Virginia Sea Grant

Abstract. Marine heatwaves (MHWs), characterized by anomalously high sea surface temperatures, are occurring with increasing frequency and intensity, profoundly impacting ocean circulation, biogeochemistry, and marine ecosystems. The MHW known as the Blob, which persisted in the subarctic NE Pacific from 2014 to 2015, significantly affected surrounding ecosystems. Warming-induced solubility reduction is expected to raise the partial pressure of carbon dioxide (pCO2) in the surface water, causing outgassing of CO2 to the atmosphere. Outgassing of CO2 is another source of atmospheric CO2 in addition to anthropogenic fossil fuel burning. However, moored observations at Ocean Station Papa (OSP; 145° W, 50° N) shows a moderate decrease in oceanic pCO2 during the Blob, resisting the warming-induced outgassing of CO2. This response is opposite of what is expected from warming alone, and instead has been attributed to reductions in dissolved inorganic carbon (DIC), although the mechanisms driving this reduction have remained unclear. We employed a regional model that accurately reproduces the temporal variability of oceanic pCO2 at OSP to investigate the cause of decrease pCO2 during the Blob. The analysis of model outputs indicates that the observed oceanic pCO2 decline resulted from the offset between warming-induced solubility reduction (increasing pCO2) and weakened physical transport of DIC (decreasing pCO2), with the latter dominating. Both horizontal and vertical transports played important roles. The near-surface carbon budget over the broad region was primarily driven by changes in the vertical transport. The decrease in DIC during the Blob resulted from the suppression of upwelling of DIC-rich subsurface waters in the winter of 2013. In this period, the horizontal transport also contributed substantially to DIC reduction. In particular, at OSP, the effect of the horizontal transport was comparable to that of the vertical transport, reflecting the northward advection of low-DIC water masses. These findings indicate that changes in physical circulation were the primary driver of the moderately enhanced CO2 uptake observed during the Blob. This study provides a critical insight into the complexity of biogeochemical response to extreme warming events and underscores the importance of resolving physical transport processes in assessing oceanic carbon uptake during MHWs.

This study presents a novel approach integrating near-field passive remote sensing technologies with advanced machine learning algorithms and edge detection through image segmentation to free surface water levels from live oblique imagery in tidal environments. The sensor is capable of full color monitoring by the optical sensor during daylight hours, which affords greater pixel density for the machine learning model and edge detection, while nighttime monitoring relies on the more limited range and relatively monochromatic color profile of the infrared projection sensor, leading to diminished accuracy at night. Cameras were strategically deployed at twelve sites across the United States, capturing images at six-minute intervals over a three-month period. The final machine learning model was trained on a week's worth of images taken every 6 minutes, translating to approximately 1,700 images at each of the twelve sites. This was done to train a model to run over the following 3 months to accurately predict 80% of the six-minute water levels within 1.5 cm, and 97% of the water levels within 10 cm accuracy. Statistical analyses demonstrated that the system reliably produced continuous surface water level measurements, with an aggregate root mean square error of less than 1 cm when cameras were positioned within approximately 10 meters of the target observation area. To verify vertical measurement accuracy, USGS A-style staff gauges were installed within each camera's field of view. Water levels inferred from imagery were cross-validated with nearby radar sensors at each location. A generalized machine learning model that doesn't fully rely on site-specific flooding imagery has been in development, but there are many factors that convolute its universal effectiveness. The resulting data machine learning model products are in the process of being integrated into the USGS Water Data System, with the goal of enabling both internal and public access to real-time water level estimates derived from the machine learning water level estimation framework.

As brackish turbid waters exit San Francisco Bay, one of the largest estuaries in the U.S. West Coast, they form the San Francisco Bay Plume (SFBP), which spreads offshore and influences the Gulf of the Farallones (GoF), an ecologically significant region in the California Current System that is also home to three National Marine Sanctuaries. This paper provides the first observationally based investigation of the spatio-temporal variability of the SFBP, using a plume tracking algorithm applied to more than two decades (2002-2023) of ocean color data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard satellites Aqua and Terra. The turbid SFBP spreads radially, extending 10-20 km offshore around 50% of the time, and during extreme discharge events (<1% of the time), the plume can reach nearly 60 km offshore to the shelf break. The greatest variability in frequency of plume occurrence was observed 10-20 km offshore and it was largely explained by the seasonal cycle (80% of total variance), linked primarily to seasonal changes in river discharge. Largest plume areas (determined by summing up all pixel areas weighted by their respective fraction of plume occurrence) were observed during winter and smallest during summer, occupying on average 24% and 1.5% of GoF area, respectively. Beyond 20-30 km offshore, variability in frequency of plume occurrence was dominated by the intraseasonal band (50-80% of total variance), attributed to plume response to synoptic wind-forcing and/or filaments and eddies, while the interannual band played a secondary role in the plume variability (<20% of total variance). Finally, a multivariable linear regression model of the turbid SFBP area was created to explore the potential predictability of the plume’s influence in the GoF. The model included the annual and semi-annual cycles and discharge anomalies (deseasoned and detrended), and despite its simplicity, it explained over 78% of total variance of the turbid SFBP area. Therefore, it could be a useful tool for scientists and stakeholders to better understand how management actions on freshwater supply can have consequences offshore beyond the Golden Gate and help guide future management decisions in this ecologically important region.

Abstract Estuarine exchange flow controls the salt balance and regulates biogeochemistry in an estuary. The Albemarle‐Pamlico estuarine system (APES) is the largest coastal lagoon in the U.S. and historically susceptible to a series of environmental issues including salt water intrusion and eutrophication, yet its estuarine exchange flow is poorly understood. Here, we investigate the estuarine exchange flow in the APES, its tributary estuaries (Pamlico and Neuse), and sub‐basin Albemarle Sound using the total exchange flow analysis framework based on results from a deterministic numerical model. We find the following: (a) Dynamics controlling estuarine exchange flow in the APES vary spatially and depend on timescales considered. At inlets, estuarine exchange flows respond to both tidal prism and residual water levels at weather‐to‐spring/neap timescales. At a long quasi‐steady timescale represented as annual means, estuarine exchange flow is dominated by barotropic flow. Within the tributary estuaries, estuarine exchange flows at timescales of wind periods are controlled by wind‐induced straining, whereas the quasi‐steady state condition is dominated by gravitational circulation. At Albemarle Sound, exchange flow is dominated by the residual water levels at weather‐to‐spring/neap timescales, while at quasi‐steady state it is controlled by barotropic flow. (b) At the quasi‐steady annual timescale, the salt content decreases with river discharge. At the weather‐to‐spring/neap timescales, salt content is insensitive to variations in estuarine exchange flow, except for within Albemarle Sound. (c) Estuarine exchange flow likely influences the biogeochemistry of the APES by playing a key role in regulating the flushing efficiency and material exchange, a role that has been previously overlooked.

Carcinonemertes carcinophila is a nemertean worm from a family of marine symbionts specialized in eating the eggs of decapod crustaceans. This species infests the Atlantic blue crab, Callinectes sapidus, a native to the Western Atlantic and Gulf of Mexico waters. Its host, the mature female blue crab, is euryhaline, migrating from low to high salinity waters during its adult life, rather than being exclusively marine. Unlike C. carcinophila, most species of marine nemerteans are stenohaline, living exclusively in high salinity waters. The salinity tolerance of C. carcinophila has not been well examined. This study used field-collected frequency data to assess the infestation intensity of nemerteans in relation to salinity regimes, and microcosm experiments to investigate the salinity tolerance and survival of C. carcinophila under hyposaline stress. These investigations also provide information on the nemertean's life history in relation to the spawning migration of female blue crabs. A multi-stage General Linear Model was used to test our hypothesized positive relationship between salinity and the probability of nemertean abundance on mature female crabs. Experiments confirmed that salinities of 20-30 psu were ideal for the survival of C. carcinophila and revealed the distinct ability of this species to acclimate rapidly to mesohaline conditions as low as 10 psu. This species was also able to withstand oligohaline stress (5 psu) for up to 39 hours. The wide range in salinity tolerance (10-30 psu) indicates that C. carcinophila has evolved to survive in similar euryhaline environments as its host. In addition to the wide salinity tolerance of the worm, the ability to withstand hyposaline stress indicates that rapid salinity changes in the blue crab's natural environment does not limit the reliability of C. carcinophila as a biomarker for the spawning history of blue crabs.

Abstract Background and aims Containerized soilless substrates are highly porous to ensure adequate air storage to overcome the “container” effect- the lower part of the container nears saturation which can decrease root health and growth. Substrate porosity is dynamic, evolving over time. As roots fill pores, substrate decomposition and in-situ particle movement change the physical structure, shifting its storage properties and performance. Research is sparse in understanding how developing roots change their morphology throughout production (temporally) and while growing throughout the three-dimensional substrate matrix (spatially). Thus, it would be beneficial to understand how root development impacts container moisture characteristics. This study aimed to quantify root morphological development and water storage (θ) spatiotemporally in conventional or engineered soilless substrate systems. Methods Helianthus annus ‘Rio Carnival’ was grown in 30.5 cm tall PVC columns in a conventional (non-stratified; 100% of the container is filled with a single composite) bark- or peat-based substrates or engineered (stratified; fine-bark atop coarse-bark; peatlite layered over pine bark) systems. Columns were frozen after roots were partially- (22 d) or fully-grown (43 d) and were separated in five vertical sections. Root morphology and θ were measured within each layer. Results The results showed that stratified systems overall stored less water, especially in coarser sub-stratas. Partially rooted columns generally stored more water and fully rooted columns drained more. Plants grown in stratified systems had greater fine root development than when grown conventionally. Conclusion Container-grown roots can be engineered to produce more fibrous root systems by spatially manipulating substrate θ.