Articles | Volume 26, issue 3
https://doi.org/10.5194/nhess-26-1537-2026
© Author(s) 2026. 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-26-1537-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Mar 2026
Research article |
| 25 Mar 2026
| 25 Mar 2026
Tracking the slopes: a spatio-temporal prediction model for backcountry skiing activity in the Swiss Alps using user-generated content
Leonie Schäfer
CORRESPONDING AUTHOR
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
Digital Society Initiative, University of Zurich, Rämistrasse 69, 8001 Zürich, Switzerland
Frank Techel
WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, 7260 Davos, Switzerland
Günter Schmudlach
Skitourenguru GmbH, Markusstrasse 12, 8006 Zürich, Switzerland
Ross S. Purves
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
Related authors
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Frank Techel, Karsten Müller, Christopher Marquardt, and Christoph Mitterer
Nat. Hazards Earth Syst. Sci., 26, 1161–1181, https://doi.org/10.5194/nhess-26-1161-2026, https://doi.org/10.5194/nhess-26-1161-2026, 2026
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We examined how avalanche forecasters across Europe use the EAWS (European Avalanche Warning Services) Matrix, a decision-support tool for determining regional avalanche danger levels. Although warning services apply the Matrix differently, we identified both consistent patterns and notable inconsistencies in its application. Our findings highlight where the Matrix works well and where clarification is needed, supporting more consistent and transparent avalanche information for the public.
Karsten Müller, Frank Techel, and Christoph Mitterer
Nat. Hazards Earth Syst. Sci., 25, 4503–4525, https://doi.org/10.5194/nhess-25-4503-2025, https://doi.org/10.5194/nhess-25-4503-2025, 2025
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This paper presents the updated EAWS (European Avalanche Warning Services) Matrix, developed to support consistent avalanche danger assessments across Europe. It links snowpack stability, its frequency, and avalanche size to the five danger levels. Based on expert surveys and operational testing, the Matrix supports expert judgment and aligns with the Conceptual Model of Avalanche Hazard while addressing known ambiguities in practice.
Frank Techel, Ross S. Purves, Stephanie Mayer, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 25, 3333–3353, https://doi.org/10.5194/nhess-25-3333-2025, https://doi.org/10.5194/nhess-25-3333-2025, 2025
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We tested how well fully data- and model-driven avalanche forecasts compare to human-made forecasts, which also integrate added context like field observations or model output. Using data from Switzerland over three winters, we found that models – even without this extra input – performed nearly as well. While human forecasts still have a slight edge, model predictions already offer reliable support for daily avalanche forecasting.
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
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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.
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
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
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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
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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.
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
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
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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
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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
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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
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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
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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.
Veronika Hutter, Frank Techel, and Ross S. Purves
Nat. Hazards Earth Syst. Sci., 21, 3879–3897, https://doi.org/10.5194/nhess-21-3879-2021, https://doi.org/10.5194/nhess-21-3879-2021, 2021
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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.
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
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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.
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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 used GPS tracks and online engagement data to model and predict daily backcountry skiing base rates in the Swiss Alps based on a set of snow, weather, temporal and environmental variables.
Backcountry skiing is a popular form of recreation in Switzerland and worldwide, despite...
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