Integrated watershed modeling for simulation of radio-cesium migration after flood events in the catchment near the Fukushima Dai-ichi Nuclear Power Plant

統合型流域モデルを用いた福島第一原子力発電所近辺の集水域における出水後の放射性セシウムのシミュレーション

佐久間 一幸   ; 操上 広志  ; Malins, A.  ; 山田 進  ; 舟木 泰智 ; 新里 忠史  ; 町田 昌彦  ; 北村 哲浩   

Sakuma, Kazuyuki; Kurikami, Hiroshi; Malins, A.; Yamada, Susumu; Funaki, Hironori; Niizato, Tadafumi; Machida, Masahiko; Kitamura, Akihiro

福島第一原子力発電所の事故後、広範な地域において多量の放射性セシウムによって汚染されている。汚染レベルや空間線量率の将来的な予測は、福島第一原子力発電所近くに住む人々にとって非常に重要な情報である。放射性セシウムは土壌に強く吸着されていることが知られており、放射性セシウムの将来予測を行う上で、土砂の挙動を予測することが重要と考えられる。そこで、広範な陸域解析に特化したモデルGETFLOWSを用いて、川内村荻野沢流域を対象に、詳細な3次元水理地質モデルを作成した。そのモデルを用いて、強度の異なる複数の降雨に対する水、土砂の輸送量および放射性セシウム137の再分布の計算を行った。本発表では、川内村荻野沢流域を対象に構築されたモデルを用いて、降雨イベント毎の土砂と放射性セシウム137の再分布予測の結果を報告する。

The environments of Fukushima near the Fukushima Dai-ichi Nuclear Power Plant have been contaminated by the explosion accident of the plant caused by the Great East Japan Earthquake on 11 March 2011. The contamination level and air-dose rate behavior at present and in future are significant concern for the people used to live nearby. Most dominant radioactive material is Cs at present and its migration is considered to be driven by soil erosion and subsequent transport. To estimate the amount of soil sedimentation and the Cs migration, a three-dimensional hydrological model of the catchment was developed focused on the Ogi-no-sawa catchment, located 15 km southwest of the Fukushima Dai-ichi Nuclear Power Plant. Base on the developed hydrological model, top soil transport and resulting radio-cesium movement was simulated. For the modeling and simulation, physics based code the General-purpose terrestrial fluid-flow simulator GETFLOWS model, which is one of the tools for watershed modeling, was applied. The simulation results were compared with monitored data of the amount of water discharge and concentration of suspended solids for model testing. As a result of the study, the soil and Cs redistribution patterns at various scales of flood events could be predicted based on the results of modeling and simulation.