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

• Published in: Journal of geophysical research. Solid earth Vol. 124; no. 9; pp. 9600 - 9621
• Main Authors: Rutarindwa, Regis, Spiller, Elaine T., Bevilacqua, Andrea, Bursik, Marcus I., Patra, Abani K.
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 01-09-2019
• Subjects: Computer applications; Computer models; Computer simulation; Data; Density; Density currents; Electric power generation; Emulators; Flood mapping; Geophysics; Hazard assessment; hazard forecasting; History; Information systems; Lava; Mapping; Mathematical models; Monte Carlo simulation; Physics; Power plants; Probabilistic analysis; Probability theory; pyroclastic density currents; Statistical analysis; Statistical methods; Uncertainty; Uncertainty analysis; uncertainty quantification; Volcanic gases; Weather forecasting; Long Valley
• ISSN: 2169-9313; 2169-9356
• DOI: 10.1029/2019JB017352

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.
Plain Language Summary
We present a method to forecast the probability of inundation by hot volcanic flows of rock and gas. In some sense, we can think of a natural hazard forecast much like a weather forecast. Instead of how likely is it to rain tomorrow, we might ask how likely is our town or the nearby power plant to get inundated by a volcanic flow? The weather forecasting analogy is, however, flawed in an important way when dealing with rare events. Large‐scale, highly destructive volcanic flows are rare events, of course, and it is human nature to think that such events will happen as they have in the past. But often the scale (here think mass of the flowing material) varies randomly, and sometimes an event bigger in scale than any in the historical recorded will happen. Thus, to generate hazard forecasts, we must rely on a combination of models—models of the physics governing the volcanic flows as well as models that describe the probabilistic nature of historical data. Unfortunately, the typical process of combining these models would require thousands of hours on a super computer. Instead, we build a surrogate model of the physical volcanic flow model that alleviates the computational bottleneck.
Key Points
Statistical emulators of computationally intensive physical models enable rapid assessment of hazard threats due to volcanic avalanches
Probabilistic hazard maps allow one to visualize the probability of hazard threat under different probabilistic scenario models
Impacts of aleatoric and epistemic uncertainties in vent opening models on volcanic hazards can be quantified in probabilistic hazard maps