Revisiting and enhancing methods of representative wave height selection for medium-term coastal bed level evolution applications

Accurate prediction of medium-term coastal bed level evolution via numerical modelling is essential for effective coastal management but remains computationally demanding when process-based models are forced with full wave climate datasets. Representative Morphological Wave Height (RMWH) methods offer a practical alternative by reducing wave input while preserving essential morphological principles. This study systematically revisits classical RMWH approaches and proposes three enhanced variants aimed at improving accuracy without increasing computational cost. The examined methods include: (i) the classical RMWH formulation, (ii) an energy-flux-based subdivision of directional bins, (iii) an approach accounting for interchangeable dominance of longshore and cross-shore sediment transport depending on wave incidence angle, and (iv) a variant combining RMWH with an Artificial Neural Network (ANN) to eliminate sea-states unable to initiate sediment motion. All variants were implemented using a coupled process-based numerical model to simulate annual coastal bed evolution at the port of Rethymno, Greece, and benchmarked against a brute-force simulation. Model performance was quantified using the Brier Skill Score (BSS). Results show that the proposed enhancements substantially improve predictive skill compared to the classical RMWH approach, with BSS values increasing from “Good” to “Excellent” while maintaining run-time reductions exceeding 450%. The ANN-assisted RMWH variant exhibited the best overall performance. Further validation against field bathymetric measurements from Eresos beach, Greece, and comparison with two established wave input reduction methods confirmed the robustness and efficiency of the proposed method. The findings underline that enhanced RMWH methods provide an efficient and reliable framework for medium-term coastal bed evolution modelling.