In this study, we combine reconstructions of volcanological data sets and inputs from a structured expert judgment to produce a first long‐term probability map for vent opening location for the next Plinian or sub‐Plinian eruption of Somma‐Vesuvio. In the past, the volcano has exhibited significant spatial variability in vent location; this can exert a significant control on where hazards materialize (particularly of pyroclastic density currents). The new vent opening probability mapping has been performed through (i) development of spatial probability density maps with Gaussian kernel functions for different data sets and (ii) weighted linear combination of these spatial density maps. The epistemic uncertainties affecting these data sets were quantified explicitly with expert judgments and implemented following a doubly stochastic approach. Various elicitation pooling metrics and subgroupings of experts and target questions were tested to evaluate the robustness of outcomes. Our findings indicate that (a) Somma‐Vesuvio vent opening probabilities are distributed inside the whole caldera, with a peak corresponding to the area of the present crater, but with more than 50% probability that the next vent could open elsewhere within the caldera; (b) there is a mean probability of about 30% that the next vent will open west of the present edifice; (c) there is a mean probability of about 9.5% that the next medium‐large eruption will enlarge the present Somma‐Vesuvio caldera, and (d) there is a nonnegligible probability (mean value of 6–10%) that the next Plinian or sub‐Plinian eruption will have its initial vent opening outside the present Somma‐Vesuvio caldera.

Numerical modeling of tephra dispersal and deposition is essential for evaluation of volcanic hazards. Many models consider reasonable physical approximations in order to reduce computational times, but this may introduce a certain degree of uncertainty in the simulation outputs. The important step of uncertainty quantification is dealt in this paper with respect to a coupled version of a plume model (PLUME‐MoM) and a tephra dispersal model (HYSPLIT). The performances of this model are evaluated through simulations of four past eruptions of different magnitudes and styles from three Andean volcanoes, and the uncertainty is quantified by evaluating the differences between modeled and observed data of plume height (at different time steps above the vent) as well as mass loading and grain size at given stratigraphic sections. Different meteorological data sets were also tested and had a sensible influence on the model outputs. Other results highlight that the model tends to underestimate plume heights while overestimating mass loading values, especially for higher‐magnitude eruptions. Moreover, the advective part of HYSPLIT seems to work more efficiently than the diffusive part. Finally, though the coupled PLUME‐MoM/HYSPLIT model generally is less efficient in reproducing deposit grain sizes, we propose that it may be used for hazard map production for higher‐magnitude eruptions (sub‐Plinian or Plinian) for what concern mass loading.

This study presents new and revised data sets about the spatial distribution of past volcanic vents, eruptive fissures, and regional/local structures of the Somma‐Vesuvio volcanic system (Italy). The innovative features of the study are the identification and quantification of important sources of uncertainty affecting interpretations of the data sets. In this regard, the spatial uncertainty of each feature is modeled by an uncertainty area, i.e., a geometric element typically represented by a polygon drawn around points or lines. The new data sets have been assembled as an updatable geodatabase that integrates and complements existing databases for Somma‐Vesuvio. The data are organized into 4 data sets and stored as 11 feature classes (points and lines for feature locations and polygons for the associated uncertainty areas), totaling more than 1700 elements. More specifically, volcanic vent and eruptive fissure elements are subdivided into feature classes according to their associated eruptive styles: (i) Plinian and sub‐Plinian eruptions (i.e., large‐ or medium‐scale explosive activity); (ii) violent Strombolian and continuous ash emission eruptions (i.e., small‐scale explosive activity); and (iii) effusive eruptions (including eruptions from both parasitic vents and eruptive fissures). Regional and local structures (i.e., deep faults) are represented as linear feature classes. To support interpretation of the eruption data, additional data sets are provided for Somma‐Vesuvio geological units and caldera morphological features. In the companion paper, the data presented here, and the associated uncertainties, are used to develop a first vent opening probability map for the Somma‐Vesuvio caldera, with specific attention focused on large or medium explosive events.

Tephra fallout hazard assessment is undertaken with probabilistic maps that rely on numerical models. Regarding maps production, the input parameters of the model (including atmospheric conditions), the physical approximations of the numerical simulations, and the probabilities of occurrence of different eruption types in specific time frames are among the most critical sources of uncertainty. We therefore present a tephra fallout hazard assessment study for two active volcanoes (Cotopaxi and Guagua Pichincha) in Ecuador. We utilize PLUME-MoM/HYSPLIT models, and a procedure for uncertainty quantification where: i) the uncertainty on eruptive source parameters and eruption type occurrence is quantified through expert elicitation; ii) we implement a new procedure for correlations between the different parameters, and iii) we use correction coeffients to take into account the uncertainty of the numerical model. Maps of exceedance probability given a deposit thickness threshold, and thickness maps given a probability of exceedence, are produced 1) for two eruptive scenarios (sub-plinian and Plinian) and 2) as a combination of these scenarios in case the next eruption will be sub-Plinian or Plinian. These maps are described according to the uncertainty distribution of eruption type occurrence probabilities, considering their 5^th percentile, mean, and 95^th percentile values. We finally present hazard curves describing exceeding probabilities in 10 sensitive sites within the city of Quito. Additional information includes the areal extent and the population potentially affected by different isolines of tephra accumulation. This work indicates that full uncertainty quantification helps in providing more robust scientific information, improving the hazard assessment reliability.