Beyond eruptive scenarios: assessing tephra fallout hazard from Neapolitan volcanoes

Sandri, Laura; Costa, Antonio; Selva, Jacopo; Tonini, Roberto; Macedonio, Giovanni; Folch, Arnau; Sulpizio, Roberto

doi:10.1038/srep24271

Cited by 64 publications

(114 citation statements)

References 39 publications

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“…The natural variability of these eruptions, in terms of tephra-fallout dispersal and PDC inundation, is quite large (Cioni et al, 2008;Gurioli et al, 2010;Sandri et al, 2016;Tierz et al, 2016a). In this paper, we quantify this aleatory uncertainty in pyroclastic volume over different catchments around SommaVesuvius (Figure 5), by computing probabilistic hazard curves (e.g., Rougier et al, 2013) of this variable for tephra fallout and dense PDCs.…”

Section: Tephra-fallout and Pdc-deposit Volumesmentioning

confidence: 99%

“…For the RI node, we collect information on maximum yearly rainfall intensities in the central Campanian region where Somma-Vesuvius is located (AdBCC, 2015) while, in the case of the PV node, we utilize the probabilistic hazard assessment of Sandri et al (2016) for tephra fallout and develop further the hazard analysis of Tierz et al (2014) for dense PDCs to quantify the aleatory uncertainty in PV. Details on the procedure to select the hydrological catchments as well as the parameterization of the RI node are given in the Supplementary Material.…”

Section: Multihaz Applied To Somma-vesuviusmentioning

confidence: 99%

“…In this paper, we quantify this aleatory uncertainty in pyroclastic volume over different catchments around SommaVesuvius (Figure 5), by computing probabilistic hazard curves (e.g., Rougier et al, 2013) of this variable for tephra fallout and dense PDCs. Our hazard assessment can be expressed as either conditional on the occurrence of a small, medium or large eruption (the sizes defined as in Sandri et al, 2016;Tierz et al, 2016a), or accounting for the aleatory uncertainty in the eruption size (e.g., Sandri et al, 2016).…”

Section: Tephra-fallout and Pdc-deposit Volumesmentioning

confidence: 99%

“…We select this volcanic system for three reasons: (1) there are data available to quantify the aleatory uncertainty linked to tephra fallout (e.g., Sandri et al, 2016) and dense PDCs (e.g., Tierz et al, 2014); (2) the volcano has generated syn-eruptive volcaniclastic flows during/after mid-large explosive eruptions (e.g., Rosi et al, 1993;Sulpizio et al, 2006) and is prone to form them at many locations around it (e.g., Bisson et al, 2010Bisson et al, , 2014; and (3) the relatively high population density nearby.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks

et al. 2017

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Volcanic water-sediment flows, commonly known as lahars, can often pose a higher threat to population and infrastructure than primary volcanic hazardous processes such as tephra fallout and Pyroclastic Density Currents (PDCs). Lahars are volcaniclastic flows of water, volcanic debris and entrained sediments that can travel long distances from their source, causing severe damage by impact and burial. Lahars are frequently triggered by intense or prolonged rainfall occurring after explosive eruptions, and their occurrence depends on numerous factors including the spatio-temporal rainfall characteristics, the spatial distribution and hydraulic properties of the tephra deposit, and the pre-and post-eruption topography. Modeling (and forecasting) such a complex system requires the quantification of aleatory variability in the lahar triggering and propagation. To fulfill this goal, we develop a novel framework for probabilistic hazard assessment of lahars within a multi-hazard environment, based on coupling a versatile probabilistic model for lahar triggering (a Bayesian Belief Network: Multihaz) with a dynamic physical model for lahar propagation (LaharFlow). Multihaz allows us to estimate the probability of lahars of different volumes occurring by merging varied information about regional rainfall, scientific knowledge on lahar triggering mechanisms and, crucially, probabilistic assessment of available pyroclastic material from tephra fallout and PDCs. LaharFlow propagates the aleatory variability modeled by Multihaz into hazard footprints of lahars. We apply our framework to Somma-Vesuvius (Italy) because: (1) the volcano is strongly lahar-prone based on its previous activity, (2) there are many possible source areas for lahars, and (3) there is high density of population nearby. Our results indicate that the size of the eruption preceding the lahar occurrence and the spatial distribution of tephra accumulation have a paramount role in the lahar initiation and potential impact. For instance, lahars with initiation volume ≥10 5 m 3 along the volcano flanks are almost 60% probable to occur after large-sized eruptions (∼VEI ≥ 5) but 40% after medium-sized eruptions (∼VEI4). Some simulated lahars can propagate for 15 km or reach combined flow depths of 2 m and speeds of 5-10 m/s, even over flat terrain. Probabilistic multi-hazard frameworks like the one presented here can be invaluable for volcanic hazard assessment worldwide.

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Section: Tephra-fallout and Pdc-deposit Volumesmentioning

confidence: 99%

Section: Multihaz Applied To Somma-vesuviusmentioning

confidence: 99%

Section: Tephra-fallout and Pdc-deposit Volumesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks

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“…The doubly stochastic features and BMA approach implemented on logic tree schemes permit the combination of different conceptual models and the production of averaged estimates, including uncertainty quantification. Our map represents crucial input information for the development of quantitative long-term hazard zonation of potential hazardous eruptive phenomena in the LVVR, such as pyroclastic density currents and tephra fallout (Miller et al, 1982;Bayarri et al, 2009Bayarri et al, , 2015Neri et al, 2015;Volentik & Houghton, 2015;Sandri et al, 2016). The vent opening map may also be used as the basis for generating updated short-term probability assessments, once monitoring information is integrated.…”

Section: Discussionmentioning

confidence: 99%

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The Long Valley volcanic region is an active volcanic area situated at the eastern base of the Sierra Nevada escarpment and dominated by a 32 km wide resurgent caldera created~760 ka. Eruptions after 180 ka have been localized at Mammoth Mountain on the western rim of the caldera and along the Mono-Inyo Craters volcanic chain stretching about 45 km northward. Three different probability models have been developed and then combined in a logic tree to estimate the long-term spatial probability of vent opening. These models include (i) an anisotropic kernel density estimator based on past vent locations, (ii) a new Bayesian model for coupling new vents with pre-existing faults, and (iii) a uniformly distributed probability map. The model combination procedure relies on Bayesian model averaging. Using a doubly stochastic framework enables us to incorporate some of the main sources of epistemic uncertainty about the interpretation of the volcanic system, thereby exploring their effect. Our vent-opening probability maps show two higher likelihood regions for new vent opening, one around Mammoth Mountain to the south, and the other along the Mono-Inyo Craters to the north. The spatial vent opening probability, conditional on an eruption, is estimated as~64% in the northern region and~36% in the southern region, with an uncertainty of about ±20%. These findings provide a rational basis for the hazard mapping of a potential eruption in the Long Valley volcanic region, suggesting that the hazard associated with Mammoth Mountain volcanism should be fully evaluated.

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

et al. 2018

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The 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.

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Beyond eruptive scenarios: assessing tephra fallout hazard from Neapolitan volcanoes

Cited by 64 publications

References 39 publications

A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks

A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks

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

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