Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Parameters
• Instruments
• Project Information
• Funding

This dataset includes results of analysis on sediment core bottom water samples collected in the back barrier island bays on Virginia’s Eastern Shore in July 2017 - initial pH, alkalinity, sulfate, DIC, and DOC.

Sediment cores were collected by divers, sealed in the field with rubber stoppers and returned to the lab for processing. Pore waters were collected by inserting rhizon samplers (Seeberg-Elverfeldt et al., 2005) through pre-drilled holes in the core tubes. Samples were collected in gas-tight glass syringes and filtered through 0.45 µm nylon filters into storage vials. Alkalinity samples were titrated within 12hr of collection; other samples were returned to the lab for analysis, using techniques routinely used in my lab: alkalinity and initial pH - Hu and Burdige (2008); sulfate, DIC, ammonium and DOC - Burdige and Komada (2011), Komada et al. (2016); sulfide - Cline (1969), Abdulla et al. (in prep.).

Alkalinity and initial pH were determined by Gran Titration using a Metrohm automatic titrator (model 785 DMP Titrino) combined with a Cole-Parmer pH electrode, calibrated using pH 4.00, 7.00 and 10.00 NIST-traceable buffers (Hu and Burdige, 2008).  

Sulfate was determined by ion chromatography and conductivity detection with a Thermo-Fisher Dionex ICS-5000 ion chromatograph.

DOC was determined by high-temperature combustion using a Shimadzu TOC-V total carbon analyzer (Burdige and Komada, 2011; Komada et al. 2016).  

DIC was determined by FIA analysis using a home-built system consisting of a Rainin Rabbit peristaltic pump and a Dionex CDM-II conductivity detector (Hall and Aller, 1992; Lustwerk and Burdige, 1995).  

BCO-DMO Processing Notes:
- added conventional header with dataset name, PI name, version date
- modified parameter names to conform with BCO-DMO naming conventions
- added columns for site, lat, and lon
- reformatted collection date m/d/yyyy H:MM to YYYY-MM-DD

NSF abstract:

This research will develop a quantitative understanding of the factors controlling carbon cycling in seagrass meadows that will improve our ability to quantify their potential as blue carbon sinks and predict their future response to climate change, including sea level rise, ocean warming and ocean acidification. This project will advance a new generation of bio-optical-geochemical models and tools (ECHOES) that have the potential to be transform our ability to measure and predict carbon dynamics in shallow water systems.

This study will utilize cutting-edge methods for evaluating oxygen and carbon exchange (Eulerian and eddy covariance techniques) combined with biomass, sedimentary, and water column measurements to develop and test numerical models that can be scaled up to quantify the dynamics of carbon cycling and sequestration in seagrass meadows in temperate and tropical environments of the West Atlantic continental margin that encompass both siliciclastic and carbonate sediments. The comparative analysis across latitudinal and geochemical gradients will address the relative contributions of different species and geochemical processes to better constrain the role of seagrass carbon sequestration to global biogeochemical cycles. Specifically the research will quantify: (i) the relationship between C stocks and standing biomass for different species with different life histories and structural complexity, (ii) the influence of above- and below-ground metabolism on carbon exchange, and (iii) the influence of sediment type (siliciclastic vs. carbonate) on Blue Carbon storage. Seagrass biomass, growth rates, carbon content and isotope composition (above- and below-ground), organic carbon deposition and export will be measured. Sedimentation rates and isotopic composition of PIC, POC, and iron sulfide precipitates, as well as porewater concentrations of dissolved sulfide, CO2, alkalinity and salinity will be determined in order to develop a bio-optical-geochemical model that will predict the impact of seagrass metabolism on sediment geochemical processes that control carbon cycling in shallow waters. Model predictions will be validated against direct measurements of DIC and O2 exchange in seagrass meadows, enabling us to scale-up the density-dependent processes to predict the impacts of seagrass distribution and density on carbon cycling and sequestration across the submarine landscape.

Status, as of 09 June 2016: This project has been recommended for funding by NSF's Division of Ocean Sciences.