Articles | Volume 25, issue 2
https://doi.org/10.5194/nhess-25-625-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-625-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Feb 2025
Research article |
| 11 Feb 2025
| 11 Feb 2025
A quantitative module of avalanche hazard – comparing forecaster assessments of storm and persistent slab avalanche problems with information derived from distributed snowpack simulations
Florian Herla
CORRESPONDING AUTHOR
Department of Geography, School of Resource & Environmental Management, and Centre for Natural Hazards Research, Simon Fraser University, Burnaby, BC, Canada
Pascal Haegeli
Department of Geography, School of Resource & Environmental Management, and Centre for Natural Hazards Research, Simon Fraser University, Burnaby, BC, Canada
Simon Horton
Avalanche Canada, Revelstoke, BC, Canada
Patrick Mair
Department of Psychology, Harvard University, Cambridge, MA, USA
Related authors
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
Short summary
Short summary
We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 24, 2727–2756, https://doi.org/10.5194/nhess-24-2727-2024, https://doi.org/10.5194/nhess-24-2727-2024, 2024
Short summary
Short summary
Snowpack simulations are increasingly employed by avalanche warning services to inform about critical avalanche layers buried in the snowpack. However, validity concerns limit their operational value. We present methods that enable meaningful comparisons between snowpack simulations and regional assessments of avalanche forecasters to quantify the performance of the Canadian weather and snowpack model chain to represent thin critical avalanche layers on a large scale and in real time.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
Short summary
Short summary
We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
John Sykes, Pascal Haegeli, Roger Atkins, Patrick Mair, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 1255–1292, https://doi.org/10.5194/nhess-25-1255-2025, https://doi.org/10.5194/nhess-25-1255-2025, 2025
Short summary
Short summary
We model the decision-making of professional ski guides and develop decision support tools to assist with determining appropriate terrain based on current conditions. Our approach compares a manually constructed Bayesian network with machine learning classification models. The models accurately capture the real-world decision-making outcomes in 85–93 % of cases. Our conclusions focus on strengths and weaknesses of each model and discuss ramifications for practical applications in ski guiding.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
Short summary
Short summary
We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 24, 2727–2756, https://doi.org/10.5194/nhess-24-2727-2024, https://doi.org/10.5194/nhess-24-2727-2024, 2024
Short summary
Short summary
Snowpack simulations are increasingly employed by avalanche warning services to inform about critical avalanche layers buried in the snowpack. However, validity concerns limit their operational value. We present methods that enable meaningful comparisons between snowpack simulations and regional assessments of avalanche forecasters to quantify the performance of the Canadian weather and snowpack model chain to represent thin critical avalanche layers on a large scale and in real time.
John Sykes, Håvard Toft, Pascal Haegeli, and Grant Statham
Nat. Hazards Earth Syst. Sci., 24, 947–971, https://doi.org/10.5194/nhess-24-947-2024, https://doi.org/10.5194/nhess-24-947-2024, 2024
Short summary
Short summary
The research validates and optimizes an automated approach for creating classified snow avalanche terrain maps using open-source geospatial modeling tools. Validation is based on avalanche-expert-based maps for two study areas. Our results show that automated maps have an overall accuracy equivalent to the average accuracy of three human maps. Automated mapping requires a fraction of the time and cost of traditional methods and opens the door for large-scale mapping of mountainous terrain.
Abby Morgan, Pascal Haegeli, Henry Finn, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 23, 1719–1742, https://doi.org/10.5194/nhess-23-1719-2023, https://doi.org/10.5194/nhess-23-1719-2023, 2023
Short summary
Short summary
The avalanche danger scale is a critical component for communicating the severity of avalanche hazard conditions to the public. We examine how backcountry recreationists in North America understand and use the danger scale for planning trips into the backcountry. Our results provide an important user perspective on the strengths and weaknesses of the existing scale and highlight opportunities for future improvements.
John Sykes, Pascal Haegeli, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 22, 3247–3270, https://doi.org/10.5194/nhess-22-3247-2022, https://doi.org/10.5194/nhess-22-3247-2022, 2022
Short summary
Short summary
Automated snow avalanche terrain mapping provides an efficient method for large-scale assessment of avalanche hazards, which informs risk management decisions for transportation and recreation. This research reduces the cost of developing avalanche terrain maps by using satellite imagery and open-source software as well as improving performance in forested terrain. The research relies on local expertise to evaluate accuracy, so the methods are broadly applicable in mountainous regions worldwide.
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.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
Short summary
Short summary
We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 22, 1973–2000, https://doi.org/10.5194/nhess-22-1973-2022, https://doi.org/10.5194/nhess-22-1973-2022, 2022
Short summary
Short summary
Avalanche bulletins include travel and terrain statements to provide recreationists with tangible guidance about how to apply the hazard information. We examined which bulletin users pay attention to these statements, what determines their usefulness, and how they could be improved. Our study shows that reducing jargon and adding simple explanations can significantly improve the usefulness of the statements for users with lower levels of avalanche awareness education who depend on this advice.
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 985–993, https://doi.org/10.5194/nhess-22-985-2022, https://doi.org/10.5194/nhess-22-985-2022, 2022
Short summary
Short summary
To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 21, 3219–3242, https://doi.org/10.5194/nhess-21-3219-2021, https://doi.org/10.5194/nhess-21-3219-2021, 2021
Short summary
Short summary
Avalanche warning services publish condition reports to help backcountry recreationists make informed decisions about when and where to travel in avalanche terrain. We tested how different graphic representations of terrain information can affect users’ ability to interpret and apply the provided information. Our study shows that a combined presentation of aspect and elevation information is the most effective. These results can be used to improve avalanche risk communication products.
Cited articles
Atkins, R.: An avalanche characterization checklist for backcountry travel decisions, in: Proceedings of the 2004 International Snow Science Workshop, 19–24 September 2004, Jackson Hole, WY, USA, 462–468, https://arc.lib.montana.edu/snow-science/item/1118 (last access: 29 January 2025), 2004. a
Bartelt, P., Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: 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
Bellaire, S. and Jamieson, J. B.: Forecasting the formation of critical snow layers using a coupled snow cover and weather model, Cold Reg. Sci. Technol., 94, 37–44, https://doi.org/10.1016/j.coldregions.2013.06.007, 2013. a, b
Bellaire, S., van erwijnen, A., Mitterer, C., and Schweizer, J.: On forecasting wet-snow avalanche activity using simulated snow cover data, Cold Reg. Sci. Technol., 144, 28–38, https://doi.org/10.1016/j.coldregions.2017.09.013, 2017. a
Calonne, N., Richter, B., Löwe, H., Cetti, C., Ter Schure, J., Van Herwijnen, A., Fierz, C., Jaggi, M., and Schneebeli, M.: The RHOSSA campaign: Multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack, The Cryosphere, 14, 1829–1848, https://doi.org/10.5194/tc-14-1829-2020, 2020. a
Campbell, C., Conger, S., Gould, B., Haegeli, P., Jamieson, J. B., and Statham, G.: Technical Aspects of Snow Avalanche Risk Management–Resources and Guidelines for Avalanche Practitioners in Canada, Revelstoke, BC, Canada, ISBN 978-1-926497-00-6, 2016. a
Canadian Avalanche Association: Observation Guidelines and Recording Standards for Weather, Snowpack, and Avalanches, Tech. rep., Revelstoke, BC, Canada, ISBN 978-1-926497-04-4, https://www.avalancheassociation.ca/page/OGRS-Updates-2024 (last access: 29 January 2025), 2024. a
Chambers, J. M., Cleveland, W. S., Kleiner, B., and Tukey, P. A.: Graphical methods for data analysis, CRC Press, ISBN 9781351080750, https://doi.org/10.1201/9781351072304, 2018. a
Conway, H. and Wilbour, C.: Evolution of snow slope stability during storms1, Cold Reg. Sci. Technol., 30, 67–77, https://doi.org/10.1016/S0165-232X(99)00009-9, 1999. a
EAWS: Standards – Avalanche Danger Scale, https://www.avalanches.org/standards/avalanche-danger-scale/ (last access: 29 January 2025), 2023a. a
EAWS: Standards – Avalanche Problems, https://www.avalanches.org/standards/avalanche-problems/ (last access: 29 January 2025), 2023b. a
Guikema, S.: Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs, Risk Anal., 40, 1117–1123, https://doi.org/10.1111/risa.13476, 2020. a
Haegeli, P. and McClung, D. M.: Expanding the snow-climate classification with avalanche-relevant information: Initial description of avalanche winter regimes for southwestern Canada, J. Glaciol., 53, 266–276, https://doi.org/10.3189/172756507782202801, 2007. a
Hendrick, M., Techel, F., Volpi, M., Olevski, T., Pérez-Guillén, C., Herwijnen, A. V., and Schweizer, J.: Automated prediction of wet-snow avalanche activity in the Swiss Alps, J. Glaciol., 50, 1–14, https://doi.org/10.1017/jog.2023.24, 2023. a, b, c
Herla, F., Horton, S., Mair, P., and Haegeli, P.: Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating of snowpack model output for avalanche forecasting, Geosci. Model Dev., 14, 239–258, https://doi.org/10.5194/gmd-14-239-2021, 2021. a, b, c
Herla, F., Haegeli, P., and Mair, P.: A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting, The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, 2022. a, b
Herla, F., Haegeli, P., Horton, S., and Mair, P.: A quantitative module of avalanche hazard – Data and Code, OSF [code], https://doi.org/10.17605/OSF.IO/W7PJY, 2024a. a, b
Herla, F., Haegeli, P., Horton, S., and Mair, P.: A large-scale validation of snowpack simulations in support of avalanche forecasting focusing on critical layers, Nat. Hazards Earth Syst. Sci., 24, 2727–2756, https://doi.org/10.5194/nhess-24-2727-2024, 2024b. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Horton, S. and Haegeli, P.: Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting, The Cryosphere, 16, 3393–3411, https://doi.org/10.5194/tc-16-3393-2022, 2022. a, b
Horton, S., Herla, F., and Haegeli, P.: An R package for snow profile analysis and visualization, in: Proceedings of the 2020 Virtual Snow Science Workshop VSSW, 4–6 October 2020, Fernie, BC, Canada, https://vssw2020.com/poster-submissions-2/ (last access: 29 January 2025), 2020a. a
Horton, S., Nowak, S., and Haegeli, P.: Enhancing the operational value of snowpack models with visualization design principles, Nat. Hazards Earth Syst. Sci., 20, 1557–1572, https://doi.org/10.5194/nhess-20-1557-2020, 2020b. a
Hothorn, T. and Zeileis, A.: Partykit: A modular toolkit for recursive partytioning in R, J. Mach. Learn. Res., 16, 3905–3909, 2015. a
Hothorn, T., Hornik, K., and Zeileis, A.: Unbiased recursive partitioning: A conditional inference framework, J. Comput. Graph. Stat., 15, 651–674, https://doi.org/10.1198/106186006X133933, 2006. a
Klassen, K.: What's the problem? A primer on defining avalanche character, Avalanche J., 105, 10–12, 2014. a
LaChapelle, E. R.: The fundamental processes in conventional avalanche forecasting, J. Glaciol., 26, 75–84, https://doi.org/10.3189/s0022143000010601, 1980. a, b, c
Lazar, B., Trautman, S., Cooperstein, M., Greene, E., and Birkeland, K. W.: North American Avalanche Danger Scale: Do Backcountry Forecasters Apply It Consistently?, in: Proceedings of the 2016 International Snow Science Workshop, Breckenridge, CO, USA, 3–7 October 2016, 457–465, https://arc.lib.montana.edu/snow-science/item/2307 (last access: 29 January 2025), 2016. a
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK model for the Swiss avalanche warning Part III: Meteorological forcing, thin layer formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184, https://doi.org/10.1016/S0165-232X(02)00072-1, 2002a. a
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A physical SNOWPACK model for the Swiss avalanche warning Part II. Snow microstructure, Cold Reg. Sci. Technol., 35, 147–167, https://doi.org/10.1016/S0165-232X(02)00073-3, 2002b. a
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks, B. Am. Meteorol. Soc., 100, 2473–2490, https://doi.org/10.1175/BAMS-D-19-0001.1, 2019. a
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.: Evaluating snow models with varying process representations for hydrological applications, Water Resources Res., 51, 2707–2723, https://doi.org/10.1002/2014WR016498, 2015. a
Mayer, S., Techel, F., Schweizer, J., and Van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, 2023. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u
McClung, D. M.: The Elements of Applied Avalanche Forecasting, Part I: The Human Issues, Nat. Hazards, 26, 111–129, https://doi.org/10.1023/a:1015665432221, 2002a. a, b
McClung, D. M.: The Elements of Applied Avalanche Forecasting, Part II: The Physical Issues and the Rules of Applied Avalanche Forecasting, Nat. Hazards, 26, 131–146, https://doi.org/10.1023/a:1015604600361, 2002b. a, b
McClung, D. M.: Dimensions of dry snow slab avalanches from field measurements, J. Geophys. Res.-Earth, 114, F01006, https://doi.org/10.1029/2007JF000941, 2009. a, b, c, d
Menard, C. B., Essery, R., Krinner, G., Arduini, G., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Dai, Y., Decharme, B., Dutra, E., Fang, X., Fierz, C., Gusev, Y., Hagemann, S., Haverd, V., Kim, H., Lafaysse, M., Marke, T., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Schädler, G., Semenov, V. A., Smirnova, T., Strasser, U., Swenson, S., Turkov, D., Wever, N., and Yuan, H.: Scientific and human errors in a snow model intercomparison, B. Am. Meteorol. Soc., 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1, 2021. a
Milbrandt, J. A., Bélair, S., Faucher, M., Vallée, M., Carrera, M. L., and Glazer, A.: The pan-canadian high resolution (2.5 km) deterministic prediction system, Weather Forecast., 31, 1791–1816, https://doi.org/10.1175/WAF-D-16-0035.1, 2016. a
Mitterer, C., Techel, F., Fierz, C., and Schweizer, J.: An operational supporting tool for assessing wet-snow avalanche danger, in: Proceedings of the International Snow Science Workshop, 7–11 October 2013, Grenoble–Chamonix Mont-Blanc, 334–338, https://arc.lib.montana.edu/snow-science/item/1860 (last access: 29 January 2025), 2013. a
Monti, F., Gaume, J., van Herwijnen, A., and Schweizer, J.: Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack, Nat. Hazards Earth Syst. Sci., 16, 775–788, https://doi.org/10.5194/nhess-16-775-2016, 2016. a
Morin, S., Fierz, C., Horton, S., Bavay, M., Dumont, M., Hagenmuller, P., Lafaysse, M., Mitterer, C., Monti, F., Olefs, M., Snook, J. S., Techel, F., 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, 1098–1107, https://doi.org/10.1016/J.COLDREGIONS.2019.102910, 2020. a, b, c
Nowak, S. and Bartram, L.: I'm Not Sure: Designing for Ambiguity in Visual Analytics, in: Proceedings of Graphics Interface 2022, 16–19 May 2022, Montréal, Quebec, 114–126, https://doi.org/10.20380/GI2022.13, 2022. a
Nowak, S., Bartram, L., and Haegeli, P.: Designing for Ambiguity: Visual Analytics in Avalanche Forecasting, in: Proceedings – 2020 IEEE Visualization Conference, Institute of Electrical and Electronics Engineers Inc., 81–85, ISBN 9781728180144, https://doi.org/10.1109/VIS47514.2020.00023, 2020. a
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
Quéno, L., Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Dumont, M., and Karbou, F.: Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts, The Cryosphere, 10, 1571–1589, https://doi.org/10.5194/tc-10-1571-2016, 2016. a
Raleigh, M. S., Lundquist, J., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015. a
R Core Team: R: A Language and Environment for Statistical Computing, https://www.R-project.org/ (last access: 29 January 2025), 2023. a
Reuter, B. and Schweizer, J.: Describing Snow Instability by Failure Initiation, Crack Propagation, and Slab Tensile Support, Geophys. Res. Lett., 45, 7019–7027, https://doi.org/10.1029/2018GL078069, 2018. 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, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t
Revuelto, J., Lecourt, G., Lafaysse, M., Zin, I., Charrois, L., Vionnet, V., Dumont, M., Rabatel, A., Six, D., Condom, T., Morin, S., Viani, A., and Sirguey, P.: Multi-criteria evaluation of snowpack simulations in complex alpine terrain using satellite and in situ observations, Remote Sens., 10, 1171, https://doi.org/10.3390/rs10081171, 2018. a
Richter, B., Schweizer, J., Rotach, M. W., and Van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019. a
Richter, B., Van Herwijnen, A., Rotach, M. W., and Schweizer, J.: Sensitivity of modeled snow stability data to meteorological input uncertainty, Nat. Hazards Earth Syst. Sci., 20, 2873–2888, https://doi.org/10.5194/nhess-20-2873-2020, 2020. a
Schirmer, M., Schweizer, J., and Lehning, M.: Statistical evaluation of local to regional snowpack stability using simulated snow-cover data, Cold Reg. Sci. Technol., 64, 110–118, https://doi.org/10.1016/j.coldregions.2010.04.012, 2010. a
Schmucki, E., Marty, C., Fierz, C., and Lehning, M.: Evaluation of modelled snow depth and snow water equivalent at three contrasting sites in Switzerland using SNOWPACK simulations driven by different meteorological data input, Cold Reg. Sci. Technol., 99, 27–37, https://doi.org/10.1016/j.coldregions.2013.12.004, 2014. a
Schweizer, J.: On the predictability of snow avalanches, in: Proceedings of the 2008 International Snow Science Workshop, 21–27 September 2008, Whistler, BC, p. 688, https://arc.lib.montana.edu/snow-science/item/115 (last access: 29 January 2025), 2008. a
Schweizer, J., Kronholm, K., Jamieson, J. B., and Birkeland, K. W.: Review of spatial variability of snowpack properties and its importance for avalanche formation, Cold Reg. Sci. Technol., 51, 253–272, https://doi.org/10.1016/j.coldregions.2007.04.009, 2007. a
Shandro, B. and Haegeli, P.: Characterizing the nature and variability of avalanche hazard in western Canada, Nat. Hazards Earth Syst. Sci., 18, 1141–1158, https://doi.org/10.5194/nhess-18-1141-2018, 2018. a
Statham, G., Haegeli, P., Birkeland, K. W., Greene, E., Israelson, C., Tremper, B., Stethem, C., Mcmahon, B., White, B., and Kelly, J.: The North American Public Avalanche Danger Scale, Tech. rep., ISBN 4037621568, https://arc.lib.montana.edu/snow-science/item/353 (last access: 29 January 2025), 2010. a, b
Statham, G., Holeczi, S., and Shandro, B.: Consistency and Accuracy of Public Avalanche Forecasts in Western Canada, in: Proceedings of the 2018 International Snow Science Workshop, 7–12 October 2018, Innsbruck, Austria, https://arc.lib.montana.edu/snow-science/item/2806 (last access: 29 January 2025), 2018b. a
Techel, F., Mitterer, C., Ceaglio, E., Coléou, C., Morin, S., Rastelli, F., and Purves, R. S.: Spatial consistency and bias in avalanche forecasts – a case study in the European Alps, Nat. Hazards Earth Syst. Sci., 18, 2697–2716, https://doi.org/10.5194/nhess-18-2697-2018, 2018. a
Trottet, B., Simenhois, R., Bobillier, G., Bergfeld, B., van Herwijnen, A., Jiang, C., and Gaume, J.: Transition from sub-Rayleigh anticrack to supershear crack propagation in snow avalanches, Nat. Phys., 18, 1094–1098, https://doi.org/10.1038/s41567-022-01662-4, 2022. a
Vernay, M., Lafaysse, M., Mérindol, L., Giraud, G., and Morin, S.: Ensemble forecasting of snowpack conditions and avalanche hazard, Cold Reg. Sci. Technol., 120, 251–262, https://doi.org/10.1016/j.coldregions.2015.04.010, 2015. a
Viallon-Galinier, L., Hagenmuller, P., and Lafaysse, M.: Forcing and evaluating detailed snow cover models with stratigraphy observations, Cold Reg. Sci. Technol., 180, 103163, https://doi.org/10.1016/j.coldregions.2020.103163, 2020. a
Viallon-Galinier, L., Hagenmuller, P., and Eckert, N.: Combining modelled snowpack stability with machine learning to predict avalanche activity, The Cryosphere, 17, 2245–2260, https://doi.org/10.5194/tc-17-2245-2023, 2023. a
Short summary
We present a spatial framework for extracting information about avalanche problems from detailed snowpack simulations and compare the numerical results against operational assessments from avalanche forecasters. Despite good agreement in seasonal summary statistics, a comparison of daily assessments revealed considerable differences, while it remained unclear which data source represented reality the best. We discuss how snowpack simulations can add value to the forecasting process.
We present a spatial framework for extracting information about avalanche problems from detailed...
Altmetrics
Final-revised paper
Preprint