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Meteorological information used for disaster prevention has developed rapidly in terms of both type and specificity. The latest forecasting models can predict weather with very high resolutions that can characterize disaster risk at the local level. However, this development can lead to an overdependency on the information and a wait-and-see attitude by the public. At the same time, residents share and use various types of information for disaster response, such as local conditions, in addition to official disaster information. Our research in Japan verified the practicality and efficiency of synergistically integrating these types of information by examining actual evacuation cases. The current numerical forecasting models sufficiently identify locality from the viewpoint of various administrative scales such as prefectures, municipalities, and school districts, but the improvements to these models have failed to improve residents' judgment in successful evacuation cases. We therefore analyzed the relationship between meteorological information and residents' disaster response and confirmed that they were strongly correlated and were contributing factors in preventing disasters. We revealed differences between a community's disaster prevention culture and the disaster information provided. This led us to propose a new concept in community disaster prevention that we call the "disaster response switch," which can serve as a data-driven risk management tool for communities when used in combination with advanced meteorological disaster information. [ABSTRACT FROM AUTHOR]

The effect of river runoff on the salinity distribution in the coastal regions of Japan is investigated with a nested-grid model system. High-resolution river runoff data calculated by a hydrological cycle model are used in this study. Freshwater fluxes from rivers result in extremely low salinity waters along the coast of Japan. Fixed line observations conducted by prefectural fisheries research institutes show that such low salinity waters also exist in the real ocean. The use of the river runoff data noted above leads to reduction in root mean square errors (RMSEs) of sea surface salinity between observational data and the model result. The model experiments also show that freshwater inputs from large rivers can affect the salinity distribution far away from their mouths, which suggests that we should care about such rivers far away from the area of interest as well as local rivers. The experiment with runoff from non-Class-A rivers shows the smaller RMSE than the experiment without river runoff. Such small rivers also contribute a lot to improving reproducibility of the salinity distribution in the coastal areas. These results clearly show that precise data for river discharge are essential for realistic coastal modeling. [ABSTRACT FROM AUTHOR]