| Project/Area Number |
16360250
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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 | 一般 |
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| Research Field |
水工水理学
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| Research Institution | Mie University |
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Principal Investigator |
KUZUHA Yasuhisa Mie University, Graduate School of Bioresources, Professor, 大学院生物資源学研究科, 教授 (50373220)
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| Co-Investigator(Kenkyū-buntansha) |
KOMATSU Yosuke Rissho University, Geo-Environmental Science, Senior Lecturer, 地球環境科学部, 講師 (90386516)
KISHII Tokuo Kanazawa Institute of Technology, College of Environmental Engineering and Architecture, Professor, 環境建築学部, 教授 (30360374)
SHO Kenjiro Nagoya Institute of Technology, Graduate School of Engineering, Lecturer, システムマネジメント工学科, 助手 (40283478)
IIZUKA Satoshi National Research Institute for Earth Science and Disaster Prevention, Storm, Flood, and Landsilide Research Department, Senior Researcher, 総合防災研究部門, 主任研究員 (40414403)
NAKAGAWA Katsuhiro National Institute of Information and Communieation Technology, Okinawa Subtropical Environment Remote Sensing Center, Senior Researcher, 沖縄亜熱帯計測技術センター, 主任研究員 (80359009)
友杉 邦雄 京都大学, 防災研究所, 助教授 (50027265)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥12,600,000 (Direct Cost: ¥12,600,000)
Fiscal Year 2006: ¥2,600,000 (Direct Cost: ¥2,600,000)
Fiscal Year 2005: ¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 2004: ¥7,100,000 (Direct Cost: ¥7,100,000)
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| Keywords | PUB / Stochastic model / Regional Flood Frequency Analysis / Scaling / Uncertainty / goodness-of-fit / IDF curve / Downscaling / 確率降水量 / PUB(未観測域での水文予測) / 頻度解析 / スケールダウン / 地域洪水頻度解析 / ランダムカスケード / 気象・水文予測 / 水文観測 / 歴史洪水 / リモートセンシング |
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| Research Abstract |
The main objectives of this project are as follows;
(1) Development of prediction technique by which one can predict meteorological and hydrological data in ungauged or poorly-gauged basins (PUB).
(2) Development of method for reduction of various kind of uncertainty in terms of prediction.
As for (1), we tried to downscale relatively (spatially / temporally) coarse data to more fine data. 'Regional flood frequency analysis' is appropriate for the downscaling. Moreover, application of scaling is also effective. These techniques are included in so-cold 'statistical analyses'. We tried to use MM5 (meso-scale meteorological program) and found that combination of statistical analysis and meteorological program is more effective (unpublished). Moreover, we developed new method by which goodness-of-fit is evaluated when IDF curve is obtained, namely, when one evaluate which stochastic distribution is appropriate for hydrological data.
As for (2), we constructed new observation station in Kanto district. However, we have not finished analysis of these data at the stage.
Moreover, we obtained a lot of results on scaling by which one can downscale spatially coarse data. Especially, we constructed new rainfall-runoff model and obtained new result on scaling by using the model. Scaling is potential appropriate method for PUB.
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