2018 Fiscal Year Final Research Report
Development of the optimal operation method of dam reservoirs according to national situation in the Chao Phraya River basin, Thailand
| Project/Area Number |
15H05222
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 海外学術 |
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| Research Field |
Hydraulic engineering
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| Research Institution | Toyama Prefectural University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
呉 修一 富山県立大学, 工学部, 准教授 (00646995)
宮本 守 国立研究開発法人土木研究所, 土木研究所(水災害・リスクマネジメント国際センター), 研究員 (80391621)
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| Research Collaborator |
Shimosaka Masashi
Dotani Kentaro
Horiuchi Yusuke
Zenkouji Shingo
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Keywords | 貯水池操作 / 大規模ダム貯水池 / タイ国 / チャオプラヤー川流域 / 機械学習 / JRA55 |
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| Outline of Final Research Achievements |
In 2011, massive floods occurred in the Chao Phraya River basin (CPRB) in Thailand. There is also a risk of droughts in the CPRB during the dry season. The aim of this study was to develop a science-based reservoir operation system that can easily be put into practice. To accomplish this aim, we studied the optimum operation of these two reservoirs. On the basis of our observations, we propose a new reservoir operation method for reducing the risk of droughts and floods. This method was developed and validated with the aid of historical hydrological and rainfall data. The volume of water to be released is determined by the accumulated daily rainfall data, daily inflow data, and storage volume. The reservoir operation method proposed provides better stabilization between the reservoir’s water discharge and storage volume. This reduces the risk of drought and allows for water discharge without increasing the risk of flooding in the lower section of the basin.
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| Free Research Field |
水資源学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では大規模貯水池運用を利水と治水の両面を満足する大規模貯水池運用モデルの提案ができた．実運用に適用できるように，2つの大規模ダム貯水池における過去の貯水池操作全てについて約40～50年連続的に検証したことはこれまでにない学術的成果である．本提案モデルは，タイ国王立灌漑局とタイ電力公社で直接成果発表しており，基盤(B)国際の事業として十分な効果があったと考えている．さらに，発展的に実施した機械学習を利用した降水量季節予報モデル開発は非常に良い精度であったことは今後の研究発展に大きく寄与するものであり，別の研究事業に引き継がれている．
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