Modelling phytoplankton succession and nutrient transfer along the Scheldt estuary (Belgium, The Netherlands)

Gypens, Nathalie; Delhez, Eric; Vanhoutte-Brunier, Alice; Burton, S.; Thieu, Vincent; Passy, Paul; Liu, Yuhong; Callens, Julie; Rousseau, Véronique; Lancelot, Christiane

doi:10.1016/j.jmarsys.2012.10.006

Cited by 20 publications

(16 citation statements)

References 57 publications

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“…The large interannual variation in OC concentration in deposits at the brackish marsh (between 3.7 ± 0.3% and 11.1 ± 2.2%) indicates that there is a large amount of labile OC present in summer deposits (Figure a), which is confirmed by the higher F 14 C in summer deposits (Figure ). It is not clear why deposition of labile OC is so substantial in this section of the estuary in comparison to the salt and freshwater sections, as phytoplankton blooms are present along the entire estuary (Gypens et al., ; Muylaert, Sabbe, & Vyverman, ). This additional labile OC is, however, only present in the topsoil layers, below which there is a steep decline in OC concentration with depth (ca.…”

Section: Discussionmentioning

confidence: 98%

Long‐term organic carbon sequestration in tidal marsh sediments is dominated by old‐aged allochthonous inputs in a macrotidal estuary

Broek

Vandendriessche

Poppelmonde

et al. 2018

Global Change Biology

114

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Tidal marshes are vegetated coastal ecosystems that are often considered as hotspots of atmospheric CO sequestration. Although large amounts of organic carbon (OC) are indeed being deposited on tidal marshes, there is no direct link between high OC deposition rates and high OC sequestration rates due to two main reasons. First, the deposited OC may become rapidly decomposed once it is buried and, second, a significant part of preserved OC may be allochthonous OC that has been sequestered elsewhere. In this study we aimed to identify the mechanisms controlling long-term OC sequestration in tidal marsh sediments along an estuarine salinity gradient (Scheldt estuary, Belgium and the Netherlands). Analyses of deposited sediments have shown that OC deposited during tidal inundations is up to millennia old. This allochthonous OC is the main component of OC that is effectively preserved in these sediments, as indicated by the low radiocarbon content of buried OC. Furthermore, OC fractionation showed that autochthonous OC is decomposed on a decadal timescale in saltmarsh sediments, while in freshwater marsh sediments locally produced biomass is more efficiently preserved after burial. Our results show that long-term OC sequestration is decoupled from local biomass production in the studied tidal marsh sediments. This implies that OC sequestration rates are greatly overestimated when they are calculated based on short-term OC deposition rates, which are controlled by labile autochthonous OC inputs. Moreover, as allochthonous OC is not sequestered in-situ, it does not contribute to active atmospheric CO sequestration in these ecosystems. A correct assessment of the contribution of allochthonous OC to the total sedimentary OC stock in tidal marsh sediments as well as a correct understanding of the long-term fate of locally produced OC are both necessary to avoid overestimations of the rate of in-situ atmospheric CO sequestration in tidal marsh sediments.

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Section: Discussionmentioning

confidence: 98%

Long‐term organic carbon sequestration in tidal marsh sediments is dominated by old‐aged allochthonous inputs in a macrotidal estuary

Broek

Vandendriessche

Poppelmonde

et al. 2018

Global Change Biology

114

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“…Values for 18 biogeochemical parameters included in the biogeochemical reaction network of the C-GEM modeling platform (Table 3) were compiled by a literature review comprising 49 model applications for tidal estuarine systems in temperate regions (Table 5). The review comprises models of different complexity ranging from 0-to 3-D models, which were developed and applied to investigate different aspect of estuarine biogeochemistry, such as water quality control (e.g., HydroQual Inc., 1987;Lin et al, 2007), bacterial and phytoplankton dynamics (e.g., Robson and Hamilton, 2004;Macedo and Duarte, 2006;Gypens et al, 2013), or estuarine nutrient budgets (e.g., Soetaert and Herman, 1995;Arndt et al, 2009) at different timescales ranging from months to several years. The review covers the following 12 parameters, assumed to be temperature-independent:…”

Section: Literature Reviewmentioning

confidence: 99%

“…The review reveals that the temperature dependence of the aerobic and the denitrification rate constants (k ox and k denit , respectively) is typically expressed as an exponential increase of the rate constants with temperature (e.g., Regnier et al, 1997;Hofmann et al, 2008;Arndt et al, 2009;Volta et al, 2014). On the other hand, the temperature dependence of autotrophic parameters, such as the nitrification rate constant (k nit ), the maximum specific photosynthetic rate (P B max ), and the phytoplankton maintenance and mortality rate constants (k maint and k mort , respectively) can be implemented as exponential functions, where the parameter value increases with temperature (e.g., Peterson and Festa, 1984;Guillaud et al, 2000;Laruelle et al, 2009), or as Gaussian functions, where the value increases until an optimum temperature is reached and then progressively decreases (e.g., Garnier et al, 1995;Kim and Cerco, 2003;Gypens et al, 2013;Zheng et al, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

“…6.08 × 10 −5 5.05 × 10 −5 2.73 × 10 −6 6.08 × 10 −5 5.05 × 10 −5 2.73 × 10 −5 S2 9.64 × 10 −5 8.00 × 10 −5 4.33 × 10 −6 9.64 × 10 −5 8.00 × 10 −5 2.73 × 10 −5 S3 1.53 × 10 −4 1.27 × 10 −4 6.86 × 10 −6 1.53 × 10 −4 1.27 × 10 −4 2.73 × 10 −5 S4 2.42 × 10 −4 2.01 × 10 −4 1.09 × 10 −5 2.42 × 10 −4 2.01 × 10 −4 2.73 × 10 −5 S5 3.84 × 10 −4 3.19 × 10 −4 1.72 × 10 −5 3.84 × 10 −4 3.19 × 10 −4 2.73 × 10 −5 BS 6.08 × 10 −4 5.05 × 10 −4 2.73 × 10 −5 6.08 × 10 −4 5.05 × 10 −4 2.73 × 10 −5 S6 9.64 × 10 −4 8.00 × 10 −4 4.33 × 10 −5 9.64 × 10 −4 8.00 × 10 −4 2.73 × 10 −5 S7 1.53 × 10 −3 1.27 × 10 −3 6.86 × 10 −5 1.53 × 10 −3 1.27 × 10 −3 2.73 × 10 −5 S8 2.42 × 10 −3 2.01 × 10 −3 1.09 × 10 −4 2.42 × 10 −3 2.01 × 10 −3 2.73 × 10 −5 S9 3.84 × 10 −3 3.19 × 10 −3 1.72 × 10 −4 3.84 × 10 −3 3.19 × 10 −3 2.73 × 10 −5 S10 6.08 × 10 −3 5.05 × 10 −3 2.73 × 10 −4 6.08 × 10 −3 5.05 × 10 −3 2.73 × 10 −5 1018 C. Volta et al: Linking biogeochemistry to hydro-geometrical variability in tidal estuaries , , Guillaud et al (2000), Cugier et al (2005), Laruelle et al (2009) 1.07 N. America Chester River and Eastern Bay; Kim and Cerco (2003), Cerco and Noel (2004) Arndt et al ( , 2009, Vanderborght et al (2007) Lung and Paerl, 1988), Chesapeake Bay; Satilla estuary; Lung (1986) (via Lung and, Cerco and Cole (1994), Urdaibai estuary; Scheldt estuary Zheng et al (2004), Garcia et al (2010) Cerco and Cole (1994), Kim and Cerco (2003) Eastern Bay 45.00 Europe Scheldt estuary Regnier and Steefel (1999), Arndt et al ( , 2009, Vanderborght et al (2007), Hofmann et al (2008, Volta et al (2014) Cerco and Cole (1994), Kim and Cerco (2003) Eastern Bay 80.00 Europe Scheldt estuary Gypens et al (2013) 100.00 Europe Scheldt estuary Arndt et al ( , 2009, Vanderborght et al (2007), Volta et al (2014) Regnier and Steefel (1999), Arndt et al ( , 2009, Vanderborght et al (2007) Regnier and Steefel (1999),…”

Section: Sensitivity Analysis 1 (Sa1)mentioning

confidence: 99%

“…Outlier values are indicated in italic. 7.16 × 10 −41 P B max (T ) = P B max,L • exp −(T − 5.5) 2 /1.6 2 Europe Scheldt estuary Gypens et al (2013) 1.07 × 10 −6 P B max (T ) = P B max,L • exp −(T − 12) 2 /5 2 Europe Hypothetical river-coastal zone Billen and Garnier (1997) 1.02 × 10 −5 P B max (T ) = P B max,L • exp −(T − 37) 2 /17 2 Europe Seine estuary Garnier et al (1995) 1.17 × 10 −5 P B max (T ) = P B max,L • exp(0.07 • T ) Europe Bay of Seine Peterson and Festa (1984), , Guillaud et al (2000) 1.23 × 10 −5 P B max (T ) = P B max,L • exp −(T − 25) 2 /5 2 Europe Scheldt estuary Arndt et al (2011a) 1.38 × 10 −5 P B max (T ) = P B max,L • exp −(T − 21) 2 /13 2 Europe Seine estuary Garnier et al (1995) 1.49 × 10 −5 P B max (T ) = P B max,L • exp −(T − 13) 2 /21 2 Europe Hypothetical river-coastal zone Billen and Garnier (1997) 1.66 × 10 −5 P B max (T ) , Guillaud et al (2000) 2.76 × 10 −5 P B max (T ) = P B max,L • exp −(T − 37) 2 /12 2 Europe Scheldt estuary Gypens et al (2013) 2.80 × 10 −5 P B max (T ) = P B max,L • exp −(T − 15) 2 /12 2 Europe Scheldt estuary Gypens et al (2013) 3.31 × 10 −5 P B max (T ) = P B max,L • exp −(T − 17) 2 /37 2 Europe Hypothetical river-coastal zone Billen and Garnier (1997) 3.75 × 10 −5 P B max (T ) = P B max,L • exp − 0.0018 • (T − 16) 2 N. America Chester River and Eastern Bay Kim and Cerco (2003) 3.86 × 10 −5 P B max (T ) = P B max,L • exp − 0.0025 • (T − 20) 2 N. America Chesapeake Bay Cerco (2000) 4.38 × 10 −5 P B max (T ) = P B max,L • exp − 0.0025 • (T − 25) 2 N. America Chesapeake Bay Cerco (2000) 6.10 × 10 −5 P B max (T ) = P B max,L • exp − 0.0035 • (T − 29) 2 N. America Chester River and Eastern Bay Kim and Cerco (2003) 6.90 × 10 −5 P B max (T ) = P B max,L • exp −(T − 21) 2 /13 2 Europe Scheldt estuary Gypens et al (2013) Table B2. Continued.…”

Section: Sensitivity Analysis 1 (Sa1)mentioning

confidence: 99%

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Linking biogeochemistry to hydro-geometrical variability in tidal estuaries: a generic modeling approach

Volta¹,

Laruelle²,

Arndt³

et al. 2015

Preprint

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Abstract. This study applies the Carbon-Generic Estuary Model (C-GEM) modeling platform to simulate the estuarine biogeochemical dynamics – in particular the air-water CO2 exchange – in three idealized end-member systems covering the main features of tidal alluvial estuaries. C-GEM uses a generic biogeochemical reaction network and a unique set of model parameters extracted from a comprehensive literature survey to perform steady-state simulations representing average conditions for temperate estuaries worldwide. Climate and boundary conditions are extracted from published global databases (e.g. World Ocean Atlas, GLORICH) and catchment model outputs (GlobalNEWS2). The whole-system biogeochemical indicators Net Ecosystem Metabolism (NEM), C and N filtering capacities (FCTC and FCTN, respectively) and CO2 gas exchanges (FCO2) are calculated across the three end-member systems and are related to their main hydrodynamic and transport characteristics. A sensitivity analysis, which propagates the parameter uncertainties, is also carried out, followed by projections of changes in the biogeochemical indicators for the year 2050. Results show that the average C filtering capacities for baseline conditions are 40, 30 and 22% for the marine, mixed and riverine estuary, respectively. This translates into a first-order, global CO2 outgassing flux for tidal estuaries between 0.04 and 0.07 Pg C yr−1. N filtering capacities, calculated in similar fashion, range from 22% for the marine estuary to 18 and 15% for the mixed and the riverine estuary, respectively. Sensitivity analysis performed by varying the rate constants for aerobic degradation, denitrification and nitrification over the range of values reported in the literature significantly widens these ranges for both C and N. Simulations for the year 2050 indicate that all end-member estuaries will remain net heterotrophic and while the riverine and mixed systems will only marginally be affected by river load changes and increase in atmospheric pCO2, the marine estuary is likely to become a significant CO2 sink in its downstream section. In the decades to come, such change of behavior might strengthen the overall CO2 sink of the estuary-coastal ocean continuum.

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Modelling Estuarine Biogeochemical Dynamics: From the Local to the Global Scale

et al. 2013

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Estuaries act as strong carbon and nutrient filters and are relevant contributors to the atmospheric CO2 budget. They thus play an important, yet poorly constrained, role for global biogeochemical cycles and climate. This manuscript reviews recent developments in the modelling of estuarine biogeochemical dynamics. The first part provides an overview of the dominant physical and biogeochemical processes that control the transformations and fluxes of carbon and nutrients along the estuarine gradient. It highlights the tight links between estuarine geometry, hydrodynamics and scalar transport, as well as the role of transient and nonlinear dynamics. The most important biogeochemical processes are then discussed in the context of key biogeochemical indicators such as the net ecosystem metabolism (NEM), air-water CO2 fluxes, nutrient-filtering capacities and element budgets. In the second part of the paper, we illustrate, on the basis of local estuarine modelling studies, the power of reaction-transport models (RTMs) in understanding and quantifying estuarine biogeochemical dynamics. We show how a combination of RTM and high-resolution data can help disentangle the complex process interplay, which underlies the estuarine NEM, carbon and nutrient fluxes, and how such approaches can provide integrated assessments of the air-water CO2 fluxes along river-estuary-coastal zone continua. In addition, trends in estuarine biogeochemical dynamics across estuarine geometries and environmental scenario are explored, and the results are discussed in the context of improving the modelling of estuarine carbon and CO2 dynamics at regional and global scales

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Modelling phytoplankton succession and nutrient transfer along the Scheldt estuary (Belgium, The Netherlands)

Cited by 20 publications

References 57 publications

Long‐term organic carbon sequestration in tidal marsh sediments is dominated by old‐aged allochthonous inputs in a macrotidal estuary

Long‐term organic carbon sequestration in tidal marsh sediments is dominated by old‐aged allochthonous inputs in a macrotidal estuary

Linking biogeochemistry to hydro-geometrical variability in tidal estuaries: a generic modeling approach

Modelling Estuarine Biogeochemical Dynamics: From the Local to the Global Scale

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