A behavioral template beach profile model for predicting seasonal to interannual shoreline evolution

Abstract

[1] This contribution presents a simple behavioral template model for beach profile evolution which is calibrated and tested against a 6-year time series of shoreline position, derived from a coastal imaging system at the Gold Coast, Australia. From a coastal management perspective, the position of the shoreline is arguably one of the most important morphodynamic parameters to be monitored and predicted and therefore forms the focus of this paper. The template model attempts to encapsulate some of the current understanding of coastal dynamics deduced from both scientific investigations and observations. The model framework is based on a simple relaxation model, whereby the profile approaches a template exponentially with time under conditions of constant forcing. The template evolves dynamically in response to incident wave forcing such that when a threshold value of the dimensionless fall velocity (W = H s /wT p , where H s = offshore significant wave height, T p = peak wave period, and w = sediment fall velocity) is exceeded, the beach face erodes and flattens, and the shoreline retreats landward, forming an erosional scarp and a breakpoint bar. Conversely, when the W falls below threshold, progradation and steepening of the shoreface ensues, accompanied by the evolution of a swash bar and breakpoint step. The Gold Coast data are used to calibrate the threshold value of W (%4.0) which is close to the time series mean (3.8). Testing the calibrated model on unseen data shows that it is able to reproduce the dominant seasonal/interannual shoreline succession observed at this site and up to 77% of the detrended shoreline variability.

Statistical modeling of interannual shoreline change driven by North Atlantic climate variability spanning 2000–2014 in the Bay of Biscay

“…However, four seasonal weather regimes have significant correlation with p-values ranging from 0.05 to 0.15: in winter, ZO high occurrence increases erosion rate (R=-0.56); in spring, AR high occurrence is associated with a decreased accretion rate (R=-0.60); in summer, GA and BL high occurrences lead to an increase (R=0.61) and a decrease in accretion rate (R=-0.67), respectively. It has been proven on many wavedominated coasts that shoreline change rate is proportional to the incident wave energy, and the energy disequilibrium between this energy and the equilibrium energy for which the coast is stable (Davidson and Turner 2009;Yates et al 2009). Thus, the statistically significant relationships identified here may be related to weather regime-driven modulation of incoming wave energy.…”

“…Shoreline evolution on timescales from hours (cf. storms) to years has recently been simulated with fair skill using wave-driven empirical equilibrium-based models (e.g., Davidson and Turner 2009;Yates et al 2009;Davidson et al 2013;Castelle et al 2014;Splinter et al 2014a). These models can also reproduce the interannual shoreline variability that sometimes exceeds the seasonal variability (e.g., Castelle et al 2014).…”

Climate controls on longshore sediment transport

“…Both cross-shore and longshore processes affect the temporal evolution of the shoreline. As observed on other wave-dominated coasts, a strong seasonal signal in the shoreline is present (Davidson and Turner, 2009). Net longshore transport for the region is frequently quoted as 500,000 m 3 /yr to the north (Dyson et al, 2002;Patterson, 2007;Castelle et al, 2009), referring to the seminal studies completed by Delft Hydraulics Laboratory (DHL, 1970(DHL, , 1992.…”

Shoreline Response to a Sequence of Typhoon and Monsoon Events

“…Observations of waves and shoreline changes were used to calibrate the parametrical model of equilibrium shoreline position ShoreFor [2,31] accounting for cross-shore transport processes. This model was chosen as it was applied successfully at various sites for predicting the daily to seasonal shoreline response to waves compared to other models more dedicated to long-term interannual evolution [32].…”