Here, we demonstrate that reductions in the depth of inlets or estuary channels can be used to reduce or prevent coastal flooding. A validated hydrodynamic model of Jamaica Bay, New York City (NYC), is used to test nature-based adaptation measures in ameliorating flooding for NYC's two largest historical coastal flood events. In addition to control runs with modern bathymetry, three altered landscape scenarios are tested: (1) increasing the area of wetlands to their 1879 footprint and bathymetry, but leaving deep shipping channels unaltered; (2) shallowing all areas deeper than 2 m in the bay to be 2 m below Mean Low Water; (3) shallowing only the narrowest part of the inlet to the bay. These three scenarios are deliberately extreme and designed to evaluate the leverage each approach exerts on water levels. They result in peak water level reductions of 0.3%, 15%, and 6.8% for Hurricane Sandy, and 2.4%, 46% and 30% for the Category-3 hurricane of 1821, respectively (bay-wide averages). These results suggest that shallowing can provide greater flood protection than wetland restoration, and it is particularly effective at reducing "fast-pulse" storm surges that rise and fall quickly over several hours, like that of the 1821 storm. Nonetheless, the goal of flood mitigation must be weighed against economic, navigation, and ecological needs, and practical concerns such as the availability of sediment.

This article presents the results and validation of a comprehensive, multi-decadal, hindcast simulation performed using the New York Harbor Observing and Prediction System´s (NYHOPS) three-dimensional hydrodynamic model. Meteorological forcing was based on three-hourly gridded data from the North American Regional Reanalysis of the US National Centers for Environmental Prediction. Distributed hydrologic forcing was based on daily United States Geologic Survey records. Offshore boundary conditions for NYHOPS at the Mid-Atlantic Bight shelf break included hourly subtidal water levels from a larger-scale model ran for the same period, tides, and temperature and salinity profiles based on the Simple Ocean Data Assimilation datasets. The NYHOPS model’s application to hindcast total water level and 3D water temperature and salinity conditions in its region over three decades was validated against observations from multiple agencies. Average indices of agreement were: 0.93 for storm surge (9 cm RMSE, 90% of errors less than 15 cm), 0.99 for water temperature (1.1 °C RMSE, 99% of errors less than 3 °C), and 0.86 for salinity (1.8 psu RMSE, 96% of errors less than 3.5 psu). The model’s skill in simulating bottom water temperature, validated against historic data from the Long Island Sound bottom trawl survey, did not drift over the years, a significant and encouraging finding for multi-decadal model applications used to identify climatic trends, such as the warming presented here. However, the validation reveals residual biases in some areas such as small tributaries that receive urban discharges from the NYC drainage network. With regard to the validation of storm surge at coastal stations, both the considerable strengths and remaining limitations of the use of North American Regional Reanalysis (NARR) to force such a model application are discussed.