Optimizing long-term morphodynamic predictions in response to dominant forcing: A case study on the Belgian Continental Shelf

Accurate prediction of long-term morphodynamic evolution in mixed-energy coastal systems requires models that resolve key hydrodynamic processes and reproduce the correct sequence and magnitude of forcing events. However, determining what constitutes the ‘correct’ events remains challenging because the governing processes operate across a wide range of temporal scales. In this study, we develop a hydro-morphodynamic modelling framework based on the TELEMAC suite to simulate multi-decadal morphological changes along the Belgian Part of the North Sea (BPNS). After validation against hydrodynamic and suspended particulate matter observations, a 10-year benchmark run is used to establish a target morphology against which 43 tests to optimise boundary conditions for morphological acceleration using the MORFAC approach are evaluated. The optimisation results show that predictive morphodynamic skill is linked to the co-occurrence of energetic wave events with the spring-neap tidal cycle. We subsequently introduce a new metric, the -factor, which quantifies the wave-tide interaction and can be used for MORFAC-based optimisation. Optimal performance is achieved when synthetic wave series preserve the natural alignment of high-energy waves with spring tides, consistent with the benchmark run. In addition to the wave-tide timing ( -factor), the optimization test shows that the predictive skill also depends strongly on the combined choice of value and wave schematization. When applied to hindcast the 1984-2022 BPNS evolution, the optimized model reproduces the large-scale development of the deeper sandbanks but fails to capture the coastward migration of a shallower bank. This mismatch is attributed to missing wave-induced cross-shore sediment transport processes and is subsequently resolved by including effects of wave nonlinearity and Stokes-drift on sediment transport in the model equations. A final sensitivity analysis demonstrates a significant risk of model equifinality: an inaccurate representation of wave conditions and their temporal alignment with tidal cycles can still appear to produce correct morphological behaviour when compensated by parameter tuning, thereby hiding the shortcomings in the physical forcing. This underscores the need for physically informed and deliberate modelling choices when predicting long-term morphological changes.