Modeling long period swell in Southern California: Practical boundary conditions from buoy observations and global wave model predictions

Accurate, unbiased, high-resolution (in space and time) nearshore wave predictions are needed to drive models of beach erosion, coastal flooding, and alongshore transport of sediment, biota and pollutants. On highly sheltered shorelines, wave predictions are sensitive to the directions of onshore propagating waves, and nearshore model prediction error is often dominated by uncertainty in offshore boundary conditions. Offshore islands and shoals, and coastline curvature, create complex sheltering patterns over the 250km span of southern California (SC) shoreline. Here, regional wave model skill in SC was compared for different offshore boundary conditions created using offshore buoy observations and global wave model hindcasts (National Oceanographic and Atmospheric Administration Wave Watch 3, WW3). Spectral ray-tracing methods were used to transform incident offshore swell (0.04-0.09Hz) energy at high directional resolution (1-deg). Model skill is assessed for predictions (wave height, direction, and alongshore radiation stress) at 16 nearshore buoy sites between 2000 and 2009. Model skill using buoy-derived boundary conditions is higher than with WW3-derived boundary conditions. Buoy-driven nearshore model results are similar with various assumptions about the true offshore directional distribution (maximum entropy, Bayesian direct, and 2^nd derivative smoothness). Two methods combining offshore buoy observations with WW3 predictions in the offshore boundary condition did not improve nearshore skill above buoy-only methods. A case example at Oceanside harbor shows strong sensitivity of alongshore sediment transport predictions to different offshore boundary conditions. Despite this uncertainty in alongshore transport magnitude, alongshore gradients in transport (e.g. the location of model accretion and erosion zones) are determined by the local bathymetry, and are similar for all predictions.