Waves and tidal currents resuspend and transport shelf sediments, influencing sediment distributions and bedform morphology with implications for various disciplines including benthic habitats, marine operations, and marine spatial planning. Shelf-scale assessments of wave-tide-dominance of sand transport tend not to fully include wave-tide interactions, which nonlinearly enhance bed shear stress and apparent roughness, change the current profile, modulate wave forcing, and can dominate net sand transport. Assessment of the contribution of wave-tide interactions to net sand transport requires computationally/labor intensive coupled numerical modeling, making comparison between regions or climate conditions challenging. Using the Northwest European Shelf, we show the dominant forcing mode and potential magnitude of net sand transport is predictable from readily available, uncoupled wave, tide, and morphological data in a computationally efficient manner using a k-Nearest Neighbor algorithm. Shelf areas exhibit different dominant forcing modes for similar wave exceedance conditions, related to differences in depth, grain size, tide range, and wave exposure. Wave-tide interactions dominate across most areas in energetic combined conditions. Meso-macrotidal areas exhibit tide-dominance while shallow, fine-grained, microtidal regions show wave-dominance over a statistically representative year, with wave-tide interactions dominating extensively >30 m depth. Sediment transport mode strongly affects seabed morphology. Sand wave geometry varies significantly between predicted dominance classes with increased wave length and asymmetry, and decreased height, for increasing wave-dominance. This approach efficiently indicates where simple noninteractive wave and tide processes may be sufficient for modeling sediment transport, and enables efficient interregional comparisons and sensitivity testing to changing climate conditions with applications globally.
Plain Language SummaryThe transport of sand across the marine environment is important to understand, as it influences the fate of sediments, pollutants, and can affect seabed habitats. In marine settings, sand transport results primarily from the forces exerted by the tide and waves, and these forces interact in a nonlinear way. Numerical models can be used to calculate sand transport rates, however to understand what processes are driving sand transport under different conditions and across large areas requires complex modeling which takes time and resources. Here, we show we can predict the magnitude and dominant forcing using a machine learning algorithm trained with readily available data for the Northwest European Shelf. Different forces drive net sand transport depending on water depth, sand grain size, tide range, and wave exposure. Areas with the largest tides are dominated by tidal forces over a year, while shallow areas with fine sand which are exposed to energetic waves are dominated by wave forces. We show that sand waves on the seabed increase in length, becom...