| Project/Area Number |
21K14386
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 25030:Disaster prevention engineering-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MA Wenchao 東京大学, 生産技術研究所, 特任研究員 (60743101)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2023: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | flood forecasting / ensemble member / ensemble system |
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| Outline of Research at the Start |
This project will develop a world-leading and computing cost-effective ensemble forecasting system for issuing quick and reliable flood warnings. We will estimate the return period of river discharge for each forecasted ensemble member, and to compare multiple sourced ensemble forecasting results.
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| Outline of Final Research Achievements |
This study considers the increasingly severe flood disasters worldwide and aims to construct a real-time global flood forecasting dataset using ensemble forecasting meteorological forcing data. The project benefits from land surface modeling systems advancements and the hydrodynamic model Cama-Flood. The grid-based hydrodynamic model can consider global watershed systems and provide effective output results for high-risk flood areas worldwide. Additionally, as a world-leading provider of meteorological forecast data, ECMWF's multi-dataset meteorological forecasts provide crucial data support for this research.
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| Academic Significance and Societal Importance of the Research Achievements |
Flood is the most severe, widespread, and destructive natural disaster threatening human survival. This study combines existing leading meteorological forecast data with hydrodynamic models, providing a valuable research approach for developing flood forecasting methods and technologies.
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