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Page 1: Integrated Sakurajima Volcanic Ash Fall Distribution Under ... · Integrated Sakurajima Volcanic Ash Fall Distribution Under Climate Change An Analysis based on JRA-55 Data using

Integrated Sakurajima Volcanic Ash Fall DistributionUnder Climate Change

An Analysis based on JRA-55 Data using Neural Network

1. Background• A volcanic eruption is one of the events which emit several

dangerous pollutants that led to catastrophes.• Volcanic ashes, apparently hampering not only the resident who

lived near the volcano but also the other citizens in the fartherarea that can get indirect impact from that.

• The volcanic ash released by large-scale eruptions may causeserious damage to critical infrastructures, buildings, andcause health problem.

PUFF Model

DPRI, Kyoto University

3. Methodology

• Integrated volcanic ashfall risk assessment in large-scaleeruption.a. Systematically estimate the risk of volcanic ashfall from probable large-

scale volcanic eruption in Sakurajima.b. Historical weather data is used for risk estimation to validate and

understand ashfall risk.c. Get critical characteristic from historical eruption data and past climate

data for verifying implemented policies obtained from the prior result.

• Confirmation of risk assessment between historicalweather data and predictive weather data that will enhancethe decision support system for volcanic disaster riskmanagement for the development of early warningsystem.a. Compare daily ash fallout map between results obtained from both

predictive data and historical data on general occasion.b. Compare daily ash fallout map between results obtained from both

predictive data and historical data on specific occasion.c. Thorough analysis by comparing similarities and anomalies detection

between both results.

• The confirmed ash fallout distribution data, added withpopulation data, will be extended to the development ofintegrated evacuation planning that consideredevacuation willingness of the potential endangeredresident.

2. Objectives

4. Results on Annual and Monthly Observation

5. Result under Typhoons No. 24 (Sep 28th-30th)

6. Conclusions• Ash fallout distribution maps produced from PUFF model with JRA-55 data give

risk assessment coverage confirming the risk threshold on different riskcomponents.

• In typhoon approaching situation, the alterations in ashfall dispersion course isdetected by using JRA-55 data.

• Lastly, by conducting ashfall risk-assessment on large-scale eruption scenariofor long period and confirming the development of ashfall early warning system,we can build more comprehensive evacuation planning based on peoplebehavior towards the ashfall hazard.

7. Future Works

𝑟𝑟𝑖𝑖 𝑡𝑡 :Position vector of the 𝑖𝑖−th particle at time 𝑡𝑡𝑆𝑆𝑖𝑖:Initial locations of the particles as a source term𝑉𝑉:Local wind velocity to transport the particle

𝑍𝑍:Diffusion velocity𝐺𝐺:Gravitational fallout velocity∆𝑡𝑡:Time step (300𝑠𝑠)

Probability Distribution

Eruption Scenario

Risk Map for 0.1 mm thick ash fall in 2018

The risk threshold for ash fall hazard

Risk Map for 5 cm thickash fall in 2017

Risk Map for 5 cm thickash fall in January 2017

Risk Map for 5 cm thickash fall in August 2017

Risk Map for 50 cm thickash fall in August 2017

Risk Map for 50 cm thickash fall in October 2017

TyphoonTrajectory

Risk Map on Sep 28th, 2018 Risk Map on Sep 29th, 2018 Risk Map on Sep 30th, 2018

Haris Rahadianto

Master StudentGraduate School of Informatics - Kyoto University

[email protected] - JAPAN

Haris Rahadianto・Subhajyoti Samaddar・Hirokazu Tatano

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