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Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019

Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operationa...

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Autores principales: Ma, Wenchao, Ishitsuka, Yuta, Takeshima, Akira, Hibino, Kenshi, Yamazaki, Dai, Yamamoto, Kosuke, Kachi, Misako, Oki, Riko, Oki, Taikan, Yoshimura, Kei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119424/
https://www.ncbi.nlm.nih.gov/pubmed/33986352
http://dx.doi.org/10.1038/s41598-021-89522-8
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author Ma, Wenchao
Ishitsuka, Yuta
Takeshima, Akira
Hibino, Kenshi
Yamazaki, Dai
Yamamoto, Kosuke
Kachi, Misako
Oki, Riko
Oki, Taikan
Yoshimura, Kei
author_facet Ma, Wenchao
Ishitsuka, Yuta
Takeshima, Akira
Hibino, Kenshi
Yamazaki, Dai
Yamamoto, Kosuke
Kachi, Misako
Oki, Riko
Oki, Taikan
Yoshimura, Kei
author_sort Ma, Wenchao
collection PubMed
description Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.
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spelling pubmed-81194242021-05-14 Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019 Ma, Wenchao Ishitsuka, Yuta Takeshima, Akira Hibino, Kenshi Yamazaki, Dai Yamamoto, Kosuke Kachi, Misako Oki, Riko Oki, Taikan Yoshimura, Kei Sci Rep Article Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding. Nature Publishing Group UK 2021-05-13 /pmc/articles/PMC8119424/ /pubmed/33986352 http://dx.doi.org/10.1038/s41598-021-89522-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ma, Wenchao
Ishitsuka, Yuta
Takeshima, Akira
Hibino, Kenshi
Yamazaki, Dai
Yamamoto, Kosuke
Kachi, Misako
Oki, Riko
Oki, Taikan
Yoshimura, Kei
Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019
title Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019
title_full Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019
title_fullStr Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019
title_full_unstemmed Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019
title_short Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019
title_sort applicability of a nationwide flood forecasting system for typhoon hagibis 2019
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119424/
https://www.ncbi.nlm.nih.gov/pubmed/33986352
http://dx.doi.org/10.1038/s41598-021-89522-8
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