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Scientific Investigations Report 2014–5066

Prepared in cooperation with the Minnesota Department of Natural Resources

Water Quality and Algal Community Dynamics of Three Sentinel Deepwater Lakes in Minnesota Utilizing CE-QUAL-W2 Models

By Erik A. Smith, Richard L. Kiesling, Joel M. Galloway, and Jeffrey R. Ziegeweid

Thumbnail of and link to report PDF (4.46 MB)Abstract

Water quality, habitat, and fish in Minnesota lakes will potentially be facing substantial levels of stress in the coming decades primarily because of two stressors: (1) land-use change (urban and agricultural) and (2) climate change. Several regional and statewide lake modeling studies have identified the potential linkages between land-use and climate change on reductions in the volume of suitable lake habitat for coldwater fish populations. In recent years, water-resource scientists have been making the case for focused assessments and monitoring of sentinel systems to address how these stress agents change lakes over the long term. Currently in Minnesota, a large-scale effort called “Sustaining Lakes in a Changing Environment” is underway that includes a focus on monitoring basic watershed, water quality, habitat, and fish indicators of 24 Minnesota sentinel lakes across a gradient of ecoregions, depths, and nutrient levels. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Department of Natural Resources, developed predictive water quality models to assess water quality and habitat dynamics of three select deepwater lakes in Minnesota. The three lakes (Lake Carlos in Douglas County, Elk Lake in Clearwater County, and Trout Lake in Cook County) were assessed under recent (2010–11) meteorological conditions. The three selected lakes contain deep, coldwater habitats that remain viable during the summer months for coldwater fish species.

Hydrodynamics and water-quality characteristics for each of the three lakes were simulated using the CE-QUAL-W2 model, which is a carbon-based, laterally averaged, two-dimensional water-quality model. The CE-QUAL-W2 models address the interaction between nutrient cycling, primary production, and trophic dynamics to predict responses in the distribution of temperature and oxygen in lakes.

The CE-QUAL-W2 models for all three lakes successfully predicted water temperature, on the basis of the two metrics of absolute mean error and root mean square error, using measured inputs of water temperature and nutrients. One of the main calibration tools for CE-QUAL-W2 model development was the vertical profile temperature data, available for all three lakes. For all three lakes, the absolute mean error and root mean square error were less than 1.0 degree Celsius and 1.2 degrees Celsius, respectively, for the different depth ranges used for vertical profile comparisons. In Lake Carlos, simulated water temperatures compared better to measured water temperatures in the epilimnion than in the hypolimnion. The reverse was true for the other two lakes, Elk Lake and Trout Lake, where the simulated results were slightly better for the hypolimnion than the epilimnion. The model also was used to approximate the location of the thermocline throughout the simulation periods, approximately April to November, in all three lake models. Deviations between the simulated and measured water temperatures in the vertical lake profile commonly were because of an offset in the timing of thermocline shifts rather than the simulated results missing thermocline shifts altogether.

First posted June 19, 2014

For additional information, contact:
Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112

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Suggested citation:

Smith, E.A., Kiesling, R.L., Galloway, J.M., and Ziegeweid, J.R., 2014, Water quality and algal community dynamics of three deepwater lakes in Minnesota utilizing CE-QUAL-W2 models: U.S. Geological Survey Scientific Investigations Report 2014–5066, 73 p.,

ISSN 2328-0328 (online)





Methods for Model Development

Model Calibration

Model Limitations

Sensitivity Analysis


References Cited

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