Probabilistic Volcanic Hazard Assessment Research Articles

A multi-methodological approach to record dynamics and timescales of the plumbing system of Zaro (Ischia Island, Italy)

Determining the time spans of processes related to the assembly of eruptible magma at active volcanoes is fundamental to understand magma chamber dynamics and assess volcanic hazard. This information can be recorded in the chemical zoning of crystals. Nevertheless, this kind of study is still poorly employed for the active volcanoes of the Neapolitan area (Southern Italy), in particular, for Ischia island where the risk is extremely high and this information can provide the basis for probabilistic volcanic hazard assessment. For these reasons, we acquired chemical composition on clinopyroxene crystals erupted at Ischia during the Zaro eruption (6.6 ± 2.2 ka) and performed numerical simulations of the input of mafic magma into a trachytic reservoir, in order to investigate various aspects of pre-eruptive dynamics occurring at different timescales. This event emplaced a ~ 0.1 km3 lava complex, in which the main trachytic lava flows host abundant mafic to felsic enclaves. Previous petrological investigation suggested that mafic magma(s) mixed/mingled with a trachytic one, before the eruption. In this work, the clinopyroxene zoning patterns depict the growth of crystals in different magmatic environments, recording sequential changes occurred in the plumbing system before the eruption. The evolution of the plumbing system involved a hierarchy of timescales: a few hours for magma mingling caused by mafic recharge(s) and likely occurred multiple times over a decade during which a dominant magmatic environment was sustained before the eruption. Such timescales must be considered in volcanic hazard assessment at Ischia and similar active volcanoes in densely populated areas.

Probabilistic volcanic mass flow hazard assessment using statistical surrogates of deterministic simulations

Probabilistic volcanic hazard assessments require (1) an identification of the hazardous volcanic source; (2) estimation of the magnitude-frequency relationship for the volcanic process; (3) quantification of the dependence of hazard on magnitude and external conditions; and (4) estimation of hazard exceedance from the magnitude-frequency and hazard intensity relationship. For volcanic mass flows, quantification of the hazard is typically undertaken through the use of computationally expensive mass flow simulators. However, this computational expense restricts the number of samples that can be used to produce a probabilistic assessment and limits the ability to rapidly update hazard assessments in response to changing source probabilities. We develop an alternate approach to defining hazard intensity through a surrogate model that provides a continuous estimate of simulation outputs at negligible computational expense, demonstrated through a probabilistic hazard assessment of dome collapse (block-and-ash) flows at Taranaki volcano, New Zealand. A Gaussian Process emulator trained on a database of simulations is used as the surrogate model of hazard intensity across the input space of possible dome collapse volumes and configurations, which is then sampled using a volume-frequency relationship of dome collapse flows. The demonstrated technique is a tractable solution to the problem of probabilistic volcanic hazard assessment, with the surrogates providing a good approximation of the simulator, and is generally applicable to volcanic hazard and geo-hazard assessments that are limited by the demands of numerical simulations and changing source probabilities.

On the feasibility and usefulness of high-performance computing in probabilistic volcanic hazard assessment: An application to tephra hazard from Campi Flegrei

For active volcanoes, knowledge about probabilities of eruption and impacted areas becomes valuable information for decision-makers to develop short- and long-term emergency plans, for which probabilistic volcanic hazard assessment (PVHA) is needed. High-resolution or spatially extended PVHA requires extreme-scale high-performance computing systems. Within the framework of ChEESE (Center of Excellence for Exascale in Solid Earth; www.cheese-coe.eu), an effort was made to generate exascale-suitable codes and workflows to collect and process in some hours the large amount of data that a quality PVHA requires. To this end, we created an optimized HPC-based workflow coined PVHA_HPC-WF to develop PVHA for a volcano. This tool uses the Bayesian event tree methodology to calculate eruption probabilities, vent-opening location(s), and eruptive source parameters (ESPs) based on volcano history, monitoring system data, and meteorological conditions. Then, the tool interacts with the chosen hazard model, performing a simulation for each ESP set or volcanic scenario (VS). Finally, the resulting information is processed by proof-of-concept-subjected high-performance data analytics (HPDA) scripts, producing the hazard maps which describe the probability over time of exceeding critical thresholds at each location in the investigated geographical domain. Although PVHA_HPC-WF can be adapted to other hazards, we focus here on tephra (i.e., lapilli and ash) transport and deposition. As an application, we performed PVHA for Campi Flegrei (CF), Italy, an active volcano located in one of the most densely inhabited areas in Europe and under busy air traffic routes. CF is currently in unrest, classified as being in an attention level by the Italian Civil Protection. We consider an approximate 2,000 × 2,000 × 40 km computational domain with 2 km grid resolution in the horizontal and 40 vertical levels, centered in CF. To explore the natural variability and uncertainty of the eruptive conditions, we consider a large number of VSs allowing us to include those of low probability but high impact, and simulations of tephra dispersal are performed for each of them using the FALL3D model. Results show the potential of HPC to timely execute a vast range of simulations of complex numerical models in large high-resolution computational domains and analyze great volumes of data to obtain quality hazard maps.

Probabilistic volcanic hazard assessment at an active but under-monitored volcano: Ceboruco, Mexico

A probabilistic volcanic hazard assessment (PVHA) for Ceboruco volcano (Mexico) is reported using PyBetVH, an e-tool based on the Bayesian Event Tree (BET) methodology. Like many volcanoes, Ceboruco is under-monitored. Despite several eruptions in the late Holocene and efforts by several university and government groups to create and sustain a monitoring network, this active volcano is monitored intermittently rather than continuously by dedicated groups. With no consistent monitoring data available, we look at the geology and the eruptive history to inform prior models used in the PVHA. We estimate the probability of a magmatic eruption within the next time window (1 year) of ~ 0.002. We show how the BET creates higher probabilities in the absence of monitoring data, which if available would better inform the prior distribution. That is, there is a cost in terms of higher probabilities and higher uncertainties for having not yet developed a sustained volcano monitoring network. Next, three scenarios are developed for magmatic eruptions: i) small magnitude (effusive/explosive), ii) medium magnitude (Vulcanian/sub-Plinian) and iii) large magnitude (Plinian). These scenarios are inferred from the Holocene history of the volcano, with their related hazardous phenomena: ballistics, tephra fallout, pyroclastic density currents, lahars and lava flows. We present absolute probability maps (unconditional in terms of eruption size and vent location) for a magmatic eruption at Ceboruco volcano. With PyBetVH we estimate and visualize the uncertainties associated with each probability map. Our intent is that probability maps and uncertainties will be useful to local authorities who need to understand the hazard when considering the development of long-term urban and land-use planning and short-term crisis management strategies, and to the scientific community in their efforts to sustain monitoring of this active volcano.

Probabilistic Volcanic Hazard Assessment of the 22.5–28°S Segment of the Central Volcanic Zone of the Andes

Evaluation of volcanic hazards typically focusses on single eruptive centres or spatially restricted areas, such as volcanic fields. Expanding hazard assessments across wide regions (e.g., large sections of a continental margin) has rarely been attempted, due to the complexity of integrating temporal and spatial variability in tectonic and magmatic processes. In this study, we investigate new approaches to quantify the hazards of such long-term active and complex settings, using the example of the 22.5–28°S segment of the Central Volcanic Zone of the Andes. This research is based on the estimation of: 1) spatial probability of future volcanic activity (based on kernel density estimation using a new volcanic geospatial database), 2) temporal probability of future volcanic events, and 3) areas susceptible to volcanic flow and fall processes (based on computer modeling). Integrating these results, we produce a set of volcanic hazard maps. We then calculate the relative probabilities of population centres in the area being affected by any volcanic phenomenon. Touristic towns such as La Poma (Argentina), Toconao (Chile), Antofagasta de la Sierra (Argentina), Socaire (Chile), and Talabre (Chile) are exposed to the highest relative volcanic hazard. In addition, through this work we delineate five regions of high spatial probability (i.e., volcanic clusters), three of which correlate well with geophysical evidence of mid-crustal partial melt bodies. Many of the eruptive centres within these volcanic clusters have poorly known eruption histories and are recommended to be targeted for future work. We hope this contribution will be a useful approach to encourage probabilistic volcanic hazard assessments for other arc segments.

VIGIL: A Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling

Probabilistic volcanic hazard assessment is a standard methodology based on running a deterministic hazard quantification tool multiple times to explore the full range of uncertainty in the input parameters and boundary conditions, in order to probabilistically quantify the variability of outputs accounting for such uncertainties. Nowadays, different volcanic hazards are quantified by means of this approach. Among these, volcanic gas emission is particularly relevant given the threat posed to human health if concentrations and exposure times exceed certain thresholds. There are different types of gas emissions but two main scenarios can be recognized: hot buoyant gas emissions from fumaroles and the ground and dense gas emissions feeding density currents that can occur, e.g., in limnic eruptions. Simulation tools are available to model the evolution of critical gas concentrations over an area of interest. Moreover, in order to perform probabilistic hazard assessments of volcanic gases, simulations should account for the natural variability associated to aspects such as seasonal and daily wind conditions, localized or diffuse source locations, and gas fluxes. Here we present VIGIL (automatized probabilistic VolcanIc Gas dIspersion modeLling), a new Python tool designed for managing the entire simulation workflow involved in single and probabilistic applications of gas dispersion modelling. VIGIL is able to manage the whole process from meteorological data processing, needed to run gas dispersion in both the dilute and dense gas flow scenarios, to the post processing of models’ outputs. Two application examples are presented to show some of the modelling capabilities offered by VIGIL.

Probabilistic Volcanic Hazard Assessment for National Park Infrastructure Proximal to Taranaki Volcano (New Zealand)

Hazard assessment for infrastructure proximal to a volcanic vent raises issues that are often not present, or not as severe in hazard assessments for more distal infrastructure. Proximal regions are subject to a greater number of hazardous phenomena, and variability in impact intensity increases with the hazard magnitude. To probabilistically quantify volcanic hazard to infrastructure, multiple volcanic hazards and their effects on exposed elements need to be considered. Compared to single-hazard assessments, multi-hazard assessments increase the size and complexity of determining hazard occurrence and magnitude, typically introducing additional uncertainties in the quantification of risk. A location-centred approach, focusing on key locations rather than key hazards, can simplify the problem to one requiring identification of hazards with the potential to affect the location, followed by assessment of the probability of these hazards and their triggering eruptions. The location-centred approach is more compatible to multi-source hazards and allows for different hazard estimation methodologies to be applied as appropriate for the infrastructure type. We present a probabilistic quantification of volcanic hazard using this location centred approach for infrastructure within Te Papakura o Taranaki National Park, New Zealand. The impact to proposed park infrastructure from volcanic activity (originating from Mt. Taranaki) is quantified using a probability chain to provide a structured approach to integrate differing hazard estimation methods with eruption probability estimates within asset lifetimes.This location-centered approach provides quantitative estimates for volcanic hazards that significantly improve volcanic hazard estimates for infrastructure proximal to the Taranaki summit vent. Volcanic mass flows, predominantly pyroclastic surges or block and ash flows, are most likely (probability >0.8) to affect walking tracks if an eruption occurs. The probability of one or more eruption(s) in the next 50 years is estimated at 0.35–0.38. This use of probability chains and a location centered assessment demonstrates a technique that can be applied to proximal hazard assessments globally.

Probabilistic Volcanic Hazard Assessment for Pyroclastic Density Currents From Pumice Cone Eruptions at Aluto Volcano, Ethiopia

Aluto volcano, in the Main Ethiopian Rift, is a peralkaline caldera system, which comprises conglomerations of rhyolite (obsidian) lavas and enigmatic pumice cones. Recent work has found that pumice cone eruptions are highly unsteady, and form convective eruption plumes that frequently collapse to generate pyroclastic density currents (PDCs). We develop a methodology and present results for the first probabilistic volcanic hazard assessment (PVHA) for PDCs at a pumice cone volcano. By doing so, we estimate the conditional probability of inundation by PDCs around Aluto volcano (Ethiopia), incorporating the aleatory uncertainty in PDC hazard. We employ a Monte Carlo energy cone modelling approach, which benefits from parameterisation informed by field investigations and volcanic plume modelling. We find that despite the relatively modest eruptions that are likely to occur, the wide distribution of past vent locations (and thus the high uncertainty of where future vents might open), results in a broad area being potentially at risk of inundation by PDCs. However, the aleatory uncertainty in vent opening means that the conditional probabilities are lower (= 0.05) regions of Aluto caldera. The Monte Carlo energy cone modelling approach provides a quantitative, accountable and defendable background and long-term PVHA for PDCs from Aluto. These results could be combined in the future with hazard assessments relating to tephra fall and/or lava to develop a comprehensive volcanic hazard map for the caldera.

Long-Term Probabilistic Volcanic Hazard Assessment Using Open and Non-open Data: Observations and Current Issues

Probabilistic volcanic hazard assessment (PVHA) has become the paradigm to quantify volcanic hazard over the last decades. Substantial aleatory and epistemic uncertainties in PVHA arise from complexity of physico-chemical processes, impossibility of their direct observation and, importantly, a severe scarcity of observables from past eruptions. One factor responsible for data scarcity is the infrequency of moderate/large eruptions; other factors include lack of discoverability and accessibility to volcanological data. Open-access databases can help alleviate data scarcity and have significantly contributed to long-term PVHA of eruption onset and size, while are less common for data required in other PVHA components (e.g. vent opening). Making datasets open is complicated by economical, technological, ethical and/or policy-related challenges. International synergies (e.g. Global Volcanism Program, WOVOdat, Global Volcano Model, EPOS) will be key to facilitate the creation and maintenance of open-access databases that support Next-Generation PVHA. Additionally, clarification of some misconceptions about PVHA can also help progress. Firstly, PVHA should be understood as an expansion of deterministic, scenario-based hazard assessments. Secondly, a successful PVHA should sometimes be evaluated by its ability to deliver useful and usable hazard-related messages that help mitigate volcanic risk. Thirdly, PVHA is not simply an end product but a driver for research: identifying the most relevant sources of epistemic uncertainty can guide future efforts to reduce the overall uncertainty. Broadening of the volcanological community expertise to statistics or engineering has already brought major breakthroughs in long-term PVHA. A vital next step is developing and maintaining more open-access datasets that support PVHA worldwide.

Probabilistic Hazard From Pyroclastic Density Currents in the Neapolitan Area (Southern Italy)

AbstractThe metropolitan area of Napoli (∼3 M inhabitants) in southern Italy is located in between two explosive active volcanoes: Somma‐Vesuvius and Campi Flegrei. Pyroclastic density currents (PDCs) from these volcanoes may reach the city center, as witnessed by the Late Quaternary stratigraphic record. Here we compute a novel multivolcano Probabilistic Volcanic Hazard Assessment of PDCs, in the next 50 years, by combining the probability of PDC invasion from each volcano (assuming that they erupt independently) over the city of Napoli and its surroundings. We model PDC invasion with the energy cone model accounting for flows of very different (but realistic) mobility and use the Bayesian Event Tree for Volcanic Hazard to incorporate other volcano‐specific information such as the probability of eruption or the spatial variability in vent opening probability. Worthy of note, the method provides a complete description of Probabilistic Volcanic Hazard Assessment, that is, it yields percentile maps displaying the epistemic uncertainty associated with our best (aleatory) hazard estimation. Since the probability density functions of the model parameters of the energy cone are unknown, we propose an ensemble of different hazard assessments based on different assumptions on such probability density functions. The ensemble merges two plausible distributions for the collapse height, reflecting a source of epistemic (specifically, parametric) uncertainty. We also apply a novel quantification for a spatially varying epistemic uncertainty associated to PDC simulations.

Suitability of energy cone for probabilistic volcanic hazard assessment: validation tests at Somma-Vesuvius and Campi Flegrei (Italy)

Pyroclastic density currents (PDCs) are gravity-driven hot mixtures of gas and volcanic particles which can propagate at high speed and cover distances up to several tens of kilometers around a given volcano. Therefore, they pose a severe hazard to the surroundings of explosive volcanoes able to produce such phenomena. Despite this threat, probabilistic volcanic hazard assessment (PVHA) of PDCs is still in an early stage of development. PVHA is rooted in the quantification of the large uncertainties (aleatory and epistemic) which characterize volcanic hazard analyses. This quantification typically requires a big dataset of hazard footprints obtained from numerical simulations of the physical process. For PDCs, numerical models range from very sophisticated (not useful for PVHA because of their very long runtimes) to very simple models (criticized because of their highly simplified physics). We present here a systematic and robust validation testing of a simple PDC model, the energy cone (EC), to unravel whether it can be applied to PVHA of PDCs. Using past PDC deposits at Somma-Vesuvius and Campi Flegrei (Italy), we assess the ability of EC to capture the values and variability in some relevant variables for hazard assessment, i.e., area of PDC invasion and maximum runout. In terms of area of invasion, the highest Jaccard coefficients range from 0.33 to 0.86 which indicates an equal or better performance compared to other volcanic mass-flow models. The p values for the observed maximum runouts vary from 0.003 to 0.44. Finally, the frequencies of PDC arrival computed from the EC are similar to those determined from the spatial distribution of past PDC deposits, with high PDC-arrival frequencies over an ∼8-km radius from the crater area at Somma-Vesuvius and around the Astroni crater at Campi Flegrei. The insights derived from our validation tests seem to indicate that the EC is a suitable candidate to compute PVHA of PDCs.

PyBetVH: A Python tool for probabilistic volcanic hazard assessment and for generation of Bayesian hazard curves and maps

PyBetVH is a completely new, free, open-source and cross-platform software implementation of the Bayesian Event Tree for Volcanic Hazard (BET_VH), a tool for estimating the probability of any magmatic hazardous phenomenon occurring in a selected time frame, accounting for all the uncertainties. New capabilities of this implementation include the ability to calculate hazard curves which describe the distribution of the exceedance probability as a function of intensity (e.g., tephra load) on a grid of points covering the target area. The computed hazard curves are (i) absolute (accounting for the probability of eruption in a given time frame, and for all the possible vent locations and eruptive sizes) and (ii) Bayesian (computed at different percentiles, in order to quantify the epistemic uncertainty). Such curves allow representation of the full information contained in the probabilistic volcanic hazard assessment (PVHA) and are well suited to become a main input to quantitative risk analyses.PyBetVH allows for interactive visualization of both the computed hazard curves, and the corresponding Bayesian hazard/probability maps. PyBetVH is designed to minimize the efforts of end users, making PVHA results accessible to people who may be less experienced in probabilistic methodologies, e.g. decision makers. The broad compatibility of Python language has also allowed PyBetVH to be installed on the VHub cyber-infrastructure, where it can be run online or downloaded at no cost. PyBetVH can be used to assess any type of magmatic hazard from any volcano. Here we illustrate how to perform a PVHA through PyBetVH using the example of analyzing tephra fallout from the Okataina Volcanic Centre (OVC), New Zealand, and highlight the range of outputs that the tool can generate.

Brief Communication: The effect of submerged vents on probabilistic hazard assessment for tephra fallout

Abstract. Many volcanic systems are partially or entirely submerged, implying that vents may open underwater. The effect of submerged vents on probabilistic volcanic hazard assessment (PVHA) for tephra fallout has always been neglected, introducing potentially uncontrolled biases. We present a strategy to quantify the effect of submerged vents on PVHA for tephra fallout, based on a simplified empirical model in which the efficiency of tephra production decreases as a function of the water depth above the eruptive vent. The method is then applied to Campi Flegrei caldera, comparing its results to those of two reference end-member models and their statistical mixing.

The correlation between run-up and repose times of volcanic eruptions

Volcanoes commonly make precursory signals before an eruption. The intensity of those precursory phenomena vary substantially in size and duration for different volcano. The precursors are thought to be related to magma ascent beneath the volcanic edifice. So a measure of the time of the precursory activity, or run-up time ( t), should be related to the dynamics of magma ascent and so to the magma viscosity. Another observable strongly related to the magma viscosity is the interevent repose time (r). The time between eruptions is controlled by the recharge of the magma chamber and the accumulation of pressure. Both of these processes are also sensitive to magma viscosity. In this study, we investigate the relationship among repose time, run-up time and viscosity by using welldocumented eruptions around the world during the last 60 years to compile a database of 54 eruptions. Since the details of the magma viscosity are subject to large uncertainties, we use a large dataset which encompasses extreme variations and enables us to use silica content as a first-order proxy for gross viscosity differences. Thus we have a data set where viscosity of the magma varies by 7 orders of magnitude and thus becomes the most dominant parameter in the system. The data collected show a strong correlation between run-up times and repose times and magma viscosity; fitting the data with a regression line we have: log( t)= 1.1log(r) -3.5 the slope significantly greater than zero at the 99% confidence level (P-value<0.01). We observe that repose time is approximately 10 4 x viscosity and run-up time is 10 x viscosity. The correlation between run-up time and viscosity is well-predicted by a model of diking where the propagation velocity of magma moving toward the surface is controlled by a combination of viscous flow and brittle failure of the crust. The repose time is less precisely modeled, but is consistent with magma recharge models inferred in previous published works. This strong correlation between precursory activity, repose phase and magma chemical composition could be very useful to put some constraint on the eruption behavior both for physical modeling the eruptive dynamics and for a probabilistic volcanic hazard assessment.

Combining long- and short-term probabilistic volcanic hazard assessment with cost-benefit analysis to support decision making in a volcanic crisis from the Auckland Volcanic Field, New Zealand

By using BET_VH, we propose a quantitative probabilistic hazard assessment for base surge impact in Auckland, New Zealand. Base surges resulting from phreatomagmatic eruptions are among the most dangerous phenomena likely to be associated with the initial phase of a future eruption in the Auckland Volcanic Field. The assessment is done both in the long-term and in a specific short-term case study, i.e. the simulated pre-eruptive unrest episode during Exercise Ruaumoko, a national civil defence exercise. The most important factors to account for are the uncertainties in the vent location (expected for a volcanic field) and in the run-out distance of base surges. Here, we propose a statistical model of base surge run-out distance based on deposits from past eruptions in Auckland and in analogous volcanoes. We then combine our hazard assessment with an analysis of the costs and benefits of evacuating people (on a 1 × 1-km cell grid). In addition to stressing the practical importance of a cost-benefit analysis in creating a bridge between volcanologists and decision makers, our study highlights some important points. First, in the Exercise Ruaumoko application, the evacuation call seems to be required as soon as the unrest phase is clear; additionally, the evacuation area is much larger than what is recommended in the current contingency plan. Secondly, the evacuation area changes in size with time, due to a reduction in the uncertainty in the vent location and increase in the probability of eruption. It is the tradeoff between these two factors that dictates which cells must be evacuated, and when, thus determining the ultimate size and shape of the area to be evacuated.

Testing forecasts of a new Bayesian time-predictable model of eruption occurrence

In this paper we propose a model to forecast eruptions in a real forward perspective. Specifically, the model provides a forecast of the next eruption after the end of the last one, using only the data available up to that time. We focus our attention on volcanoes with open conduit regime and high eruption frequency. We assume a generalization of the classical time predictable model to describe the eruptive behavior of open conduit volcanoes and we use a Bayesian hierarchical model to make probabilistic forecasts. We apply the model to Kilauea volcano eruptive data and Mount Etna volcano flank eruption data. The aims of the proposed model are: (1) to test whether or not the Kilauea and Mount Etna volcanoes follow a time predictable behavior; (2) to discuss the volcanological implications of the time predictable model parameters inferred; (3) to compare the forecast capabilities of this model with other models present in literature. The results obtained using the MCMC sampling algorithm show that both volcanoes follow a time predictable behavior. The numerical values inferred for the parameters of the time predictable model suggest that the amount of the erupted volume could change the dynamics of the magma chamber refilling process during the repose period. The probability gain of this model compared with other models already present in literature is appreciably greater than zero. This means that our model provides better forecast than previous models and it could be used in a probabilistic volcanic hazard assessment scheme.

Bayesian Hierarchical Time Predictable Model for eruption occurrence: an application to Kilauea Volcano

SUMMARY The physical processes responsible for volcanic eruptions are characterized by a large number of degrees of freedom, often non-linearly coupled. This extreme complexity leads to an intrinsic deterministic unpredictability of such events that can be satisfactorily described by a stochastic process. Here, we address the long-term eruption forecasting of open conduit volcanoes through a Bayesian Hierarchical Modelling information in the catalogue of past eruptions, such as the time of occurrence, the duration, and the erupted volumes. The aim of the model is twofold: (1) to get new insight about the physics of the process, using the model to test some basic physical hypotheses of the eruptive process and (2) to build a stochastic model for long-term eruption forecasting; this is the basic component of Probabilistic Volcanic Hazard Assessment that is used for rational land use planning and to design Emergency plan. We apply the model to Kilauea eruption occurrences and check its feasibility to be included in Probabilistic Volcanic Hazard Assessment.

Principles of volcanic risk metrics: Theory and the case study of Mount Vesuvius and Campi Flegrei, Italy

Despite volcanic risk having been defined quantitatively more than 30 years ago, this risk has been managed without being effectively measured. The recent substantial progress in quantifying eruption probability paves the way for a new era of rational science‐based volcano risk management, based on what may be termed “volcanic risk metrics” (VRM). In this paper, we propose the basic principles of VRM, based on coupling probabilistic volcanic hazard assessment and eruption forecasting with cost‐benefit analysis. The VRM strategy has the potential to rationalize decision making across a broad spectrum of volcanological questions. When should the call for evacuation be made? What early preparations should be made for a volcano crisis? Is it worthwhile waiting longer? What areas should be covered by an emergency plan? During unrest, what areas of a large volcanic field or caldera should be evacuated, and when? The VRM strategy has the paramount advantage of providing a set of quantitative and transparent rules that can be established well in advance of a crisis, optimizing and clarifying decision‐making procedures. It enables volcanologists to apply all their scientific knowledge and observational information to assist authorities in quantifying the positive and negative risk implications of any decision.

Probabilistic volcanic hazard and risk assessment quantifying long‐ and short‐term volcanic hazard: Building up a common strategy for Italian volcanoes, Erice, Italy, 8 November 2006

The term “hazard” can lead to some misunderstanding. In English, hazard has the generic meaning “potential source of danger,” but for more than 30 years [e.g., Fournier d'Albe, 1979], hazard has been also used in a more quantitative way, that reads, “the probability of a certain hazardous event in a specific time‐space window.” However, many volcanologists still use “hazard” and “volcanic hazard” in purely descriptive and subjective ways. A recent meeting held in November 2006 at Erice, Italy, entitled “Quantifying Long‐ and Short‐Term Volcanic Hazard: Building up a Common Strategy for Italian Volcanoes” (http://www.bo.ingv.it/ erice2006) concluded that a more suitable term for the estimation of quantitative hazard is “probabilistic volcanic hazard assessment” (PVHA).

Estimation of Volcanic Hazards from Tephra Fallout

The goal of probabilistic volcanic hazard assessment is to translate complex volcanological data and numerical models into practical hazard estimates for communities potentially affected by volcanic eruptions. Probabilistic volcanic hazard assessment quantifies volcanic hazards and illustrates uncertainties about the magnitude and consequences of volcanic activity. Planning based on probabilistic volcanic hazard assessment has the potential of mitigating the effects of volcanic eruptions when they occur. This paper presents an approach developed to estimate volcanic hazards related to tephra fallout and illustrates this approach with a tephra fallout hazard assessment for the city of Leon, Nicaragua, and the surrounding area. Tephra fallout from eruptions of Cerro Negro volcano has caused damage to property and adverse health effects and has disrupted life in this area. By summarizing the geologic and historical records of past eruptions of Cerro Negro on a probability tree, it is shown that the inhabitan...