This study examines the integrated influence of sea level rise (SLR) and future morphology on tidal hydrodynamics along the Northern Gulf of Mexico (NGOM) coast including seven embayments and three ecologically and economically significant estuaries. A large‐domain hydrodynamic model was used to simulate astronomic tides for present and future conditions (circa 2050 and 2100). Future conditions were simulated by imposing four SLR scenarios to alter hydrodynamic boundary conditions and updating shoreline position and dune heights using a probabilistic model that is coupled to SLR. Under the highest SLR scenario, tidal amplitudes within the bays increased as much as 67% (10.0 cm) because of increases in the inlet cross‐sectional area. Changes in harmonic constituent phases indicated that tidal propagation was faster in the future scenarios within most of the bays. Maximum tidal velocities increased in all of the bays, especially in Grand Bay where velocities doubled under the highest SLR scenario. In addition, the ratio of the maximum flood to maximum ebb velocity decreased in the future scenarios (i.e., currents became more ebb dominant) by as much as 26% and 39% in Weeks Bay and Apalachicola, respectively. In Grand Bay, the flood‐ebb ratio increased (i.e., currents became more flood dominant) by 25% under the lower SLR scenarios, but decreased by 16% under the higher SLR as a result of the offshore barrier islands being overtopped, which altered the tidal prism. Results from this study can inform future storm surge and ecological assessments of SLR, and improve monitoring and management decisions within the NGOM.

Models of alongshore sediment transport during quiescent conditions, storm‐driven barrier island morphology, and poststorm dune recovery are integrated to assess decadal barrier island evolution under scenarios of increased sea levels and variability in storminess (intensity and frequency). Model results indicate barrier island response regimes of keeping pace, narrowing, flattening, deflation (narrowing and flattening), and aggradation. Under lower storminess scenarios, more areas of the island experienced narrowing due to collision. Under higher storminess scenarios, more areas experienced flattening due to overwash and inundation. Both increased sea levels and increased storminess resulted in breaching when the majority of the island was not keeping pace and deflation was the dominant regime due to increased overtopping. Under the highest storminess scenario, the island was unable to recover elevation after storms and drowned in just 10 years.

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.

Dune erosion is an important aspect to consider when assessing coastal flood risk, as dune elevation loss makes the protected areas more susceptible to flooding. However, most advanced dune erosion numerical models are computationally expensive, which hinders their application in early‐warning systems. Based on a combination of probabilistic and process‐based numerical modeling, we develop an efficient statistical tool to predict dune erosion during storms. The analysis focuses on Dauphin Island, AL, in the northern Gulf of Mexico, where we combine synthetic sea storms with a calibrated and validated XBeach model to develop and test a range of different surrogate models for their ability to predict barrier island geometric parameters under storm conditions. Surrogate models are developed by combining the oceanographic forcing from 100 optimally sampled sea storm events covering the entire multivariate parameter space (used as XBeach input) and associated changes in the dune system (XBeach output). We test four surrogate models using a k‐fold approach for validation. All models perform well in predicting changes in dune elevation, barrier island area, and width but are less accurate in predicting alterations in the cross‐shore locations of dune morphological features. Multivariate adaptive regression splines is identified as the best surrogate model based on its fast development and good performance, attaining a modified Mielke index of 0.81 for dune crest height. As demonstrated at Dauphin Island, our approach shows potential to be used in an operational framework to predict dune response (in particular crest elevation change) when water level and wave forecasts are available.

Around the world, wetland vulnerability to sea‐level rise (SLR) depends on different factors including tidal regimes, topography, creeks and estuary geometry, sediment availability, vegetation type, etc. The Plum Island estuary (PIE) is a mesotidal wetland system on the east coast of the United States. This research applied a newly updated Hydro‐MEM (integrated hydrodynamic‐marsh) model to assess the impacts of intermediate‐low (50 cm), intermediate (1 m), and intermediate‐high (1.5 m) SLR on marsh evolution by the year 2100. Model advancements include capturing vegetation change, inorganic and below and aboveground organic matter portion of marsh platform accretion, and mudflat creation. Although the results indicate a low vulnerability marsh at the PIE, the vegetation changes from high to low marsh under all SLR scenarios (2%–22%), with the higher bounds belonging to higher rise scenarios. Lower SLR produces more productive marsh (13% gain in high productivity regions), whereas the highest SLR scenario causes increased tidal inundation, which leads to loss in productivity (12% change from high to low productivity regions), generation of mudflats (17% of the domain land), and marsh migration to higher lands. Sensitive nonlinear tidal flow changes, which may be increased or decreased with SLR as a result of mudflat creation, marsh migration, and bottom friction change, emphasize the importance of integrated modeling approaches that include dynamic marsh feedbacks in hydrodynamic modeling and varying hydrodynamic effects on the marsh system.