Probabilistic reconstruction (or forecasting) of distal runouts of large magnitude ignimbrite PDC flows sensitive to topography using mass-dependent inversion models.

Abstract

1. A method to model the minimum volume and mass to inundate an at-risk city from a volcano source site We use a Bayes Belief Network (BBN) related to each model to characterize uncertainties on the MinVol estimate, including in the calculational scheme correlation coefficients between the input variables and the calculated MinVol [Ababei (2016); UNINET BBN: https://lighttwist-software.com/ accessed 25 Nov 2019]. The large rectangular panels in each BNN illustrate the shape of the uncertainty distribution for the minimum volume needed to reach the at-risk city, based on the uncertainties in the relevant input variables, with the mean value and standard deviation shown in the lower frame of the panel. Larger correlation values (marked on linking arc arrows) highlight the more sensitive variables. Negative correlation means that an increase of that input decreases the MinVol required to reach the at-risk city. Positive correlations produce the opposite effect. The correlation coefficients are based on the joint DM distribution from elicitation and on the 130 km maximum runout distance. Further research is merited to develop better constraints on the uncertainty distributions of the most influential input parameters. Acknowledgements: This work was supported by Aspinall & Associates. We thank Charles Connor, Laura Connor, Britt Hill and the other experts participating in the elicitation sessions. .

Reproducing pyroclastic density currents deposits of the AD 79 eruption of the Somma-Vesuvius volcano using the box-model approach

“…Our approach differs from considering constant stress acting on the basal area (Dade and Huppert, 1998). Constant stress dynamics have been further explored in literature, and they can lead to different equations if the basal area grows linearly or with the square of the radius (Kelfoun et al, 2009;Kelfoun, 2011;Ogburn and Calder, 2017;Aspinall et al, 2019).…”

“…Then, once set a vent location, we assess the capability of topographic reliefs to block the current. In particular, the invasion areas are obtained by using the so-called energy-conoid model, based on the assumption of nonlinear, monotonic decay of flow kinetic energy with distance (Neri et al, 2015;Bevilacqua, 2016;Esposti Ongaro et al, 2016;Bevilacqua et al, 2017;Aspinall et al, 2019;Aravena et al, 2020). In more detail, first we determine the maximum height ℎ of an obstacle the flow can overcome.…”

“…thicknesses/total volume, maximum runout and total grain size distribution -TGSD, have been calculated in Cioni et al (2020) and partially processed to fit with PyBox input requirements (see sections 3.1.1 and 3.1.3). Densities of single grain sizes (Barberi et al, 1989; section 3.1.2) and emplacement temperatures of PDCs (T=600 K for both EU3pf and EU4; Cioni et al, 2004) are derived from the literature.…”

“…In order to provide density values for each GS, we used the mass fractions of the different components (juveniles, lithics and crystalssee Table S1 from the Supporting Information) calculated by Gurioli (1999). Such values were associated to the averaged density measurements for these three components presented in Barberi et al (1989), through which we extrapolated the weighted mean (with respect to mass fraction) mean density of each grain size class for both EU3pf and EU4b/c units ( https://doi.org/10.5194/se-2020-138 Preprint. Discussion started: 9 October 2020 c Author(s) 2020.…”

Tree‐Branching‐Based Enhancement of Kinetic Energy Models for Reproducing Channelization Processes of Pyroclastic Density Currents

“…Kinetic energy is compared with the potential energy associated with the topographic obstacles encountered by the PDC to calculate the runout distance. Because kinetic models are able to generate results rapidly, they have been adopted to assess volcanic hazard using a probabilistic approach (Aspinall et al, 2019; Bevilacqua et al, 2017; Neri, Esposti Ongaro, et al, 2015; Sandri et al, 2018; Tierz, Sandri, Costa, Sulpizio, et al, 2016; Tierz, Sandri, Costa, Zaccarelli, et al, 2016). This approach allows the consideration of the natural variability observed in the eruptive process by sampling the model input parameters within their expected variation ranges, as shown in Tierz, Sandri, Costa, Zaccarelli, et al (2016), where the energy cone model was systematically applied to capture the variability of relevant parameters for hazard assessment (e.g., inundation area and maximum runout distance) in Somma‐Vesuvius and Campi Flegrei (Italy).…”