Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

Tierz, Pablo; Stefanescu, E. R.; Sandri, Laura; Sulpizio, Roberto; Valentine, Greg A.; Marzocchi, Warner; Patra, Abani

doi:10.1029/2017jb015383

Cited by 29 publications

(55 citation statements)

References 80 publications

(253 reference statements)

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“…Probabilistic assessments of volcanic hazards based on simulations of physical processes have gained traction over the past several years Biass, Bonadonna, Di Traglia, et al, 2016;Biass, Bonadonna, Connor, et al, 2016;Cappello et al, 2015;Connor et al, 2012;Dalbey et al, 2008;Gallant et al, 2018;Mastin et al, 2014;Mead & Magill, 2017;Sandri et al, 2016Sandri et al, , 2018Tierz et al, 2018;Volentik & Houghton, 2015). Computer model emulators (also known as statistical surrogates) are an efficient tool in this type of modeling particularly when the physical model is computationally expensive to exercise (Bayarri et al, 2009(Bayarri et al, , 2015Spiller et al, 2014).…”

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confidence: 99%

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

et al. 2019

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Ideally, probabilistic hazard assessments combine available knowledge about physical mechanisms of the hazard, data on past hazards, and any precursor information. Systematically assessing the probability of rare, yet catastrophic hazards adds a layer of difficulty due to limited observation data. Via computer models, one can exercise potentially dangerous scenarios that may not have happened in the past but are probabilistically consistent with the aleatoric nature of previous volcanic behavior in the record. Traditional Monte Carlo‐based methods to calculate such hazard probabilities suffer from two issues: they are computationally expensive, and they are static. In light of new information, newly available data, signs of unrest, and new probabilistic analysis describing uncertainty about scenarios the Monte Carlo calculation would need to be redone under the same computational constraints. Here we present an alternative approach utilizing statistical emulators that provide an efficient way to overcome the computational bottleneck of typical Monte Carlo approaches. Moreover, this approach is independent of an aleatoric scenario model and yet can be applied rapidly to any scenario model making it dynamic. We present and apply this emulator‐based approach to create multiple probabilistic hazard maps for inundation of pyroclastic density currents in the Long Valley Volcanic Region. Further, we illustrate how this approach enables an exploration of the impact of epistemic uncertainties on these probabilistic hazard forecasts. Particularly, we focus on the uncertainty of vent opening models and how that uncertainty both aleatoric and epistemic impacts the resulting probabilistic hazard maps of pyroclastic density current inundation.

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confidence: 99%

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

et al. 2019

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“…These disturbances are previously computed by the numerical model

S P H y s i c s

based on the

S m o o t h e d P a r t i c l e H y d r o d y n a m i c s

theory (Monaghan, ). In the setup of the

S P H y s i c s

model the

P D C

s entering the sea (which were calculated by Tierz et al, ) correspond to the case of tsunamis generated by sliding wedge. For each scenarios the wedge is equal to the pyroclastic flow thickness

\geq

1 m at the coastal point, the slope is fixed at the average coastal slope, and the specific weight is a background value related to a generic

P D C

s density.…”

Section: Modeling Potential Tsunamigenic Sources and Tsunami Wavesmentioning

confidence: 99%

“…Eruptive size classes. We rely on a set of TITAN2D (Patra et al, 2005) simulations for dense PDCs from the summit crater of Somma-Vesuvio, performed by Tierz et al (2018) considering a wide range of possible eruptive size classes (small, medium, and large) that ultimately reflect different total erupted masses (see also Sandri et al, 2016). We have available 64 simulations for each size class.…”

Section: Pyroclastic Density Currentsmentioning

confidence: 99%

“…The study has the main following purposes: (a) dealing simultaneously with the different principal types of tsunamigenic sources in a coherent multidisciplinary probabilistic framework and (b) evaluating the long‐term comprehensive tsunami hazard in a

B a y e s i a n

analysis, hence merging prior information—for example, modellng—and past/historical data. This scheme is here applied in the context of the multirisk problem in the Campania region (Faenza et al, ; Perfetti et al, ; Sandri et al, ; Tierz et al, , ). Natural hazards have posed relevant threats to the city of Naples and the surrounding areas.…”

Section: Introductionmentioning

confidence: 99%

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Multisource Bayesian Probabilistic Tsunami Hazard Analysis for the Gulf of Naples (Italy)

et al. 2020

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A methodology for a comprehensive probabilistic tsunami hazard analysis is presented for the major sources of tsunamis (seismic events, landslides, and volcanic activity) and preliminarily applied in the Gulf of Naples (Italy). The methodology uses both a modular procedure to evaluate the tsunami hazard and a Bayesian analysis to include the historical information of the past tsunami events. In the SourceModule the submarine earthquakes and the submarine mass failures are initially identified in a gridded domain and defined by a set of parameters, producing the sea floor deformations and the corresponding initial tsunami waves. Differently volcanic tsunamis generate sea surface waves caused by pyroclastic density currents from Somma‐Vesuvius. In the PropagationModule the tsunami waves are simulated and propagated in the deep sea by a numerical model that solves the shallow water equations. In the ImpactModule the tsunami wave heights are estimated at the coast using the Green's amplification law. The selected tsunami intensity is the wave height. In the BayesianModule the probabilistic tsunami analysis computes the long‐term comprehensive Bayesian probabilistic tsunami hazard analysis. In the prior analysis the probabilities from the scenarios in which the tsunami parameter overcomes the selected threshold levels are combined with the spatial, temporal, and frequency‐size probabilities of occurrence of the tsunamigenic sources. The prior probability density functions are integrated with the likelihood derived from the historical information based on past tsunami data. The posterior probability density functions are evaluated to produce the hazard curves in selected sites of the Gulf of Naples.

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“…In practice this has typically been accomplished by accumulating the statistics of the processes through ensemble deterministic modelling (Neri et al, 2015;Biass et al, 2016;Tierz et al, 2016;Bevilacqua et al, 2017;Gallant et al, 2018;Patra et al, 2018b). Polynomial Chaos approximations and Gaussian model surrogates can provide fast algorithms to obtains these statistics (Bursik et al, 2012;Madankan et al, 2012;Stefanescu et al, 2012;Spiller et al, 2014;Bayarri et al, 2015;Tierz et al, 2018). Owing to the nature of the studied processes, the results are typically displayed in map form and contain a large amount of spatial information, the interpretation of which has become a source of extensive study (Calder et al, 2015;Thompson et al, 2015).…”

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confidence: 99%

Statistical theory of probabilistic hazard maps: a probability distribution for the hazard boundary location

Hyman¹,

Bevilacqua²,

Bursik³

2018

Preprint

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Abstract. The study of volcanic mass flow hazards in a probabilistic framework centers around systematic experimental numerical modelling of the hazardous phenomenon and the subsequent generation and interpretation of a probabilistic hazard map (PHM). For a given volcanic flow (e.g., lava flow, lahar, pyroclastic flow, etc.), the PHM is typically interpreted as the point-wise probability of flow material inundation. In the current work, we present new methods for calculating spatial representations of the mean, standard deviation, median, and modal locations of the hazard's boundary as ensembles of many deterministic runs of a physical model. By formalizing its generation and properties, we show that a PHM may be used to construct these statistical measures of the hazard boundary which have been unrecognized in previous probabilistic hazard analyses. Our formalism shows that a typical PHM for a volcanic mass flow not only gives the point-wise inundation probability, but also represents a set of cumulative distribution functions for the location of the inundation boundary with a corresponding set of probability density functions. These distributions run over curves of steepest ascent on the PHM. Consequently, 2D space curves can be constructed on the map which represent the mean, median and modal locations of the likely inundation boundary. These curves give well-defined answers to the question of the likely boundary location of the area impacted by the hazard. Additionally, methods of calculation for higher moments including the standard deviation are presented which take the form of map regions surrounding the mean boundary location. These measures of central tendency and variance add significant value to spatial probabilistic hazard analyses, giving a new statistical description of the probability distributions underlying PHMs. The theory presented here may be used to construct improved hazard maps, which could prove useful for planning and emergency management purposes. This formalism also allows for application to simplified processes describable by analytic solutions. In that context, the connection between the PHM, its moments, and the underlying parameter variation is explicit, allowing for better source parameter estimation from natural data, yielding insights about natural controls on those parameters.

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Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

Cited by 29 publications

References 80 publications

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

Multisource Bayesian Probabilistic Tsunami Hazard Analysis for the Gulf of Naples (Italy)

Statistical theory of probabilistic hazard maps: a probability distribution for the hazard boundary location

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