net ecosystem exchange

Eddy flux measurements during 2017 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts, PIE LTER.

Abstract: 

We deployed an eddy covariance system to measure ecosystem-atmosphere exchange of CO2  above a low marsh system (Spartina alterniflora) located within the Shad creek catchment off Plum Island Sound, Rowley MA. The data represents CO2 exchange for all July to October 2017. This site was established in 2015.

Core Areas: 

Data set ID: 

505

Keywords: 

Short name: 

MAR-SH-EddyFluxTower-2017

Data sources: 

MAR-SH-EddyFluxTower-2017_csv
MAR-SH-EddyFluxTower-2017_xls

Methods: 

We deployed one eddy covariance system to measure NEE of low marsh at PIE. The location of the tower is in the central low marsh area along the Rowley River, within the Shad Creek catchment. Deployment of the system is seasonal (mid-April to mid-November). The site was equipped with a Campbell Scientific® Closed Path System (CPEC200). Micrometeorological instrumentation was mounted on a tower at a height of 4.2m above the marsh  surface.

Environmental data were recorded as 10min averages. Air temperature and relative humidity were monitored at the same height as the anemometer (Campbell Scientific HC2S3 enclosed in a naturally aspirated radiation shield). A four-component net radiometer (Hukseflux NR01) was mounted 2 m aboveground of the low marsh. At the same height, one sensor (LI190SB, Licor) monitored reflected photosynthetically active radiation (PAR), incoming PAR was measured at the separate high marsh met station. In addition, a pressure transducer (Campbell Scientific CS456) recorded water table height at the high marsh. Soil temperature at a depth of 2cm, 6cm, 10cm, 20cm and 40cm was measured with (TCAV-L; Campbell Scientific; Logan, Utah, USA), and soil heat flux at a depth of 8 cm was measured with two soil heat flux plates (HFP01-SC; Campbell Scientific; Logan, Utah, USA).  This data was recorded on a separate CR3000 datalogger.     

Turbulent fluxes of momentum, sensible heat, latent heat and CO2 were determined by the eddy covariance technique (Baldocchi et al. 1988). Half hourly CO2 and H2O fluxes were calculated as the covariance between the turbulent departures from the mean of the 10 Hz vertical wind speed measured with a 3D sonic anemometer (CSAT3; Campbell Scientific; Logan, Utah, USA) and the CO2 and H2O dry mixing ratio measured with the closed path analyzer.  Fluxes were processed using EdiRe software (Robert Clement, University of Edinburgh) and reported using the meteorological sign convention where negative NEE indicates carbon uptake and positive NEE indicates carbon loss from the ecosystem.  Two coordinate rotations were performed on the wind components, and the time lag between wind and CO2 mixing ratio measurements was determined and removed for each averaging interval of 30min. For every 30 min period, a factor for the correction of the frequency attenuation of the flux was calculated according to Moore [1986] and applied to the flux. Fluxes were calculated using the Edire software (version 1.5.0.32, R. Clement, University of Edinburgh, UK). Afterward, fluxes were filtered for system malfunctioning and calibration periods, integral turbulence characteristics, stationarity, and wind direction [Foken etal., 2012]. We also excluded measurements when less than 80% of the flux was generated within the study area. Thresholds in friction velocity (u∗) for nighttime fluxes were determined using REddyProc .

To continuously monitor aboveground biomass, we calculated a broadband normalized difference vegetation index (NDVI) based on the approach of Wilson and Meyers [2007]. Incoming (i) and reflected (r) Solar (S) and photosynthetically active radiation (PAR) measurements were converted into red and near-infrared reflectance. Solar zenith effects were removed by using data exclusively around solar noon (10 A.M.–2 P.M. EST). In our system, spring tides occurred around noon, so that simultaneous radiation measurements recorded the effect of tidal inundation at that time. A decrease in NDVI would reflect that during inundation the amount of biomass that was air exposed was smaller than under nonflooded conditions. We included this effect in our NEE model by creating two continuous time series of NDVI to simulate flooded and nonflooded conditions: NDVIall which included spring tide effects, and a reference time series, NDVIref, which represented nonflooded conditions.

NEE is gap-filled with a modified PLIRTLE model (NEE=GPP+Reco), using NDVI_all, air temperature and PAR as input. GPP_all and Reco_all are estimated using the two sub-models of the PLIRTLE model. GPP_ref and Reco_ref are modelled with NDVI_ref as input variable and thus represent the fluxes occurring if no tidal inundation had occurred.

References:

Baldocchi et al. [1988]: Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods. Ecology, Vol. 69, No. 5, pp. 1331-1340.

Moore [1986]: Frequency  response correction s for eddy correlation systems. Boundary Layer Meteorology,  Vol. 37, pp. 17-35.

Foken et al. [2012]: Corrections and Data Quality Control. In: Aubinet, Vesala, Papale (editors):  Eddy covariance - a practical guide to measurement and data analysis.

Papale et al. [2006]: Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation . Biogeosciences, Vol. 3, pp. 571-583.

Maintenance: 

Data collection and processing complete.

Version 01: July 25, 2019, data and metadata updates to comply with importation to DEIMS7 and LTER Data Portal. Used MarcrosExportEML_HTML (working)pie_excel2007_Jun2019.xlsm 6/7/19 12:58 PM for QA/QC to EML 2.1.0.

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