8 research outputs found

    Controls on Sediment Bed Erodibility in a Muddy, Partially-Mixed Tidal Estuary

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    he objectives of this study are to better understand controls on bed erodibility in muddy estuaries, including the roles of both sediment properties and recent hydrodynamic history. An extensive data set of erodibility measurements, sediment properties, and hydrodynamic information was utilized to create statistical models to predict the erodibility of the sediment bed. This data set includes \u3e160 eroded mass versus applied stress profiles collected over 15 years along the York River estuary, a system characterized by “depth-limited erosion,” such that the critical stress for erosion increases rapidly with depth into the bed. For this study, erodibility was quantified in two ways: the mass of sediment eroded at 0.2 Pa (a stress commonly produced by tides in the York), and the normalized shape of the eroded mass profile for stresses between 0 and 0.56 Pa. In models with eroded mass as the response variable, the explanatory variables with the strongest influence were (in descending order) tidal range squared averaged over the previous 8 days (a proxy for recent bottom stress), salinity or past river discharge, sediment organic content, recent water level anomalies, percent sand, percent clay, and bed layering. Results support the roles of 1) recent deposition and bed disturbance increasing erodibility and 2) cohesion/consolidation and erosion/winnowing of fines decreasing erodibility. The most important variable influencing the shape of the eroded mass profile was eroded mass at 0.2 Pa, such that more (vs. less) erodible cases exhibited straighter (vs. more strongly curved) profiles. Overall, hydrodynamic variables were the best predictors of eroded mass at 0.2 Pa, which, in turn, was the best predictor of profile shape. This suggests that calculations of past bed stress and the position of the salt intrusion can serve as useful empirical proxies for muddy bed consolidation state and resulting erodibility of the uppermost seabed in estuarine numerical models. Observed water content averaged over the top 1 cm was a poor predictor of erodibility, likely because typical tidal stresses suspend less than 1 mm of bed sediment. Future field sampling would benefit from higher resolution observations of water content within the bed’s top few millimeters

    Supporting Data: Controls on Sediment Bed Erodibility in a Muddy, Partially-Mixed Tidal Estuary, York River, Virginia

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    Dataset consists of all sampling cruises with data that were analyzed and used in the statistical modeling associated with Wright (2021) and Wright et al. (2022). Each cruise folder includes erodibility data that was analyzed using a Gust Microcosm along with sediment and water column characteristics

    VIMS 2019 Potomac River Estuary Data in Support of: Improved Penetrometer Performance in Stratified Sediment for Cost-Effective Characterization, Monitoring and Management of Submerged Munitions Sites (SERDP project: MR18-1233)

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    This work complements the efforts by the Virginia Tech Department of Civil & Environmental Engineering in SERDP MR18-1233, as described in the project’s final report (Stark et al, 2020) and in the Master’s thesis by Dennis Kiptoo (Kiptoo, 2020). Previous work on this project, conducted in the York River during 2018-2918 worked to improve calibration of the Bluedrop free fall penetrometer (FFP) with high resolution sampling of a variety of sediment types (Massey et al, 2020a). Calibration methods developed (Kiptoo, 2020) were used to rapidly identify different sediment types from a grid of 59 Bluedrop PPF stations sampled on the morning of August 5, 2019 on the Potomac River in Wades Bay, just down river from Mallows Bay Park in Nanjemoy, MD. The Bluedrop FFP was deployed numerous times at each station, and the data were retained and processed by Virginia Tech. The Bluedrop stations were arranged in a grid of 8 transects (A-H) perpendicular to shore, spaced ~200 meters apart. Each transect had 5 to 10 stations, depending the on distance of the first one from the shore line, also spaced ~200 meters apart, with stations identified as A1, A2 etc. along the transect. Exact locations for these stations, along with the water depth and temperature at the station, were collected with the GPS onboard the R/V Pintail, are described in the attached VIMS data report CHSD-2020-02 (Massey et al, 2020b). Detailed methodologies, including data processing, station maps and figures from the processed data can also be found in the report. Four distinct sediment types were identified from the Bluedrop FFP results, and the identified regions of these sediment types are indicated on the station map in the data report. A sediment sampling station was selected within each of the regions identified. One sediment station (corresponding to Bluedrop stations C1, G3, G6 and D6, was sampled each day over a period of 4 days from August 5-8, 2019, respectively. At each of the sites, a GOMEX box core was used to collect several sediment grab samples of which sub-cores were collected to minimize edge effects that would disturb the sediment/water interface. At each site, the top ten centimeters, if possible, from two 4” diameter sub-cores were sliced in 1 cm increments and combined for later analysis in the lab for grain size (sand, silt, and clay) distribution (data stored under Grainsize) as well as percent moisture and percent volatile content by loss on ignition at 550 degree C (data stored under Moisture). Two additional 4” diameter cores were analyzed for sediment erodibility using a GUST Microcosm (data stored under GUST), and two rectangular cores were imaged by digital X-ray analysis (data stored under Xray). Salinity and temperature profiles were collected at each site with a Sontek Castaway CTD (data stored under CTD). At each muddy sediment station (C1, G3, and G6), several gravity core samples were collected. One core was imaged by digital X-ray analysis (data stored under Xray) and sliced and analyzed for grain size (sand, silt, and clay) distribution (data stored under Gravity Core). The other gravity cores samples were retained by Virginia Tech personnel for analyses done in their lab (Kiptoo, 2020). The gravity core would not penetrate sufficiently into sandy sediment, therefore there are no gravity cores for D6. Digital X-ray images were taken from a core from each site after it was sliced lengthwise (data stored under Xrays). The cores were then subsampled in 5 cm intervals and analyzed for grain size (sand, silt, and clay) distribution (data stored under grain size). Additional gravity cores were retained by Virginia Tech Personnel from each site for later analysis at their lab. At D6, samples were also collected for Virginia Tech personnel using the GOMEX grab. Acoustic Doppler Current Profiler (ADCP) transect data were collected on August 6th. One ADCP transect was collected along each Bluedrop transect perpendicular to the river flow (A-H). ADCP can be used to look at the general velocity flow field around the sediment sample station as well as provide an approximate measure of the bathymetry along the transects. Chirp transects were collected on the same day as ADCP transects along the numbered transects (1-10), and the data were retained by Virginia Tech personnel

    Impact of Optimized Breastfeeding on the Costs of Necrotizing Enterocolitis in Extremely Low Birthweight Infants

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    To estimate risk of NEC for ELBW infants as a function of preterm formula and maternal milk (MM) intake and calculate the impact of suboptimal feeding on NEC incidence and costs

    Master Sampling Spreadsheet and Timeline for MUDBED Project

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    The MUlti-Disciplinary Benthic Exchange Dynamics (MUDBED) project was a \u3e15-year collaboration of multiple VIMS scientists in characterizing sediment transport within the York River estuary

    Impact of Optimized Breastfeeding on the Costs of Necrotizing Enterocolitis in Extremely Low Birthweight Infants

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    To estimate risk of NEC for ELBW infants as a function of preterm formula and maternal milk (MM) intake and calculate the impact of suboptimal feeding on NEC incidence and costs
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