Davina L. Passeri

Ecogeomorphic state transitions in coastal barrier systems will be impacted and likely amplified by projected accelerated sea‐level rise and enhanced storm intensity over the next several decades. When, where, and how these transitions will take place remain uncertain and complex, yet are of primary interest to coastal managers looking to prepare for future changes. Here, we apply the new Mesoscale Explicit Ecogeomorphic Barrier model to generate probabilistic projections of future ecogeomorphic state transitions up to year 2050 for the undeveloped North Core Banks (NCB), North Carolina, United States. In our full probabilistic projection, which accounts for stochasticity from uncertainty in sea‐level rise and high‐water event (HWE) projections, loss of barrier interior to passive sea‐level rise submergence dominates ecogeomorphic state transitions, suggesting that the biggest threat to many undeveloped barriers in the next several decades will come from the back‐barrier rather than ocean‐side. Additionally, this projection suggests that dune and unvegetated interior states are most volatile, with the intensity of state transitions varying significantly alongshore. In projections simulating a range of deterministic sea‐level rise rates, higher rates enhance: overwash frequency; loss of beach and dune states; and interior submergence. We also find that state transitions are less sensitive to HWE intensity than sea‐level rise, and that more frequent, moderate HWEs—rather than the largest events—may be the principal drivers of mesoscale ecogeomorphic state change. Our projections highlight important future mesoscale trends of ecogeomorphic state transitions for NCB that are likely relevant to other stable, sediment‐rich barrier systems of U.S. coastlines.

Wave runup is an important nearshore process that impacts total water level, sediment transport, and coastal design. Current methods for forecasting wave runup implement an empirical model that considers offshore wave height, wave period, and generalized beach slope. In this study, the authors generated wave runup forecasts from offshore wave conditions and a system of polynomial equations derived from numerical simulations at three different still water datums for each beach profile. They developed a process-based methodology that incorporated site-specific cross-shore topobathy into the phase-resolving numerical model. A comparison between the system of equations, deterministic hydrodynamic simulations, and observed high-water marks was made using Hurricanes Matthew (2016) and Irma (2017) for 12 cases, and it showed that the polynomials were capable of being consistent with the results from full simulation runs, while not requiring hours of runtime when a forecast was needed—the differences between the polynomial and the observed high water marks ranged from 3 to 32 cm for the Irma hindcast and 9–70 cm for Matthew. Then, using forcings from Hurricanes Ian and Nicole (2022), the model predicted the occurrence of dune collision, overwash, and inundation for the coast of Georgia and suggested that wave runup was impacted by the still water level and local topobathy.

Coastal wetlands are being transformed by global change, impacting the ecological and societal benefits provided by these ecosystems. Synthesizing knowledge of historical and expected future transformations in coastal wetlands can help inform forward-looking planning and stewardship efforts. Here, we review anticipated future ecological transformations in coastal wetlands of the conterminous United States and contrast them with past transformations. We examine trends at the national scale but focus in detail on the following six wetland-rich regions: (1) Everglades, (2) Mississippi River delta, (3) Chesapeake Bay, (4) San Francisco Estuary, (5) Puget Sound, and (6) Great Lakes. This review is among the first to integrate and compare coastal wetland transformations in the Great Lakes with their oceanic counterparts. Between 1850 and 1970, wetland losses across the nation were high because coastal wetlands were viewed as flooded wastelands needing to be drained, filled, or excavated to make room for other land uses. Over the last 50 years, growing public and scientific appreciation of the ecological and societal benefits provided by coastal wetlands has fostered tremendous gains in wetland restoration and legal protection. Looking to the future, climate change and accelerated sea-level rise have become critical threats to coastal wetlands and are emerging as major drivers of ecological loss and transformation. This review synthesizes regional knowledge regarding past, present, and future changes to help coastal scientists, environmental managers, and the public better anticipate and prepare for future coastal wetland transformations due to climate change, accelerated sea-level rise, land-use change, and other aspects of global change.

Local to global scale projections of future coastal landscape change are essential to improve land and resource management decisions that aim to prepare for and reduce risk exposure to impending coastal hazards. However, the availability of actionable knowledge is often limited due to the complexity of drivers of change, their consequences, and uncertainties that span disciplines. Varying spatial and temporal scales of physical processes that occur on landscapes with differing levels of coastal development and modification complicate decision‐making. This paper reviews advances in observational approaches, modeling, and quantification of uncertainty to support the next generation of coastal change projections. We also highlight transdisciplinary opportunities for making this information more accessible and useful to support decision‐making in the coastal zone. This includes consideration of different levels of user engagement to address specific needs, as well as resources and mechanisms that can support knowledge transfer broadly. A central recommendation is the development of a coordinated framework to support research integration including three components: accounting for relevant drivers, processes, and feedbacks; working with users to identify their information needs; and generating meaningful projections and uncertainty. Actions that would support such frameworks include the evaluation of models through coordinated research approaches and multi‐model comparisons; ongoing investments in observations and data‐driven approaches necessary to understand this complex environment; expansion of modeling capabilities using novel techniques; prioritization of co‐development efforts between researchers and users; and continued training and support for transdisciplinary research.

An ecological threshold is the point at which a comparatively small environmental change triggers an abrupt and disproportionately large ecological response. In the face of accelerating climate change, there is concern that abrupt ecosystem transformations will become more widespread as critical ecological thresholds are crossed. There has been ongoing debate, however, regarding the prevalence of ecological thresholds across the natural world. While ecological thresholds are ubiquitous in some ecosystems, thresholds have been difficult to detect in others. Some studies have even concluded that threshold responses are uncommon in the natural world and overly emphasized in the ecological literature. As ecologists who work in ecosystems chronically exposed to high abiotic stress, we consider ecological thresholds and ecosystem transformations to be critical concepts that can greatly advance understanding of ecological responses to climate change and inform ecosystem management. But quantifying ecological thresholds can be challenging, if not impossible, without data that are strategically collected for that purpose. Here, we present a conceptual framework built upon linkages between abiotic stress, climate‐driven ecological threshold responses, and the risk of ecosystem transformation. We also present a simple approach for quantifying ecological thresholds across abiotic stress gradients. We hypothesize that climate‐driven threshold responses are especially influential in ecosystems chronically exposed to high abiotic stress, where autotroph diversity is low and foundation species play a prominent ecological role. Abiotic conditions in these environments are often near physiological tolerance limits of foundation species, which means that small abiotic changes can trigger landscape‐level ecological transformations. Conversely, the alleviation of stress near thresholds can allow foundation species to thrive and spread into previously inhospitable locations. We provide examples of this climate‐driven threshold behavior from four high‐stress environments: coastal wetlands, coral reefs, drylands, and alpine ecosystems. Our overarching aim in this review is to clarify the strong relationships between abiotic stress, climate‐driven ecological thresholds, and the risk of ecosystem transformation under climate change.

Numerical simulation of sediment transport and subsequent morphological evolution rely on accurate parameterizations of sediment characteristics. However, these data are often not available or are spatially and/or temporally limited. This study approaches the problem of limited sediment grain-size data with a series of simulations assessing model sensitivity to sediment parameters and initial bed composition configurations in Delft3D, leading to improved modeling practices. A previously validated Delft3D sediment transport and morphology model for Dauphin Island, Alabama, USA, is used as the benchmark case. A method for the generation of representative sediment grain sizes and their spatially varying distributions is presented via end-member analysis of in situ surficial sediment samples. Derived sediment classes and their spatial distributions are applied to two sensitivity case simulations with increasing bed composition complexity. First, multiple sediment classes are applied in a single fully mixed layer, regardless of sediment type. Second, multiple sediment classes are applied in a thin, fully mixed transport layer with underlayers containing only the non-cohesive sediment classes below. Simulations were carried out in a probabilistic, Delft3D MorMerge configuration to capture long-term morphology change for 10 years. We found there is sensitivity to the inclusion of additional sediment classes and sediment distribution made evident in bed level and morphology change. Inclusion of highly mobile fine sediments altered model results in each sensitivity case. The model was also found to be sensitive to initial bed composition in terms of bed level and morphology change, with notable differences between sensitivity cases on decadal timescales, indicating an armoring effect in the second sensitivity case, which used the transport and underlayer bed configuration. The results of this study offer guidance for numerical modelers concerned with sediment behavior in coastal and estuarine environments.

Coastal change and evolution are the product of physical drivers (e.g., waves) tightly coupled with human behavior. As climate change impacts intensify, demand is increasing for information on where, when, and how coastal areas may change in the future. Although considerable research investments have been made in understanding the physical drivers and processes that modify and shape coastal environments, many do not account for human behavior, compromising the accuracy of comprehensive future change predictions. We outline four social science approaches—historic case studies, simulations, longitudinal studies, and longitudinal studies supported by experimental data—that can be coupled with physical change information to support transdisciplinary understanding of future change. A fundamental need for each approach is more and better empirical data to better gauge human behavior. In addition, foundational investments in transdisciplinary collaboration help research teams support the integration of these approaches.