Accurately estimating the probability of storm surge occurrences is necessary for flood risk assessments. This research models Long Island Sound using a coupled coastal circulation and wave model (FVCOM-SWAVE) to hindcast the 44 highest storms between 1950–2018 and fitted Poisson-GPD distributions to modelled water levels and wave heights. Floodwater elevations and significant wave heights for 10% (1/10), 3% (1/30), 2% (1/50), and 1% (1/100) annual exceedance probabilities are provided for all Connecticut coastal towns. The results show that both water levels and their corresponding return intervals are higher along the western coast of Connecticut than the eastern coast, whereas significant wave heights increase eastward. Comparing our model results with those from the North Atlantic Coast Comprehensive Study (NACCS) shows that the mean NACCS results are higher for water levels and lower for significant wave heights for longer return periods. Likewise, the Federal Emergency Management Agency (FEMA) results in large errors compared to our results in both eastern and western coastal Connecticut regions. In addition to evaluating historical risks, we also added a sea-level height offset of 0.5 m for 2050 estimates in order to examine the effect of rising sea-levels on the analysis. We find that sea-level rise reduces the return period of a 10-year storm to two years. We advise periodically updating this work as improved sea-level rise projections become available.

Breakwaters influence coastal wave climate and circulation by blocking and dissipating wave energy. In a large harbor, these effects are combined with wave generation, refraction and reflection. Accurate representation of these processes is essential to the determination of coastal circulation and wave processes. MIKE21SW and SWAN are two third-generation spectral wave models which are used widely in coastal research and engineering applications. Recently improved versions of the models are able to consider the influence of breakwater structures. In this study, we used available observations to evaluate the accuracy of model simulations of waves in New Haven Harbor, Connecticut, USA, an estuary with three detached breakwaters near the mouth. The models were executed on their optimum unstructured triangular grid. The boundary conditions were derived from a bottom mounted Acoustic Doppler Current Profilers (ADCP) on the offshore side of the breakwaters. Wind forcing was applied using data from the Central Long Island Sound buoy. We found that both models were largely consistent with observations during storms. However, MIKE21SW predicted some of storm peaks slightly better. SWAN required the finer grid to achieve the optimum condition, but as it uses a fast, fully implicit algorithm, the computational times were similar. Also, the sensitivity analysis represents that wind forcing and the breakwaters have significant impact on the results.

The geometry of the Long Island Sound (LIS) renders the wave field fetch-limited and leads to marked differences between western and eastern areas. The mechanisms that contribute to the formation and dissipation of waves in the LIS are not well understood. We evaluated the ability of the wave module of a wave-coupled hydrodynamic model to simulate different wind–wave scenarios. We were unable to capture wave statistics correctly using existing meteorological model results for wind forcing due to the low resolution of the models and their inability to resolve the LIS coastline sufficiently. To solve this problem, we modified the wind fields using in situ wind observations from buoys. We optimized both the Komen and Jansen parameterizations for the LIS to better present the peak winds during storms. Waves in the LIS develop more quickly than simple theory predicts due to quadruplet nonlinear wave–wave interaction effects. Removing quadruplet nonlinear wave–wave interaction increases the time to full saturation by 50%. The spatial distribution of wave energy density input reveals the complex interaction between wind and waves in the LIS, with the area of greatest exposure receiving higher wave energy density. The interaction of nonlinear wave–wave interaction and whitecapping dissipation defines the shape of the directional spectrum along the LIS. Dissipation due to whitecapping and shoaling are the main parameters modulating a fully developed wave field.

Seasonal variations in solar insolation and wind create an annual water temperature cycle that impacts circulation and biological processes. The waters of Long Island Sound (LIS) warm from March‐February until August‐October and then begin to cool. Ship surveys show that the vertical temperature structure becomes almost uniform during this season when the area experiences low air temperatures and high winds. However, no observations have resolved the temporal evolution of the vertical structure of temperature during these cooling periods because conditions inhibit ship operations. We report glider measurements of the vertical structure of water temperatures and salinities from 22 October to 4 November 2014, in eastern LIS. We find that 20 m of water can cool at approximately during intervals of cold air and strong winds. We use the data to estimate heat content tendencies and infer surface fluxes. We also estimate the surface heat fluxes using buoy‐mounted instruments and show they are consistent. The net heat flux to the atmosphere exceeds 600 W/m² during the gilder deployment and approximately 66% of this is due to latent heat transfer. Using the buoy fluxes and products of an operation regional model, we show that the agreement with the fluxes derived from heat budget is improved by using local wind observations. In addition to confirming the extremely large cooling rates, our results demonstrate that gliders can be used in a complex region with strong tidal currents to resolve the temperature structure and heat budget during the severe weather of the cooling season.

Breakwaters influence coastal wave climate and circulation by blocking and dissipating wave energy. Accurate representation of these effects is essential to the determination of coastal circulation and wave processes. MIKE21SW and SWAN are two third-generation spectral wave models which are used widely in coastal research and engineering applications. Recent improved versions of the models are able to consider the influence of breakwater structures. In this study, we used available observations to evaluate the accuracy of model simulations of waves in New Haven Harbor, Connecticut, USA, an estuary with three detached breakwaters near the mouth. We then compare the accuracy and computational efficiency of MIKE21SW and SWAN. Both models were executed on the same unstructured triangular grid. The boundary conditions were derived from a bottom mounted ADCP on the offshore side of the breakwaters. Wind forcing was applied using data from the Central Long Island Sound buoy. We find that both models are largely consistent with observations during storms. The MIKE21SW significant wave height and wave direction simulations were slightly superior; however, SWAN is more efficient and faster due to its implementation of a fully implicit technique for time integration.

Estimating surface heat fluxes via direct covariance measurements or bulk formulae is observation‐intensive and costly. We present a methodology whereby we estimate net surface heat fluxes as the difference between the depth‐integrated heat tendencies and the depth‐integrated horizontal heat exchanges in a hydrodynamic model. We calibrate the model to achieve a good representation of mixing and advection and then assimilate satellite sea surface temperature (SST) observations into the model at an 8‐day scale. The SST data assimilation forces a good representation of observed temperatures and heat tendencies both at the surface and throughout the water column. We estimate the horizontal heat exchange directly from the model output and then infer the surface fluxes required to close the budget. When we apply this methodology to a model with prescribed surface heat fluxes and without data assimilation, we can recover the prescribed fluxes with an RMS error of ±10 W m⁻² and an r² of 0.998. When we compare our results to those estimated using Coupled Ocean‐Atmosphere Response Experiment bulk formulae with observations in western Long Island Sound, we find similarly good agreement.