Satellite-driven modeling approach for monitoring lava flow hazards during the 2017 Etna eruption

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Annalisa Cappello
Gaetana Ganci
Giuseppe Bilotta
Alexis Herault
Vito Zago
Ciro Del Negro

Abstract

The integration of satellite data and numerical modeling represents an efficient strategy to find immediate answers to the main issues raised at the onset of a new effusive eruption. Satellite thermal remote sensing can provide a variety of products suited to timing, locating, and tracking the radiant character of lava flows, including the opening times of eruptive vents. The time-series analysis of thermal satellite data can also provide estimates of the time-averaged discharge rate and volume. High-spatial resolution multispectral satellite data complement field observations for monitoring the lava emplacement in terms of flow length and area. All these satellite-derived parameters can be passed as input to physics-based numerical models in order to produce more accurate and reliable forecasts of effusive scenarios during ongoing eruptions. Here, we demonstrate the potential of the integrated application of satellite remote sensing techniques and lava flow models during the 2017 eruptive activity of Mt Etna. Remote sensing data from SEVIRI are analyzed by the HOTSAT system to output hotspot location, lava thermal flux, and effusion rate estimation. This output is used to drive, as well as to continuously update, lava flow simulations performed by the physics-based MAGFLOW model. We also show how Landsat-8 and Sentinel-2 satellite data complement the field observations to track the flow front position in time and add valuable data on lava flow advancement with which to iteratively validate the numerical simulations.

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How to Cite
1.
Cappello A, Ganci G, Bilotta G, Herault A, Zago V, Del Negro C. Satellite-driven modeling approach for monitoring lava flow hazards during the 2017 Etna eruption. Ann. Geophys. [Internet]. 2019Oct.10 [cited 2021Oct.16];62(2):VO227. Available from: https://www.annalsofgeophysics.eu/index.php/annals/article/view/7792
Section
Special Issue: MeMoVolc

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