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.
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
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The Cryosphere, 17, 4691–4704, https://doi.org/10.5194/tc-17-4691-2023, https://doi.org/10.5194/tc-17-4691-2023, 2023
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Publication in NHESS not foreseen
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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
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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
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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
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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
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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
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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
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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.
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...