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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-19
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-19
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Feb 2020

19 Feb 2020

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A revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Socio-hydrologic data assimilation: Analyzing human-flood interactions by model-data integration

Yohei Sawada1 and Risa Hanazaki2 Yohei Sawada and Risa Hanazaki
  • 1Institute of Engineering Innovation, School of Engineering,the University of Tokyo, Tokyo, Japan
  • 2Institute of Industrial Science, the University of Tokyo, Tokyo, Japan

Abstract. In socio-hydrology, human-water interactions are simulated by mathematical models. Although the integration of these socio-hydrologic models and observation data is necessary to improve the understanding of the human-water interactions, the methodological development of the model-data integration in socio-hydrology is in its infancy. Here we propose to apply sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In this experiment, the flood risk model's parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We tested if data assimilation can contribute to accurately reconstructing the historical human-flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Our results highlight that it is important to sequentially constrain both state variables and parameters when the input forcing is uncertain. Our proposed method can accurately estimate the model's unknown parameters even if the true model parameter temporally varies. The small amount of empirical data can significantly improve the simulation skill of the flood risk model. Therefore, sequential data assimilation is useful to reconstruct historical socio-hydrological processes by the synergistic effect of models and data.

Yohei Sawada and Risa Hanazaki

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Yohei Sawada and Risa Hanazaki

Yohei Sawada and Risa Hanazaki

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Latest update: 28 Sep 2020
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Short summary
In socio-hydrology, human-water interactions are investigated. Researchers have two major methodologies in socio-hydrology: mathematical modeling and empirical data analysis. Here we propose the new method to bring the synergic effect of models and data in socio-hydrology. We apply sequential data assimilation, which has been widely used in geoscience, to a flood risk model to analyze the human-flood interactions by model-data integration.
In socio-hydrology, human-water interactions are investigated. Researchers have two major...
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