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Hashimoto, Shoji*; Tanaka, Taku*; Komatsu, Masabumi*; Gonze, M.-A.*; Sakashita, Wataru*; Kurikami, Hiroshi; Nishina, Kazuya*; Ota, Masakazu; Ohashi, Shinta*; Calmon, P.*; et al.
Journal of Environmental Radioactivity, 238-239, p.106721_1 - 106721_10, 2021/11
Times Cited Count:11 Percentile:56.59(Environmental Sciences)This study was aimed at analysing performance of models for radiocesium migration mainly in evergreen coniferous forest in Fukushima, by inter-comparison between models of several research teams. The exercise included two scenarios of countermeasures against the contamination, namely removal of soil surface litter and forest renewal, and a specific konara oak forest scenario in addition to the evergreen forest scenario. All the models reproduced trend of time evolution of radiocesium inventories and concentrations in each of the components in forest such as leaf and organic soil layer. However, the variations between models enlarged in long-term predictions over 50 years after the fallout, meaning continuous field monitoring and model verification/validation is necessary.
Hashimoto, Shoji*; Tanaka, Taku*; Komatsu, Masabumi*; Gonze, M.-A.*; Sakashita, Wataru*; Kurikami, Hiroshi; Nishina, Kazuya*; Ota, Masakazu; Ohashi, Shinta*; Calmon, P.*; et al.
no journal, ,
We applied modelling approaches to evaluate the past and future dynamics of Cs in forests. In the model inter-comparison exercise using Fukushima data, six models with diverse model structures, processes, parameters and numerical approaches joined this exercise and the performance and uncertainties of the state-of-the-art models were explored. The inter-comparison revealed that, after appropriate calibration, the models reproduced the observed data reliably and the ranges of calculated trajectories were narrow in the early phase after the fallout. However, the envelope of the calculated model end points enlarged in long-term simulations over 50 years after the fallout. The model-inter comparison exercise emphasizes the importance of decadal data for various forest types and repetitive verification/validation processes using holistic, long-term data to improve the models and to update the forecasting capacity of the models.