Simulations of Tephra Fall Deposits From a Bending Eruption Plume and the Optimum Model for Particle Release

Abstract

We developed a simulation code named WT, which calculates tephra fallout from eruption plumes bent by wind. This proposed model assumes a series of radial particle sources arraying along the theoretically predicted trajectory of the plume center. To validate WT, we reconstructed the tephra dispersal during the 2011 sub-Plinian eruption of Shinmoedake in Japan. This eruption was ideal for the validation because it was observed through various approaches; however, high-resolution data of the fluctuating plume height made it difficult to determine a representative eruption time and plume height for running the simulations. Also, the amount of particle segregation along the plume could not be determined a priori. We thus implemented inversion calculations to study the optimum particle segregation pattern for possible ranges of time and the plume height, and the misfit between the observed and calculated mass loadings on the ground was evaluated. After this process and the following analysis, the optimum eruption time was determined for one of three major explosions (18:00 Japan Standard Time on 26 January). The optimum plume height was estimated to be 4 km above sea level, slightly lower than the estimation derived by the weather satellite (5 km). When wind shear exists, the WT model has a significant advantage in reconstructing tephra dispersal over the classical code named Tephra2, the prototype of WT. The WT inversion implies a simple segregation model from a well-mixed plume, and further studies on particle segregation patterns will help to improve the accuracy of forward WT simulations. Plain Language Summary Volcanic ash fall can disrupt livelihood in downwind areas. Considering that modern societies greatly depend on complex supply chains, urban functions, including transportation as well as electric and medical systems, can be disrupted by such events. Although volcanic ash itself is not lethal, ashfall events can result in life-threating crises. An example of such a potential disaster area is the Tokyo megalopolis, which spreads from the foot of Mt. Fuji to more than 100 km downwind of the prevailing wind. To develop appropriate mitigation measures, reliable ash fall simulations are critically important. However, existing models oversimplify the shape of eruption clouds and often fail to reconstruct wide range ash accumulations. Thus, we developed a simulation code named WT. This code calculates ashfall from an eruption plume bent by wind, whereas many existing simulation models assume a vertical eruption plume rising above the crater. Compared with the existing extensively used models that are, WT showed significant advantages in ash fall reconstruction when the direction of wind at high altitudes was substantially different from that on the ground. Although some important parameters for implementing WT still remain uncertain, further studies are expected to improve the ash fall simulations significantly.

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

“…For recent eruptions, users could adopt best-fit historically observed wind profiles, or use them to construct the priors for wind conditions with the non-simplified wind profile. For example, NOAA NCEP/NCAR REANALYSIS data (Kalnay et al, 1996) provide a large number of wind profiles globally, based on best-fit wind profiles to observations via weather models (e.g., Connor et al, 2019;Mannen et al, 2020).…”

“…We refrain from going into details in the physics of tephra transport and how the interaction between variables would affect the estimates. This is because tephra dispersal processes that are not taken into account by Tephra2 did affect tephra dispersal during the eruption (Mannen et al, 2020) .…”

“…The tephra deposit data we are using are reported in White et al (2017). Detailed description of the dataset can be found in White et al (2017); Mannen et al (2020). Tephra thickness and grain size distributions were measured at 55 locations downwind from the vent.…”

“…Estimating ESPs can be treated as an inverse problem (e.g., Tarantola, 2005;Kaipio and Somersalo, 2006), and requires the use of different statistical and engineering techniques (Suzuki et al, 1983;Carey and Sparks, 1986;Bursik et al, 1992b;Sparks et al, 1992;Mannen, 2006;Klawonn et al, 2012Klawonn et al, , 2014Maeno et al, 2014;Biass et al, 2016;Poret et al, 2017;Koyaguchi et al, 2017;White et al, 2017;Yang et al, 2019;Mannen et al, 2020). Previous workers have presented different methods to implement inversion to obtain ESPs from the characteristics of tephra deposits, such as deposit thickness and grain size.…”

“…Tephra2 is a widely-used volcanic ash transport and deposition model (Bonadonna et al, 2010;Connor et al, 2011). It has been coupled with different statistical and engineering techniques for forward and inverse modeling of tephra fall deposits and volcanic hazard analysis (Connor and Connor, 2006;Mannen, 2006;Volentik et al, 2010;Fontijn et al, 2011;Biass et al, 2012;Mannen, 2014;Magill et al, 2015;Biass et al, 2016Biass et al, , 2017Takarada, 2017;Wild et al, 2019;Connor et al, 2019;Mannen et al, 2020;Williams et al, 2020). Tephra2 assumes that tephra particles with different grain sizes are released from a vertical column with column radius increasing with height (accounted for by an additional diffusion term; Suzuki et al, 1983), and their transport is subject to wind advection, horizontal turbulent diffusion, and falling at terminal velocities.…”