We describe a new method for the reconstruction (or forecast) of probabilities that distal geographic locations were inundated by a large pyroclastic density current (PDC) in terms of the flow mass and related uncertainties. Using appropriate model input uncertainty distributions, derived from expert judgements using the equal weights combination rule, we can estimate the mass amount needed to reach a marginal locality at any given confidence level and compare this with ambiguous or inexact peripheral field data. Our analysis relies on different versions of the Huppert and Simpson (1980) integral formulation of axisymmetric gravity-driven particle currents. We focus on models which possess analytical solutions, enabling us to utilize a very fast functional approach for enumerating results and uncertainties. In particular, we adapt the ‘energy conoid’ approach to generate inundation maps along radial directions, based on comparison of the mass-dependent kinetic energy of the flow with the potential energy control by topography in the direction of flow at distal ranges. We focus on two alternative conceptual models: (i) Model 1 assumes the entire amount of solid material originates from a prescribed height above the volcano and flows as a granular current slowed by constant friction; (ii) Model 2 is a multi-phase formulation and includes, in addition to suspended particles, interstitial gas thermally buoyant with respect to surrounding cold air. In the latter case, the flow stops propagating at the surface when the solid fraction becomes less than a critical value, and there is lift-off of the remaining mixture of gas and small particulates. Our model parameters can be further constrained where there is reliable field data or information from analogue eruptions. Finally, we used a Bayes Belief Network related to each inversion model to evaluate probabilistically the uncertainties on the mass required, estimating correlation coefficients between input variables and the calculated mass. For any major magnitude ignimbrite PDC scenario, our method provides a rational basis for assessing the probability of distal flow inundation at critical peripheral locations when there is major uncertainty about the actual or predicted extent of flow runout. Example case histories are illustrated.

The study focuses on the estimation and modeling of the temporal rates of major explosions and paroxysms at Stromboli volcano (also named small-scale and large-scale paroxysms respectively). The analysis was further motivated by the paroxysm of July 3rd 2019, which raised, once again, the attention of the scientific community and civil protection authorities on the volcanic hazards of Stromboli. In fact, at the present state of knowledge, major explosions and paroxysms cannot be forecasted based on monitoring data, and a full probabilistic assessment based on past eruption data would be quite useful for scientific and civil protection purposes. In the study we perform a time series analysis either considering the last ~150 years of reconstructed activity and the most recent 35 years. We included the estimation of event rates and rate changes in time. Results clearly highlight that the activity is non-homogeneus in time, with a significant low of activity between about 1960 and 1990. Maximum values of event rates were computed during the first half of last century, for both major explosions and paroxysms, whereas the rate of paroxysms is significantly lower in the last decades with respect to maximum rates. We also accomplish a statistical analysis of the inter-event times, enabling us to determine if the data can be modeled as a Poisson process or not, e.g. if it shows time dependent distributions, recurring cycles, or temporal clusters. The uncertainty quantification on the current and future rates is mainly related to the choice of the modeling assumptions. The study represents a crucial progress towards quantitative hazard and risk assessments at Stromboli, which is particularly relevant for the thousands of people (e.g. tourists, guides and volcanologists) that regularly climb the volcano every year.

The ice sheets covering Antarctica and Greenland present the greatest uncertainty in, and largest potential contribution to, future sea level rise. The uncertainty arises from a paucity of suitable observations covering the full range of ice sheet behaviors, incomplete understanding of process influences, and limitations in defining key boundary conditions for the numerical models. To investigate the impact of these uncertainties on ice sheet projections we undertook a structured expert judgement study. Here, we interrogate the findings of that study to identify the dominant drivers of uncertainty in projections and their relative importance as a function of ice sheet and time. We find that for the 21st century, Greenland surface melting, in particular the role of surface albedo effects, and West Antarctic ice dynamics, specifically the role of ice shelf buttressing, dominate the uncertainty. The importance of these effects holds under both a high-end 5°C global warming scenario and another that limits global warming to 2°C. During the 22nd century the dominant drivers of uncertainty shift. Under the 5°C scenario, East Antarctic ice dynamics dominate the uncertainty in projections, driven by the possible role of ice flow instabilities. These dynamic effects only become dominant, however, for a temperature scenario above the Paris Agreement 2°C target and beyond 2100. Our findings identify key processes and factors that need to be addressed in future modeling studies in order to reduce uncertainties in ice sheet projections.