Among recent eruptions, those of Sarychev-Peak Volcano in 2009 and Kliuchevskoi Volcano in 2013 are introduced in this chapter as examples to illustrate successes and ongoing challenges of international eruption response coordination.

In this respect, VAACs Anchorage and Tokyo are working on a smoother handover and consistent information issuances using English. JCAB/ATMC and other aviation-related organizations are making similar efforts in a volcanic ash exercise in 2015 to grasp all the relevant information in one sheet in chronological order. The aim is to overcome language barriers, using a VOLKAM Sheet mainly for discussion on re-routing by sharing the present situation of the eruption phase, volcanic ash area, influence onto the traffic flow and aviation operations, as well as the expected coordination and actions by relevant organizations. The planning process itself for re-routing in the exercise is also meaningful: that will contribute to shorten the time required for coordination in a real case. In addition, an effort to share the present situation of each ANSP, responding to a re-routing request, by creating a matrix on such information has begun; members of CPWG are working on this task aiming at establishing a standardized procedure among all the ANSPs, as a long-term ideal outcome.

In this way, the volcanic ash- and/or aviation-related organizations will continue their work seeking for better coordination and operations, respectively.

**Acknowledgements** Information as for the on-going efforts in JCAB given by Mr. Takayuki Harada, the deputy chief air traffic controller in JCAB, has been a great help to flesh out the article as well as making it well-balanced in highlighting efforts taken by both information providers and users. Some of the figures were created using Generic Mapping Tools open-source software (GMT; Wessel, P. and W.H.F. Smith, New, improved version of Generic Mapping Tools released, EOS Trans. Amer. Geophys. U., Vol. 79, No. 47, p. 579, 1998).

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Decision-Making: Preventing
Miscommunication and Creating
Shared Meaning Between
Stakeholders

Emma E.H. Doyle and Douglas Paton

Abstract

The effective management and response to either volcanic eruptions or (often prolonged) periods of heightened unrest, is fundamentally dependent upon effective relationships and communication between science advisors, emergency managers and key decision makers. To optimise the effectiveness of the scientific contribution to effective prediction and management decision making, it is important for science advisors or scientific advisory bodies to be cognisant of the many different perspectives, needs and goals of the diverse organisations involved in the response. Challenges arise for scientists as they may need to be embedded members of the wider response multi-agency team, rather than independent contributors of essential information. Thus they must add to their competencies an understanding of the different roles, responsibilities, and needs of each member organisation, such that they can start to provide information implicitly rather than in response to explicit requests. To build this shared understanding, the team situational awareness (understanding of the situation in time and space), and the wider team mental model (a representation of the team functions and responsibilities), requires participating in a response environment together. Facilitating the availability of this capability has training and organizational development implications for scientific agencies and introduces a need for developing new inter-agency relationships and liaison mechanisms well before a

<footnote>E.E.H. Doyle (✉)
Joint Centre for Disaster Research, Massey
University, PO Box 756, Wellington 6140,
New Zealand
e-mail: e.e.hudson-doyle@massey.ac.nz</footnote>

<footnote>D. Paton
Faculty of Engineering, Health, Science and the
Environment, School of Psychological and Clinical
Sciences, Charles Darwin University, Darwin,
NT 0909, Australia
e-mail: Douglas.Paton@cdu.edu.au</footnote>
volcanic crisis occurs. In this chapter, we review individual and team decision making, and the role of situational awareness and mental models in creating “shared meaning” between agencies. The aim is to improve communication and information sharing, as well as furthering the understanding of the impact that uncertainty has upon communication and ways to manage this. We then review personal and organisational factors that can impact response and conclude with a brief review of methods available to improve future response capability, and the importance of protocols and guidelines to assist this in a national or international context.

# 1 Introduction

Whether it involves a period of unrest (e.g., Long Valley, CA in 1982), an ongoing eruption (e.g., Soufrière Hills Volcano, WI), or responding to a blue sky eruption (e.g., Ruapehu, NZ, 2007; Mt Ontake, Japan, 2014), the response to complex volcanic crises requires the coordinated and complementary contributions of numerous organisations and agencies. The degree to which this can effectively be achieved depends on whether the quality and degree of relationship and network building conducted before, during, and after, a crisis can facilitate the shared understanding required for communicated information to enhance effective decision making.

The challenge in this task is twofold. The first relates to the need to bring representatives from diverse sources together (Paton et al. 1998; Doyle et al. 2015), including technical advisors (such as geologists, geophysicists, engineers, and social scientists), emergency management (civil defence, fire service, police, army, national and local government), lifeline organisations (lifelines companies, transport, water), as well as community organisations and special interest groups (e.g. neighbourhood support and volunteer groups, Rotary, Lions club, etc.). A major challenge to developing effective crisis management arises because these representatives bring with them different objectives, priorities and interpretive and operational beliefs (Paton et al. 1998; Doyle et al. 2015). The second task is thus

how to facilitate the ability of these representatives to collaborate and share knowledge in order to effectively respond to a crisis.

Recognition of the diverse consequences volcanic crises create can result in organisations appreciating why they need to be part of a multi-agency group response. However, this appreciation does not automatically translate into acceptance of either the need to develop new roles and responsibilities, or that crisis response goals may need to be reconciled with the political or economic pressures that each representative brings with them to the crisis response environment. A further challenge to the scientific community arises from the need for some of them to be embedded members of the wider response multi-agency team, rather than independent contributors of essential information. For example, to enhance interagency communications during recent hazard events and exercises in NZ, members of the GNS Science team were situated as liaison officers within the Emergency Operations Centre (EOC) and responded within the emergency management team itself. In addition, the crisis response context can introduce a need to deal with demands that would rarely, if ever, be encountered in routine work contexts and that can elevate levels of stress and interfere with decision making.

The atypical demands that can impair response during a disaster were evident in evaluation of the multi-agency response to the Ruapehu 1995–1996 eruptions. These demands included:
<table><caption>**Table 1** Potential stressors that negatively impact on response capability and personal and team performance when responding to or managing crisis events and disasters (after Paton 1996)</caption><tr><td>Degree of warning or change in conditions (low warning times or rapid change increases physical and psychological demands)</td></tr><tr><td>Degree of uncertainty from event and organizational sources</td></tr><tr><td>Time of day (stress greater at night and when having to respond at the end of a working day)</td></tr><tr><td>Presence of traumatic stimuli (such as sensationalised news coverage)</td></tr><tr><td>Lack of opportunity for effective action (attributions about perceived response failure can be internalised rather than more accurately attributed to environmental factors outside of their control)</td></tr><tr><td>Knowing victims or families</td></tr><tr><td>Intense media interest or public scrutiny directed at event management and those responsible</td></tr><tr><td>Higher than usual or expected responsibility</td></tr><tr><td>Higher than usual physical, time and emotional demands (including cumulative stress over time)</td></tr><tr><td>Contact with those affected</td></tr><tr><td>Resource availability and adequacy (and how these change over time)</td></tr><tr><td>Co-ordination problems</td></tr><tr><td>Conflict between agencies</td></tr><tr><td>Inadequate and changing role definition</td></tr><tr><td>Inappropriate leadership practices</td></tr><tr><td>Single versus multiple threats</td></tr></table>

intense media interest or public scrutiny, resource availability and adequacy, co-ordination problems, a lack of defined responsibility for co-ordination of response, inadequate communication with other organisations, conflict between agencies, and inadequate and changing role definition (Paton 1996; Paton et al. 1998, 1999) (Table 1). The IAVCEI Subcommittee for Crisis Protocols (IAVCEI 1999) provides additional examples of problems commonly experienced in volcanological response (see Table 2), each of which correspond to the disaster stressors identified by Paton (1996; see Table 1).

While some potential event-related stressors reflect the dynamics of hazard impacts (e.g., volcanic ash affecting communication infrastructure), others reflect inadequacies in crisis communication systems and the expertise available to use them (Paton et al. 1998, 1999; Johnston et al. 1999). In the absence of the development of appropriate crisis management procedures and training in crisis management, which is the norm, the associated negative reactions can detrimentally affect performance and decision making (e.g., physiological and

psychological symptoms of anxiety and fear, “tunnel vision”, failure to prioritise, “freezing” and loss of concentration; Flin 1996; Flin et al. 1997; Klein 1997; Paton et al. 1999). It thus becomes important to identify the management systems and procedures, and personal and team capabilities, required to facilitate effective multi-agency response and use this to inform the training needs and training strategies adopted in all response agencies. Evaluation of previous volcanic crisis management experiences can provide a good starting point for this process.

Mitigating these issues prior to a crisis, particularly at the science:decision-maker interface is important, as effective multi-organisational management needs to be built on “consensus about task goals and priorities; co-operation and team framework; a sense of group identity; a strong sense of community within the organisation; and the breakdown of bureaucracy and formalities” (Paton et al. 1999, p. 17). Addressing these issues requires an appreciation of how a shared understanding of response needs can be achieved prior to a crisis, such that this multi-organisational, multi-level, multi-team
<table><caption>**Table 2** Common problems of professional interaction of volcanologists during crises, as identified in the IAVCEI subcommittee for crisis protocols (IAVCEI 1999)</caption><thead><tr><td>Problem</td><td>Detail</td></tr></thead><tbody><tr><td>Poor communication and teamwork among scientists</td><td><ul><li>Failure to value diverse scientific expertise, approach, and experience</li><li>Overselling of new methods</li><li>Failure to honour prior work on a volcano, and, in the reverse direction, failure to share study opportunities</li><li>Failure to share information and scarce logistical resources</li><li>Failure to work as a single scientific team, and thus loss of potential synergism, i.e., loss of a cooperative result that is greater than the sum of individual results</li><li>Failure of scientists to use a single voice for public statements</li><li>Failure of science-funding agencies, job supervisors, and promotion panels to give full credit for self-sacrifice and teamwork during volcanic crises</li></ul></td></tr><tr><td>Leadership problems</td><td><ul><li>Leaders without leadership skills</li><li>Failure of leaders to recognize the limits of their own technical expertise</li><li>Confusion about team roles, policies, and procedures</li><li>Failure to encourage those who can and wish to help</li><li>Failure to develop (a) respect for scientific differences within a team, (b) a method for developing consensus, and (c) a means for acknowledging differences that cannot be resolved</li><li>Failure to balance risk and rewards of dangerous field work</li><li>Failure to recognize and minimize fatigue</li></ul></td></tr><tr><td>Issues for visiting scientists, invited and uninvited</td><td><ul><li>Scientists who arrive at a crisis without invitation</li><li>Invitations from other than the primary scientific team, e.g., from a competing or peripheral local group</li><li>Unilateral foreign funding decisions</li><li>Cultural differences regarding scientific discussion and decision making</li><li>Public statements by visiting scientists</li><li>Pre-emption of research and publication opportunities by visitors, while local scientists are still busy managing the crisis</li></ul></td></tr><tr><td>Unwise and unwelcome warnings</td><td><ul><li>Warnings from pseudo-scientists</li><li>Warnings or forecasts from scientists from other fields</li><li>Warnings or forecasts by volcanologists working in isolation, either on-site or far from the volcano in question</li><li>Exaggerated statements of risk, or, conversely, overly reassuring statements about safety of an area when significant risk exists</li><li>Outdated warnings or forecasts in need of change</li></ul></td></tr><tr><td>Poor communication between scientists and public officials</td><td><ul><li>Unfamiliarity with each other's needs and expectations, methods, expertise, and limits</li><li>A conscious decision to withhold or delay some hazards information</li><li>Official scepticism of scientific advice</li><li>Procedural failures in communication with public officials:<ul><li>Failure to put warnings in writing, for clarity and later accountability</li><li>Failure to distribute warnings to all key parties. Failure to establish a clear "chain of communication" between scientists, public officials, and external agencies such as civil defines</li><li>Failure to confirm that officials truly understand our warnings</li></ul></li></ul></td></tr><tr><td>Ineffective relations with news media</td><td><ul><li>Inadequate interaction with the news media</li><li>Premature or excessive interaction with the news media</li></ul></td></tr></tbody></table>
response is managed effectively, and how com-
munication impacts the quality of decision
making. In lieu of real events, these capabilities
can be developed through shared exercises, sce-
nario planning, and other relationship building
activities, which have training and organizational
development implications for scientific agencies.

In this chapter we review the fundamentals of
decision making at the individual, team,
multi-team, organisational and agency levels
(Sect. 2), by drawing on psychological and criti-
cal incident management research. We review the
concepts of mental models, which are an indi-
vidual’s representation of a situation such as a
response environment including needs, responsi-
bilities, and interdependencies; or a representation
of a system such as volcanic unrest which incor-
porates their internal, personalised, experiential,
and contextual understanding of how the volcanic
system operates. We discuss how these mental
models contribute to a “shared meaning” between
agencies (Sect. 3), and how that relates to com-
munication and information sharing; as well as the
impact that uncertainty has upon communication
and ways to manage this. We then review a
number of personal and organisational factors that
can impact response (Sect. 4), and conclude (in
Sect. 5) with a brief review of the methods
available to enhance response, and the importance
of protocols and guidelines to assist this in a
national or international context. Throughout this
chapter we focus on the response phase of a crisis.
There are however many other complementary
approaches to enhance risk communication with
communities living with volcanic risk, which we
do not consider here, including community-based
disaster risk management and other participatory
techniques (see review in Barclay et al. 2008; and
also Williams and Dunn 2003; Cronin et al. 2004;
Gaillard 2006; Cadag and Gaillard 2014).

## 2 Introducing Decision Making

During a volcanic crisis, several decision making
styles and processes are required. Decision
making itself has been studied extensively across
a range of fields. Here we focus on those

supported through research in crisis and risk
management contexts (Lipshitz et al. 2001;
Doyle and Johnston 2011).

2.1 Individual Processing Systems

Considering first the processes occurring at an
individual level, the field of psychology offers us
understanding of the theory of two “parallel
processing systems” (Epstein 1994; Sloman
1996; Chaiken and Trope 1999; Slovic et al.
2004). The first, known as either Type 1 or the
affective processing system, involves rapid,
unconscious, action-oriented processing, and
results in people interpreting risk as an emotional
state or feeling (e.g., fear, dread, anxiety; Epstein
1994; Loewenstein et al. 2001; Slovic et al. 2004;
Doyle et al. 2014b), and can thus reduce or
increase risk perceptions. These are assumed to
be the default response “unless intervened by
distinctive higher order” Type 2 processes (Evans
and Stanovich 2013a), or analytical processing
systems (Epstein 1994), which heavily load
working memory, and utilise hypothetical think-
ing, more deliberate computational cognitive
processes (and thus longer decision times). These
are learnt processes that apply rules and proce-
dures (algorithms, normative rules and logic) to
the analyses of data and to justify actions (i.e., to
respond to demands rather than reacting to them).

As of Doyle et al. (2014b, p. 78) we consider
that “the adoption of the affective and analytical
processing systems [is] not an either-or situation,
but rather a more complex balancing act influ-
enced by the degree of uncertainty or threat in the
decision context, and ... relative experiences”
(Keren and Schul 2009; Kruglanski and
Gigerenzer 2011; Evans and Stanovich 2013a, b;
Keren 2013; Osman 2013; Thompson 2013).
Thus, if time permits, scientists tend to adopt the
analytical process due to their formal training in
data analysis and decision making. Meanwhile,
non-scientists adopt a more affective process
dependent upon prior experience, time pressures
and operating procedures. However, when sci-
entists are called upon to respond to atypical
demands (particularly in a multi-agency context),
if there are no formal or procedural rules to abide by, the affective system usually prevails (Loewenstein et al. 2001) and decision making effectiveness is compromised as a result (Weber 2006). Mitigating this problem calls for all those interacting in decision making to receive training which develops competency in different decision making styles, and, importantly, practice using them in simulated and actual crisis events.

## 2.2 Incident Management and Naturalistic Decision Making

The analytical (or Type 2) decision making has been identified as having four steps (Flin 1996, p. 141–142): (1) identifying the problem; (2) generating a set of options; (3) evaluating these options; (4) implementing the preferred option (Saaty 2008). However, this assumes a ‘perfect’ environment. In reality, most decisions are made in uncertain ‘naturalistic settings’ defined by: ill-structured problems; uncertain dynamic environments; shifting, ill-defined, or competing goals; action/feedback loops; time stress; high stakes; multiple players; and influences from organizational goals and norms (Zsambok 1997; Crichton and Flin 2001; Klein 2008; Doyle and Johnston 2011). Research into incident management has identified four distinct ‘naturalistic decision making’ processes seen in these conditions (Crego and Spinks 1997; Pasqual and Henderson 1997; Crichton and Flin 2002): (1) recognition primed and intuition led action; (2) action based on written or memorized procedures; (3) analytical comparison of different options; and (4) creative designing of a novel course of action; ordered in terms of decreasing pressure and time commitments.

Within a crisis, an individual decision maker (whether scientist or emergency manager) may move along this spectrum of decision processes depending upon the evolving conditions, and will not be limited to just one decision making style (see Martin et al. 1997, p. 283; Doyle and Johnston 2011). Those operating at a strategic level should use the analytic style to

accommodate the broader perspective required under these circumstances (Paton et al. 1998, 1999; Paton and Flin 1999). For those working at a tactical/coordinating level, an analytical approach should be adopted in (relatively) high time, low risk circumstances (such as when planning courses of action between eruption episodes and identifying future eruptive scenarios). However, in an eruption phase, rapid decisions need to be taken in minutes, making adoption of naturalistic decision making styles essential. For example, in Exercise Ruaumoko (which simulated the response to the lead up to an eruption in Auckland; MCDEM 2008; McDowell 2008), on site science-liaison officers often found themselves having to give almost instantaneous responses to officials during the peak of the crisis, which would have encouraged more recognition primed decision making.

Fundamental to all of these decision making processes, and an effective decision resulting from those processes, is individual and team situational awareness (SA, Endsley 1997; Martin et al. 1997), which is the understanding of the situation and needs in both time and space. This encompasses a capacity to use key environmental cues to comprehend the current situation (in relation to goals) and to project future status. Training in this essential competence enhances the ability of decision makers to anticipate and make proactive decisions that deal more effectively with emergent issues. During the initial and on-going situation assessment, individuals and team members play crucial roles in this process (Sarna 2002). As stated by Doyle and Johnston (2011, p. 75), “a decision maker may make the correct decision based on his or her perception of the situation, but if his or her situation assessment is incorrect, this may negatively influence his or her decision (Crichton and Flin 2002)”. Because the inputs into decision making can come from different professionals and/or from team members that may be geographically dispersed (e.g., in an EOC, and at the volcano), decision making training must include the development of distributed decision-making skills (where the decision-making responsibility does not lie with a single entity, but rather is
distributed throughout the responding organisa-
tions; Flin 1996; Paton et al. 1998; Kapucu and
Garayev 2011). For distributed decision making
to work effectively, decision makers must have
some shared meaning (mental model) about the
event and their respective roles in defining and
resolving response problems.

3 Shared Meaning in Multi-agency
Response: Mental Models

An individual’s mental model of a hazard is
defined by Bostrom et al. (2008, p. 308) as “how
people understand and think about the hazard,

and their causal beliefs”. For incident manage-
ment, this represents a mental “map” of the
operating environment. This must encompass
event characteristics and hazard consequences
and each other’s differing needs, responsibilities,
roles, and demands, as well as the interdepen-
dencies that contribute to effective problem
solving and decision making (Rogalski and
Samurçay 1993; Flin 1996; Paton and Jackson
2002). A shared mental model allows a dis-
tributed team to share understanding of the task
at hand, anticipate and proactively respond to
information needs (Lipshitz et al. 2001; Pollock
et al. 2003), and make shared decisions (Orasanu
1994; Salas et al. 1994).

<figure><img src="image_185.png" /><figcaption>**Fig. 1** Example of the information flow, communication network and many agencies involved in an eruption, as observed from the response to the 1995 Ruapehu eruption by Paton et al. (1999). CAA Civil Aviation Authority;</figcaption></figure>

DOC Department of Conservation; ECNZ Electricity Corporation of New Zealand; GNS Institute of Geological and Nuclear Sciences (now GNS Science); MAF Ministry of Agriculture and Fisheries
<figure><img src="image_186.png" /><figcaption>**Fig. 2** a Examples of the various mental models within an individual's over-arching or super-ordinate mental model during a volcanic crisis. b A poor shared mental model between individuals. c A good shared mental model between individuals</figcaption></figure>

From an incident management perspective, the major challenge is the need for participants to be able to continue to use their “routine” and “expert” mental models of the constituent hazard consequences and individual response. These need to complement an over-arching or super-ordinate mental model, which is a cooperative mental model that integrates individual mental models that describe their understanding of: their role within their organisation, how they relate to others within their organisation, and their organisation’s role within, and communication with, the wider response team (see Figs. 1 and 2). It is this super-ordinate model that facilitates communication.

## 3.1 Shared Meaning in Multi-agency Response: Communication

Information received by emergency managers from scientists will be considered with respect to other demands and issues placed on emergency managers considering the risk to lives, economies and infrastructure. The communication of risk information between emergency managers and the public, or scientists and emergency managers, is also subject to the mental models gulf (Morgan et al. 2002). This is where there is a

gap between “what experts know and the plan they develop, versus what key public know and prefer” (Heath et al. 2009, p. 129; see also Doyle et al. 2014a). By minimising this gulf, communication between advisors and key decision makers can move from *explicit* requests for information (which can result in an increase in time delays, pressures and stress, impacting decision making effectiveness—particularly if reformatting of that information is also required; Klein 1997; Crichton and Flin 2002) through to *implicit* supply of advice by the advisors as they recognise ahead of time what information the decision maker needs. Effective teams have been shown to be dominated by communication styles such as this (Paton and Flin 1999; Lipshitz et al. 2001; Paton and Jackson 2002; Kowalski-Trakofler et al. 2003). Table 3 describes the characteristics of teams that display these effective communication and advice provision styles.

Implicit communication also facilitates the maintenance of situational awareness during periods of dynamic information as it allows decision makers to focus on task management (see review in Doyle et al. 2015; Paton and Jackson 2002; Paton 2003; Wilson et al. 2007; Owen et al. 2013). For this to occur, these scientists and experts must recognise and understand the needs of the decision makers, as well as
<table><caption>**Table 3** Characteristics of teams that display effective communication and advice provision styles during a multi-agency crisis response</caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>Provision of science advice should involve (Paton and Jackson 2002; Paton 2003)</td><td>Effective team communication involves (Wilson et al. 2007; Owen et al. 2013)</td><td>Effective team and interagency science advice communication (Doyle et al. 2015)</td></tr><tr><td>Anticipation and definition of information needs</td><td>Accurate and timely information exchange, correct phraseology and ‘closed-loop’ communication techniques</td><td>Should consider the ability of diverse stakeholders to interpret data communicated, and apply to resolve response issues while operating in a collaborative environment</td></tr><tr><td>Organized networks between information providers and recipients</td><td>Coordinated behaviour based on shared knowledge, performance monitoring, back up and adaptability</td><td>Needs the development of a super-ordinate team identity and the ability to switch between agency and shared mental models as required</td></tr><tr><td>Established capability to “provide, access, collate, interpret and disseminate information compatible with decision needs and systems”</td><td>Co-operative team orientation, efficacy, trust and cohesion</td><td>May involve stakeholders who rarely interact with one another outside the context of managing a volcanic crisis, and so depends on emergent team dynamics and concepts such as “swift-trust” (Meyerson et al. 1996)</td></tr></tbody></table>

their timelines and thresholds, supplying information that is useful, useable, and used (Rovins et al. 2014).

During the Ruapehu 1995–1996 eruptions, comparison of pre-existing networks with information providers revealed both incomplete networks and inconsistencies with respect to information sources, in particular with the main information provider GNS Science (Paton et al. 1998, 1999). This resulted in agencies seeking information in an ad hoc basis (i.e. through explicit requests) which would have contributed to communication difficulties, as evidenced by the 37% of organisations who reported inadequate communication during the response (ibid). One way of mitigating this issue involves advisors developing a capacity to, where possible, anticipate others information needs (Doyle and Johnston 2011; Doyle et al. 2015).

Advisors must recognise the very specific information and advice needs of decision makers prior to an event and have procedures in place in advance of an event to provide that information in a timely manner directly where it is needed within the organisational structure. For this to effectively occur scientists (either as on-site science advisors, or off-site expert panels) must not just be external experts to a multi-agency response team, but must be considered part of

the extended and distributed team handling the emergency management response (Doyle and Johnston 2011). Integrating Science Advisory Groups (SAGs) into a wider response team offers the opportunity for technical and scientific experts to directly inform effective planning, intelligence gathering, and decision making of the emergency personnel and government officials.

The Auckland Volcanic Science Advisory Group (AVSAG), an example of such a SAG, was tested during Exercise Ruaumoko (MCDEM 2008; McDowell 2008). From this it was identified that the AVSAG process facilitated the provision of valuable advice in a clear, timely manner. A clear advantage was the presence of a science advisor in both the National Crisis Management Centre and the Auckland Civil Defence and Emergency Management (CDEM) Group EOC, maintaining shared situational awareness between the SAG and the emergency management team. However, having two on-site liaison officers at different EOCs did result in a divergence of advice in these two locations at the peak of the crisis (Cronin 2008), as shared situational awareness could not be maintained between them.

These past experiences, through both response and exercises, has identified that this SAG
approach is beneficial as it provides “one trusted
source” for science information during a crisis
(MCDEM 2008; Smith 2009), facilitates an
integration of a wide range of expert opinions
required to manage uncertainty during decision
making (as recommended by Lipshitz et al.
2001) and can help combat issues that may arise
due to conflict between scientists (Barclay et al.
2008). It also enabled the volcanologists to speak
with “a single voice” to reduce confusion, as
advocated by the IAVCEI protocols (1999).

## 3.2 Shared Meaning: Uncertainty

If the volcanic crisis environment was perfectly predictable, the development of the relationships and competencies discussed above would be a relatively straightforward task. However, volcanic crises present evolving, emergent demands, and a highly uncertain response environment. The IAVCEI protocols (1999) highlight that uncertainty should always be acknowledged. This raises two issues. Firstly, how uncertainty should be communicated. Secondly, how uncertainty influences the quality of the relationships between individuals.

Regarding the first, reviews by Doyle et al. (2014a, b), identified that there is much discourse as to whether revealing the uncertainties associated with a risk assessment will strengthen or decrease trust in a risk assessor and their message, and how it impacts decision-making behaviour (Miles and Frewer 2003; Wiedemann et al. 2008). On the one hand, the communication of uncertainty has been suggested to enhance credibility and trustworthiness. On the other, however, studies have suggested that it can decrease people's trust and the credibility of the provider, as it can allow people to justify inaction or their own agenda, or to perceive the risk as being higher or lower than it is depending on their personal attitudes (Johnson and Slovic 1995, 1998; Smithson 1999; Miles and Frewer 2003; Johnson 2003; Wiedemann et al. 2008; Doyle et al. 2014b). The role of ethics in whether or not to communicate uncertainty has also become a focus of recent discussion across

disciplines, including whether communicating
this uncertainty actually enhances or diminishes
the autonomy of the receiver of the message, and
whether it produces an overall benefit or can
actually cause harm (Han 2013; Austin et al.
2015; Grasso and Markowitz 2015). Keohane
et al. (2014) suggest that scientists ‘should
understand their own ethical choices in using
scientific information to communicate to audi-
ences’ (p. 343), and identify five principles for
scientific communication under uncertain condi-
tions: honesty, precision, audience relevance,
process transparency, and specification of
uncertainty about conclusions.

To address how to manage and communicate
uncertainty, many disciplines including vol-
canology, climate change, and meteorology
(IAVCEI 1999; Moss and Schneider 2000; Gill
et al. 2008; Mastrandrea et al. 2010; see also
Moss and Schneider 2000; Patt and Dessai 2005;
Budescu et al. 2009; Doyle and Potter 2016),
have established guidelines that advocate the
clear and transparent communication of uncer-
tainty, a documentation of all processes related to
uncertainty, and the use of formalised proba-
bilistic terms and frameworks for assessment and
communication (see Table 4). In volcanology, it
has become increasingly popular to use proba-
bility statements in communications (Doyle et al.
2014a), which involve knowledge of both the
dynamical phenomena and the uncertainties
involved (Sparks 2003). Further, the use of
probabilistic cost benefit analysis and Bayesian
Event Trees has been driven by a desire to make
objective and traceable decisions via quantitative
volcanic risk metrics (Aspinall and Cooke 1998;
Marzocchi and Woo 2007; Woo 2008; Lindsay
et al. 2009).

However, probabilistic statements, whether in
numeric or linguistic formats, can commonly be
misinterpreted because their framing, direction-
ality and probabilistic format can bias people’s
understanding, thereby affecting people’s action
choices (see Fig. 3; e.g., Teigen and Brun 1999;
Karelitz and Budescu 2004; Honda and Yamag-
ishi 2006; Joslyn et al. 2009; Budescu et al.
2009; Lipkus 2010). This has been identified
as a particular issue in volcanic crisis
<table><caption>**Table 4** A summary of the existing guidelines for communicating uncertainty from the volcanological, weather and climate change communities</caption><tr><td>Budescu et al. (2009)</td><td><ul><li>“Make every possible effort to differentiate between the ambiguity of a target event and its underlying uncertainty”</li><li>“Specify the various sources of uncertainty underlying key events and outline their nature and magnitude, to the degree that this is possible”</li><li>“Use both verbal terms and numerical values to communicate uncertainty”</li><li>“Adjust the width of the numerical ranges to match the uncertainty of the target events”</li></ul></td></tr><tr><td>World meteorological office (Gill et al. 2008)</td><td>Uncertainties should be communicated:<ul><li>“For improved decision making”—especially when they have many options available to them, to weigh up contingencies</li><li>“Helps manage user expectations”—a more open, honest and effective relationship</li><li>“Promotes user confidence”—If users understand forecasts have a degree of uncertainty… they can tune their decision-making to manage this uncertainty …</li><li>“As it reflects the state of science”</li></ul></td></tr><tr><td>Moss and Schneider (2000, p. 37) (as used by the IPCC; see also Patt and Dessai 2005; Mastrandrea et al. 2010)</td><td><ul><li>Identify the most important factors and uncertainties that are likely to affect the conclusions</li><li>Document ranges and distributions in the literature [… the key causes of uncertainty …]</li><li>Make an initial determination of the appropriate level of precision [… after considering the state of the science and the nature of the uncertainties…]</li><li>Characterize the distribution of values that a parameter, variable, or outcome may take</li><li>Using standard terms, rate and describe the state of scientific information on which the conclusions and/or estimates are based</li><li>Prepare a “traceable account” of how the estimates were constructed</li><li>Use formal probabilistic frameworks for assessing expert judgment</li><li>Consider target audience and develop a pluralistic approach (Patt and Dessai 2005):<ul><li>Sophisticated part in numeric format</li><li>General chapters using verbal and narrative phrase</li><li>Formalise translation between numerical and verbal probabilities</li></ul></li></ul></td></tr><tr><td>IAVCEI (1999) subcommittee for crisis protocols</td><td><ul><li>Uncertainty should be acknowledged</li><li>Forecasts, warnings, and other important public statements are best when written first</li><li>Date-stamped, team-approved hazard maps, together with their assumptions, should also be entered into the formal record of warnings. Competing or uncoordinated, multiple hazards maps are confusing to the public and should be avoided</li><li>Scientific caution in the face of uncertainty is good, but it needs to be balanced against the legitimate information needs of decision makers and the public at risk. If the data do not allow a definitive forecast,</li></ul></td></tr></table>

(continued)
<table><caption>**Table 4** (continued)</caption><tr><td></td><td>factual statements about what is known are an important step. Warnings of serious events that are known to be possible, issued before such events can be forecast as probable, may hasten precautions and save lives<br/>• Use probabilities to calibrate qualitative assessments of risk. Avoid commonly used adjectives such as “soon” or “high-” or “low-(risk),” because they mean different things to different people. Probabilities and comparisons to familiar non-volcanic risks help to avert misunderstanding that risk is higher or lower than it actually is<br/>• Under no circumstances should hazard be intentionally overstated or understated. Any decision to “err on the safe side” should be a conscious, openly discussed decision. Never disregard what seems like a low-probability, “worst case” event, because such events can and do occur (e.g., Mount St. Helens and Pinatubo). Instead, estimate the probabilities of worst-case and lesser scenarios, as above, to put the “worst-case scenario” in proper perspective</td></tr><tr><td>Doyle et al. (2014a, b) see also the operational guidelines in Doyle and Potter (2016)</td><td>• There is a need to adopt formal numerical and verbal probability translation tables that are specific to volcanology<br/>• If communicating time window forecasts, be consistent in the use of either “within” or “in” throughout out all statements, bulletins and reports; particularly for long window forecast statements where “within the next X days” has a statistically significant different interpretation to “in the next X days”<br/>• Scientists should make all possible efforts to communicate forecasts, likelihoods, and probabilities over a range of relevant time windows, including a probability forecast for a shorter immediate time window in particular (such as the first 24 h)<br/>• Any formalised communication strategy should be accompanied by exercises, simulations, and education programs with both the decision-makers and the public to help facilitate a greater understanding of the complexities inherent in these uncertain forecasts</td></tr></table>

communications (IAVCEI Subcommittee for Crisis Protocols 1999; Cronin 2008; Haynes et al. 2008; Solana et al. 2008; McGuire et al. 2009; Doyle et al. 2014a; Doyle and Potter 2016). Thus scientists communicating in a multi-agency volcanic response must consider the best practice guidelines that have been established across a range of disciplines to address this issue (see Table 4).

The second issue regarding uncertainty in a crisis is its influence on the quality of the relationships between responding individuals, and

thus the quality of mental models and performance, discussed next.

## 4 People and Organizations

A common thread running through the previous sections concerned the crucial role shared mental models played in cooperative action. It is important to appreciate how organizational and work-family relationships influence the ease with which respective agency representatives can
<figure><img src="image_187.png" /><figcaption>**Fig. 3** The factors that affect the interpretation of forecasts, and the influences on resultant decision making (Doyle et al. 2014b, after the graphical abstract of Doyle et al. 2014a)</figcaption></figure>

"participate" in a super-ordinate mental model and the degree to which they cope with and adapt to crisis demands (i.e., how organizational factors influence susceptibility to stress and thus performance).

## 4.1 Organizational Characteristics

Organizational characteristics influence the mental models that agency representatives bring to the crisis management environment. The organizational socialization (the norms, customs, and ideologies of that organisation) and the organizational cultural change that occurs when organisations interact, will influence the thinking and behaviour of people within an organization, as well as their mental models of communication between organizations. These processes then spill over to affect how volcanic crisis events and their consequences are responded to and interpreted (Paton et al. 2009; Paton and Norris 2014). The knowledge and interpretive processes that representatives bring to the crisis response role as a result of their organizational cultural provides the foundation (capabilities represented by pre-existing mental models) for their individual contribution. However, as introduced above, the need for diverse organizational representatives to integrate their respective contributions in complementary ways requires the development of a

super-ordinate mental model defined by the collective and multifaceted demands of the crisis event. The ease with which this can be achieved is complicated by the fact that this is done in a climate of uncertainty. Interaction under uncertainty can result in pre-existing beliefs dominating which may or may not be amenable to alteration. This then prevents the development of inter-agency trust and undermines collective performance (Paton et al. 2009). When dealing with atypical, challenging crisis events, emergency management representatives, by virtue of the need for them to play complementary roles in defining and managing complex events, become more reliant on others for information and guidance about how to respond.

## 4.2 Trust

Faced with uncertainty, when decision makers are reliant on one another for information and decision making, trust plays a pivotal role in facilitating sustainable collaboration in multi-agency crisis response contexts (Siegrist and Cvetkovich 2000; Mayer et al. 1995). Trust influences organizational intention to collaborate and share information between stakeholders (Mohr and Spekman 1994; Kapucu 2006).

Trust was identified as a key issue for relationships between scientists and officials during
the eruptions of Soufriere Hills Volcano, Montserrat, WI, where Haynes et al. (2007) identified that this trust was influenced by a number of factors including: competence; integrity; value similarity; openness; and conflicting messages of safety and danger. Trust in the scientists was based upon high perceived reliability, competence, openness and integrity; while trust in government authorities was based upon high perceived levels of competence, reliability, and fairness.

The diversity of, for example, agencies, organizational cultures and operating practices brought together into a crisis management environment, and the need for diverse volcanic hazard consequences to be managed by agency representatives that differ in levels of familiarity with each other, can threaten the degree to which crisis management activities are characterized by trust (Dietz et al. 2010). In part, this reflects the lack of familiarity between interacting agencies. It is also influenced by how organizational cultural characteristics ingrained in routine work, such as hierarchical reporting practices and levels of bureaucracy (including command and control expectations), influence the relationships that emerge in an inter-agency crisis management context (Dietz et al. 2010; Dirks and Ferrin 2001).

Scientists tend to bring experience of working in organizational cultures that emerge in flatter, more organic organizational cultures in which information flow is common. This makes it easier for them to engage in practices that focus on sharing information and building trust. However, representatives from government departments and emergency services agencies, whose routine culture is characterized by generally higher levels of hierarchical relationships and reporting, tend to be predisposed towards maintaining their own agency-based independence. This fosters an emergent culture of rivalry among organizations in ways that work against information sharing between agencies during a crisis (Waugh and Streib 2006; Iannella and Henricksen 2007; Marcus et al. 2006; Marincioni 2007). Furthermore, these predisposing cultural features can result in relationships that are characterized by

in- and out-group differentiation. These in- and out- groups then affect the quality of information flow and increases the likelihood of information being restricted to members within their own organization (or includes those with whom they are familiar), rather than sharing information across all stakeholders (Militello et al. 2007; Owen 2013).

These factors interact to not only constrain information flow, but in the process, introduce significant challenges to the development of the level of trust required for effective collaboration and decision making under conditions of uncertainty (McKnight et al. 1988; Banai and Reisel 1999; Siegrist and Cvetkovich 2000). Thus the agency representatives brought together for response needs may not have the mutual interaction experiences needed to forge trust in each other, with aspects such as cultural diversity adding to this challenge. Thus, there is an important need to develop trust in situ while responding in a high demand environment.

If trust is absent, those working in an EOC setting are more likely to focus on task demands in ways that reflect their core expertise and normal operating practices, rather than functional collaboration in ways that ensure they work in complementary ways to resolve multi-faceted response problems (Pollock et al. 2003). This reduces their capacity to contribute effectively to the emerging needs of the response (Pollock et al. 2003). The dynamic, prolonged nature of volcanic crisis response thus requires different ways of ensuring the development of trust. The concept of *swift trust* represents an approach to trust building in situations where people must collaborate on complex, evolving volcanic crisis tasks under high risk, low time constraints that preclude the development of trust through normal means (Goodman and Goodman 1976; Meyerson et al. 1996; Hyllengren et al. 2011; Faraj and Xiao 2006; Robert et al. 2009; Lester and Vogelgesang 2012; Crisp and Jarvenpaa 2013).

For swift trust to develop, “team” members must be assigned specific roles (that align with key response issues and needs) in the temporary work group (Meyerson et al. 1996). This can be
facilitated by establishing a super-ordinate mental model that makes the key contributions of all agencies to effective whole-of-incident management clear. It can be developed pre-event via use of techniques such as cross training in organizational crisis management training (Blickensderfer et al. 1998). Second, swift trust emerges if members are informed that there is a high likelihood of future collaboration (in incident reviews, simulations) with those with whom they are collaborating (Goodman and Goodman 1976; Meyerson et al. 1996). Finally, swift trust develops by ensuring that all participants identify that success relates to the super-ordinate management as much as it does to how they contribute their personal expertise (Curnin et al. 2015). This developmental task focuses on how the input of different representatives is necessary to develop a holistic response to multifaceted demands that exceed the expertise of any one agency. Doing so facilitates role clarification, and increases the capacity of team members to understand that each stakeholder brings to the collective task, their specialist skills and knowledge as required to fulfil one of several specialist roles in the multi-agency team (Kramer 1999). This, in turn, enables the development and performance of collaborative working practices and supports emergent multi-agency coordination.

## 4.3 Work-Family

Another element that has a significant bearing on people's stress management and performance in high risk settings and that has not been considered in volcanic crisis response contexts concerns how “work-family” relationships affect how well people cope with working in high risk, high stress contexts (Paton and Norris 2014). Organizations that take steps to facilitate family involvement in the employment experience, for their personnel who work in high risk settings, record better communication and trust between personnel and management, and thus more effective stress management in high risk work settings. Family involvement includes, for example, providing support services for partners

and children when personnel are deployed, providing regular updates for partners and other family members when personnel are deployed, providing roles (e.g., administrative roles, media liaison roles) and setting up peer support programs for partners (Paton and Kelso 1991; Paton and Norris 2014). In contrast, in organizations where these kinds of family engagements are not offered, factors such as lack of information about what is happening in the field, and the resultant increased anxiety amongst family members, will increase perceived psychological stress in partners and children and lessens their availability and effectiveness as support resources for personnel deployed to deal with volcanic crises (Paton et al. 2009). Awareness of these issues is imperative for volcanic crisis response, where responding scientists may be geographically dispersed and away from family during periods of high risk and high stress work. Under such circumstances, ensuring partners are involved as much as possible to facilitate opportunities for personnel to remain connected with family, and access social support from this quarter, can provide cost-effective stress management resources for those deployed (Paton and Kelso 1991). This aspect of a comprehensive crisis management strategy plays a key role in supporting performance and well-being, particularly when personnel can be deployed and working in high demand, high stress contexts over prolonged periods of time.

## 5 Concluding Remarks: Developing Future Response Capacity

We have highlighted how good shared mental models of the response situation between individuals within and across organisations, characterised by good situational awareness, strong inter-organisational networks, and high trust between responding organisations and individuals, have been shown to enhance communication and thus decision making. However, developing and maintaining such shared mental models is itself an important task. Research has shown that shared experience, through training, can help
improve the quality of such mental models (Cannon-Bowers and Bell 1997; Crego and Spinks 1997; Paton et al. 2000; Pliske et al. 2001; Borodzicz and van Haperen 2002).

Ideally, multi-organisational and multi-disciplinary planning activities, collaborative exercises and simulations should be undertaken with all team members and advisors to help in the development of similar mental models of the task (see review in Paton and Jackson 2002; Doyle and Johnston 2011; Doyle et al. 2015). A comprehensive suite of training and relationship building activities prior to an event, and a detailed analysis of event and exercise response, can help enhance this future response capability and identify areas for improvement. This is particularly important given the rarity of volcanic and other hazard events, and thus a lack of opportunity for real world experience. This training and exercising needs to develop both individual and team situational awareness and explore how and when each is appropriate for response, within evolving, dynamic response environments (Doyle et al. 2015). Team situational awareness can be developed in post-event and post-exercise reviews that include identifying inter-agency relationship issues as opportunities for development (and not as problems). Through the analysis of past events, lessons for successful communication, advice provisions and distributed decision-making can also be learnt.

The above processes describe group learning from crises, exercises and training, which is identified by Borodzicz and van Haperen (2002) to occur along three dimensions: personal, interpersonal, and institutional. Several training methods have been identified that can enhance naturalistic decision-making (Cannon-Bowers and Bell 1997), enhance decision skills (Pliske et al. 2001), train effective teams (Salas et al. 1997), and develop effective critical incident and team based simulations (Flin 1996; Crego and Spinks 1997) all of which are relevant for volcanologists and scientific advisory groups. These include cross training, positional rotation, scenario planning, collaborative exercises and simulations, shared exercise writing tasks including co-writing, swapped writing and 'train the

trainer' type tasks, in addition to workshops, seminars and specific knowledge sharing activities (Doyle and Johnston 2011; Doyle et al. 2015).

Adopting such an evaluative approach has greatly enhanced the response environment in New Zealand, resulting in a formation of a number of scientific advisory groups with formalised Terms of Reference, protocols for communication and networking with emergency management and key response organisations (e.g., CPVAG 2009). These, accompanied by regular workshops and meetings to facilitate relationship building and shared understanding, will help improve the communication and information flow and thus the shared situational awareness in a crisis. Being part of an exercise schedule (i.e. Exercise Ruaumoko; MCDEM 2008), also provided Auckland CDEM and the associated science agencies a focus to develop the Auckland Volcanic Science Advisory Group structure, including arranging formal contract agreements for the participating scientists (McDowell 2008; Cronin 2008).

The development of formalised protocols, Terms of Reference and the use of established guidelines for response and communication (such as those issued by IAVCEI, WMO, and IPCC; IAVCEI 1999; Gill et al. 2008; Mastrandrea et al. 2010) can greatly enhance response processes by reducing ambiguity about 'what to do', 'what to communicate', 'how to communicate', and 'who to communicate to', particularly in high stress, high pressure, high consequence events. As stated by IAVCEI (1999), responding scientists must also identify a team plan for crisis response (Table 5), and we suggest that such plans and procedures should be tested prior to an event. Simulations should aim to reproduce reality as closely as possible, reflecting the realities of advisory processes in turbulent conditions (Rosenthal and 't Hart 1989; Borodzicz and van Haperen 2002). However, evaluation of exercises and events must minimise the risk of creating an optimistic bias that overestimates future response preparedness and capability (Paton et al. 1998).

In conclusion, it is also important for future work to consider the role that international
<table><caption>**Table 5** Steps identified by IAVCEI (1999, p. 332) to form a team plan for volcanologists responding to a crisis</caption><tr><td>1.</td><td>Clear identification of scientific, warning, and other tasks (including communications with civil defence, news media, and others)</td></tr><tr><td>2.</td><td>Clear identification of responsibility (group or individual) for each task, including that of team leader</td></tr><tr><td>3.</td><td>Clear identification of a mechanism for selecting team leader(s)</td></tr><tr><td>4.</td><td>Procedures and policies on likely issues of scientific interaction, including:<br/>(a) Rights and responsibilities for data and sample sharing<br/>(b) Resolution of differences in scientific approach and/or interpretation<br/>(c) Preparation and release of forecasts, warnings, and other public statements<br/>(d) Restrictions of access to hazardous areas (and application/approval procedures for access permits)<br/>(e) Requirements and roles of visiting scientists<br/>(f) Communication, within and outside the scientific team; and<br/>(g) Publication of scientific results, and distribution of authorship</td></tr></table>

frameworks and initiatives will have on any protocols and procedures developed for volcanic science advisors at a local or national level. For example, recently the UN Office for Disaster Risk Reduction's Hyogo Framework for Action (HFA) 2005–2015 (United Nations International Strategy for Disaster Reduction (UNISDR) 2007) has been reviewed. Changes include a reconsideration of the role of science and technology, the role of local science and local knowledge, and the role of international science advice mechanisms as ratified in the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR: UNISDR 2015). Volcanologists must be aware of the impact of any changes to international frameworks such as the HFA and the SFDRR, as they will affect regional and local frameworks and support, including both funding, resources, and the legitimacy of any formalised local approaches that may be developed to maintain “shared meaning” with stakeholders during a volcanic crisis.

**Acknowledgements** EEHD was supported by a Foundation for Research Science and Technology NZ S&T Postdoctoral Fellowship MAUX0910 2010–2014, and funding from EQC and GNSScience 2014–2016.

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# Using Statistics to Quantify and Communicate Uncertainty During Volcanic Crises

Rosa Sobradelo and Joan Martí

**Abstract**

For decades, and especially in recent years, there has been an increasing amount of research using statistical modelling to produce volcanic forecasts, so that people could make better decisions. This research aims to add confidence by arming users with quantitative summaries of the chaos and uncertainty of extreme situations, in the form of probabilities—that is to say the measure of the likeliness that an event will occur.

**Introduction**

Probabilistic terms and associated jargon are often part of the working environment of volcanologists. Research activities about volcanic hazard and the quantification of volcanic risk even led to officially defining volcanic hazard in terms of probability (Blong 2000). The last decade has produced a comprehensive framework of studies, surveys and computer-assisted procedures for transforming field data into probabilities

of occurrence of a particular scenario (Newhall and Hoblitt 2002; Marzocchi et al. 2004, 2008, 2010; Aspinall 2006; Martí et al. 2008; Neri et al. 2008; Sobradelo and Martí 2010, 2015; Sobradelo et al. 2013). Following the successful development of probabilistic tools, came the challenge of communicating their results. Research and operational strategies started to incorporate the enhancement of the communication of these probabilistic forecasts to decision makers and the public (Marzocchi and Woo 2007; Marzocchi et al. 2012; Sobradelo et al. 2014). At the same time, extensive work has been done in the psychological and sociological aspects on the perception and interpretation of uncertainty, for both volcanology and across other hazards. Despite this extensive use, sometimes there is confusion surrounding the statistical interpretation of probabilities, partly due to unclear statistical concepts like: What is a probability? What is statistical science? How much can I rely on a probability estimate? What are they used for?

R. Sobradelo (✉)
Willis Research Network (Willis Towers Watson),
London, UK
e-mail: sobradelo@gmail.com

R. Sobradelo
UCL Hazard Centre Honorary Fellow Member,
University College London, London, UK

J. Martí
Institute of Earth Sciences Jaume Almera, CSIC,
Barcelona, Spain
What is uncertainty? How does uncertainty and probability relate to each other? Why are statistics and probabilities sometimes misunderstood? Why is it that scientists and/or users (officials) don't fully appreciate the uncertainty surrounding a probability estimate?

In this chapter we try to address the above questions by focusing on the statistical meaning of probability estimates and their role in the quantification and communication of uncertainty. We hope to provide some insights into best practices for the use and communication of statistics during volcanic crises.

## Quantifying and Communicating Uncertainty in Volcanology

Volcanology is by nature an inexact science. Deciphering the nature of unrest signals (volcanic reactivation), and determining whether or not an unrest episode may be an indication of a new eruption, requires knowledge on the volcano's past, current and future behaviour. In order to achieve such a complex objective experts in field studies, volcano monitoring, experimental and probabilistic modelling, amongst other, work together under pressure and tight time constrains. It is important that these stakeholders communicate on a level that caters for the needs and expectations of all disciplines; in other words, it is important to agree on a common technical language. This is particularly relevant when volcano monitoring is carried out on a systematic survey basis without continuous scientific scrutiny of monitoring protocols or interpretation of data.

By definition, uncertainty is the state of being uncertain. It is used to refer to something that is doubtful or unknown. It means lack of confidence about something. Hence, it is directly related to the amount of knowledge we have about a process. A forecast, in the form of a probability estimate, is an attempt to quantify this uncertainty and support decision-making. Forecasting potential outcomes of volcanic reactivation (unrest) usually implies high levels of scientific uncertainty. Anticipating whether a particular volcanic unrest will end with an eruption and where

(temporal and spatial uncertainty) requires scientific knowledge of how the volcano has behaved in the past, and scientific interpretation of precursor signals. Whilst this may be less challenging for volcanoes that erupt often, it is far more difficult for volcanoes with long eruptive recurrence and less data available, and even more so for those without historical records.

The main goal of volcano (eruption) forecasting is to be able to respond to questions of *how, where, and when* an eruption will happen (Sparks 2003). To address those questions we often use probabilities in an attempt to quantify the intrinsic variability due to the complexity of the process. The communication of those probabilities will have to adapt to the recipient of that information. Making predictions on the future behaviour of a volcano follows similar reasoning as in other natural phenomena (storms, landslides, earthquakes, tsunamis, etc.). Each volcano has its own characteristics depending on magma composition, physics, rock rheology, stress field, geodynamic environment, local geology, etc., which makes its behaviour unique. What is indicative in one volcano may not be relevant in another. All this makes the task of volcano forecasting challenging and difficult, especially when it comes to communicating uncertainty to population and decision-makers.

During a volcanic emergency, relevant questions are first how to quantify the uncertainty that accompanies any scientific forecast, and second, how to communicate it to policy-makers, the media and the public. Scientific communication during volcanic crises is incredibly challenging, with no standardized procedures on how this should be done among the stakeholders involved (scientists, governmental agencies, media and local populations). Of particular importance is the communication link between scientists and decision-makers (often Civil Protection agents). It is necessary to translate the scientific understanding of volcanic activity into a series of scenarios that are clear to decision-making authorities. Direct interaction between volcanologists and the general public is also important both during times of quiescence and activity. Information that comes directly from the scientific community has a
special impact on risk perception and on the trust
that people place on scientific information.
Therefore, the effective management of a volcanic
crisis requires the identification of practical
actions, to improve communication strategies at
different stages and across different stakeholders:
scientists-to-scientists, scientists-to-technicians,
scientists-to-Civil Protection, scientists-to-
decision makers, and scientists-to-the general
public.

The Role of Statistics
and Probabilities
in the Quantification of Uncertainty

Concepts, Definitions
and Misconceptions

Formally speaking, *Statistics* is a body of principles and methods for extracting useful information from data, assessing the reliability of that information, measuring and managing risk, and supporting decision-making in the face of uncertainty. Rather than drowning in a flood of numbers, statistics helps to make better management decisions and gives a competitive advantage over intuition, experience and hunches alone.

*Probability* shows the likelihood, or chances, for each of the various future outcomes, based on a set of assumptions about how the world works. It allows handling randomness (uncertainty) in a consistent, rational manner and forms the foundation for statistical inference (drawing conclusions from data), sampling, linear regression, forecasting, and risk management.

With statistics, we go from observed data to
generalizations about how the world works. For
example, if we observe that the seven hottest
years on record occurred in the most recent
decade, we may conclude (perhaps without jus-
tification) that there is global warming. With
probability, we start from an assumption about
how the world works, and then figure out what
type of data we are likely to see under that
assumption. In the above example, we could

assume the null hypothesis, *H₀*: There is no
*global warming*, and then test how likely is it to
observe the seven hottest years within the last
decade if *H₀* was true. We then use the observed
data to look for significant statistical evidence to
reject *H₀* in favour of the alternative, *H₁*: Some
*phenomena related to global warming may be
ongoing*. To some extent, we could say that
probability provides the justification for statistics.

However, there is no precise definition for
probability. All attempts to define it must ulti-
mately rely on circular reasoning. According to
the Oxford Dictionary, probability is “the state of
being probable; the extent to which something is
likely to happen or be the case”. Roughly speak-
ing, the probability of a random event is the
“chance” or “likelihood” that the event will occur.
To each random event A we attach a number P(A),
called the probability of A, which represents the
likelihood that A will occur. The three most useful
approaches to obtaining a definition of probability
are: the classical, the relative frequency, and the
subjective (Jaynes 2003; Colyvan 2008), dis-
cussed further below.

The number of volcanic eruptions of magni-
tude greater than 1 in the next *t* years in a par-
ticular area is an example of a random variable,
*Y*. When we try to quantify the value of *Y* we are
implying that a *true value* exists, and we want to
anticipate to it, so that we can make advanced
decisions. That is, we want to estimate a range of
values that we think will contain the *true value* of
the random variable *Y*. The most common way
of showing this range of values is by presenting a
*best estimate* ± *confidence margin*. Here, we
could distinguish between two types of uncer-
tainty, the one surrounding the *best estimate*,
type *A*, and the one that accounts for the level of
confidence that we have in that best estimate,
type *B*. It is not enough to provide a *best guess*
(point estimate) for a parameter, we also need to
say something about how far from the true
parameter value such an estimator is likely to be.
The confidence interval is one way of conveying
our uncertainty about a parameter. With that, we
report a range of numbers, in which we hope the
true parameter will lie.
## Measures of Uncertainty

Probability can be used as a measure of uncertainty, both type A and B. The way we understand probabilities depends on the degree of numeracy we have. It is common in our daily lives to make choices with some level of uncertainty, for instance, whether or not to order the fish of the day in a new restaurant, or whether to buy one or two bags of fruit in a new shop. To make those simple decisions, we unconsciously go through previous knowledge on similar experiences to work out some kind of odds of making the right choice. Suppose now that we are being rushed to make up our mind at the restaurant, we will have to rush our decision. The main difference between this and the decision of whether to evacuate a populated area threatened by a destructive volcanic event is the penalty or loss for making the wrong decision. In the first case, the loss is negligible to our daily lives, but a wrongly timed evacuation decision could have serious consequences. For this reason, the interpretation of probability must be in the context of how much we are willing to lose if we make the wrong decision. The difference between probability, the extent to which something is likely to happen; and risk, a situation involving exposure to danger; means that the relevance of a probability estimate for the occurrence of an event will depend on the associated risk, this is, on how much exposure to danger is in the occurrence of the event. Suppose the odds are one to ninety nine (1:99) that our car breaks down in the middle of a trip. We would most likely still take our family on that trip. Instead, suppose we are given the same odds for an airplane crash. We would most likely not want to take our loved ones on that plane. In both cases the probability is the same, but the risk is different. This illustrates how probability estimates must be interpreted in the context of their associated risk.

Clearly emotions, values, beliefs, culture and interpersonal dynamics play a significant role in decision-making processes. Extensive work in the field of psychology and sociology has examined perceptions and interpretation of uncertainty for both volcanology and across

other hazards (weather, tsunami, operational earthquake forecasting, climate change) (Fischhoff 1994; Cosmides and Tooby 1996; Kuhberger 1998; Windschitl and Weber 1999; Bruine De Bruin et al. 2000; Gigerenzer et al. 2005; Patt and Dessai 2005; Risbey and Kandlikar 2007; Morss et al. 2008; Budescu et al. 2009; McClure et al. 2009; Joslyn et al. 2009; Mastrandrea et al. 2010; Jordan et al. 2011; Eiser et al. 2012; Doyle et al. 2014a, b). However, that is not the scope of this chapter. For the purpose of our argument, we focus on the ‘rational side’ of decision-making. That is, the quantification of uncertainty using statistical theory.

What makes statistics so unique is its ability to quantify uncertainty, so that statisticians can make a categorical statement about their level of uncertainty, with complete assurance. But the statements have to be made taking into account all possible factors (sources of uncertainty) and making sure the data are correctly selected to eliminate all sources of bias. These could have a significant impact and involve matters of life and death. So far we assumed that the probability estimates have been calculated using the right methods. For the restaurant or supermarket examples this could be a simple arithmetic mean. Forecasting the occurrence of a volcanic event will require more elaborated mathematical modelling. The accuracy in the probability estimate will depend largely on the model selection.

## Disciplines and Schools of Thought

To quantify uncertainty using statistics there are three main disciplines statisticians rely on: (i) data analysis, (ii) probability, and (iii) statistical inference (Cooke 1991; Pollack 2003; Kirkup and Frenkel 2006). The first step is always the data analysis, that is, the gathering, display and summary of the data. In the case of volcanoes, we look at past and monitoring data, and we make the necessary adjustments for any inconsistencies (e.g.: Sobradelo and Martí 2015). The second step is the formal study of the laws of chance, also called the laws of probability, whose birthplace is in the 17th century for no other
reason than to be used in gambling (Cooke 1991). Probabilities are the result of applying probability models to describe the world, and this is done using the concept of random variables, that is, the numerical outcome of a random experiment or a random process we are trying to understand, so that we can forecast its future outcome (height, weight, income, eruptive events in the last 500 years, number of seismic events in one day, etc.). Finally, we use the above so that we can make inferences in the real world with a certain degree of confidence (Rice 2006).

Approaches to developing probability models, associated with different schools of thought, are: (1) the classical, based on gambling ideas, which assumes that the game is fair and all elementary outcomes have the same probability; (2) the relative (objective) frequency approach which believes that if an experiment can be repeated, then the probability estimate that an event will occur is equivalent to the proportion of times the event occurs in the long run; and (3) the personal (subjective) probability approach which believes that most of the events in life are not repeatable (Cooke 1991; Jaynes 2003). They base the probability on their personal belief of the likelihood of an outcome, and then update that probability as they receive new evidence (Cosmides and Tooby 1996). An objectivist uses either the classical or the frequency definition of probability. Subjectivists, also called Bayesians, apply formal laws of chance to their own personal probabilities. What makes the Bayesian approach subjective is the choice of models and a priori beliefs to define the prior probabilities, even if the rules and observed data to update and compute the posterior probabilities are quite “objective”. The Bayesian approach claims that any state of uncertainty can be described with a probability distribution, making it suitable for the study of volcanic areas where very little or no data exists, other than theoretical models or expert scientific beliefs. These initial probabilities get updated each time new information arrives, making the approach quite dynamic and easy to apply.

For many years there has been controversy over the “frequentist” versus “Bayesian” methods.

However, neither the Bayesian nor the frequentist approaches are universally applicable (Jaynes 2003). For each situation, some approaches and models are more suitable than others to produce probability estimates as accurately as possible with high confidence. It is the task of the statistician to decide and justify the model selection to ensure reliability of the results. But a brilliant analysis is worthless unless the results are successfully communicated, including its degree of statistical uncertainty.

Often presented as an alternative to the probabilistic approach, is the deterministic approach. Events are completely determined by cause-effect chains (causality), without any room for random variation. Here, a given input will always produce the same output, as opposed to probabilistic models that use ranges of values for variables in the form of probability distributions. This approach is sometimes used in fields with a lot of data, like in weather forecasting, or where the underlying process can be explained with physics-based models, such as in seismology. In any case, the reliability of probabilistic versus deterministic forecasts is sometimes a cause of debate, and is often a mixed of both, a deterministic and a probabilistic approach, the preferred option.

## How Reliable Is a Forecast: Data and Methodology

By giving an expected value for a forecast we are already quantifying a measure of uncertainty. This value will have an interpretation based on the degree of confidence which the estimate is made with, which will depend on the type, amount, quality and consistency of the evidence upon which the estimate is made, usually past data or theoretical models.

The degree of confidence, or certainty, is quantified and expressed via the variance or standard deviation (squared root of the variance). Suppose we have three measurements of a random process (e.g. inter-event time in years) of 2, 3, and 4 years, and want to draw some conclusion about the inter-event time based on these values.
We use 3 years, a simple arithmetic mean, as the estimate of the inter-event time. The three measurements are equally distant and symmetrical around the mean. The variance, which measures the dispersion of the values around the mean, is 1, and the median, which is the value in the middle, is 3 as well as the mean. Suppose we do the same exercise with measurements 1, 3, and 5, we still get a mean of 3, but now we can see the values 1, and 5 are two units away from the mean, and so the variance, as a measure of dispersion around the mean, is now 4, instead of 1. Note, however, that the values are symmetrically distributed around the mean, and that the mean and median are still the same as before, 3. The only thing that has changed is the variance, now larger. The lower the variability around the point estimate, the more reliable is our estimate. Let's take a sample with 10 measurements: 1, 1.4, 2, 2.1, 2.2, 2.3, 3, 4, 5, 7. The estimated inter event time, based on a simple arithmetic mean, is still 3, but we based this estimate on 10 rather than 3 observations. The more data we have to compute our estimates, the more confident we are in these results (Rice 2006).

Apart from the reliability of the data to pro-
duce an estimate, a crucial aspect of a forecast is
the correct choice of methodology to model this.
Most of the time we do not know the underlying
distribution of a random process (e.g. number of
volcanic eruptions in a time interval and partic-
ular area, assumed to be random), and so we
make assumptions to help us find a function
within a family of known distributions (Normal
or Gaussian, Exponential, Binomial, Beta, Pois-
son, Chi-Squared, Log-normal, etc.) that would
be suitable to model this unknown process (see
Rice 2006; Gonick and Smith 2008; McKillup
and Dyar 2010 for details on these distributions).
This facilitates making inferences and forecasts
based on the conveniently known properties of
these functions. The choice of the distribution
family depends on the characteristics of the
sample data (how many observations are there,
whether it is a symmetrical or a skewed distri-
bution, what type of measurement was used,
etc.). To select the most appropriate distribution,
it is important that the data is an unbiased and

representative sample of the population. There-
fore, the data gathering process and a preliminary
and exhaustive analysis of the dataset are crucial
to reduce uncertainty and increase confidence in
the final results. Needless to say, the choice of
distribution and assumptions about the sample
data add uncertainty to the results, and must be
taken into account when presenting the final
outcome.

Arithmetical means are pure descriptive mea-
sures used to sum up the information from the data
sample. In practice, we would not use a simple
arithmetical mean to estimate probabilities and
make inferences about complex processes. There
are a large number of statistical modelling tech-
niques (not the scope of this chapter) based on the
type of data we have, its distribution, quality and
quantity and the type of question we want to
answer. In the end, the reliability of the proba-
bility estimate (whereas an inter event time of
3 years or not) will depend on the accuracy, reli-
ability and amount of data used to reach that
conclusion, together with the statistical model and
approach. That is why a probability estimate
should always be presented with some measure of
its variability (estimated error, usually given by
the variance or standard deviation) and it should
be made clear that it is an estimate based on the
available data, and that we have assumed that a
future behaviour of the random event will follow
the same pattern we have observed in this dataset.
This might in fact not be the case, and that is why
sometimes we hear about time series data being
"stationary or not", meaning that depending on
what time interval the data comes from, the pat-
tern observed may be different. In short, there are
many assumptions and sources of uncertainty
around a probability estimate that have to be taken
into consideration when interpreting probability.

Taking a bigger picture view, ultimately all
we are doing is drawing some general conclu-
sions about an unknown process (the inter-event
time) from some samples of observations. We do
not have access to all the possible observations of
this process, but still want to anticipate the future
value of this event, so we can be better prepared
should the event strike. This is the reason why
we use statistical approaches to model random
events, unless we can see into the future, a probability estimate can never be either 0 or 100%.

## Using Probabilities to Communicate Uncertainty

Since the late 1990s there has been significant focus on improving communications during volcanic crises (IAVCEI 1999; McGuire et al. 2009; Aspinall 2010; Donovan et al. 2012a, b; Marzocchi et al. 2012; Sobradelo et al. 2014). A common factor that emerges is the value of probabilities as a way to communicate scientific forecasts and their associated uncertainties, for natural hazards in general (Cooke 1991; Colyvan 2008; Stein and Stein 2013), or more specific for volcano forecasting (Aspinall and Cook 1998; Marzocchi et al. 2004; Aspinall 2006; Sobradelo and Martí 2010; Marzocchi and Bebbington 2012; Donovan et al. 2012c). However, it also requires the need to communicate the uncertainty that accompanies any forecast on the future behaviour of a natural system.

Making predictions on the future behaviour of a volcano involves analysis of past data, monitoring of the current situation and identification of possible scenarios. Quite often, these predictions are challenging to quantify and communicate due to lack of data and past experience. An added source of complexity is when the probability estimates are very small, <1%. Most lay people are not familiar with decimals or small fractions. A layman will easily understand a probability of 0.2 or 20%, but not so well one of 0.0002 or 0.02%, even when both are associated to the same level of risk. Scientists responsible for the communication of volcanic forecasts have the difficult task of selecting the scientific language to deliver a clear message to a non-scientific audience.

The uncertainty that accompanies the identification and interpretation of eruption precursors derives from the unpredictably of the volcano as a natural system (aleatory or deep uncertainties) and from our lack of knowledge on the behaviour of the system (epistemic or shallow uncertainties) (Cox 2012; Stein and Stein 2013). These

uncertainties will depend on how well we know the volcanic system. Active volcanoes with high eruption frequencies can be more easily predicted (i.e. they are reasonably well known and so past events are good predictors of future ones, shallow uncertainties). In contrast, deep uncertainties are associated to probability estimates based on poorly known parameters or poor understanding of the system, this is usually the case for volcanoes characterised by low eruption frequencies.

In everyday life we are often quite unaware that we use probabilities (commonly known as “common sense”) to evaluate the degree of uncertainty we face. The question is whether we prefer or understand better the mathematical expression of probability (e.g.: 20% chance of an event occurring) or more verbal statements such as likely, improbable, certainly, to make our decisions. Greater precision does not necessarily imply greater understanding of what the message really is, as it will be perceived differently (Slovic 2016).

Some countries, like USA, prefer to use probabilities to express uncertainties with weather forecasts, while some European countries prefer to use verbal expressions. In both cases, people react according to the forecast. There are different ways in which probabilities (and uncertainties) can be described. These include words, numbers, or graphics. The use of words to explain probabilities tend to use language that appeals to people’s intuition and emotions (Lipkus 2007). However, it usually lacks precision as it tends to introduce significant ambiguity by the use of non-precise words such as “probable”, “likely”, “doubtful”, etc. A probability is the “measure” of the likeliness that an event will occur, so it makes sense to expect a numerical value (e.g. percentages) associated to that measure. However, in volcanology most of the time there is insufficient observational data to present probabilistic forecasts with enough level of confidence. Using only numerical expressions may fail when the audience has a low level of numeracy. The interpretation of probabilistic terms can vary greatly depending on the educational level of the receptor and whether verbal or numerical expressions are used (Budestu et al. 2009; Spiegelhalter et al. 2011; Doyle et al. 2014a; Gigerenzer 2014). To minimise this
problem, a combination of verbal uncertainty
terms (e.g.: very likely) with quantitative specifi-
cations (e.g.: <90% probability) has been recom-
mended, for example, to better understand results
from Intergovernmental Panel on Climate Change
(IPCC) (Budescu et al. 2009, 2012). Climate sci-
entists working within the IPCC have adopted a
lexicon to communicate uncertainty through ver-
bal probability expressions ranging from “very
likely”, “likely”, “about as likely as not unlikely”,
“very unlikely” and “exceptionally unlikely” to
refer to probabilities (e.g. IPCC 2005, 2007). The
terms are assigned specific numerical meanings
but are typically presented in verbal format only,
so that a probability of occurrence of 1% will be
interpreted as “very unlikely” for that particular
event, and a probability of 66% will be seen as
“likely” for the event to happen. Similarly, any-
thing in the range of 33–66% would be perceived
as “about as likely as not unlikely”.

Since 2011 it has been increasingly common
to use graphics to represent probabilities in nat-
ural hazards (Kunz et al. 2011; Spiegelhalter et al.
2011; Stein and Geller 2012). The advantage of
communicating uncertainties (or probabilities)
visually is that people are everyday better pre-
pared and trained to use and understand info-
graphics, as an immediate consequence of the
globalised use of internet and informatics, and a
graphic can be adapted to stress the importance of
the content of the communication and can be
adapted to the needs and capabilities of the
audience (Spiegelhalter et al. 2011).

In addition to considering the way probabilities
(and uncertainties) are communicated, there is a
need to consider the local context of the particular
society in which the volcanic crisis is occurring.
“Odds” is an expression of relative probability
that is well understood by many communities (e.g.
gambling, games of chance) and can be effective
also to communicate volcano forecasting if it is
correctly adapted for the purpose. Regulations
(i.e. legal and commonly accepted norms) fre-
quently determine the articulation of uncertainty
and risk used to manage environmental and nat-
ural hazards. Finally, culture is of key importance
in communication (Oliver-Smith and Hoffmann
1999; Eiser et al. 2012). The way in which risk is

perceived may change depending on cultural
beliefs of each society, and in the same way the
cultural diversity of societies facing a volcanic
threat may imply that communication methods
that work in one country or culture may not work
in another. Therefore, it is important to investigate
and gain in-depth understanding of the particular
cultural aspects of each society in order to define
the best communication procedures and lan-
guages in each case. There are numerous studies
that demonstrate the importance of public educa-
tion, pre-crisis education programmes, and risk
perception to better understand scientific com-
munication during crisis (e.g. Bird et al. 2009;
Budescu et al. 2012; Dohaney et al. 2015). Most
of them agree that better educated populations on
natural hazards understand better risk communi-
cation and behave in a more orderly way for
managing a crisis. There are additional sociolog-
ical and qualitative aspects to consider when
communicating probabilities beyond the scope of
this chapter, but address issues around risk per-
ception, trust, decision-making, and managing
disasters e.g. Kilburn 1978; Fiske 1984; Tazieff
1977; Paton et al. 1999; Chester et al. 2002;
Sparks 2003; Haynes et al. 2007, 2008; Baxter
et al. 2008; Solana et al. 2008; Fearnley 2013;
Doyle et al. 2015.

What Should Be Communicated?

The key questions focus around what can be
forecasted. Should the forecasting of the outcome
of a volcano be determining whether it will erupt
of not? How big or explosive will it be? When?
Where? What is the dimension of the problem?
These are basic questions that civil protection asks
to the scientist once an alert has been declared,
and the process of managing a volcanic crisis has
started (IAVCEI 1999; McGuire et al. 2009;
Aspinall 2010; Donovan et al. 2012a, b; Mar-
zocchi et al. 2012; Sobradelo et al. 2014). Usually,
scientists can answer these questions with
approximations (probabilities) based on knowl-
edge of previous cases from the same volcano, or
from other volcanoes with similar characteristics,
knowledge of the past eruptive history of the
volcano, warning signals (geophysical and geo-
chemical monitoring), and knowledge about the
significance of these warning signs. Whilst giving
probabilities as an outcome of a volcano forecast
may be relatively easy for the scientist (depending
on the degree of information available), it may not
be fully understood by the decision-maker or any
other recipient of such information. It is necessary
to find a clear and precise way to communicate
this information between scientists and key
decision-makers, to avoid misunderstandings and
misinterpretations that could lead to an incorrect
management of the volcanic emergency and,
consequently, to a disaster.

In recent years, a way used to improve the
communication of statistics, as well as decision-
maker needs, is through the development of
exercises where a volcanic crisis is simulated and
all key players involved in risk management, such
as scientists, civil protection, decision-makers,
population and media are invited to participate, as
in a real case. Exercises have been carried out at
different volcanoes such as Vesuvius (MESIMEX,
Barberi and Zuccaro 2004), or Campi Flegrei,
Cotopaxi and Dominica (VUELCO Project, www.
vuelco.com), New Zealand (DEVORA), among
others. These simulations facilitate interaction and
cooperation between the stakeholders, and the
sharing and exchanging of procedures, method-
ologies and technologies among them, including
scientific communication. They present an
opportunity for learning the exact role and
responsibilities that each key player has in the
management of a volcanic crisis, as well as
exchanging concerns and feedback on specific
matters.

Whilst volcanic forecasts centre on scientific
data and probabilities as much as possible, sci-
entists may also recommend safe behaviour
directly to the public, providing advice that saves
people's lives (e.g. going up a hill if a lahar
threatens). Often this is beyond the legal
requirements of the scientists, who are required
to comment on the volcanic science only, but
they could feel a moral duty to assist (Fearnley
2013). However, this should not imply or be
confused with making decisions on how to
manage a volcanic emergency (e.g. evacuation),

as this frequently falls under the remit of civil
protection (or other such government organisa-
tions), although in some countries such as
Indonesia the scientists and the civil protection
organisations work together rather than having
distinct roles; it is dependent on the governance
structures of the country.

When Should a Volcano Forecast Be
Communicated?

Ideally, forecasts should be communicated as
early as possible, and then with increasing fre-
quency if, or when, an eruption nears. This means
there should be a permanent flow of information
between scientists, the vulnerable populations,
and policy-makers on the eruptive characteristics
of the volcano, its current state of activity, and its
associated hazards, even when volcanoes do not
show signs for alarm. This is to aid preparation for
when an emergency starts and things need to
move much faster. However, in many cases sci-
entific communication in hazard assessment and
volcano forecasting is just restricted to volcanic
emergencies. When volcanic unrest starts and
escalates, the origin of this unrest needs to be
investigated to assess the level of hazard expected.
Good detection and interpretation of precursors
will help predict what will happen with a con-
siderable degree of confidence. This means that
scientific communication during a volcanic crisis
needs to be constant and permanently updated
with the arrival of each new piece of data. The
longer it takes to make a decision, the greater the
potential losses are likely to be as vulnerability
increases. This constitutes the main challenge in
communicating forecasts and probabilities during
a volcanic crisis. In essence, the relationship
between the decrease of uncertainty in the inter-
pretation of the warning signs of pre-eruptive
processes to acceptable (reliable) levels, and the
time required to make a correct decision, is a
function of the degree of the scientific knowledge
of the volcanic process and of the effectiveness of
scientific communication. Therefore, scientific
communication during a volcanic crisis needs to
be effective from the start.
## Conclusion

In order to improve scientific communication during a volcanic crisis it is recommended that the communication protocols and procedures used by the different volcano observatories and scientific advisory committees are compared for each level of communication: scientist-scientist, scientist-technician, scientist-Civil Protection, scientist-general public. Experience from other natural hazards helps, as do clear and effective ways to show probabilities and associated uncertainties. Although each cultural and socio-economic situation will have different communication requirements, comparing different experiences will help improve each particular communication approach, thus reducing uncertainty in communicating volcano forecasts.

Finally it is worth mentioning that a crucial aspect in facilitating risk communication is education. This, however, is a long-term task that requires to be conducted permanently in societies threatened by natural hazards. Risk perception depends on cultural beliefs but also on whether or not a society has been educated on its natural environment and potential hazards. In the same way scientific communication is better perceived and understood when the population have previous knowledge on the existence and potential impacts of natural hazards. There are numerous studies that demonstrate the importance of public education, pre-crisis education programmes, and risk perception to better understand scientific communication during crisis (e.g. Bird et al. 2009; Budescu et al. 2012; Dohaney et al. 2015). Most of them agree that better educated populations on natural hazards understand better risk communication and behave in a more orderly way for managing a crisis. Therefore, best practices on communication should also consider improving education of population on natural hazards, their potential impacts and the ways to minimise the associated risks, as well as on how to behave during the implementation of emergency plans in a crisis.

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<figure><img src="image_188.png" /></figure>
Insurance and a Volcanic Crisis—A
Tale of One (Big) Eruption, Two
Insurers, and Innumerable Insureds

Russell Blong, Catherine Tillyard and George Attard

Abstract

Although probabilistic insurance loss models, particularly for ash fall, are
currently being developed volcanic risk has been widely ignored by
insurers and policy holders alike. Volcanic eruption cover is often grouped
in insurance and reinsurance policies with earthquake and tsunami cover.
Many volcanic eruptions include several perils occurring in different
spaces around the volcano, with widely varying intensities and conse-
quences, sometimes all at once, sometimes sequentially, and sometimes
repeatedly. Given the possibly large differences in hazard characteristics,
event durations and potential losses the policy alignment with earthquake
and tsunami covers can be unfortunate. Does ‘volcanic activity’ have the
same meaning as ‘volcanic eruption’? Do the terms ‘ash fall’ and
‘pyroclastic fall’ have identical meanings to an insurer—or to a
volcanologist? Some policies cover all volcanic perils while others
include only named volcanic perils such as pyroclastic flows, ash falls,
and/or lava flows. Often the intent of the coverage is not clear—were
some volcanic perils missing from a list excluded by accident or design?
Does a policy that covers damage occasioned by a fall of volcanic ash also
cover the cost of clean-up, removal, transport and appropriate storage of
the ash—even if the fall of 5–10 mm of ash causes almost no property
damage? Clear communication between the insurance sector and policy
holders (and the media) is dependent upon informed understanding of the
nature of volcanic perils and volcanic eruptions, insurance wordings, and
the potential losses to property and business interruption covers. This
chapter explores these issues using examples of policy wordings, evidence
from past eruptions, insurance case law, and potential losses in future
eruptions.

<footnote>R. Blong (✉)
Aon Benfield, Sydney, Australia
e-mail: russell.blong@aonbenfield.com</footnote>

<footnote>C. Tillyard
London, UK</footnote>

<footnote>G. Attard
Singapore, Singapore</footnote>
# 1 Introduction

Our aim is to highlight the challenges presented by volcanic eruptions with respect to insurance and communication. In particular, we wish to provide stakeholders with a range of perspectives on (i) the important issues as the insurance industry might see them; and (ii) the (possible) views of some other stakeholders including the policy holder, re/insurer and regulator. We begin by focussing on a single realistic—yet hypothetical—disaster scenario for a large (VEI 6) eruption, the sort of eruption that might occur somewhere in the world on average once in 50 years or so (Deligne et al. 2010). Clearly, this is just one eruption scenario out of the thousands that are possible—even more likely—but it emphasises that there are numerous insurance-related issues to consider. Our main concern is with the possible approaches of two insurance companies at opposite ends of the corporate resilience spectrum—one that has an embedded strategy to quickly adapt to disruptions while maintaining continuous business operations and safeguarding people, assets and overall brand equity, and another that has considered risk resilience in less detail and may have difficulty continuing to function (or remaining solvent) in the aftermath of a large event where thousands of policyholders want to make a claim.

We then delve into three insurance-related issues:

* modelling and communicating risk from volcanic activity

* the Contract Wordings used in insurance policies and what they might mean in relation to an eruption and its aftermath; and

* examples of insurance case law from around the world and what these court determinations might mean for insurers, policy holders and others experiencing the consequences of a large eruption.

While the eruption scenario and its insurance consequences are set in the (near) future, the details are firmly grounded in (recent) experience.

For the purposes of this chapter, we have not considered governments’ role in bridging the insurance protection gap or disaster risk financing, nor have we delved deeply into the ability of insurance as a signalling device to drive risk mitigation and risk management practices. In addition, we have not considered broader macroeconomic impacts—natural hazards have unexpected consequences beyond direct economic and insured loss including broad disruption from operational challenges, effects on counterparties, trading relationships and financial markets.

# 2 Modelling and Communicating Volcanic Risk

Assessment and communication of natural catastrophe risk within the insurance industry is often through the use of various catastrophe models. Traditionally the perils covered by these models are: earthquake (and now correlating tsunami); hurricane; windstorms; winterstorms; tornado; and flood. Increasingly there is a move towards modelling terrorism, pandemic and cyber risks.

Models vary in their levels of sophistication; from fully probabilistic models to simpler deterministic, scenario type models. The aim is to give a re/insurer a view on the probability of loss against their portfolio (of say residential houses in the UK) from one or more of these perils. For example, once every two hundred years an insurer might incur a loss of £100 m or more from flood. Companies use these results to understand the risk from natural catastrophes that they are exposed to, and to help them determine how much reinsurance (insurance for insurers) they need to buy.

There are currently few commercially available probabilistic catastrophe models for volcanic risk. Those that are available tend to focus on one hazard such as ash or pyroclastic density currents (PDCs). Development of new models is driven by industry demand; to date volcanoes have not caused sufficient insured losses to create a demand.
<figure><img src="image_189.png" /><figcaption>**Fig. 1** Example of a volcano RDS for Mexico City and Popocatepetl volcano. The exposure of two separate insurance portfolios has been overlain in blue and green on scenario hazard layers for (i) ash and pumice fall >10 cm, and (ii) lahars</figcaption></figure>

Developing deterministic scenario models is much more straightforward. Realistic Disaster Scenarios (RDSs) are commonly used within re/insurance to give companies a view of loss from a single event (Fig. 1). However, these do not always include a view on how frequently such an event might be expected.

Two of the challenges with respect to communication of volcanic risk within a re/insurance organisation are:

* Volcanic risk often goes unmodelled. Therefore, do re/insurers have an appreciation of their exposure to volcanic eruptions and the potential for loss? Non-modelled (and poorly modelled) perils have gained more focus in recent years and there is a growing pressure on the industry to form a view (e.g. ABI 2014)

* If an insurer has a view on volcanic risk, how does it compare that to the natural catastrophe risks that companies and boards are used to dealing with (e.g. earthquakes and hurricanes)? The return periods of volcanic eruptions can be very long (thousands of years) and may seem insignificant when compared to more frequently occurring perils such as earthquake or hurricane. However, volcanic eruptions have the potential to cause very large losses.

If a company does manage all of the above in terms of modelling and view of volcanic risk, there are many other challenges that still remain.

## 3 An Eruption Scenario

This scenario is based on a VEI 6 eruption. No location is specified, but it could be in almost any volcanic part of the world. We limit discussion to just one hypothetical country, but the reality may be that more than a single country is affected by ash fall, thus giving rise to even more insurance issues as policy conditions, interpretations and government responses vary significantly around the world.

A couple of middle-sized earthquakes stir the volcano to life producing a few throat-clearing but minor eruptions over a period of a few years. Minor ground shaking (volcanic tremor) continues intermittently throughout this time. Minor damage is limited to areas on the volcano's slopes. Mandatory evacuation areas extend only five kilometres from the vent. Local 'experts', including volcanologists, speculate privately and argue publically about the future course of the eruption.

Then, one bright early spring morning the volcano stirs to life with a massive Plinian eruption, producing pyroclastic density currents
(PDCs) on all sides of the cone out to 15 km radius from the vent. A tsunami produced by the PDCs down one side of the volcano damages ships in the harbour, harbour installations, other infrastructure, cargo on the wharf, and destroys an elite coastal residential area.

An ash column rises to an altitude exceeding 25 km with the upper atmosphere winds spreading the ash fall nearly 1000 km downwind—more than 1 m thick a few km from the vent, thinning to a few cm 500 km away and to just a millimetre near the margin of the more than 100,000 km² experiencing ash fall. Evacuation is ordered from the city and surrounding areas but the reality is it is too little, too late. The ash fall lasts just 24 h or so, but is accompanied by complete darkness. Property and other damage are widespread across a sizeable city's commercial centre and most but not all suburbs and surrounding areas. In areas near the volcano, PDCs destroy property. Where ash falls are more than 300–400 mm thick severe damage to buildings and other insured property is widespread; with falls of 100 mm or less only weaker buildings are damaged significantly but even these falls are enough to hinder transport and create substantial issues for communications, electricity, water distribution and sewage networks and other associated impacts from volcanic ash (Wilson et al. 2015). Across the very large areas where ash fall are only a few mm thick, some agricultural crops are severely damaged and livestock are distressed.

At the height of the eruption the volcanic cone collapses leaving a caldera 5 km across. Over the next few days rainfall compacts the ash to about half its freshly-fallen thickness and increases its density significantly; a hardened surface crust forms, strengthened by sulphur and other volatiles. Rainfall erodes and redistributes some of the ash, further clogging roads and drainage systems.

Late advice, clogged roads, a reluctance to evacuate, and a desire to remain to protect property meant hundreds of citizens didn't evacuate. In more severely affected areas the remaining citizens are presumed dead but the threat of further PDCs and the heat retained in the deposits

prevents access to the city or emergency assistance for some days, longer in some districts. Elsewhere emergency services are overloaded but the cleanup begins.

Airspace is closed across most of three countries for nearly a week. There is widespread disruption to commerce and the tourist industry.

The government enforces an exclusion zone across the remaining suburbs near the foot of the volcano for the best part of two months, fearing further eruptions.

A month or two after the eruption it is evident that some of the remaining metal roofs, even relatively new roofs, are corroding quickly. From 1500–2000 km downwind, beyond the region of ash fall, insurance claims are emerging for tarnished metal and silver, clothes and other exposed fabrics destroyed by acidic vapours.

Five months later, although the volcano is now doing nothing more than quietly steaming, the rainy season begins. Secondary lahars (volcanic mudflows), resulting from intense rainstorms on the volcano's slopes, carve deep channels, threaten life, limb, property, and infrastructure (especially bridges) even at distances of 20–30 km from the caldera, with burial (or erosion) of some remaining insured property.

## 4 A Range of Insurance Responses

It is unrealistic to regard all insurers as similar. Table 1 shows a selection of eruption-related issues that are likely to arise, characterising an insurer at the ‘well-prepared’ end of the insurance company response spectrum.

At the other end of the insurance industry spectrum are the insurers who have not thought much about volcanic risk even though their policy wordings imply that they will compensate policy holders for volcanic eruption-related damage. As the volcano moves “inexorably” towards an eruption some companies will fail to think strategically or operationally about the impending damage, the myriad claims, communication with their clients and reinsurers or to recognise the potential consequences for the company itself.
<table><caption>**Table 1** The prepared insurer</caption><thead><tr><td>Timeframe</td><td>The prepared insurer</td></tr></thead><tbody><tr><td>Before the eruption—internal</td><td><ol><li>Aware the volcano is restive; engaged with local volcano and emergency management experts; developed reasonable understanding of potential eruption styles, magnitudes and associated types of damage; reviewed and understood implications of eruption-related policy wordings; modelled potential losses associated with each scenario; established realistic estimates of the number of claims anticipated and assessments of the time required to close 50, 90% of claims</li><li>Discussed requirements, potential issues and possible problems with claims adjusters and repairers; communicated a range of scenarios and their implications to the Board and across senior management; informed all staff of crisis roles and taken staff feedback into account</li><li>Revisited operational, tactical and strategic aspects of crisis plan—test off-site data backup facilities, work from home/off site arrangements, denial of access to office buildings, absence of key personnel, alternative communications, support for staff experiencing losses/damage/stress</li><li>Engage with media organisations. Provision of information to insureds using email and social media. Assess transport and infrastructure needs/priorities</li></ol></td></tr><tr><td>Before the eruption— engagement with insureds</td><td><ol><li>Generated footprints for several key scenarios; identified at-risk policy holders using GIS technologies</li><li>Communicated information regarding policy coverage (including FAQ on making temporary repairs, deductibles, limits on alternative accommodation, looting, underinsurance and the limitations of cover), social media information sites, preparedness, evacuation and damage mitigation advice, how to contact your insurer, and how to make a claim should damage occur</li><li>Drew policyholders attention to valuable sources of information such as http:// volcanoes.usgs.gov/hazards/index.php and http://www.gns.cri.nz/Home/Learning/ Science-Topics/Volcanoes/Volcanic-Hazards and http://link.springer.com/article/10. 1186%2Fs13617-014-0010-x#page-1</li><li>Announces refusal to extend cover under existing policies or to offer new policies in areas within 200 km of the volcano</li></ol></td></tr><tr><td>After the eruption</td><td>Mobilised staff support procedures. Engaged with media; put social media plan into operation. Small team communicated frequently to deal with any unforeseen/emerging issues and to discuss issues arising from possible ongoing eruptions which could last for weeks, months, years</td></tr><tr><td>Summary</td><td>The prepared insurer has considered reputational, credit, market, liquidity, operational and environmental risks long before the rare catastrophe occurs. The staff has been engaged in corporate resilience decision making. This insurer can be fairly confident that the policyholder who insured with them this year will be back next year</td></tr></tbody></table>

Nearly all insurance companies will lie somewhere on the spectrum between the extremely well-prepared and proactive company described in the table and the rare 'response' just summarised.

## 5 Insurance Policy Wordings— Some Examples and Issues

Insurance policies contain a number of clauses or definitions that may not have been tested or thought about in detail until after an event occurs. While policies differ from country to country and insurer

to insurer, all contain a standard clause defining a 'loss occurrence'; this usually refers to all individual losses arising out of and directly occasioned by one catastrophe. Commonly, the duration and extent of any 'loss occurrence' is limited to '72 consecutive hours as regards earthquake, sea-quake, tidal wave and/or volcanic eruption' (LP098A; see further discussion regarding IUA01-033 at http://www.iuaclauses.com/site/cms/contentDocumentView.asp?chapter=9).

Around the world many wordings are similar: 'volcanic eruption' is often grouped together with earthquake and tsunami, even with
‘seaquake’ and ‘other convulsions of nature’. Some policies/contracts refer to ‘volcanic activity’, ‘volcanic eruption’ (seismic events) or even ‘losses caused by volcano’. In plain English, these different wordings do not have the same meaning—that may or may not have been the intent.

Some New Zealand examples help to highlight potential interpretation and communication issues with wordings:

i. On Christmas Eve 1953, water in volcano Ruapehu's crater lake breached the crater wall. The resulting mudflow/lahar destroyed a rail bridge pier and an overnight express train (Fig. 2) plunged into the Whangaehu River, taking 151 lives. The insured loss was minimal but could be considerably more today.

The Smithsonian Institution Global Volcanism Program in Washington D.C. is the world repository for volcanic information. The GVP database holds information on every known eruption in the world in the last 10,000 years. Volcanologists, historians and others contribute, check and update information while GVP staff

scour the relevant literature to ensure the database is as accurate as possible.

The Smithsonian database shows that Ruapehu was not in eruption between 1952 and 1956 (or to be more precise between July 1952 and the 18th November 1956) (http://volcano.si.edu/volcano.cfm?vn=241100). Certainly the Ruapehu crater wall was breached on December 24th 1953 and a lahar rushed down the Whangaehu Valley ultimately taking 151 lives, but the breach was not the result of an eruption.

Would an insurance contract defining a loss occurrence using the expression ‘volcanic eruption’ provide cover? How would a policy relying on the seemingly broader expression ‘volcanic activity’ respond?

ii. In 2005 several houses in Rotorua, New Zealand suffered damage caused by hydrothermal activity (Fig. 3)—steam, gas and hot water heated by igneous activity—at several locations within the city. There were no insurance issues as the New Zealand Earthquake Commission (EQC) policy, which provides additional cover to all privately insured residential property in the

<figure><img src="image_190.png" /><figcaption>**Fig. 2** The locomotive destroyed in the Ruapehu, New Zealand, lahar 1953 (AAVK W3493 D1952 Archives New Zealand The Department of Internal Affairs Te Tari Taiwhenua)</figcaption></figure>
<figure><img src="image_191.png" /><figcaption>**Fig. 3** Suburban hydrothermal activity, Rotorua, New Zealand (Photo B Scott, GNS Sciences, with permission)</figcaption></figure>

country, states: "If your house is damaged by earthquake, natural landslip, tsunami, volcanic eruption or hydrothermal activity (as defined in the Earthquake Commission Act 1993 and any amendments) we will pay."

However, if the words 'hydrothermal activity' had been omitted from the EQC policy so that any claim relied on 'volcanic eruption' (or on 'volcanic activity') would the damage have been covered?

iii. Another policy wording, probably used only once, stated: "volcanic activity and resulting earthquake, tsunami, lahar, lava flow, ash fall and/or fire following any of these perils" Notwithstanding it is agreed that no loss occurrence shall last more than 672 h [28 days].

This clause apparently aimed to be all-inclusive, but perhaps having the implication that if a hazard associated with 'volcanic activity' was not named, then it was not covered under this loss occurrence clause. If this implication is correct, it is unfortunate that the list does not include 'pyroclastic density current' or similar phenomena as the insurance claim for a building impacted by a PDC is likely to be around 110%

of the sum insured: 100% damage + 10% for debris removal.

We might also note that the above definition may raise other issues. Does 'resulting earthquakes' mean that only damage produced by earthquakes that occur *after* volcanic activity has begun is covered? Would 'ash fall' (Fig. 4) include cover for damage produced by 'volcanic bombs'? A volcanologist or sedimentologist could readily argue (with the support of scientific literature and practice) that 'volcanic ash' refers only to particles less than 2 mm in diameter (less than 4 mm in diameter in some definitions). The Greek word 'tephra' (used by Aristotle) which covers all airborne volcanic particles from fine dust and ash to blocks the size of houses would be more all-encompassing than 'ash fall'. We needn't worry about adding a Greek word to the definition; after all 'lava' is Greek, 'tsunami' is Japanese, and 'lahar' is Indonesian!

The Text Box below gives further examples from real-life insurance policies with a few questions highlighted in red. All of these examples emphasise that insurance Wordings need to keep the coverage simple and re/insurers need to think carefully about what their policies (intentionally or otherwise) include or exclude. Policy holders also need to think carefully about the Wordings in policies and, if necessary, to engage in dialogue with their insurers.
<figure><img src="image_192.png" /><figcaption>**Fig. 4** A Rabaul (Papua New Guinea) suburban house severely damaged (total loss) by 600–800 mm ash fall from Tavurvur volcano in 1994. The timber-framed roof</figcaption></figure>

has collapsed inwards and the walls spread outwards. The sub-floor area has been buried by air fall ash and minor lahars (Photo R Blong)

* **Definition of Volcanic Eruption: Volcanic Eruption** is a form of a volcanic activity ejecting volcanic materials i.e. such as lava flows, pyroclastics and or volcanic gasses onto the earth's surface either from a central vent or from fissures of a volcano. **What about Lahars? Ground deformation? Was the intent to exclude damage produced by these and other volcanic hazards?**

* **Definition of Tsunami: Tsunami** is a great sea wave produced by submarine earth movement such as subduction of crustal plates or by submarine volcanic eruption. **The Krakatau eruption of 1883 was not a submarine eruption but the tsunami produced on August 26 (either by massive pyroclastic flows striking the sea surface or by collapse of the volcano) killed more than 34,000 people and produced extensive damage on the nearby coasts of Sumatra and Java. Was the underwriting intent to include all volcanically-generated tsunami?**

* "However, earthquake or volcanic eruption shall happen by natural cause, but excluding earthquake or volcanic eruption caused by any material from space (Earthquake or Volcanic Eruption or Tidal Wave or Tsunami Endorsement)." **Very likely, something was lost in translation (possibly three languages were involved in this translation).**

* **Definition of Loss:** A key question for insurers on Montserrat during the 1990s was the definitions in the original contracts with the insureds on the island, and **physical damage** versus **loss of use**. A number of the properties on the island had suffered no physical damage after eruptions in 1997; however they were included in the exclusion zone. **Is this covered under the insurance contract?** The insurer in this example paid claims to the household owners on the basis of denial of access/loss of use.
# 6 The Hours Clause

As implied in the foregoing examples ‘loss occurrence’ clauses in insurance policies (particularly reinsurance policies) usually contain an Hours Clause; commonly this refers to a loss occurrence being limited to a period such as 72 consecutive hours (3 days), 168 h (1 week), or occasionally 672 h (28 days). Usually, the insurer can specify to the reinsurer when the Loss Occurrence begins, but if the damaging event lasts longer than the specified Hours Clause then a new Loss Occurrence event begins. Generally, for each Loss Occurrence the insurer will pay the retention (which might be tens of millions of dollars) with the reinsurers footing the bill for the rest of the damage. Clearly, with the large sums potentially at stake the number of Loss Occurrences agreed is of major concern to both insurers and reinsurers. This will also be of interest to individual insurance policyholders when, say, a fall of tephra produces damage, followed two months later by another damaging tephra fall. In most cases the policyholder will have to pay the deductible twice.

The eruption of Soufriere Hills on Montserrat is a good example of how even defining a loss occurrence can be complicated when considering volcanic eruptions.

In 1995 Soufrière Hills, the volcano on Montserrat in the Caribbean began erupting. The eruption continues today. In 1997 parts of the capital, Plymouth, were destroyed by PDCs. Most of the surrounding areas in the southern part of the island were relatively undamaged. In 2014 most of the houses in areas near Plymouth remain only lightly damaged by ash fall (Fig. 5) and the occasional hurricane but stand empty as the southern end of the island is located in a government-ordained exclusion zone.

During June to August 1997 a number of explosive eruptions led to the expansion of the exclusion zone on the island. Properties within this zone, however, had not necessarily suffered any physical damage from the eruptions.

A large part of the discussion between one insurer on the island and its reinsurers was how to define ‘loss occurrence’. The loss in this example was ‘loss of use’. The question was whether the June 25th 1997 eruption was the ‘occurrence’ that led to those properties being in the exclusion zone, and effectively a total loss.

Explosive stages of volcanic eruptions can last from a few minutes to more than a year with a median value of less than 10 h (Jenkins et al. 2007). In many cases it is too dangerous for loss assessors to determine damage for days or weeks after an eruption where explosive phases are irregularly interspersed between less violent stages of activity. PDCs only a few tens of cm thick are probably too hot and dangerous to walk on for days or weeks. After an eruption has ceased heavy rains can redistribute ash as mud-flows down streets and through buildings. Volcanic eruptions can produce long periods where loss assessment is not possible. This can become even more complicated when a policy is renewed with a different insurer between eruptions spaced a few days or weeks apart.

When large eruptions occur huge volumes of unconsolidated volcanic ash are deposited on surrounding slopes. As ash fall or PDCs have usually destroyed all vegetation there is little to hold the volcanic sediments in place so erosion is usually rapid—as a rough rule of thumb as much as half the sediment will be eroded within a year or two of the eruption. This means that huge volumes of sediment move into rivers and overflow burying lower-lying land. This process can continue for years as illustrated below. Only the upper half of the Bacolor (Philippines) church remains above ground (Fig. 6), buried over a period of several years by lahars from the cataclysmic eruption of Pinatubo in 1991 (one of the two largest eruptions in the world in the 20th century).

Both the Montserrat example and the burial of the Bacolor church, and numerous other buildings, villages and infrastructure, more than 30 km east of Pinatubo, illustrates that the direct
<figure><img src="image_193.png" /><figcaption>**Fig. 5** A lightly-damaged but abandoned shoe shop in the government-ordained exclusion zone in Plymouth, Montserrat in 2004 (*Photo* C Tillyard)</figcaption></figure>

<figure><img src="image_194.png" /><figcaption>**Fig. 6** The San Guillermo parish church, Bacolor (Philippines), buried by lahars years after the 1991 Pinatubo eruption (*Photos* R Blong)</figcaption></figure>

consequences of a volcanic eruption can continue for years even after the volcano has ceased erupting. Such scenes also illustrate that insurers, policy writers and insureds need to understand the consequences and the timeframes of volcanic eruptions to ensure that policies reflect not only underwriting intent but also volcanic reality.

# 7 Clean-up Costs

Clean up costs resulting from ash fall over an urban area remains an important issue for policyholders, insurers and reinsurers to consider.
We can imagine an ash fall about 10 mm thick across a city of about 200,000 people that covers an area of roughly 100 km². The ash has to be removed before it blows and washes into the storm water system which probably has (illegal) links to the sewage system and/or blows around aggravating respiratory problems, creating other health issues, and exacerbating wear and tear on machinery, vegetation including commercial crops, and nerves (Hayes et al. 2015).

A 10 mm fall of volcanic ash will likely cause little damage to well-constructed buildings, but the ash needs to be removed from building roofs and gutters, prevented from damaging sensitive equipment including most electronics, the electrical generation and distribution network, communication networks, airport runways, and roads (where a few mm obscures road markings and makes the surface slippery). The cleanup process may need to be repeated several times to remove most of the ash, or because ash continues to fall.

For our scenario city of 200,000 people the 10 mm ash fall has a volume of about 1 million cubic meters (say 10,000 truckloads); it is not simply a matter of trucking the ash to another location where it can continue to blow around. The clean-up will require planning. Suitable dump sites might be 20 km outside the city. Dump sites require maintenance so that ash doesn't continue to blow around.

Cleanup can be expensive. A repeat of the 1707 eruption of Fuji in Japan, for example, would spread ash across the Tokyo and Yokohama urban areas (and elsewhere). The cost of cleanup and removal of ash from the urban areas has been estimated to cost more than USD10 billion (Christina Magill, Risk Frontiers, pers. comm., September 2015). The damage bill could be quite limited but the cleanup costs will be substantial.

Not all of this cost would fall to insurers but have re/insurers considered the potential costs? Would policies cover the cost of debris removal and cleanup when there is no or little material damage? After the 1980 Mount St. Helens (USA) eruption, around 90% of insurers in eastern Washington paid policyholders an hourly

rate to remove ash from roofs and building surrounds. Would this practice continue?

## 8 Other Stakeholders

Here we briefly summarise some of the possible issues and considerations of other insurance industry stakeholders:

* Loss adjusters are likely to have minimal (if any) experience of forensically examining the myriad claims' issues arising in the aftermath of an eruption.

* Like insurers, reinsurers may not have considered the implications of the contract wording, impacts of volcanic eruption in their pricing, or which aspects of eruption-related damage might be covered under the catastrophe bonds they issued.

* Regulators will be working through the impacts of the event on the industry and specifically policyholders including the solvency positions of insurers.

* The media are focused on a lot of local examples of tragedy, isolated insurance issues and massive generalisations, failing to appreciate the complexity of numerous issues from insurers' points of view or the diversity in insurance response.

* Government involvement is significant. Local governments have clean-up issues and desperately need support from state government officials to contribute funds to reinstate infrastructure, assist the clean-up process, provide handouts, and deal with those who are uninsured and/or underinsured. Nobody much has thought about where all the cleaned up ash is going to be dumped. Political motivation may also play a part, for example the assumption that re/insurance companies have deep pockets with politicians publically urging the industry to be generous, even outside policy conditions/limitations.

* It is really difficult to contemplate the legal responses to the insurance issues generated by our eruption scenario, but the following
examples from insurance case law provide
insights for both insurers and insureds into
the arcane worlds of policy wordings and the
law.

## 9 Insurance Case Law

There is a rather limited amount of case law
relating to insurance and volcanic eruptions
available. Contract wordings (and their intent)
are usually of paramount importance. The
examples below allow few conclusions to be
reached. Nonetheless, these brief summaries
provide valuable examples of some of the issues
that arise in interpreting insurance contracts.

### 9.1 Philippines, 1991

The eruption of Pinatubo in early June, 1991,
covered a wide area in ash fall including two
insured properties in Angeles City. Six days later
Typhoon 'Diding' (known internationally as
Typhoon Yunya) swept across central Luzon
bringing significant rainfall to Angeles City.
Subsequently the rooves of the insured properties
collapsed. The insureds claimed that the proxi-
mate cause of the damage was the typhoon
whereas the insurer claimed the damage resulted
from the eruption of Pinatubo, that volcanic
eruption was an excluded peril and that the losses
were not covered under the policy.

Clause 6 of the policy read (in part): 'This
insurance does not cover any loss or damage
occasioned by or through or in consequence,
directly or indirectly, of any of the following
occurrences, namely:

(a) Earthquake, volcanic eruption or other con-
vulsion of nature'.

The Appeal Court found: 'An examination of
the records reveals that no damage was sustained
by the insured properties at the height of the
volcanic upheaval. True, there may have been
volcanic ashes deposited on the roofs but these

did not result in any untoward incident until the
typhoon came on June 15, 1991 which bought
more significant amount of ash fall in the affected
area, caused the same to be soaked with rain-
water thereby making it heavy which lead to the
damage of the insured properties. Consequently,
it can be deduced that the proximate cause of
the roofs caving in and the subsequent entry of
the water inside the insured premises was the
typhoon and not the volcanic debris' (Court of
Appeals, Manila 1993).

While this was bad news for the insurer, the
Appeal Court also found that the lower court's
awarding of attorney's fees to the defendant-
appellant was uncalled for (Court of Appeals,
Manila 1993).

### 9.2 New Zealand, 1995

Eruptions of Ruapehu in 1995 and 1996 during
the ski seasons caused Ruapehu Alpine Lifts
(RAL), a ski lift operator, to cease operating
because of ash fall on the snow. The ash was
corrosive, the ski field was closed and RAL lost
business. RAL had a material damage policy and
the material damage claim was met, but claims
under Business Interruption (BI) were declined
even though the cover provided for 'Earthquake,
geothermal activity or volcanic eruption'. The
quantum of loss of NZD4.669 million plus
GST + interest + any licence fees properly pay-
able was not in dispute. The standard BI policy
wording did not exclude snow from being
'property' whether natural or artificially created
(possibly an important point as RAL had
groomed and shaped the snow).

The issue revolved around the sense and
meaning of the terms in the policy and the
intention of the parties to the policy. Did ash fall
onto snow constitute damage to 'building and
other property' so as to fall within the cover of
the consequential loss policy?; the argument was
over whether snow is 'property', whether
'building' in 'building and other property' limits
the nature of 'other property' and whether the
cover was also limited by 'for the purposes of the
Business'. 'Other property' it appears is not
confined to fixed assets but includes plant, machinery, and stock; in its wider sense ‘property’ can include debts, goodwill, rights, interests and claims.

RAL won the case with costs in June 1998 (High Court of New Zealand 1998). State Insurance appealed to the New Zealand High Court but lost in May 1999.

## 9.3 United States, 1980

The appeal arose from a dispute between two insurance companies and their insureds following the May 18 1980 eruption of Mount St. Helens in which pyroclastic density currents melted snow and ice on the volcano. These PDCs combined with sediment eroded from the large debris avalanche deposit and river channels and with torrential rain from the eruption cloud, groundwater and the waters of Spirit Lake to produce mudflows down the Toutle Valley. At a distance of 30–40 km from the volcano and about 10 h after the eruption began, the appellants homes were destroyed by mudflows/lahars or by mudflows/lahars preceded by water damage from flooding.
All three policies stated the following:

### Section 1—Exclusions

We do not cover loss resulting directly or indirectly from:

...

2. Earth Movement. Direct loss by fire, explosion, theft, or breakage of glass or safety glazing materials resulting from earth movement is covered.

3. Water damage, meaning:

(a) flood ....

The term ‘Earth Movement’ was not specifically defined (evidently it had been defined in earlier years but had been omitted from this policy in order to simplify the language).

The insurers rejected the insured’s claims on the basis that the damage was excluded as ‘earth movement’. The trial court assumed the movement of Mount St. Helens was an ‘explosion’ within the terms of the insurance policies, and noted that the true meaning of ‘explosion’ was to be determined by jurors. It further determined that the mudflows which destroyed the appellants’ homes would not have occurred without the eruption of Mount St. Helens; that is, the eruption was a proximate cause of damage to the appellants’ homes. However, this was not a unanimous decision by the bench (Supreme Court of Washington 1983).

## 9.4 Iceland, 2010

The minor eruption of Eyjafjallojökull in 2010 produced an ash cloud which grounded more than 100,000 flights across Europe, disrupted the travel plans of around 10 million airline passengers and produced substantial economic losses to passengers, airlines and third parties (Alexander 2013).

Two insurance-related issues are of interest.

i. Costs of the disruption to insurers were relatively small as most BI policies require physical damage to an insured item to trigger the policy. As there was no damage to airplanes or to airports outside Iceland insurers were generally not liable for the losses sustained. However, policies do tend to cover ‘damage caused to third parties by negligent air traffic control guidance’ and it remains a question as to whether airspace was closed for longer than was strictly necessary—although the hazards to aviation (and jet engines and modern aircraft in particular) presented by volcanic ash have been well-known for at least 30 years, and a global network of VAAC (Volcanic Ash Advisory Centres) has been established for about two decades, surprisingly little effort was made to determine the ‘safe’ concentrations of volcanic ash through which aircraft could fly before the Eyjafjallajökull eruption.
More recently, non-damage Business Interruption (NDBI) policies have been offered whereby components of a BI policy without preceding property damage have been available as extensions to existing policies. NDBI policies indemnify an airline for any cancelled flight arising from airspace or airport closure by a third-party authority or airport operator caused by non-manmade events. However, there has been surprisingly limited uptake of such policies, perhaps emphasising (a) the extensions are too restrictive or too expensive; (b) a disconnect between the ways insurers see policy wording and the ways risk managers in industry view wordsings; (c) short-term thinking on long-term issues; and/or (d) the need for all parties to view risk within a holistic corporate resilience framework.

ii. Under European Union regulations air passengers are not entitled to compensation when 'force majeure' is involved. However, not all travel policies are the same (Australian travel policies, for example, tended to provide cover for the disruption resulting from the Eyjafjallajökull ash cloud). Moreover, some European insurers made refunds on abandoned travel and some insurers paid goodwill gestures.

More significantly, both a UK lower court and the UK Financial Ombudsman Service regarded the ash cloud as falling under 'poor weather conditions' which were covered by at least some travel policies. The FOS Ombudsman concluded that 'it would be fair and reasonable for the insurer to treat the wind-borne ash cloud as poor weather conditions under Ms B's travel policy; it would not be fair and reasonable for the insurer to decline Ms B's claim; and Ms B's claim should succeed and the insurer should pay the benefit available under her policy plus interest (at 8% per year simple)' (UK FOS Final Decision, March 2011; see also Brannigan 2010).

More recently there have been discussions among various parties regarding travel insurance cover for events such as the Eyjafjallajökull ash cloud but it will still be important to read the fine print.

Most interestingly for insurers and insureds alike the UK Financial Ombudsman Services (2011) made an important point in providing an opinion on Ms B's claim: 'It is a general principle of English courts that an ambiguous contractual term must be given the interpretation that is less favourable to the party who supplied the wording, which was the insurer in this case. So although I consider the “poor weather” encompasses ash on the wind, if there is any ambiguity about it, this principle will apply'.

## 10 Conclusions

Within the re/insurance industry, risk from volcanic hazards is often unmodelled and poorly understood. We need to understand more about the nature of volcanic hazards and to recognize that there are quite a few things happening before, during, and after volcanic eruptions that produce consequences including damage to a wide range of insured assets.

Not all insurance policies are the same. Insurers, reinsurers, loss adjusters, policy holders, and other players in the insurance space, need to ensure that policy wordings reflect both volcanic reality and underwriting intent.

Not all insurance companies are the same. Many are well-organised, experienced, pro-active and resilient, whilst others may have difficulty responding or even surviving an uncommon event that produces a large number of claims well beyond their experience. In that sense, insurers and the other insurance-related players in a volcanic crisis, are no different to other commercial organisations (or governments, or non-government organisations, or families, or individuals for that matter) experiencing an unusual event—some insurers have limited resilience; some have what we might call 'planned resilience'; others will adapt quickly to changed circumstances. Some insurers (and some policyholders) will survive, some will thrive.
## References

ABI, Association of British Insurers 2014 Non-modelled risks—a guide to more complete catastrophe risk assessment for (re)insurers. https://www.abi.org.uk/~/media/Files/Documents/Publications/Public/2014/prudential%20regulation/Nonmodelled%20risks%20a%20guide%20to%20more%20complete%20catastrophe%20risk%20assessment%20for%20reinsurers.pdf

Alexander D (2013) Volcanic ash in the atmosphere and risks for civil aviation: a study in european crisis management. Int J Disaster Risk Sci 4(1):9–19

Brannigan VM (2010) Alice's adventures in volcano land: the use and abuse of expert knowledge in safety regulation. Eur J Risk Regul 107–113

Court of Appeals, Manila (1993) Republic of the Philippines Court of Appeals, Manila, Sixteenth Division, Leonor Infante and CA G.R> CV No 43449 Swagman Hotel and Travel, Inc., Decision, 7 p

Deligne NI, Coles SG, Sparks RSJ (2010) Recurrence rates of large explosive volcanic eruptions. J Geophys Res 115:B06203. doi:10.1029/2009JB006554

Hayes JL, Wilson TM, Magill C (2015) Tephra fall clean-up in urban environments. J Volcanol Geoth Res 304:359–377

High Court of New Zealand (1998) 61-404 Ruapehu Alpine Lifts Limited v State Insurance Limited, June 1998, CCH Australia Limited, 61-404, 74,432–74,443

Jenkins SF, Magill CR, McAneney KJ (2007) Multi-stage volcanic events: a statistical investigation. J Volcanol Geoth Res 161(4):275–288

Supreme Court of Washington (1983), 98 Wn.2d 533; 656 P.2d 1077; 1983 Wash. LEXIS 1340

UK Financial Ombudsman Service (2011) Final Decision, Complaint by Ms B, March 2011, 10 p

Wilson TM, Jenkins S, Stewart C (2015) Impacts from volcanic ash fall. In: Papale P (ed) Volcanic hazards, risks, and disasters. Elsevier, Amsterdam, pp 47–86

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# Challenges and Benefits of Standardising Early Warning Systems: A Case Study of New Zealand's Volcanic Alert Level System

Sally H. Potter, Bradley J. Scott, Carina J. Fearnley, Graham S. Leonard and Christopher E. Gregg

## Abstract

Volcano early warning systems are used globally to communicate volcano-related information to diverse stakeholders ranging from specific user groups to the general public, or both. Within the framework of a volcano early warning system, Volcano Alert Level (VAL) systems are commonly used as a simple communication tool to inform society about the status of activity at a specific volcano. Establishing a VAL system that is effective for multiple volcanoes can be challenging, given that each volcano has specific behavioural characteristics. New Zealand has a wide range of volcano types and geological settings, including rhyolitic calderas capable of very large eruptions (>500 km³) and frequent unrest episodes, explosive andesitic stratovolcanoes, and effusive basaltic eruptions at both caldera and volcanic field settings. There is also a range in eruption frequency, requiring the VAL system to be used for both frequently active ‘open-vent’ volcanoes, and reawakening ‘closed-vent’ volcanoes. Furthermore, New Zealand’s volcanoes are situated in a variety of risk settings ranging from the Auckland Volcanic Field, which lies beneath a city of 1.4 million people; to Mt. Ruapehu, the location of popular ski fields that are occasionally impacted by ballistics and lahars, and produces tephra that falls in distant cities. These wide-ranging characteristics and their impact on society provide opportunities to learn from New Zealand’s

S.H. Potter (✉) · B.J. Scott · C.J. Fearnley · G.S. Leonard · C.E. Gregg
Department of Science and Technology, University College London, Gower Street, London WC1E 6BT, UK
e-mail: S.Potter@gns.cri.nz

C.J. Fearnley
e-mail: c.fearnley@ucl.ac.uk

S.H. Potter · G.S. Leonard
GNS Science, 1 Fairway Drive, Avalon, Lower Hutt 5010, New Zealand
e-mail: G.Leonard@gns.cri.nz

B.J. Scott
GNS Science, 114 Karetoto Road, RD4, Taupo 3384, New Zealand
e-mail: B.Scott@gns.cri.nz

C.E. Gregg
Department of Geosciences, East Tennessee State University, Box 70357, Johnson, TN 37614, New Zealand
e-mail: GREGG@mail.etsu.edu
experience with VAL systems, and the adoption of a standardised single VAL system for all of New Zealand’s volcanoes following a review in 2014. This chapter outlines the results of qualitative research conducted in 2010–2014 with key stakeholders and scientists, including from the volcano observatory at GNS Science, to ensure that the resulting standardised VAL system is an effective communication tool. A number of difficulties were faced in revising the VAL system so that it remains effective for all of the volcanic settings that exist in New Zealand. If warning products are standardised too much, end-user decision making and action can be limited when unusual situations occur, e.g., there may be loss of specific relevance in the alert message. Specific decision-making should be based on more specific parameters than the VAL alone, however wider VAL system standardisation can increase credibility, a known requirement for effective warning, by ensuring that warning sources are clear, trusted and widely understood. With a credible source, user groups are less likely to look for alternatives or confirmation, leading to faster action. Here we consider volcanic warnings within the wider concept of end-to-end multi-hazard early warning systems including detection, evaluation, notification, decision-making and action elements (based on Carsell et al. 2004).

Keywords

Volcanic Alert Level • Early warning system • Standardisation • New Zealand

# 1 Early Warning Systems and Standardisation

An Early Warning System (EWS) can be defined as a system designed to provide “hazard monitoring, forecasting and prediction, disaster risk assessment, communication and preparedness activities, systems and processes that enables individuals, communities, governments, businesses and others to take timely action to reduce disaster risks in advance of hazardous events” (UN 2003). They are recognized as “a means of getting information about an impending emergency, communicating that information to those that need it, and facilitating good decisions and timely response by people in danger” (Mileti and Sorensen 1990, p. 2–1). Essentially EWSs facilitate the provision of timely warnings to minimize loss of life and to reduce economic and

social impact on vulnerable populations (Garcia and Fearnley 2012).

The operation of an EWS presents numerous challenges due to variations in: scale (global, national, regional, local); temporality (rapid onset, slow onset, frequent, infrequent); function (safety, property, environment); and hazard (e.g., weather, climate, geohazards). A Volcano Alert Level (VAL) system is a communication tool within a volcano EWS, which simplifies the communication of volcanologists’ interpretation of data (Newhall 2000). The VAL system is disseminated with supporting information that provides more specific details and local context to enable responding agencies, the public, and other stakeholders to make informed decisions (Fearnley 2011; Potter et al. 2014). The levels can be labelled using words, numbers, colours, and/or symbols, and summarise information from