Effects of eruption source parameter variation and meteorological dataset on tephra fallout hazard assessment: example from Vesuvius (Italy)
© Macedonio et al. 2016
Received: 19 June 2015
Accepted: 25 February 2016
Published: 8 March 2016
In this study, using the tephra dispersal model HAZMAP, we investigate the effect of using different meteorological datasets and eruption source parameters on tephra fallout hazard assessment for a sub-Plinian eruption of Vesuvius, which is considered as a reference case for hazard assessment analysis. We analyze the effect of using different meteorological data, from: i) radio-sounding carried out at the meteorological station of Brindisi (Italy) between 1962 and 1976 and between 1996 and 2012, and at Pratica di Mare (Rome, Italy) between 1995 and 2013; ii) meteorological models of the National Oceanic and Atmospheric Administration (NOAA), and of the European Centre for Medium-Range Weather Forecasts (ECMWF). Furthermore, we consider the effects of perturbing reference eruptive source parameters. In particular, we vary the total mass, the total grain-size distribution, the column height, and the effective atmospheric diffusion coefficient to evaluate how these parameters affect the hazard probability maps. Moreover, the effect of the seasonal variation of the wind field and the effect of the rain on the deposit loading are considered. Results show that the parameter that mostly affects hazard maps is, as expected, the total erupted mass; furthermore, keeping constant the erupted mass, the most important control on hazard is due to the particle terminal settling velocity distribution which is a function of the total grain-size distribution, particle density and shape. Within the considered range variations, the hazard depends less on the use of different meteorological datasets, column height and effective diffusion coefficient.
KeywordsStatistical study Meteorological datasets Eruption source parameters Probability maps Vesuvius area
Risk assessment for tephra fallout in the highly urbanized area around Vesuvius (more than 1 million people) is an important and difficult goal. The type of explosive activity may be defined on the basis of the information derived from the past behavior of the volcano, currently quiescent since 1944. The Somma strato-volcano, the oldest edifice, formed between 37 and 20 ka mainly by effusive activity (e.g. Andronico et al. 1995; Cioni et al. 2003). Recent activity of Vesuvius was characterized by different eruptive styles depending on the conditions of the magmatic system. They ranged from Strombolian activities to sub-Plinian and Plinian eruptions (e.g. Cioni et al. 2008; Neri et al. 2008; Rolandi et al. 1993; Rosi et al. 1987; Santacroce et al. 2008; Todesco et al. 2002). The largest Plinian event is the “Pomici di Base” eruption occurred around 18 ka (Bertagnini et al. 1998) formed a caldera that was modified after other major explosive events (Cioni et al. 1999). Other Plinian eruptions occurred at 8 ka, the “Pomici di Mercato” eruption (e.g. Aulinas et al. 2008; Mele et al. 2011); 3.8 ka, the “Pomici di Avellino” eruption (e.g. Cioni et al. 2000) and in A.D. 79, the “Pompei” eruption (e.g. Andronico and Cioni 2002; Barberi et al. 1989). In between these major eruptions, Vesuvius explosive activity was characterized by several sub-Plinian eruptions. The two most recent of these major events occurred in A.D. 472 (Rolandi et al. 2004; Rosi and Santacroce 1983) and in A.D. 1631 (Barberi et al. 1989; Rolandi et al. 1993; Rosi et al. 1993). Finally, the most frequent low energy activity spans from Violent Strombolian to continuous ash emission (Barberi et al. 1989; Cioni et al. 2008). Tephra fallout from all these eruptions seriously affected the Vesuvius area in the last 18,000 years (e.g. Cioni et al. 2003) but the impact of a future eruption will be substantially higher because of the intense urbanization of the area.
Macedonio et al. (1990) suggested that a shallow magma chamber at Vesuvius is currently supplied at a constant rate and an event similar to the A.D. 1631 eruption is expected. This scenario represented the “Maximum Expected Event” (MEE) at Vesuvius for a short-medium time window (Barberi et al. 1990; Cioni et al. 2003; Macedonio et al. 1990).
The first probability map for tephra fallout at Vesuvius was obtained by Barberi et al. (1990). They considered an eruption with total mass of 1–2 ×1011 kg of tephra, wind velocity distribution obtained by the analysis of a radio-sounding station located in Brindisi (Italy) in the period between 1962 and 1976, and results from a tephra dispersal model. This study allowed the evaluation of the extent of the area around Vesuvius more likely to be affected by roof collapses, which is mainly located in the east sector of the volcano. New assessment of tephra fallout was carried out by Cioni et al. (2003) who evaluated the conditional probability of a mass loading greater than a given threshold, considering an event with total mass equal to 5 ×1011 kg, column heights spanning from 12 to 22 km, the same wind velocity profiles recorded at Brindisi, and four different total grain-size distributions. Other probability maps for different eruption scales, such as Plinian, sub-Plinian, and Violent Strombolian events, were computed by Macedonio et al. (2008) with the dispersal model HAZMAP (Macedonio et al. 2005), using NCEP/NCAR re-analysis wind data from 1968 to 2003. These authors highlighted the different impact of those events with respect to the MEE.
Tephra transport and dispersal models of various types have been extensively used for hazard assessment at several volcanoes around the world (e.g. Barberi et al. 1990; Barsotti et al. 2015; Bonadonna et al. 2002; 2005; Bonasia et al. 2011; 2012; Capra et al. 2008; Costa et al. 2009; Hill et al. 1998; Jenkins et al. 2015; Macedonio et al. 2008; Scaini et al. 2012; Scollo et al. 2008; 2013; Sieron et al. 2014). However, the eruption source parameters of the considered reference scenario are commonly kept fixed an their variability and uncertainty are rarely taken into account. Moreover, meteorological data usually come from a single database.
In this paper we present the main outcomes of a detailed investigation of the effects of meteorological datasets and key eruption source parameters on the fallout hazard maps for the reference scenario adopted for the Emergency Plan of Vesuvius (DPC 2001; 2015; Regione Campania 2015). The goal is to highlight the role of the uncertainty and the natural variability on the probability maps commonly used for hazard assessment.
In order to analyze the effect that the use of different wind datasets and the variation of the main eruptive source parameters have on the fallout hazard maps, we used the HAZMAP model (Macedonio et al. 2005). The HAZMAP model (freely available at http://datasim.ov.ingv.it ) is based on the solution of a simplified 2D equations of diffusion, transport and sedimentation of small particles, describing the dispersion of tephra generated by a volcanic convective column. The HAZMAP code was previously used to produce hazard maps at Vesuvius (Macedonio et al. 2008) and Campi Flegrei (Costa et al. 2009).
The investigation of the effects of tephra transport model on the hazard maps is outside the scope of this work, which is mainly focused on the analysis of the effects of the meteorological and the volcanological source parameters. In a previous work, Scollo et al. (2008) found that, at Etna volcano, larger differences between the output of tephra transport models are related to topographic effects, which strongly affect the dispersal of fine particles for column heights lower than 12 km; however these difference decrease in simulating higher eruption columns (Scollo et al. 2008). With respect to Scollo et al. (2008), who deal with a sensitivity analysis of tephra transport at Etna for small to moderate eruptions, the present work is focused on a sub-Plinian eruption at Vesuvius.
Here, we considered three different meteorological radio-soundings datasets and three reanalysis models, a range of total mass from 1011 to 1012 kg, effective atmospheric (horizontal) diffusion coefficients from 1000 – 10,000 m/s2, eruption column height from 14 – 22 km, and a few different total grain-size distribution. The effects of these variations were assessed on the area having probability greater than 5 % of exceeding a given deposit loading (e.g. 300, 500 and 1000 kg/m2). The other considered input parameters are described below.
radio-soundings at Brindisi station, about 300 km East of Vesuvius (here named BRIN) between 1962 and 1976. This dataset consists in 3125 observations (some records are missing).
radio-soundings at Brindisi (LIBR station) in the period between 1 July 1996 and 31 December 2012. This dataset consists of 18,482 records. Only records containing data between ground level and 25,000 m above sea level, and with more than 8 data in this altitude range, were selected for this work, resulting in a dataset containing 12,788 records.
radio-soundings at Pratica di Mare, Rome, about 190 km North-West of Vesuvius (LIRE station) in the period between 1 November 1995 and 21 December 2013. This dataset consists of 21211 records. Only records containing data between ground level and 25,000 m above sea level, and with more than 8 data in this altitude range, were selected for this work, resulting in a dataset containing 14,976 records.
daily average data from the NOAA NCEP/NCAR Reanalysis 1 (NOAA1, 7305 records) model (Kalnay et al. 1996), spatial resolution 2.52° × 2.52°, in the period between 1991 and 2010, available in NetCDF format.
6-h averaged data from the global model of NOAA-CIRES 20th Century (NOAA2, 29,220 records), spatial resolution 2° × 2°, between 1991 and 2010, available in the NetCDF format (Compo et al. 2006; Compo et al. 2011).
6-h averaged data of the global model ECMWF ERA-Interim (ERAI, 29,220 records), spatial resolution 0.75° × 0.75°, between 1991 and 2010, available in GRIB format (Berrisford et al. 2011).
Meteorological datasets used in this study
1962 – 1976
1.7.1996 – 31.12.2012
≈ 6 h
1.11.1995 – 21.12.2013
≈ 6 h
NCEP-NCAR Rean. I
1.1.1991 – 31.12.2010
NOAA-CIRES Rean. II
1.1.1991 – 31.12.2010
1.1.1991 – 31.12.2010
Figures 1 and 2 show the polar diagrams of the different winds datasets. For tephra dispersal simulations wind data are averaged over horizontal layers 500 m thick; however, for sake of simplicity, the figures show data averaged over thicker layers (4–6 km for the BRIN station and 5 km for the others).
Figure 1 shows the polar diagrams of wind direction and intensity at the Brindisi station (BRIN), recorded in the period between 1962 and 1976, averaged on different horizontal layers 4–6 km thick. The plots show the direction towards which the wind blows (not the direction of provenance). These data were previously used by Barberi et al. (1990) and Macedonio et al. (1990) for the estimation of the fallout hazard zone a Vesuvius.
Figure 2 shows the polar diagrams of the winds, averaged on different horizontal layers 5 km thick, for the LIBR, LIRE, NOAA1, NOAA2 and ERAI datasets used in the present work. Again, here, the plots show the direction towards which the wind blows. A comparison between the plots shows no major differences among the datasets.
Wind direction and speed show a moderate seasonal variation, similar to that described by Costa et al. (2009). A statistics on this effect is reported in Fig. 3 for the ECMWF ERA-Interim (ERAI) dataset. The other datasets do not show significant difference with respect the ECMWF dataset.
The total erupted mass is the most important input parameter having a first order effect. The estimation of the total mass is commonly derived by field data analysis (Bonadonna and Costa 2012; Bonadonna and Houghton 2005; Pyle 1989) or by best-fit procedure (Bonasia et al. 2010; 2012; Connor and Connor 2006; Costa et al. 2009; Pfeiffer et al. 2005; Scollo et al. 2008). Keeping fixed the other parameters, the effect of the total mass on the deposit loading, neglecting or simplifying other non-linear phenomena (e.g. the aggregation process, the variability of the wind, etc.), follows a linear relationship.
A total mass of 2 × 1011 kg was used by Barberi et al. (1990) for assessing volcanic hazard from sub-Plinian eruptions at Vesuvius. This value derived from an estimate of the mass erupted during the main fallout phase of the Vesuvius A.D. 1631 sub-Plinian eruption obtained by Rosi et al. (1993), who found values in the range 4.5 × 1010 and 1.35 × 1011 kg, using the method of deposit thinning. However this kind of estimation can have a large uncertainty, depending on the quality and spatial distribution of the data, up to a factor of four (Bonadonna and Costa 2012).
Moreover, the area with probability greater than a given value (e.g. 5 %) of loading (e.g. 300 kg/m2) does not follow a linear relationship for wide ranges of the total erupted mass.
In this paper we use, as reference, the same total mass adopted by Cioni et al. (2003), who proposed a value of 5 × 1011 kg as representative of sub-Plinian eruptions at Vesuvius. This value was also adopted by the Italian Department of Civil Protection for the reference scenario at Vesuvius (DPC 2012). Moreover, we performed a sensitivity analysis on the extension of the hazard zone by varying the total mass between 1011 and 1012 kg, in agreement with the corresponding volume range proposed by Cioni et al. (2008) for the sub-Plinian (type I) eruptions.
Total grain-size distribution
The Total Grain Size Distribution (TGSD) is very difficult to estimate (e.g. Bonadonna et al. 2015). The TGSD is usually estimated from data obtained from the grain-size analysis of samples collected in several places where the thickness (or mass) is also known. The TGSD may be evaluated using weighted average based on the isomasses (Carey and Sigurdsson 1982), on statistical methods (Bonadonna and Houghton 2005) or using best-fit techniques (e.g. Bonasia et al. 2010; Costa et al. 2012; 2014; Volentik et al. 2010). However, depending on the quality of tephra sampling, the TGSD may have large uncertainties (Barsotti et al. 2010; Bonadonna and Houghton 2005; Bonadonna et al. 2015). Note also that a high percentage of fine particles (up to 50 % Rose and Durant 2009) may fall as aggregates and this phenomenon is often not considered in the analysis of probability maps. Due to the lack of specific studies, the TGSD of Vesuvius has typically been considered similar to the TGSD reconstructed for the 79 AD eruption (e.g. Barberi et al. 1990; Macedonio et al. 1988; 2008). During this eruption, magma fragmentation and juvenile/lithic ratio changed with time (i.e. between the “White” and “Gray” phases). Macedonio et al. (1988) used the TGSD of the “Gray” phase as derived from the analysis of the deposit of the pyroclastic flow associated to the 79 AD eruption. The TGSD of the “White” phase was assumed to have the same TGSD of the “Gray” phase but with a lower proportion of lithic. Because of the lack of data, the TGSD of smaller scale sub-Plinian eruptions was assumed to be the same of the 79 AD Plinian eruption but with a different juveniles/lithics ratio (Macedonio et al. 1990).
In this work we considered the TGSD for Plinian and Sub-Plinian eruptions of Macedonio et al. (2008), and converted the TGSD to settling velocity distribution, as described below.
An analysis of particle types characterizing the TGSD of the Vesuvius 79 A.D. eruption can be found in Macedonio et al. (1988). Lithic particles usually show constant density whereas juvenile particles show a density variable with the size (e.g. lower density for particles of greater size) due to the presence of larger bubbles inside larger clasts. The variation of the density of juvenile particles with their size was analyzed by different authors (e.g. Bonadonna and Phillips 2003; Walker 1971). For sake of consistency, we used the same density variation as Macedonio et al. (1990), who adopted a simplified function for describing the particle density variations. However, as shown by Pfeiffer et al. (2005), particle density has a second order effect on the settling velocity with respect to the particle dimensions.
It is well known that smaller particles (diameter ranging from sub-micron to tens of micron) fall as aggregates with greater settling velocity than the single particles and deposit in areas much closer to the volcano (Bonadonna et al. 2002; Carey and Sigurdsson 1982; Cornell et al. 1983; James et al. 2003). A few models aimed at describing the aggregation processes, with different degree of complexity, have been proposed (Armienti et al. 1988; Bonadonna et al. 2002; Cornell et al. 1983; Costa et al. 2010; Folch et al. 2010; Macedonio et al. 1988). One of the simplest and more commonly used approach was suggested by Cornell et al. (1983) in order to reproduce field observations of the Campanian Ignimbrite eruption (see also Costa et al. 2012). Cornell et al. (1983) assumed that 50 % of volcanic ash with a diameter between 44 and 63 μm (4 < Φ < 4.6), 75 % of volcanic ash with a diameter between 31 and 44 μm (4.6 < Φ < 5) and 100 % of volcanic ash with diameter less than 32 μm (Φ ≥ 5) fall as aggregates of 200 μm diameter and density of 200 kg/m3 (i.e. with terminal settling velocity of about 0.2 m/s). It is worth noting that, because of the discretization in settling velocity classes, different authors (e.g. Cioni et al. 2003; Macedonio et al. 1988), implicitly accounted for aggregation processes. In fact, since for computational reasons, velocity classes were typically discretized in steps of 0.5 m/s and the lowest velocity class, where all the fines were included, was 0.5 m/s, that implies the class of 0.5 m/s was implicitly considered as the effective settling velocity class of the aggregates, a value similar to that used by Armienti et al. (1988) for the 1980 Mt. St. Helens eruption.
Terminal settling velocity
The evaluation of particle terminal settling velocity from the Total Grain Size Distribution (TGSD) is not trivial because of the irregular and variable shape of the real pyroclast particles (Armienti et al. 1988; Pfeiffer et al. 2005; Wilson and Huang 1979). Usually, it is obtained combining both theoretical models and experimental measurements (e.g. Arastoopour et al. 1982; Bagheri et al. 2015; Dellino et al. 2005; Ganser 1993; Wilson and Huang 1979). The terminal settling velocity depends on particle size, shape, and density and plays an important role on tephra dispersal (e.g. Pfeiffer et al. 2005). Normally, the densest and greatest particles fall near the volcanic vent, less dense and finest particles are instead dispersed farther from the volcanic vent. This natural trend is not valid in presence of significant volcanic ash aggregation, as discussed above.
It is worth noting that, due to the variation of the air density and viscosity with the altitude, the particle settling velocity has different values at different heights and changes, therefore, during the particle fall. For this reason, in this work, the terminal velocity is evaluated at each height by adopting the same approach used by Pfeiffer et al. (2005), who considered an empirical settling velocity-altitude relationship for each particle size.
Column height is an important input parameter of tephra dispersal models. The height reached by the column increases with the mass eruption rate (e.g. Wilson and Walker 1987) and, for this reason, it is often used to estimate the mass eruption rate from column observations. Furthermore, particles leaving the eruption column at different heights are affected by different winds and can be transported in different directions. In this study, the column height is set to 18 km, within the range 15–20 km proposed by Cioni et al. (2008) for sub-Plinian (type I) eruptions, and this value has been varied of about 20 %, i.e. from 12 to 22 km. The vertical distribution of the particle mass in the column is parameterized according to Suzuki (1983), assuming a shape coefficient of 4. This parameterization was subsequently adopted, among others, by Armienti et al. (1988); Macedonio et al. (2005) and generalized by Pfeiffer et al. (2005).
Horizontal diffusion coefficient
The effective horizontal atmospheric diffusion coefficient is an empirical parameter adopted in plume models, that describes the effective spreading due to atmospheric turbulence and gravitational spreading at the Neutral Buoyancy Level (Costa et al. 2013). It is usually obtained by best-fit procedure (e.g. Bonadonna et al. 2002; Bonasia et al. 2010; Macedonio et al. 1988) and typically ranges between 102 and 104 m2/s. Studies on Vesuvius eruptions have shown that the horizontal diffusion coefficient, for sub-Plinian and Plinian eruptions, for the target area, ranges between 1000 and 10,000 m2/s (Bonasia et al. 2010; Macedonio et al. 1988).
Computational and input parameters
5 × 1011 kg
1011– 1012 kg
12 – 22 km
Horizontal diffusion coefficient
1000 – 10000 m2/s
Column shape (Suzuki coefficient)
see Table 1
Settling velocity distribution
VSET–1990, –79, –1631,–2008
400 × 400 m
Effect of the meteorological dataset
Effect of the total mass
The erupted mass is the primary parameter affecting the extension of the hazard zone. As we mentioned above, keeping fixed all other parameters, a variation in the total mass produced a linear variation in the deposit loading. In general, the relationship between the total mass and the extension of the area having a given probability of being covered by a deposit of a given threshold is not linear, because of the non-linearity of the operation of thresholding (thresholding produces a null hazard area when the erupted mass is insufficient for reaching the given loading).
Effect of the terminal settling velocity
Effect of the column height
Effect of the horizontal diffusion coefficient
This work presents the results of a sensitivity analysis on tephra fall probability maps, based on a reference scenario, with the aim to evaluate the variability due to the use of different meteorological datasets and different eruption source parameters. The considered reference event is a sub-Plinian eruption of Vesuvius with a total mass of 5 × 1011 kg and a column height of 18 km. For comparison purposes, this study refers to the area with probability greater than 5 % of tephra loading equal to 300 kg/m2, here referred as “hazard zone”, although also loading of 500 and 1000 kg/m2 were investigated. These tephra loads are comparable with the collapse thresholds of roofs from low to medium-high resistance (Spence et al. 2005; Zuccaro et al. 2008).
From the simulation outcomes, we found that the total erupted mass has a first order effect on the extension of the hazard zone. The particles settling velocity also plays a crucial role, whereas the use of different meteorological datasets, and column height within the considered range do not affect significantly the hazard extension. Simulations show that there are some particles sizes that give a greater contribution on tephra loading in the target area. This effect is due to the fact that the particles having a different terminal settling velocity disperse on a different area. Since the particle settling velocity distribution, or the associated TGSD, is not easy to estimate, the probability maps are affected by large uncertainties. In fact, as mentioned above, the TGSD can be assessed by field analysis using statistical approaches (e.g. Bonadonna and Costa 2013; Bonadonna and Houghton 2005) or by inverse modeling (Bonasia et al. 2010; Mannen 2006) and the quality of this estimation strongly depends on the quantity and quality of the data (e.g. Barsotti et al. 2010; Bonadonna et al. 2015). The lacking of proximal points, often inaccessible, and distal points, often eroded or unavailable (e.g. in the sea), may in fact seriously affect the assessment of the total grain-size distribution (Bonadonna and Houghton 2005; Bonadonna et al. 2015; Bonasia et al. 2010). Our analysis shows that at the investigate latitude (≈42°N) the wind field shows important seasonal variations at altitudes greater than about 15 km. For the considered reference scenario (column height 18 km), this leads to a variation of about 15 % in the extension of the hazard zone between Summer and Winter.
As a final remark, concerning the tephra loading, we need to consider the potential effect of rain. In fact, tephra fall deposits are porous and incoherent. In case of rain, tephra deposits are able to absorb water within the pores up to a maximum level, beyond which the deposit becomes unstable and mobilized. Typically, the limit of instability is reached for volume fractions of water in the range of 23–47 % (Pierson 1986). Before mobilization, the load is given by the sum of the deposit load plus the load of the rain stored by the deposit (Macedonio and Costa 2012). An estimation of the contribution of the rain to the deposit load, applied to the pyroclastic deposits in the Neapolitan area was considered by Macedonio and Costa (2012) that account for the statistics of the rains in the Neapolitan area (Fiorillo and Wilson 2004; Macedonio and Costa 2012). In order to estimate the maximum tephra load, Macedonio and Costa (2012) assumed that the deposit absorbs all the water up to the limit of re-mobilization or up to the maximum available water. In the latter case, they considered the maximum events of rain in the Neapolitan area during the last century (about 200 mm of rain per month, and the extreme event of 500 mm). This study demonstrated that, in case of rain, the load can increase easily of 100–200 kg/m2. On the basis of these considerations, for our study, the absorption of 100 kg/m2 of water in a deposit can increase the hazard area (area with probability greater than 5 % of a loading equal to 300 kg/m2) from 972 to 1660 km2, for the reference scenario.
We thank Susanna Jenkins and an anonymous referee for their helpful suggestions. We also acknowledge the Editor Thomas Wilson for the time and effort spent in managing the paper. We thanks the Aeronautica Militare for producing radio-sounding data in Italy, and the University of Wyoming and NOAA where they can be freely downloaded. NCEP Reanalysis and 20th Century Reanalysis V2 data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. For the ERA-Interim data, we thanks the ECMWF. For the free software we thanks the UNIDATA Program Center for providing the netCDF and NCL libraries and the developers of the “Generic Mapping Tools” (GMT) and Gnuplot. Numerical simulations with HAZMAP-2.4.4 (cuda version) were performed on the Linux Cluster at Osservatorio Vesuviano funded by the Project PON-MIUR “VULCAMED” (Project PONa3_00278). This work was partially supported by the MED-SUV Project funded by the European Union (FP7 Grant Agreement n.308665). This work benefited of the agreement between Istituto Nazionale di Geofisica e Vulcanologia and the Italian Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile (DPC). This paper does not necessarily represent DPC official opinion and policies.
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