Observations of sand bar evolution on a natural beach

Gallagher, Edith L.; Elgar, Steve; Guza, R. T.

doi:10.1029/97jc02765

Cited by 371 publications

(386 citation statements)

References 44 publications

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“…Depending on the magnitude of the forcing conditions, the role of waves can quickly change from accretive small wave conditions to erosive storm wave conditions. Although simplistic, this approach holds also when describing subaqueous sandbars that tend to move slowly onshore during calm, accretive conditions and more rapidly migrate offshore during stormy, erosive conditions (Gallagher et al, 1998;Plant et al, 2006). However, such simplistic models do not provide the level of detail needed by planners to define coastal hazard zones or by engineers to estimate fill quantities needed for post-storm re-nourishment.…”

Section: Introductionmentioning

confidence: 99%

Beach response to a sequence of extreme storms

Coco¹,

Sénéchal²,

Rejas³

et al. 2014

Geomorphology

180

107

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Section: Introductionmentioning

confidence: 99%

Beach response to a sequence of extreme storms

Coco¹,

Sénéchal²,

Rejas³

et al. 2014

Geomorphology

180

107

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“…Grain properties clearly play an important role in the transport formulation [Bagnold, 1963]. However, grain properties may be constant in time [e.g., Gallagher et al, 1998], and for the purpose of simplifying this problem we will not consider these effects. In this case a generalized transport relationship is where U describes the near-bed velocities and Ha describes the time-varying incident wave field, which may be multivariate and include, for example, wave height and wave period.…”

Section: Description Of Feedbackmentioning

confidence: 99%

Role of morphologic feedback in surf zone sandbar response

Plant¹,

Freilich²,

Holman³

2001

J. Geophys. Res.

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Abstract. Several aspects of feedback mechanisms associated with surf zone sandbar response have been characterized using bathymetric surveys, sampled approximately monthly over a 16-year period at the Army Corps of Engineers' Field Research Facility (North Carolina). The measured bathymetry was alongshore averaged and modeled by the superposition of two Gaussian-shaped sandbars on an underlying planar slope. A third, half-Gaussian-shaped bar represented steepening at the shoreline. The rms error between the measured bathymetry and the profile model was 0.10 m (estimated over 322 different surveys). The model explained 99% of the profile variance that remained after first removing the linear, cross-shore trend from each observed profile. Bar response, which was extracted from the modeled profiles, was compared to a local hydrodynamic forcing variable F (F was defined as the ratio of the wave height to water depth, evaluated at bar crest locations). At low values of F (i.e., nonbreaking conditions), bars migrated onshore, and their amplitude tended to decay. At high values of F (i.e., breaking conditions), bars migrated offshore, with relatively little change in amplitude. The transition between onshore and offshore migration occurred at a value of F that was consistent with the onset of wave breaking. Bar migration was associated with a stabilizing feedback mechanism, which drove bar crests toward an equilibrium position at the wave breakpoint. However, we observed that the rate of bar response showed no reduction for any nonzero choice of F, indicating that bars never reached equilibrium. Systematic bar amplitude decay was observed under nonbreaking conditions. Bar amplitude decay could drive F farther away from breaking conditions, allowing further bar amplitude decay. This is a destabilizing feedback mechanism, potentially leading to bar destruction.

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“…The most obvious example thereof is the organization of sandy bed material in spatially quasi-periodic patterns known as alongshore sandbars, 0.5-3 m high submarine ridges of sand located approximately parallel to shore and occurring singularly or in multiples of up to four or five bars. The crossshore oriented dynamics of sandbars constitutes an important part of the morphological dynamics of the entire nearshore zone and comprises variability on timescales of a few days to years (e.g., Gallagher, Elgar, and Guza (1998), Plant, Holman, Freilich, and Birkemeier (1999), Ruessink, Wijnberg, Holman, Kuriyama, and Van Enckevort (2003)). …”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network modeling of nearshore sandbar behavior

et al. 2007

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The temporal evolution of nearshore sandbars (alongshore ridges of sand fringing coasts in water depths less than 10 m and of paramount importance for coastal safety) is commonly predicted using process-based models. These models are autoregressive and require offshore wave characteristics as input, properties that find their neural network equivalent in the NARX (Nonlinear AutoRegressive model with eXogenous input) architecture. Earlier literature results suggest that the evolution of sandbars depends nonlinearly on the wave forcing and that the sandbar position at a specific moment contains 'memory', that is, time-series of sandbar positions show dependencies spanning several days. Using observations of an outer sandbar collected daily for over seven years at the double-barred Surfers Paradise, Gold Coast, Australia several data-driven models are compared. Nonlinear and linear models as well as recurrent and nonrecurrent parameter estimation methods are applied to investigate the claims about nonlinear and long-term dependencies. We find a small performance increase for long-term predictions (>40 days) with nonlinear models, indicating that nonlinear effects expose themselves for larger prediction horizons, and no significant difference between nonrecurrent and recurrent methods meaning that the effects of dependencies spanning several days are of no importance.

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Observations of sand bar evolution on a natural beach

Cited by 371 publications

References 44 publications

Beach response to a sequence of extreme storms

Beach response to a sequence of extreme storms

Role of morphologic feedback in surf zone sandbar response

Recurrent neural network modeling of nearshore sandbar behavior

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