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.

中文翻译：

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使用哪种数据同化方法以及何时使用：释放海岸线建模中观测的潜力

海岸线预测对于海岸管理至关重要。在这个来自不同来源的数据量不断增加的时代，必须利用观测来确保海岸线预测的可靠性。数据同化已成为一种强大的工具，可以弥合情景和不精确的时空观测与描述海岸动力学物理学的不完整数学方程之间的差距。本研究旨在通过评估不同数据同化算法的有效性（考虑不同的观测数据特征和初始系统知识）来最大限度地发挥这一潜力，以指导海岸线模型提供尽可能接近现实世界的结果。两种统计算法（随机系综和扩展卡尔曼滤波器）和一种变分算法（4D-Var）被纳入平衡跨岸模型和单线沿岸模型中。进行孪生实验程序以确定这些同化算法在准确性、数据收集活动的长度和采样频率方面的观测要求。类似地，在合成场景下评估所需的初始系统知识和同化方法跟踪系统非平稳性的能力。结果表明，对于噪声观测，卡尔曼滤波器变体的性能优于 4D-Var。然而，4D-Var 在初始系统知识方面的限制较少，并且可以更准确地跟踪跨境过程的非平稳参数化。研究结果在两个真实的海滩上得到了证明，这两个海滩由不同的流程控制，并使用不同的数据源进行校准。在这篇文章中，扩展了迄今为止在海岸线建模中同化的沿海过程，首次在海岸线建模领域应用了 4D-Var 算法，并就可用观测数据而言同化方法最有利的指南提供数据和系统知识。