Observed relationships between microstructure patches and the gradient Richardson number in a thermally stratified lake

Yeates, Peter; Gómez-Giraldo, Andrés; Imberger, Jörg

doi:10.1007/s10652-013-9269-4

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…The strong stratification at the level of the thermocline inhibited vertical mixing (Figures c, e, and 9). The horizontal and vertical eddy down‐gradient diffusion coefficients for these strongly stratified metalimnetic shear flows [ Yeates et al ., ] were also estimated by inserting a set of tracers in one grid cell at three locations at a depth of the thermocline and using the simulations to track their rate of spread, horizontally (Figures h–j) and vertically (Figure b). From the rates of spread, the horizontal diffusion coefficient varied from 0.9 m 2 s −1 for the tracer release near Monte Isola to 1.7 and 2.5 m 2 s −1 for the tracers released in the south and north of the lake, respectively (Figures h–j), while the vertical diffusion coefficient,

κ_{M; V M}

, was around 10 −6 m 2 s −1 (Figure b), the same order of magnitude as those estimated from microstructure measurements (not shown).…”

Section: Discussionmentioning

confidence: 99%

“…The time scale for vertical transport in the metalimnion, T M;VT , [ Cuypers et al ., ; Valerio et al ., ] is given by the period of the basin‐scale internal wave modes induced by the wind and the time scale for the horizontal transport in the metalimnion can be expressed as

T_{M; H T} \sim \frac{L}{u}

, [ Marti and Imberger , ], where u is the velocity scale induced by the internal mode in the metalimnion. The time scale for vertical mixing in the metalimnion, T M;VM , [ Eckert et al ., ; Yeates et al ., ] may be calculated from,

T_{M; V M} \sim \frac{h_{M}^{2}}{κ_{M; V M}}

where h M is the metalimnion thickness scale [ Imberger , ] and

κ_{M; V M}

is the vertical diffusion coefficient in the metalimnion. The time scale for horizontal mixing in the metalimnion, T M;HM , [ Eckert et al ., ] is given by

T_{M; H M} \sim \frac{L^{2}}{κ_{M; H M}}

where

κ_{M; H M}

is the horizontal dispersion coefficient in the metalimnion.…”

Section: Methodsmentioning

confidence: 99%

“…The time scale for vertical transport in the metalimnion, T M;VT , [Cuypers et al, 2011;Valerio et al, 2012] is given by the period of the basin-scale internal wave modes induced by the wind and the time scale for the horizontal transport in the metalimnion can be expressed as T M;HT $ L u , [Marti and Imberger, 2008], where u is the velocity scale induced by the internal mode in the metalimnion. The time scale for vertical mixing in the metalimnion, T M;VM , [Eckert et al, 2002;Yeates et al, 2013] may be calculated from,…”

Section: 1002/2015wr017555mentioning

confidence: 99%

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Using time scales to characterize phytoplankton assemblages in a deep subalpine lake during the thermal stratification period: Lake Iseo, Italy

Marti

Imberger

Garibaldi

et al. 2016

Water Resources Research

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A combination of field observations and 3‐D hydrodynamic simulations were used to identify the phytoplankton species and to estimate the various time scales of the dominant physical and biological processes in Lake Iseo, a deep subalpine lake located in northern Italy, during a stratified period (July 2010). By ordering the rate processes time scales, we derive a phytoplankton patch categorization and growth interpretation that provides a general framework for the spatial distribution of phytoplankton concentration in Lake Iseo and illuminates the characteristics of their ecological niches. The results show that the diurnal surface layer was well mixed, received strong diurnal radiation, had low phosphorus concentrations and the phytoplankton biomass was sustained by the green alga Sphaerocystis schroeterii. The vertical mixing time scales were much shorter than horizontal mixing time scales causing a depth‐uniform chlorophyll a concentration. The horizontal patch scale was determined by horizontal dispersion balancing the phytoplankton growth time scale, dictating the success of the observed green algae. The strongly stratified nutrient‐rich metalimnion had mild light conditions and Diatoma elongatum and Planktothrix rubescens made up the largest proportions of the total phytoplankton biomass at the intermediate and deeper metalimnetic layers. The vertical transport time scales were much shorter than horizontal transport and vertical dispersion leading to growth niche for the observed phytoplankton. The study showed that time‐scale hierarchy mandates the essential phytoplankton attributes or traits for success in a particular section of the water column and/or water body.

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κ_{M; V M}

, was around 10 −6 m 2 s −1 (Figure b), the same order of magnitude as those estimated from microstructure measurements (not shown).…”

Section: Discussionmentioning

confidence: 99%

T_{M; H T} \sim \frac{L}{u}

T_{M; V M} \sim \frac{h_{M}^{2}}{κ_{M; V M}}

where h M is the metalimnion thickness scale [ Imberger , ] and

κ_{M; V M}

is the vertical diffusion coefficient in the metalimnion. The time scale for horizontal mixing in the metalimnion, T M;HM , [ Eckert et al ., ] is given by

T_{M; H M} \sim \frac{L^{2}}{κ_{M; H M}}

where

κ_{M; H M}

is the horizontal dispersion coefficient in the metalimnion.…”

Section: Methodsmentioning

confidence: 99%

Section: 1002/2015wr017555mentioning

confidence: 99%

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Using time scales to characterize phytoplankton assemblages in a deep subalpine lake during the thermal stratification period: Lake Iseo, Italy

Marti

Imberger

Garibaldi

et al. 2016

Water Resources Research

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“…The daily cycle of a convective mixed layer has been extensively studied in lakes, oceans and numerical models (Imberger, 1985;Brainerd and Gregg, 1993a,b;D'Asaro et al, 2002;Nagai et al, 2005;Yeates et al, 2013). A quantity of general interest is the kinetic energy dissipation rate ǫ [W Kg −1 ].…”

Section: Vertical Dissipation Dispersion and Diffusivitymentioning

confidence: 99%

Material transport in a convective surface mixed layer under weak wind forcing

et al. 2015

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Flows in the upper ocean mixed layer are responsible for the transport and dispersion of biogeochemical tracers, phytoplankton and buoyant pollutants, such as hydrocarbons from an oil spill. Material dispersion in mixed layer flows subject to diurnal buoyancy forcing and weak winds (|u 10 | = 5 ms −1) are investigated using a non-hydrostatic model. Both purely buoyancy-forced and combined wind-and buoyancy-forced flows are sampled using passive tracers, as well as 2D and 3D particles to explore characteristics of horizontal and vertical dispersion. It is found that the surface tracer patterns are determined by the convergence zones created by convection cells within a time scale of just a few hours. For pure convection, the results displayed the classic signature of Rayleigh-Benard cells. When combined with a wind stress, the convective cells become anisotropic in that the along-wind length scale gets much larger than the crosswind scale. Horizontal relative dispersion computed by sampling the flow fields using both 2D and 3D passive particles is found to be consistent with the Richardson regime. Relative dispersion is an order of magnitude higher and 2D surface releases transition to Richardson regime faster in the wind-forced case. We also show that the buoyancy-forced case results in significantly lower amplitudes of scale-dependent horizontal relative diffusivity, k D (ℓ), than those reported by Okubo (1970), while the wind-and buoyancy-forced case shows a good agreement with Okubo's diffusivity amplitude, and the scaling is consistent with Richardson's 4/3rd law, k D ∼ ℓ 4/3. These modeling results provide a framework for measuring material dispersion by mixed layer flows in future observational programs.

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“…Direct measurements of the buoyancy flux using a laser Doppler forward velocimeter and a temperature microstructure sensor have enabled (7.1.23) to be evaluated (Yeates et al, 2012). Direct measurements of the buoyancy flux using a laser Doppler forward velocimeter and a temperature microstructure sensor have enabled (7.1.23) to be evaluated (Yeates et al, 2012).…”

Section: Mixing In Environmental Flowsmentioning

confidence: 99%