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Science at the policy interface: volcano-monitoring
technologies and volcanic hazard management
Amy Donovan, Clive Oppenheimer, Michael Bravo
To cite this version:
Amy Donovan, Clive Oppenheimer, Michael Bravo. Science at the policy interface: volcano-monitoring
technologies and volcanic hazard management. Bulletin of Volcanology, Springer Verlag, 2012, 74 (5),
pp.1005-1022. �10.1007/s00445-012-0581-5�. �insu-00723551�
Science at the policy interface: volcano-monitoring technologies and
volcanic hazard management
Amy Donovan
1, 2
, Clive Oppenheimer
3, 4, 1
and Michael Bravo
2
(1)
Department of Geography, University of Cambridge, Downing Place, Cambridge, CB2
3EN, UK
(2)
Scott Polar Research Institute, University of Cambridge, Lensfield Road, Cambridge,
CB2 1ER, UK
(3)
Le Studium, Institute for Advanced Studies, Orléans and Tours, France
(4)
Institut des Sciences de la Terre d‘Orléans, University of Orléans, 1a rue de la Férollerie,
45071 Orléans, Cedex 2, France
Abstract
This paper discusses results from a survey of volcanologists carried out on the Volcano
Listserv during late 2008 and early 2009. In particular, it examines the status of volcano
monitoring technologies and their relative perceived value at persistently and potentially
active volcanoes. It also examines the role of different types of knowledge in hazard
assessment on active volcanoes, as reported by scientists engaged in this area, and
interviewees with experience from the current eruption on Montserrat. Conclusions are drawn
about the current state of monitoring and the likely future research directions, and also about
the roles of expertise and experience in risk assessment on active volcanoes; while local
knowledge is important, it must be balanced with fresh ideas and expertise in a combination
of disciplines to produce an advisory context that is conducive to high-level scientific
discussion.
Keywords Science and policy – Risk – Uncertainty – Volcano monitoring – Volcanic hazards
Introduction: monitoring volcanoes and scientific progression in
volcanology
The last decade has witnessed extensive growth in the availability of technologies for
monitoring volcanoes. Much of this development has involved collaborations between
volcano observatories and researchers around the world. At the same time, however, resources
at observatories may be stretched and there can a great deal of pressure on scientists to justify
their purchase of new equipment. The use of different monitoring technologies in integrated
monitoring systems on active volcanoes has also increased significantly in the last decade,
with wider funding of multidisciplinary international projects (e.g. Galle et al. 2010). New
technologies have been developed to monitor long-recognised valuable signals of volcanic
activity including seismicity, gas geochemistry and ground deformation, and to develop the
application of other areas such as electromagnetic field surveillance (e.g. Zlotnicki et al.
2006). With new observatories being set up to monitor volcanoes, a key question for scientists
concerns budgeting: governments and relevant authorities can be reluctant to fund multiple
monitoring techniques if they are unconvinced of their worth. For example, the UK
Department for International Development was criticised in 2004 for failing to fund research
as well as baseline monitoring on Montserrat (House of Commons Science and Technology
Committee 2004).
The role of scientists as policy advisors has been much discussed in the social scientific
literature, particularly in the fields of climate change, biotechnology and medicine (e.g.
Shackley and Wynne 1995, 1996; Jasanoff 1990, 2004, 2005; Rayner 2003; Wynne et al.
2007; Brown 2009; Fischer 2010; Hulme and Mahony 2010). Expertise in the political
context can be questioned for political and social reasons, as well as specialist ones, placing
scientists under pressure to justify results and recommendations to laypeople. The
democratising of expertise (Fischer 2010; Brown 2009) has implications for volcanologists,
particularly given that volcanological advice may be required under crisis conditions, and may
feed into policy decisions about costly evacuations (e.g. Marzocchi and Woo 2009). While
many volcanoes are not adequately monitored (Ewert and Newhall 2004; Ewert et al. 2005),
monitoring networks have frequently provided the main source of information about volcanic
unrest (e.g. Sigmundsson et al 2010; Voight et al. 1999; Newhall and Punongbayan 1996),
and the importance of monitoring active volcanoes is widely asserted in the literature (e.g.
Tilling 2008). However, the relative infrequency of volcanic eruptions has also provoked
political criticism of budgeting for volcano monitoring. Where resources for monitoring are
limited, scientists have to justify their choice of technologies and techniques.
Volcano monitoring and the convergence of disciplines
Monitoring volcanoes typically involves the integration of a number of disciplines, the most
common being seismology, ground deformation and gas geochemistry (based on a survey of
observatory websites). Seismology is generally regarded as the most reliable of these.
However, the practice of volcano seismology varies widely between locations. The
monitoring of a particular volcano typically involves a network of seismic stations, preferably
more than four to allow location of hypocentres. There are several different typologies of
seismic signal, and there is some variation between volcanoes (McNutt 1996). Types include
volcano-tectonic (high frequency) earthquakes, attributed to brittle fracture of rock at depth,
long-period (low frequency) earthquakes, which may relate to fluid transport and deformation
(e.g. Kumagai and Chouet 1999); and volcanic tremor (e.g. Benoit and McNutt 1997). In
addition, some volcanoes generate hybrid earthquakes, which have both high- and low-
frequency components (e.g. Lahr et al. 1994; De Angelis et al., 2007), very long period
earthquakes (e.g. Rowe et al. 1998), and/or deep high-frequency earthquakes (McNutt 1996).
The use of broadband seismometers has significantly improved the resolution and range of
signals detectable from volcanoes, and this has generated new methods for the interpretation
and analysis of signals (e.g. Roman et al. 2006; Sandri et al. 2004; Neuberg et al. 1998, 2006;
Chouet 1996; Chouet et al. 2003; McNutt 1996).
Ground deformation monitoring involves the use of tiltmeters, electronic distance
measurements and Global Positioning System (GPS) receivers to monitor surface movements
at volcanoes: inflation may be caused, for example, by rising and/or vesiculating magma.
Similarly, during an eruption, the ground may deflate as magma is discharged. Ground
deformation model typically posit a source with simple geometry and try to fit it to the
observed deformation (Mogi 1958; Jousset et al. 2003; Fialko et al. 2003), and finite element
modelling methods have enabled increasingly detailed modelling of crustal dynamics (e.g.
Foroozan et al. 2010; Fialko et al. 2003). However, ground deformation can also be caused by
hydrothermal activity, and this has been the source of divided opinion on interpretation of
data, a key example being Campi Flegrei in Italy (Bellucci et al. 2005; Bonafede 1991). The
eruption of Soufriere Hills Volcano, Montserrat, has produced a well-studied deformation
pattern of inflation during phases of quiescence, and deflation during extrusive episodes
(Voight et al. 1998, 1999, 2010; Wadge et al. 2006, 2010; Foroozan et al. 2010).
Gas geochemical monitoring can provide information about the depth and amount of magma
in the crust. Currently, some observatories still carry out in situ sampling with Giggenbach
bottles, while some have spectrometers (Galle et al. 2010; Oppenheimer et al. 2003). In recent
years, ultraviolet spectroscopy for SO2 measurement has become widespread, using the
differential optical absorption spectroscopy (DOAS) technique, which is more practical and
affordable than correlation spectroscopy (e.g. McGonigle and Oppenheimer 2003). The use of
spectroscopy is growing, but the instruments are vulnerable and several are needed to provide
good coverage of the drifting plume (Edmonds et al. 2003; Salerno et al. 2009a, b; Burton et
al. 2009; Galle et al. 2010). Data processing is time-consuming and labour-intensive (e.g.
Kern et al. 2010), and spectroscopic flux measurements may be subject to high errors. A
further development is the SO2 camera (e.g. Mori and Burton 2006). While SO2 is perhaps
the most commonly monitored volcanic gas because it is abundant at active volcanoes but not
otherwise present in large quantities in the atmosphere, recent results using Fourier-Transform
infrared spectroscopy (FTIR) to monitor HCl, HF and H2S have been shown to be promising
(e.g. Edmonds et al. 2001; Burton et al. 2003). Multigas sensors have also been deployed to
analyse H2O, CO2 and SO2 together (Aiuppa et al. 2010; Edmonds et al. 2010).
There are a variety of other monitoring methods, including infrasound (e.g. Ripepe et al.
2010), resistivity (e.g. Jackson et al. 1985), microgravity (e.g. Rymer 1994) and petrological
laboratory tools (e.g. Corsaro and Miraglia 2005; Cashman and Taggart 1983). These are
currently in various stages of development, and are mostly employed at observatories with
healthier funding, or by university scientists in collaboration with observatories. There is
therefore a very wide breadth of expertises required for volcano monitoring, and therefore a
complex communicative process across disciplines. At some observatories, this also involves
communication with social scientists, whose role includes risk perception surveys and
outreach. There have recently been a number of attempts to use public participation in
workshops as a means of developing risk management plans and maps (e.g. Cronin et al.
2004).
Uncertainty and expertise: applying volcano monitoring and other types of
knowledge in advisory contexts
There are many sources of uncertainty during a volcanic eruption, including instrument error,
model error, choice of models, processing error, interpretative error, population behaviour,
‗unknown unknowns‘ and language issues. In the provision of scientific advice, monitoring
data and its analysis may be combined with modelling results, geological data, local
knowledge about the physical and social characteristics of the area, and social scientific data,
requiring interdisciplinary communication between scientists, as well as communication with
policymakers. Interdisciplinary communication for the purpose of providing expert advice has
been discussed in other fields. Collins (2004) and Collins and Evans (2007) discuss the
concept of ‗interactional expertise‘: the ability to engage with academic disciplines at a level
that allows one to understand and draw on multiple disciplines, but not necessarily contribute
to cutting-edge research (‗contributory expertise‘). Interactional expertise would ideally
include a working knowledge of the uncertainties inherent in particular types of data, for
example.
Recent studies in the science and policy field have discussed the relationship between risk and
uncertainty (Wynne 1992; Stirling 2007; Spiegelhalter and Riesch 2011). The climate change
discourse in particular has yielded some important results in this area (Giddens 2010; Hulme
2009; Morgan et al. 2009), since political decisions about climate change have to be based on
uncertain science—and this has on occasion been extremely controversial, not least in the so-
called ‗climategate‘ episode. Fundamentally, these discourses relate to the public perception
of science; in the UK, the public generally view science as a source of certainty, whereas in
practice it is characterised by uncertainty not only in the form of error, but also in subjective
judgements, model parameterization, data collection and representation of results. A schema
drawn up by Stirling (2007), which in turn draws on the work of Wynne (1992) shows risk,
uncertainty, ambiguity and ignorance as definitions of incomplete knowledge of likelihood
and/or outcome. It has been suggested that there is a ‗closing down‘ towards risk in the case
of technological governance (Stirling 2008), where risk involves knowledge of both the
outcome and its probability. The danger of this is that non-technical types of knowledge are
omitted. Figure 2 shows a schema based on Wynne (1992) and Stirling (2007), adapted for
volcanic risk. This diagram demonstrates the ways in which some of the different types of
knowledge mentioned in this paper seek to reduce uncertainty to something that can be
represented quantitatively.
The extent of the dependence on science in volcanic crises may be much greater than for other
areas of scientific governance. There is nothing that can be done reliably to reduce the
volcanic activity itself—the requirement is therefore to decide on the necessity of costly
evacuations, and long-term land-use planning. While this should be a decision for
policymakers, the absolute dependence on scientific advice often means that in practice
scientists are asked to make decisions, perhaps in the form of alert levels, which may be
directly linked to particular civil protection actions. However, many scientists feel very
strongly that this is extremely dangerous and far exceeds their role as scientists. Difficulties
arise when the boundaries become unclear, or people are put under pressure. This is perhaps
particularly likely in a crisis situation at a volcano where scientists, officials and the public are
poorly prepared and decisions have to be made quickly under high levels of uncertainty.
Bayesian Event Trees (Marzocchi et al. 2004, 2008) and volcanic risk metrics (Marzocchi and
Woo 2007, 2009) provide innovative quantitative approaches to decision making, exemplified
for the Auckland Volcanic Field by Lindsay et al. (2010). This does however involve pre-
existing collaborative relationships between local officials and scientists, and a supply of
scientific data about the volcanic conditions.
Marzocchi and Zechar (2011) discuss the different types of uncertainty involved in seismic
hazard assessment—a field that has arguably encountered some similar challenges to
volcanology. Seismic hazard assessments vary in the degree to which subjective probabilistic
methods are used, with some being heavily dependent on expert judgement (e.g. Hanks et al.
2009). As Marzocchi and Zechar (2011) note, probabilities are frequently subjective and
cannot be verified or falsified (unless P = 0 or 1), and all Bayesian methods involve a degree
of subjectivity. However, these methods are more effective at representing uncertainty not
simply from the variability of the natural system (aleatory) but also from lack of knowledge
(epistemic). An important summary of recent relevant developments in earthquake hazard
assessment is provided by Jordan et al. (2011), who note the importance of probabilistic
forecasts for risk managers. It should also be noted that the use of probabilistic methods does
not take into account the broader social context both of science and its application (Fig. 1).
These are uncertainties that are not easily quantified.
