Subgrid FD: a model balancing computational efficiency and accuracy for salinity simulation in tidal river systems

Salinity distribution is a critical determinant in the management of water resources and the protection of ecological integrity in coastal regions, drawing particular concerns in the context of increasing saltwater intrusion. This study aims to establish a framework for the efficient and robust simulation of hydrodynamic-salinity distribution profiles in tidal river systems. The proposed modelling approach, termed Subgrid FD, is developed based on a two-dimensional subgrid methodology to enhance computational efficiency. It combines a flow resistance mechanism to ensure computational convergence when addressing wetting-drying boundaries and dispersion effects to account for the impact of uneven velocity distributions. The applicability of the Subgrid FD model is validated through its implementation in the Sanya River, China, where it exhibits robust predictive performance, achieving optimal root mean square error values of 0.04 m for hydrodynamic simulations and 1.7‰ for salinity predictions. Using the calibrated model, a comprehensive correlation analysis between hydrodynamic parameters and salinity distribution reveals that salinity demonstrates dynamic variability in response to tidal cycles and geomorphological features. The present work provides a theoretical and methodological basis for aquatic environmental management in tidal river systems; however, further refinement of subgrid-scale parameterization in low-gradient reaches is required to enhance model applicability.