Refinement of Regression Models to Estimate Real-Time Concentrations of Contaminants in the Menomonee River Drainage Basin, Southeast Wisconsin (2008-2011)

In 2008, the U.S. Geological Survey and the Milwaukee
Metropolitan Sewerage District initiated a study to develop
regression models to estimate real-time concentrations and
loads of chloride, suspended solids, phosphorus, and bacteria
in streams near Milwaukee, Wisconsin. To collect monitoring
data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee
River drainage basin. The sensors continuously measured four
potential explanatory variables: water temperature, specific
conductance, dissolved oxygen, and turbidity. Discrete waterquality samples were collected and analyzed for five response
variables: chloride, total suspended solids, total phosphorus,
Escherichia coli bacteria, and fecal coliform bacteria. Using
the first year of data, regression models were developed to
continuously estimate the response variables on the basis
of the continuously measured explanatory variables. Those
models were published in a previous report. In this report,
those models are refined using 2 years of additional data, and
the relative improvement in model predictability is discussed.
In addition, a set of regression models is presented for a new
site in the Menomonee River Basin, Underwood Creek at
Wauwatosa.
The refined models use the same explanatory variables as
the original models. The chloride models 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. Total suspended solids and
total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and
turbidity as explanatory variables