Effects of seasonal fluctuations of surface heat flux and wind stress on mixing and vertical diffusivity of water column in deep lakes

Saber, Ali; James, David E.; Hayes, Donald F.

doi:10.1016/j.advwatres.2018.07.006

Cited by 27 publications

(24 citation statements)

References 33 publications

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“…Our study, however, clearly demonstrates that simulations over several seasons with a 3D model can indeed reproduce temperature dynamics and mixing intensities close to reality. The simulated K z values in our study are mainly between 1.0 × 10 −6 and 1 × 10 −4 m 2 s −1 (Figure ), which is within the range calculated from observed water temperature in previous studies (Imboden, Lemmin, Joller, & Schurter, ; Saber, James, & Hayes, ; Wahl & Peeters, ; Yang et al, ). However, a slight, but systematic, overestimation of hypolimnetic K z was also visible in our study, particularly at the start of the stratified season.…”

Section: Discussionsupporting

confidence: 91%

Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model

Dong

Hupfer

et al. 2019

Hydrological Processes

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Vertical turbulent diffusivity (K z ), which can be estimated from water temperature, is a key factor in the evolution of water quality in lentic waters. In this study, we analysed the capability of a three-dimensional hydrodynamic model (EFDC) to capture water temperature and vertical diffusivity in Lake Arendsee in the Northern German plain. Of particular interest to us is to evaluate the model performance for capturing the diffusion minimum within the metalimnion and analyse the response of the metalimnetic K z to meteorological forcing, namely changing wind speed and warming. The comparison confirmed that the calibrated model could reproduce both stratification dynamics and vertical diffusion profiles in the lake. The model was also shown to be able to capture the duration and vertical extent of the metalimnetic diffusion minimum. The scenario results illustrate that, compared to air temperature, wind velocity appeared to be the more influential meteorological variable on the vertical exchange within the metalimnion. While increasing wind velocities mostly affected the minimum values of K z in the metalimnion and thus led to intensified vertical exchange, the reduction of wind velocity mostly affected the depth of minimal K z , but not its absolute value.

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Section: Discussionsupporting

confidence: 91%

Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model

Dong

Hupfer

et al. 2019

Hydrological Processes

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“…Saber et al () estimated the average annual water column vertical diffusivities of about 5 × 10 −5 m 2 s −1 in Boulder Basin during nearly the same time period as this study. Fischer and Smith () reported internal waves with an amplitude of 6 m in Las Vegas Bay (depth of 25 m).…”

Section: Resultssupporting

confidence: 62%

“…Decreased forecast accuracies, indicated by higher RMSE and lower R values, primarily occurred between 6 and 16 m and from 41 to 56 m. This could stem from changes in the mixing intensity of the water column. Saber et al () reported SML depths between 8 and 10 m in Boulder Basin during the stratification period; however, the SML could increase to about 50 to 60 m during the cold winter days. Thermal stratification, particularly in deep lakes, limits the interactions between well‐mixed and stratified zones, resulting in different fluctuation patterns of targets in these layers.…”

Section: Resultsmentioning

confidence: 98%

“…During stratification, turbulent kinetic energy (TKE) generated from surface heat loss and wind stress can produce noticeably greater vertical diffusivities in the top 10 to 15 m of the water column of deep lakes compared to deeper metalimnetic and hypolimnetic layers. Available TKE during cold winter days can result in vertical diffusivities of 1.5 orders of magnitude greater than summer values in the top 60–70 m of the water column (Saber et al ).…”

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confidence: 99%

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Long‐term forecast of water temperature and dissolved oxygen profiles in deep lakes using artificial neural networks conjugated with wavelet transform

Saber

James

Hayes

2019

Limnology & Oceanography

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Forecasting water quality in inland waters can improve management practices to protect water resources. This study proposes a novel data‐driven framework to forecast water quality profiles over long time periods in Boulder Basin of Lake Mead, a deep monomictic subtropical lake. Hourly meteorological data were used to estimate lake–atmosphere heat exchange. Heat fluxes combined with 6‐hourly measured water quality profiles up to 106 m depth were used to train six different artificial neural networks to forecast water temperature, dissolved oxygen, and conductivity profiles up to 240 d ahead. A model incorporating heat fluxes, winds, and stationary wavelet decomposition generated correlation coefficients > 0.88 and relative errors < 4% throughout the water column for up to 240‐d ahead forecasts. Internal wave motions at the thermocline resulted in larger relative errors of forecasts in the metalimnion compared to other depths. Greater atmospheric influences on water temperature and dissolved oxygen resulted in larger forecast errors compared to conductivity. An autocovariance method successfully determined appropriate forecasting lead times at different depths, improving forecast accuracies.

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“…A lake gains energy with a positive buoyancy flux and loses energy with a negative buoyancy flux. Studies have shown that negative buoyancy fluxes combined with high winds can break up summer lake stratification 70 (Augusto-Silva et al, 2019;Liu et al, 2019;Saber et al, 2018). Therefore, besides winds, surface thermal forcing (buoyancy flux) is an essential factor that affects lake water mixing.…”

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confidence: 99%

Improving lake mixing process simulations in the Community Land Model by using K profile parameterization

Zhang¹,

Jin²,

Wang³

et al. 2019

Preprint

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Abstract. We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM assumes that mixing is driven primarily by wind, and it produces large biases in thermal process simulations. We improved the CLM lake model by using KPP, where vertical mixing was driven by winds and surface thermal forcing, the latter representing the net heat flux in the lake boundary layer. We chose an Arctic Alaskan lake to evaluate this improved lake model. Results demonstrated that KPP could reproduce the observed lake mixing and significantly improved lake temperature simulations when compared to the original mixing scheme in CLM. Our newly improved model better represents the transition between stratification and turnover due to surface thermal forcing combined with high winds. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services.

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Effects of seasonal fluctuations of surface heat flux and wind stress on mixing and vertical diffusivity of water column in deep lakes

Cited by 27 publications

References 33 publications

Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model

Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model

Long‐term forecast of water temperature and dissolved oxygen profiles in deep lakes using artificial neural networks conjugated with wavelet transform

Improving lake mixing process simulations in the Community Land Model by using K profile parameterization

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