Development of Regression-Based Models to Predict Fecal Bacteria Numbers at Select Sites within the Illinois River Watershed, Arkansas and Oklahoma, USA

Abstract

The Illinois River Watershed is a multifacet basin with ecological and economic importance to its local stakeholders in northwest Arkansas and northeast Oklahoma, USA. The numbers, transport and sources of fecal bacteria in streams was identified as a research priority of the USDA NRI Water and Watershed Program in 2006, and the objective of this study was to evaluate the relation between fecal bacteria and other measured physicochemical parameters in water samples collected from selected sites throughout the Illinois River Watershed. An existing database (i.e., National Water Information Systems, NWIS) from the US Geological Survey (USGS) was used in this project. The data obtained includes discharge, pH, temperature, dissolved oxygen, Escherichia coli (E. coli), fecal coliform, and fecal streptococci among several other physic-chemical parameters. A synthetic model, based on multiregression analysis, was developed to predict fecal bacteria numbers at these selected sites based on available USGS NWIS data, and the multiple regressions were significant at almost every site for all three bacteria groups. However, the physicochemical parameters used in the equations were very different across sites and fecal bacteria groups, suggesting that the development of such predictive models is site and bacteria group specific even within one watershed.

Relationship between Coliform Bacteria and Water Quality Factors at Weir Stations in the Nakdong River, South Korea

“…However, most focused on marine systems [7,13,14,[17][18][19][20][21][22][23] rather than on rivers [6,9,11], because coastal zones are often used for recreational purposes and are the final repositories of pollutants. Some studies have used regression analyses to determine the relationship between coliform bacteria and water quality factors in rivers [24][25][26][27]. Those studies are important because they can help easily predict the concentration of coliform bacteria using water quality factors.…”

Development and implementation of a regression model for predicting recreational water quality in the Cuyahoga River, Cuyahoga Valley National Park, Ohio 2009-11

“…This is especially true in the river where concentrations can change greatly over short periods of time in response to decay, dilution, dispersion, and transport of bacteria (Myers and others, 1998). Predictive models have been shown to work well in other river and lake systems (Christensen and others, 2002;David and Haggard, 2011;Elaria and Vogel, 2005;Francy and Darner, 2007;Francy and others, 2009;Nevers and Whitman, 2006;Nevers and others, 2007;Rasmussen and Ziegler, 2003;Chattahoochee Riverway Project, 2011), and may be able to provide reliable results of the current day's fecal-indicator bacteria concentrations at CVNP, thus allowing park managers to determine whether they might promote river use.…”

Advance hydrological studies utilizing open access internet databases

“…The open data access initiatives taken by the U.S. governmental agencies make the government‐owned mega databases readily available to the public, presenting scientists with unprecedented research opportunities. Numerous environmental studies have been reported utilizing the three databases described in this paper, the NCEI of the NOAA (Herkert, Martinez, & Hornbuckle, ; Lubenow, Fairley, Lindsey, & Larson, ; Rayne & Forest, ; Stow, Cha, Johnson, Confesor, & Richards, ; Zhang, Wang, Hamilton & Lauer, ), the NWIS of the USGS (Bartrons, Papeş, & Diebel, ; Conyers & Fonstad, ; David & Haggard, ; Saat, Werth, Schaeffer, Yoon, & Barkan, ; Tesoriero, Terziotti, & Abrams, ), and the GeoTracker of the SWRCB of California (Adamson, Anderson, Mahendra & Newell, ; McHugh, Kulkarni, Newell, Connor, & Garg, ; Pineda & Liu, ). We anticipate that, with an increased level of awareness of the existence of these open access databases, many more environmental and hydrological studies will be reported utilizing these publically accessible data sources in the future.…”