Abstract: Shoreline predictions are essential for coastal management. In this era of increasing amounts of data from different sources, it is imperative to use observations to ensure the reliability of shoreline forecasts. Data assimilation has emerged as a powerful tool to bridge the gap between episodic and imprecise spatiotemporal observations and the incomplete mathematical equations describing the physics of coastal dynamics. This research seeks to maximize this potential by assessing the effectiveness of different data assimilation algorithms considering different observational data characteristics and initial system knowledge to guide shoreline models towards delivering results as close as possible to the real world. Two statistical algorithms (stochastic ensemble and extended Kalman filters) and one variational algorithm (4D-Var) are incorporated into an equilibrium cross-shore model and a one-line longshore model. A twin experimental procedure is conducted to determine the observation requirements for these assimilation algorithms in terms of accuracy, length of the data collection campaign and sampling frequency. Similarly, the initial system knowledge needed and the ability of the assimilation methods to track the system nonstationarity are evaluated under synthetic scenarios. The results indicate that with noisy observations, the Kalman filter variants outperform 4D-Var. However, 4D-Var is less restrictive in terms of initial system knowledge and tracks nonstationary parametrizations more accurately for cross-shore processes. The findings are demonstrated at two real beaches governed by different processes with different data sources used for calibration. In this contribution, the coastal processes assimilated thus far in shoreline modelling are extended, the 4D-Var algorithm is applied for the first time in the field of shoreline modelling, and guidelines on which assimilation method can be most beneficial in terms of the available observational data and system knowledge are provided..

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