Reconstructing eruptive source parameters from tephra deposit: a numerical study of medium-sized explosive eruptions at Etna volcano

Abstract

Since the 1970s, multiple reconstruction techniques have been proposed and are currently used, to extrapolate and quantify eruptive parameters from sampled tephra fall deposit datasets. Atmospheric transport and deposition processes strongly control the spatial distribution of tephra deposit; therefore, a large uncertainty affects mass derived estimations especially for fall layer that are not well exposed. This paper has two main aims: the first is to analyse the sensitivity to the deposit sampling strategy of reconstruction techniques. The second is to assess whether there are differences between the modelled values for emitted mass and grainsize, versus values estimated from the deposits. We find significant differences and propose a new correction strategy. A numerical approach is demonstrated by simulating with a dispersal code a mild explosive event occurring at Mt. Now at Icelandic Meteorological Office, Reykjavík, Iceland using available tephra information collected after the eruption. A full synthetic deposit is created by integrating the deposited mass computed by the model over the computational domain (i.e., an area of 7.5 × 10 4 km 2 ). A statistical analysis based on 2000 sampling tests of 50 sampling points shows a large variability, up to 50 % for all the reconstruction techniques. Moreover, for some test examples Power Law errors are larger than estimated uncertainty. A similar analysis, on simulated grain-size classes, shows how spatial sampling limitations strongly reduce the utility of available information on the total grain size distribution. For example, information on particles coarser than φ(−4) is completely lost when sampling at 1.5 km from the vent for all columns with heights less than 2000 m above the vent. To correct for this effect an optimal sampling strategy and a new reconstruction method are presented. A sensitivity study shows that our method can be extended to a wide range of eruptive scenarios including those in which aggregation processes are important. The new correction method allows an estimate of the deficiency for each simulated class in calculated mass deposited, providing reliable estimation of uncertainties in the reconstructed total (whole deposit) grainsize distribution.

Temporal models for the episodic volcanism of Campi Flegrei caldera (Italy) with uncertainty quantification

“…Triangular sampling was adopted for them with 5th and 95th percentiles corresponding to ±50% relative errors, consistently with the findings of Klawonn et al [] and Engwell et al []. The possibility of assuming even larger uncertainties [e.g., Spanu et al , ] or an underestimation bias on such estimates was also considered. Additional assessments carried out assuming [−25%, +100%] relative errors affecting the volumes did not affect significantly the main outcomes of the analysis.…”

Modeling Eruption Source Parameters by Integrating Field, Ground‐Based, and Satellite‐Based Measurements: The Case of the 23 February 2013 Etna Paroxysm

“…In contrast, the Castiglione site (Figure ) shows a finer field deposit than the computed one. These discrepancies can be attributed not only to the sample positions from the main plume axis but also to the sampling distance from the source (Spanu et al, ). In fact, the coarser material (−4 ≥ Φ ≥ −2) deposits within a narrow area from the vent, highlighting the difficulty to correctly capture the coarse tail distribution through the Voronoi tessellation method when the deposit is not adequately sampled (Andronico, Scollo, Cristaldi, et al, ).…”

“…Hereinafter, we define the very fine ash as particle matter below 10 μm (hereinafter PM 10 ). Nonetheless, the TGSD strongly depends on the sampling distance from the source (Costa, Pioli, et al, ), the number of available samples (Bonadonna et al, ; Bonadonna & Houghton, ), and the spatial distribution (Bonadonna et al, ; Spanu et al, ). Moreover, the fine ash fraction within the TGSD is likely underestimated due to the long atmospheric residence time ranging from hours to days (Rose & Durant, ), preventing very fine ash from sampling at reasonable distance (Costa, Pioli, et al, ).…”

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

“…As mentioned above, although the paucity and spatial distribution prevent the 7 field samples from representing fully the whole TGSD (Andronico et al, 2014;Beckett et al, 2015;Bonadonna et al, 2015;Costa et al, 2016a;Spanu et al, 2016), we first estimated the TGSD (hereinafter Field TGSD; Fig. 5) on the basis of the individual GSDs using the Voronoi tessellation 10 method (Bonadonna and Houghton, 2005).…”

“…Nonetheless, the mode shift can also be attributed to the sampling distance from the source as explained in Spanu et al (2016).…”