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.

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.

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.

Volcanoes within monogenetic volcanic fields often are arranged in alignments and clusters, which are related to effects of magma source geometry in the upper mantle, principal stress orientations, and crustal structures on their magma feeding systems. We use cluster analysis with dendrogram, vent morphometric analysis, and field structural data to explore the relationships between volcanoes and tectonic features in the Plio-Pleistocene part of the Lunar Crater Volcanic Field (LCVF; Pancake Range, Nevada, USA), which includes 96 monogenetic volcanic edifices totaling 119 vents. Structural analysis identified three main sets of faults with dip-slip kinematics (mostly normal with a few examples of thrust faults), striking N-S, E-W, and NE-SW. The NE-SW set comprises dip-slip faults with a dominant normal component of movement which are consistent with the modern state of stress based upon the World Stress Map database. Spatial distribution pattern analysis suggests a clustered distribution of vents in the LCVF, and GIS-based spatial density analysis shows that these clusters trend mostly NE-SW. Morphometric study of the monogenetic cones, which provides information on feeder dike orientation where dikes are not directly exposed, suggests dominant NNE-SSW to NE-SW orientations of near-surface inferred dikes. An amount of 27 out of 31 inferred feeder dikes within the LCVF is parallel to the present orientation of the greatest principal horizontal stress (σ Hmax ) as suggested by World Stress Map data derived from hydrofracturing and earthquake focal mechanisms. In some cases, dike strike is parallel with that of preexisting Quaternary dip-slip faults. We suggest that the spatial distribution of vents is related to domains of different scales of partial melting and compositional heterogeneity in the upper mantle source, which is substantiated by geochemical data. The relationship of feeder dikes with respect to shallow tectonic structures, although somewhat ambiguous at LCVF, is consistent with behavior that is intermediate between volcanic fields with highand low-long-term magma fluxes.

The quantification of the maximum runout, invaded area, volume and total grain-size distribution (TGSD) of pyroclastic density currents (PDC) is a critically important task because such parameters represent the needed input quantities for physical modeling and for hazard assessment of PDCs. In this work, new and well-established methods for the quantification of these parameters are applied to a large stratigraphic dataset of three PDC units from two eruptions of Somma-Vesuvius (the AD 79 Pompeii and the AD 472 Pollena eruptions), representative of a large spectrum of transport and depositional processes. Maximum runout and invaded area are defined on the basis of the available volcanological and topographical constraints. The related uncertainties are evaluated with an expert judgement procedure, which considered the different sectors of the volcano separately. Quite large uncertainty estimates of dispersal area (20-40%) may have important implications in terms of hazard assessment. The testing of different methods for estimating the volume (and mass) of a PDC deposit suggests that integration, over the invaded area, of thickness (and deposit density) data using the triangulated irregular network method can minimize and localize data extrapolation. Such calculations, however, bear an intrinsic additional uncertainty (at least 10% of the total PDC deposit) related to loss or new formation of fine material during transport (at least 10% of the total PDC deposit). Different interpolation methods for TGSD produce multimodal distributions, likely reflecting the different response of each grain size class to transport and deposition processes. These data, when integrated with information on the related co-ignimbrite deposits, can give a more accurate picture of the pyroclastic mixture feeding the current. Response to Reviewers: Dear Associate Editor, we have addressed all the requests for the revision, which are detailed in the following letter and in the manuscript with tracked changes. Furthermore, we have embedded figure captions in each figure's file, and we have checked the formatting of the references and the bibliography. Please let us know if you need also the figures withouth the captions.

Future occurrence of explosive eruptive activity at Cotopaxi and Guagua Pichincha volcanoes, Ecuador, is assessed probabilistically, utilizing expert elicitation. Eight eruption types were considered for each volcano.Type event probabilities were evaluated for the next eruption at each volcano and for at least one of each type within the next 100 years. For each type, we elicited relevant eruption source parameters (duration, average plume height and total tephra mass). We investigated the robustness of these elicited evaluations by deriving probability uncertainties using three expert scoring methods. For Cotopaxi, we considered both rhyolitic and andesitic magmas. Elicitation findings indicate that the most probable next eruption type is an andesitic hydrovolcanic/ash-emission (~26-44% median probability), which has also the highest median probability of recurring over the next 100 years. However, for the next eruption at Cotopaxi, the average joint probabilities for sub-Plinian or Plinian type eruption is of order 30-40% -a significant chance of a violent explosive event. It is inferred that any Cotopaxi rhyolitic eruption could involve a longer duration and greater erupted mass than an andesitic event, likely producing a prolonged emergency. For Guagua Pichincha, future eruption types are expected to be andesitic/dacitic, and a vulcanian event is judged most probable for the next eruption (median probability ~40-55%); this type is expected to be most frequent over the next 100 years, too. However, there is a substantial probability (possibly >40% in average) that the next eruption could be sub-Plinian or Plinian, with all that implies for hazard levels.

Abstract. We use PyBox, a new numerical implementation of the box-model approach, to reproduce pyroclastic density current (PDC) deposits from the Somma–Vesuvius volcano (Italy). Our simplified model assumes inertial flow front dynamics and mass deposition equations and axisymmetric conditions inside circular sectors. Tephra volume and density and total grain size distribution of EU3pf and EU4b/c, two well-studied PDC units from different phases of the 79 CE Pompeii eruption, are used as input parameters. Such units correspond to the deposits from variably dilute, turbulent PDCs. We perform a quantitative comparison and uncertainty quantification of numerical model outputs with respect to the observed data of unit thickness, inundation areas and grain size distribution as a function of the radial distance to the source. The simulations consider (i) polydisperse conditions, given by the total grain size distribution of the deposit, or monodisperse conditions, given by the mean Sauter diameter of the deposit; (ii) axisymmetric collapses either covering the whole 360∘ (round angle) or divided into two circular sectors. We obtain a range of plausible initial volume concentrations of solid particles from 2.5 % to 6 %, depending on the unit and the circular sector. Optimal modelling results of flow extent and deposit thickness are reached on the EU4b/c unit in a polydisperse and sectorialized situation, indicating that using total grain size distribution and particle densities as close as possible to the real conditions significantly improves the performance of the PyBox code. The study findings suggest that the simplified box-model approach has promise for applications in constraining the plausible range of the input parameters of more computationally expensive models. This could be done due to the relatively fast computational time of the PyBox code, which allows the exploration of the physical space of the input parameters.

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