Improving on mass flow rate estimates of volcanic eruptions

Degruyter, Wim; Bonadonna, Costanza

doi:10.1029/2012gl052566

Cited by 156 publications

(304 citation statements)

References 35 publications

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“…Degruyter and Bonadonna Degruyter & Bonadonna (2012 recently developed a new analytical expression for the calculation of the MER for both vertically-rising and bent-over plumes. This method relates the MER to the plume height and the average wind speed as:…”

Section: Statistics In Volcanologymentioning

confidence: 99%

“…The plume height is then converted to MER, deterministically inputting values for the empirical constants of Wilson & Walker (1987) and Mastin et al (2009). Since the method of Degruyter & Bonadonna (2012) accounts for the effect of the wind on the MER, TError either accepts the wind speed as an independent variable or as propagated from the method of Carey & Sparks (1986), i.e., directly related to the downwind and crosswind range. In TError, the input wind should be the maximum wind speed at the tropopause, interpolated by the code below and above the tropopause according to the atmospheric model of Bonadonna & Phillips (2003).…”

Section: Bonadonna and Costa 2012mentioning

confidence: 99%

“…Similarly, an identical RIU for the constant of Mastin et al (2009) results in ROD values for MER of +55% and −33%, respectively. The method of Degruyter & Bonadonna (2012) using the wind speed propagated from Carey & Sparks (1986) shows dependencies on clast diameter, crosswind and downwind ranges, where the crosswind range and the clast diameter influence both plume height and wind speed. RIUs of ±10% on clast diameter, crosswind range and downwind range result in symmetrical ROD values for MER of −11%/ + 15%, −15%/ + 16% and −13%/ + 14%, respectively.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The TError package includes an implementation of one model for the calculation of plume height (Carey & Sparks, 1986), three parameterizations for the determination of the MER (Degruyter & Bonadonna, 2012;Mastin et al, 2009;Wilson & Walker, 1987) and three strategies for the calculation of erupted volume (Fierstein & Nathenson, 1992;Bonadonna & Houghton, 2005;Bonadonna & Costa, 2012). Erupted mass and duration are also quantified.…”

Section: The Terror Packagementioning

confidence: 99%

“…During the last three decades, models have been developed to address the need of the characterization of explosive eruptions, including empirical, statistical and analytical models. These models allow for the characterization of plume height (Bonadonna & Costa, 2013;Burden et al, 2011;Carey & Sparks, 1986), MER (Degruyter & Bonadonna, 2012;Mastin et al, 2009;Sparks et al, 1997;Sparks, 1986;Wilson & Walker, 1987;Woodhouse et al, 2013) and erupted tephra volume (Bonadonna & Costa, 2012;Bonadonna & Houghton, 2005;Burden et al, 2013;Fierstein & Nathenson, 1992;Legros, 2000;Pyle, 1989). These methods, with the exception of Burden et al (2013), mostly depend on the compilation of isopleth maps (i.e.…”

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: Statistics In Volcanologymentioning

confidence: 99%

Section: Bonadonna and Costa 2012mentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: The Terror Packagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Biass¹,

Bagheri²,

Aeberhard³

et al. 2013

SIV

View full text Add to dashboard Cite

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Testing the accuracy of a 1‐D volcanic plume model in estimating mass eruption rate

Mastin

2014

JGR Atmospheres

111

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During volcanic eruptions, empirical relationships are used to estimate mass eruption rate from plume height. Although simple, such relationships can be inaccurate and can underestimate rates in windy conditions. One-dimensional plume models can incorporate atmospheric conditions and give potentially more accurate estimates. Here I present a 1-D model for plumes in crosswind and simulate 25 historical eruptions where plume height H obs was well observed and mass eruption rate M obs could be calculated from mapped deposit mass and observed duration. The simulations considered wind, temperature, and phase changes of water. Atmospheric conditions were obtained from the National Center for Atmospheric Research Reanalysis 2.5°model. Simulations calculate the minimum, maximum, and average values (M min , M max , and M avg ) that fit the plume height. Eruption rates were also estimated from the empirical formula M empir = 140H obs 4.14 (M empir is in kilogram per second, H obs is in kilometer). For these eruptions, the standard error of the residual in log space is about 0.53 for M avg and 0.50 for M empir . Thus, for this data set, the model is slightly less accurate at predicting M obs than the empirical curve.The inability of this model to improve eruption rate estimates may lie in the limited accuracy of even well-observed plume heights, inaccurate model formulation, or the fact that most eruptions examined were not highly influenced by wind. For the low, wind-blown plume of 14-18 April 2010 at Eyjafjallajökull, where an accurate plume height time series is available, modeled rates do agree better with M obs than M empir .

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Separation of ash and sulfur dioxide during the 2011 Grímsvötn eruption

Moxnes

Kristiansen

Stohl

et al. 2014

J. Geophys. Res. Atmos.

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Modeling the transport of volcanic ash and gases released during volcanic eruptions is crucially dependent on knowledge of the source term of the eruption, that is, the source strength as a function of altitude and time. For the first time, an inversion method is used to estimate the source terms of both volcanic sulfur dioxide (SO 2 ) and ash. It was applied to the explosive volcanic eruption of Grímsvötn, Iceland, in May 2011. The method uses input from the particle dispersion model, FLEXPART (flexible particle dispersion model), a priori source estimates, and satellite observations of SO 2 or ash total columns from Infrared Atmospheric Sounding Interferometer to separately obtain the source terms for volcanic SO 2 and fine ash. The estimated source terms show that SO 2 was emitted mostly to high altitudes (5 to 13 km) during about 18 h (22 May, 00-18 UTC) while fine ash was emitted mostly to low altitudes (below 4 km) during roughly 24 h (22 May 06 UTC to 23 May 06 UTC). FLEXPART simulations using the estimated source terms show a clear separation of SO 2 (transported mostly northwestward) and the fine ash (transported mostly southeastward). This corresponds well with independent satellite observations and measured aerosol mass concentrations and lidar measurements at surface stations in Scandinavia. Aircraft measurements above Iceland and Germany confirmed that the ash was located in the lower atmosphere. This demonstrates that the inversion method, in this case, is able to distinguish between emission heights of SO 2 and ash and can capture resulting differences in transport patterns.

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Improving on mass flow rate estimates of volcanic eruptions

Cited by 156 publications

References 35 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

Testing the accuracy of a 1‐D volcanic plume model in estimating mass eruption rate

Separation of ash and sulfur dioxide during the 2011 Grímsvötn eruption

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