| Project/Area Number |
18560497
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | Kyoto University |
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Principal Investigator |
TACHIKAWA Yasuto Kyoto University, Graduate School of Engineering, Associate Professor (40227088)
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| Co-Investigator(Kenkyū-buntansha) |
TAKARA Kaoru Kyoto University, Disaster Prevention Research Institute, Professor (80144327)
SAYAMA Takahiro Kyoto University, Disaster Prevention Research Institute, Assistant Professor (70402930)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,490,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥390,000)
Fiscal Year 2007: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2006: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | distributed hydrologic model / runoff prediction / ungauged basins / predictive uncertainty / design flood / flood control planning / river planning / probable rainfall / Ungauged Basins / 降雨時系列 |
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| Research Abstract |
To obtain design flood, at first some patterns of spatiotemporal rainfall distributions with the magnitude of pre-determined return period is determined, then the rainfall distributions are converted to river discharges using a runoff model. Here, the given rainfall distributions have various patterns and the runoff model has limitations. Thus, the estimated river discharges include uncertainties which come from various sources. Therefore, it is important to evaluate the predictive uncertainty and clarify the sources of the uncertainty to determine design flood.
The predictive uncertainty in flood prediction mainly comes from 1) input uncertainty (insufficiency of time and space rainfall observation), 2) model structure uncertainty (insufficiency of the understandings of hydrologic processes), and 3) parameter uncertainty (insufficiency of model parameter identification). These uncertainties essentially stem from insufficient hydrologic observations. To solve the problem, to enhance the observation system is essential, but not practical because the target of prediction is flood which happens e.g. once a hundred years. Therefore, it is requested to develop a hydrologic prediction method for ungauged basins which fully utilize the information at gauged catchments. In this research, followings are conducted to obtain design flood at ungauged basins :
1) Development of a intensity-duration-frequency relationships based on scaling characteristics of rainfall extremes
2) Development of a rainfall time series generation method which reflects characteristics of rainfall extremes
3) Development of a method to assess the performance of rainfall-runoff models in terms of predictive uncertainty.
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