the importance of biogeochemical water quality...
TRANSCRIPT
The Importance of Biogeochemical Water Quality
Modeling
Mark Rowe
National Research Council Research Associate
NOAA Great Lakes Environmental Research Laboratory
Ann Arbor, Michigan
Biogeochemical Water Quality Models
• Quantitative hypothesis testing
• Forecasting
LM3-Eutro 5-km model grid and tributary load locations
Simulation of Primary Production in Lake Michigan with the LM3-PP Model
M. D. Rowe, J. J. Pauer, W. Melendez, R. G. Kreis, Jr.
• Observation
– Updated total nitrogen load
greater than previous estimates
• Hypothesis
– The nitrogen budget for Lake
Michigan can balance with the
greater load if denitrification is
included
A reactive nitrogen budget for Lake Michigan
M. D. Rowe, R. G. Kreis, Jr., D. M. Dolan
• 15-year trend in simulated nitrate is consistent with
observations, indicating N budget is plausible
• Observation
– Quagga mussel population expanded rapidly in Lake Michigan
over the period 2000 – 2010.
• Hypothesis
– Quagga mussel population is approaching carrying capacity
Modeling the effect of nutrients and Dreissenid mussels on primary
production in Lake Michigan
M. D. Rowe, J. J. Pauer, P. DePetro, R. G. Kreis
Biomass observations: Nalepa et al. 2010 JGLR 36: 5-19
Ration =g C ingested( )
g C biomass( )i day
• The simulation suggests quagga mussels are
approaching carrying capacity due to limited food
availability
Simulating the Direct Impact of Dreissenid Mussel Grazing on
Phytoplankton Concentration in Lake Michigan as a Function of
Turbulence Parameterization
M. D. Rowe, J. Wang, H. A. Vanderploeg, E. J. Anderson, T. F. Nalepa, J. R.
Liebig, S. A. Pothoven, T. H. Johengen, G. L. Fahnenstiel
FVCOM Currents and
Vertical Eddy Diffusivity
Improved Estimates of
Mussel Spatial Distribution
Conclusion
• Biogeochemical water quality models are useful to
– Put detailed process measurements into a larger context
– Test hypotheses within constraints of mass balance and process
rates
– Identify gaps in knowledge and needs for further research
– Predict future variability and trends