Articles | Volume 21, issue 12
https://doi.org/10.5194/nhess-21-3879-2021
© Author(s) 2021. 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-21-3879-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland? Consistency between forecasters and avalanche danger
Veronika Hutter
School of Life Sciences, Technical University of Munich, Germany
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
WSL Institute for Snow and Avalanche Research (SLF), Davos, Switzerland
Department of Geography, University of Zurich, Zurich, Switzerland
Ross S. Purves
Department of Geography, University of Zurich, Zurich, Switzerland
Related authors
No articles found.
Inhye Kong, Jan Seibert, and Ross S. Purves
Hydrol. Earth Syst. Sci., 29, 3795–3808, https://doi.org/10.5194/hess-29-3795-2025, https://doi.org/10.5194/hess-29-3795-2025, 2025
Short summary
Short summary
This study examines the timing and topics of newspaper coverage of droughts in England. Media attention correlated with drought-prone hydroclimatic conditions, particularly low precipitation and low groundwater levels, but also showed a seasonality bias, with more coverage in spring and summer, as exemplified by the 2022 summer drought. The findings reveal complex media dynamics in science communication, suggesting potential gaps in how droughts are framed by scientists versus the media.
Frank Techel, Karsten Müller, Christopher Marquardt, and Christoph Mitterer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3349, https://doi.org/10.5194/egusphere-2025-3349, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We studied how avalanche forecasters across Europe used a new tool called the EAWS Matrix to assess avalanche danger levels. Despite different approaches, many services used the Matrix in similar ways. Our findings can help to further improve the Matrix and support more consistent avalanche forecasts, leading to more reliable and credible avalanche information for people in snow-covered mountain regions.
Annika Kunz, Ross S. Purves, and Bruna Rohling
AGILE GIScience Ser., 6, 33, https://doi.org/10.5194/agile-giss-6-33-2025, https://doi.org/10.5194/agile-giss-6-33-2025, 2025
Leonie Schäfer, Frank Techel, Günter Schmudlach, and Ross S. Purves
EGUsphere, https://doi.org/10.5194/egusphere-2025-2344, https://doi.org/10.5194/egusphere-2025-2344, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Backcountry skiing is a popular form of recreation in Switzerland and worldwide, despite numerous avalanche accidents and fatalities that are recorded each year. There is a need for spatially explicit information on backcountry usage for effective risk estimations and avalanche forecast verification. We successfully used GPS tracks and online engagement data to model daily backcountry skiing base rates in the Swiss Alps based on a set of snow, weather, temporal and environmental variables.
Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 1331–1351, https://doi.org/10.5194/nhess-25-1331-2025, https://doi.org/10.5194/nhess-25-1331-2025, 2025
Short summary
Short summary
This study assesses the performance and explainability of a random-forest classifier for predicting dry-snow avalanche danger levels during initial live testing. The model achieved ∼ 70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
Short summary
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Frank Techel, Stephanie Mayer, Ross S. Purves, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-158, https://doi.org/10.5194/nhess-2024-158, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We evaluate fully data- and model-driven predictions of avalanche danger in Switzerland and compare them with human-made avalanche forecasts as a benchmark. We show that model predictions perform similarly to human forecasts calling for a systematic integration of forecast chains into the forecasting process.
Inhye Kong and Ross S. Purves
AGILE GIScience Ser., 5, 33, https://doi.org/10.5194/agile-giss-5-33-2024, https://doi.org/10.5194/agile-giss-5-33-2024, 2024
Karsten Müller, Frank Techel, and Christoph Mitterer
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-48, https://doi.org/10.5194/nhess-2024-48, 2024
Preprint under review for NHESS
Short summary
Short summary
Avalanche forecasting is crucial for mountain safety. Tools like the European Avalanche Danger Scale and Matrix set standards for forecasters, but consistency still varies. We analyzed the use of the EAWS Matrix, aiding danger level assignment. Our analysis shows inconsistencies, suggesting further need for refinement and training.
Stephanie Mayer, Frank Techel, Jürg Schweizer, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, https://doi.org/10.5194/nhess-23-3445-2023, 2023
Short summary
Short summary
We present statistical models to estimate the probability for natural dry-snow avalanche release and avalanche size based on the simulated layering of the snowpack. The benefit of these models is demonstrated in comparison with benchmark models based on the amount of new snow. From the validation with data sets of quality-controlled avalanche observations and danger levels, we conclude that these models may be valuable tools to support forecasting natural dry-snow avalanche activity.
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
Short summary
Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
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.
Mina Karimi, Mohammad Saadi Mesgari, Ross Stuart Purves, and Omid Reza Abbasi
Abstr. Int. Cartogr. Assoc., 5, 54, https://doi.org/10.5194/ica-abs-5-54-2022, https://doi.org/10.5194/ica-abs-5-54-2022, 2022
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
Short summary
Short summary
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Stefan S. Ivanovic and Ross Purves
AGILE GIScience Ser., 3, 39, https://doi.org/10.5194/agile-giss-3-39-2022, https://doi.org/10.5194/agile-giss-3-39-2022, 2022
Frank Techel, Stephanie Mayer, Cristina Pérez-Guillén, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, https://doi.org/10.5194/nhess-22-1911-2022, 2022
Short summary
Short summary
Can the resolution of forecasts of avalanche danger be increased by using a combination of absolute and comparative judgments? Using 5 years of Swiss avalanche forecasts, we show that, on average, sub-levels assigned to a danger level reflect the expected increase in the number of locations with poor snow stability and in the number and size of avalanches with increasing forecast sub-level.
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.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
Short summary
Short summary
Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
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.
Cited articles
Brabec, B. and Stucki, T.: Verification of avalanche bulletins by
questionnaires, in: Proceedings 25 Years of Snow Avalanche Research at NGI, Norges Geotekniske Institutt NGI, 12–16 May 1998, Voss, Norway, edited by: Hestnes, E., NGI publication, 203,
79–83, 1998. a
EAWS: Memorandum of understanding for the European Avalanche Warning Services
(EAWS), memorandum, European Avalanche Warning Services EAWS, available at: https://www.avalanches.org/about/memorandum-of-understanding/ (last access: 22 December 2021), 2017. a
EAWS: Definition of avalanche danger, avalanche danger level and their
contributing factors; presented at EAWS General Assembly, Davos, Switzerland,
2021, EAWS working group Matrix and Scale, working group members: Müller,
K., Bellido, G., Bertrando, L., Feistl, T., Mitterer, C., Palmgren, P.,
Sofia, S., and Techel, F., presented at: EAWS General Assembly, Davos, Switzerland, June 2021, 2021a. a
EAWS: Standards: Information pyramid,
available at: https://www.avalanches.org/standards/information-pyramid/ (last access:
17 November 2021), 2021c. a
Ebert, P. A. and Milne, P.: Methodological and conceptual challenges in rare and severe event forecast-verification, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2021-215, in review, 2021. a
Elder, K. and Armstrong, B.: A quantitative approach for verifying avalanche
hazard ratings, in: Symposium at Davos 1986 on Avalanche Formation, Movement
and Effects, vol. 162 of International Association of Hydrological
Sciences Publication, 593–603, 1987. a
Giraud, G., Lafeuille, J., and Pahaut, E.: Evaluation de la qualité de la
prévision du risque d'avalanche, Int. Ass. Hydrol. Sci. Publ., 162,
583–591, 1987. a
Gordon, N. and Shaykewich, J.: Guidelines of performance assessment of public
weather services, World Meteorological Organization, Geneva, Switzerland, available at: https://library.wmo.int/index.php?lvl=notice_display&id=11898#.YcHrS1kxlZc (last access: 21 December 2021), WMO/TD No. 1023, 2000. a
Kuhn, T.: A survey and classification of controlled natural languages,
Computational Linguistics, 40, 121–170, https://doi.org/10.1162/COLI_a_00168, 2014. a, b
Landis, J. R. and Koch, G. G.: The measurement of observer agreement for
categorical data, Biometrics, 33, 159–174, https://doi.org/10.2307/2529310, 1977. a, b, c, d
Lazar, B., Trautmann, S., Cooperstein, M., Greene, E., and Birkeland, K.: North
American avalanche danger scale: Do backcountry forecasters apply it
consistently?, in: Proceedings ISSW 2016. International Snow Science
Workshop, 2–7 October 2016, Breckenridge, Co., 457–465, 2016. a
LWD Steiermark: Ergebnisse der Online-Umfrage des LWD Steiermark 2015, Lawinenwarndienst Steiermark, Graz, 2015. a
MacEachren, A. M.: How maps work: representation, visualization, and design,
Guilford Press, New York, 2004. a
Moner, I., Orgué, S., Gavaldà, J., and Bacardit, M.: How big is big:
results of the avalanche size classification survey, in: Proceedings ISSW
2013. International Snow Science Workshop, 7–11 October 2013, Grenoble –
Chamonix Mont-Blanc, France, 2013. a
Müller, K., Mitterer, C., Engeset, R., Ekker, R., and Kosberg, S.: Combining
the conceptual model of avalanche hazard with the Bavarian matrix, in:
Proceedings ISSW 2016, International Snow Science Workshop, 2–7 October
2016, Breckenridge, Co., USA, 472–479, 2016. a
Murphy, A. H.: What is a good forecast? An essay on the nature of goodness in
weather forecasting, Weather Forecast., 8, 281–293,
https://doi.org/10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2, 1993. a
Newcombe, R. G.: Interval estimation for the difference between independent
proportions: comparison of eleven methods, Stat. Med., 8,
873–890,
https://doi.org/10.1002/(sici)1097-0258(19980430)17:8<873::aid-sim779>3.0.co;2-i, 1998. a
Ogden, C. K. and Richards, I. A.: The Meaning of Meaning: A Study of the
Influence of Language upon Thought and of the Science of Symbolism, vol. 29,
Harcourt, Brace, 1925. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
available at: https://www.R-project.org/ (last access: 1 July 2020), 2020. a
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, b, c
SLF: Avalanche bulletin interpretation guide, WSL Institute for Snow and
Avalanche Research SLF,
available at: http://www.slf.ch/lawineninfo/zusatzinfos/interpretationshilfe/interpretationshilfe_e.pdf (last access: 20 October 2018),
edition December 2017, 53 pp., 2017. a
SLF: Avalanche bulletin interpretation guide, WSL Institute for Snow and
Avalanche Research SLF,
available at: https://www.slf.ch/files/user_upload/SLF/Lawinenbulletin_Schneesituation/Wissen_zum_Lawinenbulletin/Interpretationshilfe/Interpretationshilfe_EN.pdf (last access: 15 September 2020),
edition December 2019, 52 pp., 2019. a
St. Clair, A., Finn, H., and Hageli, P.: Where the rubber of the RISP model
meets the road: Contextualizing risk information seeking and processing with
an avalanche bulletin user typology, Int. J. Dis. Risk
Red., 66, 102626, https://doi.org/10.1016/j.ijdrr.2021.102626, 2021. a, b, c
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, 2018a. a
Statham, G., Holeczi, S., and Shandro, B.: Consistency and accuracy of public
avalanche forecasts in Western Canada, in: Proceedings ISSW 2018,
International Snow Science Workshop, 7–12 October 2018, Innsbruck, Austria,
1491–1496, 2018b. a
Techel, F.: How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland?, EnviDat [data set], https://doi.org/10.16904/envidat.266, 2021. a
Techel, F. and Schweizer, J.: On using local avalanche danger level estimates
for regional forecast verification, Cold Reg. Sci. Technol., 144,
52–62, https://doi.org/10.1016/j.coldregions.2017.07.012, 2017. 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, b, c, d
Thumlert, S., Statham, G., and Jamieson, B.: The likelihood scale in avalanche
forecasting, The Avalanche Review, 38, 31–33, 2020. a
Williams, K.: Credibility of avalanche warnings, J. Glaciol., 26,
93–96, https://doi.org/10.1017/S0022143000010625, 1980. a
Winkler, K. and Kuhn, T.: Fully automatic multi-language translation with a
catalogue of phrases – successful employment for the Swiss avalanche
bulletin, Lang. Resour. Eval., 51, 13–35,
https://doi.org/10.1007/s10579-015-9316-5, 2017. a, b, c
Winkler, K. and Techel, F.: Users rating of the Swiss avalanche forecast, in:
Proceedings ISSW 2014, International Snow Science Workshop, 29 September–3
October 2014, Banff, Canada, 437–444, 2014. a
Winkler, K., Bächtold, M., Gallorini, S., Niederer, U., Stucki, T.,
Pielmeier, C., Darms, G., Dürr, L., Techel, F., and Zweifel, B.: Swiss
avalanche bulletin: automated translation with a catalogue of phrases, in:
Proceedings ISSW 2013, International Snow Science Workshop, 7–11 October
2013, Grenoble – Chamonix Mont-Blanc, France, 437–441, 2013. a
Winkler, K., Schmudlach, G., Degraeuwe, B., and Techel, F.: On the correlation
between the forecast avalanche danger and avalanche risk taken by backcountry
skiers in Switzerland, Cold Reg. Science Technol., 188, 103299,
https://doi.org/10.1016/j.coldregions.2021.103299, 2021. a
Short summary
How is avalanche danger described in public avalanche forecasts? We analyzed 6000 textual descriptions of avalanche danger in Switzerland, taking the perspective of the forecaster. Avalanche danger was described rather consistently, although the results highlight the difficulty of communicating conditions that are neither rare nor frequent, neither small nor large. The study may help to refine the ways in which avalanche danger could be communicated to the public.
How is avalanche danger described in public avalanche forecasts? We analyzed 6000 textual...
Altmetrics
Final-revised paper
Preprint