A Comparative Analysis of In-Situ Wave Measurements and Reanalysis Models for Predicting Coastline Evolution: A Case Study of IJmuiden, The Netherlands

This study investigates the influence of wave-climate datasets derived from in situ measurements and reanalysis models on predictive modelling accuracy for coastline evolution, focusing on the IJmuiden coastal stretch in The Netherlands. By analyzing wave parameters, sediment dynamics, and nourishment interventions, the research evaluates the performance of a numerical model in simulating shoreline changes over a 40-year period. Using the LTC (Long-Term Configuration) model, scenarios incorporating artificial sand nourishment volumes of 200,000 m3/year and 250,000 m3/year were tested against conditions without nourishment. The results highlighted the critical role of significant wave height, direction, and dataset variability in sediment accretion and erosion patterns. Datasets from in situ measurements (Measured-YM6) and reanalysis sources (ERA5, AENWS-WPR, and AENWS-WPR North) demonstrate variable performance, with ERA5 proving to be the most reliable under both nourished and non-nourished scenarios. The findings emphasize the importance of integrating high-resolution wave datasets into numerical models to improve predictions, optimize nourishment strategies, and enhance coastal resilience against erosion. The study underscores the necessity of nourishment interventions to mitigate sediment loss, stabilize shorelines, and support sustainable coastal-management practices in the face of climate change.