Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-1911-2022
https://doi.org/10.5194/nhess-22-1911-2022
Research article
 | 
09 Jun 2022
Research article |  | 09 Jun 2022

On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger

Frank Techel, Stephanie Mayer, Cristina Pérez-Guillén, Günter Schmudlach, and Kurt Winkler

Related authors

A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
Alessandro Maissen, Frank Techel, and Michele Volpi
EGUsphere, https://doi.org/10.5194/egusphere-2023-2948,https://doi.org/10.5194/egusphere-2023-2948, 2024
Short summary
Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations
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
Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice
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
A random forest model to assess snow instability from simulated snow stratigraphy
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
Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland
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

Related subject area

Other Hazards (e.g., Glacial and Snow Hazards, Karst, Wildfires Hazards, and Medical Geo-Hazards)
Brief communication: The Lahaina Fire disaster – how models can be used to understand and predict wildfires
Timothy W. Juliano, Fernando Szasdi-Bardales, Neil P. Lareau, Kasra Shamsaei, Branko Kosović, Negar Elhami-Khorasani, Eric P. James, and Hamed Ebrahimian
Nat. Hazards Earth Syst. Sci., 24, 47–52, https://doi.org/10.5194/nhess-24-47-2024,https://doi.org/10.5194/nhess-24-47-2024, 2024
Short summary
Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations
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
Automated Avalanche Terrain Exposure Scale (ATES) mapping – Local validation and optimization in Western Canada
John Sykes, Håvard Toft, Pascal Haegeli, and Grant Statham
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-112,https://doi.org/10.5194/nhess-2023-112, 2023
Revised manuscript accepted for NHESS
Short summary
Early warning system for ice collapses and river blockages in the Sedongpu Valley, southeastern Tibetan Plateau
Wei Yang, Zhongyan Wang, Baosheng An, Yingying Chen, Chuanxi Zhao, Chenhui Li, Yongjie Wang, Weicai Wang, Jiule Li, Guangjian Wu, Lin Bai, Fan Zhang, and Tandong Yao
Nat. Hazards Earth Syst. Sci., 23, 3015–3029, https://doi.org/10.5194/nhess-23-3015-2023,https://doi.org/10.5194/nhess-23-3015-2023, 2023
Short summary
Fire risk modeling: an integrated and data-driven approach applied to Sicily
Alba Marquez Torres, Giovanni Signorello, Sudeshna Kumar, Greta Adamo, Ferdinando Villa, and Stefano Balbi
Nat. Hazards Earth Syst. Sci., 23, 2937–2959, https://doi.org/10.5194/nhess-23-2937-2023,https://doi.org/10.5194/nhess-23-2937-2023, 2023
Short summary

Cited articles

Birkeland, K.: Spatial patterns of snow stability through a small mountain range, J. Glaciol., 47, 176–186, https://doi.org/10.3189/172756501781832250, 2001. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Bühler, Y., von Rickenbach, D., Stoffel, A., Margreth, S., Stoffel, L., and Christen, M.: Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping, Nat. Hazards Earth Syst. Sci., 18, 3235–3251, https://doi.org/10.5194/nhess-18-3235-2018, 2018. a, b, c
EAWS: European Avalanche Danger Scale (2018/19), https://www.avalanches.org/wp-content/uploads/2019/05/European_Avalanche_Danger_Scale-EAWS.pdf (last access: 1 May 2022), 2018. a, b
EAWS: Standards: avalanche size, https://www.avalanches.org/standards/avalanche-size/ (last access: 13 May 2022), 2019. a
Download
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.
Altmetrics
Final-revised paper
Preprint