International Journal of Marine Science, 2024, Vol.14, No.3, 182-192 http://www.aquapublisher.com/index.php/ijms 188 community-level impacts, and understanding the scales over which climate will change and living systems will respond (Stetson et al., 2006). By integrating these strategies, coastal communities can better adapt to the changing climate and mitigate its impacts on marine ecosystems and their associated economic and social systems. 8 Technological and Methodological Advances 8.1 Innovations in modeling techniques Recent advancements in coastal circulation modeling have led to more accurate and comprehensive predictions of coastal dynamics. Innovations such as the coupling of wave and circulation models have been shown to significantly improve forecast accuracy by addressing the non-linear interactions between strong currents and wind waves. This approach has been successfully applied in regions like the German Bight, demonstrating improved model performance during extreme events (Staneva et al., 2015). Additionally, the integration of high-resolution nested models, like those developed for the West Florida coastal ocean, has facilitated better downscaling from deep ocean models to estuarine environments. These models have shown high accuracy in simulating tidal and low-frequency variability, demonstrating their utility in both hindcasts and forecasts (Zheng and Weisberg, 2012). Recent studies have also highlighted the importance of data assimilation and real-time integration of observational data to enhance model predictions, as seen in the California Current System (Moore et al., 2011). 8.2 Advances in observation technologies The development and deployment of advanced observation technologies have been crucial in validating and improving coastal models. The evaluation of various operational ocean forecasting services during extreme events, such as Storm Gloria, has underscored the capabilities of integrated observation and modeling systems. These systems, which include high-resolution models nested within regional models, have demonstrated their effectiveness in predicting storm-induced ocean circulation and enhancing preparedness for maritime disasters (Figure 2) (Sotillo et al., 2021). Sotillo et al. (2021) demonstrated the hierarchical structure of ocean forecasting systems, ranging from global to regional and then to local coastal forecasting systems nested within each other. Through this nesting, high-resolution regional and coastal forecasts can obtain boundary conditions from large-scale global forecasts, thereby enhancing the accuracy and timeliness of the predictions. Furthermore, the use of high-frequency (HF) radar, satellite sensing, and autonomous gliders provides an integrated dataset for model validation and assimilation. For example, the NYHOPS system utilizes these technologies to validate surface current forecasts, significantly enhancing the credibility of the system's predictions (Kuang et al., 2012). 8.3 Integrative approaches Integrative approaches that combine observations, modeling, and data assimilation are essential for advancing coastal circulation studies. The synergy between models and observational data allows for the optimization of observational networks and the development of robust predictive systems. Coastal Ocean Forecasting Systems (COFS) exemplify this integration, linking observational and modeling components to provide seamless data sets and forecasts across different scales (Kourafalou et al., 2015). These integrative systems not only enhance scientific understanding but also address practical applications such as sea-level rise monitoring, coastal management, and disaster preparedness. For instance, ensemble ocean circulation modeling has been used to improve trajectory forecasting, demonstrating the value of integrating multiple observational platforms and enhancing forecast accuracy (Melsom et al., 2012). 9 Challenges and Future Directions 9.1 Technical and methodological challenges One of the primary technical challenges in evaluating coastal circulation and its response to climate change is the
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