related applications such as road management, well-documented avalanches are important. To enlarge the information, webcams may be used. We propose supporting the mapping of avalanches from webcams with a machine learning model that interactively works together with the human. Relying on that model, there is a 90% saving of time compared to the "traditional" mapping. This gives a better base for safety-critical decisions and planning in avalanche-prone mountain regions.
Francis Meloche, Francis Gauthier, and Alexandre Langlois
The Cryosphere, 18, 1359–1380, https://doi.org/10.5194/tc-18-1359-2024, https://doi.org/10.5194/tc-18-1359-2024, 2024
Short summary
Short summary
Snow avalanches are a dangerous natural hazard. Backcountry recreationists and avalanche practitioners try to predict avalanche hazard based on the stability of snow cover. However, snow cover is variable in space, and snow stability observations can vary within several meters. We measure the snow stability several times on a small slope to create high-resolution maps of snow cover stability. These results help us to understand the snow variation for scientists and practitioners.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
Short summary
Short summary
Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Philipp Weißgraeber and Philipp L. Rosendahl
The Cryosphere, 17, 1475–1496, https://doi.org/10.5194/tc-17-1475-2023, https://doi.org/10.5194/tc-17-1475-2023, 2023
Short summary
Short summary
The work presents a mathematical model that calculates the behavior of layered snow covers in response to loadings. The information is necessary to predict the formation of snow slab avalanches. While sophisticated computer simulations may achieve the same goal, they can require weeks to run. By using mathematical simplifications commonly used by structural engineers, the present model can provide hazard assessments in milliseconds, even for snowpacks with many layers of different types of snow.
Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer
The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, https://doi.org/10.5194/tc-16-4593-2022, 2022
Short summary
Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
Simon Horton and Pascal Haegeli
The Cryosphere, 16, 3393–3411, https://doi.org/10.5194/tc-16-3393-2022, https://doi.org/10.5194/tc-16-3393-2022, 2022
Short summary
Short summary
Snowpack models can help avalanche forecasters but are difficult to verify. We present a method for evaluating the accuracy of simulated snow profiles using readily available observations of snow depth. This method could be easily applied to understand the representativeness of available observations, the agreement between modelled and observed snow depths, and the implications for interpreting avalanche conditions.
Arnaud Caiserman, Roy C. Sidle, and Deo Raj Gurung
The Cryosphere, 16, 3295–3312, https://doi.org/10.5194/tc-16-3295-2022, https://doi.org/10.5194/tc-16-3295-2022, 2022
Short summary
Short summary
Snow avalanches cause considerable material and human damage in all mountain regions of the world. We present the first model to automatically inventory avalanche deposits at the scale of a catchment area – here the Amu Panj in Afghanistan – every year since 1990. This model called Snow Avalanche Frequency Estimation (SAFE) is available online on the Google Engine. SAFE has been designed to be simple and universal to use. Nearly 810 000 avalanches were detected over the 32 years studied.
Hippolyte Kern, Nicolas Eckert, Vincent Jomelli, Delphine Grancher, Michael Deschatres, and Gilles Arnaud-Fassetta
The Cryosphere, 15, 4845–4852, https://doi.org/10.5194/tc-15-4845-2021, https://doi.org/10.5194/tc-15-4845-2021, 2021
Short summary
Short summary
Snow avalanches are a major component of the mountain cryosphere that often put people, settlements, and infrastructures at risk. This study investigated avalanche path morphological factors controlling snow deposit volumes, a critical aspect of snow avalanche dynamics that remains poorly known. Different statistical techniques show a slight but significant link between deposit volumes and avalanche path morphology.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 15, 4335–4356, https://doi.org/10.5194/tc-15-4335-2021, https://doi.org/10.5194/tc-15-4335-2021, 2021
Short summary
Short summary
Extreme snowfall can cause major natural hazards (avalanches, winter storms) that can generate casualties and economic damage. In the French Alps, we show that between 1959 and 2019 extreme snowfall mainly decreased below 2000 m of elevation and increased above 2000 m. At 2500 m, we find a contrasting pattern: extreme snowfall decreased in the north, while it increased in the south. This pattern might be related to increasing trends in extreme snowfall observed near the Mediterranean Sea.
Bastian Bergfeld, Alec van Herwijnen, Benjamin Reuter, Grégoire Bobillier, Jürg Dual, and Jürg Schweizer
The Cryosphere, 15, 3539–3553, https://doi.org/10.5194/tc-15-3539-2021, https://doi.org/10.5194/tc-15-3539-2021, 2021
Short summary
Short summary
The modern picture of the snow slab avalanche release process involves a
dynamic crack propagation phasein which a whole slope becomes detached. The present work contains the first field methodology which provides the temporal and spatial resolution necessary to study this phase. We demonstrate the versatile capabilities and accuracy of our method by revealing intricate dynamics and present how to determine relevant characteristics of crack propagation such as crack speed.
Jürg Schweizer, Christoph Mitterer, Benjamin Reuter, and Frank Techel
The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, https://doi.org/10.5194/tc-15-3293-2021, 2021
Short summary
Short summary
Snow avalanches threaten people and infrastructure in snow-covered mountain regions. To mitigate the effects of avalanches, warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of danger levels and to improve consistency and accuracy of avalanche forecasts.
Pascal Haegeli, Bret Shandro, and Patrick Mair
The Cryosphere, 15, 1567–1586, https://doi.org/10.5194/tc-15-1567-2021, https://doi.org/10.5194/tc-15-1567-2021, 2021
Short summary
Short summary
Numerous large-scale atmosphere–ocean oscillations including the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, the Pacific North American Teleconnection Pattern, and the Arctic Oscillation are known to substantially affect winter weather patterns in western Canada. Using avalanche problem information from public avalanche bulletins, this study presents a new approach for examining the effect of these atmospheric oscillations on the nature of avalanche hazard in western Canada.
Frank Techel, Karsten Müller, and Jürg Schweizer
The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-14-3503-2020, https://doi.org/10.5194/tc-14-3503-2020, 2020
Short summary
Short summary
Exploring a large data set of snow stability tests and avalanche observations, we quantitatively describe the three key elements that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. The findings will aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.
Xingyue Li, Betty Sovilla, Chenfanfu Jiang, and Johan Gaume
The Cryosphere, 14, 3381–3398, https://doi.org/10.5194/tc-14-3381-2020, https://doi.org/10.5194/tc-14-3381-2020, 2020
Short summary
Short summary
This numerical study investigates how different types of snow avalanches behave, how key factors affect their dynamics and flow regime transitions, and what are the underpinning rules. According to the unified trends obtained from the simulations, we are able to quantify the complex interplay between bed friction, slope geometry and snow mechanical properties (cohesion and friction) on the maximum velocity, runout distance and deposit height of the avalanches.
Jürg Schweizer, Christoph Mitterer, Frank Techel, Andreas Stoffel, and Benjamin Reuter
The Cryosphere, 14, 737–750, https://doi.org/10.5194/tc-14-737-2020, https://doi.org/10.5194/tc-14-737-2020, 2020
Short summary
Short summary
Snow avalanches represent a major natural hazard in seasonally snow-covered mountain regions around the world. To avoid periods and locations of high hazard, avalanche warnings are issued by public authorities. In these bulletins, the hazard is characterized by a danger level. Since the danger levels are not well defined, we analyzed a large data set of avalanches to improve the description. Our findings show discrepancies in present usage of the danger scale and show ways to improve the scale.
Bettina Richter, Jürg Schweizer, Mathias W. Rotach, and Alec van Herwijnen
The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, https://doi.org/10.5194/tc-13-3353-2019, 2019
Short summary
Short summary
Information on snow stability is important for avalanche forecasting. To improve the stability estimation in the snow cover model SNOWPACK, we suggested an improved parameterization for the critical crack length. We compared 3 years of field data to SNOWPACK simulations. The match between observed and modeled critical crack lengths greatly improved, and critical weak layers appear more prominently in the modeled vertical profile of critical crack length.
Yves Bühler, Elisabeth D. Hafner, Benjamin Zweifel, Mathias Zesiger, and Holger Heisig
The Cryosphere, 13, 3225–3238, https://doi.org/10.5194/tc-13-3225-2019, https://doi.org/10.5194/tc-13-3225-2019, 2019
Short summary
Short summary
We manually map 18 737 avalanche outlines based on SPOT6 optical satellite imagery acquired in January 2018. This is the most complete and accurate avalanche documentation of a large avalanche period covering a big part of the Swiss Alps. This unique dataset can be applied for the validation of other remote-sensing-based avalanche-mapping procedures and for updating avalanche databases to improve hazard maps.
Anselm Köhler, Jan-Thomas Fischer, Riccardo Scandroglio, Mathias Bavay, Jim McElwaine, and Betty Sovilla
The Cryosphere, 12, 3759–3774, https://doi.org/10.5194/tc-12-3759-2018, https://doi.org/10.5194/tc-12-3759-2018, 2018
Short summary
Short summary
Snow avalanches show complicated flow behaviour, characterized by several flow regimes which coexist in one avalanche. In this work, we analyse flow regime transitions where a powder snow avalanche transforms into a plug flow avalanche by incorporating warm snow due to entrainment. Prediction of such a transition is very important for hazard mitigation, as the efficiency of protection dams are strongly dependent on the flow regime, and our results should be incorporated into avalanche models.
Cited articles
Ancey, C., Gervasoni, C., and Meunier, M.: Computing extreme avalanches, Cold
Reg. Sci. Technol., 39, 161–180,
https://doi.org/10.1016/j.coldregions.2004.04.004, 2004. a
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche
warning: Part I: numerical model, Cold Reg. Sci. Technol., 35,
123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002. a
Bois, P., Obled, C., and Good, W.: Multivariate data analysis as a tool for
day-by-day avalanche forecast, in: Snow Mechanics Symposium, 1974. a
Bourova, E., Maldonado, E., Leroy, J.-B., Alouani, R., Eckert, N., Bonnefoy-Demongeot, M., and Deschatres, M.: A new web-based system to improve the monitoring of snow avalanche hazard in France, Nat. Hazards Earth Syst. Sci., 16, 1205–1216, https://doi.org/10.5194/nhess-16-1205-2016, 2016. a, b
Bradley, A. P.: The use of the area under the ROC curve in the evaluation of
machine learning algorithms, Pattern Recogn., 30, 1145–1159,
https://doi.org/10.1016/s0031-3203(96)00142-2, 1997. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/a:1010933404324, 2001. a, b, c
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification
and regression trees, Cole Advanced Books and Software, https://doi.org/10.1201/9781315139470, 1984. a
Brun, E., Martin, E., Simon, V., Gendre, C., and Coleou, C.: An Energy and Mass
Model of Snow Cover Suitable for Operational Avalanche Forecasting, J.
Glaciol., 35, 333–342, https://doi.org/10.3189/S0022143000009254, 1989. a
Bründl, M. and Margreth, S.: Integrative risk management: The example of snow
avalanches, in: Snow and Ice-Related Hazards, Risks, and Disasters,
Elsevier, 259–296, https://doi.org/10.1016/b978-0-12-817129-5.00002-0, 2021. a
Buser, O.: Two Years Experience of Operational Avalanche Forecasting using the
Nearest Neighbours Method, Ann. Glaciol., 13, 31–34,
https://doi.org/10.3189/s026030550000759x, 1989. a, b, c
Castebrunet, H., Eckert, N., and Giraud, G.: Snow and weather climatic control on snow avalanche occurrence fluctuations over 50 yr in the French Alps, Clim. Past, 8, 855–875, https://doi.org/10.5194/cp-8-855-2012, 2012. a
Chawla, N. V., Japkowicz, N., and Kotcz, A.: Editorial, ACM SIGKDD
Explorations Newsletter, 6, 1–6, https://doi.org/10.1145/1007730.1007733, 2004. a
Chen, C., Liaw, A., and Breiman, L.: Using random forest to learn
imbalanced data, University of California, Berkeley, 110,
https://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf (last access: 1 May 2023),
2004. a
Choubin, B., Borji, M., Mosavi, A., Sajedi-Hosseini, F., Singh, V. P., and
Shamshirband, S.: Snow avalanche hazard prediction using machine learning
methods, J. Hydrol., 577, 123929,
https://doi.org/10.1016/j.jhydrol.2019.123929, 2019. a, b
Coléou, C. and Morin, S.: Vingt-cinq ans de prévision du risque d’avalanche
à Météo-France, La Météorologie, 100, 79–84, https://doi.org/10.4267/2042/65147,
2018. a
Conway, H. and Wilbour, C.: Evolution of snow slope stability during storms,
Cold Reg. Sci. Technol., 30, 67–77,
https://doi.org/10.1016/S0165-232X(99)00009-9, 1999. a, b
Decharme, B., Boone, A., Delire, C., and Noilhan, J.: Local evaluation of the
Interaction between Soil Biosphere Atmosphere soil multilayer diffusion
scheme using four pedotransfer functions, J. Geophys. Res.-Atmos., 116, D20126, https://doi.org/10.1029/2011JD016002, 2011. a
Dekanová, M., Duchon, F., Dekan, M., Kyzek, F., and Biskupič, M.: Avalanche
forecasting using neural network, in: 2018 ELEKTRO, IEEE, Mikulov, Czech Republic, 1–5,
https://doi.org/10.1109/elektro.2018.8398359, 2018. a
Durand, Y., Giraud, G., Brun, E., Mérindol, L., and Martin, E.: A
computer-based system simulating snowpack structures as a tool for regional
avalanche forecasting, J. Glaciol., 45, 469–484,
https://doi.org/10.3189/S0022143000001337, 1999. a
Durand, Y., Laternser, M., Giraud, G., Etchevers, P., Lesaffre, B., and
Mérindol, L.: Reanalysis of 44 Yr of Climate in the French Alps
(1958–2002): Methodology, Model Validation, Climatology, and Trends for Air
Temperature and Precipitation, J. Appl. Meteorol.
Clim., 48, 429–449, https://doi.org/10.1175/2008JAMC1808.1, 2009. a, b
Eckert, N. and Giacona, F.: Towards a holistic paradigm for long-term snow
avalanche risk assessment and mitigation, Ambio, 52, 711–732, https://doi.org/10.1007/s13280-022-01804-1, 2022. a
Eckert, N., Parent, E., Faug, T., and Naaim, M.: Bayesian optimal design of an
avalanche dam using a multivariate numerical avalanche model, Stoch.
Env. Res. Risk A., 23, 1123–1141,
https://doi.org/10.1007/s00477-008-0287-6, 2009. a
Eckert, N., Coleou, C., Castebrunet, H., Deschatres, M., Giraud, G., and Gaume,
J.: Cross-comparison of meteorological and avalanche data for characterising
avalanche cycles: The example of December 2008 in the eastern part of the
French Alps, Cold Reg. Sci. Technol., 64, 119–136,
https://doi.org/10.1016/j.coldregions.2010.08.009, 2010a. a
Eckert, N., Naaim, M., and Parent, E.: Long-term avalanche hazard assessment
with a Bayesian depth-averaged propagation model, J. Glaciol., 56,
563–586, https://doi.org/10.3189/002214310793146331, 2010b. a
Eckert, N., Keylock, C., Castebrunet, H., Lavigne, A., and Naaim, M.: Temporal
trends in avalanche activity in the French Alps and subregions: from
occurrences and runout altitudes to unsteady return periods, J.
Glaciol., 59, 93–114, https://doi.org/10.3189/2013JoG12J091, 2013. a
Eckert, N., Naaim, M., Giacona, F., Favier, P., Lavigne, A., Richard, D.,
Bourrier, F., and Parent, E.: Repenser les fondements du zonage
règlementaire des risques en montagne
“récurrents”, LHB, 104,
38–67, https://doi.org/10.1051/lhb/2018019, 2018. a
Favier, P., Eckert, N., Bertrand, D., and Naaim, M.: Sensitivity of avalanche
risk to vulnerability relations, Cold Reg. Sci. Technol., 108,
163–177, https://doi.org/10.1016/j.coldregions.2014.08.009, 2014. a
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., Mcclung, D., Nishimura, K.,
Satyawali, P., and Sokratov, S.: The International Classification for
Seasonal Snow on the Ground, IHP-VII Technical Documents in Hydrology, 83,
https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 1 May 2023), 2009. a
Gassner, M. and Brabec, B.: Nearest neighbour models for local and regional avalanche forecasting, Nat. Hazards Earth Syst. Sci., 2, 247–253, https://doi.org/10.5194/nhess-2-247-2002, 2002. a
Gaume, J., van Herwijnen, A., Chambon, G., Wever, N., and Schweizer, J.: Snow fracture in relation to slab avalanche release: critical state for the onset of crack propagation, The Cryosphere, 11, 217–228, https://doi.org/10.5194/tc-11-217-2017, 2017. a
Giacona, F., Eckert, N., and Martin, B.: A 240-year history of avalanche risk in the Vosges Mountains based on non-conventional (re)sources, Nat. Hazards Earth Syst. Sci., 17, 887–904, https://doi.org/10.5194/nhess-17-887-2017, 2017. a
Giacona, F., Eckert, N., Corona, C., Mainieri, R., Morin, S., Stoffel, M.,
Martin, B., and Naaim, M.: Upslope migration of snow avalanches in a warming
climate, P. Natl. Acad. Sci. USA, 118, e2107306118, https://doi.org/10.1073/pnas.2107306118,
2021. a
Giard, D., Poli, P., Morin, S., Cohuet, J.-B., Marin, F., Souverain, C.,
Coléou, C., Regimbeau, A., and Créau, M.:
L'approche participative au service de
l'observation météorologique, La
Météorologie, 100, p. 105, https://doi.org/10.4267/2042/65152, 2018. a
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical
Learning, Springer New York, https://doi.org/10.1007/978-0-387-84858-7, 2009. a, b, c
Heierli, J., Gumbsch, P., and Zaiser, M.: Anticrack Nucleation as Triggering
Mechanism for Snow Slab Avalanches, Science, 321, 240–243,
https://doi.org/10.1126/science.1153948, 2008. a
Hendrikx, J., Owens, I., Carran, W., and Carran, A.: Avalanche activity in an
extreme maritime climate: The application of classification trees for
forecasting, Cold Reg. Sci. Technol., 43, 104–116,
https://doi.org/10.1016/j.coldregions.2005.05.006, 2005. a
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a
INRAE: Programmes institutionnels d’observation des avalanches soutenus par le ministère de l’environnement, https://www.avalanches.fr/, last access: 1 May 2023. a
Jomelli, V., Delval, C., Grancher, D., Escande, S., Brunstein, D., Hetu, B.,
Filion, L., and Pech, P.: Probabilistic analysis of recent snow avalanche
activity and weather in the French Alps, Cold Reg. Sci. Technol.,
47, 180–192, https://doi.org/10.1016/j.coldregions.2006.08.003, 2007. a
Karas, A., Karbou, F., Giffard-Roisin, S., Durand, P., and Eckert, N.:
Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for
Snow Avalanche Debris Monitoring, IEEE T. Geosci.
Remote, 60, 1–17, https://doi.org/10.1109/tgrs.2021.3131853, 2022. a, b
Kern, H., Eckert, N., Jomelli, V., Grancher, D., Deschatres, M., and Arnaud-Fassetta, G.: Brief communication: Weak control of snow avalanche deposit volumes by avalanche path morphology, The Cryosphere, 15, 4845–4852, https://doi.org/10.5194/tc-15-4845-2021, 2021. a
Keylock, C. J., McClung, D. M., and Magnússon, M. M.: Avalanche risk
mapping by simulation, J. Glaciol., 45, 303–314,
https://doi.org/10.3189/S0022143000001805, 1999. a
Kronholm, K., Vikhamar-Schuler, D., Jaedicke, C., Isaksen, K., Sorteberg, A.,
and Kristensen, K.: Forecasting snow avalanche days from meteorological data
using classification trees; Grasdalen, Western Norway, in: Proceedings of the
International Snow Science Workshop, Telluride, Colorado, Citeseer, 1–6,
2006. a, b, c, d, e, f, g, h
LaChapelle, E. R.: Snow Avalanches: A review of Current Research and
Applications, J. Glaciol., 19, 313–324,
https://doi.org/10.3189/s0022143000215633, 1977. a, b
Lafaysse, M., Morin, S., Coléou, C., Vernay, M., Serça, D., Besson,
F., Willemet, J.-M., Giraud, G., Durand, Y., and Météo-France, D.:
Towards a new chain of models for avalanche hazard forecasting in French
mountain ranges, including low altitude mountains, in: Proceedings of
International Snow Science Workshop Grenoble–Chamonix Mont-Blanc, vol. 7,
162–166,
2013. a
Lavigne, A., Eckert, N., Bel, L., and Parent, E.: Adding expert contributions
to the spatiotemporal modelling of avalanche activity under different
climatic influences, J. Roy. Stat. Soc. C, 64, 651–671, https://doi.org/10.1111/rssc.12095, 2015. a
Le Roux, E., Evin, G., Eckert, N., Blanchet, J., and Morin, S.: Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019, The Cryosphere, 15, 4335–4356, https://doi.org/10.5194/tc-15-4335-2021, 2021. a
Lehning, M., Fierz, C., Brown, B., and Jamieson, B.: Modeling snow instability
with the snow-cover model SNOWPACK, Ann. Glaciol., 38, 331–338,
https://doi.org/10.3189/172756404781815220, 2004. a
Mayer, S., van Herwijnen, A., Ulivieri, G., and Schweizer, J.: Evaluating the
performance of an operational infrasound avalanche detection system at three
locations in the Swiss Alps during two winter seasons, Cold Reg. Sci. Technol., 173, 102962, https://doi.org/10.1016/j.coldregions.2019.102962, 2020. a
Mitterer, C. and Schweizer, J.: Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction, The Cryosphere, 7, 205–216, https://doi.org/10.5194/tc-7-205-2013, 2013. a
Mitterer, C., Techel, F., Fierz, C., and Schweizer, J.: An operational
supporting tool for assessing wet-snow avalanche danger, in: Proceedings
ISSW, vol. 33,
2013. a
Mitterer, C., Heilig, A., Schmid, L., van Herwijnen, A., Eisen, O., and
Schweizer, J.: Comparison of measured and modelled snow cover liquid water
content to improve local wet-snow avalanche prediction, in: International
Snow Science Workshop Proceedings,
2016. a
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet,
A., Hagenmuller, P., Lafaysse, M., Ližar, M., Mitterer, C., Monti, F.,
Müller, K., Olefs, M., Snook, J. S., van Herwijnen, A., and Vionnet, V.:
Application of physical snowpack models in support of operational avalanche
hazard forecasting: A status report on current implementations and prospects
for the future, Cold Reg. Sci. Technol., 170, 102910,
https://doi.org/10.1016/j.coldregions.2019.102910, 2020. a, b, c, d
Mosavi, A., Shirzadi, A., Choubin, B., Taromideh, F., Hosseini, F. S., Borji,
M., Shahabi, H., Salvati, A., and Dineva, A. A.: Towards an Ensemble Machine
Learning Model of Random Subspace Based Functional Tree Classifier for Snow
Avalanche Susceptibility Mapping, IEEE Access, 8, 145968–145983,
https://doi.org/10.1109/access.2020.3014816, 2020. a
Obled, C. and Good, W.: Recent Developments of Avalanche Forecasting by
Discriminant Analysis Techniques: A Methodological Review and Some
Applications to the Parsenn Area (Davos, Switzerland), J. Glaciol.,
25, 315–346, https://doi.org/10.3189/S0022143000010522, 1980. a, b
Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022. a, b, c, d, e, f, g
Pozdnoukhov, A., Matasci, G., Kanevski, M., and Purves, R. S.: Spatio-temporal avalanche forecasting with Support Vector Machines, Nat. Hazards Earth Syst. Sci., 11, 367–382, https://doi.org/10.5194/nhess-11-367-2011, 2011. a
Reuter, B., Schweizer, J., and van Herwijnen, A.: A process-based approach to estimate point snow instability, The Cryosphere, 9, 837–847, https://doi.org/10.5194/tc-9-837-2015, 2015. a
Reuter, B., Viallon-Galinier, L., Horton, S., van Herwijnen, A., Mayer, S.,
Hagenmuller, P., and Morin, S.: Characterizing snow instability with
avalanche problem types derived from snow cover simulations, Cold Reg. Sci. Technol., 194, 103462,
https://doi.org/10.1016/j.coldregions.2021.103462, 2022. a, b, c, d
Roch, A.: Les déclenchements d'avalanche, IAHS-AISH P., 69, 86–99,
https://iahs.info/uploads/dms/069021.pdf (last access: 12 January 2022), 1966 a
Rubin, M. J., Camp, T., Herwijnen, A. V., and Schweizer, J.: Automatically
Detecting Avalanche Events in Passive Seismic Data, in: 2012 11th
International Conference on Machine Learning and Applications, IEEE,
https://doi.org/10.1109/icmla.2012.12, 2012. a
Scapozza, C.: Entwicklung eines dichte-und temperaturabhängigen
Stoffgesetzes zur Beschreibung des visko-elastischen Verhaltens von Schnee,
PhD thesis, ETH Zurich, https://doi.org/10.3929/ethz-a-004680249, 2004. a
Schirmer, M., Lehning, M., and Schweizer, J.: Statistical forecasting of
regional avalanche danger using simulated snow-cover data, J.
Glaciol., 55, 761–768, https://doi.org/10.3189/002214309790152429, 2009. a
Schweizer, J.: On recent advances in avalanche research, Cold Reg. Sci. Technol., 144, 1–5, https://doi.org/10.1016/j.coldregions.2017.10.014,
2017. a
Schweizer, J. and Föhn, P. M. B.: Avalanche forecasting – an
expert system approach, J. Glaciol., 42, 318–332,
https://doi.org/10.3189/s0022143000004172, 1996. a, b
Schweizer, J. and Jamieson, J. B.: A threshold sum approach to stability
evaluation of manual snow profiles, Cold Reg. Sci. Technol., 47,
50–59, https://doi.org/10.1016/j.coldregions.2006.08.011, 2007. a
Schweizer, J., Bruce Jamieson, J., and Schneebeli, M.: Snow avalanche
formation, Rev. Geophys., 41, 1016, https://doi.org/10.1029/2002RG000123, 2003. a, b
Schweizer, J., Bellaire, S., Fierz, C., Lehning, M., and Pielmeier, C.:
Evaluating and improving the stability predictions of the snow cover model
SNOWPACK, Cold Reg. Sci. Technol., 46, 52–59,
https://doi.org/10.1016/j.coldregions.2006.05.007, 2006. a, b
Schweizer, J., Mitterer, C., Techel, F., Stoffel, A., and Reuter, B.: On the
relation between avalanche occurrence and avalanche danger level, The
Cryosphere, 14, 737–750, https://doi.org/10.5194/tc-14-737-2020, 2020. a
Sielenou, P. D., Viallon-Galinier, L., Hagenmuller, P., Naveau, P., Morin, S.,
Dumont, M., Verfaillie, D., and Eckert, N.: Combining random forests and
class-balancing to discriminate between three classes of avalanche activity
in the French Alps, Cold Reg. Sci. Technol., 187, 103276,
https://doi.org/10.1016/j.coldregions.2021.103276, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Singh, A. and Ganju, A.: Artificial Neural Networks for snow avalanche
forecasting in Indian Himalaya, in: Proceedings of 12th International
Conference of International Association for Computer Methods and Advances in
Geomechanics, IACMAG, vol. 16, 2008. a
Statham, G., Haegeli, P., Greene, E., Birkeland, K., Israelson, C., Tremper,
B., Stethem, C., McMahon, B., White, B., and Kelly, J.: A conceptual model of
avalanche hazard, Nat. Hazards, 90, 663–691,
https://doi.org/10.1007/s11069-017-3070-5, 2018. a, b
Stethem, C., Jamieson, B., Schaerer, P., Liverman, D., Germain, D., and Walker,
S.: Snow Avalanche Hazard in Canada – a Review, Nat. Hazards, 28,
487–515, https://doi.org/10.1023/a:1022998512227, 2003. a
van Herwijnen, A. and Schweizer, J.: Monitoring avalanche activity using a
seismic sensor, Cold Reg. Sci. Technol., 69, 165–176,
https://doi.org/10.1016/j.coldregions.2011.06.008, 2011. a
van Herwijnen, A., Gaume, J., Bair, E. H., Reuter, B., Birkeland, K. W., and
Schweizer, J.: Estimating the effective elastic modulus and specific fracture
energy of snowpack layers from field experiments, J. Glaciol., 62,
997–1007, https://doi.org/10.1017/jog.2016.90, 2016.
a
van Herwijnen, A., Heck, M., Richter, B., Sovilla, B., and Techel, F.: When Do
Avalanches Release: Investigating Time Scales in Avalanche Formation, in:
Proceedings, International Snow Science Workshop, 2018. a
Vernay, M., Lafaysse, M., Hagenmuller, P., Nheili, R., Verfaillie, D., and
Morin, S.: The S2M meteorological and snow cover reanalysis in the French
mountainous areas (1958–present), version 2020.2, Aeris [data set], https://doi.org/10.25326/37, 2020. a, b
Viallon-Galinier, L., Hagenmuller, P., Reuter, B., and Eckert, N.: Modelling
snowpack stability from simulated snow stratigraphy: Summary and
implementation examples, Cold Reg. Sci. Technol., 201, 103596,
https://doi.org/10.1016/j.coldregions.2022.103596, 2022. a
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b
Wilhelm, C., Wiesinger, T., and Ammann, W. J.: The avalanche winter 1999 in
Switzerland – an overview, in: Proceedings ISSW 2000, International Snow
Science Workshop, Big Sky, Montana, USA, 1–6 October 2000, 487–494,
2001. a
Zeidler, A. and Jamieson, B.: A nearest-neighbour model for forecasting
skier-triggered dry-slab avalanches on persistent weak layers in the Columbia
Mountains, Canada, Ann. Glaciol., 38, 166–172,
https://doi.org/10.3189/172756404781815194, 2004. a
Zgheib, T., Giacona, F., Granet-Abisset, A.-M., Morin, S., and Eckert, N.: One
and a half century of avalanche risk to settlements in the upper Maurienne
valley inferred from land cover and socio-environmental changes, Global
Environ. Chang., 65, 102149, https://doi.org/10.1016/j.gloenvcha.2020.102149,
2020. a
Zgheib, T., Giacona, F., Granet-Abisset, A.-M., Morin, S., Lavigne, A., and
Eckert, N.: Spatio-temporal variability of avalanche risk in the French Alps,
Reg. Environ. Change, 22, 1, https://doi.org/10.1007/s10113-021-01838-3, 2022. a
Short summary
Avalanches are a significant issue in mountain areas where they threaten recreationists and human infrastructure. Assessments of avalanche hazards and the related risks are therefore an important challenge for local authorities. Meteorological and snow cover simulations are thus important to support operational forecasting. In this study we combine it with mechanical analysis of snow profiles and find that observed avalanche data improve avalanche activity prediction through statistical methods.
Avalanches are a significant issue in mountain areas where they threaten recreationists and...
