http://lod.bco-dmo.org/id/dataset/810274
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2020-04-29
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Distance-weighted data flow averages for the York River estuary in 2018 and 2019
2020-04-29
publication
2020-04-29
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2022-03-11
publication
https://doi.org/10.26008/1912/bco-dmo.810274.1
Iris C. Anderson
Virginia Institute of Marine Science
principalInvestigator
Mark J. Brush
Virginia Institute of Marine Science
principalInvestigator
Kimberly S. Reece
Virginia Institute of Marine Science
principalInvestigator
Bongkeun Song
Virginia Institute of Marine Science
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Anderson, I. C., Brush, M. J., Reece, K., Song, B. (2022) Distance-weighted data flow averages for the York River estuary in 2018 and 2019. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2020-04-29 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.810274.1 [access date]
Distance-weighted data flow averages for the York River estuary in 2018 and 2019 Dataset Description: Methods and Sampling: <p><strong>High resolution sampling surveys in the York River estuary (YRE):</strong>&nbsp;performed in 2018 during Feb, March, June, August, and October and in 2019 during Feb, April, July, and October at dawn, dusk, and the following dawn along a zig-zag cruise track, covering approximately equal areas of shoal and channel, a total distance of 30 km, using a DataFlow system modified from Madden and Day (1992). All sampling is performed during spring tides, the period following de-stratification in the YRE when the system is well-mixed (Haas 1977). This high resolution sampling plan enables us to capture day-time primary production (dawn to dusk) and night-time respiration (dusk to dawn) while sampling water masses exposed to the same tidal stage. The DataFlow system captures continuous measurements of water quality parameters and allows grab sampling (at five channel and five shoal stations) for DIC and other parameters shown in different spread sheets and used for multiple regression analyses. The pCO₂-DataFlow system is instrumented with a pCO₂ analyzer, a multi-parameter datasonde (YSI 6600V2), Wet Labs CDOM sensor, Garmin global positioning system (GPS MAP 546S), and data acquisition system. The system continuously samples surface water (approximately every 30 m at an average speed of 20 knots) from a stern-mounted water intake located 0.5 m below the water surface with a pump, which delivers water in parallel to (1) a shower-head equilibrator and (2) a flow-through cell attached to the YSI which is configured to measure water temperature, salinity, chl-<em>a</em>&nbsp;fluorescence, DO, pH, and turbidity. pCO₂ in the equilibration chamber is determined by recirculating a carrier gas at a flow of approximately 1.5 L min⁻¹ through the equilibrator chamber and a nondispersive infrared absorbance detection analyzer (LI-COR, LI-840). The mole fraction of CO₂ (xCO₂) is corrected for headspace pressure and temperature to determine surface-water pCO₂ with an attainable accuracy of ±4 µatm over the functional range of 100 to 5,000 ppmv (Crosswell et al. 2012). All measurements are taken at 2-second intervals, and the lag time between DataFlow and LI-COR data is measured and corrected for each sampling run. At the beginning, during, and end of each survey, ambient atmospheric air and two CO₂ gas standards (Praxair Inc.) are measured for calibration and verification of the absorbance detection analyzer.</p>
<p><strong>Calculation of Air-Water CO2 and O2 Fluxes</strong>: Data in these spreadsheets are used to calculate CO2, O2 fluxes and metabolic parameters as described below. Air-water CO2 and O2 exchanges are driven by the differences between the gas concentrations in the surface water and the atmosphere (ΔpCO2, ΔpO2) scaled by the gas solubility, and the rate of exchange is set by the gas-transfer velocity (k, cm h-1). Input data include: K0, the solubility coefficient (K0(CO2): Weiss 1974; K0(O2): Benson and Krause 1984); k600 the gas exchange coefficient; ΔpCO2 and ΔpO2 and ScSST is the Schmidt number at ambient sea-surface temperature and salinity (Wanninkhof 1992). The gas solubility and Schmidt numbers for CO2 and O2 are determined based on temperature and salinity. The transfer velocity k is parameterized as a function of wind speed normalized to 10 m above the water surface (u10). We calculate air-water CO2 fluxes using the Jiang et al. (2008) parameterization, which provides a conservative estimate of k and has been widely used in reviews of estuarine CO2 fluxes (Crosswell et al 2014).</p>
<p><strong>Spatial and Diel Interpolation of Dawn-Dusk Data for Calculation of Daily CO2 and O2 Fluxes:</strong>&nbsp;Data from each dawn–dusk survey is spatially averaged by calculating distance-weighted means of ΔpCO2 and delta pO2 (difference water and atmospheric concentrations), water temperature, salinity and other parameters within a series of five boxes of the YRE. Data are used to calculate CO2 and O2 fluxes, and metabolic parameters as described below.</p>
<p><strong>Open Water measurements of metabolism (based on diel variations of DO):&nbsp;</strong>NEM gross primary production (GPP), and respiration (R) are determined from changes in DO concentrations in Dataflow samples collected at dawn, dusk, and the following dawn using the open water method (Kemp and Boynton 1980). ∆DO (dawn to dusk; dusk to dawn), corrected for air/sea exchanges of O2 are used to calculate NEM, GPP, and R. To scale metabolic measurements to regional estimates, the surface areas and water volumes of the YRE were determined for each spatial element using ArcGIS 10 and bathymetric data. Daily average hourly O2 fluxes (mmol DO m-2 d-1) for each box are multiplied by their respective spatial element areas and scaled to annual O2 fluxes. Daily fluxes for each spatial element are summed to provide seasonal and annual totals (moles O2 per m2 per y).</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1737258 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1737258
completed
Iris C. Anderson
Virginia Institute of Marine Science
757-876-9436
P.O Box 1346
Gloucester Point
VA
23062
USA
iris@vims.edu
pointOfContact
Mark J. Brush
Virginia Institute of Marine Science
804-684-7402
P.O Box 1346
Gloucester Point
VA
23062
USA
brush@vims.edu
pointOfContact
Kimberly S. Reece
Virginia Institute of Marine Science
804-684-7407
P.O Box 1346
Gloucester Point
VA
23062
USA
kreece@vims.edu
pointOfContact
Bongkeun Song
Virginia Institute of Marine Science
804-684-7411
P.O Box 1346
Gloucester Point
VA
23692
USA
songb@vims.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
Segments
box
pCO2_1
Temp
Salinity
DO_1
DO_2
DO_3
pH
Turbidity
Chlorophyll_a
DIC
Wet Labs CDOM sensor
Garmin global positioning system (GPS MAP 546S)
pCO2 analyzer
YSI 6600V2
theme
None, User defined
sample identification
site
Partial pressure of CO2
water temperature at measurement depth
salinity
O2 saturation
dissolved Oxygen
pH
turbidity
chlorophyll a
dissolved inorganic Carbon
featureType
BCO-DMO Standard Parameters
Fluorometer
Global Positioning System Receiver
pCO2 Sensor
YSI Sonde 6-Series
instrument
BCO-DMO Standard Instruments
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Alteration of carbon fluxes by intense phytoplankton blooms in a microtidal estuary
https://osprey.bco-dmo.org/project/747588
Alteration of carbon fluxes by intense phytoplankton blooms in a microtidal estuary
<p><em>NSF Award Abstract:</em><br />
Estuaries, coastal water bodies where rivers mix with ocean water, are hotspots for the processing of carbon and nutrients moving from land to the coastal ocean. Within estuaries land-based nutrient inputs can cause intense blooms of single-celled algae called phytoplankton, which can have significant impacts on the ecosystem. As blooms move down-estuary some of the phytoplankton material is buried on the bottom, and some is decomposed, resulting in low oxygen conditions (hypoxia), harmful to marine life, and production of carbon dioxide (CO2), the major greenhouse gas, which can exchange with the atmosphere. The remaining phytoplankton material can be exported to the ocean. The type and amount of carbon exported from the estuary depend both on its biological activity and physical factors such as fresh water discharge, temperature, and light availability. If phytoplankton production is greater than decomposition, the estuary will take up atmospheric CO2 and export phytoplankton carbon to the coastal ocean. On the other hand, if decomposition is greater than production the estuary will be a source of CO2 to the atmosphere and dissolved CO2 to the coastal ocean. The investigators expect that intense phytoplankton blooms will greatly amplify carbon exchanges with the atmosphere, coastal ocean, and bottom sediments. As intense phytoplankton blooms increase in the future due to increased nutrient inputs and temperature, low oxygen events may become more frequent with potential negative impacts on fisheries and increased export of carbon to the coastal ocean and atmosphere. This study will fill critical gaps identified by the Coastal Carbon Synthesis Program in knowledge of how microtidal estuaries transform and export C to the atmosphere, benthos, and coastal ocean. In addition, there will be a strong teaching and training component to this project, with support for graduate and undergraduate students. The graduate student will be partnered with secondary teachers to gain teaching experience and enrich the middle school educational programs. Summer undergraduate interns will be recruited for a summer program from Hampton University, a historically Black college. There will be public outreach through participation in existing programs at VIMS.</p>
<p>Estuaries serve as critical hotspots for the processing of carbon (C) as it transits from land to the coastal ocean. Recent attempts to synthesize what is known about sources and fates of C in estuaries have noted large data gaps; thus, the role of estuaries, especially those that are microtidal, as important sources of carbon dioxide (CO2) to the atmosphere and total organic carbon (TOC) and dissolved inorganic carbon (DIC) to the coastal ocean, or as a C sink in bottom sediments, remains uncertain. Intensive phytoplankton blooms are becoming increasingly frequent in many estuaries and are likely to have important and yet unknown impacts on the C cycle. The trophic status of an estuary will determine in large part the species of C exported to the atmosphere, bottom sediments, and coastal ocean. The overarching objective of this project is to identify the impacts of intense phytoplankton blooms on C speciation, net C fluxes and exchanges in the Lower York River Estuary (LYRE), a representative mesotrophic, microtidal mid-Atlantic estuary. Metabolic processes are hypothesized to be spatially and temporally dynamic, driving the speciation, abundance, and fates of C in the LYRE. High spatiotemporal resolution sampling in the LYRE will capture rates of C cycling under both baseline conditions throughout most of the year, and during periods when the estuary is perturbed by widespread and intense, but patchy, late summer phytoplankton blooms. The short-term effects of physical drivers (wind, temperature, salinity, fresh water discharge, nutrient and organic carbon loads) and biological drivers (metabolic rates, bacterial and phytoplankton abundances and composition) on C transformations, speciation, and exchanges will be assessed. Expected longer term variations in the C cycle due to anthropogenic and natural disturbances will be predicted through use of modeling. In addition, laboratory manipulations will examine the impacts of specific organisms dominating intensive phytoplankton blooms on benthic metabolism, processing of organic C by the microbial community, and C fluxes to the water column.</p>
LYRE
largerWorkCitation
project
eng; USA
oceans
-76.8037
-76.3685
37.265
37.5217
2018-02-06
2019-10-15
York River Estuary, Virginia
0
BCO-DMO catalogue of parameters from Distance-weighted data flow averages for the York River estuary in 2018 and 2019
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/810356.rdf
Name: Segments
Units: unitless
Description: <p>Sampling time point</p>
http://lod.bco-dmo.org/id/dataset-parameter/810357.rdf
Name: box
Units: unitless
Description: <p>Box into which the estuary has been subdivided. 1.00 is at the mouth; 5.00 at the head. All data were collected continuously during cruises along the entire estuary. Data within a single box were distance weighted, representing a distance weighted average for the box.</p>
http://lod.bco-dmo.org/id/dataset-parameter/810358.rdf
Name: pCO2_1
Units: micro atmospheres (uatm)
Description: <p>Delta partial pressure CO2 in water and atmosphere</p>
http://lod.bco-dmo.org/id/dataset-parameter/810359.rdf
Name: Temp
Units: degrees Celsius
Description: <p>Water temperature</p>
http://lod.bco-dmo.org/id/dataset-parameter/810360.rdf
Name: Salinity
Units: unitless
Description: <p>Salinity</p>
http://lod.bco-dmo.org/id/dataset-parameter/810361.rdf
Name: DO_1
Units: percent saturation
Description: <p>Dissolved oxygen</p>
http://lod.bco-dmo.org/id/dataset-parameter/810362.rdf
Name: DO_2
Units: milligrams per liter (mg/L)
Description: <p>Dissolved oxygen</p>
http://lod.bco-dmo.org/id/dataset-parameter/810363.rdf
Name: DO_3
Units: mg/L at saturation
Description: <p>Dissolved oxygen</p>
http://lod.bco-dmo.org/id/dataset-parameter/810364.rdf
Name: pH
Units: unitless
Description: <p>pH</p>
http://lod.bco-dmo.org/id/dataset-parameter/810365.rdf
Name: Turbidity
Units: units relative to standard
Description: <p>Turbidity</p>
http://lod.bco-dmo.org/id/dataset-parameter/810366.rdf
Name: Chlorophyll_a
Units: micrograms per liter (ug/L)
Description: <p>in situ chlorophyll a</p>
http://lod.bco-dmo.org/id/dataset-parameter/810367.rdf
Name: DIC
Units: milligrams per liter (mg/L)
Description: <p>Dissolved inorganic carbon</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
13748
https://darchive.mblwhoilibrary.org/bitstream/1912/28083/1/dataset-810274_york-river-flow-distance-weighted-averages__v1.tsv
download
https://doi.org/10.26008/1912/bco-dmo.810274.1
download
onLine
dataset
<p><strong>High resolution sampling surveys in the York River estuary (YRE):</strong>&nbsp;performed in 2018 during Feb, March, June, August, and October and in 2019 during Feb, April, July, and October at dawn, dusk, and the following dawn along a zig-zag cruise track, covering approximately equal areas of shoal and channel, a total distance of 30 km, using a DataFlow system modified from Madden and Day (1992). All sampling is performed during spring tides, the period following de-stratification in the YRE when the system is well-mixed (Haas 1977). This high resolution sampling plan enables us to capture day-time primary production (dawn to dusk) and night-time respiration (dusk to dawn) while sampling water masses exposed to the same tidal stage. The DataFlow system captures continuous measurements of water quality parameters and allows grab sampling (at five channel and five shoal stations) for DIC and other parameters shown in different spread sheets and used for multiple regression analyses. The pCO₂-DataFlow system is instrumented with a pCO₂ analyzer, a multi-parameter datasonde (YSI 6600V2), Wet Labs CDOM sensor, Garmin global positioning system (GPS MAP 546S), and data acquisition system. The system continuously samples surface water (approximately every 30 m at an average speed of 20 knots) from a stern-mounted water intake located 0.5 m below the water surface with a pump, which delivers water in parallel to (1) a shower-head equilibrator and (2) a flow-through cell attached to the YSI which is configured to measure water temperature, salinity, chl-<em>a</em>&nbsp;fluorescence, DO, pH, and turbidity. pCO₂ in the equilibration chamber is determined by recirculating a carrier gas at a flow of approximately 1.5 L min⁻¹ through the equilibrator chamber and a nondispersive infrared absorbance detection analyzer (LI-COR, LI-840). The mole fraction of CO₂ (xCO₂) is corrected for headspace pressure and temperature to determine surface-water pCO₂ with an attainable accuracy of ±4 µatm over the functional range of 100 to 5,000 ppmv (Crosswell et al. 2012). All measurements are taken at 2-second intervals, and the lag time between DataFlow and LI-COR data is measured and corrected for each sampling run. At the beginning, during, and end of each survey, ambient atmospheric air and two CO₂ gas standards (Praxair Inc.) are measured for calibration and verification of the absorbance detection analyzer.</p>
<p><strong>Calculation of Air-Water CO2 and O2 Fluxes</strong>: Data in these spreadsheets are used to calculate CO2, O2 fluxes and metabolic parameters as described below. Air-water CO2 and O2 exchanges are driven by the differences between the gas concentrations in the surface water and the atmosphere (ΔpCO2, ΔpO2) scaled by the gas solubility, and the rate of exchange is set by the gas-transfer velocity (k, cm h-1). Input data include: K0, the solubility coefficient (K0(CO2): Weiss 1974; K0(O2): Benson and Krause 1984); k600 the gas exchange coefficient; ΔpCO2 and ΔpO2 and ScSST is the Schmidt number at ambient sea-surface temperature and salinity (Wanninkhof 1992). The gas solubility and Schmidt numbers for CO2 and O2 are determined based on temperature and salinity. The transfer velocity k is parameterized as a function of wind speed normalized to 10 m above the water surface (u10). We calculate air-water CO2 fluxes using the Jiang et al. (2008) parameterization, which provides a conservative estimate of k and has been widely used in reviews of estuarine CO2 fluxes (Crosswell et al 2014).</p>
<p><strong>Spatial and Diel Interpolation of Dawn-Dusk Data for Calculation of Daily CO2 and O2 Fluxes:</strong>&nbsp;Data from each dawn–dusk survey is spatially averaged by calculating distance-weighted means of ΔpCO2 and delta pO2 (difference water and atmospheric concentrations), water temperature, salinity and other parameters within a series of five boxes of the YRE. Data are used to calculate CO2 and O2 fluxes, and metabolic parameters as described below.</p>
<p><strong>Open Water measurements of metabolism (based on diel variations of DO):&nbsp;</strong>NEM gross primary production (GPP), and respiration (R) are determined from changes in DO concentrations in Dataflow samples collected at dawn, dusk, and the following dawn using the open water method (Kemp and Boynton 1980). ∆DO (dawn to dusk; dusk to dawn), corrected for air/sea exchanges of O2 are used to calculate NEM, GPP, and R. To scale metabolic measurements to regional estimates, the surface areas and water volumes of the YRE were determined for each spatial element using ArcGIS 10 and bathymetric data. Daily average hourly O2 fluxes (mmol DO m-2 d-1) for each box are multiplied by their respective spatial element areas and scaled to annual O2 fluxes. Daily fluxes for each spatial element are summed to provide seasonal and annual totals (moles O2 per m2 per y).</p>
Specified by the Principal Investigator(s)
<p>BCO-DMO Processing:<br />
- formatted date/time to&nbsp;YYYY-MM-DDThh:mm;<br />
- moved the box numbers from a row to a column.</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
Wet Labs CDOM sensor
Wet Labs CDOM sensor
PI Supplied Instrument Name: Wet Labs CDOM sensor PI Supplied Instrument Description:Wet Labs FLCD-SB CDOM sensor Instrument Name: Fluorometer Instrument Short Name:Fluorometer Instrument Description: A fluorometer or fluorimeter is a device used to measure parameters of fluorescence: its intensity and wavelength distribution of emission spectrum after excitation by a certain spectrum of light. The instrument is designed to measure the amount of stimulated electromagnetic radiation produced by pulses of electromagnetic radiation emitted into a water sample or in situ. Community Standard Description: http://vocab.nerc.ac.uk/collection/L05/current/113/
Garmin global positioning system (GPS MAP 546S)
Garmin global positioning system (GPS MAP 546S)
PI Supplied Instrument Name: Garmin global positioning system (GPS MAP 546S) PI Supplied Instrument Description:The pCO₂-DataFlow system is instrumented with a pCO₂ analyzer, a multi-parameter datasonde (YSI 6600V2), Wet Labs CDOM sensor, Garmin global positioning system (GPS MAP 546S), and data acquisition system. Instrument Name: Global Positioning System Receiver Instrument Short Name:GPS Instrument Description: The Global Positioning System (GPS) is a U.S. space-based radionavigation system that provides reliable positioning, navigation, and timing services to civilian users on a continuous worldwide basis. The U.S. Air Force develops, maintains, and operates the space and control segments of the NAVSTAR GPS transmitter system. Ships use a variety of receivers (e.g. Trimble and Ashtech) to interpret the GPS signal and determine accurate latitude and longitude. Community Standard Description: http://vocab.nerc.ac.uk/collection/L05/current/POS03/
pCO2 analyzer
pCO2 analyzer
PI Supplied Instrument Name: pCO2 analyzer PI Supplied Instrument Description:The pCO₂-DataFlow system is instrumented with a pCO₂ analyzer, a multi-parameter datasonde (YSI 6600V2), Wet Labs CDOM sensor, Garmin global positioning system (GPS MAP 546S), and data acquisition system. Instrument Name: pCO2 Sensor Instrument Short Name:pCO2 Sensor Instrument Description: A sensor that measures the partial pressure of CO2 in water (pCO2)
YSI 6600V2
YSI 6600V2
PI Supplied Instrument Name: YSI 6600V2 PI Supplied Instrument Description:The pCO₂-DataFlow system is instrumented with a pCO₂ analyzer, a multi-parameter datasonde (YSI 6600V2), Wet Labs CDOM sensor, Garmin global positioning system (GPS MAP 546S), and data acquisition system. Instrument Name: YSI Sonde 6-Series Instrument Short Name:YSI Sonde 6-Series Instrument Description: YSI 6-Series water quality sondes and sensors are instruments for environmental monitoring and long-term deployments. YSI datasondes accept multiple water quality sensors (i.e., they are multiparameter sondes). Sondes can measure temperature, conductivity, dissolved oxygen, depth, turbidity, and other water quality parameters. The 6-Series includes several models. More from YSI. Community Standard Description: http://vocab.nerc.ac.uk/collection/L22/current/TOOL0737/