Abstract

Modeling lava flow propagation is important to determine potential hazards to local populations. Thermo-rheological models such as PyFLOWGO track downflow cooling and rheological responses for open-channel, cooling-limited flows. The dominant radiative cooling component is governed partly by the lava emissivity, which is a material property that governs the radiative efficiency. Emissivity is commonly treated as a constant in cooling models, but is shown here to vary with temperature. To establish the effect of temperature on emissivity, high spatiotemporal, multispectral thermal infrared data were acquired of a small flow emplaced from a tumulus. An inverse correlation between temperature and emissivity was found, which was then integrated into the PyFLOWGO model. Incorporating a temperature-dependent emissivity term results in a ∼5% increase in flow length and < 75% lower total cumulative heat flux for the small flow. To evaluate the scalability of this relationship, we applied the modified PyFLOWGO model to simulations of the 2018 Lower East Rift Zone fissure 8 flow, emplaced between May 27 and June 3. Our model improves the emplacement match because of the ~ 30% lower heat flux resulting in a ∼7% longer flow compared to modeling using a constant emissivity (0.95). This 5–7% increase in length prior to ocean entry, realized by an accurate temperature-dependent emissivity term, is critical for developing the most accurate model of future flow hazard assessments, particularly if population centers lie in the flow’s path.

Highlights

  • Lava flow modeling is a powerful tool for quantitatively forecasting lava propagation and subsequently improving the accuracy and reliability of lava hazard assessments (Ramsey and Harris 2013)

  • The temperature-dependent emissivity relationship we found became the basis for a new PyFLOWGO radiant heat flux module

  • Variable emissivity is shown to have a measureable effect on heat flux (> 30% decrease), which translates to the potential of longer flows (~ 7% increase)

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Summary

Introduction

Lava flow modeling is a powerful tool for quantitatively forecasting lava propagation and subsequently improving the accuracy and reliability of lava hazard assessments (Ramsey and Harris 2013). This importance is reinforced by recent eruptions at Kīlauea (Hawai’i), Piton de la Fournaise (La Réunion), Etna (Italy), and Pacaya (Guatemala), which produced lava flows that posed serious risks to local societies. More than 60 separate episodes of lava flows were observed over a 35-year period on the East Rift Zone with approximately 4.4 ­km of lava emplaced, mostly as a series of tube-fed sheet-like and ropey pāhoehoe flows (Wolfe et al 1987; Heliker and Mattox 2003; Orr et al 2013; Neal et al 2019)

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