Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1359-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-1359-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Mar 2024
Research article |
| 26 Mar 2024
| 26 Mar 2024
Snow mechanical property variability at the slope scale – implication for snow mechanical modelling
Laboratoire de géomorphologie et de gestion des risques en montagne (LGGRM), Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, Rimouski, Canada
Centre for Northern Studies, Université Laval, Québec, Canada
Francis Gauthier
Laboratoire de géomorphologie et de gestion des risques en montagne (LGGRM), Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, Rimouski, Canada
Centre for Northern Studies, Université Laval, Québec, Canada
Alexandre Langlois
Groupe de Recherche Interdisciplinaire sur les Milieux Polaires (GRIMP), Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, Canada
Centre for Northern Studies, Université Laval, Québec, Canada
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Paul Billecocq, Alexandre Langlois, and Benoit Montpetit
The Cryosphere, 18, 2765–2782, https://doi.org/10.5194/tc-18-2765-2024, https://doi.org/10.5194/tc-18-2765-2024, 2024
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Snow covers a vast part of the globe, making snow water equivalent (SWE) crucial for climate science and hydrology. SWE can be inversed from satellite data, but the snow's complex structure highly affects the signal, and thus an educated first guess is mandatory. In this study, a subgridding framework was developed to model snow at the local scale from model weather data. The framework enhanced snow parameter modeling, paving the way for SWE inversion algorithms from satellite data.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Tom Birien and Francis Gauthier
Nat. Hazards Earth Syst. Sci., 23, 343–360, https://doi.org/10.5194/nhess-23-343-2023, https://doi.org/10.5194/nhess-23-343-2023, 2023
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On highly fractured rockwalls such as those found in northern Gaspésie, most rockfalls are triggered by weather conditions. This study highlights that in winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 °C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. This knowledge could be used to implement a risk management strategy.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
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Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
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To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Alain Royer, Alexandre Roy, Sylvain Jutras, and Alexandre Langlois
The Cryosphere, 15, 5079–5098, https://doi.org/10.5194/tc-15-5079-2021, https://doi.org/10.5194/tc-15-5079-2021, 2021
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Dense spatially distributed networks of autonomous instruments for continuously measuring the amount of snow on the ground are needed for operational water resource and flood management and the monitoring of northern climate change. Four new-generation non-invasive sensors are compared. A review of their advantages, drawbacks and accuracy is discussed. This performance analysis is intended to help researchers and decision-makers choose the one system that is best suited to their needs.
Nataniel M. Holtzman, Leander D. L. Anderegg, Simon Kraatz, Alex Mavrovic, Oliver Sonnentag, Christoforos Pappas, Michael H. Cosh, Alexandre Langlois, Tarendra Lakhankar, Derek Tesser, Nicholas Steiner, Andreas Colliander, Alexandre Roy, and Alexandra G. Konings
Biogeosciences, 18, 739–753, https://doi.org/10.5194/bg-18-739-2021, https://doi.org/10.5194/bg-18-739-2021, 2021
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Microwave radiation coming from Earth's land surface is affected by both soil moisture and the water in plants that cover the soil. We measured such radiation with a sensor elevated above a forest canopy while repeatedly measuring the amount of water stored in trees at the same location. Changes in the microwave signal over time were closely related to tree water storage changes. Satellites with similar sensors could thus be used to monitor how trees in an entire region respond to drought.
Alexandre Roy, Alain Royer, Olivier St-Jean-Rondeau, Benoit Montpetit, Ghislain Picard, Alex Mavrovic, Nicolas Marchand, and Alexandre Langlois
The Cryosphere, 10, 623–638, https://doi.org/10.5194/tc-10-623-2016, https://doi.org/10.5194/tc-10-623-2016, 2016
C. Papasodoro, E. Berthier, A. Royer, C. Zdanowicz, and A. Langlois
The Cryosphere, 9, 1535–1550, https://doi.org/10.5194/tc-9-1535-2015, https://doi.org/10.5194/tc-9-1535-2015, 2015
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Located at the far south (~62.5° N) of the Canadian Arctic, Grinnell and Terra Nivea Ice Caps are good climate proxies in this scarce data region. Multiple data sets (in situ, airborne and spaceborne) reveal changes in area, elevation and mass over the past 62 years. Ice wastage sharply accelerated during the last decade for both ice caps, as illustrated by the strongly negative mass balance of Terra Nivea over 2007-2014 (-1.77 ± 0.36 m a-1 w.e.). Possible climatic drivers are also discussed.
A. Roy, A. Royer, B. Montpetit, P. A. Bartlett, and A. Langlois
The Cryosphere, 7, 961–975, https://doi.org/10.5194/tc-7-961-2013, https://doi.org/10.5194/tc-7-961-2013, 2013
Related subject area
Discipline: Snow | Subject: Natural Hazards
Combining modelled snowpack stability with machine learning to predict avalanche activity
Can Saharan dust deposition impact snowpack stability in the French Alps?
A closed-form model for layered snow slabs
A random forest model to assess snow instability from simulated snow stratigraphy
Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting
Snow Avalanche Frequency Estimation (SAFE): 32 years of monitoring remote avalanche depositional zones in high mountains of Afghanistan
Brief communication: Weak control of snow avalanche deposit volumes by avalanche path morphology
Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019
Dynamic crack propagation in weak snowpack layers: insights from high-resolution, high-speed photography
Avalanche danger level characteristics from field observations of snow instability
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On the importance of snowpack stability, the frequency distribution of snowpack stability, and avalanche size in assessing the avalanche danger level
The mechanical origin of snow avalanche dynamics and flow regime transitions
On the relation between avalanche occurrence and avalanche danger level
Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK
Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps
Cold-to-warm flow regime transition in snow avalanches
Léo Viallon-Galinier, Pascal Hagenmuller, and Nicolas Eckert
The Cryosphere, 17, 2245–2260, https://doi.org/10.5194/tc-17-2245-2023, https://doi.org/10.5194/tc-17-2245-2023, 2023
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Avalanches are a significant issue in mountain areas where they threaten recreationists and human infrastructure. Assessments of avalanche hazards and the related risks are therefore an important challenge for local authorities. Meteorological and snow cover simulations are thus important to support operational forecasting. In this study we combine it with mechanical analysis of snow profiles and find that observed avalanche data improve avalanche activity prediction through statistical methods.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
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Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Philipp Weißgraeber and Philipp L. Rosendahl
The Cryosphere, 17, 1475–1496, https://doi.org/10.5194/tc-17-1475-2023, https://doi.org/10.5194/tc-17-1475-2023, 2023
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The work presents a mathematical model that calculates the behavior of layered snow covers in response to loadings. The information is necessary to predict the formation of snow slab avalanches. While sophisticated computer simulations may achieve the same goal, they can require weeks to run. By using mathematical simplifications commonly used by structural engineers, the present model can provide hazard assessments in milliseconds, even for snowpacks with many layers of different types of snow.
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.
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.
Arnaud Caiserman, Roy C. Sidle, and Deo Raj Gurung
The Cryosphere, 16, 3295–3312, https://doi.org/10.5194/tc-16-3295-2022, https://doi.org/10.5194/tc-16-3295-2022, 2022
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Snow avalanches cause considerable material and human damage in all mountain regions of the world. We present the first model to automatically inventory avalanche deposits at the scale of a catchment area – here the Amu Panj in Afghanistan – every year since 1990. This model called Snow Avalanche Frequency Estimation (SAFE) is available online on the Google Engine. SAFE has been designed to be simple and universal to use. Nearly 810 000 avalanches were detected over the 32 years studied.
Hippolyte Kern, Nicolas Eckert, Vincent Jomelli, Delphine Grancher, Michael Deschatres, and Gilles Arnaud-Fassetta
The Cryosphere, 15, 4845–4852, https://doi.org/10.5194/tc-15-4845-2021, https://doi.org/10.5194/tc-15-4845-2021, 2021
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Snow avalanches are a major component of the mountain cryosphere that often put people, settlements, and infrastructures at risk. This study investigated avalanche path morphological factors controlling snow deposit volumes, a critical aspect of snow avalanche dynamics that remains poorly known. Different statistical techniques show a slight but significant link between deposit volumes and avalanche path morphology.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 15, 4335–4356, https://doi.org/10.5194/tc-15-4335-2021, https://doi.org/10.5194/tc-15-4335-2021, 2021
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Extreme snowfall can cause major natural hazards (avalanches, winter storms) that can generate casualties and economic damage. In the French Alps, we show that between 1959 and 2019 extreme snowfall mainly decreased below 2000 m of elevation and increased above 2000 m. At 2500 m, we find a contrasting pattern: extreme snowfall decreased in the north, while it increased in the south. This pattern might be related to increasing trends in extreme snowfall observed near the Mediterranean Sea.
Bastian Bergfeld, Alec van Herwijnen, Benjamin Reuter, Grégoire Bobillier, Jürg Dual, and Jürg Schweizer
The Cryosphere, 15, 3539–3553, https://doi.org/10.5194/tc-15-3539-2021, https://doi.org/10.5194/tc-15-3539-2021, 2021
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The modern picture of the snow slab avalanche release process involves a
dynamic crack propagation phasein which a whole slope becomes detached. The present work contains the first field methodology which provides the temporal and spatial resolution necessary to study this phase. We demonstrate the versatile capabilities and accuracy of our method by revealing intricate dynamics and present how to determine relevant characteristics of crack propagation such as crack speed.
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.
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.
Frank Techel, Karsten Müller, and Jürg Schweizer
The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-14-3503-2020, https://doi.org/10.5194/tc-14-3503-2020, 2020
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Exploring a large data set of snow stability tests and avalanche observations, we quantitatively describe the three key elements that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. The findings will aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.
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
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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.
Jürg Schweizer, Christoph Mitterer, Frank Techel, Andreas Stoffel, and Benjamin Reuter
The Cryosphere, 14, 737–750, https://doi.org/10.5194/tc-14-737-2020, https://doi.org/10.5194/tc-14-737-2020, 2020
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Snow avalanches represent a major natural hazard in seasonally snow-covered mountain regions around the world. To avoid periods and locations of high hazard, avalanche warnings are issued by public authorities. In these bulletins, the hazard is characterized by a danger level. Since the danger levels are not well defined, we analyzed a large data set of avalanches to improve the description. Our findings show discrepancies in present usage of the danger scale and show ways to improve the scale.
Bettina Richter, Jürg Schweizer, Mathias W. Rotach, and Alec van Herwijnen
The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, https://doi.org/10.5194/tc-13-3353-2019, 2019
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Information on snow stability is important for avalanche forecasting. To improve the stability estimation in the snow cover model SNOWPACK, we suggested an improved parameterization for the critical crack length. We compared 3 years of field data to SNOWPACK simulations. The match between observed and modeled critical crack lengths greatly improved, and critical weak layers appear more prominently in the modeled vertical profile of critical crack length.
Yves Bühler, Elisabeth D. Hafner, Benjamin Zweifel, Mathias Zesiger, and Holger Heisig
The Cryosphere, 13, 3225–3238, https://doi.org/10.5194/tc-13-3225-2019, https://doi.org/10.5194/tc-13-3225-2019, 2019
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We manually map 18 737 avalanche outlines based on SPOT6 optical satellite imagery acquired in January 2018. This is the most complete and accurate avalanche documentation of a large avalanche period covering a big part of the Swiss Alps. This unique dataset can be applied for the validation of other remote-sensing-based avalanche-mapping procedures and for updating avalanche databases to improve hazard maps.
Anselm Köhler, Jan-Thomas Fischer, Riccardo Scandroglio, Mathias Bavay, Jim McElwaine, and Betty Sovilla
The Cryosphere, 12, 3759–3774, https://doi.org/10.5194/tc-12-3759-2018, https://doi.org/10.5194/tc-12-3759-2018, 2018
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Snow avalanches show complicated flow behaviour, characterized by several flow regimes which coexist in one avalanche. In this work, we analyse flow regime transitions where a powder snow avalanche transforms into a plug flow avalanche by incorporating warm snow due to entrainment. Prediction of such a transition is very important for hazard mitigation, as the efficiency of protection dams are strongly dependent on the flow regime, and our results should be incorporated into avalanche models.
Cited articles
Bellaire, S. and Schweizer, J.: Measuring spatial variations of weak layer and slab properties with regard to snow slope stability, Cold Reg. Sci. Technol., 65, 234–241, https://doi.org/10.1016/J.COLDREGIONS.2010.08.013, 2011. a, b, c, d
Birkeland, K. W.: Spatial patterns of snow stability throughout a small mountain range, J. Glaciol., 47, 176–186, https://doi.org/10.3189/172756501781832250, 2001. a, b
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: A review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305, 1995. a
Calonne, N., Richter, B., Löwe, H., Cetti, C., ter Schure, J., Van Herwijnen, A., Fierz, C., Jaggi, M., and Schneebeli, M.: The RHOSSA campaign: multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack , The Cryosphere, 14, 1829–1848, https://doi.org/10.5194/tc-14-1829-2020, 2020. a
Campbell, C. and Jamieson, B.: Spatial variability of slab stability and fracture characteristics within avalanche start zones, Cold Reg. Sci. Technol., 47, 134–147, https://doi.org/10.1016/j.coldregions.2006.08.015, 2007. a
Chilès, J.-P. and Delfiner, P.: Geostatistics: Modelling Spatial Uncertainty, John Wiley & Sons, Ltd, New-York, https://doi.org/10.1002/9781118136188, 1999. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a
Dale, M. R. T. and Fortin, M.-J.: Spatial Analysis: A guide for Ecologists, Cambridge University Press, 2nd edn., https://doi.org/10.1017/CBO9780511978913, 2014. a, b
Feick, S., Kronholm, K., and Schweizer, J.: Field observations on spatial variability of surface hoar at the basin scale, J. Geophys. Res.-Earth Surf., 112, 1–16, https://doi.org/10.1029/2006JF000587, 2007. a
Fyffe, B. and Zaiser, M.: The effects of snow variability on slab avalanche release, Cold Reg. Sci. Technol., 40, 229–242, https://doi.org/10.1016/j.coldregions.2004.08.004, 2004. a, b
Gao, J. and Xia, Z. G.: Fractals in physical geography, Prog. Phys. Geogr., 20, 178–191, https://doi.org/10.1177/030913339602000204, 1996. a, b
Gaume, J., Chambon, G., Eckert, N., and Naaim, M.: Influence of weak-layer heterogeneity on snow slab avalanche release: Application to the evaluation of avalanche release depths, J. Glaciol., 59, 423–437, https://doi.org/10.3189/2013JoG12J161, 2013. a, b
Gaume, J., Schweizer, J., Herwijnen, A., Chambon, G., Reuter, B., Eckert, N., and Naaim, M.: Evaluation of slope stability with respect to snowpack spatial variability, J. Geophys. Res.-Earth Surf., 119, 1783–1799, https://doi.org/10.1002/2014jf003193, 2014. a, b, c, d
Gaume, J., Chambon, G., Eckert, N., Naaim, M., and Schweizer, J.: Influence of weak layer heterogeneity and slab properties on slab tensile failure propensity and avalanche release area, The Cryosphere, 9, 795–804, https://doi.org/10.5194/tc-9-795-2015, 2015. a, b
Gaume, J., van Herwijnen, A., Chambon, G., Wever, N., and Schweizer, J.: Snow fracture in relation to slab avalanche release: critical state for the onset of crack propagation, The Cryosphere, 11, 217–228, https://doi.org/10.5194/tc-11-217-2017, 2017. a
Gauthier, D. and Jamieson, B.: Evaluation of a prototype field test for fracture and failure propagation propensity in weak snowpack layers, Cold Reg. Sci. Technol., 51, 87–97, https://doi.org/10.1016/J.COLDREGIONS.2007.04.005, 2008. a
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010. a, b
Guy, Z. M. and Birkeland, K. W.: Relating complex terrain to potential avalanche trigger locations, Cold Reg. Sci. Technol., 86, 1–13, https://doi.org/10.1016/j.coldregions.2012.10.008, 2013. a
Habermann, M., Schweizer, J., and Jamieson, J. B.: Influence of snowpack layering on human-triggered snow slab avalanche release, Cold Reg. Sci. Technol., 54, 176–182, https://doi.org/10.1016/j.coldregions.2008.05.003, 2008. a
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. a, b
Hägeli, P. and McClung, D. M.: Hierarchy theory as a conceptual framework for scale issues in avalanche forecast modeling, Ann. Glaciol., 38, 209–214, https://doi.org/10.3189/172756404781815266, 2004. a
Harper, J. T. and Bradford, J. H.: Snow stratigraphy over a uniform depositional surface: Spatial variability and measurement tools, Cold Reg. Sci. Technol., 37, 289–298, https://doi.org/10.1016/S0165-232X(03)00071-5, 2003. a
Hesterberg, T., Choi, N. H., Meier, L., and Fraley, C.: Least angle and l1 penalized regression: A review, Statistical Surveys, 2, 61–93, https://doi.org/10.1214/08-SS035, 2008. a
Johnson, J. B. and Schneebeli, M.: Characterizing the microstructural and micromechanical properties of snow, Cold Reg. Sci. Technol., 30, 91–100, https://doi.org/10.1016/S0165-232X(99)00013-0, 1999. a, b, c
Kronholm, K. and Birkeland, K. W.: Integrating spatial patterns into a snow avalanche cellular automata model, Geophys. Res. Lett., 32, L19504, https://doi.org/10.1029/2005GL024373, 2005. a
Kronholm, K. and Birkeland, K. W.: Reliability of sampling designs for spatial snow surveys, Comput. Geosci., 33, 1097–1110, https://doi.org/10.1016/j.cageo.2006.10.004, 2007. a
Kronholm, K. and Schweizer, J.: Snow stability variation on small slopes, Cold Reg. Sci. Technol., 37, 453–465, https://doi.org/10.1016/S0165-232X(03)00084-3, 2003. a, b, c
Landry, C., Birkeland, K., Hansen, K., Borkowski, J., Brown, R., and Aspinall, R.: Variations in snow strength and stability on uniform slopes, Cold Reg. Sci. Technol., 39, 205–218, https://doi.org/10.1016/j.coldregions.2003.12.003, 2004. a
Löwe, H. and van Herwijnen, A.: A Poisson shot noise model for micro-penetration of snow, Cold Reg. Sci. Technol., 70, 62–70, https://doi.org/10.1016/j.coldregions.2011.09.001, 2012. a, b, c
Lutz, E. and Birkeland, K. W.: Spatial patterns of surface hoar properties and incoming radiation on an inclined forest opening, J. Glaciol., 57, 355–366, https://doi.org/10.3189/002214311796405843, 2011. a, b
Lutz, E., Birkeland, K. W., Kronholm, K., Hansen, K., and Aspinall, R.: Surface hoar characteristics derived from a snow micropenetrometer using moving window statistical operations, Cold Reg. Sci. Technol., 47, 118–133, https://doi.org/10.1016/j.coldregions.2006.08.021, 2007. a, b
Marra, G. and Wood, S. N.: Practical variable selection for generalized additive models, Computational Statistics and Data Analysis, 55, 2372–2387, https://doi.org/10.1016/j.csda.2011.02.004, 2011. a
Meloche, F., Gauthier, F., Langlois, A., and Boucher, D.: The Northeastern Rainy Continental snow climate: A snow climate classification for the Gaspé Peninsula, Québec, Canada, in: International Snow Science Workshop, Innsbruck, Austria, 1025–1029, 2018. a
Meloche, J., Langlois, A., Rutter, N., McLennan, D., Royer, A., Billecocq, P., and Ponomarenko, S.: High-resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut, Hydrol. Process., 36, e14546, https://doi.org/10.1002/HYP.14546, 2022. a, b, c, d
Monti, F., Gaume, J., van Herwijnen, A., and Schweizer, J.: Snow instability evaluation: calculating the skier-induced stress in a multi-layered snowpack, Nat. Hazards Earth Syst. Sci., 16, 775–788, https://doi.org/10.5194/nhess-16-775-2016, 2016. a, b, c, d
Mott, R., Schirmer, M., and Lehning, M.: Scaling properties of wind and snow depth distribution in an Alpine catchment, J. Geophys. Res.-Atmos., 116, 1–8, https://doi.org/10.1029/2010JD014886, 2011. a, b
Mullen, R. S. and Birkeland, K. W.: Mixed Effect and Spatial Correlation Models for Analyzing a Regional Spatial dataset, International snow science workshop, 21–27 September 2008, Whistler, Canada, 8, https://arc.lib.montana.edu/snow-science/item/65 (last access: 10 July 2023), 2008. a
Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A.: Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models, SOIL, 3, 191–210, https://doi.org/10.5194/soil-3-191-2017, 2017. a, b, c
Pebesma, E. J.: Multivariable geostatistics in S: the gstat package, Comput. Geosci., 30, 683–691, https://doi.org/10.1016/j.cageo.2004.03.012, 2004. a
Pielmeier, C. and Marshall, H. P.: Rutschblock-scale snowpack stability derived from multiple quality-controlled SnowMicroPen measurements, Cold Reg. Sci. Technol., 59, 178–184, https://doi.org/10.1016/j.coldregions.2009.06.005, 2009. a
Proksch, M., Löwe, H., and Schneebeli, M.: Density, specific surface area, and correlation length of snow measured by high-resolution penetrometry, J. Geophys. Res.-Earth Surf., 120, 346–362, https://doi.org/10.1002/2014JF003266, 2015. a, b
Pulwicki, A., Flowers, G. E., Radic, V., and Bingham, D.: Estimating winter balance and its uncertainty from direct measurements of snow depth and density on alpine glaciers, J. Glaciol., 64, 781–795, https://doi.org/10.1017/JOG.2018.68, 2018. a, b
Reuter, B. and Schweizer, J.: Describing Snow Instability by Failure Initiation, Crack Propagation, and Slab Tensile Support, Geophys. Res. Lett., 45, 7019–7027, https://doi.org/10.1029/2018GL078069, 2018. a
Reuter, B., Herwijnen, A. V., and Schweizer, J.: Simple drivers of snow instability, Cold Reg. Sci. Technol., 120, 168–178, https://doi.org/10.1016/j.coldregions.2015.06.016, 2015a. a, b
Reuter, B., Schweizer, J., and van Herwijnen, A.: A process-based approach to estimate point snow instability, The Cryosphere, 9, 837–847, https://doi.org/10.5194/tc-9-837-2015, 2015b. a
Reuter, B., Proksch, M., Löwe, H., Van Herwijnen, A., and Schweizer, J.: Comparing measurements of snow mechanical properties relevant for slab avalanche release, J. Glaciol., 65, 55–67, https://doi.org/10.1017/jog.2018.93, 2019. a, b
Revuelto, J., Billecocq, P., Tuzet, F., Cluzet, B., Lamare, M., Larue, F., and Dumont, M.: Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas, Hydrol. Process., 34, 5384–5401, https://doi.org/10.1002/hyp.13951, 2020. a, b, c, d
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019. a, b, c
Rosendahl, P. L. and Weißgraeber, P.: Modeling snow slab avalanches caused by weak-layer failure – Part 2: Coupled mixed-mode criterion for skier-triggered anticracks, The Cryosphere, 14, 131–145, https://doi.org/10.5194/tc-14-131-2020, 2020. a
Sappington, J. M., Longshore, K. M., and Thompson, D. B.: Quantifying Landscape Ruggedness for Animal Habitat Analysis: A Case Study Using Bighorn Sheep in the Mojave Desert, J. Wildlife Manage., 71, 1419–1426, https://doi.org/10.2193/2005-723, 2007. a
Schirmer, M. and Lehning, M.: Persistence in intra-annual snow depth distribution: 2.Fractal analysis of snow depth development, Water Resour. Res., 47, 1–14, https://doi.org/10.1029/2010WR009429, 2011. a
Schirmer, M., Wirz, V., Clifton, A., and Lehning, M.: Persistence in intra-annual snow depth distribution: 1.Measurements and topographic control, Water Resour. Res., 47, 1–16, https://doi.org/10.1029/2010WR009426, 2011. a
Schweizer, J. and Kronholm, K.: Snow cover spatial variability at multiple scales: Characteristics of a layer of buried surface hoar, Cold Reg. Sci. Technol., 47, 207–223, https://doi.org/10.1016/j.coldregions.2006.09.002, 2007. a
Schweizer, J. and Reuter, B.: A new index combining weak layer and slab properties for snow instability prediction, Nat. Hazards Earth Syst. Sci., 15, 109–118, https://doi.org/10.5194/nhess-15-109-2015, 2015. a, b
Schweizer, J., Kronholm, K., Jamieson, J. B., and Birkeland, K. W.: Review of spatial variability of snowpack properties and its importance for avalanche formation, Cold Reg. Sci. Technol., 51, 253–272, https://doi.org/10.1016/j.coldregions.2007.04.009, 2008a. a, b, c, d
Schweizer, J., McCammon, I., and Jamieson, J. B.: Snowpack observations and fracture concepts for skier-triggering of dry-snow slab avalanches, Cold Reg. Sci. Technol., 51, 112–121, https://doi.org/10.1016/J.COLDREGIONS.2007.04.019, 2008b. a
Skøien, J. O. and Blöschl, G.: Sampling scale effects in random fields and implications for environmental monitoring, Environ. Monitor. A., 114, 521–552, https://doi.org/10.1007/s10661-006-4939-z, 2006. a, b, c
Stethem, C., Jamieson, B., Schaerer, P., Liverman, D., Germain, D., and Walker, S.: Snow avalanche hazard in Canada – A review, Nat. Hazards, 28, 487–515, https://doi.org/10.1023/A:1022998512227, 2003. a
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. a, b
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields, Water Resour. Res., 43, W07409, https://doi.org/10.1029/2006WR005317, 2007. a, b
Veitinger, J., Sovilla, B., and Purves, R. S.: Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach, The Cryosphere, 8, 547–569, https://doi.org/10.5194/tc-8-547-2014, 2014. a, b
Weiss, A.: Topographic position and landforms analysis, Poster presentation, ESRI User Conference, San Diego, CA, 64, 227–245, 2001. a
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., and Reynolds, J. M.: “Structure-from-Motion” photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, 179, 300–314, https://doi.org/10.1016/J.GEOMORPH.2012.08.021, 2012. a
Wirz, V., Schirmer, M., Gruber, S., and Lehning, M.: Spatio-temporal measurements and analysis of snow depth in a rock face, The Cryosphere, 5, 893–905, https://doi.org/10.5194/tc-5-893-2011, 2011. a
Wood, S. N.: Generalized additive models: an introduction with R, 2nd edn., Chapman and Hall/CRC, https://doi.org/10.1201/9781315370279, 2017. a
Short summary
Snow avalanches are a dangerous natural hazard. Backcountry recreationists and avalanche practitioners try to predict avalanche hazard based on the stability of snow cover. However, snow cover is variable in space, and snow stability observations can vary within several meters. We measure the snow stability several times on a small slope to create high-resolution maps of snow cover stability. These results help us to understand the snow variation for scientists and practitioners.
Snow avalanches are a dangerous natural hazard. Backcountry recreationists and avalanche...
