Numerical Modeling of Thermal Bar Evolution in Lake Ontario using the EFDC Model Rumana Reaz Arifin

Transcription

1 Numerical Modeling of Thermal Bar Evolution in Lake Ontario using the EFDC Model Rumana Reaz Arifin 14 th Great Lakes Beach Association Conference November 14, 2014

2 Layout Introduction EFDC Lake Ontario Model Calibration Processes Analysis of Results Graphical Evolution of Spring Thermal Bar Summary 1

3 Lake Ontario Modeling Smallest of 5 lakes; Average depth > all lakes except Lake Superior. Surface area 19,000 km 2 ; Mean depth 86 m, maximum 244 m. Steep depth/area ratio aids in exhibition of thermal bar formation. Lake Superior Lake Michigan Lake Huron Canada Lake Erie Lake Ontario USA Hydrodynamic model is Environmental Fluid Dynamics Code Hydrodynamics (EFDC). Open source; Applied to over 100 Dynamics Near Field Dye Temperature Salinity (E, u, v, w, mixing) modeling studies (lake, reservoir, Plume estuary, river). 2 Drifter

4 Spring Thermal Bar and Summer Stratification Thermal bar: vertical wall of sinking water at maximum density (temperature 4 C). Thermal stratification: warm surface layers (epilimnion); cold deep layers (hypolimnion); transition zone (metalimnion), with strong density gradient (thermocline). 3

5 EFDC Lake Ontario Model Curvilinear grid resolution: ~2km Lake bathymetry (NOAA) interpolated over the grid. Inflow: Niagara river. Outflow: St. Lawrence river. Bathymetry following sigma vertical coordinate; 20 vertical layers. 4

6 Meterological and Flow Input Parameters Hourly time series input data from 7 weather stations around Lake Ontario. Interpolated using inverse distance weighing approach. Meterological Parameters Wind Speed and Direction Air Temperature and Pressure Relative Humidity Solar Radiation Cloud Cover Precipitation Flow Parameters Flow Rate for Niagara and St. Lawrence River. Water Temperature for Niagara River. Simulation Period 5 th April to 31 st July, 2011 Initialized with 3 0 C temperature. Ontario Canada New York USA 5

7 EFDC Heat Module Flow Chart 6

8 Updating Evaporation Algorithm Anomalous negative evaporation (condensation) is persistent through summer months, Lake water level and surface heat flux are affected by anomalous evaporation rate, leading to high surface temperatures. 7

9 Updating Evaporation Algorithm (contd.) EFDC Evaporation Algorithm: Updated Evaporation Algorithm (Quinn, 1977): E in m/day; RE in watt/m 2 C e = Evaporation coefficient WSP = Wind speed at 10m (m/s) Ta = Air temperature at 2m Tw = Simulated water temperature SVPW = Saturation vapor pressure at Tw (mbar) VPA = Actual vapor pressure at Ta (mbar) P atm = Atmospheric pressure (mbar) (Phillips and Irbe, 1977; Croley II, 1989): adjustment made for over-land to over-lake wind profile. q sw = Specific humidity of water q aa = Specific humidity of air Specific humidity is computed based on vapor pressure as: ρ a, ρ w = Air and Water density (kg/m 3 ) λv = Latent heat of vaporization (MJ/kg) 8

10 Updating Evaporation Algorithm (contd.) 9

11 Computation of Solar Radiation Attenuation Coefficients Applicable for Lake Ontario Rate at which incoming solar radiation is depleted, as it descends through water column. Lake Ontario is a mesotrophic (medium productivity) lake. USEPA study (1994) recommended value m -1. Period and Location Lake spatial average in summer, 2011 Lake spatial average, 2008 Annual ( ) mean at nearshore tributaries Annual ( ) mean at nearshore (30m depth) Annual ( ) mean at offshore (100m depth) TSS (mg/l) Chlorophyll α (μg/l) Computed β (m -1 ), (TSS only) Computed β(m -1 ), (TSS and Chl α) (computed with Chl α 1.75 μg/l) Study Reference USEPA TMDL Report for Lake Ontario (2011) 1.75 Yurista et al. (2012) (computed with Chl α 1.75 μg/l) USEPA Field Data Report for Lake Ontario Tributaries Makarewicz et al.(2012) Makarewicz et al.(2012) 10

12 Sensitivity Analysis of Solar Radiation Attenuation Coefficients Vertical Temperature Profile Incoming Shortwave Radiation Distribution (Beer-Lambert Law): RSN = SOLRAD*(f r *exp(-β f H(1-Z)) +((1-f r )*exp(-β s H(1-Z))) β f = Fast Scale Attenuation Coefficient β s = Slow Scale Attenuation Coefficient Decided for βf = 0.5/m and βs = 0.15/m Observed profile courtesy: Dr. Ram Yerubandi Rao, Environment Canada 11

13 Surface Temperature Profiles Surface temperature profiles compared with observed profiles from Great Lakes Observation System (GLOS). 12

14 Vertical Temperature Profiles Shallow Location Vertical temperature profiles compared with observed profiles from Environment Canada. Observed profile courtesy: Dr. Ram Yerubandi Rao Environment Canada 13

15 Vertical Temperature Profiles Deep Location Vertical temperature profiles compared with observed profiles from Environment Canada. Observed profile courtesy: Dr. Ram Yerubandi Rao Environment Canada 14

16 Lake-wide Surface Temperature Profiles Lake-wide surface temperature profiles compared with AVHRR satellite imagery from NOAA. Temperature Difference Legends Overall, simulated temperatures are predicted well (0.5-1 C difference from NOAA imagery) in mixed phase (period of thermal bar evolution). 15

17 Result Analysis Overall the EFDC Lake Ontario model is Performing well while the lake is in mixed phase (thermal bar evolution period), RMSE values for vertical profiles < C. Not capturing density driven lake stratification phase so well (late May/early June and onwards), RMSE values keep increasing. Capturing overall good surface profiles (RMSE values are C), major deviation occurs as lake gets into stratified phase. 16

18 Evolution of Spring Thermal Bar (Surface View) 17

19 Evolution of Spring Thermal Bar (North-South Sectional View) 18

20 Summary EFDC Lake Ontario model has simulated Overall good result in lake mixed phase (RMSE value C). Evolution of thermal bar process. These are achieved by Updating evaporation algorithm, to simulate correct evaporation heatflux. Setting appropriate solar radiation coefficient values (by computation and sensitivity analysis), to absorb enough incoming radiation in water column. Update is needed for vertical mixing coefficient parametrization, to better capture summer stratification pattern. 19

21 Thank You 20