| Project/Area Number |
18K05879
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 41030:Rural environmental engineering and planning-related
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| Research Institution | Okayama University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
工藤 亮治 岡山大学, 環境生命科学学域, 准教授 (40600804)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
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| Keywords | メタ統計的極値分布 / 区間最大値法 / 極値統計 / 地域頻度解析 / 地域分類 / 日雨量時系列 / 極値 / 経年変化 / 外れ値 / 水文統計 / ベイズ理論 |
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| Outline of Final Research Achievements |
The purpose of this study was to examine a method for more accurate estimation of extreme values from limited data at the beginning, and initially used the Markov chain Monte Carlo method based on Bayesian statistical ideas that have been widely used in recent years. Firstly, extreme statistical analysis using the conventional annual maximum value method and threshold excess method were applied and evaluated, but as we proceeded with the research, we found a meta-statistical extreme value (MEV) distribution that uses all rainfall time series data. Comparison between the application results of the MEV method and those of the annual maximum method showed that the return level of rainfall can be estimated by suppressing the influence of a small number of extremely large peculiar data by the MEV method.
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| Academic Significance and Societal Importance of the Research Achievements |
近年，洪水・渇水などの極端な気象現象の発生頻度が増加している。これらの極端現象は気候変動に伴うとされ，その規模・発生頻度は経年的に変化している。これらの統計的推定には，従来，年最大値のみを用いる区間最大値法が用いられてきたが，解析に用いることができるデータのサイズが限られるため，少数の極端な値を持つ値により，確率年・確率雨量が大きな影響を受け，その推定精度に問題があった。ここで研究対象としてMEV法は，大正期間中の観測データ全てを用いて極値を推定する手法であり，限られた数の極端なデータの影響が小さく，安定した確率雨量，確率年の推定が期待される。
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