Articles | Volume 16, issue 8
https://doi.org/10.5194/tc-16-3295-2022
© 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/tc-16-3295-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2022
Research article |
| 24 Aug 2022
| 24 Aug 2022
Snow Avalanche Frequency Estimation (SAFE): 32 years of monitoring remote avalanche depositional zones in high mountains of Afghanistan
Arnaud Caiserman
CORRESPONDING AUTHOR
Mountain Societies Research Institute, University of Central Asia, Khorog, 736000, Tajikistan
Roy C. Sidle
Mountain Societies Research Institute, University of Central Asia, Khorog, 736000, Tajikistan
Deo Raj Gurung
Aga Khan Agency for Habitat, Dushanbe, 734013, Tajikistan
Related authors
Anushilan Acharya, Jakob F. Steiner, Khwaja Momin Walizada, Salar Ali, Zakir Hussain Zakir, Arnaud Caiserman, and Teiji Watanabe
Nat. Hazards Earth Syst. Sci., 23, 2569–2592, https://doi.org/10.5194/nhess-23-2569-2023, https://doi.org/10.5194/nhess-23-2569-2023, 2023
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All accessible snow and ice avalanches together with previous scientific research, local knowledge, and existing or previously active adaptation and mitigation solutions were investigated in the high mountain Asia (HMA) region to have a detailed overview of the state of knowledge and identify gaps. A comprehensive avalanche database from 1972–2022 is generated, including 681 individual events. The database provides a basis for the forecasting of avalanche hazards in different parts of HMA.
Anushilan Acharya, Jakob F. Steiner, Khwaja Momin Walizada, Salar Ali, Zakir Hussain Zakir, Arnaud Caiserman, and Teiji Watanabe
Nat. Hazards Earth Syst. Sci., 23, 2569–2592, https://doi.org/10.5194/nhess-23-2569-2023, https://doi.org/10.5194/nhess-23-2569-2023, 2023
Short summary
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All accessible snow and ice avalanches together with previous scientific research, local knowledge, and existing or previously active adaptation and mitigation solutions were investigated in the high mountain Asia (HMA) region to have a detailed overview of the state of knowledge and identify gaps. A comprehensive avalanche database from 1972–2022 is generated, including 681 individual events. The database provides a basis for the forecasting of avalanche hazards in different parts of HMA.
Jianqiang Zhang, Cees J. van Westen, Hakan Tanyas, Olga Mavrouli, Yonggang Ge, Samjwal Bajrachary, Deo Raj Gurung, Megh Raj Dhital, and Narendral Raj Khanal
Nat. Hazards Earth Syst. Sci., 19, 1789–1805, https://doi.org/10.5194/nhess-19-1789-2019, https://doi.org/10.5194/nhess-19-1789-2019, 2019
Short summary
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The aim of this study is to investigate the differences in the mappable characteristics of earthquake-triggered and rainfall triggered landslides in terms of their frequency–area relationships, spatial distributions and relation with causal factors, as well as to evaluate whether separate susceptibility maps generated for specific landslide size and triggering mechanism are better than a generic landslide susceptibility assessment including all landslide sizes and triggers.
Related subject area
Discipline: Snow | Subject: Natural Hazards
Combining modelled snowpack stability with machine learning to predict avalanche activity
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Brief communication: Weak control of snow avalanche deposit volumes by avalanche path morphology
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Dynamic crack propagation in weak snowpack layers: insights from high-resolution, high-speed photography
Avalanche danger level characteristics from field observations of snow instability
Using avalanche problems to examine the effect of large-scale atmosphere–ocean oscillations on avalanche hazard in western Canada
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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.
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
Abermann, J., Eckerstorfer, M., Malnes, E., and Hansen, B. U.: A large wet
snow avalanche cycle in West Greenland quantified using remote sensing and
in situ observations, Nat. Hazards, 97, 517–534,
https://doi.org/10.1007/s11069-019-03655-8, 2019.
Avalanche.org: Accidents, https://avalanche.org/avalanche-accidents/, last
access: 30 June 2021.
European Agency for Asylum: Badakhshan, https://euaa.europa.eu/country-guidance-afghanistan-2020/badakhshan, last access: 15 August 2022.
Bair, E. H., Rittger, K., Ahmad, J. A., and Chabot, D.: Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan, The Cryosphere, 14, 331–347, https://doi.org/10.5194/tc-14-331-2020, 2020.
Barbolini, M., Pagliardi, M., Ferro, F., and Corradeghini, P.: Avalanche
hazard mapping over large undocumented areas, Nat. Hazards, 56, 451–464,
https://doi.org/10.1007/s11069-009-9434-8, 2011.
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016.
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, 2018a.
Bühler, Y., von Rickenbach, D., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., and Kühne, R.: Linking modelled potential
release areas with avalanche dynamic simulations: an automated
approach for efficient avalanche hazard indication mapping, International
snow science workshop proceedings, Innsbruck, Austria, 7–12 October 2018, 810–814, 2018b.
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.
Caiserman A.: Snow Avalanche Frequency Estimation (SAFE), Zenodo [code], https://doi.org/10.5281/zenodo.6973757, 2022.
Chabot, D. and Kaba, A.: Avalanche forecasting in the Central
Asian countries of Afghanistan, Pakistan and Tajikistan, International
Snow Science Workshop, Breckenridge, United States, 10 February 2016, https://arc.lib.montana.edu/snow-science/item/2310 (last access: 15 August 2022), 2016.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479,
https://doi.org/10.3189/2013JoG12J154, 2013.
Eckerstorfer, M., Malnes, E., Frauenfelder, R., Doomas, U., and
Brattlien, K.: Avalanche Debris Detection Using Satellite-Borne Radar and Optical Remote Sensing, International Snow Science Workshop 2014 Proceedings, Banff, Canada, 131–138, https://www.researchgate.net/profile/Markus-Eckerstorfer/publication/265395539 (last access: 15 August 2022), 2014.
Eckerstorfer, M., Bühler, Y., Frauenfelder, R., and Malnes, E.: Remote
sensing of snow avalanches: Recent advances, potential, and limitations,
Cold Reg. Sci. Technol., 121, 126–140,
https://doi.org/10.1016/j.coldregions.2015.11.001, 2016.
Eckerstorfer, M., Malnes, E., and Müller, K.: A complete snow avalanche
activity record from a Norwegian forecasting region using Sentinel-1
satellite-radar data, Cold Reg. Sci. Technol., 144, 39–51,
https://doi.org/10.1016/j.coldregions.2017.08.004, 2017.
European Avalanche Warning Services: Fatalities, https://www.avalanches.org/fatalities/, last access: 30 June
2021.
FAO: Special report
FAO/WFP crop and food supply assessment mission
to Afghanistan, https://www.fao.org/3/j0156e/j0156e00.htm, last
access: 17 October 2021.
Global Facility for Disaster and Recovery: Afghanistan Multi-Hazard Risk Assessment, World Bank, Kabul, Afghanistan, 108 pp., https://www.gfdrr.org/sites/default/files/publication/Afghanistan_MHRA.pdf (last access: 15 August 2022), 2018.
Greene, E., Birkeland, K., Elder, K., McCammon, I., Staples, M.,
and Sharaf, D.: Observation Guidelines for Avalanche Professionals
in the U.S. American Avalanche Association, Pagosa
Springs, Victor, United States of America, 104 pp., https://issuu.com/americanavalanche/docs/aaa_swag_sample (last access: 15 August 2022), 2016.
Gubler, H.: Measurements and modelling of snow avalanche
speeds, in: Proceedings of the Davos Symposium, Avalanche
Formation, Movment and Effects, Davos, Switzerland, September 1987, https://www.semanticscholar.org/paper/Measurements-and-modelling-of-snow-avalanche-speeds-Gubler/ca94f82fc1f509b7b3fb7a1fe0fc1f871414800f (last access: 15 August 2022), 1987.
Guimbert, S.: Struture and performance of the Afghan economy,
World, Washington D.C, United States, 47 pp., https://documents1.worldbank.org/curated/en/819001468740686597/pdf/308610PAPER0SASPR0no1010Afghan0eco.pdf (last access: 15 August 2022), 2004.
Hafner, E. and Bühler, Y.: SPOT6 Avalanche outlines 24 January
2018, https://opendata.swiss/en/dataset/spot6-avalanche-outlines-24-january-2018 (last access: 15 August 2022), 2018.
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, 2021a.
Hafner, E., Leinss, S., Techel, F., and Bühler, Y.: Satellite avalanche mapping validation data, EnviDat [data set],
https://doi.org/10.16904/envidat.202, 2021b.
Hammond, J. C., Saavedra, F. A., and Kampf, S. K.: Global snow zone maps and
trends in snow persistence 2001–2016, Int. J. Climatol.,
38, 4369–4383, https://doi.org/10.1002/joc.5674, 2018.
Karas, A., Karbou, F., Giffard-Roisin, S., Durand, P., and Eckert, N.: Automatic Color Detection-Based Method Applied to Sentinel-1 SAR Images for Snow Avalanche Debris Monitoring, IEEE T. Geosci. Remote, 60, 1–17, https://doi.org/10.1109/TGRS.2021.3131853, 2022.
Keylock, C. J., McClung, D. M., and Magnusson, M. M.: Avalanche
risk mapping by simulation, J. Glaciology, 45, 303–314, https://doi.org/10.3189/S0022143000001805, 1999.
Kravtsova, V. I.: Snow Cover Mapping of Afghanistan's Mountains with Space
Imagery, Mapping Sciences and Remote Sensing, 27, 295–302,
https://doi.org/10.1080/07493878.1990.10641815, 1990.
Leinss, S., Wicki, R., Holenstein, S., Baffelli, S., and Bühler, Y.: Snow avalanche detection and mapping in multitemporal and multiorbital radar images from TerraSAR-X and Sentinel-1, Nat. Hazards Earth Syst. Sci., 20, 1783–1803, https://doi.org/10.5194/nhess-20-1783-2020, 2020.
Louge, M. Y., Turnbull, B., and Carroll, C.: Volume growth of a powder snow
avalanche, Ann. Glaciol., 53, 57–60,
https://doi.org/10.3189/2012AoG61A030, 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,
2009.
Malnes, E., Eckerstorfer, M., and Vickers, H.: First Sentinel-1 detections of avalanche debris, The Cryosphere Discuss., 9, 1943–1963, https://doi.org/10.5194/tcd-9-1943-2015, 2015.
Martinez-Vazquez, A. and Fortuny-Guasch, J.: A GB-SAR Processor for Snow
Avalanche Identification, IEEE T. Geosci. Remote, 46, 3948–3956, https://doi.org/10.1109/TGRS.2008.2001387, 2008.
Mohanty, A., Hussain, M., Mishra, M., Kattel, D. B., and Pal, I.: Exploring
community resilience and early warning solution for flash floods, debris
flow and landslides in conflict prone villages of Badakhshan, Afghanistan,
Int. J. Disast. Risk Re., 33, 5–15,
https://doi.org/10.1016/j.ijdrr.2018.07.012, 2019.
Office for the Coordination of Humanitarian Affairs: Districts Affected by Avalanches –
Badakhshan Province, https://reliefweb.int/map/afghanistan/afghanistan-districts-affected-avalanches
%C2%A0%E2%80%90-badakhshan-province-19-jan-2012-location (last access: 15 August 2022), 2012.
Palma, J.: Climate in Crisis: How Risk Information Can Build Resilience in Afghanistan,
https://blogs.worldbank.org/endpovertyinsouthasia/climate-crisis-how-risk-information-can-build-resilience-afghanistan,
last access: 30 June 2021.
Prokop, A.: Assessing the applicability of terrestrial laser scanning for
spatial snow depth measurements, Cold Reg. Sci. Technol., 54,
155–163, https://doi.org/10.1016/j.coldregions.2008.07.002, 2008.
Prokop, A., Schön, P., Singer, F., Pulfer, G., Naaim, M., and Thibert,
E.: Determining Avalanche Modelling Input Parameters Using Terrestrial Laser
Scanning Technology, International Snow Science Workshop, Grenoble,
Chamonix Mont-Blanc, 7–11 October 2013, 770–774, https://hal.archives-ouvertes.fr/hal-00950086/document (last access: 15 August 2022), 2013.
Qureshi, S.: Water resources management in Afghanistan: The issues and options, IWMI, 30 pp., https://books.google.com.tj/books?hl=fr&lr=&id=qJMAbmFqAFoC&oi=fnd&pg=PA10&dq=Water+resources+management+in+Afghanistan:+the+50+issues+and+options,&ots=TzN-kHTxZX&sig=-GMrnmUSiN77y73QcKBAz7LYldU&redir_esc=y#v=onepage&q&f=false (last access: 15 August 2022), 2002.
Schaffhauser, A., Adams, M., Fromm, R., Jörg, P., Luzi, G., Noferini,
L., and Sailer, R.: Remote sensing based retrieval of snow cover properties,
Cold Reg. Sci. Technol., 54, 164–175,
https://doi.org/10.1016/j.coldregions.2008.07.007, 2008.
Singh, D. K., Mishra, V. D., Gusain, H. S., Gupta, N., and Singh, A. K.:
Geo-spatial Modeling for Automated Demarcation of Snow Avalanche Hazard
Areas Using Landsat-8 Satellite Images and In Situ Data, J. Indian Soc. Remote
Sens., 47, 513–526, https://doi.org/10.1007/s12524-018-00936-w, 2019.
Singh, K. K., Singh, D. K., Thakur, N. K., Dewali, S. K., Negi, H. S.,
Snehmani, and Mishra, V. D.: Detection and mapping of snow avalanche debris
from Western Himalaya, India using remote sensing satellite images, Geocarto
Int., 37, 2561–2579, https://doi.org/10.1080/10106049.2020.1762762,
2020.
Smith, W. D., Dunning, S. A., Brough, S., Ross, N., and Telling, J.: GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): a new tool for identifying and monitoring supraglacial landslide inputs, Earth Surf. Dynam., 8, 1053–1065, https://doi.org/10.5194/esurf-8-1053-2020, 2020.
Soteres, R. L., Pedraza, J., and Carrasco, R. M.: Snow avalanche
susceptibility of the Circo de Gredos (Iberian Central System, Spain),
J. Maps, 16, 155–165,
https://doi.org/10.1080/17445647.2020.1717655, 2020.
Tompkin, C. and Leinss, S.: Backscatter Characteristics of Snow Avalanches
for Mapping With Local Resolution Weighting, IEEE J. Sel. Top.
Appl., 14, 4452–4464,
https://doi.org/10.1109/JSTARS.2021.3074418, 2021.
USAID: Afghanistan Avalanches, https://www.usaid.gov/humanitarian-assistance/afghanistan, last access: 15 August 2022.
Vickers, H., Eckerstorfer, M., Malnes, E., Larsen, Y., and Hindberg, H.: A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging, Earth and Space Science, 3, 446–462, https://doi.org/10.1002/2016EA000168, 2016.
Yang, J., Li, C., Li, L., Ding, J., Zhang, R., Han, T., and Liu, Y.:
Automatic Detection of Regional Snow Avalanches with Scattering and
Interference of C-band SAR Data, Remote Sensing, 12, 2781,
https://doi.org/10.3390/rs12172781, 2020.
Yariyan, P., Avand, M., Abbaspour, R. A., Karami, M., and Tiefenbacher, J.
P.: GIS-based spatial modeling of snow avalanches using four novel ensemble
models, Sci. Total Environ., 745, 141008,
https://doi.org/10.1016/j.scitotenv.2020.141008, 2020.
Zhang, J., Gurung, D. R., Liu, R., Murthy, M. S. R., and Su, F.: Abe Barek
landslide and landslide susceptibility assessment in Badakhshan Province,
Afghanistan, Landslides, 12, 597–609,
https://doi.org/10.1007/s10346-015-0558-5, 2015.
Short summary
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.
Snow avalanches cause considerable material and human damage in all mountain regions of the...
