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
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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.
                                            
                                            
                                        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
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                                                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
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                                                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
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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
                            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
                                    Revised manuscript accepted for NHESS 
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                                                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
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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.
                                            
                                            
                                        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.
                                            
                                            
                                        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
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                                                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.
                                            
                                            
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                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...
                    
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