2019 Fiscal Year Annual Research Report
| Project/Area Number |
17F17372
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
鼎 信次郎 東京工業大学, 環境・社会理工学院, 教授 (20313108)
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| Co-Investigator(Kenkyū-buntansha) |
DHANAPALA ARACHC SACHINDRA 東京工業大学, 環境・社会理工学院, 外国人特別研究員
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| Project Period (FY) |
2017-11-10 – 2020-03-31
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| Keywords | Environmental Flow / Climate Change |
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| Outline of Annual Research Achievements |
In the previous fiscal year, it was found that models developed with Parallel Genetic Programming (PGP) algorithm (introduced in this research) show better generalisation skills and produce fewer unphysically large outliers compared to that of models developed with the traditional single deme GP. However, both conventional GP and PGP algorithms were not able to capture the extremes in the observed time series accurately. In a preliminary investigation, it was found that a monthly downscaling approach coupled with a monthly to daily disaggregation method has a better potential to simulate extremes, but the overall performance of the model is not as good as that of a daily downscaling model. Also, disaggregation methods are known to perform poorly when months with similar totals/averages with significantly different intra-monthly distributions are present in observations. In this year, a comprehensive investigation on the improvement to the simulation of extremes in precipitation, temperature and streamflows was conducted focussing on the use of disaggregation. Potential methods to combine the daily simulations with monthly to daily disaggregated simulations were also investigated. Streamflows were projected into the future with the improved methodology, availability of water to satisfy the needs of the environment was investigated. Results were summarized into manuscripts and submitted as papers to journals based on the finding of these new analyses.
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| Research Progress Status |
令和元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 |
令和元年度が最終年度であるため、記入しない。
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