Net ecosystem production and carbon dioxide fluxes in the Scheldt estuarine plume
© Borges et al; licensee BioMed Central Ltd. 2008
Received: 26 September 2007
Accepted: 08 September 2008
Published: 08 September 2008
A time series of 4 consecutive years of measurements of the partial pressure of CO2 (pCO2) in the Scheldt estuarine plume is used here to estimate net ecosystem production (NEP).
NEP in the Scheldt estuarine plume is estimated from the temporal changes of dissolved inorganic carbon (DIC). The strong seasonal variations of NEP are consistent with previous reports on organic carbon dynamics in the area. These variations are related to successive phytoplankton blooms that partly feed seasonally variable heterotrophy the rest of the year. On an annual time scale the Scheldt estuarine plume behaves as a net heterotrophic system sustained with organic carbon input from the Scheldt inner estuary and the Belgian coast. During one of the years of the time-series the estuarine plume behaved annually as a net autotrophic system. This anomalous ecosystem metabolic behaviour seemed to result from a combination of bottom-up factors affecting the spring phytoplankton bloom (increased nutrient delivery and more favourable incoming light conditions). This net autotrophy seemed to lead to a transient aa accumulation of organic carbon, most probably in the sediments, that fed a stronger heterotrophy the following year.
The present work highlights the potential of using pCO2 data to derive detailed seasonal estimates of NEP in highly dynamic coastal environments. These can be used to determine potential inter-annual variability of NEP due to natural climatic oscillations or due to changes in anthropogenic impacts.
The flows of carbon and nutrients in the coastal ocean are disproportionately high in comparison with its surface area because of the massive inputs of organic matter and nutrients from land. Large amounts of matter and energy are exchanged between the coastal ocean and the open ocean across continental slopes and the coastal ocean represents one of the most biogeochemically active areas of the biosphere [e.g., ]. The production, degradation, export and burial of organic matter in coastal waters are in general much higher than in the open ocean [e.g., ].
The metabolic status of an ecosystem is quantified by the net ecosystem production (NEP) that corresponds to the difference between gross primary production (GPP) and ecosystem respiration (autotrophic and heterotrophic respiration) in both the pelagic and benthic compartments. This will determine if an ecosystem exports organic carbon to adjacent systems (net autotrophic; NEP > 0) or if an ecosystem requires external organic carbon inputs to sustain its ecosystem metabolism (net heterotrophic; NEP < 0). However, the ecosystem metabolic status of the coastal ocean as net autotrophic or net heterotrophic has been the subject of a long lived debate [1–6]. One of the reasons for this debate is the lack of data for resolving the temporal variability of carbon cycling in the highly dynamic coastal ecosystems, and for adequately describing the diversity and spatial heterogeneity of these ecosystems [1, 7–9]. A recent exhaustive literature review of ecosystem metabolic estimates in European coastal waters did not reach an unambiguous conclusion on their trophic status, although these are among the most thoroughly studied sites in the world .
Reliable estimates of the ecosystem metabolic status are hampered by the conceptual problems associated with 14C estimation of primary production [e.g., [10, 11]], the strong spatial heterogeneity within an ecosystem [e.g., for estuaries [12, 13]], and the high temporal variability [e.g., ] which cannot be easily measured with classical incubation based approaches. Gazeau et al.  reviewed the advantages and caveats of several methods to estimate NEP, and recommended the use of integrative mass balance approaches. A commonly applied integrative mass balance approach is the Land-Ocean Interaction in the Coastal Zone (LOICZ) method based on the budget of dissolved inorganic phosphorus (DIP) . In turbid environments such as inner and outer estuaries, the LOICZ DIP budgets can provide highly unrealistic NEP estimates [e.g., [12, 13]] due to complex abiotic processes of desorption/adsorption from/on suspended matter [e.g., ].
Results and discussion
where DIC1 and DIC2 are DIC values from 2 consecutive cruises, FCO21 and FCO22 are the air-sea CO2 fluxes (FCO2) from 2 consecutive cruises, Δt is the time interval between 2 consecutive cruises, and d is the depth of the mixed layer depth.
Such an approach is suited for permanently well-mixed systems such as the Belgian coastal zone (BCZ), as knowledge of the mixed layer depth is not required. This method relies on the assumption that the production and degradation of organic matter, and air-sea CO2 exchange are the main drivers of CO2 dynamics (and that other processes such as CaCO3 production/dissolution are negligible). Such an assumption holds true in the BCZ based on current understanding of CO2 dynamics in this region [9, 20–24]. The major caveat of the method is the assumption that the net advective input/output of CO2 is constant between two steps of the computation. This source of uncertainty can be assumed minimal in the present case, because for time steps of the computations lower than the water residence time, the invariance of CO2 advective inputs/outputs can be assumed constant. The average time step of the computations was 21 d for an average water residence time of 60 d .
Average Scheldt river fresh water discharge (Q) from January and December of the previous year, flux of dissolved inorganic nitrogen (FDIN) from the Scheldt river, flux of total inorganic phosphorous (FPtot) from the Scheldt river, winter-time DIN and PO42- concentrations in the Belgian coastal zone and annual averages of the partial pressure of CO2 (pCO2), air-sea gradient of pCO2 (ΔpCO2), air-sea CO2 flux (FCO2) and net ecosystem production (NEP) at a fixed station in the Scheldt plume near the Zeebrugge harbor.
(106 mol d-1)
(106 mol d-1)
(mol m-2 yr-1)
(mol m-2 yr-1)
-4.2 ± 0.2
-3.8 ± 0.2
2.4 ± 0.1
-5.7 ± 0.2
On an annual scale the Scheldt river plume behaved as a net heterotrophic system in 2001, 2002 and 2004, but behaved as a net autotrophic system in 2003 (Table 1). Using a simple organic matter input/output budget, Borges and Frankignoulle  showed previously that the annual emission of CO2 to the atmosphere is only partly due to the input of CO2 from the Scheldt inner estuary and that net heterotrophy of the Scheldt estuarine plume is also important. The net heterotrophy of the Scheldt river plume must be sustained by external inputs of organic carbon that could originate from the Belgian coast and/or from the Scheldt inner estuary. Based on the input of organic matter from the Scheldt inner estuary reported by Soetaert and Herman  and a Scheldt plume surface area ranging between 2000 and 800 km2 , we computed a potential organic matter degradation ranging between 0.3 and 0.6 mol m-2 yr-1. Wollast  provides a higher estimate of the input of organic matter from the Scheldt inner estuary that can sustain a potential organic matter degradation ranging between 0.8 and 2.0 mol m-2 yr-1. Finally, Wollast  estimated the input of organic carbon from the Belgian coast that can sustain a potential organic matter degradation ranging between 0.7 and 1.8 mol m-2 yr-1. The potential degradation of these inputs of allochtonous organic matter from the Scheldt inner estuary and the Belgian coast are of the same order of magnitude as the annual NEP values we computed (Table 1).
The much stronger springtime NEP observed in 2003 compared to the other years is consistent with the remote sensed chlorophyll-a concentration from Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors showing that the peak springtime chlorophyll-a concentration was higher in 2003 than in 2004 (Fig. 2). It is known that the quality of satellite chlorophyll-a data may be suspect in coastal regions because of masking of phytoplankton absorption by absorption from coloured dissolved organic matter and/or non algal particles. For the region considered here such effects give a detection limit of about 3–5 μg L-1 for chlorophyll-a concentration for the Sea-viewing Wide Field-of-view (SeaWiFS) and MODIS sensors, and possibly a lower limit for MERIS. This is seen here as a background (artificial) concentration at the level of this detection limit. However, the high phytoplankton biomass in spring are detected every year quite coherently by all three sensors, despite different atmospheric correction, overpass time and chlorophyll-a retrieval algorithms, giving confidence in the satellite detection of these blooms. SeaWiFS data suggest that peak springtime chlorophyll-a concentration was also higher in 2003 compared to 2002 (Fig. 2).
The stronger annual heterotrophy in 2004 than in 2001 and 2002 could be due to a transient accumulation of part of the excess organic matter produced in 2003, since FPtot and winter-time PO42- concentrations were actually higher in 2004 than in 2002. The water residence time in the BCZ is highly variable but can be as long as 216 d  and is assumed to be on average 60 d . Hence, we hypothesize that part of the non-steady accumulation of organic matter from 2003 to 2004 occurred in the sediments. Sedimentation of organic matter is important in the BCZ, representing about 20% of annual GPP , and gives bottom sediments that are exceptionally rich in organic carbon compared to the rest of the North Sea [30, 34].
The present work highlights the potential of using pCO2 data to derive detailed seasonal estimates of NEP in highly dynamic coastal environments, and to determine potential inter-annual variability of NEP due to natural climatic oscillations or due to changes in anthropogenic impacts. On a longer term, such an approach should also allow estimation of decadal changes in NEP that could be used as an indication of the effectiveness of nutrient control policies for reducing eutrophication of coastal waters.
Automated measurements of pCO2 have been obtained since September 2000 on all the cruises carried out by the research vessel Belgica. A non-dispersive infrared gas analyzer (IRGA, Li-Cor®, Li-6262) and an equilibrator were used to measure the pCO2 (for details on design and performance tests refer to ). The IRGA was calibrated weekly using pure nitrogen (Air Liquide Belgium) and two gas mixtures with a CO2 molar fraction of 366 and 810 ppm (Air Liquide Belgium) that were calibrated against National Oceanic and Atmospheric Administration standards of a CO2 molar fraction of 361 and 774 ppm. The temperature at the outlet of the equilibrator was monitored with a platinum resistance thermometer (PT100, Metrohm®). The pCO2 values were corrected for the temperature difference between in-situ seawater and water in the equilibrator using the algorithm given by Copin-Montégut [36, 37]. The overall accuracy of pCO2 measurements is estimated to be better than ± 3 μatm. Salinity and temperature were measured using a SeaBird® SBE21 thermosalinograph. Salinity, temperature and pCO2 were sampled from the seawater supply of the ship (pump inlet at a depth of 2.5 m) and logged at a 1 min frequency.
The k values were computed using hourly wind speed values from the Vlakte van de Raan meteorological station (3.24°E 51.52°N) provided by the Royal Netherlands Meteorological Institute, and the k-wind parameterization given by Nightingale et al. , established in the Southern Bight of the North Sea, close to our study area. Monthly values of atmospheric pCO2 data obtained at station Kollumerwaard in the Netherlands (6.17°E 53.20°N) were provided by the Dutch National Air Quality Monitoring Network. Atmospheric pCO2 data were converted into pCO2 in wet air according to Dickson and Goyet .
where SSS is sea surface salinity, and TA is in μmol kg-1, established from 742 measurements in surface waters (salinity range 19.5–35.4) from 26 cruises carried out in the BCZ from 1996 to 2001 [[20, 21], Borges unpublished, Schiettecatte unpublished]. TA was measured using the Gran electrotitration method, with an estimated accuracy of ± 3 μmol kg-1. DIC was computed from pCO2 measurements and TA estimates from SSS, using the carbonic acid constants of Mehrbach et al.  refitted by Dickson and Millero .
where GR is in J cm-2 d-1 and PAR is in μmolE m-2 s-1
Level-3 SeaWiFS chlorophyll-a concentration data were extracted from the Ocean Color Time-Series Online Visualization and Analysis web site http://reason.gsfc.nasa.gov/Giovanni/. Level-2 MODIS chlorophyll-a concentration data were derived from the L1A MODIS data, distributed by NASA Goddard Space Flight Center Ocean Color group http://oceancolor.gsfc.nasa.gov/. The L1A radiance data measured by the sensor at the top of atmosphere are processed using the SeaWiFS Data Analysis System software with the atmospheric correction of Ruddick et al.  to obtain atmospherically corrected radiances. These are then converted to chlorophyll-a concentrations using the OC3 algorithm . Two chlorophyll-a parameters are included in MERIS level-2 products http://envisat.esa.int/dataproducts/meris/. The "algal pigment index 1" is computed using a ratio of water reflectances at blue and green bands [45, 46] and represents chlorophyll-a concentration for oceanic case 1 waters. The "algal pigment index 2" is designed to represent chlorophyll-a concentration for coastal case 2 waters and computed using a neural-network multiband inversion technique . The MERIS chlorophyll-a concentration used in this study was taken either from the algal pigment index 2 if the MERIS case 2 water flag was set or algal pigment index 1 otherwise. These chlorophyll-a data were removed if the product confidence flag was raised, thus excluding unreliable data. It is expected that the MERIS chlorophyll-a data will be more reliable in this region than those from SeaWiFS and MODIS because the case 2 algorithm is better suited to waters with high yellow substance absorption.
We are grateful to the crew of R.V. Belgica for help in running the underway pCO2 system, Management Unit of the North Sea Mathematical Models for thermosalinograph and GPS data, Youngje Park and Bouchra Nechad for processing the MERIS and MODIS data, Véronique Rousseau for providing the PAR data, Jack J. Middelburg and Frédéric Gazeau for providing the river Scheldt nutrient data, Management Unit of the Mathematical Models of the North Sea for providing the nutrient data in the BCZ. This research was supported by the European Union in the framework of EUROTROPH (EVK3-CT-2000-00040), and CARBOOCEAN (511176-2), by the Belgian Federal Science Policy Office in the framework of CANOPY (EV/12/20C), SOLAS.BE (OA/00/025), COMETS (OA/00/014), and BELCOLOUR-2 (SR/00/104), and by the Fonds National de la Recherche Scientifique (2.4545.02) where AVB is a research associate.
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