2022 Fiscal Year Final Research Report
Understanding of flood conditions in mountain rivers during heavy rainfall for the improvement of prediction accuracy.
| Project/Area Number |
18K05741
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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 40010:Forest science-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Asano Yuko 東京大学, 大学院農学生命科学研究科(農学部), 講師 (80376566)
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| Co-Investigator(Kenkyū-buntansha) |
内田 太郎 筑波大学, 生命環境系, 教授 (60370780)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Keywords | 極端洪水 / 基岩中の水の流れ / 基岩の透水性 / トレーサ / 斜面 / 河道 / 流域の規模 / 付加体堆積岩 |
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| Outline of Final Research Achievements |
Extreme floods during heavy rainfall events are difficult to observe and their phenomena have not been fully elucidated, so runoff prediction has been based on limited information obtained from observations during small- to medium-scale runoff events. However, observations of large runoff events in this study revealed that the peak delay in runoff occurs mainly in the river channel, not on the slope, and that the subsurface flow on the slope may occur both in the surface soil and in the base rock depending on the permeability characteristics of the bedrock. Under very wet conditions, flow processes both in soil and bedrock are quite rapid. Under climate change condition, it is necessary to make predictions for heavy rainfall that has never been experienced before, and incorporating the actual conditions obtained in this study into prediction models is expected to improve the prediction accuracy of runoff during heavy rainfall.
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| Free Research Field |
森林水文学、砂防学
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| Academic Significance and Societal Importance of the Research Achievements |
豪雨時の極端洪水は、観測が困難で現象解明が進んでいないため、小中規模出水時の観測に基づく限られた情報を基に流出予測が行われてきた。流出遅れは主に斜面における表土中の水移動で生じていると考えられてきたが、本研究では大規模出水時の観測から、流出ピーク遅れは斜面ではなく主に河道で生じていること、斜面の地中の流れは基岩の透水特性により、表土中と基岩中の両方で生じている場合があり、湿潤条件下ではいずれもかなり早いことが明らかとなった。気候変動下においては、これまで経験したことのない豪雨に対する予測を行う必要があり、本研究で得た実態を予測モデルに組み込むことにより、豪雨時流出の予測精度向上が期待できる。
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