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

Biass, Sébastien; Bonadonna, Costanza; Connor, L.; Connor, Charles B.

doi:10.1186/s13617-016-0050-5

Cited by 46 publications

(57 citation statements)

References 73 publications

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“…Probabilistic assessments of volcanic hazards based on simulations of physical processes have gained traction over the past several years Biass, Bonadonna, Di Traglia, et al, 2016;Biass, Bonadonna, Connor, et al, 2016;Cappello et al, 2015;Connor et al, 2012;Dalbey et al, 2008;Gallant et al, 2018;Mastin et al, 2014;Mead & Magill, 2017;Sandri et al, 2016Sandri et al, , 2018Tierz et al, 2018;Volentik & Houghton, 2015). Computer model emulators (also known as statistical surrogates) are an efficient tool in this type of modeling particularly when the physical model is computationally expensive to exercise (Bayarri et al, 2009(Bayarri et al, , 2015Spiller et al, 2014).…”

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confidence: 99%

“…In this sense, we anticipate that the approach presented here and variations thereof will prove useful for a wide range of volcanic hazards-lahars, tephra fallout, and pyroclastic surges. In particular, we imagine that such an approach could be useful to overcome computational challenges described in Biass, Bonadonna, Di Traglia, et al (2016), Gallant et al (2018), and Mead and Magill (2017).…”

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confidence: 99%

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Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

et al. 2019

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Ideally, probabilistic hazard assessments combine available knowledge about physical mechanisms of the hazard, data on past hazards, and any precursor information. Systematically assessing the probability of rare, yet catastrophic hazards adds a layer of difficulty due to limited observation data. Via computer models, one can exercise potentially dangerous scenarios that may not have happened in the past but are probabilistically consistent with the aleatoric nature of previous volcanic behavior in the record. Traditional Monte Carlo‐based methods to calculate such hazard probabilities suffer from two issues: they are computationally expensive, and they are static. In light of new information, newly available data, signs of unrest, and new probabilistic analysis describing uncertainty about scenarios the Monte Carlo calculation would need to be redone under the same computational constraints. Here we present an alternative approach utilizing statistical emulators that provide an efficient way to overcome the computational bottleneck of typical Monte Carlo approaches. Moreover, this approach is independent of an aleatoric scenario model and yet can be applied rapidly to any scenario model making it dynamic. We present and apply this emulator‐based approach to create multiple probabilistic hazard maps for inundation of pyroclastic density currents in the Long Valley Volcanic Region. Further, we illustrate how this approach enables an exploration of the impact of epistemic uncertainties on these probabilistic hazard forecasts. Particularly, we focus on the uncertainty of vent opening models and how that uncertainty both aleatoric and epistemic impacts the resulting probabilistic hazard maps of pyroclastic density current inundation.

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confidence: 99%

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Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

et al. 2019

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“…Four-time daily wind profiles from the National Oceanographic and Atmospheric Administration (NOAA, 2017) were 5 analysed for the period 1997-2015 using the Matlab package TephraProb (Biass et al, 2016c). Wind conditions were found to be very similar for one year (2013), 10 years (2006-2015) and the full dataset (19 years, from 1997-2015) (see Figs S1 to S6, for full wind analysis).…”

Section: Wind Datamentioning

confidence: 99%

“…Appendix C Eruption and atmospheric parameters sampled for the Eruption Range Scenario Figure C1 Parameters sampled for the Eruption Range Scenario. a) particle density, b) mass eruption rate, c) plume height, d) wind speed and direction (created in TephraProb) (Biass et al, 2016c;NOAA, 2017). Plume height depends on mass eruption rate and 5 wind speed.…”

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Exposure-based risk assessment and emergency management associated with the fallout of large clasts

Osman

Rossi

Bonadonna

et al. 2018

Preprint

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Fallout of ballistic blocks and bombs ejected from eruptive vents represents a well-known hazard in proximal areas.However, fallout of large clasts sedimenting from plume margins extending to medial areas with the potential to produce severe injuries to people and cause damage to infrastructure, is often overlooked. Recent eruptive events at Mount Etna (Italy) provide a clear example where large-clast fallout from plume margins (>5 cm) has posed a real threat both to the many visitors reaching the summit area and to local infrastructure, and, therefore, has been selected as a case study. To quantify this hazard, 15 a new particle-sedimentation model was calibrated with field data and then used for probabilistic hazard assessments. For a fully probabilistic scenario the hazard zone covered 72 km 2 and included some 125 km of paths and roads, and 15 buildings; evacuation on foot to a safe area was estimated at almost 4 hours, but this could be reduced to less than 3 hours if two shelters were provided. Our results show the importance of integrating probabilistic hazard analysis of large-clast fallout within effective strategies of risk management and reduction, especially in the case of volcanoes where visitors can reach the summit 20 areas.

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“…Such expertise could be in the form of a quantitative or descriptive physical model, an empirical or semi-empirical relationship, or their combination. Estimating ESPs can be treated as an inverse problem (e.g., Tarantola, 2005;Kaipio and Somersalo, 2006), and requires the use of different statistical and engineering techniques (Klawonn et al, 2012(Klawonn et al, , 2014Biass et al, 2016;Poret et al, 2017;Koyaguchi et al, 2017;White et al, 2017). Previous workers have presented different methods to implement inversion to obtain ESPs from the characteristics of tephra deposits, such as deposit thickness and grain size.…”

Section: Introductionmentioning

confidence: 99%

Tephra Deposit Inversion by Coupling TEPHRA2 With the Metropolis-Hastings Algorithm and Its Implications

Yang¹,

Pitman²,

Bursik³

et al. 2020

Preprint

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Abstract In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model TEPHRA2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works, and to show how inputs affect its performance. Results are presented as sample posterior distribution estimates for variables of interest. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; and iii) capture correlations between variables of interest in the estimated Eruption Source Parameters. A limitation is that some of the inputs need to be specifed subjectively. How and why such inputs affect the performance of the algorithm and how to specify them properly are explained and listed. Correlation between variables of interest are well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters, and wind direction and speed at each elevation level, as this increases the number of variables to be estimated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from TEPHRA2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from the deposit.

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

Cited by 46 publications

References 73 publications

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

Exposure-based risk assessment and emergency management associated with the fallout of large clasts

Tephra Deposit Inversion by Coupling TEPHRA2 With the Metropolis-Hastings Algorithm and Its Implications

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