Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, Shiliang Shan, Enda Murphy
{"title":"Insight on the Coastal Response to Combined Tides, Storm Surges, and Surface Waves in a Macrotidal Bay From Real-Time Predictions","authors":"Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, Shiliang Shan, Enda Murphy","doi":"10.1029/2024JC022076","DOIUrl":null,"url":null,"abstract":"<p>Hazardous sea surface conditions can develop during storm events, when wind-generated waves and storm surges coincide with high astronomical tides. For better understanding of these conditions, a novel and computationally efficient real-time forecast model (COASTLINES-BoF) was developed for a macrotidal bay that is exposed to wind waves, the Bay of Fundy in Atlantic Canada. This forecasting system simulates the combined effects of tides, storm surge, and waves. Spatiotemporally varying meteorological forecasts drive the model, with water levels and ocean waves applied at the open boundary in the Atlantic Ocean, implemented with input from large-scale ocean forecast models. Analysis of the real-time performance indicates that the model accurately predicts total water levels compared to observations. Modeled significant wave heights agree with buoy observations and altimeter data. During Post-tropical Hurricane Fiona in 2022, a peak water level residual (combined storm surge and water level change driven by wave-current interactions) of over 0.9 m was forecast in the upper Bay of Fundy. Sensitivity analysis indicates that 0.6 m of the water level increase resulted from wind and pressure effects, and an additional 0.3 m water level contribution is a result of wave-current interaction. The high accuracy, use of open data to drive and validate the model, and relatively low computational demand make this approach a useful way to gain insight into the coastal response to a wide range of conditions. This method can be applied to predict marine environmental conditions in other coastal regions.</p>","PeriodicalId":54340,"journal":{"name":"Journal of Geophysical Research-Oceans","volume":"130 9","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024JC022076","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research-Oceans","FirstCategoryId":"89","ListUrlMain":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024JC022076","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
Hazardous sea surface conditions can develop during storm events, when wind-generated waves and storm surges coincide with high astronomical tides. For better understanding of these conditions, a novel and computationally efficient real-time forecast model (COASTLINES-BoF) was developed for a macrotidal bay that is exposed to wind waves, the Bay of Fundy in Atlantic Canada. This forecasting system simulates the combined effects of tides, storm surge, and waves. Spatiotemporally varying meteorological forecasts drive the model, with water levels and ocean waves applied at the open boundary in the Atlantic Ocean, implemented with input from large-scale ocean forecast models. Analysis of the real-time performance indicates that the model accurately predicts total water levels compared to observations. Modeled significant wave heights agree with buoy observations and altimeter data. During Post-tropical Hurricane Fiona in 2022, a peak water level residual (combined storm surge and water level change driven by wave-current interactions) of over 0.9 m was forecast in the upper Bay of Fundy. Sensitivity analysis indicates that 0.6 m of the water level increase resulted from wind and pressure effects, and an additional 0.3 m water level contribution is a result of wave-current interaction. The high accuracy, use of open data to drive and validate the model, and relatively low computational demand make this approach a useful way to gain insight into the coastal response to a wide range of conditions. This method can be applied to predict marine environmental conditions in other coastal regions.