A novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model free parameters can vary in time, adjusting to potential non-stationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (Dual State-Parameter Ensemble Kalman Filter) within the established shoreline evolution model, ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real-world shoreline dataset, revealing that time-varying model free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modelling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate.

The steepness of the beach face is a fundamental parameter for coastal morphodynamic research. Despite its importance, it remains extremely difficult to obtain reliable estimates of the beach-face slope over large spatial scales (1000’s of km of coastline). In this letter, a novel approach to estimate this slope from time-series of satellite-derived shoreline positions is presented. This new technique uses a frequency-domain analysis to find the optimum slope that minimises high-frequency tidal fluctuations relative to lower-frequency erosion/accretion signals. A detailed assessment of this new approach at 8 locations spanning a range of tidal regimes, wave climates and sediment grain sizes shows strong agreement (R = 0.9) with field measurements. The automated technique is then applied across 1000’s of beaches in eastern Australia and California USA, revealing similar regional-scale distributions along these two contrasting coastlines and highlights the potential for new global-scale insight to beach-face slope spatial distribution, variability and trends.

Accurately mapping the evolving bathymetry under energetic wave breaking is challenging, yet critical for improving our understanding of sandy beach morphodynamics. Though remote sensing is one of the most promising opportunities for reaching this goal, existing depth-inversion algorithms using linear approaches face major theoretical and/or technical issues in the surf zone, limiting their accuracy over this region. Here, we present a new depth-inversion approach relying on Boussinesq theory for quantifying nonlinear dispersion effects in nearshore waves. Using high-resolution datasets collected in the laboratory under diverse wave conditions and beach morphologies, we demonstrate that this approach results in enhanced levels of accuracy in the surf zone (errors typically within 10%) and presents a major improvement over linear methods. The new nonlinear depth-inversion approach provides significant prospects for future practical applications in the field using existing remote sensing technologies, including continuous lidar scanners and stereo imaging systems.