Daiya SHIOJIRI Assistant Professor


Assistant Professor

  • Chiba University Institute for Advanced Academic Research / Center for Environmental Remote Sensing

  • Keywords

    Hydrology, Data assimilation, Land surface model, Hydrological observation, Soil water, Groundwater, Runoff, Observation placement, Dimensionality reduction, Machine learning

  • Professional Memberships

    Japan Society of Civil Engineers, The Japan Society of Hydrology and Water Resources

Research Theme

Investigating global water cycle using data science and physical modeling


Water, a vital component of our daily lives, constantly circulates through various processes such as precipitation, evaporation, infiltration into the ground, and runoff into rivers. Water related disasters, such as water shortages and floods, can occasionally happen during the water cycle processes. We must have a thorough understanding of the water cycle process in order to monitor and prevent disasters. For this objective, I am developing a new system that combines a physical model with data science technology.

One challenge in conventional hydrological research that I am addressing is the scarcity of observations. This limitation arises from the global scale of the hydrological cycle, making comprehensive data collection a formidable task. While various studies have developed models for hydrological processes, the lack of observational data has prevented verification of the accuracy of the models. In this study, I ai m to overcome the issue of insufficient observations by applying data science techniques, including data assimilation.