Climate change, manifested through sea level rise, increased temperatures, and altered storm tracks, poses significant challenges to coastal regions, leading to heightened risks of flooding and intensified impacts of winter storms. The Chignecto Isthmus, identified as one of Canada’s most vulnerable locations, is a low-lying land formation connecting the provinces of New Brunswick and Nova Scotia, and separating the Bay of Fundy from the Northumberland Strait. The Isthmus hosts an extensive network of coastal dykes, many of which date back to the 17th century, providing crucial protection to the low-lying communities and infrastructure from flood hazards driven by tides and storm surges in the Bay of Fundy. However, the impacts of climate change threaten the integrity of these existing dykes, posing risks to communities and infrastructure situated landward of the dykes. A stark example occurred in September 2022 when post-tropical cyclone Fiona struck Atlantic Canada. This extreme weather event, recorded as the costliest in the region, emphasized the vulnerability of these coastal areas. Notably, the peak storm surge of Fiona occurred approximately mid-way between neap and spring. Considering the substantial tidal ranges in the Bay of Fundy, the potential devastation from a post-tropical storm on the Chignecto Isthmus is likely underestimated. This research endeavors to comprehensively evaluate the hydrodynamic impacts on the region, aiming to inform and fortify adaptation strategies for the coastal dyke infrastructure, addressing the evolving challenges posed by climate change. The research is based on the calibrated and validated TELEMAC 2D tidal hydrodynamics model of Cumberland Basin and adjacent coastal floodplains, developed by the Ocean, Coastal and River Engineering Research Centre of the National Research Council Canada. The research focuses on assessing the contributions of tide and storm surge to overall water levels at Chignecto Isthmus, particularly in the aftermath of post-tropical storm Fiona. The research will be expanded to additional hypothetical extreme scenarios to enhance our understanding of the potential impacts and vulnerabilities associated with extreme weather events in the region.

Field surveys following major coastal disasters, such as the Chile tsunami in 2010 or the Tohoku tsunami in Japan in 2011, have pointed out the lack of resilience of coastal communities to such events and the need to better understand associated risks. While the primary cause of destruction during tsunamis remains related with the hydraulic loads (hydrostatic, hydrodynamics, wave impact, etc.), it has been demonstrated that debris loading is also a major cause of damage on structures, mainly via debris impact and damming. In the past decade, multiple reports have studied these issues in extreme events, however, those studies mainly focused on positively buoyant debris, like wood logs or empty containers, leaving a gap in knowledge. Indeed, Stolle et al., in a field survey following the Indonesian tsunami in 2018, identified the study of neutrally and negatively buoyant debris as one of five major needs in debris loading research, with even ASCE7-16 Chapter 6 containing limited recommendation on the load associated with those type of debris. To address this gap, small scale experiments were conducted with negatively buoyant debris at the Institut National de la Recherche Scientifique(INRS), in Québec, Canada.

Numerical models solving the wave action balance equation have been widely used to simulate wind waves in estuaries. In-situ measurements, albeit sparse, are crucial to the calibration and validation of numerical wave models. High spatial resolution to resolve complex bathymetric and geometric features of estuaries and coupling with estuarine circulation models to consider the effects of ambient currents and water levels changes are necessary. This results in a high computational cost. In recent years, the utilization of machine learning (ML) in coastal and ocean engineering has gained significant attention. This interest is primarily driven by the accessibility of ML algorithms and the accumulation of comprehensive datasets from numerical simulations, field measurements, and laboratory experiments. ML refers to a computational system capable of learning and improving from experience, typically in the form of datasets, without the need for explicit programming. This paper reviews and synthesizes recent advances in wave prediction in estuaries, ranging from small shallow bays to large estuaries, including Delaware Bay and Chesapeake Bay.

Coastal wetlands serve as natural protective barriers against wave energy and storm surges, and understanding how waves interact with these ecosystems is crucial. This study focuses on the impact of typhoons on wave evolution in coastal wetlands, aiming to enhance our knowledge of these interactions. The research was conducted in Eastern Chongming Island, Shanghai, during Severe Typhoon Muifa, comparing wave distribution and significant wave height between normal and typhoon conditions. The findings suggest that typhoons lead to greater wave energy attenuation due to increased wave breaking, and wave nonlinearity intensifies during typhoon events. This research contributes to both coastal defense strategies and broader coastal science.