On the morphological development of embayed beaches

Daly, Christopher; Winter, Christian; Bryan, Karin R.

doi:10.1016/j.geomorph.2015.07.040

Cited by 18 publications

(15 citation statements)

References 38 publications

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“…Empirical models for bay morphology assume that the bay has achieved equilibrium, yet little is known about how long it takes for bays to attain equilibrium and what controls this response time. Daly et al [] found that the progression to equilibrium from an initially straight coast between two headlands could be well described by an exponential function, with a rapid initial response increasing bay size, and the rate of change in bay size declining through time. However, for large bays (600 m headland separation) they were not able to run simulations long enough for equilibrium conditions to emerge.…”

Section: Introductionmentioning

confidence: 99%

Exploring the sensitivities of crenulate bay shorelines to wave climates using a new vector-based one-line model

Hurst

Barkwith

Ellis

et al. 2015

J. Geophys. Res. Earth Surf.

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We use a new exploratory model that simulates the evolution of sandy coastlines over decadal to centennial timescales to examine the behavior of crenulate-shaped bays forced by differing directional wave climates. The model represents the coastline as a vector in a Cartesian reference frame, and the shoreface evolves relative to its local orientation, allowing simulation of coasts with high planform-curvature. Shoreline change is driven by gradients in alongshore transport following newly developed algorithms that facilitate dealing with high planform-curvature coastlines. We simulated the evolution of bays from a straight coast between two fixed headlands with no external sediment inputs to an equilibrium condition (zero net alongshore sediment flux) under an ensemble of directional wave climate conditions. We find that planform bay relief increases with obliquity of the mean wave direction, and decreases with the spread of wave directions. Varying bay size over 2 orders of magnitude (0.1-16 km), the model predicts bay shape to be independent of bay size. The time taken for modeled bays to attain equilibrium was found to scale with the square of the distance between headlands, so that, all else being equal, small bays are likely to respond to and recover from perturbations more rapidly (over just a few years) compared to large bays (hundreds of years). Empirical expressions predicting bay shape may be misleading if used to predict their behavior over planning timescales.

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Section: Introductionmentioning

confidence: 99%

Exploring the sensitivities of crenulate bay shorelines to wave climates using a new vector-based one-line model

Hurst

Barkwith

Ellis

et al. 2015

J. Geophys. Res. Earth Surf.

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“…The talus width, in plan view, is also a user-specified value. The beach profile is, in the present version of the framework, assumed to be a Dean profile (Dean, 1991):…”

Section: Cliff Erosionmentioning

confidence: 99%

“…The beach equilibrium profile currently assumed in CoastalME is the Dean profile (Eq. 8; Dean, 1991). By allowing the shore platform to adopt any slope, we do not need to use the analytical expressions used in COVE to calculate the shoreline changes as a function of volume changes, but instead use an iterative numerical scheme.…”

Section: Alongshore Sediment Transportmentioning

confidence: 99%

“…However, quantitative prediction of morphological coastal changes at mesoscales (decades to centuries and tens to hundreds of kilometres) is scientifically challenging. Physics-based, reductionist models that represent small-scale processes have proven to be of limited use in this task, both because of the accumulation of small errors over long timescales (de Vriend et al, 1993) because of the omission of processes that govern long-term change (Murray, 2007;Werner, 2003) and computational limitations (Daly et al, 2015). Faced with this impasse, coastal geomorphologists have begun to adopt simpler behaviourally based approaches or large-scale coastal behavioural (LSCB) models (Terwindt and Battjes, 1990).…”

Section: Introductionmentioning

confidence: 99%

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Coastal Modelling Environment version 1.0: a framework for integrating landform-specific component models in order to simulate decadal to centennial morphological changes on complex coasts

et al. 2017

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Abstract. The ability to model morphological changes on complex, multi-landform coasts over decadal to centennial timescales is essential for sustainable coastal management worldwide. One approach involves coupling of landformspecific simulation models (e.g. cliffs, beaches, dunes and estuaries) that have been independently developed. An alternative, novel approach explored in this paper is to capture the essential characteristics of the landform-specific models using a common spatial representation within an appropriate software framework. This avoid the problems that result from the model-coupling approach due to between-model differences in the conceptualizations of geometries, volumes and locations of sediment. In the proposed framework, the Coastal Modelling Environment (CoastalME), change in coastal morphology is represented by means of dynamically linked raster and geometrical objects. A grid of raster cells provides the data structure for representing quasi-3-D spatial heterogeneity and sediment conservation. Other geometrical objects (lines, areas and volumes) that are consistent with, and derived from, the raster structure represent a library of coastal elements (e.g. shoreline, beach profiles and estuary volumes) as required by different landform-specific models. As a proof-of-concept, we illustrate the capabilities of an initial version of CoastalME by integrating a cliffbeach model and two wave propagation approaches. We verify that CoastalME can reproduce behaviours of the component landform-specific models. Additionally, the integration of these component models within the CoastalME framework reveals behaviours that emerge from the interaction of landforms, which have not previously been captured, such as the influence of the regional bathymetry on the local alongshore sediment-transport gradient and the effect on coastal change on an undefended coastal segment and on sediment bypassing of coastal structures.

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“…Beyond small-scale morphological patterns of sandy coastlines (e.g., beach cusps and rip channels), evolving over daily to weekly timescales [Almar et al, 2008;Gallop et al, 2011;Castelle and Coco, 2012;Stokes et al, 2015], the morphology of embayed beaches also vary at the scale of the embayment (O(1 km)). Related patterns develop over monthly to interannual timescales [Ojeda et al, 2011;Daly et al, 2015] and dominate beach behavior [Clarke and Eliot, 1982;Short and Trembanis, 2004;Ojeda et al, 2011;Loureiro et al, 2012;Turki et al, 2013a]. They typically account for the cross-shore migration and the rotation of the beach shoreline and nearshore sandbars [Ojeda and Guillén, 2008;Ojeda et al, 2011;van de Lageweg et al, 2013], as well as variations of their curvature [Loureiro et al, 2012;Ratliff and Murray, 2014].…”

Section: Introductionmentioning

confidence: 99%

Shore and bar cross‐shore migration, rotation, and breathing processes at an embayed beach

2017

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A principal component analysis (PCA) is used to decompose data on the coupled morphodynamics of the shoreline and nearshore sandbar of a typical single‐barred embayed beach (Tairua Beach, New Zealand). Dynamic patterns are classified into simultaneous modes, where the bar and shoreline move at the same time, and nonsimultaneous modes, where the shore moves independently from the bar, and vice versa. Two simultaneous modes accounting for 65% of the variance of the shoreline and barline dominate the system. One mode describes inverse shoreline and sandbar cross‐shore migrations (alongshore averaged), occurring with simultaneous rotations in the same direction. The other mode accounts for migration in the same direction accompanied by variations of the barline curvature (similar to “breathing modes” previously described in embayed beach shoreline modeling studies). Two nonsimultaneous modes of lesser importance account separately for independent shoreline and barline rotations (10 to 15% of the variance explained). A PCA applied to the shore and sandbar behaviors modeled by four standard equilibrium models simulating shore and sandbar cross‐shore migrations and rotations show that these are interrelated because of a correlation between wave energy and direction. Shore and bar rotations are coupled partially because the shape of the bay induces a correlation of their respective drivers, the wave angle of incidence and the alongshore gradient of wave energy. However, this correlation depends on the wave energy. This, in combination with different shore and sandbar response times (quantified using the models), also explains the independent rotations reflected by the nonsimultaneous modes.

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On the morphological development of embayed beaches

Cited by 18 publications

References 38 publications

Exploring the sensitivities of crenulate bay shorelines to wave climates using a new vector-based one-line model

Exploring the sensitivities of crenulate bay shorelines to wave climates using a new vector-based one-line model

Coastal Modelling Environment version 1.0: a framework for integrating landform-specific component models in order to simulate decadal to centennial morphological changes on complex coasts

Shore and bar cross‐shore migration, rotation, and breathing processes at an embayed beach

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