Campi Flegrei (CF) is an example of an active caldera containing densely populated settlements at very high risk of pyroclastic density currents (PDCs). We present here an innovative method for assessing background spatial PDC hazard in a caldera setting with probabilistic invasion maps conditional on the occurrence of an explosive event. The method encompasses the probabilistic assessment of potential vent opening positions, derived in the companion paper, combined with inferences about the spatial density distribution of PDC invasion areas from a simplified flow model, informed by reconstruction of deposits from eruptions in the last 15 ka. The flow model describes the PDC kinematics and accounts for main effects of topography on flow propagation. Structured expert elicitation is used to incorporate certain sources of epistemic uncertainty, and a Monte Carlo approach is adopted to produce a set of probabilistic hazard maps for the whole CF area. Our findings show that, in case of eruption, almost the entire caldera is exposed to invasion with a mean probability of at least 5%, with peaks greater than 50% in some central areas. Some areas outside the caldera are also exposed to this danger, with mean probabilities of invasion of the order of 5-10%. Our analysis suggests that these probability estimates have location-specific uncertainties which can be substantial. The results prove to be robust with respect to alternative elicitation models and allow the influence on hazard mapping of different sources of uncertainty, and of theoretical and numerical assumptions, to be quantified.

The Long Valley volcanic region is an active volcanic area situated at the eastern base of the Sierra Nevada escarpment and dominated by a 32 km wide resurgent caldera created~760 ka. Eruptions after 180 ka have been localized at Mammoth Mountain on the western rim of the caldera and along the Mono-Inyo Craters volcanic chain stretching about 45 km northward. Three different probability models have been developed and then combined in a logic tree to estimate the long-term spatial probability of vent opening. These models include (i) an anisotropic kernel density estimator based on past vent locations, (ii) a new Bayesian model for coupling new vents with pre-existing faults, and (iii) a uniformly distributed probability map. The model combination procedure relies on Bayesian model averaging. Using a doubly stochastic framework enables us to incorporate some of the main sources of epistemic uncertainty about the interpretation of the volcanic system, thereby exploring their effect. Our vent-opening probability maps show two higher likelihood regions for new vent opening, one around Mammoth Mountain to the south, and the other along the Mono-Inyo Craters to the north. The spatial vent opening probability, conditional on an eruption, is estimated as~64% in the northern region and~36% in the southern region, with an uncertainty of about ±20%. These findings provide a rational basis for the hazard mapping of a potential eruption in the Long Valley volcanic region, suggesting that the hazard associated with Mammoth Mountain volcanism should be fully evaluated. This study is structured as follows. First, the LVVR is described. Second, the volcanic datasets (past vent locations and ages, tectonic structures) are presented. Third, the implementation of the main uncertainty sources and details of the statistical models are given. Fourth, the vent opening probability maps, with uncertainty quantification, are presented and discussed.

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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.

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Abstract. The San Salvador volcanic complex (El Salvador) and Nejapa-Chiltepe volcanic complex (Nicaragua) have been characterized by a significant variability in eruption style and vent location. Densely inhabited cities are built on them and their surroundings, including the metropolitan areas of San Salvador (∼2.4 million people) and Managua (∼1.4 million people), respectively. In this study we present novel vent opening probability maps for these volcanic complexes, which are based on a multi-model approach that relies on kernel density estimators. In particular, we present thematic vent opening maps, i.e., we consider different hazardous phenomena separately, including lava emission, small-scale pyroclastic density currents, ejection of ballistic projectiles, and low-intensity pyroclastic fallout. Our volcanological dataset includes: (1) the location of past vents, (2) the mapping of the main fault structures, and (3) the eruption styles of past events, obtained from critical analysis of the literature and/or inferred from volcanic deposits and morphological features observed remotely and in the field. To illustrate the effects of considering the expected eruption style in the construction of vent opening maps, we focus on the analysis of small-scale pyroclastic density currents derived from phreatomagmatic activity or from low-intensity magmatic volcanism. For the numerical simulation of these phenomena we adopted the recently developed branching energy cone model by using the program ECMapProb. Our results show that the implementation of thematic vent opening maps can produce significantly different hazard levels from those estimated with traditional, non-thematic maps.

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.