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- Title
Multisource Bayesian Probabilistic Tsunami Hazard Analysis for the Gulf of Naples (Italy).
- Authors
Grezio, Anita; Cinti, Francesca Romana; Costa, Antonio; Faenza, Licia; Perfetti, Paolo; Pierdominici, Simona; Pondrelli, Silvia; Sandri, Laura; Tierz, Pablo; Tonini, Roberto; Selva, Jacopo
- Abstract
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. Plain Language Summary: Probabilistic analyses are essential to estimate the natural hazards caused by infrequent and devastating events and to elaborate risk assessments aiming to mitigate and reduce the impact of the natural disasters on society. Probabilistic tsunami hazard analyses use procedures that trace and weight the different tsunami sources (submarine earthquakes, aerial/submarine slides, volcanic activity, meteorological events, and asteroid impacts) with varying probability of occurrence. The scope of the present methodology is the reduction of possible biases and underestimations that arise by focusing on a single tunamigenic source. The multisource probabilistic tsunami hazard analysis is applied to a real case study, the Gulf of Naples (Italy), where relevant threats due to natural events exist in a high densely populated district. The probabilistic hazard procedure takes into account multiple tsunamigenic sources in the region and provides a first‐order prioritization of the different sources in a long‐term comprehensive analysis. The methodology is based on a Bayesian approach that merges computational hazard quantification (based on source‐tsunami simulations) and past data, appropriately including in the quantification the epistemic uncertainty. For the first time a probabilistic analysis of the tsunami hazard in the region is presented taking into consideration multiple tsunamigenic sources. Key Points: A comprehensive methodology is proposed to evaluate the probabilistic hazard posed by different tsunamigenic sourcesA modular Bayesian methodology is used to treat different levels of epistemic uncertainty in a case studyThree major tsunamigenic sources (earthquakes, submarine landslides, and volcanic pyroclastic flows) are analyzed in the Gulf of Naples, Italy
- Subjects
BAY of Naples (Italy); BAYESIAN analysis; TSUNAMIS; TSUNAMI hazard zones; SHALLOW-water equations; DENSITY currents
- Publication
Journal of Geophysical Research. Oceans, 2020, Vol 125, Issue 2, p1
- ISSN
2169-9275
- Publication type
Article
- DOI
10.1029/2019JC015373