Modelling the influence of turbulence on phytoplankton photosynthesis

Belyaev, V. I.

doi:10.1016/0304-3800(92)90010-c

Cited by 15 publications

(7 citation statements)

References 2 publications

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“…Similarly to DO results, the baseline scenario exhibited the best model performance of CHLA simulation; CHLA responded even more sensitively than DO in terms of CCs and RMSEs (Figure 10e). According to previous studies, turbulence intensity and nutrient availability are dominant environmental factors controlling phytoplankton patchiness [50,51]; our (Table 2); blue dashed line, results using NARR wind-driven mixing and observed wind-driven reaeration) dissolved oxygen at two sampling sites in Chesapeake Bay (Figure 1…”

Section: Sensitivity To Vertical Eddy Viscositysupporting

confidence: 70%

Application of an Unstructured Grid-Based Water Quality Model to Chesapeake Bay and Its Adjacent Coastal Ocean

Xia¹,

Jiang²

2016

JMSE

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To provide insightful information on water quality management, it is crucial to improve the understanding of the complex biogeochemical cycles of Chesapeake Bay (CB), so a three-dimensional unstructured grid-based water quality model (ICM based on the finite-volume coastal ocean model (FVCOM)) was configured for CB. To fully accommodate the CB study, the water quality simulations were evaluated by using different horizontal and vertical model resolutions, various wind sources and other hydrodynamic and boundary settings. It was found that sufficient horizontal and vertical resolution favored simulating material transport efficiently and that winds from North American Regional Reanalysis (NARR) generated stronger mixing and higher model skill for dissolved oxygen simulation relative to observed winds. Additionally, simulated turbulent mixing was more influential on water quality dynamics than that of bottom friction: the former considerably influenced the summer oxygen ventilation and new primary production, while the latter was found to have little effect on the vertical oxygen exchange. Finally, uncertainties in riverine loading led to larger deviation in nutrient and phytoplankton simulation than that of benthic flux, open boundary loading and predation. Considering these factors, the model showed reasonable skill in simulating water quality dynamics in a 10-year (2003-2012) period and captured the seasonal chlorophyll-a distribution patterns. Overall, this coupled modeling system could be utilized to analyze the spatiotemporal variation of water quality dynamics and to predict their key biophysical drivers in the future.

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Section: Sensitivity To Vertical Eddy Viscositysupporting

confidence: 70%

Application of an Unstructured Grid-Based Water Quality Model to Chesapeake Bay and Its Adjacent Coastal Ocean

Xia¹,

Jiang²

2016

JMSE

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“…In marine ecosystems, small organisms with little or no motility, such as phytoplankton cells, are immersed in a turbulent environment that influences numerous biological processes, including photosynthesis (Belyaev 1992), nutrient uptake (Lazier and Mann 1989, Bowen Featured article 542 • D. Macías et al et al 1993), encounter and grazing rates (Rothschild and Osborn 1988) and even community composition (Margalef 1978, Falkowski andOliver 2007). The distribution of plankton, in particular its patchiness, is also related to the turbulent nature of movements in the upper ocean (Platt 1972) in both the horizontal and vertical dimensions (Abraham 1998, Franks 2005, d'Ovidio et al 2010.…”

Section: Introductionmentioning

confidence: 99%

Turbulence as a driver for vertical plankton distribution in the subsurface upper ocean

Macías¹,

Rodríguez-Santana²,

Ramirez-Romero³

et al. 2013

Sci. Mar.

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SUMMARY: Vertical distributions of turbulent energy dissipation rates and fluorescence were measured simultaneously with a high-resolution micro-profiler in four different oceanographic regions, from temperate to polar and from coastal to open waters settings. High fluorescence values, forming a deep chlorophyll maximum (DCM), were often located in weakly stratified portions of the upper water column, just below layers with maximum levels of turbulent energy dissipation rate. In the vicinity of the DCM, a significant negative relationship between fluorescence and turbulent energy dissipation rate was found. We discuss the mechanisms that may explain the observed patterns of planktonic biomass distribution within the ocean mixed layer, including a vertically variable diffusion coefficient and the alteration of the cells' sinking velocity by turbulent motion. These findings provide further insight into the processes controlling the vertical distribution of the pelagic community and position of the DCM.Keywords: turbulence, deep chlorophyll maximum, vertical plankton distribution. RESUMEN: La turbulencia como mecanismo forzante de la distribución vertical del plancton en el océano subsuperficial. -Las distribuciones verticales de la tasa de disipación de la energía turbulenta y de la fluorescencia han sido medidas simultáneamente usando un microperfilador de alta resolución en cuatro regiones oceánicas distintas, desde zonas templadas a polares y desde regiones costeras al océano abierto. Valores altos de fluorescencia, conformando un máximo profundo de clorofila (MPC) fueron detectados habitualmente en regiones poco estratificadas de la columna de agua y justo por debajo de capas con valores máximos de la tasa de disipación de la energía turbulenta. En las cercanías del MPC se encontró una relación estadísticamente significativa y negativa entre los valores de fluorescencia y energía turbulenta. En este trabajo se discuten los posibles mecanismos que pueden explicar estas relaciones, incluyendo el efecto de la variación vertical del coeficiente de difusión y la posible alteración de la velocidad de sedimentación de las células de fitoplancton por el movimiento turbulento. Estos resultados proporcionan un mayor conocimiento sobre los procesos que controlan la distribución vertical de la comunidad pelágica y la posición de los MPC.Palabras clave: turbulencia, máximo profundo de clorofila, distribución vertical del plancton.

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“…Turbulence can inhibit growth rates of some phytoplankton species (Belayev 1992, Thomas & Gibson 1992, increase coagulation and sedimentation rates of phytoplankton cells (Ki~irboe et al 1990(Ki~irboe et al , hebesell 1991, enhance encounter rates between zooplankton predators and their prey (Rothschild & Osborn 1988, Marrase et al 1990, Sundby & Fossum 1990, MacKenzie & Leggett 1991, and increase development, metabolic and ingestion rates in zooplankton (Saiz & Alcaraz 1991, Saiz et al 1992). …”

Section: Introductionmentioning

confidence: 99%

Wind-based models for estimating the dissipation rates of turbulent energy in aquatic environments: empirical comparisons

Mackenzie¹,

Leggett²

1993

Mar. Ecol. Prog. Ser.

135

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The rate at which turbulent kinetic energy is dissipated influences growth, encounter probability, coagulation rates and vertical distribution of plankton. In this study we quantified the effectiveness with which boundary (wall) layer theory represents turbulent dissipation rates (E, W m-3) measured within natural surface mixing layers. This model explained 58 "/o of the variance in 818 literature-derived estimates of turbulent dissipation rates measured at 11 different geographic sites. The residual mean square error (RMSE) associated with the regression of loglo observed dissipation rate vs log,o predicted dissipation rate showed that ca 68 % of surface layer dissipation rates observed in nature were within a factor + 5.2-fold of dissipation rates estimated using boundary layer theory.Dissipation rates in more complex mixing environments, where turbulence was known to be caused by additional hydrographic phenomena (free convection, breaking of waves in the upper 1.5 m of the water column, current shear, upwelling), exceeded the boundary layer prediction by 1.5-to 26-fold depending on the mechanism associated with turbulence-generation. We found no evidence that turbulence near the surface (0 to 5 or 0 to 10 m) during high winds (27.5 or 2 10 m S -' ) was higher than the boundary layer prediction. When all data were combined into one data set, n = 1088), a multlple regression model having wind speed (W) and sampling depth ( 2 ) as inputs (log F = 2 6881og W -1.3221ogz -4.812) explained 54 % of the variance in surface layer turbulent dissipation rates (RMSE = + 5.5-fold). The potential for developing more precise empirical models of mixlng layer turbulent d~ssipation rates is h~g h and can be achieved by reporting wind conditions prior to, and during, turbulence measurements more thoroughly, and by collecting replicate turbulence profiles. The existing theoretical and empirical models are, however, adequate for many biological applications such as estimating the nature and magnitude of interactions among, and distributions of, many plankton taxa a s a result of wind forcing.

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Modelling the influence of turbulence on phytoplankton photosynthesis

Cited by 15 publications

References 2 publications

Application of an Unstructured Grid-Based Water Quality Model to Chesapeake Bay and Its Adjacent Coastal Ocean

Application of an Unstructured Grid-Based Water Quality Model to Chesapeake Bay and Its Adjacent Coastal Ocean

Turbulence as a driver for vertical plankton distribution in the subsurface upper ocean

Wind-based models for estimating the dissipation rates of turbulent energy in aquatic environments: empirical comparisons

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