Forecasting human-water interactions: data assimilation approach

To mitigate the negative impacts of water conflicts and associated social phenomena, monitoring and forecasting human-water interactions are crucially important. Although the integration of process-based models and big data is necessary to monitor and predict the unprecedented social phenomena, 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 weather forecasting, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment and a real-data experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In these experiments, the flood risk models parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We show that data assimilation can contribute to accurately estimating the states of human-flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Therefore, sequential data assimilation is useful to monitor and predict socio-hydrological processes by the synergistic effect of models and data. In addition, we will present our recent research activities called socio-meteorology. To predict the responses of society to water hazards, understanding the interaction between society and natural scientific prediction of water hazards (i.e. weather and climate forecasting) is crucially important. We develop the stylized model to simulate the interactions of flood, social collective memory, social collective trust in weather forecasting, and preparedness actions to understand how weather forecasting alters human-water interactions.