2016
DOI: 10.5194/hess-20-991-2016
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Linking biogeochemistry to hydro-geometrical variability in tidal estuaries: a generic modeling approach

Abstract: 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 tidal estuaries characterized by increasing riverine influence, from a so-called "marine estuary" to a "riverine estuary". An intermediate case called "mixed estuary" is also considered. C-GEM uses a generic biogeochemical reaction network and a unique set of model parameters extracted from a comprehensive liter… Show more

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Cited by 15 publications
(28 citation statements)
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References 115 publications
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“…The bulk of the remaining estimates falls in the 10%-50% range, which is consistent with our results where estuarine systems characterized by short residence times of several days like small deltas only denitrify a few percent of TN in , while systems such as fjords with residence times of several years denitrify up to 38% of TN in . Our results are also in line with the study of Volta, Laruelle, Arndt, and Regnier (2016) who used a generic, physically based estuarine modeling approach spanning a wide range of estuarine geometries; these authors report mean N losses via denitrification in the range 15%-25%.…”
Section: Nitrogen Input To Riverssupporting
confidence: 91%
“…The bulk of the remaining estimates falls in the 10%-50% range, which is consistent with our results where estuarine systems characterized by short residence times of several days like small deltas only denitrify a few percent of TN in , while systems such as fjords with residence times of several years denitrify up to 38% of TN in . Our results are also in line with the study of Volta, Laruelle, Arndt, and Regnier (2016) who used a generic, physically based estuarine modeling approach spanning a wide range of estuarine geometries; these authors report mean N losses via denitrification in the range 15%-25%.…”
Section: Nitrogen Input To Riverssupporting
confidence: 91%
“…While many different studies consider flow-dependent river TA to anticorrelate with discharge (e.g., Wang et al, 2013;Evans et al, 2013), the strong Fraser River TA hysteresis demonstrated by our data links the strongest Fraser River TA fluctuations to rapid pulses in river discharge. A future shift to smaller freshets will thus likely reduce the already weak influence of flow-dependent freshwater TA in the model, supporting the use of fixed freshwater TA endmembers in large-scale acidification projection models (e.g., Volta et al, 2016). However, the present study highlights the importance of choosing a freshwater TA endmember carefully.…”
Section: Comparison To Other Rivers and Implications For Future Climatesupporting
confidence: 60%
“…This explicit representation of the organic (OC) and inorganic carbon (IC) cycles allows simulating and quantifying the different processes controlling the OC/IC ratio and the emission of CO 2 into the atmosphere along the entire river network. Earlier modeling work in tidal estuaries using C-GEM alone evidenced the intense carbon processing taking place in the widest, marine part of estuaries (Regnier et al, 2013b;Volta et al, 2014Volta et al, , 2016a. The first integrated study presented here will allow further testing the hypothesis that the most downstream section of the river/estuarine system disproportionately contributes to the retention of carbon and CO 2 outgassing at the scale of an entire river network.…”
Section: Introductionmentioning
confidence: 71%
“…Our study relies on the off-line coupling of two transient, spatially discrete models: C-GEM and pyNuts-Riverstrahler. C-GEM is a recently developed generic estuarine model (Volta et al, 2014(Volta et al, , 2016a which has already been applied to several estuaries distributed along the coast of the North Atlantic Ocean (Volta et al, 2016b;Laruelle et al, 2017a) while the pyNuts-Riverstrahler platform is a well-established model coupling watersheds and river network (Billen et al, 1994;Garnier and Billen, 1994;Thieu et al, 2015). Simulations similar to those described in Marescaux (2018) are first performed with pyNuts-Riverstrahler for year 2010 over the entire Seine watershed.…”
Section: Modeling Strategymentioning
confidence: 99%
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