This study presents a comprehensive analysis of contaminant transport in estuarine environments, focusing on the impact of tidal creeks and flats. The research employs advanced hydrodynamic models with irregular grid systems and conducts a detailed residual current analysis to explore how these physical features influence the movement and dispersion of contaminants. The methodology involves simulating residual currents and Lagrangian particle trajectories in both ‘Creek’ and ‘No Creek’ cases, under varying tidal conditions. The results indicate that tidal creeks significantly affect particle retention and transport, with notable differences observed in the dispersion patterns between the two scenarios. The ‘Creek’ case demonstrates enhanced material retention along the creek pathways, while the ‘No Creek’ case shows broader dispersion, potentially leading to increased sedimentation in open sea areas. The discussion highlights the implications of these findings for sediment dynamics, contaminant transport, and estuarine ecology, emphasizing the role of tidal creeks in modulating flow and material transport. The research underlines the necessity of incorporating detailed environmental features in estuarine models for accurate contaminant transport prediction and effective estuarine management. This study contributes to a deeper understanding of estuarine hydrodynamics and offers valuable insights for environmental policy and management in coastal regions.

This study addresses the critical challenge of predicting sediment behavior in a semi-enclosed estuary, where the interplay between artificial freshwater discharge and seawater significantly impacts turbidity. Such environments are characterized by complex hydrodynamic interactions that lead to cycles of sediment settling and resuspension, influenced by tidal forces. To tackle this problem, we employed machine learning, leveraging its capability to analyze and predict complex non-linear phenomena. Our approach involved extensive transect observations conducted over two years, encompassing 11 ebb tide and 9 flood tide cycles. These observations were crucial for training the machine learning model, ensuring it captured the nuanced dynamics of sediment behavior under varying hydrodynamic conditions. The necessity of this research lies in its potential to enhance our understanding of sediment dynamics in estuaries, a vital aspect for environmental management and engineering projects. The findings demonstrate a promising alignment between the machine learning model’s predictions and the theoretically assumed sediment behavior, highlighting the model’s effectiveness in deciphering and predicting turbidity patterns in these challenging environments.

Sea surface wind plays an essential role in the simulating and predicting ocean phenomena. However, it is difficult to obtain accurate data with uniform spatiotemporal scale. A high-resolution (10 km) sea surface wind hindcast around the Korean Peninsula (KP) is presented using the weather research and forecasting model focusing on wind speed. The hindcast data for 39 years (1979–2017) are obtained by performing a three-dimensional variational analysis data assimilation, using ERA-Interim as initial and boundary conditions. To evaluate the added value of the hindcasts, the ASCAT-L2 satellite-based gridded data (DASCAT) is employed and regarded as “True” during 2008–2017. Hindcast and DASCAT data are verified using buoy observations from 1997–2017. The added value of the hindcast compared to ERA-Interim is evaluated using a modified Brier skill score method and analyzed for seasonality and wind intensity. Hindcast data primarily adds value to the coastal areas of the KP, particularly over the Yellow Sea in the summer, the East Sea in the winter, and the Korean Strait in all seasons. In case of strong winds (10–25 m·s⁻¹), the hindcast performed better in the East Sea area. The estimation of extreme wind speeds is performed based on the added value and 50-year and 100-year return periods are estimated using a Weibull distribution. The results of this study can provide a reference dataset for climate perspective storm surge and wave simulation studies.

This study proposes improving the process of the vertical diffusion of temperature in numerical models to enhance the accuracy of sea surface temperature (SST) simulation. SST tends to be underestimated in the coastal and tidal flat regions, such as the Yellow Sea around Korea. In particular, SST in coastal areas is highly sensitive to wet/dry treatment, implying that the sensitivity of SST increases with the slope of coastal bathymetry. Therefore, during the calculation of vertical temperature diffusion terms, the numerical model’s surface boundary condition (SBC) was modified to limit excessive temperature differences below a certain depth in the coastal regions. Under wet or dry conditions defined by the wet/dry treatment, SBC and bottom boundary condition (BBC) adjustments are stabilized within a predefined depth limit. While horizontal diffusion also plays a role in the model, SST is significantly influenced by the balance of heat advection and shortwave radiation. To demonstrate this, Heat Limit Depth (HLD) was added as an input parameter into the vertical diffusion algorithm in the model to enhance sensitivity to the SBC. If the total water depth in the tidal flat is below the HLD and less than 1.0 m, the model is changed to estimate surface sediment temperature instead of SST. The improvement in the vertical diffusion term for SST was effective primarily in tidal flat areas. In contrast, the impact was less pronounced in coastal areas with average depths exceeding 5 m. The rationale for separating SBC and BBC in the improved air–sea interaction process is twofold: SBC adjustments are suitable for reducing heat flux effects, specifically in shallow depths or tidal flats, without significantly affecting the entire model domain, while combined SBC and BBC adjustments are more appropriate for inducing coastal SST changes across the domain.