2023 Fiscal Year Final Research Report
Flood risk assessment using spatio-temporal distribution of heavy rainfall generated by a deep learning model
| Project/Area Number |
21K05841
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 41030:Rural environmental engineering and planning-related
|
|---|
| Research Institution | National Agriculture and Food Research Organization |
|---|
Principal Investigator |
Minakawa Hiroki 国立研究開発法人農業・食品産業技術総合研究機構, 農村工学研究部門, 上級研究員 (70527019)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
福重 雄大 国立研究開発法人農業・食品産業技術総合研究機構, 農村工学研究部門, 研究員 (80845850)
|
|---|
| Project Period (FY) |
2021-04-01 – 2024-03-31
|
| Keywords | 流域治水 / 拡散モデル / 水害リスク / 気候変動 |
|---|
| Outline of Final Research Achievements |
A model for generating spatio-temporal heavy rainfall data was developed for use in flood risk assessment in the watershed.
The generation model, which was trained on the analyzed rainfall in the target area, was able to generate a large number of hourly rainfall spatiotemporal patterns. A watershed water cycle model was applied to flood risk assessment, and the functions of dams and paddy field dams were incorporated to enable assessment of risk and the effectiveness of flood countermeasures. The results of a comparison of the effectiveness of flood control measures in events with equivalent basin-averaged rainfall in the target watershed showed that the effectiveness differs depending on whether the rainfall amount distribution overlaps with the target area of dams and paddies, indicating that spatiotemporal information on rainfall has a significant bearing on flood risk in the region. Next, these two technologies will be linked to assess risk and propose countermeasures.
|
| Free Research Field |
水文学
|
| Academic Significance and Societal Importance of the Research Achievements |
多発する水害に対して、どのような降雨パターンでリスクが高まるか、またどのような対策を実施すればリスク低減に繋がるかを事前に十分検討できれば、よりロバストな地域防災計画の策定が可能になる。その実現には、観測数が少ないために十分な豪雨のパターンを準備できない点がネックであったが、本課題で開発した生成モデルを活用することでその課題が解消された。また、流域内で取りうる対策の効果を評価する流出モデルを組み合わせることで具体的な対応策の検討が進むなど、流域治水の促進に貢献可能な点で、本課題成果には社会的な意義があるといえる。
|
