Turbulence and Sediment Resuspension Modelling in Lake Erie

Abstract

Physical and biogeochemical observation in Lake Erie, and a field-scale numerical model was applied to study the turbulent dissipation and mixing, sediment resuspension and bottom stress parameterizations. In Chapter 2, temperature microstructure data were processed and grouped to represent characteristic turbulent kinematic energy (TKE) dissipation rate (ε) and turbulent diffusivity (K_z) profiles in distinct regions of Lake Erie. The presence of a seasonal thermocline can increase ε up to 10^(-6) (m^2 s^(-3)) and decrease K_z to molecular diffusivity in the bottom layer of the western basin and metalimnion of the central and eastern basins. The TKE budget shows efficiency of turbulent energy transfer from the wind decreased with increasing wind stress, and 36 - 54% of energy transferred from wind dissipated beneath the surface mixed layer, which was larger than that observed in smaller lakes. In Chapter 3, a three-dimensional Reynolds-averaged Navier Stokes (RANS) equation model, coupled with a water quality model, was applied to simulate sediment resuspension in Lake Erie. The model was qualitatively validated and able to reproduce the timing of resuspension. The model showed surface wave-induced bottom shear stress dominated sediment resuspension in shallow western basin, and with contributions from up- and down-welling events, and internal Poincaré wave motions, the importance of mean current-induced bottom shear stress increased in central basin. Resuspension in the deeper regions (>25 m) of the central and eastern basins was not modelled. To improve the prediction of sediment resuspension in field and model, bottom shear stress parameterizations, including surface wave stress method (τ_w), quadratic stress method (τ_c), log-law method (τ_L) and turbulent kinetic energy method (τ_TKE), were assessed in Chapter 4. In 2008-09 observations, resuspension induced by surface waves, bottom mean currents, and combination of them could be predicted by total bottom shear stress (τ_b), represented by τ_w+τ_c, τ_L, and τ_TKE. Considering required model setups (computing power, grid, etc.), τ_b= τ_w+τ_c is still the most practical parameterization method within field-scale RANS models. In comparison to observed values based on the same parameterization, the optimal algorithms of τ_w and τ_c in the RANS models showed 0.031 Pa and 0.025 Pa root-mean-square-error (RMSE), respectively.