Articles | Volume 24, issue 5
https://doi.org/10.5194/nhess-24-1779-2024
© 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/nhess-24-1779-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 May 2024
Research article |
| 21 May 2024
| 21 May 2024
AutoATES v2.0: Automated Avalanche Terrain Exposure Scale mapping
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Center for Avalanche Research and Education, UiT the Arctic University of Norway, Tromsø, Norway
John Sykes
SFU Avalanche Research Program, Department of Geography, Simon Fraser University, Burnaby, Canada
Chugach National Forest Avalanche Center, Girdwood, AK, USA
Andrew Schauer
Chugach National Forest Avalanche Center, Girdwood, AK, USA
Jordy Hendrikx
Antarctica New Zealand, Christchurch, New Zealand
Department of Geosciences, UiT the Arctic University of Norway, Tromsø, Norway
Center for Avalanche Research and Education, UiT the Arctic University of Norway, Tromsø, Norway
Audun Hetland
Center for Avalanche Research and Education, UiT the Arctic University of Norway, Tromsø, Norway
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Cited articles
Bebi, P., Kulakowski, D., and Rixen, C.: Snow avalanche disturbances in forest ecosystems – State of research and implications for management, Forest Ecol. Manage., 257, 1883–1892, https://doi.org/10.1016/j.foreco.2009.01.050, 2009.
Birkeland, K. W., Greene, E. M., and Logan, S.: In Response to Avalanche Fatalities in the United States by Jekich et al, Wildern. Environ. Med., 28, 380–382, https://doi.org/10.1016/j.wem.2017.06.009, 2017.
Blöschl, G.: Scaling issues in snow hydrology, Hydrol. Process., 13, 2149–2175, https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2149::AID-HYP847>3.0.CO;2-8, 1999.
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.
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.
CAA: Technical Aspects of Snow Avalanche Risk Management – Resources and Guidelines for Avalanche Practitioners in Canada, edited by: Campbell, C., Conger, S., Gould, B., Haegeli, P., Jamieson, B., and Statham, G., Canadian Avalanche Association, ISBN 978-1-926497-00-6, https://www.researchgate.net/publication/326271708_Technical_Aspects_of_Snow_Avalanche_Risk_Management (last access: 15 January 2024), 2016.
Campbell, C. and Gould, B.: A proposed practical model for zoning with the Avalanche Terrain Exposure Scale, in: International Snow Science Workshop Proceedings, 7 October 2013, Grenoble, Chamonix Mont-Blanc, 385–391, https://arc.lib.montana.edu/snow-science/item.php?id=1985 (last access: 20 May 2024), 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.
D'Amboise, C. J. L., Neuhauser, M., Teich, M., Huber, A., Kofler, A., Perzl, F., Fromm, R., Kleemayr, K., and Fischer, J.-T.: Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows, Geosci. Model Dev., 15, 2423–2439, https://doi.org/10.5194/gmd-15-2423-2022, 2022.
Delparte, D. M.: Avalanche terrain modeling in Glacier National Park, Canada, PhD thesis, Library and Archives Canada, 1–195, ISBN 978-0-494-38204-2, 2008.
Engeset, R. V., Pfuhl, G., Landrø, M., Mannberg, A., and Hetland, A.: Communicating public avalanche warnings – what works?, Nat. Hazards Earth Syst. Sci., 18, 2537–2559, https://doi.org/10.5194/nhess-18-2537-2018, 2018.
Fisher, K. C., Haegeli, P., and Mair, P.: Exploring the avalanche bulletin as an avenue for continuing education by including learning interventions, J. Outdoor Recreat. Tourism, 37, 100472, https://doi.org/10.1016/J.JORT.2021.100472, 2022.
Heim, A.: Bergsturz und Menschenleben, Fretz und Wasmuth, Zurich, https://books.google.no/books/about/Bergsturz_und_Menschenleben.html?id=7GkhAQAAMAAJ&redir_esc=y (last access: 20 May 2024), 1932.
Hendrikx, J., Johnson, J., and Mannberg, A.: Tracking decision-making of backcountry users using GPS tracks and participant surveys, Appl. Geogr., 144, 102729, https://doi.org/10.1016/J.APGEOG.2022.102729, 2022.
Jang, J.-S. R. and Sun, C.-T.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice-Hall, Inc., Upper Saddle River, NJ, USA, ISBN 0-13-261066-3, 1997.
Johnson, J. and Hendrikx, J.: Using Citizen Science to Document Terrain Use and Decision-Making of Backcountry Users, Citiz. Sci., 6, 8, https://doi.org/10.5334/cstp.333, 2021.
Keskinen, Z., Hendrikx, J., Eckerstorfer, M., and Birkeland, K.: Satellite detection of snow avalanches using Sentinel-1 in a transitional snow climate, Cold Reg. Sci. Technol., 199, 103558, https://doi.org/10.1016/j.coldregions.2022.103558, 2022.
Landrø, M., Hetland, A., Engeset, R. V., and Pfuhl, G.: Avalanche decision-making frameworks: Factors and methods used by experts, Cold Reg. Sci. Technol., 170, 102897, https://doi.org/10.1016/j.coldregions.2019.102897, 2020.
Larsen, H. T., Hendrikx, J., Slåtten, M. S., and Engeset, R. V.: Developing nationwide avalanche terrain maps for Norway, Nat. Hazards, 103, 2829–2847, https://doi.org/10.1007/s11069-020-04104-7, 2020.
Lee, C. and Landgrebe, D. A.: Decision boundary feature extraction for nonparametric classification, IEEE Trans. Syst. Man Cybern., 23, 433–444, https://doi.org/10.1109/21.229456, 1993.
Lied, K. and Bakkehøi, S.: Empirical calculations of snow avalanche run-out distances based on topographic parameters, J. Glaciol., 26, 165–177, 1980.
Liu, Y., Wang, Y., and Zhang, J.: New Machine Learning Algorithm: Random Forest, in: Information Computing and Applications, ICICA 2012, Lecture Notes in Computer Science, vol. 7473, edited by: Liu, B., Ma, M., and Chang, J., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-34062-8_32, 2021.
Meyes, R., Lu, M., de Puiseau, C. W., and Meisen, T.: Ablation studies in artificial neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1901.08644, 2019.
Plattner, C., Braun, L., and Brenning, A.: The spatial variability of snow accumulation at Vernagtferner, Austrian Alps, in winter 2003/2004, Z. Gletscherk. Glazialgeol., 39, 43–57, 2006.
Sampl, P. and Zwinger, T.: Avalanche simulation with SAMOS, Ann. Glaciol., 38, 393–398, https://doi.org/10.3189/172756404781814780, 2004.
Sandvoss, M., McClymont, B., and Farnden, C.: User's Guide to VRI, Timberline Forest Inventory Consultants, https://www.for.gov.bc.ca/hfd/library/documents/bib106996.pdf (last access: 4 March 2024), 2005.
Schumacher, J., Toft, H., McLean, J. P., Hauglin, M., Astrup, R., and Breidenbach, J.: The utility of forest attribute maps for automated Avalanche Terrain Exposure Scale (ATES) modelling, Scand. J. Forest Res., 37, 264–275, https://doi.org/10.1080/02827581.2022.2096921, 2022.
Schweizer, J. and Lütschg, M.: Characteristics of human-triggered avalanches, Cold Reg. Sci. Technol., 33, 147–162, https://doi.org/10.1016/S0165-232X(01)00037-4, 2001.
Sharp, E.: Evaluating the exposure of heliskiing ski guides to avalanche terrain using a fuzzy logic avalanche susceptibility model, University of Leeds, Leeds, https://doi.org/10.13140/RG.2.2.18673.94567, 2018.
Statham, G. and Campbell, C.: The Avalanche Terrain Exposure Scale v2, in: International Snow Science Workshop Proceedings, 8 October 2023, Bend, Oregon, 597–605, https://arc.lib.montana.edu/snow-science/item.php?id=2939 (last access: 20 May 2024), 2023.
Statham, G., McMahon, B., and Tomm, I.: The Avalanche Terrain Exposure Scale, in: International Snow Science Workshop Proceedings, Telluride, Colorado, 491–497, https://arc.lib.montana.edu/snow-science/item.php?id=970 (last access: 20 May 2024), 2006.
Sykes, J., Hendrikx, J., Johnson, J., and Birkeland, K. W.: Combining GPS tracking and survey data to better understand travel behavior of out-of-bounds skiers, Appl. Geogr., 122, 102261, https://doi.org/10.1016/j.apgeog.2020.102261, 2020.
Sykes, J., Toft, H. B., and Haegeli, P.: Automated Avalanche Terrain Exposure Scale (ATES) mapping – Local validation and optimization in Western Canada, OSF [code], https://doi.org/10.17605/OSF.IO/ZXJW5, 2023.
Tarboton, D. G.: A new method for the determination of flow directions and upslope areas in grid digital elevation models, Water Resour. Res., 33, 309–319, https://doi.org/10.1029/96WR03137, 1997.
Techel, F. and Zweifel, B.: Recreational avalanche accidents in Switzerland: Trends and patterns with an emphasis on burial, rescue methods and avalanche danger, in: International Snow Science Workshop Proceedings, 7 October 2013, Grenoble-Chamonix, 1106–1112, https://arc.lib.montana.edu/snow-science/item.php?id=1844 (last access: 20 May 2024), 2013.
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.
Techel, F., Mitterer, C., Ceaglio, E., Coléou, C., Morin, S., Rastelli, F., and Purves, R. S.: Spatial consistency and bias in avalanche forecasts – a case study in the European Alps, Nat. Hazards Earth Syst. Sci., 18, 2697–2716, https://doi.org/10.5194/nhess-18-2697-2018, 2018.
Thumlert, S. and Haegeli, P.: Describing the severity of avalanche terrain numerically using the observed terrain selection practices of professional guides, Nat. Hazards, 91, 89–115, https://doi.org/10.1007/s11069-017-3113-y, 2018.
Toft, H. B., Müller, K., Hendrikx, J., Jaedicke, C., and Bühler, Y.: Can big data and random forests improve avalanche runout estimation compared to simple linear regression?, Cold Reg. Sci. Technol., 211, 103844, https://doi.org/10.1016/j.coldregions.2023.103844, 2023.
Toft, H. B., Sykes, J. M., and Schauer, A.: AutoATES-v2.0, GitHub [code], https://github.com/AutoATES (last access: 19 January 2024), 2024.
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.
Werners, B.: Aggregation models in mathematical programming, Math. Model. Decis. Support, 48, 295–305, 1988.
Short summary
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.
Manual Avalanche Terrain Exposure Scale (ATES) mapping is time-consuming and inefficient for...
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