A fast GIS-based risk assessment for tephra fallout: the example of Cotopaxi volcano, Ecuador-Part II: vulnerability and risk assessment

Biass, Sébastien; Frischknecht, Corine; Bonadonna, Costanza

doi:10.1007/s11069-012-0270-x

Cited by 25 publications

(14 citation statements)

References 62 publications

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“…Third, probability maps were converted into probabilistic isomass maps, which show the typical tephra accumulation given a probability of occurrence of the hazard (e.g. Biass et al, 2013). Figure 8c shows the probability of occurrence of 1, 10, 50 and 90 % for the tephra accumulation of 100 kg m −2 compared to the accumulation of 100 kg m −2 observed for the climactic phase of the eruption (4-5 June), suggesting that this phase is within 1 % of all the possible atmospheric conditions recorded in the last 15 years.…”

Section: Perspective On the Occurrence Of A Potential New Similar Erumentioning

confidence: 99%

Chronology and impact of the 2011 Cordón Caulle eruption, Chile

Elissondo

Baumann

Bonadonna

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. We present a detailed chronological reconstruction of the 2011 eruption of the Cordón Caulle volcano (Chile) based on information derived from newspapers, scientific reports and satellite images. Chronology of associated volcanic processes and their local and regional effects (i.e. precursory activity, tephra fallout, lahars, pyroclastic density currents, lava flows) are also presented. The eruption had a severe impact on the ecosystem and on various economic sectors, including aviation, tourism, agriculture and fishing industry. Urban areas and critical infrastructures, such as airports, hospitals and roads, were also impacted. The concentration of PM 10 (particulate matter ≤ 10 µm) was measured during and after the eruption, showing that maximum safety threshold levels of daily and annual exposures were surpassed in several occasions. Probabilistic analyses suggest that this combination of atmospheric and eruptive conditions has a probability of occurrence of about 1 %. The management of the crisis, including evacuation of people, is discussed, as well as the comparison with the impact associated with other recent eruptions located in similar areas and having similar characteristics (i.e. Quizapu, Hudson and Chaitén volcanoes). This comparison shows that the regions downwind and very close to the erupting volcanoes suffered very similar problems, without a clear relation to the intensity of the eruption (e.g. health problems, damage to vegetation, death of animals, roof collapse, air traffic disruptions, road closure, lahars and flooding). This suggests that a detailed collection of impact data can be largely beneficial for the development of plans for the management of an eruptive crisis and the mitigation of associated risk of the Andean region.

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Section: Perspective On the Occurrence Of A Potential New Similar Erumentioning

confidence: 99%

Chronology and impact of the 2011 Cordón Caulle eruption, Chile

Elissondo

Baumann

Bonadonna

et al. 2016

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…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 2016;Biass et al 2017;Takarada 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 and et al (1983)), and their transport is subject to wind advection, horizontal turbulent diffusion, and falling at terminal velocities.…”

Section: Volcanic Ash Transport Model Tephra2mentioning

confidence: 99%

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

et al. 2021

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In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model Tephra2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Outputs of the algorithm are presented as sample posterior distributions for variables of interest. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works from different perspectives, and to show how inputs affect its performance. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; iii) capture correlations between variables of interest in the estimated Eruption Source Parameters; and iv) no simplification is assumed in sampling from the posterior probability distribution. A limitation is that some of the inputs need to be specified subjectively, which is designed intentionally such that the full capacity of the Bayes’ rule can be explored by users. How and why inputs of the algorithm affect its performance and how to specify them properly are explained and listed. Correlation between variables of interest in the posterior distributions exists in many of our experiments. They can be well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters and wind direction and speed at each elevation level, because this could be unnecessary or would increase the number of variables to be estimated, and these variables could be highly correlated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from Tephra2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from deposits.

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“…2 Volcanic ash transport model TEPHRA2 TEPHRA2 is a widely-used volcanic ash transport and deposition model . 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;Volentik et al, 2010;Fontijn et al, 2011;Biass et al, 2012;Magill et al, 2015;Biass et al, 2016Biass et al, , 2017Takarada, 2017;Wild et al, 2019;Connor et al, 2019). TEPHRA2 assumes that tephra particles with different grain sizes are released from a line source instantaneously, and their transport is subject to wind advection, horizontal turbulent diffusion, and falling at terminal velocities.…”

Section: Introductionmentioning

confidence: 99%

Tephra Deposit Inversion by Coupling TEPHRA2 With the Metropolis-Hastings Algorithm and Its Implications

Yang¹,

Pitman²,

Bursik³

et al. 2020

Preprint

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Abstract In this work we couple the Metropolis-Hastings algorithm with the volcanic ash transport model TEPHRA2, and present the coupled algorithm as a new method to estimate the Eruption Source Parameters of volcanic eruptions based on mass per unit area or thickness measurements of tephra fall deposits. Basic elements in the algorithm and how to implement it are introduced. Experiments are done with synthetic datasets. These experiments are designed to demonstrate that the algorithm works, and to show how inputs affect its performance. Results are presented as sample posterior distribution estimates for variables of interest. Advantages of the algorithm are that it has the ability to i) incorporate prior knowledge; ii) quantify the uncertainty; and iii) capture correlations between variables of interest in the estimated Eruption Source Parameters. A limitation is that some of the inputs need to be specifed subjectively. How and why such inputs affect the performance of the algorithm and how to specify them properly are explained and listed. Correlation between variables of interest are well-explained by the physics of tephra transport. We point out that in tephra deposit inversion, caution is needed in attempting to estimate Eruption Source Parameters, and wind direction and speed at each elevation level, as this increases the number of variables to be estimated. The algorithm is applied to a mass per unit area dataset of the tephra deposit from the 2011 Kirishima-Shinmoedake eruption. Simulation results from TEPHRA2 using posterior means from the algorithm are consistent with field observations, suggesting that this approach reliably reconstructs Eruption Source Parameters and wind conditions from the deposit.

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A fast GIS-based risk assessment for tephra fallout: the example of Cotopaxi volcano, Ecuador-Part II: vulnerability and risk assessment

Cited by 25 publications

References 62 publications

Chronology and impact of the 2011 Cordón Caulle eruption, Chile

Chronology and impact of the 2011 Cordón Caulle eruption, Chile

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

Tephra Deposit Inversion by Coupling TEPHRA2 With the Metropolis-Hastings Algorithm and Its Implications

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