VIGIL: a Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling

Dioguardi, Fabio; Massaro, Silvia; Chiodini, Giovanni; Costa, Antonio; Folch, Arnau; Macedonio, Giovanni; Sandri, Laura; Selva, Jacopo; Tamburello, Giancarlo. 2022 VIGIL: a Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling. Annals of Geophysics, 65 (1), DM107. https://doi.org/10.4401/ag-8796

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Abstract/Summary

Probabilistic volcanic hazard assessment is a standard methodology based on running a deterministic hazard quantification tool multiple times to explore the full range of uncertainty in the input parameters and boundary conditions, in order to probabilistically quantify the variability of outputs accounting for such uncertainties. Nowadays, different volcanic hazards are quantified by means of this approach. Among these, volcanic gas emission is particularly relevant given the threat posed to human health if concentrations and exposure times exceed certain thresholds. There are different types of gas emissions but two main scenarios can be recognized: hot buoyant gas emissions from fumaroles and the ground and dense gas emissions feeding density currents that can occur, e.g., in limnic eruptions. Simulation tools are available to model the evolution of critical gas concentrations over an area of interest. Moreover, in order to perform probabilistic hazard assessments of volcanic gases, simulations should account for the natural variability associated to aspects such as seasonal and daily wind conditions, localized or diffuse source locations, and gas fluxes. Here we present VIGIL (automatized probabilistic VolcanIc Gas dIspersion modeLling), a new Python tool designed for managing the entire simulation workflow involved in single and probabilistic applications of gas dispersion modelling. VIGIL is able to manage the whole process from meteorological data processing, needed to run gas dispersion in both the dilute and dense gas flow scenarios, to the post processing of models’ outputs. Two application examples are presented to show some of the modelling capabilities offered by VIGIL.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.4401/ag-8796
ISSN:	1593-5213
Date made live:	06 May 2022 10:33 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532592

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