Hazard assessment at the Quaternary La Garrotxa Volcanic Field (NE Iberia)

Bartolini, Stefania; Bolós, Xavier; Martı́, Joan; Pedra, Elisabeth Riera; Planagumà, Llorenç

doi:10.1007/s11069-015-1774-y

Cited by 17 publications

(19 citation statements)

References 53 publications

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“…Kernel density estimation (KDE), a nonparametric statistical method for estimating spatial density, is widely used by researchers as a method for predicting probable locations of future eruptive vents based solely on the observed locations of vents formed in the past (e.g., Richardson et al, 2012;Germa et al, 2013;El Difrawy et al, 2013;Bevilacqua et al, 2015;Bartolini et al, 2015;Galindo et al, 2016;Mazzarini et al, 2016;Tadini et al, 2017;van den Hove et al, 2017). In application of KDE, it is assumed that the past pattern of vent formation is governed by the same tectonic and magmatic factors that govern future vent formation.…”

Section: Introductionmentioning

confidence: 99%

How to use kernel density estimation as a diagnostic and forecasting tool for distributed volcanic vents

Connor

Germa

et al. 2019

SIV

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2019) How to use kernel density estimation as a diagnostic and forecasting tool for distributed volcanic vents, Statistics in Volcanology 4.3 : 1 − 25. AbstractVolcanic activity often results in the formation of new volcanic vents. These new vents can create hazards in unexpected areas. Therefore, the probability of new vent formation should be assessed as part of volcanic hazard assessments. This paper describes our use of kernel density estimation (KDE) as a way to estimate the spatial density of future volcanic vents. The bivariate Gaussian kernel function is described step-by-step using pseudocode. Our computer code, written in PERL, is used to calculate the spatial density of existing vents and then create a contour map using GMT (Generic Mapping Tools). Application of this method and code relies on several assumptions about the definition of volcanic events, independence of events, the type of kernel function used, and the selection of kernel bandwidth. Three examples using the code are provided: (1) for volcanic vents located west of the city of Managua (Nicaragua), (2) for volcanic vents distributed within the Arsia Mons caldera (Mars), weighted by volume, and (3) for vents of the Lassen volcanic system (northern California), sub-divided by geochemistry.

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Section: Introductionmentioning

confidence: 99%

How to use kernel density estimation as a diagnostic and forecasting tool for distributed volcanic vents

Connor

Germa

et al. 2019

SIV

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“…For volcanic fields with low recurrence rates (e.g., Auckland Volcanic Field,~10 − 4 events/year; Bebbington and Cronin 2011), or scarce temporal data (e.g., Harrat Al-Madinah; El Difrawy et al 2013), it is usually assumed that the temporal component of the hazard assessment is independent of the spatial component (Bebbington 2013(Bebbington , 2015. Following this assumption, a long-term average recurrence rate (a stationary model) can be calculated for the whole field based on the number of events over a specific time interval, which has been a common approach for several volcanic fields (e.g., El Difrawy et al 2013;Runge et al 2014;Bartolini et al 2015;Gallant et al 2018;Nieto-Torres and Martin Del Pozzo 2019).…”

Section: Spatial Temporal and Volcanic Hazard Assessmentsmentioning

confidence: 99%

“…Several techniques to integrate multiple background data have been developed during the last few years (e.g., Cappello et al, 2012Cappello et al, , 2013Alcorn et al 2013;Becerril et al 2013Becerril et al , 2014Becerril et al , 2017El Difrawy et al 2013;Bartolini et al , 2015Gallant et al 2018;Jiménez et al 2018). Most of these approaches apply a similar statistical treatment of the available volcano-structural information resulting in spatial density maps showing the probability of vent location.…”

Section: Spatial Temporal and Volcanic Hazard Assessmentsmentioning

confidence: 99%

“…The program has to be loaded as a toolbar in ArcMap (up to version 9.3) and a complete user's guide is provided. VORIS was a pioneer initiative in including different simulation tools in a single framework and it is still frequently used in volcanic hazard assessments (e.g., Alcorn et al 2013;Bartolini et al 2015;Becerril et al 2017). However, this program assumes a unique scalar bandwidth for the spatial probability analysis, does not perform a temporal analysis, does not model either ballistic projectiles or lahars, and it is not possible to combine hazards into an integrated map.…”

Section: Existing Models and Research Gapsmentioning

confidence: 99%

“…This strong bias towards spatial and temporal assessments is mainly due to the difficulty in addressing, in a single assessment, the multiple volcanic phenomena that might be produced by any subsequent eruption. Some attempts concentrate on a single volcanic phenomenon, mainly pyroclastic density currents (PDCs; Sandri et al 2012;Neri et al 2015) and lava flows (Connor et al 2012;Gallant et al 2018); there are very few studies that have included several volcanic phenomena (Alcorn et al 2013; Bartolini et al 2014Bartolini et al , 2015Becerril et al 2014Becerril et al , 2017Sandri et al 2014). Integration of multiple hazards into a single map has only been carried out qualitatively (or in one-off scenario events), because a quantitative procedure for hazard integration has been lacking.…”

Section: Introductionmentioning

confidence: 99%

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MatHaz: a Matlab code to assist with probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields

Bertin

Lindsay

Becerril³

et al. 2019

J Appl. Volcanol.

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This paper introduces an open source computer code to perform an integrated probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields. The program, named MatHaz, is a set of Matlab scripts that follows a sequential methodology. After the user has provided a set of input files, this tool first estimates the spatial probability of future volcanic vents, then the temporal probability of future volcanic events, and finally models up to five volcanic phenomena (pyroclastic density currents, ballistic projectiles, lava flows, lahars, and tephra fallout) following a probabilistic approach. These results can be combined and depicted as an integrated quantitative (and/or qualitative) volcanic hazard map, with weightings of hazard factors chosen by the user. We illustrate the use of this tool by applying it to the Carrán-Los Venados Volcanic Field in southern Chile. The opensource, replicable, and user-friendly nature of the code allows its application to any volcanic region of the world, regardless of its extent, type, and amount of volcano-structural data.

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Probabilistic E-tools for Hazard Assessment and Risk Management

Bartolini

Martı́

Sobradelo

et al. 2017

Advances in Volcanology

Self Cite

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The impact of a natural event can significantly affect human life and the environment. Although fascinating, a volcanic eruption creates similar or even greater problems than more frequent natural events due to its multi-hazard nature and the intensity and extent of its potential impact. It is possible to live near a volcanic area and take advantage of the benefits that volcanoes offer, but it is also important to be aware of the existing threats and to know how to minimise risks. In this chapter, we present an integrated approach using e-tools for assessing volcanic hazard and risk management. These tools have been especially designed to assess and manage volcanic risk, to evaluate longand short-term volcanic hazards, to conduct vulnerability analysis, and to assist decision-makers during the management of a volcanic crisis. The methodology proposed here can be implemented before an emergency in order to identify optimum mitigating actions and how these may have to be adapted as new information is obtained. These tools also allow us identifying the most appropriate probabilistic and statistical techniques for volcanological data analysis and treatment in the context of quantitative hazard and risk assessments. Understanding volcanic unrest, forecasting volcanic eruptions, and predicting the most probable scenarios, all imply a high degree of inherent uncertainty, which needs to be quantified and clearly explained when transmitting scientific information to decision-makers. Resumen El impacto de un evento natural puede afectar significativamente la vida humana y el medio ambiente. Aunque fascinante, una erupción volcánica

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Hazard assessment at the Quaternary La Garrotxa Volcanic Field (NE Iberia)

Cited by 17 publications

References 53 publications

How to use kernel density estimation as a diagnostic and forecasting tool for distributed volcanic vents

How to use kernel density estimation as a diagnostic and forecasting tool for distributed volcanic vents

MatHaz: a Matlab code to assist with probabilistic spatio-temporal volcanic hazard assessment in distributed volcanic fields

Probabilistic E-tools for Hazard Assessment and Risk Management

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