Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2399-2025
© 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-25-2399-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jul 2025
Research article |
| 21 Jul 2025
| 21 Jul 2025
Simulation of cold-powder snow avalanches considering daily snowpack and weather situations
Julia Glaus
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Centre (CERC), Davos Dorf, 7260, Switzerland
Katreen Wikstrom Jones
Alaska Division of Geological & Geophysical Surveys, Anchorage, Alaska, USA
Perry Bartelt
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Centre (CERC), Davos Dorf, 7260, Switzerland
Marc Christen
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Centre (CERC), Davos Dorf, 7260, Switzerland
Lukas Stoffel
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Johan Gaume
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Centre (CERC), Davos Dorf, 7260, Switzerland
Institute for Geotechnical Engineering, ETH Zurich, Zurich, 8057, Switzerland
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, 7260, Switzerland
Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Centre (CERC), Davos Dorf, 7260, Switzerland
Related authors
Pia Ruttner, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 1315–1330, https://doi.org/10.5194/nhess-25-1315-2025, https://doi.org/10.5194/nhess-25-1315-2025, 2025
Short summary
Short summary
Snow depth variations caused by wind are an important factor in avalanche danger, but detailed and up-to-date information is rarely available. We propose a monitoring system, using lidar and optical sensors, to measure the snow depth distribution at high spatial and temporal resolution. First results show that we can quantify snow depth changes with an accuracy on the low decimeter level, or better, and can identify events such as avalanches or displacement of snow during periods of strong winds.
Grégoire Bobillier, Bertil Trottet, Bastian Bergfeld, Ron Simenhois, Alec van Herwijnen, Jürg Schweizer, and Johan Gaume
Nat. Hazards Earth Syst. Sci., 25, 2215–2223, https://doi.org/10.5194/nhess-25-2215-2025, https://doi.org/10.5194/nhess-25-2215-2025, 2025
Short summary
Short summary
Our study investigates the initiation of snow slab avalanches. Combining experimental data with numerical simulations, we show that on gentle slopes, cracks form and propagate due to compressive fractures within a weak layer. On steeper slopes, crack velocity can increase dramatically after approximately 5 m due to a fracture mode transition from compression to shear. Understanding these dynamics provides a crucial missing piece in the puzzle of dry-snow slab avalanche formation.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 25, 1901–1912, https://doi.org/10.5194/nhess-25-1901-2025, https://doi.org/10.5194/nhess-25-1901-2025, 2025
Short summary
Short summary
The experimentally based μ(I) rheology, widely used for gravitational mass flows, is reinterpreted as a Voellmy-type relationship to highlight its link to grain flow theory. Through block modeling and case studies, we establish its equivalence to μ(R) rheology. μ(I) models shear thinning but fails to capture acceleration and deceleration processes and deposit structure. Incorporating fluctuation energy in μ(R) improves accuracy, refining mass flow modeling and revealing practical challenges.
Pia Ruttner, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 1315–1330, https://doi.org/10.5194/nhess-25-1315-2025, https://doi.org/10.5194/nhess-25-1315-2025, 2025
Short summary
Short summary
Snow depth variations caused by wind are an important factor in avalanche danger, but detailed and up-to-date information is rarely available. We propose a monitoring system, using lidar and optical sensors, to measure the snow depth distribution at high spatial and temporal resolution. First results show that we can quantify snow depth changes with an accuracy on the low decimeter level, or better, and can identify events such as avalanches or displacement of snow during periods of strong winds.
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.
Hervé Vicari, Michael Lukas Kyburz, and Johan Gaume
EGUsphere, https://doi.org/10.5194/egusphere-2024-3421, https://doi.org/10.5194/egusphere-2024-3421, 2025
Short summary
Short summary
Advances in 3D modeling have improved understanding of geophysical flows like avalanches, but using these complex simulations in hazard mapping remains challenging. This study presents a tool that converts 3D simulation results into simplified 2D maps, making results more accessible for natural hazard applications. We applied this tool to model an ice avalanche and visualized the data in GIS, aiming to support practical hazard assessment and mitigation efforts.
Alessandro Cicoira, Daniel Tobler, Rachel Riner, Lars Blatny, Michael Lukas Kyburz, and Johan Gaume
Abstr. Int. Cartogr. Assoc., 9, 4, https://doi.org/10.5194/ica-abs-9-4-2025, https://doi.org/10.5194/ica-abs-9-4-2025, 2025
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
Short summary
Short summary
In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Short summary
Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Jaeyoung Lim, Elisabeth Hafner, Florian Achermann, Rik Girod, David Rohr, Nicholas R. J. Lawrance, Yves Bühler, and Roland Siegwart
EGUsphere, https://doi.org/10.5194/egusphere-2024-2728, https://doi.org/10.5194/egusphere-2024-2728, 2024
Short summary
Short summary
As avalanches occur in remote and potentially dangerous locations, data relevant to avalanche monitoring is difficult to obtain. Uncrewed fixed-wing aerial vehicles are promising platforms for gathering aerial imagery to map avalanche activity over a large area. In this work, we present an unmanned aerial system (UAS) capable of autonomously navigating and mapping avalanches in steep mountainous terrain. We expect our work to enable efficient large-scale autonomous avalanche monitoring.
Elisabeth D. Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 18, 3807–3823, https://doi.org/10.5194/tc-18-3807-2024, https://doi.org/10.5194/tc-18-3807-2024, 2024
Short summary
Short summary
For many safety-related applications such as road management, well-documented avalanches are important. To enlarge the information, webcams may be used. We propose supporting the mapping of avalanches from webcams with a machine learning model that interactively works together with the human. Relying on that model, there is a 90% saving of time compared to the "traditional" mapping. This gives a better base for safety-critical decisions and planning in avalanche-prone mountain regions.
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.
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
Short summary
Short summary
Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Adrian Ringenbach, Peter Bebi, Perry Bartelt, Andreas Rigling, Marc Christen, Yves Bühler, Andreas Stoffel, and Andrin Caviezel
Earth Surf. Dynam., 11, 779–801, https://doi.org/10.5194/esurf-11-779-2023, https://doi.org/10.5194/esurf-11-779-2023, 2023
Short summary
Short summary
Swiss researchers carried out repeated rockfall experiments with rocks up to human sizes in a steep mountain forest. This study focuses mainly on the effects of the rock shape and lying deadwood. In forested areas, cubic-shaped rocks showed a longer mean runout distance than platy-shaped rocks. Deadwood especially reduced the runouts of these cubic rocks. The findings enrich standard practices in modern rockfall hazard zoning assessments and strongly urge the incorporation of rock shape effects.
Gregor Ortner, Michael Bründl, Chahan M. Kropf, Thomas Röösli, Yves Bühler, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 23, 2089–2110, https://doi.org/10.5194/nhess-23-2089-2023, https://doi.org/10.5194/nhess-23-2089-2023, 2023
Short summary
Short summary
This paper presents a new approach to assess avalanche risk on a large scale in mountainous regions. It combines a large-scale avalanche modeling method with a state-of-the-art probabilistic risk tool. Over 40 000 individual avalanches were simulated, and a building dataset with over 13 000 single buildings was investigated. With this new method, risk hotspots can be identified and surveyed. This enables current and future risk analysis to assist decision makers in risk reduction and adaptation.
Yu Zhuang, Aiguo Xing, Perry Bartelt, Muhammad Bilal, and Zhaowei Ding
Nat. Hazards Earth Syst. Sci., 23, 1257–1266, https://doi.org/10.5194/nhess-23-1257-2023, https://doi.org/10.5194/nhess-23-1257-2023, 2023
Short summary
Short summary
Tree destruction is often used to back calculate the air blast impact region and to estimate the air blast power. Here we established a novel model to assess air blast power using tree destruction information. We find that the dynamic magnification effect makes the trees easier to damage by a landslide-induced air blast, but the large tree deformation would weaken the effect. Bending and overturning are two likely failure modes, which depend heavily on the properties of trees.
Adrian Ringenbach, Peter Bebi, Perry Bartelt, Andreas Rigling, Marc Christen, Yves Bühler, Andreas Stoffel, and Andrin Caviezel
Earth Surf. Dynam., 10, 1303–1319, https://doi.org/10.5194/esurf-10-1303-2022, https://doi.org/10.5194/esurf-10-1303-2022, 2022
Short summary
Short summary
The presented automatic deadwood generator (ADG) allows us to consider deadwood in rockfall simulations in unprecedented detail. Besides three-dimensional fresh deadwood cones, we include old woody debris in rockfall simulations based on a higher compaction rate and lower energy absorption thresholds. Simulations including different deadwood states indicate that a 10-year-old deadwood pile has a higher protective capacity than a pre-storm forest stand.
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.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
Short summary
Short summary
Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
Aubrey Miller, Pascal Sirguey, Simon Morris, Perry Bartelt, Nicolas Cullen, Todd Redpath, Kevin Thompson, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 22, 2673–2701, https://doi.org/10.5194/nhess-22-2673-2022, https://doi.org/10.5194/nhess-22-2673-2022, 2022
Short summary
Short summary
Natural hazard modelers simulate mass movements to better anticipate the risk to people and infrastructure. These simulations require accurate digital elevation models. We test the sensitivity of a well-established snow avalanche model (RAMMS) to the source and spatial resolution of the elevation model. We find key differences in the digital representation of terrain greatly affect the simulated avalanche results, with implications for hazard planning.
Adrian Ringenbach, Elia Stihl, Yves Bühler, Peter Bebi, Perry Bartelt, Andreas Rigling, Marc Christen, Guang Lu, Andreas Stoffel, Martin Kistler, Sandro Degonda, Kevin Simmler, Daniel Mader, and Andrin Caviezel
Nat. Hazards Earth Syst. Sci., 22, 2433–2443, https://doi.org/10.5194/nhess-22-2433-2022, https://doi.org/10.5194/nhess-22-2433-2022, 2022
Short summary
Short summary
Forests have a recognized braking effect on rockfalls. The impact of lying deadwood, however, is mainly neglected. We conducted 1 : 1-scale rockfall experiments in three different states of a spruce forest to fill this knowledge gap: the original forest, the forest including lying deadwood and the cleared area. The deposition points clearly show that deadwood has a protective effect. We reproduced those experimental results numerically, considering three-dimensional cones to be deadwood.
Yves Bühler, Peter Bebi, Marc Christen, Stefan Margreth, Lukas Stoffel, Andreas Stoffel, Christoph Marty, Gregor Schmucki, Andrin Caviezel, Roderick Kühne, Stephan Wohlwend, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, https://doi.org/10.5194/nhess-22-1825-2022, 2022
Short summary
Short summary
To calculate and visualize the potential avalanche hazard, we develop a method that automatically and efficiently pinpoints avalanche starting zones and simulate their runout for the entire canton of Grisons. The maps produced in this way highlight areas that could be endangered by avalanches and are extremely useful in multiple applications for the cantonal authorities, including the planning of new infrastructure, making alpine regions more safe.
Louis Védrine, Xingyue Li, and Johan Gaume
Nat. Hazards Earth Syst. Sci., 22, 1015–1028, https://doi.org/10.5194/nhess-22-1015-2022, https://doi.org/10.5194/nhess-22-1015-2022, 2022
Short summary
Short summary
This study investigates how forests affect the behaviour of snow avalanches through the evaluation of the amount of snow stopped by the trees and the analysis of energy dissipation mechanisms. Different avalanche features and tree configurations have been examined, leading to the proposal of a unified law for the detrained snow mass. Outcomes from this study can be directly implemented in operational models for avalanche risk assessment and contribute to improved forest management strategy.
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.
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
Short summary
Short summary
Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
Short summary
Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Ryan L. Crumley, David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, Christopher Cosgrove, and Community Snow Observations Participants
Hydrol. Earth Syst. Sci., 25, 4651–4680, https://doi.org/10.5194/hess-25-4651-2021, https://doi.org/10.5194/hess-25-4651-2021, 2021
Short summary
Short summary
In this study, we use a new snow data set collected by participants in the Community Snow Observations project in coastal Alaska to improve snow depth and snow water equivalence simulations from a snow process model. We validate our simulations with multiple datasets, taking advantage of snow telemetry (SNOTEL), snow depth and snow water equivalence, and remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.
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.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
Short summary
Short summary
The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
Short summary
Short summary
In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Xingyue Li, Betty Sovilla, Chenfanfu Jiang, and Johan Gaume
The Cryosphere, 14, 3381–3398, https://doi.org/10.5194/tc-14-3381-2020, https://doi.org/10.5194/tc-14-3381-2020, 2020
Short summary
Short summary
This numerical study investigates how different types of snow avalanches behave, how key factors affect their dynamics and flow regime transitions, and what are the underpinning rules. According to the unified trends obtained from the simulations, we are able to quantify the complex interplay between bed friction, slope geometry and snow mechanical properties (cohesion and friction) on the maximum velocity, runout distance and deposit height of the avalanches.
Cited articles
Ammann, W. J.: A new Swiss test-site for avalanche experiments in the Vallée de la Sionne/Valais, Cold Reg. Sci. Technol., 30, 3–11, https://doi.org/10.1016/S0165-232X(99)00010-5, 1999. a
AvaFrame: avaframe/AvaFrame: Version 1.6.1, Zenodohttps://doi.org/10.5281/zenodo.8319432, supplement to: https://github.com/avaframe/AvaFrame/tree/1.6.1 (last access: 26 March 2025), 2023. a
Bagnold, R. A.: Experiments on a gravity-free dispersion of large solid spheres in a Newtonian fluid under shear, Proc. R. Soc. Lon. A Mat., 225, 49–63, 1954. a
Bartelt, P., Buser, O., and Platzer, K.: Fluctuation-dissipation relations for granular snow avalanches, J. Glaciol., 52, 631–643, https://doi.org/10.3189/172756506781828476, 2006. a, b
Bartelt, P., Buser, O., and Platzer, K.: Starving avalanches: frictional mechanisms at the tails of finite-sized mass movements, Geophys. Res. Lett., 34, 1–6, https://doi.org/10.1029/2007GL031352, 2007. a
Bartelt, P., Buser, O., Valero, C., and Bühler, Y.: Configurational energy and the formation of mixed flowing/powder snow and ice avalanches, Ann. Glaciol., 57, 179–188, https://doi.org/10.3189/2016AoG71A464, 2015a. a
Bartelt, P., Valero, C., Feistl, T., Christen, M., Bühler, Y., and Buser, O.: Modelling cohesion in snow avalanche flow, J. Glaciol., 61, 837–850, https://doi.org/10.3189/2015JoG14J126, 2015b. a, b, c
Bozhinskiy, A. N. and Losev, K. S.: The Fundamentals of Avalanche Science, Swiss Federal Institute for Snow and Avalanche Research (SLF), Davos, Switzerland, ISBN 978-3-905620-71-9, https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:17257/ (last access: 26 March 2025), 1998 (translated from the Russian original). a
Brabec, B. and Meister, R.: A nearest-neighbor model for regional avalanche forecasting, Ann. Glaciol., 32, 130–134, https://doi.org/10.3189/172756401781819247, 2001. a
Buser, O. and Bartelt, P.: Production and decay of random kinetic energy in granular snow avalanches, J. Glaciol., 55, 3–12, https://doi.org/10.3189/002214309788608859, 2009. a, b
Buser, O. and Bartelt, P.: Dispersive pressure and density variations in snow avalanches, J. Glaciol., 57, 857–860, https://doi.org/10.3189/002214311798043870, 2011. a
Buser, O. and Bartelt, P.: An energy-based method to calculate streamwise density variations in snow avalanches, J. Glaciol., 61, 563–575, https://doi.org/10.3189/2015JoG14J054, 2015. a
Bühler, Y., Christen, M., Kowalski, J., and Bartelt, P.: Sensitivity of snow avalanche simulations to digital elevation model quality and resolution, Ann. Glaciol., 52, 72–80, https://doi.org/10.3189/172756411797252121, 2011. a
Bühler, Y., Adams, M., Stoffel, A., and Bösch, R.: Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain applying near-infrared UAS imagery, Int. J. Remote Sens., 38, 3135–3158, https://doi.org/10.1080/01431161.2016.1275060, 2017. a
Bühler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., Kühne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on a statewide scale, Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, 2022. a
Cerda, P., Gallardo, L. A., Didier, M., Joaquin, M. O., Rodrigo, M. F., and Vera, C.: Avalanche Management in a Large Chilean Copper Mine, https://api.semanticscholar.org/CorpusID:133105900 (last access: 26 March 2025), 2016. a
Christen, M., Kowalski, J., and Bartelt, P.: RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain, Cold Reg. Sci. Technol., 63, 1–14, https://doi.org/10.1016/j.coldregions.2010.04.005, 2010. a, b, c
Dreier, L., Bühler, Y., Ginzler, C., and Bartelt, P.: Comparison of simulated powder snow avalanches with photogrammetric measurements, Ann. Glaciol., 57, 371–381, https://doi.org/10.3189/2016AoG71A532, 2016. a
Eglit, E. M.: Some mathematical models of snow avalanches, in: Advances in the Mechanics and the Flow of Granular Materials, edited by: Shahinpoor, M., vol. II, 577–588 pp., Trans Tech Publications, Clausthal-Zellerfeld, Germany, 1st Edn., 1983. a
Feistl, T., Bebi, P., Christen, M., Margreth, S., Diefenbach, L., and Bartelt, P.: Forest damage and snow avalanche flow regime, Nat. Hazards Earth Syst. Sci., 15, 1275–1288, https://doi.org/10.5194/nhess-15-1275-2015, 2015. a
Gauer, P.: A model of powder snow avalanches, in: Comptes rendus du symposium de Chamonix CISA/IKAR, Chamonix, 1995, edited by: Sivardière, F., ANENA, ANENA, Grenoble, France, https://www.torrossa.com/en/resources/an/5063963#page=57 (last access: 11 July 2025), 1995. a
Glaus, J.: Brämabühl_Avalanche_Jan2019_Dataset (Version 1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.15796702, 2025. a
Glaus, J., Wikstrom-Jones, K., Buehler, Y., Christen, M., Ruttner-Jansen, P., Gaume, J., and Bartelt, P.: RAMMS::EXTENDED – Sensitivity analysis of numerical fluicized powder avalanche simulation in three-dimensional terrain, Montana State University Library, https://arc.lib.montana.edu/snow-science/objects/ISSW2023_P2.26.pdf (last access: 26 March 2025), 2023. a, b
Glaus, J., Wikstrom-Jones, K., Kleinn, J., Stoffel, L., Ruttner-Jansen, P., Gaume, J., and Buehler, Y.: Probability-Based Avalanche Run-Out Mapping for Road Safety, Montana State University Library, https://arc.lib.montana.edu/snow-science/objects/ISSW2024_O7.10.pdf (last access: 15 July 2025), 2024. a
Gruber, U. and Bartelt, P.: Snow avalanche hazard modelling of large areas using shallow water numerical methods and GIS, Environ. Modell. Softw., 22, 1472–1481, https://doi.org/10.1016/j.envsoft.2007.01.001, 2007. a, b
Gruber, U. and Margreth, S.: Winter 1999: a valuable test of the avalanche-hazard mapping procedure in Switzerland, Ann. Glaciol., 32, 328–332, https://doi.org/10.3189/172756401781819238, 2001. a
Haff, P. K.: Grain flow as a fluid-mechanical phenomenon, J. Fluid Mechan., 134, 401–430, https://doi.org/10.1017/S0022112083003419, 1983. a, b, c
Hermann, F., Issler, D., and Keller, S.: Towards a numerical model of powder snow avalanches, in: Proc. of the Second European Computational Fluid Dynamics Conference, Stuttgart (Germany), 5–8 September 1994, edited by: Wagner, S., Hirschel, E. H., Périaux, J., and Piva, R., 948–955 pp., J. Wiley & Sons, Ltd., Chichester, UK, https://www.research-collection.ethz.ch/handle/20.500.11850/485115 (last access: 8 July 2025), 1994. a
Issler, D.: Comments on “On a Continuum Model for Avalanche Flow and Its Simplified Variants” by S. S. Grigorian and A. V. Ostroumov, Geosciences, 10, 96 pp., https://doi.org/10.3390/geosciences10030096, 2020. a
Issler, D., Jenkins, J., and McElwaine, J.: Comments on avalanche flow models based on the concept of random kinetic energy, J. Glaciol., 64, 148–164, https://doi.org/10.1017/jog.2017.62, 2018. a, b
Issler, D., Gisnås, K., Gauer, P., Glimsdal, S., Domaas, U., and Sverdrup-Thygeson, K.: NAKSIN – a new approach to snow avalanche hazard indication mapping in Norway, SSRN Scholarly Paper, https://doi.org/10.2139/ssrn.4530311, 2023. a
Issler, D., Gauer, P., Tregaskis, C., and Vicari, H.: Structure of equations for gravity mass flows with entrainment, Nat. Commun., 15, 1–58, https://doi.org/10.1038/s41467-024-48605-6, 2024. a
Jomelli, V. and Bertran, P.: Wet snow avalanche deposits in the French Alps: structure and sedimentology, Geogr. Ann. A, 83, 15–28, https://doi.org/10.1111/j.0435-3676.2001.00141.x, 2004. a
Keiler, M., Sailer, R., Jörg, P., Weber, C., Fuchs, S., Zischg, A., and Sauermoser, S.: Avalanche risk assessment – a multi-temporal approach, results from Galtür, Austria, Nat. Hazards Earth Syst. Sci., 6, 637–651, https://doi.org/10.5194/nhess-6-637-2006, 2006. a
Keylock, C. J., McClung, D., and Magnússon, M.: Avalanche risk mapping by simulation, J. Glaciol., 45, 303–314, https://doi.org/10.3189/002214399793377103, 1999. a
Köhler, A., Fischer, J.-T., Scandroglio, R., Bavay, M., McElwaine, J., and Sovilla, B.: Cold-to-warm flow regime transition in snow avalanches, The Cryosphere, 12, 3759–3774, https://doi.org/10.5194/tc-12-3759-2018, 2018. a, b, c
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a network of weather and snow stations, Cold Reg. Sci. Technol., 30, 145–157, https://doi.org/10.1016/S0165-232X(99)00022-1, 1999. a, b
Li, X., Sovilla, B., Jiang, C., and Gaume, J.: Three-dimensional and real-scale modeling of flow regimes in dense snow avalanches, Landslides, 18, 3231–3248, https://doi.org/10.1007/s10346-021-01692-8, 2021. a
McClung, D.: Dimensions of dry snow slab avalanches from field measurements, J. Geophys. Res., 114, F01031, https://doi.org/10.1029/2007JF000941, 2009. a
Platzer, K., Bartelt, P., and Jaedicke, C.: Basal shear and normal stresses of dry and wet snow avalanches after a slope deviation, Cold Reg. Sci. Technol., 49, 11–25, https://doi.org/10.1016/j.coldregions.2007.04.003, 2007a. a
Platzer, K., Bartelt, P., and Kern, M.: Measurements of dense snow avalanche basal shear to normal stress ratios (S/N), Geophys. Res. Lett., 34, L07501, https://doi.org/10.1029/2006GL028670, 2007b. a, b, c
Rauter, M., Kofler, A., Huber, A., and Fellin, W.: faSavageHutterFOAM 1.0: depth-integrated simulation of dense snow avalanches on natural terrain with OpenFOAM, Geosci. Model Dev., 11, 2923–2939, https://doi.org/10.5194/gmd-11-2923-2018, 2018. a
Salm, B.: Flow, flow transition and runout distances of flowing avalanches, Ann. Glaciol., 18, 221–226, https://doi.org/10.3189/s0260305500011551, 1993. a
Sampl, P. and Granig, M.: Avalanche simulation with SAMOS-AT, ISSW 09 – International Snow Science Workshop, Proceedings, 519–523 pp., https://arc.lib.montana.edu/snow-science/objects/issw-2009-0519-0523.pdf (last access: 26 Mach 2025), 2009. a
Sampl, P. and Zwinger, T.: Avalanche simulation with SAMOS, Ann. Glaciol., 38, 393–398, https://doi.org/10.3189/172756404781814780, 2004. a
Sheng, J., Mind'je, R., Zhang, X., Wang, Y., Zhou, H., and Li, L.: Implementation of an early warning for snowfall-triggered avalanche to road safety in the Tianshan Mountains, Cold Reg. Sci. Technol., 204, 103675, https://doi.org/10.1016/j.coldregions.2022.103675, 2022. a
Sovilla, B., Schaer, M., Kern, M., and Bartelt, P.: Impact Pressures and flow regimes in dense snow avalanches observed at the Vallée de la Sionne test site, J. Geophys. Res., 519–523, https://doi.org/10.1029/2006JF000688, 2008. a
Steinkogler, W., Gaume, J., Löwe, H., Sovilla, B., and Lehning, M.: Granulation of snow: From tumbler experiments to discrete element simulations, J. Geophys. Res.-Solid Earth, 120, 1049–1064, https://doi.org/10.1002/2014JF003294, 2015. a
Stoffel, L. and Schweizer, J.: Guidelines for avalanche control services: organization, hazard assessment and documentation – an example from Switzerland, in: Proceedings of the International Snow Science Workshop 2008, Whistler, Canada, https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:16919 (last access: 26 March 2025), 2008. a
Stoffel, L., Bartelt, P., and Margreth, S.: Scenario-based avalanche simulations including snow entrainment, snow temperature and density, International Snow Science Workshop Proceedings 2024, Tromsø, Norway, https://arc.lib.montana.edu/snow-science/item.php?id=3161 (last access: 26 March 2025), 2024. a
Swisstopo: Swiss Federal Office of Topography (swisstopo), Federal Office of Topography swisstopo, https://www.swisstopo.admin.ch/ (last access: 8 March 2024), 2024. a
Valero, C., Wikstrom J., K., Bühler, Y., and Bartelt, P.: Release temperature, snow-cover entrainment and the thermal flow regime of snow avalanches, J. Glaciol., 61, 173–184, https://doi.org/10.3189/2015JoG14J117, 2015. a, b
Vera Valero, C., Wever, N., Bühler, Y., Stoffel, L., Margreth, S., and Bartelt, P.: Modelling wet snow avalanche runout to assess road safety at a high-altitude mine in the central Andes, Nat. Hazards Earth Syst. Sci., 16, 2303–2323, https://doi.org/10.5194/nhess-16-2303-2016, 2016. a
Vera Valero, C., Wever, N., Christen, M., and Bartelt, P.: Modeling the influence of snow cover temperature and water content on wet-snow avalanche runout, Nat. Hazards Earth Syst. Sci., 18, 869–887, https://doi.org/10.5194/nhess-18-869-2018, 2018. a, b, c
Vallet, J., Gruber, U., and Dufour, F.: Photogrammetric avalanche volume measurements at Vallée de la Sionne, Switzerland, Ann. Glaciol., 32, 141–146, https://doi.org/10.3189/172756401781819689, 2001. a
Vicari, H. and Issler, D.: MoT-PSA: a two-layer depth-averaged model for simulation of powder snow avalanches on 3-D terrain, Ann. Glaciol., 65, e10, https://doi.org/10.1017/aog.2024.10, 2025. a, b
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a
Voellmy, A.: Über die Zerstörungskraft von Lawinen, Swiss Society of Engineers and Architects (SIA), https://doi.org/10.5169/seals-61878, 1955. a
Zhuang, Y., Piazza, N., Xing, A., Christen, M., Bebi, P., Bottero, A., and Bartelt, P.: Tree blow-down by snow avalanche air-blasts: dynamic magnification effects and turbulence, Geophys. Res. Lett., 50, e2023GL105334, https://doi.org/10.1029/2023GL105334, 2023a. a, b, c, d
Zhuang, Y., Xing, A., Bartelt, P., Bilal, M., and Ding, Z.: Dynamic response and breakage of trees subject to a landslide-induced air blast, Nat. Hazards Earth Syst. Sci., 23, 1257–1266, https://doi.org/10.5194/nhess-23-1257-2023, 2023b. a, b, c, d
Short summary
This study assesses RAMMS::EXTENDED's predictive power in estimating avalanche runout distances critical for mountain road safety. Leveraging meteorological data and sensitivity analyses, it offers meaningful predictions, aiding near real-time hazard assessments and future model refinement for improved decision-making.
This study assesses RAMMS::EXTENDED's predictive power in estimating avalanche runout distances...
Altmetrics
Final-revised paper
Preprint