Coastal responses to sea level rise (SLR) include inundation of wetlands, increased shoreline erosion, and increased flooding during storm events. Hydrodynamic parameters such as tidal ranges, tidal prisms, tidal asymmetries, increased flooding depths and inundation extents during storm events respond nonadditively to SLR. Coastal morphology continually adapts toward equilibrium as sea levels rise, inducing changes in the landscape. Marshes may struggle to keep pace with SLR and rely on sediment accumulation and the availability of suitable uplands for migration. Whether hydrodynamic, morphologic, or ecologic, the impacts of SLR are interrelated. To plan for changes under future sea levels, coastal managers need information and data regarding the potential effects of SLR to make informed decisions for managing human and natural communities. This review examines previous studies that have accounted for the dynamic, nonlinear responses of hydrodynamics, coastal morphology, and marsh ecology to SLR by implementing more complex approaches rather than the simplistic “bathtub” approach. These studies provide an improved understanding of the dynamic effects of SLR on coastal environments and contribute to an overall paradigm shift in how coastal scientists and engineers approach modeling the effects of SLR, transitioning away from implementing the “bathtub” approach. However, it is recommended that future studies implement a synergetic approach that integrates the dynamic interactions between physical and ecological environments to better predict the impacts of SLR on coastal systems.

Worldwide, coastal, and deltaic communities are susceptible to flooding from the individual and combined effects of rainfall excess and astronomic tide and storm surge inundation. Such flood events are a present (and future) cause of concern as observed from recent storms such as the 2016 Louisiana flood and Hurricanes Harvey, Irma, and Maria. To assess flood risk across coastal landscapes, it is advantageous to first delineate flood transition zones, which we define as areas susceptible to hydrologic and coastal flooding and their collective interaction. We utilize numerical simulations combining rainfall excess and storm surge for the 2016 Louisiana flood to describe a flood transition zone for southeastern Louisiana. We show that the interaction of rainfall excess with coastal surge is nonlinear and less than the superposition of their individual components. Our analysis provides a foundation to define flooding zones across coastal landscapes throughout the world to support flood risk assessments.

Natural disasters, such as floods, may damage power system assets and lead to widespread and long outages. The impact of flood can be alleviated by preventive actions such as installing tiger dams around power substations before the flood. In this regard, it is imperative that critical substations are identified in terms of the connected load and imposed costs to the system. This article presents a stochastic resource allocation approach for protecting power substations against flood events a day ahead of the event. Flood probability distribution functions are used to generate several flood scenarios at each substation. Using flood scenarios and substations’ fragility, damage, and repair time curves obtained from historical data, the failure probability, damage percentage, damage cost, and repair time of substations are estimated. A day-ahead risk-aware stochastic scheduling model is proposed to identify the critical substations whose protection by tiger dams maximizes grid resilience. The risk-aware approach prevents high cost and low resilience if a particular scenario with a low probability is realized. A scenario reduction method is developed to generate representative substation failure scenarios and reduce the computational cost of the optimization problem. The simulation results on a realistic 30-substation system show the effectiveness of the proposed model.

Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three- class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.

Marine tar residues originate from natural and anthropogenic oil releases into the ocean environment and are formed after liquid petroleum is transformed by weathering, sedimentation, and other processes. Tar balls, tar mats, and tar patties are common examples of marine tar residues and can range in size from millimeters in diameter (tar balls) to several meters in length and width (tar mats). These residues can remain in the ocean environment indefinitely, decomposing or becoming buried in the sea floor. However, in many cases, they are transported ashore via currents and waves where they pose a concern to coastal recreation activities, the seafood industry and may have negative effects on wildlife. This review summarizes the current state of knowledge on marine tar residue formation, transport, degradation, and distribution. Methods of detection and removal of marine tar residues and their possible ecological effects are discussed, in addition to topics of marine tar research that warrant further investigation. Emphasis is placed on benthic tar residues, with a focus on the remnants of the Deepwater Horizon oil spill in particular, which are still affecting the northern Gulf of Mexico shores years after the leaking submarine well was capped.

A growing concern of coastal communities is increased flood risk and non‐monetary consequences due to climate‐induced impacts such as sea level rise (SLR). Previous efforts have discussed the importance of future flood risk quantification using broad aggregations of monetary loss with “bathtub” SLR models rather than more physically based modeling approaches. Here we quantify actual impacts to coastal communities at the census block level using a dynamic, high‐resolution, bio geophysical modeling framework for four SLR scenarios for the year 2100. This framework accounts for future sea‐levels, landscape change, and urbanization to quantify the 1% and 0.2% annual exceedance probability (AEP) water levels. The computed AEP water levels were used to quantify building damage and populations of displaced people and people requiring long‐term shelter across the Northern Gulf of Mexico (NGOM) (Mississippi, Alabama, and the Florida panhandle). The increase in damaged buildings under SLR is linear, with an increase of 16,367 damaged buildings per 1 m of SLR (R² = 0.96) for the 1% AEP flood. The rate increases to 24,981 damaged buildings per 1 m of SLR (R² = 0.96) for the 0.2% AEP, on average. The increase in displaced people across the NGOM is 8,056 people per meter of SLR, and people requiring shelter is 300 per meter of SLR. The results in this work highlight the varying levels of risk across the NGOM and the change in risk under climate change‐induced impacts.