Articles | Volume 22, issue 10
https://doi.org/10.5194/nhess-22-3247-2022
© Author(s) 2022. 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-22-3247-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Automated snow avalanche release area delineation in data-sparse, remote, and forested regions
Geography Department, Simon Fraser University, Burnaby, British Columbia, Canada
Pascal Haegeli
School of Resource and Environmental Management, Simon Fraser University, Burnaby, British Columbia, Canada
Geography Department, Simon Fraser University, Burnaby, British Columbia, Canada
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, 7260 Davos Dorf, Switzerland
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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
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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.
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Manual Avalanche Terrain Exposure Scale (ATES) mapping is time-consuming and inefficient for large-scale applications. The updated algorithm for automated ATES mapping overcomes previous limitations by including forest density data, improving the avalanche runout estimations in low-angle runout zones, accounting for overhead exposure and open-source software. Results show that the latest version has significantly improved its performance.
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The research validates and optimizes an automated approach for creating classified snow avalanche terrain maps using open-source geospatial modeling tools. Validation is based on avalanche-expert-based maps for two study areas. Our results show that automated maps have an overall accuracy equivalent to the average accuracy of three human maps. Automated mapping requires a fraction of the time and cost of traditional methods and opens the door for large-scale mapping of mountainous terrain.
Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 2399–2419, https://doi.org/10.5194/nhess-25-2399-2025, https://doi.org/10.5194/nhess-25-2399-2025, 2025
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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.
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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
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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.
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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.
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
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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.
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Nat. Hazards Earth Syst. Sci., 25, 625–646, https://doi.org/10.5194/nhess-25-625-2025, https://doi.org/10.5194/nhess-25-625-2025, 2025
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We present a spatial framework for extracting information about avalanche problems from detailed snowpack simulations and compare the numerical results against operational assessments from avalanche forecasters. Despite good agreement in seasonal summary statistics, a comparison of daily assessments revealed considerable differences, while it remained unclear which data source represented reality the best. We discuss how snowpack simulations can add value to the forecasting process.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
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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
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-2728, https://doi.org/10.5194/egusphere-2024-2728, 2024
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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
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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.
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Nat. Hazards Earth Syst. Sci., 24, 2727–2756, https://doi.org/10.5194/nhess-24-2727-2024, https://doi.org/10.5194/nhess-24-2727-2024, 2024
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Snowpack simulations are increasingly employed by avalanche warning services to inform about critical avalanche layers buried in the snowpack. However, validity concerns limit their operational value. We present methods that enable meaningful comparisons between snowpack simulations and regional assessments of avalanche forecasters to quantify the performance of the Canadian weather and snowpack model chain to represent thin critical avalanche layers on a large scale and in real time.
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Manual Avalanche Terrain Exposure Scale (ATES) mapping is time-consuming and inefficient for large-scale applications. The updated algorithm for automated ATES mapping overcomes previous limitations by including forest density data, improving the avalanche runout estimations in low-angle runout zones, accounting for overhead exposure and open-source software. Results show that the latest version has significantly improved its performance.
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Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
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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.
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
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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
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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
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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.
Abby Morgan, Pascal Haegeli, Henry Finn, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 23, 1719–1742, https://doi.org/10.5194/nhess-23-1719-2023, https://doi.org/10.5194/nhess-23-1719-2023, 2023
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The avalanche danger scale is a critical component for communicating the severity of avalanche hazard conditions to the public. We examine how backcountry recreationists in North America understand and use the danger scale for planning trips into the backcountry. Our results provide an important user perspective on the strengths and weaknesses of the existing scale and highlight opportunities for future improvements.
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
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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.
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
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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.
Simon Horton and Pascal Haegeli
The Cryosphere, 16, 3393–3411, https://doi.org/10.5194/tc-16-3393-2022, https://doi.org/10.5194/tc-16-3393-2022, 2022
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Snowpack models can help avalanche forecasters but are difficult to verify. We present a method for evaluating the accuracy of simulated snow profiles using readily available observations of snow depth. This method could be easily applied to understand the representativeness of available observations, the agreement between modelled and observed snow depths, and the implications for interpreting avalanche conditions.
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
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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.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
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We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
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
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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.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 22, 1973–2000, https://doi.org/10.5194/nhess-22-1973-2022, https://doi.org/10.5194/nhess-22-1973-2022, 2022
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Avalanche bulletins include travel and terrain statements to provide recreationists with tangible guidance about how to apply the hazard information. We examined which bulletin users pay attention to these statements, what determines their usefulness, and how they could be improved. Our study shows that reducing jargon and adding simple explanations can significantly improve the usefulness of the statements for users with lower levels of avalanche awareness education who depend on this advice.
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
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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.
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
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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
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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.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 21, 3219–3242, https://doi.org/10.5194/nhess-21-3219-2021, https://doi.org/10.5194/nhess-21-3219-2021, 2021
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Avalanche warning services publish condition reports to help backcountry recreationists make informed decisions about when and where to travel in avalanche terrain. We tested how different graphic representations of terrain information can affect users’ ability to interpret and apply the provided information. Our study shows that a combined presentation of aspect and elevation information is the most effective. These results can be used to improve avalanche risk communication products.
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
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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.
Pascal Haegeli, Bret Shandro, and Patrick Mair
The Cryosphere, 15, 1567–1586, https://doi.org/10.5194/tc-15-1567-2021, https://doi.org/10.5194/tc-15-1567-2021, 2021
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Numerous large-scale atmosphere–ocean oscillations including the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, the Pacific North American Teleconnection Pattern, and the Arctic Oscillation are known to substantially affect winter weather patterns in western Canada. Using avalanche problem information from public avalanche bulletins, this study presents a new approach for examining the effect of these atmospheric oscillations on the nature of avalanche hazard in western Canada.
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.
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
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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.
Florian Herla, Simon Horton, Patrick Mair, and Pascal Haegeli
Geosci. Model Dev., 14, 239–258, https://doi.org/10.5194/gmd-14-239-2021, https://doi.org/10.5194/gmd-14-239-2021, 2021
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The adoption of snowpack models in support of avalanche forecasting has been limited. To promote their operational application, we present a numerical method for processing multivariate snow stratigraphy profiles of mixed data types. Our algorithm enables applications like dynamical grouping and summarizing of model simulations, model evaluation, and data assimilation. By emulating the human analysis process, our approach will allow forecasters to familiarly interact with snowpack simulations.
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
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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.
Simon Horton, Moses Towell, and Pascal Haegeli
Nat. Hazards Earth Syst. Sci., 20, 3551–3576, https://doi.org/10.5194/nhess-20-3551-2020, https://doi.org/10.5194/nhess-20-3551-2020, 2020
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We investigate patterns in how avalanche forecasters characterize snow avalanche hazard with avalanche problem types. Decision tree analysis was used to investigate both physical influences based on weather and on snowpack variables and operational practices. The results highlight challenges with developing decision aids based on previous hazard assessments.
Cited articles
Bebi, P., Kulakowski, D., and Rixen, C.: Snow avalanche disturbances in
forest ecosystems – State of research and implications for management,
Forest Ecol. Manag., 257, 1883–1892, https://doi.org/10.1016/J.FORECO.2009.01.050, 2009.
Bebi, P., Bast, A., Helzel, K., Schmucki, G., Brozova, N., and Bühler,
Y.: Avalanche Protection Forest: From Process Knowledge to Interactive Maps, in: Protective forests as Ecosystem-based solution for Disaster Risk
Reduction, IntechOpen, https://doi.org/10.5772/intechopen.99514, 2021.
Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline:
NASA's Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.
Boyd, J., Haegeli, P., Abu-Laban, R. B., Shuster, M., and Butt, J. C.:
Patterns of death among avalanche fatalities: a 21-year review, Can. Med.
Assoc. J. (CMAJ), 180, 507–12, https://doi.org/10.1503/cmaj.081327, 2009.
Brožová, N., Fischer, J. T., Bühler, Y., Bartelt, P., and Bebi,
P.: Determining forest parameters for avalanche simulation using remote
sensing data, Cold Reg. Sci. Technol., 172, 102976,
https://doi.org/10.1016/j.coldregions.2019.102976, 2020.
Bühler, Y., Hüni, A., Christen, M., Meister, R., and Kellenberger,
T.: Automated detection and mapping of avalanche deposits using airborne
optical remote sensing data, Cold Reg. Sci. Technol., 57, 99–106,
https://doi.org/10.1016/j.coldregions.2009.02.007, 2009.
Bühler, Y., Kumar, S., Veitinger, J., Christen, M., Stoffel, A., and Snehmani: Automated identification of potential snow avalanche release areas based on digital elevation models, Nat. Hazards Earth Syst. Sci., 13, 1321–1335, https://doi.org/10.5194/nhess-13-1321-2013, 2013.
Bühler, Y., Adams, M. S., Stoffel, A., and Boesch, 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.
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.
Bühler, Y., Hafner, E. D., Zweifel, B., Zesiger, M., and Heisig, H.: Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13, 3225–3238, https://doi.org/10.5194/tc-13-3225-2019, 2019.
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.
Campbell, C. and Gould, B.: A proposed practical model for zoning with the
Avalanche Terrain Exposure Scale, in: Proceedings International Snow Science Workshop, Grenoble and Chamonix-Mont-Blanc, France, 7–11 October 2013.
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.
Colorado Avalanche Information Center (CAIC):
https://avalanche.state.co.us/accidents/statistics-and-reporting/, last access: 27 April 2020.
Dash, J. P., Watt, M. S., Bhandari, S., and Watt, P.: Characterising forest
structure using combinations of airborne laser scanning data, RapidEye
satellite imagery and environmental variables, Forestry, 89, 159–169,
https://doi.org/10.1093/forestry/cpv048, 2016.
Facciolo, G., de Franchis, C., and Meinhardt, E.: MGM: A Significantly More Global Matching for Stereovision, in: Proceedings of the British Machine Vision Conference (BMVC), edited by: Xie, X., Jones, M. W., and Tam, G. K. L., BMVA Press, 90.1–90.12, https://doi.org/10.5244/C.29.90, 2015.
Feistl, T., Bebi, P., Teich, M., Bühler, Y., Christen, M., Thuro, K.,
and Bartelt, P.: Observations and modeling of the braking effect of forests
on small and medium avalanches, J. Glaciol., 60, 124–138,
https://doi.org/10.3189/2014JoG13J055, 2014.
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.
Gillies, S., Perry, M., Wurster, K., et al.: Rasterio: geospatial raster I/O for Python programmers, GitHub [code], https://github.com/rasterio/rasterio/blob/main/CITATION.txt (last access: 19 September 2022), 2013.
Gruber, U. and Haefner, H.: Avalanche hazard mapping with satellite data and a digital elevation model, Appl. Geogr., 15, 99–113,
https://doi.org/10.1016/0143-6228(94)00004-A, 1995.
Haegeli, P. and Atkins, R.: Managing the Physical Risk From Avalanches in a
Helicopter Skiing Operation–Merging and Contrasting Gps Tracking Data with
the Operational Guiding Perspective, in: Proceedings International Snow
Science Workshop, Breckenridge, CO, USA, 3–7 October 2016, 104–111, 2016.
Haegeli, P. and McClung, D. M.: Expanding the snow-climate classification
with avalanche-relevant information: initial description of avalanche winter regimes for southwestern Canada, J. Glaciol., 53, 266–276,
https://doi.org/10.3189/172756507782202801, 2007.
Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, 2021.
Hägeli, P. and McClung, D. M.: Avalanche characteristics of a
transitional snow climate – Columbia Mountains, British Columbia, Canada,
Cold Reg. Sci. Technol., 37, 255–276, https://doi.org/10.1016/S0165-232X(03)00069-7, 2003.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen,
P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern,
R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del
Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020.
Harvey, S., Schmudlach, G., Buhler, Y., Durr, L., Stoffel, A., and Christen,
M.: Avalanche Terrain Maps for Backcountry Skiing in Switzerland, in:
Proceedings International Snow Science Workshop, Innsbruck, Austria, 7–12 October 2018, 2018.
Höhle, J. and Höhle, M.: Accuracy assessment of digital elevation
models by means of robust statistical methods, ISPRS J. Photogramm., 64,
398–406, https://doi.org/10.1016/j.isprsjprs.2009.02.003, 2009.
Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., and
Zhu, Y. H.: Accuracy comparison of various remote sensing data sources in
the retrieval of forest stand attributes, Forest Ecol. Manag., 128,
109–120, https://doi.org/10.1016/S0378-1127(99)00278-9, 2000.
Jamieson, B., Haegeli, P., and Gauthier, D.: Avalanche accidents in
Canada, Canadian Avalanche Association, 5, 15–39, ISBN: 978-0-9866597-4-4,
2010.
Korzeniowska, K., Bühler, Y., Marty, M., and Korup, O.: Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery, Nat. Hazards Earth Syst. Sci., 17, 1823–1836, https://doi.org/10.5194/nhess-17-1823-2017, 2017.
Kramm, T. and Hoffmeister, D.: EVALUATION OF DIGITAL ELEVATION MODELS FOR GEOMORPHOMETRIC ANALYSES ON DIFFERENT SCALES FOR NORTHERN CHILE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1229–1235, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1229-2019, 2019.
Kumar, S., Srivastava, P. K., Snehmani, and Bhatiya, S.: Geospatial
probabilistic modelling for release area mapping of snow avalanches, Cold
Reg. Sci. Technol., 165, 102813, https://doi.org/10.1016/j.coldregions.2019.102813, 2019.
Lato, M. J., Frauenfelder, R., and Bühler, Y.: Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery, Nat. Hazards Earth Syst. Sci., 12, 2893–2906, https://doi.org/10.5194/nhess-12-2893-2012, 2012.
Maggioni, M. and Gruber, U.: The influence of topographic parameters on
avalanche release dimension and frequency, Cold Reg. Sci. Technol., 37,
407–419, https://doi.org/10.1016/S0165-232X(03)00080-6, 2003.
Margreth, S. and Funk, M.: Hazard mapping for ice and combined snow/ice
avalanches – two case studies from the Swiss and Italian Alps, Cold Reg.
Sci. Technol., 30, 159–173, https://doi.org/10.1016/S0165-232X(99)00027-0,
1999.
McClung, D. and Schaerer, P.: The avalanche handbook, 3rd edn., The Mountaineers Books,
342 pp., ISBN: 978-0-89886-809-8, 2006.
McClung, D. M.: Characteristics of terrain, snow supply and forest cover for avalanche initiation caused by logging, Ann. Glaciol., 32, 223–229,
https://doi.org/10.3189/172756401781819391, 2001.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Édouard, D.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Planet Team: Planet Application Program Interface, in: Space for Life on
Earth, https://api.planet.com (last access: 5 November 2021), 2017.
QGIS Development Team: QGIS Geographic Information System, http://qgis.org (last access: 5 November 2021), 2021.
Rouault, E., Warmerdam, F., Schwehr, K., Kiselev, A., Butler, H., Łoskot, M., Szekeres, T., Tourigny, E., Landa, M., Miara, I., Elliston, B., Kumar, C., Plesea, L., Morissette, D., Jolma, A., and Dawson, N.: GDAL, Zenodo [code], https://doi.org/10.5281/zenodo.5884351, 2022.
Rudolf-Miklau, F., Skolaut, C., and Sauermoser, S.: Avalanche Hazard
Assessment and Planning of Protection Measures, in: The Technical Avalanche
Protection Handbook, Ernst & Sohn, 91–126, https://doi.org/10.1002/9783433603840.ch04, 2015.
Schweizer, J. and Lütschg, M.: Characteristics of human-triggered
avalanches, Cold Reg. Sci. Technol., 33, 147–162, 2001.
Schneebeli, M. and Bebi, P.: Snow and Avalanche Control, Encyclopedia of Forest Sciences, 397–402, https://doi.org/10.1016/B0-12-145160-7/00271-4, 2004.
Shandro, B. and Haegeli, P.: Characterizing the nature and variability of avalanche hazard in western Canada, Nat. Hazards Earth Syst. Sci., 18, 1141–1158, https://doi.org/10.5194/nhess-18-1141-2018, 2018.
Sharp, E., Haegeli, P., and Welch, M.: Patterns in the exposure of ski
guides to avalanche terrain, in: Proceedings International Snow Science
Workshop, Innsbruck, Austria, 7–12 October 2018.
Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R.,
Porter, C., and Morin, P.: An automated, open-source pipeline for mass
production of digital elevation models (DEMs) from very-high-resolution
commercial stereo satellite imagery, ISPRS J. Photogramm., 116, 101–117,
https://doi.org/10.1016/j.isprsjprs.2016.03.012, 2016.
Sykes, J., Haegeli, P., and Bühler, Y.: Automated snow avalanche release
areas delineation in data sparse, remote, and forested regions – Code and
Data, OSF [data set, code], https://doi.org/10.17605/OSF.IO/YQ5S3, 2021.
Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.: Avalanche fatalities in the European Alps: long-term trends and statistics, Geogr. Helv., 71, 147–159, https://doi.org/10.5194/gh-71-147-2016, 2016.
Teich, M., Bartelt, P., Grêt-Regamey, A., and Bebi, P.: Snow Avalanches
in Forested Terrain: Influence of Forest Parameters, Topography, and
Avalanche Characteristics on Runout Distance, Arct. Antarct. Alp. Res., 44,
509–519, https://doi.org/10.1657/1938-4246-44.4.509, 2012.
Veitinger, J., Purves, R. S., and Sovilla, B.: Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach, Nat. Hazards Earth Syst. Sci., 16, 2211–2225, https://doi.org/10.5194/nhess-16-2211-2016, 2016.
Voellmy, A.: Über die Zerstörungskraft von Lawinen, Schweizerische
Bauzeitung, 73, 159–165, 1955 (in German).
Wallner, A., Elatawneh, A., Schneider, T., and Knoke, T.: Estimation of
forest structural information using RapidEye satellite data, Forestry, 88,
96–107, https://doi.org/10.1093/forestry/cpu032, 2015.
Waser, L., Fischer, C., Wang, Z., Ginzler, C., Waser, L. T., Fischer, C.,
Wang, Z., and Ginzler, C.: Wall-to-Wall Forest Mapping Based on Digital
Surface Models from Image-Based Point Clouds and a NFI Forest Definition,
Forests, 6, 4510–4528, https://doi.org/10.3390/f6124386, 2015.
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.
Automated snow avalanche terrain mapping provides an efficient method for large-scale assessment...
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