2023 Fiscal Year Final Research Report
Study on estimation accuracy of return level of hydrological values by applying Metastatistical Extreme Value Distribution and its
Project/Area Number |
21K05831
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 41030:Rural environmental engineering and planning-related
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Research Institution | Okayama University |
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) |
2021-04-01 – 2024-03-31
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Keywords | 確率雨量 / 経年変動 / メタ統計的極値分布 |
Outline of Final Research Achievements |
The annual maximum series (AMS) method is generally applied to estimate the return level of extreme rainfall. The sample size, however, is so small that the estimated return level and return period are strongly fluctuated even when just one or a few extreme-size minima or maxima are included by change in a target duration for the analysis. That often disturbs correct estimation of secular change in return level and return period of rainfall. In this study, we estimated the secular change in both return level and in return period of daily rainfall in Japan by applying the metastatistical extreme value (MEV) distribution to daily rainfall data. The results showed that the estimated 100-year daily rainfall by applying the MEV distribution showed similarly increasing trend nationwide as when the annual maximum method was applied. In addition, compared with the AMS method, the fluctuation range of the estimated values was suppressed, and the trend of annual change was shown more clearly.
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Free Research Field |
流域水文学,水文統計学
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Academic Significance and Societal Importance of the Research Achievements |
豪雨に伴う出水規模の評価には,従来,解析対象期間内の年最大値に極値分布を適応することにより推定する区間最大値法が用いられてきた。しかし,区間最大値法では,解析対象となるデータサイズが限られ,他のデータに比べて極端に大きさが異なるデータの影響を受けやすい難点があった。本研究では,メタ統計的極値分布を用いて解析対象期間内の全ての日雨量データを対象として確率雨量の推定を行ない,推定値の経年変化を,最大値法による推定値と比較した。その結果,解析対象区間の変化による確率雨量の推定値の変動が抑えられ,降雨規模の経年変動がより分かりやすく示された。
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