Ise Bay Reanalysis (IBRA): a 12-year ecological hydrodynamic reanalysis dataset for Ise Bay, Japan

Reanalysis datasets based on coupled ocean circulation and ecosystem models are essential tools for understanding coastal environmental variability and supporting water-quality management. However, the development of such datasets for semi-enclosed coastal systems remains limited, particularly for those incorporating subsurface biogeochemical observations such as dissolved oxygen (DO). This study presents the Ise Bay Reanalysis (IBRA), a high-resolution, 12-year (2011–2022) reanalysis dataset of hydrodynamic and biogeochemical variables for Ise Bay, Japan. The dataset was generated using the Ecological hydrodynamics simulation system of the Port and Airport Research Institute Simulator (EcoPARI-Simulator), coupled with an ensemble Kalman filter data assimilation system that assimilated in situ observations of water temperature, salinity, and bottom-layer DO at 6 h intervals. The resulting dataset has an hourly temporal resolution and resolves the entire water column. IBRA has three key distinguishing features. First, unlike many previous reanalysis datasets that rely primarily on satellite observations, IBRA assimilates continuous in situ measurements throughout the water column, including high-frequency DO observations, thereby improving the representation of subsurface dynamics. Second, the 6 h assimilation cycle enables the dataset to resolve short-term variability driven by tidal processes, which are a dominant control of coastal transport and mixing. Third, spatially coherent corrections are applied across the entire bay using an ensemble Kalman filter, ensuring spatial consistency in both physical and biogeochemical fields while avoiding artificial discontinuities often associated with simpler data assimilation approaches. The quality of IBRA was evaluated using statistical metrics and detailed comparisons against in situ observations. Data assimilation improved overall agreement with observations across multiple stations and depths, particularly for bottom-layer DO concentrations. The dataset successfully reproduced seasonal variability, vertical stratification, and the formation and persistence of hypoxic conditions in the inner bay, while also capturing well-oxygenated conditions near the bay mouth. Furthermore, the spatial distributions of temperature, salinity, and DO were realistically represented, including the intrusion of offshore water masses and associated horizontal gradients. The IBRA dataset therefore provides a dynamically consistent and comprehensive representation of physical and biogeochemical variability in a semi-enclosed coastal system. It is expected to serve as a valuable resource for studies of coastal hypoxia, water-quality assessment, and environmental management. The IBRA dataset is publicly available via Zenodo at https://doi.org/10.5281/zenodo.19808667 (Matsuzaki et al., 2026).