Volcanic hazard is a basic ingredient in risk-based decision making in land-use planning and emergency management. Volcanic hazard is defined as the probability of any particular area being affected by a destructive volcanic event within a given period of time (Fournier d'Albe,1979). Current volcanic hazard assessment is even more entangled by scarce data and relatively poor knowledge of the physical processes. Cumulatively, these difficulties prevent solution of the hazard/risk problem from a rigorous scientific perspective. In this respect, Bayesian statistics provides a suitable framework for producing a volcanic hazard/risk assessments in a rational, probabilistic form (e.g., UNESCO, 1972; Gelman et al., 1995). In this chapter we present and further develop the method proposed by Marzocchi et al. (2004) based on the event tree (Newhall and Hoblitt, 2002) scheme to estimate the probability of all the relevant possible outcomes of a volcanic crisis and, in general, to quantify volcanic hazard and risk. In Marzocchi et al. (2004) we have emphasized the volcanological aspects, dividing the strategy in three consequential steps that encompass (a) the logical sequence of acquisition of information, (b) use of past data to assess long-term volcanic hazard (from years to decades), and (c) use of monitoring observations to assess mid- to short-term volcanic hazard (from hours to a few years). There are two important points about this approach. First, the scheme can take all available information into account, from theoretical models to past data and monitoring measurements. Second, the use of these different types of data in a Bayesian framework provides a mechanism for continuously updating probabilities, and therefore both the long- and mid- to short-term volcanic hazard may be continuously revised if necessary. For example, long-term volcanic hazard assessments are often used to compare different kinds of hazards (volcanic, seismic, industrial, floods, etc.) that may impact the same area. Results of long-term hazard assessments are very useful for cost/benefit analysis of risk mitigation actions, and for appropriate land-use planning, such as location of settlements. As data and models related to hazards are continually changing, and risks may change rapidly with population growth, easy update of event trees is essential. In contrast, hazard assessment in a Bayesian framework on mid- to short-time scales assists with actions for immediate vulnerability (and risk) reduction, for instance through evacuation of people from dangerous areas (Fournier d'Albe, 1979).

A Quantitative Model for Volcanic Hazard Assessment

FURLAN, CLAUDIA
2006

Abstract

Volcanic hazard is a basic ingredient in risk-based decision making in land-use planning and emergency management. Volcanic hazard is defined as the probability of any particular area being affected by a destructive volcanic event within a given period of time (Fournier d'Albe,1979). Current volcanic hazard assessment is even more entangled by scarce data and relatively poor knowledge of the physical processes. Cumulatively, these difficulties prevent solution of the hazard/risk problem from a rigorous scientific perspective. In this respect, Bayesian statistics provides a suitable framework for producing a volcanic hazard/risk assessments in a rational, probabilistic form (e.g., UNESCO, 1972; Gelman et al., 1995). In this chapter we present and further develop the method proposed by Marzocchi et al. (2004) based on the event tree (Newhall and Hoblitt, 2002) scheme to estimate the probability of all the relevant possible outcomes of a volcanic crisis and, in general, to quantify volcanic hazard and risk. In Marzocchi et al. (2004) we have emphasized the volcanological aspects, dividing the strategy in three consequential steps that encompass (a) the logical sequence of acquisition of information, (b) use of past data to assess long-term volcanic hazard (from years to decades), and (c) use of monitoring observations to assess mid- to short-term volcanic hazard (from hours to a few years). There are two important points about this approach. First, the scheme can take all available information into account, from theoretical models to past data and monitoring measurements. Second, the use of these different types of data in a Bayesian framework provides a mechanism for continuously updating probabilities, and therefore both the long- and mid- to short-term volcanic hazard may be continuously revised if necessary. For example, long-term volcanic hazard assessments are often used to compare different kinds of hazards (volcanic, seismic, industrial, floods, etc.) that may impact the same area. Results of long-term hazard assessments are very useful for cost/benefit analysis of risk mitigation actions, and for appropriate land-use planning, such as location of settlements. As data and models related to hazards are continually changing, and risks may change rapidly with population growth, easy update of event trees is essential. In contrast, hazard assessment in a Bayesian framework on mid- to short-time scales assists with actions for immediate vulnerability (and risk) reduction, for instance through evacuation of people from dangerous areas (Fournier d'Albe, 1979).
Statistics in volcanology
9781862392083
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/1559216
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