Inland lake indicator bacteria: Long-term impervious surface and weather influences and a predictive bayesian model

Abstract

Indicator bacteria (IB) that tend to occur with human pathogens provide surveillance of waterborne disease risk. This study analyzes a long-term IB surveillance record at Geneva Lake, Wisconsin, United States. The first research objective examined the influence of urbanization on fecal coliform (FC) variability and change from 1975 to 2000. Over this period, impervious surface expansion mirrored escalating fecal coliform in 2 of the 3 urbanized subwatersheds; however, impervious surface construction in less-developed subwatersheds did not impact FC levels. Average FC levels were highest at the only municipality (Linn Hillside Road Creek) with beaches around the lake using septic systems. The second research objective developed a predictive model to forecast human health risk in periods without surveillance. A Bayesian framework communicated uncertainty surrounding beach management decisions. Existing water quality surveillance is limited by infrequent and relatively slow sample processing; thus, beach managers often do not have reliable water quality information. The predictive statistical model determined associations between biophysical conditions and E. coli levels from 2001 to 2008. More moisture (precipitation, lake discharge) increased E. coli levels at almost every sampling site. Statistical models may accurately forecast risk at some beaches and hydrologic conditions. In particular, statistical models for Lake Geneva and Williams Bay beaches exhibit high overall accuracy, good specificity, and modest sensitivity levels. [Supplementary materials are available for this article. Go to the publisher's online edition of Lake and Reservoir Management to view the supplemental file.] © 2012 Taylor & Francis Group, LLC.