Using infrared satellite data to drive a thermo‐rheological/stochastic lava flow emplacement model: A method for near‐real‐time volcanic hazard assessment

Abstract

We combine two flow simulation models to provide a computationally efficient means of predicting the final dimensions of active lava flows. By using a stochastic approach to predict all the downhill paths that a lava flow could possibly follow and a thermo–rheological model to terminate these paths once the predicted cooling–limited length for the flow has been attained, this approach allows for rapid assessment of the area likely to be inundated by lava. The 1991–1993 eruption of Mount Etna is used as a case study. Lava effusion rate exerts a strong control on the maximum length a lava flow can attain, and we demonstrate how the model can be driven by time–varying effusion rates, obtained from infrared satellite data. As such data are obtained in near–real–time, this approach allows flow simulations to be updated in a timely manner, in response to changing eruption conditions.