Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-2031-2022
© 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-22-2031-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
14 Jun 2022
Research article | Highlight paper |
| 14 Jun 2022
| 14 Jun 2022
Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland
Cristina Pérez-Guillén
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Frank Techel
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Martin Hendrick
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Michele Volpi
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Alec van Herwijnen
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Tasko Olevski
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Guillaume Obozinski
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Fernando Pérez-Cruz
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Department of Computer Science, ETH Zurich, Zurich, Switzerland
Jürg Schweizer
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
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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.
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
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Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Bettina Richter, Alec van Herwijnen, Mathias W. Rotach, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 20, 2873–2888, https://doi.org/10.5194/nhess-20-2873-2020, https://doi.org/10.5194/nhess-20-2873-2020, 2020
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We investigated the sensitivity of modeled snow instability to uncertainties in meteorological input, typically found in complex terrain. The formation of the weak layer was very robust due to the long dry period, indicated by a widespread avalanche problem. Once a weak layer has formed, precipitation mostly determined slab and weak layer properties and hence snow instability. When spatially assessing snow instability for avalanche forecasting, accurate precipitation patterns have to be known.
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.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
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Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
Cited articles
Baggi, S. and Schweizer, J.: Characteristics of wet-snow avalanche activity: 20 years of observations from a high alpine valley (Dischma, Switzerland), Nat. Hazards, 50, 97–108, https://doi.org/10.1007/s11069-008-9322-7, 2009. a
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for
meteorological data, Geosci. Model Dev., 7, 3135–3151, https://doi.org/10.5194/gmd-7-3135-2014, 2014. a
Bowler, N. E.: Explicitly accounting for observation error in categorical
verification of forecasts, Mon. Weather Rev., 134, 1600–1606, https://doi.org/10.1175/MWR3138.1, 2006. 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
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
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. a
Chen, C., Liaw, A., and Breiman, L.: Using random forest to learn imbalanced data, University of California, Berkeley, 110, 24, 2004. a
Davis, R. E., Elder, K., Howlett, D., and Bouzaglou, E.: Relating storm and
weather factors to dry slab avalanche activity at Alta, Utah, and Mammoth
Mountain, California, using classification and regression trees, Cold Reg.
Sci. Technol., 30, 79–89, https://doi.org/10.1016/S0165-232X(99)00032-4, 1999. a
Dkengne Sielenou, P., Viallon-Galinier, L., Hagenmuller, P., Naveau, P., Morin, S., Dumont, M., Verfaillie, D., and Eckert, N.: Combining random forests and class-balancing to discriminate between three classes of avalanche activity in the French Alps, Cold Reg. Sci. Technol., 187, 103276,
https://doi.org/10.1016/j.coldregions.2021.103276, 2021. a, b
Dreier, L., Harvey, S., van Herwijnen, A., and Mitterer, C.: Relating
meteorological parameters to glide-snow avalanche activity, Cold Reg. Sci.
Technol., 128, 57–68, https://doi.org/10.1016/j.coldregions.2016.05.003, 2016. a
EAWS: Information pyramid,
https://www.avalanches.org/standards/information-pyramid/ (last access: 18 June 2021), 2021b. a
EAWS: Avalanche Problems, Edited, EAWS – European Avalanche Warning
Services,
https://www.avalanches.org/wp-content/uploads/2019/05/Typical_avalanche_problems-EAWS.pdf
(last access: 18 June 2021), 2021c. a
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung,
D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The
international classification for seasonal snow on the ground, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 31 May 2022), 2009. a
Föhn, P. M. B. and Schweizer, J.: Verification of avalanche danger with
respect to avalanche forecasting, in: Les apports de la recherche scientifique à la sécurité neige glace et avalanche. Actes de
Colloque, Chamonix, 30 mai–3 juin 1995, edited by: Sivardière, F., ANENA, Grenoble, France, 151–156, 1995. a
Frénay, B. and Verleysen, M.: Classification in the presence of label
noise: a survey, IEEE T. Neural Netw. Learn. Syst., 25, 845–869, 2013. a
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
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V.: Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389–422, 2002. a
Heck, M., Van Herwijnen, A., Hammer, C., Hobiger, M., Schweizer, J., and
Fäh, D.: Automatic detection of avalanches combining array classification
and localization, Earth Surf. Dynam., 7, 491–503,
https://doi.org/10.5194/esurf-7-491-2019, 2019. a
Hendrikx, J., Murphy, M., and Onslow, T.: Classification trees as a tool for
operational avalanche forecasting on the Seward Highway, Alaska, Cold Reg.
Sci. Technol., 97, 113–120, https://doi.org/10.1016/j.coldregions.2013.08.009, 2014. a
Hendrikx, J., Dreier, L., Ulivieri, G., Sanderson, J., Jones, A., and Steinkogler, W.: Evaluation of an infrasound detection system for avalanches in Rogers Pass, Canada, in: Proceedings ISSW 2018, International Snow Science Workshop, 7–12 October 2018, Innsbruck, Austria, 171–175, 2018. a
Jamieson, B., Campbell, C., and Jones, A.: Verification of Canadian avalanche bulletins including spatial and temporal scale effects, Cold Reg. Sci.
Technol., 51, 204–213, https://doi.org/10.1016/j.coldregions.2007.03.012, 2008. a
Jamieson, J. and Johnston, C.: Refinements to the stability index for
skier-triggered dry-slab avalanches, Ann. Glaciol., 26, 296–302,
https://doi.org/10.3189/1998AoG26-1-296-302, 1998. a, b, c
Kahneman, D., Sibony, O., and Sunstein, C. R.: Noise – A flaw in human judgment, Hachette Book Group, New York, USA, 454 pp., ISBN 10 0316451401, 2021. a
LaChapelle, E. R.: The fundamental processes in conventional Alavalanche
forecasting, J. Glaciol., 26, 75–84, https://doi.org/10.3189/S0022143000010601, 1980. a, b
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a new 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, c, d
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A physical
SNOWPACK model for the Swiss avalanche warning: Part II. Snow microstructure,
Cold Reg. Sci. Technol., 35, 147–167, https://doi.org/10.1016/S0165-232X(02)00073-3, 2002. a
Maas, A., Rottensteiner, F., and Heipke, C.: Using label noise robust logistic regression for automated updating of topographic geospatial databases., in: XXIII ISPRS Congress, Commission VII 3 (2016), 133–140. https://doi.org/10.5194/isprsannals-III-7-133-2016, 2016. a
Mayer, S., van Herwijnen, A., Ulivieri, G., and Schweizer, J.: Evaluating the
performance of an operational infrasound avalanche detection system at three
locations in the Swiss Alps during two winter seasons, Cold Reg. Sci. Technol., 173, 102962, https://doi.org/10.1016/j.coldregions.2019.102962, 2020. a
McClung, D. and Schaerer, P. A.: The avalanche handbook, The Mountaineers
Books, ISBN 13 978-0898868098, 2006. a
McClung, D. M.: Predictions in avalanche forecasting, Ann. Glaciol., 31, 377–381, https://doi.org/10.3189/172756400781820507, 2000. a
Mitterer, C. and Schweizer, J.: Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction, The Cryosphere, 7, 205–216, https://doi.org/10.5194/tc-7-205-2013, 2013. a
Möhle, S., Bründl, M., and Beierle, C.: Modeling a system for decision support in snow avalanche warning using balanced random forest and weighted random forest, in: Lecture notes in computer science: Vol. 8722, Artificial intelligence: methodology, systems, and applications, Proceedings, edited by: Agre, G., Hitzler, P., Krisnadhi, A. A., and Kuznetsov, S. O., Springer, 80–91, https://doi.org/10.1007/978-3-319-10554-3_8, 2014. a
Monti, F., Schweizer, J., and Fierz, C.: Hardness estimation and weak layer
detection in simulated snow stratigraphy, Cold Reg. Sci. Technol., 103,
82–90, https://doi.org/10.1016/j.coldregions.2014.03.009, 2014. a
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
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet, A., Hagenmuller, P., Lafaysse, M., Ližar, M., and Mitterer, C.,: Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future, Cold Reg. Sci. Technol., 170, 102910, https://doi.org/10.1016/j.coldregions.2019.102910, 2020. a, b, c
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., and Vanderplas, J.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., and
Dedieu, G.: Effect of training class label noise on classification
performances for land cover mapping with satellite image time series, Remote
Sens., 9, 173, https://doi.org/10.3390/rs9020173, 2017. a
Pérez-Guillén, C.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Renkulab [code], https://renkulab.io/gitlab/deapsnow/predictions_avalanche_danger-level_switzerland, last access: 9 June 2022. a
Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Weather, snowpack and danger ratings data for automated avalanche danger level predictions, EnviDat [data set], https://doi.org/10.16904/envidat.330, 2022. a
Perla, R. I.: On contributory factors in avalanche hazard evaluation, Can.
Geotech. J., 7, 414–419, https://doi.org/10.1139/t70-053, 1970. a
Pozdnoukhov, A., Purves, R. S., and Kanevski, M.: Applying machine learning
methods to avalanche forecasting, Ann. Glaciol., 49, 107–113,
https://doi.org/10.3189/172756408787814870, 2008. a
Pozdnoukhov, A., Matasci, G., Kanevski, M., and Purves, R. S.: Spatio-temporal avalanche forecasting with Support Vector Machines, Nat. Hazards Earth Syst. Sci., 11, 367–382, https://doi.org/10.5194/nhess-11-367-2011, 2011. a
Purves, R., Morrison, K., Moss, G., and Wright, D.: Nearest neighbours for
avalanche forecasting in Scotland – development, verification and
optimisation of a model, Cold Reg. Sci. Technol., 37, 343–355,
https://doi.org/10.1016/S0165-232X(03)00075-2, 2003. a
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
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and
Rigol-Sanchez, J. P.: An assessment of the effectiveness of a random forest
classifier for land-cover classification, ISPRS J. Photogram. Remote Sens., 67, 93–104, https://doi.org/10.1016/j.isprsjprs.2011.11.002, 2012. a
Schirmer, M., Lehning, M., and Schweizer, J.: Statistical forecasting of
regional avalanche danger using simulated snow-cover data, J. Glaciol., 55,
761–768, https://doi.org/10.3189/002214309790152429, 2009. a, b
Schweizer, J.: Verifikation des Lawinenbulletins, in: Schnee und Lawinen in den Schweizer Alpen, Winter 2004/2005, Wetter, Schneedecke und Lawinengefahr, Winterbericht SLF, edited by: Pielmeier, C., Aebi, M., and Schweizer, J., Eidg. Institut für Schnee- und Lawinenforschung SLF, Davos, Switzerland, 91–99, 2007. a, b, c
Schweizer, J. and Föhn, P. M.: Avalanche forecasting – an expert system
approach, J. Glaciol., 42, 318–332, https://doi.org/10.3189/S0022143000004172, 1996. a, b
Schweizer, J. and Jamieson, J. B.: A threshold sum approach to stability
evaluation of manual snow profiles, Cold Reg. Sci. Technol., 47, 50–59,
https://doi.org/10.1016/j.coldregions.2006.08.011, 2007. a
Schweizer, J., Föhn, P., and Plüss, C.: COGENSYS Judgment Processor (PARADOCS) als Hilfmittel für die Lawinenwarnung, Interner Bericht, Report No. 675, Eidgenössisches Institut für Schnee- und Lawinenforschung, https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:30627 (last access: 9 June 2022), 1992. a
Schweizer, J., Jamieson, J. B., and Skjonsberg, D.: Avalanche forecasting for transportation corridor and backcountry in Glacier National Park (BC, Canada), in: 25 Years of Snow Avalanche Research, Voss, Norway, 12–16 May 1998, NGI Publication, Vol. 203, edited by: Hestnes, E., Norwegian Geotechnical Institute, Oslo, Norway, 238–243, 1998. a
Schweizer, J., Bellaire, S., Fierz, C., Lehning, M., and Pielmeier, C.:
Evaluating and improving the stability predictions of the snow cover model
SNOWPACK, Cold Reg. Sci. Technol., 46, 52–59,
https://doi.org/10.1016/j.coldregions.2006.05.007, 2006. a
Schweizer, J., Mitterer, C., Techel, F., Stoffel, A., and Reuter, B.: On the
relation between avalanche occurrence and avalanche danger level, The
Cryosphere, 14, 737–750, https://doi.org/10.5194/tc-14-737-2020, 2020. a, b
Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, 2021. a, b
SLF: Avalanche bulletin interpretation guide, WSL Institute for Snow and
Avalanche Research – SLF, edition December 2020, p. 53,
https://www.slf.ch/files/user_upload/SLF/Lawinenbulletin_Schneesituation/Wissen_zum_Lawinenbulletin/Interpretationshilfe/Interpretationshilfe_EN.pdf (last access: 2 June 2022), 2020. a, b
Sokolova, M. and Lapalme, G.: A systematic analysis of performance measures for classification tasks, Inform. Process. Manage., 45, 427–437, 2009. a
Statham, G., Haegeli, P., Greene, E., Birkeland, K., Israelson, C., Tremper,
B., Stethem, C., McMahon, B., White, B., and Kelly, J.: A conceptual model of
avalanche hazard, Nat. Hazards, 90, 663–691, https://doi.org/10.1007/s11069-017-3070-5, 2018. a, b
Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T.: Bias in random
forest variable importance measures: Illustrations, sources and a solution,
BMC Bioinf., 8, 1–21, https://doi.org/10.1186/1471-2105-8-25, 2007.
a
Techel, F.: On consistency and quality in public avalanche forecasting: a
data-driven approach to forecast verification and to refining definitions of
avalanche danger, PhD thesis, Department of Geography, University of Zurich, Zurich, Switzerland, https://doi.org/10.5167/uzh-199650, 2020. a, b, c, d, e
Techel, F., Zweifel, B., and Winkler, K.: Analysis of avalanche risk factors in backcountry terrain based on usage frequency and accident data in
Switzerland, Nat. Hazards Earth Syst. Sci., 15, 1985–1997,
https://doi.org/10.5194/nhess-15-1985-2015, 2015. a
Techel, F., Müller, K., and Schweizer, J.: On the importance of snowpack
stability, the frequency distribution of snowpack stability and avalanche
size in assessing the avalanche danger level, The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-2020-42, 2020a. a, b, c
Techel, F., Pielmeier, C., and Winkler, K.: Refined dry-snow avalanche danger
ratings in regional avalanche forecasts: Consistent? And better than random?,
Cold Reg. Sci. Technol., 180, 103162, https://doi.org/10.1016/j.coldregions.2020.103162, 2020b. a, b, c
Techel, F., Mayer, S., Pérez-Guillén, C., Schmudlach, G., and Winkler, K.: On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger, Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, 2022. a
van Herwijnen, A., Heck, M., and Schweizer, J.: Forecasting snow avalanches
using avalanche activity data obtained through seismic monitoring, Cold Reg.
Sci. Technol., 132, 68–80, https://doi.org/10.1016/j.coldregions.2016.09.014, 2016. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving
Richards Equation for snow improves snowpack meltwater runoff estimations in
detailed multi-layer snowpack model, The Cryosphere, 8, 257–274,
https://doi.org/10.5194/tc-8-257-2014, 2014. a
Winkler, K., Schmudlach, G., Degraeuwe, B., and Techel, F.: On the correlation between the forecast avalanche danger and avalanche risk taken by backcountry skiers in Switzerland, Cold Reg. Sci. Technol., 188, 103299,
https://doi.org/10.1016/j.coldregions.2021.103299, 2021. a, b
Zweifel, B., Hafner, E., Lucas, C., Marty, C., Techel, F., and Stucki, T.:
Schnee und Lawinen in den Schweizer Alpen. Hydrologisches Jahr 2018/19,
WSL Ber. 86, WSL-Institut für Schnee- und Lawinenforschung – SLF, Davos, 134 pp., https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:22232 (last access: 2 June 2022), 2019. a, b, c
Executive editor
The paper could have a strong impact in the entire Alpine region, where avalanche forecasting is a critical issue to manage
The paper could have a strong impact in the entire Alpine region, where avalanche forecasting is...
Short summary
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in...
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