Paper No. JAWRA-08-0106-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.
Predicting the Fate and Transport of E. coli in Two Texas River Basins Using a Spatially Referenced Regression Model1
Article first published online: 1 JUN 2009
© 2009 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 45, Issue 4, pages 928–944, August 2009
How to Cite
Puri, D., Karthikeyan, R. and Babbar-Sebens, M. (2009), Predicting the Fate and Transport of E. coli in Two Texas River Basins Using a Spatially Referenced Regression Model. JAWRA Journal of the American Water Resources Association, 45: 928–944. doi: 10.1111/j.1752-1688.2009.00337.x
- Issue published online: 29 JUL 2009
- Article first published online: 1 JUN 2009
- Received June 5, 2008; accepted February 13, 2009.
- E. coli;
- water quality;
- model selection;
- fecal coliform;
Abstract: The two main rivers of southeast Texas: Guadalupe and San Antonio have shown high temporal increase in bacteria concentration during the last decade. The SPAtially Referenced Regression On Watershed (SPARROW) attributes model, developed by the U.S. Geological Survey (USGS), has been applied to predict the fluxes and concentrations of contaminants in unmonitored streams and to identify the sources of these contaminants. This model identifies every reach as a basic network unit to distribute the sources, delivery, and attenuation factors. The model is data intensive and implements nonlinear regression to solve the parsimonious relations for describing various watershed processes. This study explored watershed and hydrological characteristics (land uses, precipitation, human and animal population, point sources, areal hydraulic load and drainage density, etc.) as the probable sources and delivery mechanisms of waterborne pathogens and their indicator (Escherichia coli [E. coli]) in the Guadalupe and San Antonio River basins. The effect of using various statistical indices for model selection on the final model’s ability to explain the various E. coli sources and transport processes was also analyzed.