Understanding and predicting shoreline variability at various temporal and spatial scales is vital for effective, data-driven coastal management. Shoreline position, a reliable indicator of beach morphological changes, has been assessed using complex numerical models. Recently, equilibrium-based shoreline evolution models (EBSEMs) have gained traction for their efficiency in simulating shoreline orientation, including cross-shore and rotational (longshore) changes. However, existing EBSEMs for shoreline rotation have been applied predominantly to microtidal beaches, with limited validation across diverse coastal environments. This study evaluates the performance and scalability of the EBSEM proposed by Jaramillo et al. (2021) in modelling shoreline rotational variability at seven embayed beaches: Narrabeen Beach (Australia), Tairua Beach (New Zealand), Blackpool Beach (United Kingdom), Poniente Beach, Llevant Beach, Cala Millor Beach, and Moncofa Beach (Spain). These sites represent diverse environmental conditions in terms of sediment size, tidal regimes, monitoring frequency, and data types. The model was tested across full monitoring periods, elevation contours, and temporal resolutions. Results show that EBSEM performs well across contrasting beach types, effectively capturing short-term and seasonal shoreline rotation patterns. However, reduced accuracy was observed in environments with high-energy events or human interventions, such as Poniente, Llevant, and Cala Millor beaches. Sensitivity analyses highlight the importance of temporal resolution and intertidal elevation in model performance. While the EBSEM shows significant potential for broader application, further refinement is needed to better capture storm-driven and anthropogenic variability. These improvements would enhance its utility for coastal adaptation planning, hazard mitigation, and long-term shoreline management in the face of climate change.

Interest in understanding long-term coastal morphodynamics has recently increased as climate change impacts become perceptible and accelerated. Multiscale, behavior-oriented and process-based models, or hybrids of the two, are typically applied with deterministic approaches which require considerable computational effort. In order to reduce the computational cost of modeling large spatial and temporal scales, input reduction and morphological acceleration techniques have been developed. Here we introduce a general framework for reducing dimensionality of wave-driver inputs to morphodynamic models. The proposed framework seeks to account for dependencies with global atmospheric circulation fields and deals simultaneously with seasonality, interannual variability, long-term trends, and autocorrelation of wave height, wave period, and wave direction. The model is also able to reproduce future wave climate time series accounting for possible changes in the global climate system. An application of long-term shoreline evolution is presented by comparing the performance of the real and the simulated wave climate using a one-line model.