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Understanding waterline variability at seasonal to interannual timescales is crucial for predicting coastal responses to climate forcing. However, relationships between large-scale climate variability and coastal morphodynamics remain underexplored beyond intensively monitored sites. This study leverages a newly developed 25-year (1997–2022) satellite-derived waterline dataset along the North American West Coast. Our results reveal distinct latitudinal patterns in seasonal waterline change, with excursions exceeding 25 m in the Pacific Northwest, decreasing to less than 10 m in Southern California and farther south. Waterline fluctuations strongly follow wave power in the Pacific Northwest (R = −0.78), northern California (R = −0.75), and Baja California (R = −0.62), while Baja California Sur aligns more with sea-level variations (R = −0.42). Interannually, waterline change exhibits latitudinal dependence: south of southern California, variability is low, with major erosion confined to strong El Niño-Southern Oscillation (ENSO) events, while northern regions show mixed responses. ENSO-driven storm track shifts modulate winter wave climate, resulting in enhanced (attenuated) erosion from southern California to Baja California Sur during El Niño (La Niña). However, further north, ENSO impacts are less consistent, reflecting a complex interplay of storm track displacement and intensification. These findings highlight the spatial complexity of ENSO-driven morphodynamics and provide a framework for assessing climate-induced coastal vulnerability. 

Abstract Coastal ecosystems such as mangroves, salt marshes, and seagrasses sequester large amounts of carbon per unit area due to their high productivity and sediment accumulation rates. However, only a handful of studies have examined carbon sequestration in coastal dunes, which are shaped by biophysical feedback between aeolian sediment transport and burial-tolerant vegetation. The goal of this study was to measure carbon storage and identify the factors that influence its variability along the foredunes of the US Outer Banks barrier islands of North Carolina. Specifically, differences in carbon stocks (above- and belowground biomass and sand), dune grass abundance, and sand supply were measured among islands, cross-shore dune profile locations, and dune grass species. Carbon varied among aboveground grass biomass (0.1 ± 0.1 kg C m⁻²), belowground grass biomass (1.1 ± 1.6 kg C m⁻³), and sand (0.9 ± 0.6 kg C m⁻³), with the largest amount in belowground grass stocks. Aboveground grass carbon stocks were comparable to those in eelgrass beds and salt marshes on a per-area basis, while sediment carbon values in our study system were lower than those in other coastal systems, including other dune locations. Additionally, sand carbon density was positively related to patterns in dune sand supply and grass abundance, reflecting a self-reinforcing vegetation-sediment feedback at both high and low sand accumulation rates. 

Abstract The risk of compound coastal flooding in the San Francisco Bay Area is increasing due to climate change yet remains relatively underexplored. Using a novel hybrid statistical-dynamical downscaling approach, this study investigates the impacts of climate change induced sea-level rise and higher river discharge on the magnitude and frequency of flooding events as well as the relative importance of various forcing drivers to compound flooding within the Bay. Results reveal that rare occurrences of flooding under the present-day climate are projected to occur once every few hundred years under climate change with relatively low sea-level rise (0.5 m) but would become annual events under climate change with high sea-level rise (1.0 to 1.5 m). Results also show that extreme water levels that are presently dominated by tides will be dominated by sea-level rise in most locations of the Bay in the future. The dominance of river discharge to the non-tidal and non-sea-level rise driven water level signal in the North Bay is expected to extend ~15 km further seaward under extreme climate change. These findings are critical for informing climate adaptation and coastal resilience planning in San Francisco Bay. 

Coastal jetties are commonly used throughout the world to stabilize channels and improve navigation through inlets. These engineered structures form artificial boundaries to littoral cells by reducing wave-driven longshore sediment transport across inlet entrances. Consequently, beaches adjacent to engineered inlets are subject to large gradients in longshore transport rates and are highly sensitive to changes in wave climate. Here, we quantify annual beach and nearshore sediment volume changes over a 9-yr time period along 80 km of wave- dominated coastlines in the U.S. Pacific Northwest. Beach and nearshore monitoring during the study period (2014–2023) reveal spatially coherent, multi-annual patterns of erosion and deposition on opposing sides of two engineered inlets, indicating a regional reversal of longshore-transport direction. A numerical wave model coupled with a longshore transport predictor was calibrated and validated to explore the causes for the observed spatial and temporal patterns of erosion and deposition adjacent to the inlets. The model results indicate that subtle but important changes in wave direction on seasonal to multi-annual time scales were responsible for the reversal in the net longshore sediment transport direction and opposing patterns of morphology change. Changes in longshore transport direction coincided with a reversal in the Pacific Decadal Oscillation (PDO) climate index, suggesting large-scale, multi-decadal climate variability may influence patterns of waves and sediment dynamics at other sites throughout the Pacific basin. 

Vegetation plays a crucial role in coastal dune building. Species‐specific plant characteristics can modulate sediment transport and dune shape, but this factor is absent in most dune building numerical models. Here, we develop a new approach to implement species‐specific vegetation characteristics into a process‐based aeolian sediment transport model. Using a three‐step approach, we incorporated the morphological differences of three dune grass species dominant in the US Pacific Northwest coast (European beachgrassAmmophila arenaria, American beachgrassA. breviligulata, and American dune grassLeymus mollis) into the model AeoLiS. First, we projected the tiller frontal area of each grass species onto a high resolution grid and then re‐scaled the grid to account for the associated vegetation cover for each species. Next, we calibrated the bed shear stress in the numerical model to replicate the actual sand capture efficiency of each species, as measured in a previously published wind tunnel experiment. Simulations were then performed to model sand bedform development within the grass canopies with the same shoot densities for all species and with more realistic average field densities. The species‐specific model shows a significant improvement over the standard model by (a) accurately simulating the sand capture efficiency from the wind tunnel experiment for the grass species and (b) simulating bedform morphology representative of each species' characteristic bedform morphology using realistic field vegetation density. This novel approach to dune modeling will improve spatial and temporal predictions of dune morphologic development and coastal vulnerability under local vegetation conditions and variations in sand delivery. 

The grand environmental challenges society faces cannot be solved without knowledge contributions from communities that are touched by the outcomes and impacts of research. Recognizing and valuing these sources, a research method known as co-production of knowledge involves collaboration of interested parties and knowledge owners in activities ranging from identifying research needs to study design, data collection, interpretation, and evaluation. We present a novel approach to the administrative and financial support for knowledge co-production developed by Oregon Sea Grant and provide examples from two funded project teams. We summarize barriers to these approaches, discuss points of symmetry, and propose good practices. One project emphasized equitable alternative strategies for chronic and acute coastal hazard adaptation and mitigation. The other project looked beyond dam removal in the Klamath River Basin to generate place-based and generalizable understanding of, and equitable decisions around, water quality and socio-ecological systems undergoing change. Both projects provide high-level lessons regarding opportunities and challenges encountered in the engaged research process, highlighting flexibility and adaptability as two important considerations. 

While there is high certainty that chronic coastal hazards like floodingand erosion, are increasing due to climate change induced sea-levelrise, there is high uncertainty surrounding the timing, intensity, andlocation of future hazard impacts. Assessments that quantify theseaspects of future hazards are critical for adaptation planning under achanging climate and can reveal new insights into the drivers of coastalhazards. In particular, probabilistic simulations of future hazardimpacts can improve these assessments by explicitly quantifyinguncertainty and by better simulating dependence structures between thecomplex multivariate drivers of hazards. In this study, a regional-scaleprobabilistic assessment of climate change induced coastal hazards isconducted for the Cascadia region, USA during the 21st century. Threeco-produced hazard proxies for beach safety, erosion, and flooding arequantified to identify areas of high hazard impacts and determine hazarduncertainty under three sea-level rise scenarios. A novel chroniccoastal hazard hotspot indicator is introduced that identifies areasthat may experience significant increases in hazard impacts compared topresent day conditions. We find that Southern Cascadia and NorthernWashington have larger hazard impacts and hazard uncertainty due totheir morphologic setting. Erosional hazards, relative to beach safetyand coastal flooding, will increase the most in Cascadia during the 21stcentury under all sea-level rise scenarios. Finally, we find that hazarduncertainty associated with wave and water level variability exceeds theuncertainty associated with sea-level-rise until the end of the century. 

Regional scale assessments of future chronic coastal hazard impacts are critical tools for adaptation planning under a changing climate. Probabilistic simulations of hazard impacts can improve these assessments by explicitly attempting to quantify uncertainty and by better simulating dependence between complex multivariate drivers of hazards. In this study, probabilistic future timeseries of total water levels (TWLs) are generated from a stochastic climate emulator (TESLA; Anderson et al., 2019) for the Cascadia region, USA for use in a chronic hazard impact assessment. This assessment focuses on three hazard metrics: collision, overtopping, and beach safety, and also introduces a novel hotspot indicator to identify areas that may experience dramatic changes in hazard impacts compared to present day conditions. Results are presented for a subset of the Cascadia region (Rockaway Beach Littoral Cell, Oregon) to demonstrate the power of the probabilistic impact assessment approach. The results highlight how useful spatially varying, scenario-based hazard impacts assessments and hotspot indicators are for identifying which areas and types of hazards may require increased adaptation support. This approach enables us to piece apart the relative uncertainty of hazards as driven by SLR versus natural variability (caused by variation in climate, weather, and hydrodynamic drivers).