A 38-year hindcast water-level product is developed for the US Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water-level modeling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modeled water-level components. Corrected water-level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water-level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water-level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water-level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water-level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

The design of coastal protection measures and the quantification of coastal risks at locations affected by tropical cyclones (TCs) are often based solely on the analysis of historical cyclone tracks. Due to data scarcity and the random nature of TCs, the assumption that a hypothetical TC could hit a neighboring area with equal likelihood to past events can potentially lead to over- and/or underestimations of extremes and associated risks. The simulation of numerous synthetic TC tracks based on (historical) data can overcome this limitation. In this paper, a new method for the generation of synthetic TC tracks is proposed. The method has been implemented in the highly flexible open-source Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE). TCWiSE uses an empirical track model based on Markov chains and can simulate thousands of synthetic TC tracks and wind fields in any oceanic basin based on any (historical) data source. Moreover, the tool can be used to determine the wind extremes, and the output can be used for the reliable assessment of coastal hazards. Validation results for the Gulf of Mexico show that TC patterns and extreme wind speeds are well reproduced by TCWiSE.

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

Parametric wind profiles are commonly applied in a number of engineering applications for the generation of tropical cyclone (TC) wind and pressure fields. Nevertheless, existing formulations for computing wind fields often lack the required accuracy when the TC geometry is not known. This may affect the accuracy of the computed impacts generated by these winds. In this paper, empirical stochastic relationships are derived to describe two important parameters affecting the TC geometry: radius of maximum winds (RMW) and the radius of gale-force winds (ΔAR35). These relationships are formulated using best-track data (BTD) for all seven ocean basins (Atlantic; S, NW, and NE Pacific; and N, SW, and SE Indian oceans). This makes it possible to (a) estimate RMW and ΔAR35 when these properties are not known and (b) generate improved parametric wind fields for all oceanic basins. Validation results show how the proposed relationships allow the TC geometry to be represented with higher accuracy than when using relationships available from literature. Outer wind speeds can be reproduced well by the commonly used Holland wind profile when calibrated using information either from best-track data or from the proposed relationships. The scripts to compute the TC geometry and the outer wind speed are freely available via the following URL: https://bit.ly/2k9py1J (last access: October 2019).