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GCE-LTER Data Set Summary

Accession: MSH-NASA-2006 Research Theme: Marsh Ecology (Research Study)
Contributors: Peter Hawman, Deepak Mishra, David Cotten, Jessica O'Connell, Caroline Narron, Lishen Mao
Title: Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the Grand Bay, Mississippi flux tower site from March 2018 to January 2019
Abstract: Eddy covariance (EC) CO2 fluxes from March 2018 to January 2019 collected over a Juncus roemerianus marsh located in the Grand Bay National Estuarine Research Reserve (NERR) in Mississippi. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height within the marsh.
DOI: 10.6073/pasta/3e78ff1dc5e4f102ddbca5e876c9c861
Key Words: air temperature, carbon dioxide, carbon fluxes, Climate Monitoring, eddy covariance, Juncus roemerianus, Long-term Data, net primary productivity, photosynthetically active radiation, salt marshes, vapor pressure, water level, wind direction, wind speed
LTER Core Area: Primary Production
Research Themes: Marsh Ecology, Meteorology
Study Period: 13-Mar-2018 to 01-Jan-2019
Study Sites:
GrandBay -- Grand Bay NERR, Moss Point, Mississippi
» Download Geographic Coverage: Google Earth
Research Protocols:

Protocol for calculating sampling error in CO2 flux measurements (Journal of Geophysical Research)

Impact of post-field data processing on eddy covariance flux estimates and energy balance closure (Meteorologische Zeitschrift)

Averaging Detrending and Filtering of Eddy Covariance Time Series (Handbook of Micrometeorology)

A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide (Journal of Hydrology)

Quality Control and Flux Sampling Problems for Tower and Aircraft Data (Journal of Atmospheric and Oceanic Technology)

Correction of flux measurements for density effects due to heat and water vapour transfer (Quarterly Journal of the Royal Meteorological Society)

Sonic Anemometer Tilt Correction Algorithms (Boundary-Layer Meteorology)

Downloads: Information

Data Table: MSH-NASA-2006 (Main data table for data set MSH-NASA-2006, 28248 records)

Access: Public (released 07-Apr-2021)

Metadata: Text (ESA FLED), XML (Ecological Metadata Language)

Data Formats: Spreadsheet (CSV) [2970.05kb],  Text File [2976.58kb],  MATLAB (GCE Toolbox) [6296.88kb],  MATLAB (Variables) [NaNkb],  Text Report [3148.61kb]

Column List:(hide)

Column Name Units Type Description
1 DateTime yyyy-mm-dd HH:MM:SS - GMT string Calendar date and time of observation
2 Wind_Speed m/s floating-point Wind speed, 30 minute mean
3 Wind_Dir degrees floating-point Wind direction, 30 minute mean
4 uStar m/s floating-point Friction velocity
5 Monin-Obukhov length meters floating-point Monin-Obukhov length
6 Z_L none floating-point Monin-Obukhov stability parameter (z-d)/L
7 V_Var m/s floating-point Variance of lateral velocity fluctuations
8 CO2_Flux umol/m^2/s floating-point Corrected carbon dioxide flux
9 QC_CO2_Flux none integer Quality flag for gas flux
10 Rand_Err_CO2_Flux umol/m^2/s floating-point Random error for carbon dioxide flux
11 CO2_Strg umol/m^2/s floating-point Estimate of storage carbon dioxide flux
12 Mean_PAR_Incident umol/m^2/s floating-point Photosynthetically active radiation, 30 minute mean
13 Mean_Temp_Air °C floating-point Air temperature, 30 minute mean
14 Mean_Temp_Soil °C floating-point Soil temperature, 30 minute mean
15 Mean_Humidity % floating-point Relative humidity, 30 minute mean
16 VPD kPa floating-point Vapor pressure deficit, 30 minute mean
17 Mean_Water_Table meters floating-point Water table level, 30 minute mean
Statistics: Generate script code to retrieve data tables for analysis in: MATLAB, R, SAS, SPSS
Citation: Hawman, Peter. 2020. Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the Grand Bay, Mississippi flux tower site from March 2018 to January 2019. Georgia Coastal Ecosystems LTER Project, University of Georgia, Long Term Ecological Research Network. http://dx.doi.org/10.6073/pasta/3e78ff1dc5e4f102ddbca5e876c9c861

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This material is based upon work supported by the National Science Foundation under grants OCE-9982133, OCE-0620959, OCE-1237140 and OCE-1832178. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.