Salt marshes are valuable ecosystems that provide important ecosystem services. Given the global scale of marsh loss due to climate change and coastal squeeze, there is a pressing need to identify the critical extrinsic (wind exposure and foreshore morphology) and intrinsic factors (soil and vegetation properties) affecting the erosion of salt marsh edges. In this study, we quantified rates of cliff lateral retreat (i.e., the eroding edge of a salt marsh plateau) using a time series of aerial photographs taken over four salt marsh sites in the Westerschelde estuary, the Netherlands. In addition, we experimentally quantified the erodibility of sediment cores collected from the marsh edge of these four marshes using wave tanks. Our results revealed the following: (i) at the large scale, wind exposure and the presence of pioneer vegetation in front of the cliff were the key factors governing cliff retreat rates; (ii) at the intermediate scale, foreshore morphology was partially related to cliff retreat; (iii) at the local scale, the erodibility of the sediment itself at the marsh edge played a large role in determining the cliff retreat rate; and (iv) at the mesocosm scale, cliff erodibility was determined by soil properties and belowground root biomass. Thus, both extrinsic and intrinsic factors determined the fate of the salt marsh but at different scales. Our study highlights the importance of understanding the scale dependence of the factors driving the evolution of salt marsh landscapes.

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.

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.

The relationship between the salinity mixing, the diffusive salt transport, and the diahaline exchange flow is examined using salinity coordinates. The diahaline inflow and outflow volume transports are defined in this study as the integral of positive and negative values of the diahaline velocity. A numerical model of the Pearl River Estuary (PRE) shows that this diahaline exchange flow is analogous to the classical concept of estuarine exchange flow with inflow in the bottom layers and outflow at the surface. The inflow and outflow magnitudes increase with salinity, while the net transport equals the freshwater discharge Q_r after sufficiently long temporal averaging. In summer, intensified salinity mixing mainly occurs in the surface layers and around the islands. The patchy distribution of intensified diahaline velocity suggests that the water exchange through an isohaline surface can be highly variable in space. In winter, the zones of intensification of salinity mixing occur mainly in deep channels. Apart from the impact of freshwater transport from rivers, the transient mixing is also controlled by an unsteadiness term due to estuarine storage of salt and water volume. In the PRE, the salinity mixing and exchange flow show substantial spring–neap variation, while the universal law of estuarine mixing m = 2SQ_r (with m being the sum of physical and numerical mixing per salinity class S) holds over longer averaging period (spring–neap cycle). The correlation between the patterns of surface mixing, the vorticity, and the salinity gradients indicates a substantial influence of islands on estuarine mixing in the PRE.