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.

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

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