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Authors: Vicari, A.* 
Ganci, G.* 
Behncke, B.* 
Cappello, A.* 
Neri, M.* 
Del Negro, C.* 
Title: Near‐real‐time forecasting of lava flow hazards during the 12–13 January 2011 Etna eruption
Journal: Geophysical Researh Letters 
Series/Report no.: /38 (2011)
Publisher: American Geophysical Union.
Issue Date: 7-Jul-2011
DOI: 10.1029/2011GL047545, 2011
Keywords: lava hazard
Subject Classification04. Solid Earth::04.01. Earth Interior::04.01.99. General or miscellaneous 
04. Solid Earth::04.04. Geology::04.04.99. General or miscellaneous 
04. Solid Earth::04.08. Volcanology::04.08.99. General or miscellaneous 
04. Solid Earth::04.08. Volcanology::04.08.03. Magmas 
04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring 
04. Solid Earth::04.08. Volcanology::04.08.07. Instruments and techniques 
04. Solid Earth::04.08. Volcanology::04.08.08. Volcanic risk 
05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks 
05. General::05.02. Data dissemination::05.02.03. Volcanic eruptions 
05. General::05.08. Risk::05.08.99. General or miscellaneous 
Abstract: Forecasting the lava flow invasion hazard in near‐real time is a primary challenge for volcano monitoring systems. The paroxysmal episode at Mount Etna on 12–13 January 2011 produced in ∼4 hours lava fountains and fast‐moving lava flows 4.3 km long. We produced timely predictions of the areas likely to be inundated by lava flows while the eruption was still ongoing. We employed infrared satellite data (MODIS, AVHRR, SEVIRI) to estimate in near‐realtime lava eruption rates (peak value of 60 m3 s−1). These time‐varying discharge rates were then used to drive MAGFLOW simulations to chart the spread of lava as a function of time. Based on a classification on durations and lava volumes of ∼130 paroxysms at Etna in the past 13 years, and on lava flow path simulations of expected eruptions, we constructed a lava flow invasion hazard map for summit eruptions, providing a rapid response to the impending hazard. This allowed key at‐risk areas to be rapidly and appropriately identified.
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