Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables.

Characterization of nonpoint source microbial contamination in an urbanizing watershed serving as a municipal water supply.

Inland watersheds in the southeastern United States are transitioning from agricultural and forested land uses to urban and exurban uses at a rate greater than the national average. This study sampled creeks representing a variety of land use factors in a rapidly urbanizing watershed that also serves as a drinking water supply. Samples were collected bimonthly under dry-weather conditions and four times during each of three storm events and assessed for microbial indicators of water quality.

HuBac and nifH source tracking markers display a relationship to land use but not rainfall.

Identification of the source of fecal pollution is becoming a priority for states and territories in the U.S. in order to meet water quality standards and to develop and implement total maximum daily loads.