Chronology and dispersal characteristics of recently (last 5000 years) erupted tephra of Cotopaxi (Ecuador): implications for long-term eruptive forecasting

Barberi, F.; Coltelli, M.; Frullani, A.; Rosi, Mauro; Almeida, Eduardo Bezerra de

doi:10.1016/0377-0273(95)00017-8

Cited by 90 publications

(89 citation statements)

References 8 publications

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“…Typical field-based input parameters used to derive ESPs relate to the compilation of isopleth and isopach maps (i.e., crosswind and downwind ranges of isopleth maps, diameter of maximum clasts, deposit thickness, and area of isopach contours). We do not aim to quantify the typical ranges of errors associated with field-based input parameters but strongly encourage systematic studies of the uncertainty related to the variability of deposits, measurement techniques and subjective interpretations following the works of Barberi et al (1995), Biass & Bonadonna (2011), Cioni et al (2011, Engwell et al (2013), Klawonn et al (2014a) and Klawonn et al (2014b). Four additional key input parameters are considered in the TError package (empirical constants of Wilson & Walker (1987) and Mastin et al (2009), distal integration limit of Bonadonna & Houghton (2005) and wind speed at the tropopause) in order to assess the sensitivity related to the choice of given values.…”

Section: Discussionmentioning

confidence: 99%

“…1 Refer to Biass & Bonadonna (2011) Layer 5 is a black scoriaceous lapilli fallout with a silica content of 58 wt.% (Barberi et al, 1995). Table 1 and Table 2 summarize the input parameters as characterized by Biass & Bonadonna (2011).…”

Section: Case Studymentioning

confidence: 99%

“…For example, Biass & Bonadonna (2011) and show that the measurement of maximum clast and the compilation of isopleth maps is not standardized, considering either 1 or 3 axes of various number (e.g., 1, 3, 5, 10) of clasts and sampling volumes. These different measuring techniques can lead to discrepancies in plume heights between 20 − 40% (Barberi et al, 1995;Biass & Bonadonna, 2011). The last kind of uncertainty is related to the subjective choices taken during the contouring of both isopleth and isopach maps.…”

Section: Introductionmentioning

confidence: 99%

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TError: towards a better quantification of the uncertainty propagated during the characterization of tephra deposits

Biass¹,

Bagheri²,

Aeberhard³

et al. 2013

SIV

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We present TError, a Matlab package designed to quantify systematically the uncertainty associated with the characterization of tephra deposits, in which the most commonly used methods to quantify eruption source parameters are implemented. Inputs of the code are a range of field-based, model-based and empirical parameters (i.e., clast diameter, crosswind and downwind ranges, thickness measurement, area of isopach contours, bulk deposit density, empirical constants and wind speed), for which the user defines an uncertainty and an associated distribution. The TError package contains two main functions. The first function deterministically varies one input parameter at a time and quantifies the sensitivity of each Eruption Source Parameter (ESP; i.e., plume height, erupted volume, mass eruption rate) to the variability of input parameters. The second function propagates input parameters as stochastic distributions of noise through all ESPs. The resulting distributions can then be used to express the uncertainty of physical parameters of explosive eruptions in a systematic way. For both functions, comprehensive reports and sets of figures assist the user in the interpretation of the results. As an example, the TError package was applied to Layer 5 of Cotopaxi volcano. Using the median, the 2 nd percentile and the 98 th percentile as central value, lower bound and upper bound respectively, a new quantification of the ESP suggests a plume height of 30 ± 1 km a.s.l, a mass eruption rate of 1.8 citation:Biass, S., G. Bagheri, W. H. Aeberhard, C. Bonadonna (2014) TError: towards a better quantification of the uncertainty propagated during the characterization of tephra deposits, Statistics in Volcanology 1.2 : 1 − 27.

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Section: Discussionmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TError: towards a better quantification of the uncertainty propagated during the characterization of tephra deposits

Biass¹,

Bagheri²,

Aeberhard³

et al. 2013

SIV

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“…was performed on November 13th, 2014 in Quito, Ecuador. The ice-capped stratovolcano, with an andesitic to (Barberi et al 1995;Hall and Mothes 2008;Biass and Bonadonna 2011). Some lahars reached the Pacific Ocean at >200 km distance (Aguilera et al 2004;Pistolesi et al 2013).…”

Section: Cotopaxi Ecuador: Retrospective Application Inspired By Thementioning

confidence: 99%

The Need to Quantify Hazard Related to Non-magmatic Unrest: From BET_EF to BET_UNREST

Sandri

Tonini

Rouwet

et al. 2017

Advances in Volcanology

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Most volcanic hazard studies focus on magmatic eruptions and their accompanying phenomena. However, hazardous volcanic events can also occur during non-magmatic unrest, defined as a state of volcanic unrest in which no migration of magma is recognised. Examples include tectonic unrest, and hydrothermal unrest that may lead to phreatic eruptions. Recent events (e.g. Ontake eruption, September 2014) have demonstrated that the successful forecasting of phreatic eruptions is still very difficult. It is therefore of paramount importance to identify indicators that define the state of non-magmatic unrest. Often, this type of unrest is driven by fluids-on-the-move, requiring alternative monitoring setups, beyond the classical seismic-geodetic-geochemical architectures. Here we present a new version of the probabilistic model BET (Bayesian Event Tree), called BET_UNREST, specifically developed to include the forecasting of non-magmatic unrest and related hazards. The structure of BET_UNREST differs from the previous BET_EF (BET for Eruption Forecasting) by adding a dedicated branch to detail non-magmatic unrest outcomes. Probabilities are calculated at each node by merging prior models and past data with new incoming monitoring data, and the results can be updated any time new data has been collected. Monitoring data are weighted through pre-defined thresholds of anomaly, as in BET_EF. The BET_UNREST model is introduced here, together with its software implementation PyBetUnrest, with the aim of creating a user-friendly, open-access, and straightforward tool to support short-term volcanic forecasting (already available on the VHub platform). The BET_UNREST model and PyBetUnrest tool are tested through three case studies in the frame of the EU VUELCO project. Resumen extendidoLa mayoría de los estudios sobre amenazas volcánicas están enfocados en las erupciones magmáticas y fenómenos relacionados. Sin embargo, fenómenos volcánicos peligrosos pueden también ocurrir durante una fase de "unrest" no-magmático, definido por el estado de unrest volcánico en el cual no se reconoce la migración de un magma. Ejemplos de esto son unrest tectónico (capaz de causar preocupación independientemente del resultado posterior) y unrest hidrotermal, que pueden resultar en erupciones freáticas. Eventos recientes (e.g. la erupción de Ontake en septiembre 2014) han demostrado que las erupciones freáticas siguen siendo difícilmente previsibles. Por estas razones, es de extrema importancia identificar señales que permitan definir un estado de unrest no-magmático. Muchas veces, este tipo de unrest es provocado por fluidos en movimiento, y requiere la instalación de un sistema de monitoreo alternativo, más allá de la clásica arquitectura sismo-geodético-química. En este capítulo, presentamos la nueva versión del modelo probabilístico BET (Arbol de Eventos Bayesiano, por sus siglas en inglés), llamado BET_UNREST, específicamente desarrollado para incluir la previsión de unrest no-magmático y sus peligros relacionados. La estructura de BET_...

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“…The evaluation of the past eruptive behaviour was based on both field data (Barberi et al 1995;Biass and Bonadonna 2011) and a thorough study of the Global Volcanism Program of the Smithsonian institution (Siebert and Simkin 2002, GVP), based on which we have decided to focus on eruptions of VEI of 3-5 (bulk volumes between 0.01 and 10 km 3 ; Newhall and Self 1982). The probability of an eruption of a given magnitude occurring within a hypothetical time period (i.e.…”

Section: Summary Of the Hazard Assessmentmentioning

confidence: 99%

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

2012

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Chronology and dispersal characteristics of recently (last 5000 years) erupted tephra of Cotopaxi (Ecuador): implications for long-term eruptive forecasting

Cited by 90 publications

References 8 publications

TError: towards a better quantification of the uncertainty propagated during the characterization of tephra deposits

TError: towards a better quantification of the uncertainty propagated during the characterization of tephra deposits

The Need to Quantify Hazard Related to Non-magmatic Unrest: From BET_EF to BET_UNREST

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

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