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Title: | Long-term ash dispersal dataset of the Sakurajima Taisho eruption for ashfall disaster countermeasure |
Authors: | Rahadianto, Haris Tatano, Hirokazu https://orcid.org/0000-0001-7209-4358 (unconfirmed) Iguchi, Masato https://orcid.org/0000-0002-4322-5854 (unconfirmed) Tanaka, Hiroshi L. Takemi, Tetsuya https://orcid.org/0000-0002-7596-2373 (unconfirmed) Roy, Sudip |
Author's alias: | 多々納, 裕一 井口, 正人 竹見, 哲也 |
Issue Date: | 2-Dec-2022 |
Publisher: | Copernicus GmbH |
Journal title: | Earth System Science Data |
Volume: | 14 |
Issue: | 12 |
Start page: | 5309 |
End page: | 5332 |
Abstract: | Abstract. A large volcanic eruption can generate large amounts ofash which affect the socio-economic activities of surrounding areas, affecting airline transportation, socio-economics activities, and humanhealth. Accumulated ashfall has devastating impacts on areas surrounding thevolcano and in other regions, and eruption scale and weather conditions mayescalate ashfall hazards to wider areas. It is crucial to discover placeswith a high probability of exposure to ashfall deposition. Here, as areference for ashfall disaster countermeasures, we present a datasetcontaining the estimated distributions of the ashfall deposit and airborneash concentration, obtained from a simulation of ash dispersal following alarge-scale explosive volcanic eruption. We selected the Taisho (1914)eruption of the Sakurajima volcano, as our case study. This was thestrongest eruption in Japan in the last century, and our study provides abaseline for a worst-case scenario. We employed one eruption scenario (OES)approach by replicating the actual event under various extended weatherconditions to show how it would affect contemporary Japan. We generated anash dispersal dataset by simulating the ash transport of the Taisho eruptionscenario using a volcanic ash dispersal model and meteorological reanalysisdata for 64 years (1958–2021). We explain the dataset production andprovide the dataset in multiple formats for broader audiences. We examinethe validity of the dataset, its limitations, and its uncertainties.Countermeasure strategies can be derived from this dataset to reduceashfall risk. The dataset is available at the DesignSafe-CI Data Depot:https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-2848v2or through the following DOI: https://doi.org/10.17603/ds2-vw5f-t920by selecting Version 2 (Rahadianto and Tatano, 2020). |
Rights: | © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. |
URI: | http://hdl.handle.net/2433/282740 |
DOI(Published Version): | 10.5194/essd-14-5309-2022 |
Appears in Collections: | Journal Articles |
This item is licensed under a Creative Commons License