The longevity of lava dome eruptions
©2016. American Geophysical Union. All Rights Reserved. Understanding the duration of past, ongoing, and future volcanic eruptions is an important scientific goal and a key societal need. We present a new methodology for forecasting the duration of ongoing and future lava dome eruptions based on a database (DomeHaz) recently compiled by the authors. The database includes duration and composition for 177 such eruptions, with "eruption" defined as the period encompassing individual episodes of dome growth along with associated quiescent periods during which extrusion pauses but unrest continues. In a key finding, we show that probability distributions for dome eruption durations are both heavy tailed and composition dependent. We construct objective Bayesian statistical models featuring heavy-tailed Generalized Pareto distributions with composition-specific parameters to make forecasts about the durations of new and ongoing eruptions that depend on both eruption duration to date and composition. Our Bayesian predictive distributions reflect both uncertainty about model parameter values (epistemic uncertainty) and the natural variability of the geologic processes (aleatoric uncertainty). The results are illustrated by presenting likely trajectories for 14 dome-building eruptions ongoing in 2015. Full representation of the uncertainty is presented for two key eruptions, Soufriére Hills Volcano in Montserrat (10-139 years, median 35 years) and Sinabung, Indonesia (1-17 years, median 4 years). Uncertainties are high but, importantly, quantifiable. This work provides for the first time a quantitative and transferable method and rationale on which to base long-term planning decisions for lava dome-forming volcanoes, with wide potential use and transferability to forecasts of other types of eruptions and other adverse events across the geohazard spectrum.
Wolpert, RL; Ogburn, SE; Calder, ES
Volume / Issue
Start / End Page
Electronic International Standard Serial Number (EISSN)
International Standard Serial Number (ISSN)
Digital Object Identifier (DOI)