Title

Probbilistically Modeling Lava Flows with MOLASSES

Document Type

Poster Session

Publication Date

12-17-2017

Abstract

Modeling lava flows through Cellular Automata methods enables a computationally inexpensive means to quickly forecast lava flow paths and ultimate areal extents. We have developed a lava flow simulator, MOLASSES, that forecasts lava flow inundation over an elevation model from a point source eruption. This modular code can be implemented in a deterministic fashion with given user inputs that will produce a single lava flow simulation. MOLASSES can also be implemented in a probabilistic fashion where given user inputs define parameter distributions that are randomly sampled to create many lava flow simulations. This probabilistic approach enables uncertainty in input data to be expressed in the model results and MOLASSES outputs a probability map of inundation instead of a determined lava flow extent. Since the code is comparatively fast, we use it probabilistically to investigate where potential vents are located that may impact specific sites and areas, as well as the unconditional probability of lava flow inundation of sites or areas from any vent.

We have validated the MOLASSES code to community-defined benchmark tests and to the real world lava flows at Tolbachik (2012-2013) and Pico do Fogo (2014-2015). To determine the efficacy of the MOLASSES simulator at accurately and precisely mimicking the inundation area of real flows, we report goodness of fit using both model sensitivity and the Positive Predictive Value, the latter of which is a Bayesian posterior statistic. Model sensitivity is often used in evaluating lava flow simulators, as it describes how much of the lava flow was successfully modeled by the simulation. We argue that the positive predictive value is equally important in determining how good a simulator is, as it describes the percentage of the simulation space that was actually inundated by lava.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Presented at the AGU Fall Meeting on December 14, 2017 in New Orleans, LA

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