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Operational Environmental Prediction: Nearshore Water Quality in the Great Lakes. David J. Schwab NOAA Great Lakes Environmental Research Laboratory Ann Arbor, MI. Factors Contributing to Nearshore Water Quality in the Great Lakes.
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Operational Environmental Prediction: Nearshore Water Quality in the Great Lakes David J. Schwab NOAA Great Lakes Environmental Research Laboratory Ann Arbor, MI
Factors Contributing to Nearshore Water Quality in the Great Lakes Climate – Meteorology – Hydrology – Hydrodynamics – Biology/Chemistry
Forecasting Beach Closings or HABs Meteorology Meteorology Change in Land-use Change in Land-use Change in Land-use Hydrology/Water Flow Bacterial Fate Hydrology/Water Flow Bacterial Fate Beach Closings Circulation and Bacterial Fate Circulation and Bacterial Fate
Outline Lake Michigan tributary modeling using nested-grid hydrodynamic models - application to beach water quality forecasting Lake Erie coupled physical/biological model - application to HAB and hypoxia forecasting
Beach Closures • Major health risk of microbial contamination by bacteria, viruses and protozoa in recreational waters • E.Coli requires a 24 hour incubation period • People may unintentionally swim in contaminated water
Lake Michigan Beach Quality Forecasting Lakewide grid (POM model) Coupled models nested grids + Burns Ditch nested model grid
Princeton Ocean Model (Blumberg and Mellor, 1987) - Fully three-dimensional nonlinear Navier-Stokes equations - Flux form of equations - Boussinesq and hydrostatic approximations - Free upper surface with barotropic (external) mode - Baroclinic (internal) mode - Turbulence model for vertical mixing - Terrain following vertical coordinate (<sigma>-coordinate) - Generalized orthogonal horizontal coordinates - Smagorinsky horizontal diffusion - Leapfrog (centered in space and time) time step - Implicit scheme for vertical mixing - Arakawa-C staggered grid - Fortran code optimized for vectorization Application to the Great Lakes - No open boundary - No tides - Uniform salinity - Seasonal thermal structure - Uniform rectangular grid - XDR used for input and output • Nested grid considerations: • 3d boundary condition for u, v, and T interpolated from coarse grid at each boundary point • Vertically integrated velocity is specified for external mode • Internal mode velocity and temperature are specified from 3-d boundary condition for inflow, use radiation condition for outflow • Water level is adjusted to maintain zero mean in nested grid subdomain
Burns Ditch 100m computational grid 24 km 6 km
Great Lakes Coastal Forecasting System - Operational Nowcast 20 day sample using vertically averaged currents
The Problem: - Excessive nutrient loading in the 1960’s led to massive algal blooms, oxygen depletion, and diminished water quality in Lake Erie. - 1972 Water Quality Agreement between the US and Canada limited P loads from municipal, industrial, and agricultural sources. - With controls, P levels decreased to acceptable levels and water quality improved. - In recent years, P levels in Lake Erie appear to be increasing, despite controls.
The Problem: - Excessive nutrient loading in the 1960’s led to massive algal blooms, oxygen depletion, and diminished water quality in Lake Erie. - 1972 Water Quality Agreement between the US and Canada limited P loads from municipal, industrial, and agricultural sources. - With controls, P levels decreased to acceptable levels and water quality improved. - In recent years, P levels in Lake Erie appear to be increasing, despite controls. Our Approach: - Incorporate phosphorus transport and fate dynamics into high resolution (hourly time scale, 2 km horizontal resolution) hydrodynamic model of Lake Erie as a first step toward spatially explicit model of entire lower food web
Lake Erie Physical Characteristics: Surface Area: 25800 km2 Throughflow ~ 6000 m3s-1 Volume: 480 km3 Retention time: 2.5 yrs Mean Depth: 18.6 m
Ecosystem Forecasting of Lake Erie Hypoxia • What are the Causes, Consequences, and Potential Remedies of Lake Erie Hypoxia? • Linked set of models to forecast: • changes in nutrient loads to Lake Erie • responses of central basin hypoxia to multiple stressors • P loads, hydrometeorology, dreissenids • potential ecological responses to changes in hypoxia • Approach • Models with range of complexity • Consider both anthropogenic and natural stressors • Use available data – IFYLE, LETS, etc. • Will assess uncertainties in both drivers and models • Apply models within an Integrated Assessment framework to inform decision making for policy and management
Hypoxia Forecasting Modeling Approach • Model ranging in complexity • Correlation-based models • 1D hydrodynamics with simple mechanistic WQ model • Vertical profiles extracted from full hydrodynamic model • TP, Carbon, Solids • 3D hydrodynamics with simple mechanistic WQ model • Physics from full hydrodynamic model • 3D hydrodynamics with complex mechanistic WQ model • WQ framework similar to Chesapeake Bay ICM model • Multi-class phyto- and zooplankton, organic and inorganic nutrients, sediment digenesis, etc • Addition of zebra mussels and other improvements
Effect of Phosphorus Controls on Lake Erie Central Basin Springtime P Concentration (Ryan et al., 1999)
Lake Erie 1994 physical/biological model • Hydrodynamics • - Great Lakes version of POM • 20 vertical levels, 2 km horizontal grid (~6500 cells) • Hourly meteorology (1994, JD 1-365) • Realistic tributary flows • Accounts for ice cover • Mass balance for P • POM hydrodynamics (2d for now) • Realistic P loading • Constant settling velocity (for now)
Computer animation of model results: • Starts in January, 1994 • Uses 2d currents from hydrodynamic model • Time dependent P loads • Combination Lax-Wendroff and upwind advection scheme • No horizontal diffusion • Initial condition: C = 10 ug/L • Settling velocity = 6.8E-7 m/s (21 m/yr)