Stratigraphic and sedimentologic framework for tephras in the Wilson Creek Formation, Mono Basin, California, USA

Yang, Qianru; Bursik, Marcus; Pouget, S.

doi:10.1016/j.jvolgeores.2019.02.007

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…We used symmetrical, normal distributions because these approximate distributions often observed in tephra fall deposits (e.g. Mannen 2006;Yang et al 2019;Maeno et al 2019). We also aimed to avoid using overly specific, skewed or bimodal distributions as these might make it difficult for our results to be applied to future studies.…”

Section: Tephra Depositmentioning

confidence: 99%

How rainfall influences tephra fall loading — an experimental approach

et al. 2021

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The load a tephra fall deposit applies to an underlying surface is a key factor controlling its potential to damage a wide range of assets including buildings, trees, crops and powerlines. Though it has long been recognised that loading can increase when deposits absorb rainfall, few efforts have been made to quantify likely load increases. This study builds on previous theoretical work, using an experimental approach to quantify change in load as a function of grainsize distribution, rainfall intensity and duration. A total of 20 laboratory experiments were carried out for ~ 10-cm thick, dry tephra deposits of varying grainsize and grading, taken to represent different eruptive scenarios (e.g. stable, waxing or waning plume). Tephra was deposited onto a 15° impermeable slope (representing a low pitch roof) and exposed to simulated heavy rainfalls of 35 and 70 mm h−1 for durations of up to 2 h. Across all experiments, the maximum load increases ranged from 18 to 30%. Larger increases occurred in fine-grained to medium-grained deposits or in inversely graded deposits, as these retained water more efficiently. The lowest increases occurred in normally graded deposits as rain was unable to infiltrate to the deposit’s base. In deposits composed entirely of coarse tephra, high drainage rates meant the amount of water absorbed was controlled by the deposit’s capillary porosity, rather than its total porosity, resulting in load increases that were smaller than expected. These results suggest that, for low pitch roofs, the maximum deposit load increase due to rainfall is around 30%, significantly lower than the oft-referenced 100%. To complement our experimental results, field measurements of tephra thickness should be supplemented with tephra loading measurements, wherever possible, especially when measurements are made at or near the site of observed damage.

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Section: Tephra Depositmentioning

confidence: 99%

How rainfall influences tephra fall loading — an experimental approach

et al. 2021

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“…A maximum, "worst-case scenario" volume is very roughly estimated to be ∼1 km 3 . This volume represents the failure of an entire large Mono-Inyo dome or column collapse associated with the largest pyroclastic eruptions of the late Quaternary, the Mammoth pumice fall of unit rfp (Hildreth et al, 2014), or Wilson Creek formation layer B7 from Mono Craters (Yang et al, 2019).…”

Section: Pdcs In the Lvvrmentioning

confidence: 99%

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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“…We used symmetrical, normal distributions because these approximate distributions that are often observed in tephra fall deposits (e.g. Mannen 2006;Yang et al 2019;Maeno et al 2019). We also aimed to avoid using overly specific, skewed or bimodal distributions as these might make it difficult for our results to be applied to future studies.…”

Section: Tephra Depositmentioning

confidence: 99%

Reducing uncertainty in building vulnerability and impact assessment for tephra hazards

Williams¹

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From her brutally honest career advice, to the stream of research projects sparked over casual tea chats, to the unrivalled organisation and strength she has displayed as a leader during this challenging pandemic.To my Thesis Advisory Committee, I thank Profs Benoit Taisne and David Lallemant for teaching me Matlab and R -an unenviable task -and for their tireless support and enthusiasm. You and Susanna have shown me academics can be successful without sacrificing kindness and generosity.

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Stratigraphic and sedimentologic framework for tephras in the Wilson Creek Formation, Mono Basin, California, USA

Cited by 7 publications

References 49 publications

How rainfall influences tephra fall loading — an experimental approach

How rainfall influences tephra fall loading — an experimental approach

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

Reducing uncertainty in building vulnerability and impact assessment for tephra hazards

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