ORIGINAL PAPER A conceptual model of avalanche hazard Grant Statham 1 • Pascal Haegeli 2,3 • Ethan Greene 5 • Karl Birkeland 4 • Clair Israelson 6 • Bruce Tremper 7 • Chris Stethem 8 • Bruce McMahon 9 • Brad White 1 • John Kelly 6 Received: 21 March 2016 / Accepted: 9 October 2017 / Published online: 2 November 2017 Ó The Author(s) 2017. This article is an open access publication Abstract This conceptual model of avalanche hazard identifies the key components of avalanche hazard and structures them into a systematic, consistent workflow for hazard and risk assessments. The method is applicable to all types of avalanche forecasting operations, and the underlying principles can be applied at any scale in space or time. The concept of an avalanche problem is introduced, describing how different types of avalanche problems directly influence the assessment and management of the risk. Four sequential questions are shown to structure the assessment of avalanche hazard, namely: (1) What type of avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our objective was to develop an underpinning for qualitative hazard and risk assessments and address this knowledge gap in the avalanche forecasting literature. We used judgmental decomposition to elicit the avalanche forecasting process from forecasters and then described it within a risk-based framework that is consistent with other natural hazards disciplines. & Grant Statham [email protected]1 Parks Canada Agency, P.O. Box 900, Banff, AB T1L 1K2, Canada 2 Simon Fraser University, Burnaby, BC, Canada 3 Avisualanche Consulting, Vancouver, BC, Canada 4 USDA Forest Service National Avalanche Centre, Bozeman, MT, USA 5 Colorado Avalanche Information Centre, Boulder, CO, USA 6 Canadian Avalanche Centre, Revelstoke, BC, Canada 7 USDA Forest Service Utah Avalanche Centre, Salt Lake City, UT, USA 8 Chris Stethem & Associates Ltd., Canmore, AB, Canada 9 Parks Canada Agency, Rogers Pass, BC, Canada 123 Nat Hazards (2018) 90:663–691 https://doi.org/10.1007/s11069-017-3070-5
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ORIGINAL PAPER
A conceptual model of avalanche hazard
Grant Statham1• Pascal Haegeli2,3 • Ethan Greene5 •
Karl Birkeland4 • Clair Israelson6 • Bruce Tremper7 •
Chris Stethem8• Bruce McMahon9 • Brad White1 •
John Kelly6
Received: 21 March 2016 /Accepted: 9 October 2017 / Published online: 2 November 2017� The Author(s) 2017. This article is an open access publication
Abstract This conceptual model of avalanche hazard identifies the key components of
avalanche hazard and structures them into a systematic, consistent workflow for hazard and
risk assessments. The method is applicable to all types of avalanche forecasting operations,
and the underlying principles can be applied at any scale in space or time. The concept of
an avalanche problem is introduced, describing how different types of avalanche problems
directly influence the assessment and management of the risk. Four sequential questions
are shown to structure the assessment of avalanche hazard, namely: (1) What type of
avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How
likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our
objective was to develop an underpinning for qualitative hazard and risk assessments and
address this knowledge gap in the avalanche forecasting literature. We used judgmental
decomposition to elicit the avalanche forecasting process from forecasters and then
described it within a risk-based framework that is consistent with other natural hazards
3 Could bury and destroy a car, damage a truck,destroy a wood frame house or break a few trees
103 100 1000
4 Could destroy a railway car, large truck, severalbuildings or a forest area of approximately 4hectares
104 500 2000
5 Largest snow avalanche known. Could destroy avillage or a forest area of approximately 40hectares
105 1000 3000
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123
an element-at-risk to determine the degree of risk and decide on mitigation strategies. This
occurs in different ways depending on the operational application (Table 1). In the case of
public forecasting, danger ratings are published to accompany an avalanche bulletin, and
the public at-large manages their own exposure and vulnerability. In operations where an
element-at-risk is being managed (e.g., transportation corridor, ski area, backcountry
guiding), the hazard assessment, which may or may not be expressed with a hazard rating,
is then combined with scenarios that estimate the exposure and vulnerability of the ele-
ment-at-risk and result in specific tactics to mitigate the risk to an acceptable level within
the operational risk band (McClung 2002a). Mirroring the hazard assessment process, the
risk assessment process also typically follows an iterative cycle (LaChapelle 1980) and
proceeds in stages through progressively smaller scales starting from regional, long-range
desktop assessments down through to decision making in real-time situations on individual
terrain features.
4.5 Operational advantages of the CMAH approach to hazard assessment
The CMAH has considerable practical benefits when implemented into an operational
avalanche forecasting application.
4.5.1 Structured workflow
The step-wise nature of the CMAH creates a logical and consistent workflow that walks
avalanche forecasters through a progression of choices. The model is flexible enough to
accommodate a variety of scales, applications and perspectives and provides a common,
standardized approach for communicating critical avalanche hazard information between
diverse operations who manage different elements-at-risk (Haegeli et al. 2014). Within
individual teams, the CMAH provides a platform for debate and decision making that is
independent of any individual. The workflow of the CMAH naturally lends itself to
software development and database capture, which facilitates operational record keeping
and future data analysis. The rich dataset that results from the CMAH may form the
Like
lihoo
d of
Ava
lanc
he(s
)
Destruc�ve Avalanche Size1 2 3 4 5
Unlikely
Possible
Likely
Very Likely
Almost Certain
Storm Slab
Persistent Slab
Fig. 3 An avalanche hazard chart showing two avalanche problems. In this example, persistent slabavalanches are possible from size 2 to 4, while storm slabs near to size 2 are likely to almost certain
684 Nat Hazards (2018) 90:663–691
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foundation of future decision aids that could be derived from patterns found within these
assessments.
For public avalanche warnings and education, the components of the CMAH can be
presented in a simplified format that provides the public with the same structured ava-
lanche hazard information that the forecasters have assessed (Statham et al. 2012). This
strengthens the link between forecasting methods and public communication. The struc-
tured workflow of the CMAH also provides a natural platform for education, with each
component of the model supplying valuable lessons on the overall composition of ava-
lanche hazard. Further, the CMAH’s explicit distinction between hazard and risk promotes
a better understating of how to manage exposure and vulnerability when interacting with
avalanche hazard.
4.5.2 Systematic breakdown of avalanche problems
Avalanche hazard assessments using the CMAH offer rich evaluations of current and
future avalanche conditions that go beyond single ratings and are highly informative for
risk mitigation decisions. Breaking down the complexity of avalanche prediction into a
series of smaller, more manageable analyses of avalanche problems allow forecasters to
isolate the individual components of avalanche hazard in order to study them specifically,
and in more detail. This results in a more thorough analysis and understanding of the
overall hazard conditions, which can guide communication and the choice of risk miti-
gation strategies more meaningfully. When undertaken in a group environment, the debate
and consensus around each hazard component draws out many important, detail-oriented
elements of the avalanche hazard.
Single danger or hazard ratings primarily serve as a tool for summarizing the avalanche
conditions and communicating them to a broader audience. Several different rating systems
exist (e.g., CAA 2014, 2016; EAWS 2016b), each of them providing a relative measure of
avalanche hazard that corresponds with a set of definitions for each hazard level. The five-
level avalanche danger scale (Statham et al. 2010a; EAWS 2016a) is the most commonly
used in public warnings (Fig. 4). For avalanche forecasters, any single rating represents the
end of the hazard assessment process, while for the public it may signal the beginning.
The CMAH provides a foundation for rating systems in North America similar to how
the ‘information pyramid’ does in Europe (SLF 2015). Although the North American
avalanche danger scale’s criteria for avalanche likelihood, size and distribution map
qualitatively from the CMAH, the link is not deterministic. Instead, the CMAH’s model
provides the platform for a detailed assessment, and a framework for data analysis and
collection. This was done deliberately to support future empirical analyses (e.g., Haegeli
et al. 2012; Shandro et al. 2016) in establishing more robust links between assessment
methods and any operational rating systems. This is in contrast to the Bavarian Matrix
(Muller et al. 2016b), which was designed specifically to determine a danger rating and
provide consistency in the use of the European avalanche danger scale.
4.5.3 Clear illustration of uncertainty
Uncertainty is inherent in all avalanche hazard and risk assessments; it can be reduced, but
never eliminated (LaChapelle 1980; Jamieson et al. 2015). Uncertainty creates doubt, and
doubt (or lack of it) manifests itself in people and their actions. High uncertainty leads to
low confidence and vice versa (Willows and Connell 2003). For these reasons, it is
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essential to recognize, accommodate and communicate uncertainty in avalanche
assessments.
The CMAH shows uncertainty in hazard assessments by illustrating ranges of likelihood
and size for each avalanche problem. Starting from an initial data point, each parameter is
given a range to show what could be possible. Figure 3 illustrates a persistent slab problem
where the potential avalanche is unlikely to possible and could range from size 2–4. The
size and shape of the resulting rectangles provide an indication of the degree of uncer-
tainty. This approach is similar to Jamieson et al. (2015) who show quantitative uncertainty
expressed as confidence intervals (whiskers) that illustrate a range of values.
5 Existing operational implementations
Since the development of the initial version of the CMAH in 2008, the framework has been
implemented in various applications in both Canada and the USA. While the adoption of
the CMAH by practitioners can be interpreted as an indication of its practical value, this
operational testing also produced valuable feedback that resulted in many important
refinements.
5.1 Examples from Canada
In 2008, the Canadian Avalanche Association’s Industry Training Program incorporated
the CMAH as core curriculum for their Level 3—Applied Avalanche Risk Management
course. Haegeli (2008) developed a database-driven online tool for facilitating the oper-
ational use of the CMAH, providing the foundation for the first statistical examination of
relationships between its components (Haegeli et al. 2012). In 2011, Parks Canada
developed AvalX to integrate the CMAH into the daily workflow of avalanche forecasters
from different agencies (Statham et al. 2012). AvalX provided the first standardized
forecasting method between different agencies and forecasters in Canada and delivered a
Fig. 4 North American public avalanche danger scale (Statham et al. 2010a)
686 Nat Hazards (2018) 90:663–691
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consistent format for avalanche safety information to the Canadian public. In 2013, the
CMAH was integrated into the InfoExTM, the daily exchange of avalanche information
among avalanche safety services hosted by the Canadian Avalanche Association (Haegeli
et al. 2014). This effectively embedded the CMAH process into the daily workflow of all
Canadian avalanche forecasters.
5.2 Examples from the USA
Incorporating the CMAH into professional training programs for avalanche workers began
in 2008. Currently, all four programs providing avalanche worker training in the USA use
the CMAH framework. The American Avalanche Institute and the American Institute for
Avalanche Research and Education both run Level 3 courses where avalanche workers
from a variety of disciplines use the CMAH to assess the avalanche hazard and ISO 31000
to manage risk for workers and clients. The American Avalanche Association’s AVPRO
course and the National Avalanche School both include the CMAH as the basis for
assessing avalanche hazard for ski area operations.
In the USA, the US Forest Service (USFS) and the Colorado Avalanche Information
Center (CAIC) produce public safety information for backcountry recreation. The USFS
program is composed of 12 regional avalanche centers, while the CAIC runs a statewide
program that also provides highway avalanche forecasts. All US operations utilize ele-
ments of the CMAH in an informal way, though the Utah Avalanche Center began using a
communication tool that included avalanche character, likelihood of triggering, and ava-
lanche size in their products in 2004. Many other USFS avalanche centers incorporated
these ideas into their products over the next decade. The CAIC formally adopted the
CMAH into its daily operations in 2012. It is embedded into the daily workflow as well as
documentation of forecast process and operational decisions. The CMAH forms the
foundation for communication between CAIC forecasters in different offices and focused
on different avalanche safety applications.
6 Conclusions
Although the existing literature on avalanche forecasting has provided a good overview of
the general nature of the assessment process and its inputs, it is missing tangible guidance
on how to undertake and assemble a hazard or risk assessment for avalanche forecasting
and backcountry operations. Our objective was to address this knowledge gap by eliciting
the essence of the avalanche forecasting process from avalanche forecasters and then
describing it within a risk-based framework that is consistent with other natural hazards
disciplines. The resulting conceptual model illustrates the key components of avalanche
hazard and structures them into a systematic, consistent workflow for hazard and risk
assessment.
Based on our experience with the CMAH to-date, we believe that the main benefits are:
1. It provides a logical framework for organizing and analyzing crucial data and evidence
that contributes to the avalanche hazard and informs risk mitigation decisions.
2. It is universally applicable to all types of avalanche forecasting operations, and the
underlying principles can be applied at any scale in space or time.
3. It formalizes the concept of an avalanche problem and that different types of problems
directly influence the assessment and management of avalanche hazard and risk.
Nat Hazards (2018) 90:663–691 687
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4. It aligns avalanche forecasters with a consistent methodology and language and
streamlines the communication of hazard information between different avalanche
operations.
5. Its risk-basis brings the practice of avalanche forecasting into line with the concepts
and methods employed in land-use planning, bridging these two disciplines of the
avalanche industry.
Although the CMAH is a step forward in the description of the avalanche hazard
assessment process, numerous challenges remain. For example, although the identification
of different types of avalanche problems (Table 4) is fundamental to avalanche forecasting,
agreeing on the specific type of problem and when to transition from one problem to
another is challenging. Furthermore, the lack of quantitative links between the components
of the CMAH—or any existing hazard rating system—leaves the process highly suscep-
tible to human error and bias. It is our hope that by capturing these judgments in a
structured manner, the CMAH will help to facilitate the development of evidence-based
decision aids that can address these challenges. Future research into the intuitive, judg-
ment-based processes used in conventional avalanche forecasting may yield important
practical results that allow forecasters to check their assessments against a model output.
Avalanche forecasting has always been difficult to explain and fraught with uncertainty.
With little in the way of rational guidance, it ultimately remains a task for human judgment
with support from technology and process. The CMAH resulted from our investigation into
the underlying, intuitive processes that forecasters have developed from thousands of days
spent observing avalanches in the mountains.
Acknowledgements We thank Roger Atkins, Steve Conger, Bruce Jamieson, Karl Klassen and Brian Lazarfor their thoughtful insights and important contributions to this work, as well as two anonymous reviewerswhose comments greatly improved the quality of this manuscript. This work was supported by the CanadianAvalanche Association and Avalanche Canada through their management of project funding provided by theGovernment of Canada’s National Search and Rescue Secretariat’s New Initiatives Fund (SAR-NIF).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Inter-national License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided you give appropriate credit to the original author(s) and thesource, provide a link to the Creative Commons license, and indicate if changes were made.
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