Use of real-time monitoring to predict concentrations of select constituents in the Menomonee River drainage basin, Southeast Wisconsin, 2008-9

The Menomonee River drainage basin in southeast Wisconsin is undergoing changes that may affect water quality. Several rehabilitation and flood-management projects are underway, including removal of concrete channels and the construction of floodwater retention basins. The city of Waukesha may begin discharging treated wastewater into Underwood Creek, thus approximately doubling the current base-flow discharge. In addition, the headwater basins, historically dominated by agriculture and natural areas, are becoming increasingly urbanized.

In an effort to monitor these and future changes to the basin, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study in 2008 to develop regression models to estimate real-time concentrations and loads of selected water-quality constituents. Water-quality sensors and automated samplers were installed at five sites in the Menomonee River drainage basin. The sensors continuously measured four explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Regression models were developed to continuously estimate the response variables on the basis of the explanatory variables.

The models to estimate chloride concentrations all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity as explanatory variables. Adjusted R² values for the chloride models ranged from 0.74 to 0.97. Models to estimate total suspended solids and total phosphorus used turbidity as the only explanatory variable. Adjusted R² values ranged from 0.77 to 0.94 for the total suspended solids models and from 0.55 to 0.75 for the total phosphorus models. Models to estimate indicator bacteria used water temperature and turbidity as the explanatory variables, with adjusted R² values from 0.54 to 0.69 for Escherichia coli bacteria models and from 0.54 to 0.74 for fecal coliform bacteria models. Dissolved oxygen was not used in any of the final models. These models may help managers measure the effects of land-use changes and improvement projects, establish total maximum daily loads, estimate important water-quality indicators such as bacteria concentrations, and enable informed decision making in the future.