Inversion is the key to dispersion: understanding eruption dynamics by inverting tephra fallout

Connor, L.; Connor, Charles B.

doi:10.1144/iavcei001.18

Cited by 94 publications

(135 citation statements)

References 21 publications

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“…These models include analytical solutions, used widely for investigations of particle sedimentation and for hazard assessments (e.g., Armienti et al, 1988;Bonadonna et al, 2005a;Bursik et al, 1992a and b;Connor et al, 2001;Connor and Connor, 2006;Glaze and Self, 1991;Hurst and Turner, 1999;Koyaguchi and Ohno, 2001a;Macedonio et al, 2005;Suzuki, 1983), and numerical models for real-time forecast of plume evolution and sedimentation (e.g., Costa et al, 2006;Searcy et al 1998). Both types have been validated with field data and are now used regularly for both these purposes.…”

Section: Introductionmentioning

confidence: 99%

A review of volcanic ash aggregation

Brown

Bonadonna

Durant

2012

Physics and Chemistry of the Earth, Parts A/B/C

237

298

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. (2012) 'A review of volcanic ash aggregation.', Physics and chemistry of the earth, parts A/B/C., 45-46 . pp. 65-78. Further information on publisher's website:http://dx.doi.org/10.1016/j.pce. 2011.11.001 Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractMost volcanic ash particles with diameters < 63 µm settle from eruption clouds as particle aggregates that cumulatively have larger sizes, lower densities, and higher terminal fall velocities than individual constituent particles. Particle aggregation reduces the atmospheric residence time of fine ash, which results in a proportional increase in fine ash fallout within 10s km to 100s km from the volcano and a reduction in airborne fine ash mass concentrations 1000s km from the volcano. Aggregate characteristics vary with distance from the volcano: proximal aggregates are typically larger (up to cm size) with concentric structures, while distal aggregates are typically smaller (sub-millimetre size). Particles comprising ash aggregates are bound through hydro-bonds (liquid and ice water) and electrostatic forces, and the rate of particle aggregation correlates with cloud liquid water availability. Eruption source parameters (including initial particle size distribution, erupted mass, eruption column height, cloud water content and temperature) and the eruption plume temperature lapse rate, coupled with the environmental parameters, determines the type and spatiotemporal distribution of aggregates. Field studies, lab experiments and modelling investigations have already provided important insights on the process of particle aggregation. However, new integrated observations that combine remote sensing studies of 2 ash clouds with field measurement and sampling, and lab experiments are required to fill current gaps in knowledge surrounding the theory of ash aggregate formation. Abstract word count: 222

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Section: Introductionmentioning

confidence: 99%

A review of volcanic ash aggregation

Brown

Bonadonna

Durant

2012

Physics and Chemistry of the Earth, Parts A/B/C

237

298

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“…In this study, we utilised the analytical tephra advection-diffusion model Tephra2 Connor and Connor 2006). Tephra2 is based on the model developed by Suzuki (1983) and subsequently modified by a number of authors: see for a full description.…”

Section: Inversion Using Tephra2mentioning

confidence: 99%

“…To replicate the phases of the Hoei eruption, we utilised inversion techniques developed by Connor and Connor (2006). A downhill simplex method was employed to determine an optimal set of eruption parameters for Tephra2 that, when applied, best replicated field measurements given by .…”

Section: −2mentioning

confidence: 99%

“…Inversion of analytical models is one method for the determination of physical parameters (e.g. Connor and Connor 2006;Bonasia et al 2010;Mannen 2014;Volentik et al 2010;Bonadonna et al 2015a, c) but has not been tested on a long-lasting, multi-phase volcanic eruption. Here, we present a detailed investigation of the use of analytical inversions based on application to a well-known and well-studied multi-phase eruption that occurred over a Editorial responsibility: S. A. Fagents and J. Fierstein…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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Fuji Volcano last erupted in AD 1707 depositing approximately 40 mm of tephra in the area that is now central Tokyo. New high-resolution data describe 17 eruptive phases occurring over a period of 16 days (Miyaji et al., J Volcanol Geotherm Res 207(3-4):113-129, 2011). Inversion techniques were used in order to best replicate geological data and eyewitness accounts, and to estimate eruption source parameters. Inversion results based on data from individual eruptive phases suggest a total erupted mass of 2.09×10 12 kg. Comparatively, results based on a single data set describing the entire eruption sequence suggest a total mass of 1.69× 10 12 kg. Values for total erupted mass determined by inversion were compared to those calculated using various curve fitting approaches. An exponential (two-segment) method, taking into account missing distal data, was found to be most compatible with inversion results, giving an erupted mass of 2.52×10 12 kg when combining individual phases and 1.59× 10 12 kg when utilising a single data set describing the Hoei sequence. Similarly, a Weibull fitting method determined a total erupted mass of 1.54×10 12 kg for the single data set and compared favourably with inversion results when enough data were available. Partitioning extended eruption scenarios into multiple phases and including detailed geological data close to the eruption source, more accurately replicated the observed deposit by taking into account subtleties such as lobes deposited during transient increases in eruption rate and variations in wind velocity or direction throughout the eruption.

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“…unit area measurements (Bonasia et al, 2010;Connor & Connor, 2006;Klawonn et al, 2012;Volentik et al, 2009) or using statistical approaches (Burden et al, 2013).…”

Section: Statistics In Volcanologymentioning

confidence: 99%

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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Inversion is the key to dispersion: understanding eruption dynamics by inverting tephra fallout

Cited by 94 publications

References 21 publications

A review of volcanic ash aggregation

A review of volcanic ash aggregation

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

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

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