Probabilistic Hazard Curves and Maps Around Somma-Vesuvius (Italy)

Journal of Geophysical Research: Solid Earth RESEARCH ARTICLE Towards Quantitative Volcanic Risk of Pyroclastic Density 10.1029/2017JB015383 Currents: Probabilistic Hazard Curves and Maps Key Points: Around Somma-Vesuvius (Italy) • We develop a three-stage methodology to quantify aleatory and epistemic uncertainty of P. Tierz1,2 , E. R. Stefanescu3,4, L. Sandri1 , R. Sulpizio5,6 , G. A. Valentine7 , dense-PDC hazard 8 3 • A comprehensive collection W. Marzocchi , and A. K. Patra of probabilistic hazard curves and maps for flow depth and speed 1Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy, 2Now at British Geological Survey, is obtained The Lyell Centre, Edinburgh, UK, 3Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, • Such hazard products represent USA, 4Now at Self-Driving Car Nanodegree Program, Udacity, Mountain View, CA, USA, 5Dipartimento di Scienze della a crucial step into the quantification 6 of volcanic risk of dense PDCs Terra e Geoambientali, Università di Bari, Bari, Italy, Istituto per la Dinamica dei Processi Ambientali, Consiglio Nazionale delle Ricerche, Milan, Italy, 7Department of Geology, University at Buffalo, Buffalo, NY, USA, 8Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Roma1, Rome, Italy Supporting Information: • Supporting Information S1 •DataSetS1 Abstract Pyroclastic density currents (PDCs) are hot flowing mixtures of gas and pyroclasts that can cause widespread loss of life and structural damage around the erupting volcano. Hazard assessments Correspondence to: that include quantification of aleatory and epistemic uncertainty are a necessary step toward calculating P. Ti e r z , [email protected] volcanic risk of PDCs in an accurate and complete manner. We develop a three-stage procedure to quantify such uncertainties for dense PDCs. First, the TITAN2D model is parameterized to simulate the PDC phenomenology at the target volcano. Second, TITAN2D is coupled with Polynomial Chaos Citation: Tierz, P., Stefanescu, E. R., Sandri, L., Quadrature to propagate aleatory uncertainty from model parameters to hazard intensity measures Sulpizio, R., Valentine, G. A., (flow depth and speed). Third, the TITAN2D-PCQ analysis is merged with the Bayesian Event Tree Marzocchi, W., & Patra, A. K. (2018). for Volcanic Hazard to include other volcano-specific aleatory uncertainty and estimates of epistemic Towards quantitative volcanic risk of pyroclastic density currents: uncertainty. A comprehensive collection of probabilistic hazard curves and maps for flow depth and Probabilistic hazard curves and maps speed around the volcano is obtained through this methodology and its application is illustrated at around Somma-Vesuvius (Italy). Somma-Vesuvius (Italy). Our results indicate that, given an eruption from the current central crater, Journal of Geophysical Research: Solid Earth, 123. exceedance probabilities are around 30% (aleatory uncertainty only) and between 10% and 60% (aleatory https://doi.org/10.1029/2017JB015383 and epistemic uncertainty), for flow depth = 1 m and flow speed = 2 m/s, over the first 2–3 km around the vent. Dense PDCs faster than 30 m/s may cover areas about 50 km2 around the vent, on average, 1 every Received 26 DEC 2017 10 eruptions. This type of probabilistic hazard assessment represents a crucial step toward quantitative Accepted 1 JUL 2018 volcanic risk of dense PDCs at Somma-Vesuvius and worldwide. Accepted article online 16 JUL 2018 1. Introduction Pyroclastic density currents (PDCs) are devastating phenomena that commonly occur during explosive or dome-extrusion eruptions. They are gravity-driven mixtures of gas and fragments of volcanic material (pyroclasts) that can travel at speeds up to many tens of meters per second, reach temperatures of several hundreds of degree Centigrade, and exert dynamic pressures above 100 kPa (e.g., Branney & Kokelaar, 2002; Sulpizio et al., 2014; Valentine, 1998). Their potential for destruction is huge as has been demonstrated in recent historical times (e.g., Auker et al., 2013). In order to quantify the volcanic risk associated with PDCs, accurate and complete volcanic hazard assessment, including uncertainty quantification, is required (e.g. Aspinall et al., 2002; Newhall & Hoblitt, 2002). Commonly, two major types of uncertainty are associated with natural hazards (e.g., Riley et al., 2016; Rougier et al., 2013): (1) aleatory uncertainty, related to the intrinsic variability or randomness of the natural system and (2) epistemic uncertainty, related to diverse sources of incomplete or limited knowledge. In practice, the two types of uncertainty can be unequivocally defined after having established a proper experimental concept (Marzocchi & Jordan, 2014), that is, the collection of exchange- able data that we aim at describing, for instance, if a specific spatial cell is invaded or not by a PDC during an eruption. Thus, the aleatory uncertainty is the intrinsic variability of the true data-generating process (i.e., the ©2018. American Geophysical Union. spatial cell may be invaded during some eruptions but may not during others), while the epistemic uncer- All Rights Reserved. tainty describes the incomplete knowledge about the aleatory variability through a statistical distribution TIERZ ET AL. 1 Journal of Geophysical Research: Solid Earth 10.1029/2017JB015383 (i.e., the true underlying frequency of invasion of the spatial cell is not known perfectly). A clear, unequivocal definition of aleatory and epistemic uncertainty is vital for any hazard assessment and its objective testing against real data (Marzocchi & Jordan, 2014). Nevertheless, this is often not provided in recent contributions to volcanic hazard assessment (e.g., Bevilacqua et al., 2017; Neri et al., 2015). Volcanic hazard assessments of PDCs that include the quantification of, at least, the aleatory uncertainty are less common than those produced for other volcanic hazardous phenomena such as tephra fallout or lava flows. However, they are becoming more customary over recent years. The majority of these hazard assessments have quantified the probability of PDC invasion around the target volcano without considering other hazard intensity measures such as the flow depth or speed (e.g., Bevilacqua et al., 2017; Neri et al., 2015; Sandri et al., 2012, 2014, 2018; Tierz, Sandri, Costa, Sulpizio, et al., 2016; Tierz, Sandri, Costa, Zaccarelli, et al., 2016). Some studies that have quantified uncertainty in relation with intensity measures have mostly focused on dividing the PDC model parameter space into regions that do or do not lead to a catastrophe (defined as a given flow depth being overcome) occurring at selected locations (e.g., Bayarri et al., 2009; Spiller et al., 2014). Other studies have provided probability maps displaying the exceedance probability associated with a given threshold of flow depth as the hazard intensity measure (e.g., Dalbey et al., 2008). However, a fully complete and accurate hazard assessment is only obtained when hazard curves, including estimation of epistemic uncertainty, are computed (e.g., Rougier et al., 2013; Tonini et al., 2015). Such volcanic hazard analyses become even more important in densely populated areas, where volcanic risk is largest. The city of Napoli, in southern Italy, is located on the eastern sector of the Campi Flegrei caldera and less than 15 km away from the current crater of Somma-Vesuvius (Figure 1). The municipality has a popula- tion of about one million people and critical infrastructure at a national level is also present in the area. The eruptive history and morphology of Somma-Vesuvius indicate that (1) the volcano is able to produce signifi- cant volumes of dense PDCs during explosive eruptions ranging from violent Strombolian to Plinian eruptions (Cioni et al., 2008; Gurioli et al., 2010, and references therein) and (2) the steep flanks of the stratovolcano may promote building of momentum (and increase of dynamic pressure) of the generated PDCs (e.g., Esposti Ongaro et al., 2008). Accordingly, volcanic risk analyses, for instance, of building resistance to PDC passage, at Somma-Vesuvius have been conducted (e.g., Spence, Baxter, & Zuccaro, 2004; Spence, Zuccaro, Petrazzuoli, & Baxter, 2004; Zuccaro et al., 2008). These risk analyses have been entirely based on single-scenario (or single-eruption) hazard assessments. However, modern hazard analyses should give quantitative answers to the following questions: (i) how likely the given scenario is (e.g., Marzocchi et al., 2004) and (ii) the likelihood of different locations around the volcano being invaded by PDCs (e.g., Sandri et al., 2018; Tierz, Sandri, Costa, Sulpizio, et al., 2016; Tierz, Sandri, Costa, Zaccarelli, et al., 2016). Such quantitative hazard assessments can then be coupled with vulnerability and exposure analyses to quantify volcanic risk in an accurate and complete manner (e.g., Marzocchi & Woo, 2009). In this paper we present, for the first time in volcanic hazard assessment of PDCs, a comprehensive collection of hazard curves, for the flow depth and speed of dense PDCs, over a grid of points surrounding the target volcano: Somma-Vesuvius (Italy). This collection is a full description of both aleatory (mean hazard curve) and epistemic uncertainty (alternative hazard curves). We develop a three-stage methodology that combines one physical simulator for dense PDCs (TITAN2D; Patra et al., 2005) and two uncertainty quantification techniques: Polynomial Chaos Quadrature (PCQ; e.g., Dalbey et al.,

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