Logistic and linear regression model documentation for statistical relations between continuous real-time and discrete water-quality constituents in the Kansas River, Kansas, July 2012 through June 2015

Abstract

The Kansas River is a primary source of drinking water for about 800,000 people in northeastern Kansas. Sourcewater supplies are treated by a combination of chemical and physical processes to remove contaminants before distribution. Advanced notification of changing water-quality conditions and cyanobacteria and associated toxin and taste-and-odor compounds provides drinking-water treatment facilities time to develop and implement adequate treatment strategies. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas State Water Plan Fund), and the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One, began a study in July 2012 to develop statistical models at two Kansas River sites located upstream from drinking-water intakes. Continuous water-quality monitors have been operated and discrete-water quality samples have been collected on the Kansas River at Wamego (USGS site number 06887500) and De Soto (USGS site number 06892350) since July 2012. Continuous and discrete water-quality data collected during July 2012 through June 2015 were used to develop statistical models for constituents of interest at the Wamego and De Soto sites. Logistic models to continuously estimate the probability of occurrence above selected thresholds were developed for cyanobacteria, microcystin, and geosmin. Linear regression models to continuously estimate constituent concentrations were developed for major ions, dissolved solids, alkalinity, nutrients (nitrogen and phosphorus species), suspended sediment, indicator bacteria (Escherichia coli, fecal coliform, and enterococci), and actinomycetes bacteria. These models will be used to provide real-time estimates of the probability that cyanobacteria and associated compounds exceed thresholds and of the concentrations of other water-quality constituents in the Kansas River. The models documented in this report are useful for characterizing changes in water-quality conditions through time, characterizing potentially harmful cyanobacterial events, and indicating changes in water-quality conditions that may affect drinking-water treatment processes.

Water-quality conditions with an emphasis on cyanobacteria and associated toxins and taste-and-odor compounds in the Kansas River, Kansas, July 2012 through September 2016

“…The purpose of this report is to (1) describe water-quality conditions, with an emphasis on cyanobacteria and associated toxins (microcystin) and taste-and-odor compounds (geosmin and MIB), in the Kansas River during July 2012 through September 2016; (2) describe the environmental factors associated with the occurrence of cyanobacteria and associated toxins and taste-and-odor compounds in the Kansas River during July 2012 through September 2016; and (3) evaluate previously published logistic regression models that used continuous water-quality data to estimate the probability of cyanobacteria, microcystin, and geosmin occurrence above relevant thresholds in the Kansas River (Foster and Graham, 2016). Detailed analyses of all regression models published by Foster and Graham (2016) and all water-quality data collected from the Kansas River during July 2012 through September 2016 are beyond the scope of this report. Quantification of cyanobacteria, cyanotoxins, and taste-and-odor compounds, and the conditions under which they are most likely to occur in the Kansas River will provide drinking-water suppliers and the State of Kansas a better understanding of associated water-quality concerns in the river.…”

“…samples have been collected at three reservoir outflow sites that are tributaries to the Kansas River: Republican River at Junction City, Kans., streamgage (downstream from Milford Lake; USGS station number 06857100), Big Blue River near Manhattan, Kans., streamgage (downstream from Tuttle Creek Lake; USGS station number 06887000), and Delaware River at Perry, Kans., streamgage (downstream from Perry Lake; USGS station number 06890900). Continuous and discrete water-quality data collected by the USGS at these sites during July 2012 through September 2016 were used to characterize water-quality conditions in the lower Kansas River Basin and to evaluate previously published (Foster and Graham, 2016) logistic regression models developed to estimate the probability of cyanobacteria, microcystin, and geosmin occurrence above relevant thresholds at the Wamego and De Soto sites. Because of the emphasis on cyanobacteria and associated toxins and taste-and-odor compounds, other regression models published by Foster and Graham (2016) are not evaluated in this report.…”

“…Continuous and discrete water-quality data collected by the USGS at these sites during July 2012 through September 2016 were used to characterize water-quality conditions in the lower Kansas River Basin and to evaluate previously published (Foster and Graham, 2016) logistic regression models developed to estimate the probability of cyanobacteria, microcystin, and geosmin occurrence above relevant thresholds at the Wamego and De Soto sites. Because of the emphasis on cyanobacteria and associated toxins and taste-and-odor compounds, other regression models published by Foster and Graham (2016) are not evaluated in this report.…”

Estimated fecal coliform bacteria concentrations using near real-time continuous water-quality and streamflow data from five stream sites in Chester County, Pennsylvania, 2007–16

“…Regression equations included turbidity, water temperature, and stream discharge as possible explanatory variables because of data availability and statistically significant regression results of other investigations in stream settings elsewhere (Baldwin and others, 2012;Foster and Graham, 2016). Other available continuous water-quality data, including pH, dissolved oxygen concentrations, and specific conductance, were considered as explanatory variables in the preliminary unpublished evaluation of Chester County data done by USGS in 2012, but none were statistically significant or stronger than the physical variables of turbidity, water temperature, and streamflow.…”

“…Other available continuous water-quality data, including pH, dissolved oxygen concentrations, and specific conductance, were considered as explanatory variables in the preliminary unpublished evaluation of Chester County data done by USGS in 2012, but none were statistically significant or stronger than the physical variables of turbidity, water temperature, and streamflow. In addition, the variables computed using the Julian Day (JD) as a fraction of the 365-day year to account for seasonal variability (warmest temperatures midsummer), sin(2πJD/365) and cos(2πJD/365) as used by Foster and Graham (2016) and hereafter referred to as seasonality variables, were also considered in regressions.…”