Reconstructing volcanic plume evolution integrating satellite and ground-based data: application to the 23 November 2013 Etna eruption

Abstract. Recent explosive volcanic eruptions recorded worldwide (e.g. Hekla in 2000, Eyjafjallajokull in 2010, Cordon-Caulle in 2011) demonstrated the necessity for a better assessment of the eruption source parameters (ESPs; e.g. column height, mass eruption rate, eruption duration, and total grain-size distribution – TGSD) to reduce the uncertainties associated with the far-travelling airborne ash mass. Volcanological studies started to integrate observations to use more realistic numerical inputs, crucial for taking robust volcanic risk mitigation actions. On 23 November 2013, Etna (Italy) erupted, producing a 10 km height plume, from which two volcanic clouds were observed at different altitudes from satellites (SEVIRI, MODIS). One was retrieved as mainly composed of very fine ash (i.e. PM20), and the second one as made of ice/SO2 droplets (i.e. not measurable in terms of ash mass). An atypical north-easterly wind direction transported the tephra from Etna towards the Calabria and Apulia regions (southern Italy), permitting tephra sampling in proximal (i.e. ∼ 5–25 km from the source) and medial areas (i.e. the Calabria region, ∼ 160 km). A primary TGSD was derived from the field measurement analysis, but the paucity of data (especially related to the fine ash fraction) prevented it from being entirely representative of the initial magma fragmentation. To better constrain the TGSD assessment, we also estimated the distribution from the X-band weather radar data. We integrated the field and radar-derived TGSDs by inverting the relative weighting averages to best fit the tephra loading measurements. The resulting TGSD is used as input for the FALL3D tephra dispersal model to reconstruct the whole tephra loading. Furthermore, we empirically modified the integrated TGSD by enriching the PM20 classes until the numerical results were able to reproduce the airborne ash mass retrieved from satellite data. The resulting TGSD is inverted by best-fitting the field, ground-based, and satellite-based measurements. The results indicate a total erupted mass of 1.2  ×  109 kg, being similar to the field-derived value of 1.3  ×  109 kg, and an initial PM20 fraction between 3.6 and 9.0 wt %, constituting the tail of the TGSD.

[1]  Arnau Folch,et al.  A model for wet aggregation of ash particles in volcanic plumes and clouds: 1. Theoretical formulation , 2010 .

[2]  M. Gouhier,et al.  Modeling Eruption Source Parameters by Integrating Field, Ground‐Based, and Satellite‐Based Measurements: The Case of the 23 February 2013 Etna Paroxysm , 2018, Journal of Geophysical Research: Solid Earth.

[3]  Augusto Neri,et al.  The VOL-CALPUFF model for atmospheric ash dispersal: 1. Approach and physical formulation , 2008 .

[4]  F. Bonnardot,et al.  Comparison of VAAC atmospheric dispersion models using the 1 November 2004 Grimsvötn eruption , 2007 .

[5]  Arnau Folch,et al.  A parametric and comparative study of different tephra fallout models , 2008 .

[6]  J. Kerkmann,et al.  Simultaneous retrieval of volcanic ash and SO2 using MSG-SEVIRI measurements , 2007 .

[7]  Arnau Folch,et al.  A model for wet aggregation of ash particles in volcanic plumes and clouds: 2. Model application , 2010 .

[8]  Arnau Folch,et al.  A review of tephra transport and dispersal models: Evolution, current status, and future perspectives , 2012 .

[9]  Boris Behncke,et al.  The 2011-2012 summit activity of Mount Etna: Birth, growth and products of the new SE crater☆ , 2014 .

[10]  Daniele Andronico,et al.  PM10 measurements in urban settlements after lava fountain episodes at Mt. Etna, Italy: pilot test to assess volcanic ash hazard to human health , 2015 .

[11]  Geoffrey Ingram Taylor,et al.  Turbulent gravitational convection from maintained and instantaneous sources , 1956, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[12]  Marcus I. Bursik,et al.  Effect of wind on the rise height of volcanic plumes , 2001 .

[13]  H. Rymer,et al.  Balancing bulk gas accumulation and gas output before and during lava fountaining episodes at Mt. Etna , 2015, Scientific reports.

[14]  Costanza Bonadonna,et al.  Improving on mass flow rate estimates of volcanic eruptions , 2012 .

[15]  Simona Scollo,et al.  Unexpected hazards from tephra fallouts at Mt Etna: The 23 November 2013 lava fountain , 2015 .

[16]  Arnau Folch,et al.  FPLUME-1.0: An integral volcanic plume model accounting for ash aggregation , 2015 .

[17]  Nicola Spinelli,et al.  Volcanic ash concentration during the 12 August 2011 Etna eruption , 2015 .

[18]  Maurizio Ripepe,et al.  Tephra sedimentation during the 2010 Eyjafjallajökull eruption (Iceland) from deposit, radar, and satellite observations , 2011 .

[19]  William I. Rose,et al.  Fine ash content of explosive eruptions , 2009 .

[20]  D. Andronico,et al.  The iron-catalysed surface reactivity and health-pertinent physical characteristics of explosive volcanic ash from Mt. Etna, Italy , 2017, Journal of Applied Volcanology.

[21]  Arnau Folch,et al.  A three-dimensional Eulerian model for transport and deposition of volcanic ashes , 2006 .

[22]  Gianfranco Vulpiani,et al.  Mass discharge rate retrieval combining weather radar and thermal camera observations , 2016 .

[23]  Costanza Bonadonna,et al.  Estimating the volume of tephra deposits: A new simple strategy , 2012 .

[24]  William I. Rose,et al.  Retrieval of sizes and total masses of particles in volcanic clouds using AVHRR bands 4 and 5 , 1994 .

[25]  A. Sobolev,et al.  Melt inclusion record of the conditions of ascent, degassing, and extrusion of volatile‐rich alkali basalt during the powerful 2002 flank eruption of Mount Etna (Italy) , 2006 .

[26]  Michele Prestifilippo,et al.  Near-source Doppler radar monitoring of tephra plumes at Etna , 2016 .

[27]  H. Webster,et al.  The Entrainment Rate for Buoyant Plumes in a Crossflow , 2010 .

[28]  Marianne Guffanti,et al.  Encounters of aircraft with volcanic ash clouds; A compilation of known incidents, 1953-2009 , 2010 .

[29]  C. Bonadonna,et al.  Physical characterization of explosive volcanic eruptions based on tephra deposits: Propagation of uncertainties and sensitivity analysis , 2015 .

[30]  Daniele Andronico,et al.  Relationship between tremor and volcanic activity during the Southeast Crater eruption on Mount Etna in early 2000 , 2003 .

[31]  Arnau Folch,et al.  FALL3D: A computational model for transport and deposition of volcanic ash , 2009, Comput. Geosci..

[32]  Albert Ansmann,et al.  Evaluating the structure and magnitude of the ash plume during the initial phase of the 2010 Eyjafjallajökull eruption using lidar observations and NAME simulations , 2011 .

[33]  S. Barsotti,et al.  Reconstructing eruptive source parameters from tephra deposit: a numerical study of medium-sized explosive eruptions at Etna volcano , 2016, Bulletin of Volcanology.

[34]  G. Macedonio,et al.  Uncertainties in volcanic plume modeling: A parametric study using FPLUME , 2016 .

[35]  C. Bonadonna,et al.  Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles , 2015 .

[36]  Larry G. Mastin,et al.  Results of the eruptive column model inter-comparison study , 2016 .

[37]  Barbara J. B. Stunder,et al.  Airborne Volcanic Ash Forecast Area Reliability , 2007 .

[38]  A. Neri,et al.  The VOL-CALPUFF Model for Atmospheric Ash , 2007 .

[39]  Luca Merucci,et al.  Eruption column height estimation of the 2011-2013 Etna lava fountains , 2014 .

[40]  Larry G. Mastin,et al.  A multidisciplinary effort to assign realistic source parameters to models of volcanic ash-cloud transport and dispersion during eruptions , 2009 .

[41]  B. Rothen‐Rutishauser,et al.  Combined exposure of diesel exhaust particles and respirable Soufrière Hills volcanic ash causes a (pro-)inflammatory response in an in vitro multicellular epithelial tissue barrier model , 2016, Particle and Fibre Toxicology.

[42]  Simona Scollo,et al.  Representivity of incompletely sampled fall deposits in estimating eruption source parameters: a test using the 12–13 January 2011 lava fountain deposit from Mt. Etna volcano, Italy , 2014, Bulletin of Volcanology.

[43]  A. Ulke New turbulent parameterization for a dispersion model in the atmospheric boundary layer , 2000 .

[44]  Sonia Calvari,et al.  Eruptive processes leading to the most explosive lava fountain at Etna volcano: The 23 November 2013 episode , 2014 .

[45]  T. Koyaguchi,et al.  A three‐dimensional numerical simulation of spreading umbrella clouds , 2009 .

[46]  R. Carluccio,et al.  The continuing story of Etna's New Southeast Crater (2012–2014): Evolution and volume calculations based on field surveys and aerophotogrammetry , 2015 .

[47]  Carlo Cavazzoni,et al.  An automatic procedure to forecast tephra fallout , 2008 .

[48]  C. Bonadonna,et al.  Assessing tephra total grain-size distribution: Insights from field data analysis , 2016 .

[49]  Antonella Longo,et al.  A computer model for volcanic ash fallout and assessment of subsequent hazard , 2005, Comput. Geosci..

[50]  A. Costa,et al.  Modelling tephra dispersal and ash aggregation: The 26th April 1979 eruption, La Soufrière St. Vincent , 2017 .

[51]  R. S. J. Sparks,et al.  Interaction between volcanic plumes and wind during the 2010 Eyjafjallajökull eruption, Iceland , 2013 .

[52]  Stephen Tait,et al.  Turbulent entrainment in jets with arbitrary buoyancy , 2005, Journal of Fluid Mechanics.

[53]  D. Byun,et al.  Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , 2006 .

[54]  Isamu Aida,et al.  RELIABILITY OF A TSUNAMI SOURCE MODEL DERIVED FROM FAULT PARAMETERS , 1978 .

[55]  Frank S. Marzano,et al.  A Multi-Sensor Approach for Volcanic Ash Cloud Retrieval and Eruption Characterization: The 23 November 2013 Etna Lava Fountain , 2016, Remote. Sens..

[56]  M. Montopoli Velocity profiles inside volcanic clouds from three‐dimensional scanning microwave dual‐polarization Doppler radars , 2016 .

[57]  P. Segall,et al.  Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008 , 2013 .

[58]  A. Costa,et al.  Hazard assessment of far-range volcanic ash dispersal from a violent Strombolian eruption at Somma-Vesuvius volcano, Naples, Italy: implications on civil aviation , 2012, Bulletin of Volcanology.

[59]  Sara Basart,et al.  Validation of the FALL3D ash dispersion model using observations of the 2010 Eyjafjallajökull volcanic ash clouds , 2012 .

[60]  Arnau Folch,et al.  Density‐driven transport in the umbrella region of volcanic clouds: Implications for tephra dispersion models , 2013 .

[61]  Peter N. Francis,et al.  Sensitivity analysis of dispersion modeling of volcanic ash from Eyjafjallajökull in May 2010 , 2012 .

[62]  A. Neri,et al.  Large Eddy Simulation of gas–particle kinematic decoupling and turbulent entrainment in volcanic plumes , 2016 .

[63]  Costanza Bonadonna,et al.  Total grain-size distribution and volume of tephra-fall deposits , 2005 .

[64]  Gary H. Ganser,et al.  A rational approach to drag prediction of spherical and nonspherical particles , 1993 .

[65]  T. Koyaguchi,et al.  Effects of wind on entrainment efficiency in volcanic plumes , 2015 .

[66]  C. Bonadonna,et al.  Plume height, volume, and classification of explosive volcanic eruptions based on the Weibull function , 2013, Bulletin of Volcanology.