the importance of biogeochemical water quality...

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