Shortwave Sand Transport in the Shallow Surf Zone.

Empirical parameterizations of the shortwave sand transport that are used in practical engineering models lack the representation of certain processes to accurately predict morphodynamics in shallow water. Therefore, measurements of near-bed velocity and suspended sand concentration, collected during two field campaigns (at the Sand Engine and Ameland, the Netherlands) and one field-scale laboratory experiment (BARDEXII), were here analyzed to study the magnitude and direction of the shortwave sand flux in the shallow surf zone. Shortwave sand fluxes dominated the total sand flux during low-energetic accretive conditions, while the mean cross-shore current (undertow) dominated the total flux during high-energetic erosive conditions. Under low-energetic conditions, the onshore-directed shortwave sand flux scales with the root-mean-square orbital velocity urms and velocity asymmetry Au but not with the velocity skewness. Under more energetic conditions the shortwave flux reduces with an increase in the cross-shore mean current u¯ and can even become offshore directed. For all data combined, the contribution of the shortwave flux to the total flux scales with (-Auurms)/|u¯| , with a high contribution of the shortwave flux (∼70%) when this ratio is high (∼ 10) and low contributions (∼0%) when this ratio is low (∼1). We argue that the velocity asymmetry is a good proxy for the net effect of several transport mechanisms in the shallow surf zone, including breaking-induced turbulence. These field and laboratory measurements under irregular waves thus support the hypothesis that the inclusion of velocity asymmetry in transport formulations would improve the performance of morphodynamic models in shallow water.

Recurrent neural network modeling of nearshore sandbar behavior.

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.