The roles of straining and dissipation in controlling stratification are derived analytically using a vertical salinity variance method. Stratification is produced by converting horizontal variance to vertical variance via straining, that is, differential advection of horizontal salinity gradients, and stratification is destroyed by the dissipation of vertical variance through turbulent mixing. A numerical model is applied to the Changjiang estuary in order to demonstrate the salinity variance balance and how it reveals the factors controlling stratification. The variance analysis reveals that dissipation reaches its maximum during spring tide in the Changjiang estuary, leading to the lowest stratification. Stratification increases from spring tide to neap tide because of the increasing excess of straining over dissipation. Throughout the spring–neap tidal cycle, straining is almost always larger than dissipation, indicating a net excess of production of vertical variance relative to dissipation. This excess is balanced on average by advection, which exports vertical variance out of the estuarine region into the plume. During neap tide, tidal straining shows a general tendency of destratification during the flood tide and restratification during ebb, consistent with the one-dimensional theory of tidal straining. During spring tide, however, positive straining occurs during flood because of the strong baroclinicity induced by the intensified horizontal salinity gradient. These results indicate that the salinity variance method provides a valuable approach for examining the spatial and temporal variability of stratification in estuaries and coastal environments.

A distinct sediment plume exists over the Yangtze Bank in the Yellow and East China Seas (YECS) in winter, but it disappears in summer. Based on satellite color images, there are two controversial viewpoints about the formation mechanism for the sediment plume. One viewpoint is that the sediment plume forms because of cross‐shelf sediment advection of highly turbid water along the Jiangsu coast. The other viewpoint is that the formation is caused by local bottom sediment resuspension and diffused to the surface layer through vertical turbulent mixing. The dynamic mechanism of the sediment plume formation has been unclear until now. This issue was explored by using a numerical sediment model in the present paper. Observed wave, current, and sediment data from 29 December 2016 to 16 January 2017 were collected near the Jiangsu coast and used to validate the model. The results indicated that the model can reproduce the hydrodynamic and sediment processes. Numerical experiments showed that the bottom sediment could be suspended by the bottom shear stress and diffuse to the surface layer by vertical mixing in winter; however, the upward diffusion is restricted by the strong stratification in summer. The sediment plume is generated locally due to bottom sediment resuspension primarily via tide‐induced bottom shear stress rather than by cross‐shelf sediment advection over the Yangtze Bank.

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The numerical simulation of estuarine dynamics requires accurate prediction for the transport of tracers, such as temperature and salinity. During the simulation of these processes, all the numerical models introduce two kinds of tracer mixing: 1) by parameterizing the tracer eddy diffusivity through turbulence models leading to a source of physical mixing and 2) discretization of the tracer advection term that leads to numerical mixing. Physical and numerical mixing both vary with the choice of horizontal advection schemes, grid resolution, and time step. By simulating four idealized cases, this study compares the physical and numerical mixing for three different tracer advection schemes. Idealized domains only involving physical and numerical mixing are used to verify the implementation of mixing terms by equating them to total tracer variance. Among the three horizontal advection schemes, the scheme that causes the least numerical mixing while maintaining a sharp front also results in larger physical mixing. Instantaneous spatial comparison of mixing components shows that physical mixing is dominant in regions of large vertical gradients, while numerical mixing dominates at sharp fronts that contain large horizontal tracer gradients. In the case of estuaries, numerical mixing might locally dominate over physical mixing; however, the amount of volume integrated numerical mixing through the domain compared to integrated physical mixing remains relatively small for this particular modeling system.