This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high‐quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high‐frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national‐scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near‐future time horizon, we project clear patterns of changing flood hazard (3σ changes in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time.

Over the past decades, the scientific community has made significant efforts to simulate flooding conditions using a variety of complex physically based models. Despite all advances, these models still fall short in accuracy and reliability and are often considered computationally intensive to be fully operational. This could be attributed to insufficient comprehension of the causative mechanisms of flood processes, assumptions in model development and inadequate consideration of uncertainties. We suggest adopting an approach that accounts for the influence of human activities, soil saturation, snow processes, topography, river morphology, and land‐use type to enhance our understanding of flood generating mechanisms. We also recommend a transition to the development of innovative earth system modeling frameworks where the interaction among all components of the earth system are simultaneously modeled. Additionally, more nonselective and rigorous studies should be conducted to provide a detailed comparison of physical models and simplified methods for flood inundation mapping. Linking process‐based models with data‐driven/statistical methods offers a variety of opportunities that are yet to be explored and conveyed to researchers and emergency managers. The main contribution of this paper is to notify scientists and practitioners of the latest developments in flood characterization and modeling, identify challenges in understanding flood processes, associated uncertainties and risks in coupled hydrologic and hydrodynamic modeling for forecasting and inundation mapping, and the potential use of state‐of‐the‐art data assimilation and machine learning to tackle the complexities involved in transitioning such developments to operation.

This study investigates the capacity of a Spartina alterniflora meadow to attenuate waves during storm events based on field observations in the Chesapeake Bay. These observations reveal that environmental conditions including the ratio between water depth and plant height (h_r), the ratio between wave height (H_S) and water depth, and current directions impact the wave height decay. Further, we present empirical representations of the bulk drag coefficient (C_d) as a function of the Keulegan‐Carpenter (KC) and Reynolds (Re) numbers, and the h_r ratio_. When applying the distinction between current directions, this representation exhibits better agreement when using the Re (ρ² = 54%) and h_r (ρ² = 77%) than with the KC (ρ² = 39%). Furthermore, we show that the representation of C_d can be improved by using a h_r‐based modified Re and KC formulation, yielding correlations of 76% (modified Re) and 78% (modified KC). The proposed expressions are validated during another storm and predicted H_S computed within the marsh results in a root‐mean‐square error of 0.014 m, overestimating the largest H_S (0.22 m) by 18%. Finally, these expressions are applied to several hypothetical sea conditions. Under similar vegetation characteristics, H_S of 1.55 and 0.8 m (close to a 10,000‐ and 100‐year recurrence interval storm) are attenuated by 50% and 70%, respectively, at 250 m from the marsh edge. This study provides evidence that validates the saltmarsh wave attenuation capacity during storms, quantifies this attenuation, and supports the transferability of the existing formulas in the literature across similar coastal marshes.

Large estuaries are especially vulnerable to coastal flooding due to the potential of combined storm surges and riverine flows. Numerical models can support flood prevention and planning for coastal communities. However, while recent advancements in the development of numerical models for storm surge prediction have led to robust and accurate models; an increasing number of parameters and physical processes’ representations are available to modelers and engineers. This study investigates uncertainties associated with the selection of physical parameters or processes involved in storm surge modeling in large estuaries. Specifically, we explored the sensitivity of a hydrodynamic model (ADCIRC) and a coupled wind-wave and circulation model system (ADCIRC + SWAN) to Manning’s n coefficient, wind waves and circulation interaction (wave setup), minimum depth (H₀) in the wetting and drying algorithm, and spatially constant horizontal eddy viscosity (ESLM) forced by tides and hurricane winds. Furthermore, sensitivity analysis to Manning’s n coefficient and the interaction of waves and circulation were analyzed by using three different numerical meshes. Manning’s coefficient analysis was divided into waterway (rivers, bay and shore, and open ocean) and overland. Overall, the rivers exhibited a larger sensitivity, and M₂ amplitude and maximum water elevations were reduced by 0.20 m and 0.56 m, respectively, by using a high friction value; similarly, high friction reduced maximum water levels up to 0.30 m in overland areas; the wave setup depended on the offshore wave height, angle of breaking, the profile morphology, and the mesh resolution, accounting for up to 0.19 m setup inside the bay; minimum depth analysis showed that H₀ = 0.01 added an artificial mass of water in marshes and channels, meanwhile H = 0.1 partially solved this problem; and the eddy viscosity study demonstrated that the ESLM = 40 values reduced up to 0.40 m the peak of the maximum water levels in the upper side of narrow rivers.