[1] One of the key processes for the formation of deltas and their fluvial networks is the deposition of mouth bars in front of prograding distributaries. Waves influence mouth bar growth, but it is not clear how and to what extent. Toward this end, we conduct a modeling study on deltas forming in sheltered bays, where waves are locally generated and both longshore currents and surf zone are absent. We focus on the simplified case of a homopycnal river plume subject to frontal wave attack, and we begin by isolating the effects of waves on jet spreading. An analytical model for the hydrodynamic interaction between incoming waves and a turbulent expanding jet is developed and tested with the numerical model Delft3D coupled to the wave model SWAN. Both the analytical model and Delft3D predict that incoming surface gravity waves increase the spreading of the jet and the interaction between wave and current boundary layers causes an increase in bottom friction. To investigate how waves influence mouth bar morphodynamics, a set of numerical simulations is run with Delft3D-SWAN utilizing a geometry and wave characteristics typical of sheltered bays. Our numerical results show that in the presence of waves, mouth bars form up to 35% closer to the river mouth and 40% faster when compared to cases without waves. The distance from the river mouth to the stagnated mouth bar decreases with increasing wave height and wave period. The timescale of bar formation is inversely proportional to wave height and directly proportional to wave period. Our modeling study suggests that wave influence on mouth bar growth is complex. Small waves, like the ones modeled here, promote mouth bar formation via increased jet spreading and faster formation time, which in turn should create deltas with more distributary channels. On the other hand, large waves suppress mouth bar formation, as seen in other studies, leading to fewer distributary channels.
This work is inspired by the sudden resurgence of the submersed aquatic vegetation (SAV) bed in the Chesapeake Bay (USA). Because the SAV bed occurs at the mouth of the Bay's main tributary (Susquehanna River), it plays a significant role in modulating sediment and nutrient inputs from the Susquehanna to the Bay. Previous model studies on the impact of submersed aquatic vegetation on the development of river mouth bars lacked a complete mechanistic understanding. This study takes advantage of new advances in 3D computational models that include explicit physical‐sedimentological feedbacks to obtain this understanding. Specifically, we used Delft3D, a state‐of‐the‐art hydrodynamic model that provides fine‐scale computations of three‐dimensional flow velocity and bed shear stress, which can be linked to sediment deposition and erosion. Vegetation is modeled using a parameterization of hydraulic roughness that depends on vegetation height, stem density, diameter, and drag coefficient. We evaluate the hydrodynamics, bed shear stresses, and sediment dynamics for different vegetation scenarios under conditions of low and high river discharge. Model runs vary the vegetation height, density, river discharge, and suspended‐sediment concentration. Numerical results from the idealized model show that dense SAV on river mouth bars substantially diverts river discharge into adjacent channels and promotes sediment deposition at ridge margins, as well as upstream bar migration. Increasing vegetation height and density forms sandier bars closer to the river mouth and alteration of the bar shape. Thus, this study highlights the important role of SAV in shaping estuarine geomorphology, which is especially relevant for coastal management. © 2019 John Wiley & Sons, Ltd.
The effectiveness of a Metamodel-Embedded Evolution Framework for model parameter identification of a Smoothed Particles Hydrodynamic (SPH) solver, called DualSPHysics, is demonstrated when applied to the generation and propagation of progressive ocean waves. DualSPHysics is an open-source code that provides GP-GPU acceleration, allowing for highly refined simulations. The automatic optimization framework combines the global-convergence capabilities of a Multi-Objective Genetic Algorithm (MOGA) with Response Surface Method (RSM) based on a Kriging approximation. The proposed Metamodel-Embedded Evolutionary framework is used to find the set of SPH model parameters that ensures an accurate reproduction of a 2 nd order Stokes wave propagating in a numeric flume tank. In order to demonstrate the consistency of the obtained results, the optimum set of parameters found by the framework is finally used to reproduce other 2 nd and 3 rd order Stokes waves propagating over the same flume tank.
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