As computational techniques advance, the scope of digital twins (DTs) is expanding to encompass entire cities, oceans, or even the Earth. Digital twins of oceans can provide highly comprehensive insights and predictions, thus enabling better-informed decision-making regarding ocean-related activities and management. Here, a numerical model of the Seto Inland Sea (SIS), Japan, was built as a basis to establish a digital twin of the SIS. Spatially varying filtering parameters and grid resolutions were applied to facilitate the robust and accurate simulation of coastal and oceanic processes even under varying extreme conditions. The modeling results were validated using observational datasets from forty-two tidal stations, one mooring system, and thirteen water thermometers. The results represented tidal variations, with NRMSE values below 0.15 and R² values exceeding 0.87 at all tidal stations. The NRMSE and R² values for currents were approximately 0.14 and 0.76, respectively. The model reproduced the extreme storm surge event causing a sea level rise of 1.5 m near Osaka City resulting from Typhoon Jebi in 2018. The model was shown to enable analyses of complex circulations and hazards in the SIS by accurately replicating barotropic and baroclinic processes. After additional modules are added, this model will serve as a basis for constructing a digital twin of the SIS.

In tsunami studies, understanding the intricate dynamics in the swash area, characterised by the shoaling effect, remains a challenge. In this study, we employed the adaptive mesh refinement (AMR) method to model tsunami inundation and propagation in the Onagawa town physical flume experiment. Using the open-source flow solver Basilisk, we implemented the Saint-Venant (SV) equations, Serre–Green–Naghdi (SGN) equations, and a nonhydrostatic multilayer (ML) extension of the SGN equations. A hydraulic bore tsunami-like wave was used as the input boundary condition. The objective was to assess the efficiency of the AMR method with nonhydrostatic tsunami models in overcoming limitations in 2D and quasi-3D models in flume experiments, particularly with respect to improving accuracy in arrival time and run-up detection. The results indicate improved performance of the SGN and SV models in determining tsunami arrival times. The ML model demonstrated enhanced wave run-up simulations on complex built-in terrain. The refined roughness coefficient determined using the ML solver captured the arrival time well in the northern section of the Onagawa model, albeit with a 1 s delay. The AMR method offered a computationally stable solution with an 86.3% reduction in computational time compared to a constant grid. While effective, the nonhydrostatic models entail the use of a great deal of computational resources.