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In this paper, we illustrate a Bayesian Event Tree to estimate Volcanic Hazard (BET_VH). The procedure enables us to calculate the probability of any kind of long-term hazardous event for which we are interested, accounting for the intrinsic stochastic nature of volcanic eruptions and our limited knowledge regarding related processes. For the input, the code incorporates results from numerical models simulating the impact of hazardous volcanic phenomena on an area and data from the eruptive history. For the output, the code provides a wide and exhaustive set of spatiotemporal probabilities of different events; these probabilities are estimated by means of a Bayesian approach that allows all uncertainties to be properly accounted for. The code is able to deal with many eruptive settings simultaneously, weighting each with its own probability of occurrence. In a companion paper, we give a detailed example of application of this tool to the Campi Flegrei caldera, in order to estimate the hazard from tephra fall. © The Author(s) 2010.

In this paper, we explore the effects of the intrinsic uncertainties upon long-term volcanic hazard by analyzing tephra fall hazard at Campi Flegrei, Italy, using the BET_VH model described in Marzocchi et al. (Bull Volcanol, 2010). The results obtained show that volcanic hazard based on the weighted average of all possible eruptive settings (i. e. size classes and vent locations) is significantly different from an analysis based on a single reference setting, as commonly used in volcanic hazard practice. The long-term hazard map for tephra fall at Campi Flegrei obtained here accounts for a wide spectrum of uncertainties which are usually neglected, largely reducing the bias intrinsically introduced by the choice of a specific reference setting. We formally develop and apply a general method to recursively integrate simulations from different models which have different characteristics in terms of spatial coverage, resolution and physical details. This outcome of simulations will be eventually merged with field data through the use of the BET_VH model. © The Author(s) 2010.

High risk volcanic events are commonly preceded by long periods of unrest during which scientists are asked to provide near real-time forecasts. The rarity of such events, inaccessibility of the underground volcanic system, non-linear behaviors, and limited datasets constitute major sources of uncertainty. In order to provide reasoned guidance in the face of uncertainties, monitoring observations and conceptual/theoretical models must be incorporated into a formal and structured probabilistic scheme using evidence science principles. As uncertainty and subjectivity are inescapable components of volcanic hazard forecasts, they must be dealt with and clearly communicated to decision-makers and society. Here, we present the set-up of an automated near-real-time tool for short-term eruption forecasting for Campi Flegrei caldera (CFc), Italy. The tool, based on a Bayesian Event Tree scheme, takes account of all the available information, and subjectivity of choices is dealt through a 5-year-long elicitation experiment with a team of about 30 of the major experts of the geological history, dynamics and monitoring of CFc. The tool provides prompt probabilistic assessment in near real-time, making it particularly suitable for tracking a rapidly evolving crisis, and it is easily reviewable once new observations and/or models become available. The quantitative rules behind the tool, which represent the group view of the elicited community of experts, are defined during a period of quiescence, thus allowing prior scrutiny of any scientific input into the model, and minimizing the external stress on scientists during an actual emergency phase. Notably, the results also show that CFc may pose a higher threat to the city of Naples than the better-known Mount Vesuvius. © 2012 Selva et al.