2015
DOI: 10.1016/j.coastaleng.2015.03.005
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Land–sea interaction and morphogenesis of coastal foredunes — A modeling case study from the southern Baltic Sea coast

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Cited by 32 publications
(26 citation statements)
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“…To better assess its potential impact on carbon budget, a numerical experiment was carried out. In this experiment, we applied a sand dune model (Zhang et al, ) to a small domain (1 × 1 km) with a grid resolution of 0.5 × 0.5 m. The model domain is assumed to be initially covered by an erodible layer (1 m thick) of sands with uniform grain size (250 μm) to mimic the sandy seafloor environment in the southern North Sea (Figure b). A constant, unidirectional current of 25 cm/s (bottom shear stress = 0.16 N/m 2 ) was specified as driving force.…”
Section: Discussionmentioning
confidence: 99%
“…To better assess its potential impact on carbon budget, a numerical experiment was carried out. In this experiment, we applied a sand dune model (Zhang et al, ) to a small domain (1 × 1 km) with a grid resolution of 0.5 × 0.5 m. The model domain is assumed to be initially covered by an erodible layer (1 m thick) of sands with uniform grain size (250 μm) to mimic the sandy seafloor environment in the southern North Sea (Figure b). A constant, unidirectional current of 25 cm/s (bottom shear stress = 0.16 N/m 2 ) was specified as driving force.…”
Section: Discussionmentioning
confidence: 99%
“…It couples some widely used models in a parallel computational structure to solve different processes. Through a module coupling interface, the model allows an integration of external functional modules to take into account‐specific processes of interest [ Zhang et al ., ]. Major functional components of the model include: A three‐dimensional circulation‐wave coupled module based on the Princeton Ocean Model [ Blumberg and Mellor , ; Mellor et al ., ] adopting a fourth‐order vertical pressure gradient scheme from McCalpin []; A bottom boundary layer (BBL) module based on the Styles‐Glenn model [ Styles and Glenn , ] taking into account the impact of stratification induced by suspended particulate matter (SPM)—on the vertical structure of current velocity in the constant stress layer; A subaqueous sediment transport module [ Zhang et al ., ] modified from ECOMSED [ HydroQual, Inc ., ] for a process‐based formulation of erosion, suspended load/bed load transport, and deposition of cohesive (one grain‐size class) and noncohesive (multiple grain‐size classes) sediment; and A bathymetry update module based on the technique of morphological update acceleration and approaches for maintaining the computational stability [ Zhang et al ., ]. …”
Section: Data Sets and Methodsmentioning
confidence: 99%
“…The modeling approach reviewed here considers the effect of increasing moisture content on lengthening the critical fetch (F c ) necessary to achieve saturated transport, however, it is also possible to model this effect as a limitation on sand supply from the surface directly (cf., de Vries et al, 2014). Other studies have attempted to scale up sediment supply to coastal dunes and model their evolution by simply calculating subaerial barrier volumes and comparing these to foredune volumetric change measurements (e.g., Miot da Silva and Hesp, 2010), or by using computational approaches that solve simplified aerodynamics and sand transport relations (e.g., Luna et al, 2011;Duran and Moore, 2014) or cellular automata approaches (e.g., Baas, 2002;Baas and Neild, 2007;Zhang et al, 2015;Keijsers et al, 2016). The utility of these modelling efforts is limited, however, by fundamental…”
Section: Advances In Modelling the Effect Of Supply-limited Conditionmentioning
confidence: 99%
“…While incorporation of complexities such as moisture and vegetation may improve some simulations (e.g., Luna et al, 2011, Zhang et al, 2015Keijsers et al, 2016), realistic parameterization of vegetation growth (e.g., seasonal phenology, gradual succession) and related roughness effects and sand trapping efficiency are generally lacking. In addition, there are limited field measurements to inform and validate such models, which increases the risk of using expedient oversimplifications (Barchyn et al, 2014).…”
Section: Advances In Modelling the Effect Of Supply-limited Conditionmentioning
confidence: 99%
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