| Project/Area Number |
21F21071
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 外国 |
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| Review Section |
Basic Section 22040:Hydroengineering-related
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| Research Institution | Kyoto University |
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Principal Investigator |
佐山 敬洋 京都大学, 防災研究所, 教授 (70402930)
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| Co-Investigator(Kenkyū-buntansha) |
TRY SOPHAL 京都大学, 防災研究所, 外国人特別研究員
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| Project Period (FY) |
2021-04-28 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2022: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2021: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Flood forecast / Flood inundation / Flood damage / Climate Change / Lower Mekong Basin |
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| Outline of Research at the Start |
本研究は、メコン川下流域を対象に洪水氾濫と農業被害をリアルタイムで予測する技術を開発する。広域を対象に高分解能でかつ不確実性を加味した氾濫予測を実現するために、本研究は、多数のアンサンブルシミュレーションとその他の水文・地形情報を入力する機械学習に基づく氾濫予測手法を開発する。また、時事刻々と変化する浸水及び被害の状況を広域俯瞰的に反映するために、衛星情報を活用した農業被害の推定技術を開発する。
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| Outline of Annual Research Achievements |
Flood forecasting and the related damage assessment in real-time are challenging and currently not available in the Lower Mekong region. This study focuses on the development of a real-time system of flood inundation in the Lower Mekong Basin (LMB). In particular, this study focuses on the developing a system to assess flood hazards and damages in the LMB simultaneously. The developed system can also be used for risk assessment of agricultural damages for future projections of extreme flood events under climate change effects.
Firstly, we have collected and evaluated the observed hydrological data. Flood inundation simulation was conducted in the LMB under the effect of climate change using a large ensemble climate data (d4PDF), MRI-AGCM3.2, and CMIP6 GCM datasets. Flood hazards and its related damages were assessed. Moreover, flood forecasting was primarily evaluated using numerical weather prediction dataset from GSMaPxNEXRA through NICAM-LETKF data assimilation in the Prek Thnot River Basin, Cambodia. Finally, the evaluation and improvement of flood forecasting has been done to improve its performance and accuracy by combining with machine learning technique. Hybrid approach of hydrological model.
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| Research Progress Status |
令和4年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和4年度が最終年度であるため、記入しない。
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