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発表 An integrated approach to predict sediment and flood disasters using a watershed scale rainfall-sediment-runoff model and a 2-D flood flow model

作成年度 2023 年度
論文名 An integrated approach to predict sediment and flood disasters using a watershed scale rainfall-sediment-runoff model and a 2-D flood flow model
論文名(和訳) 流域スケールの降雨土砂流出モデルと2次元洪水モデルを用いた土砂・洪水氾濫災害予測の統合的アプローチ
論文副題
発表会 International Conference on Climate Risk, Vulnerability and Resilience Building
誌名
巻・号・回
発表年月日 2023/04/19 ~ 2023/04/21
所属研究室/機関名 著者名(英名)
ICHARM原田 大輔(HARADA Daisuke)
抄録
Recently, several extreme flood hazards with active sediment transport have occurred in mountainous areas of Japan, as typically observed in the Akatani river flood disaster in 2017. In this event, within a watershed of 20 km2, landslide and debris flow occurred in approximately 39,000 m2 area (MLIT report, 2017), and large amounts of produced sediment were transported downstream, causing extreme damage in downstream residential areas. To mitigate the damage of such disasters, it is important to evaluate and predict such flood hazards for the planning of structural and non-structural countermeasures. In this study, we present our methods for evaluating such disasters using a watershed scale rainfall-sediment-runoff model and a 2-D flood flow model. The rainfall-sediment runoff model is composed of a distributed rainfall-runoff model, landslide and debris flow analysis, and sediment transport in the river channel, to evaluate the amount of water and sediment runoff from the watershed. In downstream areas, a 2-D flood flow model with sediment transport is prepared, in which the results of watershed model are used to determine the upstream boundary conditions. As a result of the model application to the Akatani river flood disaster event, we found the computational result practically reproduces the inundation area and elevation change due to sediment deposition. In addition, a part of the model has been implemented to the DIAS (Data Integration and Analysis System), which enables a real time sediment and flood disaster prediction up to 6 hours ahead, thus the results will also be introduced in the present study.
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