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

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.