A fast GIS-based risk assessment for tephra fallout: the example of Cotopaxi volcano, Ecuador

Abstract

In order to develop efficient strategies for risk mitigation and emergency management, planners require the assessment of both the expected hazard (frequency and magnitude) and the vulnerability of exposed elements. This paper presents a GIS-based methodology to produce qualitative to semi-qualitative thematic risk assessments for tephra fallout around explosive volcanoes, designed to operate with datasets of variable precision and resolution depending on data availability. Due to the constant increase in population density around volcanoes and to the wide dispersal of tephra from volcanic plumes, a large range of threats, such as roof collapses, damage to crops, blockage of vital lifelines and health problems, concern even remote communities. To address these issues, we have assessed the vulnerability and the risk levels for five themes relevant to tephra fallout: (1) social, (2) economic, (3) environmental, (4) physical and (5) territorial. Risk and vulnerability indices for each theme are averaged to the fourth level of administrative unit (parroquia, parish). In a companion paper, Biass and Bonadonna (this volume) present a probabilistic hazard assessment for tephra fallout at Cotopaxi volcano (Ecuador) using the advection-diffusion model TEPHRA2, which is based on field investigations and a global eruption database (Global Volcanism Program, GVP). The scope of this paper is to present a new approach to risk assessment specifically designed for tephra fallout, based on a comprehensive hazard assessment of Cotopaxi volcano. Our results show that an eruption of moderate magnitude (i.e. VEI 4) would result in the possible collapse of *9,000 houses in the two parishes located close to the volcano. Our study also reveals a high risk on agriculture, closely linked to the economic sector, and a possible accessibility problem in case of an eruption of any size, as tephra is likely to affect the only major road running from Quito to Latacunga (Panamerican This article has been previously published in Natural Hazards 64:615-639. For referencing, please use this article.

TephraProb: a Matlab package for probabilistic hazard assessments of tephra fallout

“…3b). Note that breaks-in-slope associated with completeness might vary as a function of the VEI/magnitude class and should be assessed separately (Biass and Bonadonna 2013;Dzierma and Wehrmann 2010;Jenkins et al 2012;Mendoza-Rosas and De la CruzReyna 2008).…”

“…Biass and Bonadonna 2013;Borradaile 2003). We aim at quantifying the probability that a repose interval T is smaller or equal to an arbitrary time window t:…”

“…based upon the conditional probability that the associated eruption scenario occurs; two caveats should be considered when using the GVP module. First, the GVP database is not a direct input to the probabilistic modelling in TephraProb and should be used as a support tool to develop eruption scenarios and identify critical ESPs with the full knowledge of the limitations and assumptions behind such databases (Biass and Bonadonna 2013;Dzierma and Wehrmann 2010;Jenkins et al 2012; Mendoza-Rosas and De la Cruz-Reyna 2008; Siebert et al 2010;Simkin and Siebert 1994). Second assessing the long-term probability of a future eruption is not trivial and should be achieved using a rigorous probabilistic framework in order to quantify and propagate various sources of uncertainties on final estimates (Bear-Crozier et al 2016;Connor et al 2003;Jenkins et al 2012;Marzocchi and Bebbington 2012;Sandri et al 2016;Sheldrake 2014;Thompson et al 2015).…”

Simulating a multi-phase tephra fall event: inversion modelling for the 1707 Hoei eruption of Mount Fuji, Japan

“…The relative release height of particles may be approximated by the distribution of mass within the simulated eruption plume. Previous modelling carried out with Tephra2 assumed a linear distribution of mass between the maximum plume height and a given lower limit (Bonadonna et al 2002Biass and Bonadonna 2012). Bonadonna et al (2002) tested various mass distributions by simulating Vulcanian plumes with (1) mass concentrated half-way between the neutral buoyancy height and the plume height, (2) mass distributed linearly between the neutral buoyancy height and the maximum plume height, and (3) mass distributed according to Suzuki (1983), where the highest concentration of particles are typically located around the height of neutral buoyancy.…”

Remote sensing of volcanoes and volcanic processes: integrating observation and modelling – introduction

“…roof collapse: Biass et al 2013). However, it is, perhaps, in terms of ash dispersal that numerical models have been put to most use in real-time to forecast plume evolution and sedimentation (Brown et al 2012 and references therein), particularly driven by disruption to the aviation industry by eruptions such as that of Eyjafjallajökull in 2010.…”