A novel geostatistical approach combining Euclidean and gradual-flow covariance models to estimate fecal coliform along the Haw and Deep rivers in North Carolina

Abstract

Incorporating flow in the covariance function is important for geostatistical water quality estimation that accounts for hydrological transport. Very few studies have successfully incorporated flow due to various reasons including implementation difficulties. To address this critical issue, we introduce here the first implementation of a flow weighted covariance model that uses gradual flow, and we use this model in a novel hybrid Euclidean/Gradual-flow covariance model to estimate fecal coliform along the Haw and Deep rivers from 2006 to 2010. The hybrid Euclidean/Gradual-flow model results in a 12.4% reduction in estimation mean square error compared to the Euclidean model, indicating that this is the first study to successfully incorporate gradual flow and demonstrate an improvement in estimation accuracy over the purely Euclidean approach. Furthermore, results show that the Euclidean/Gradual-flow model is more accurate and easier to implement than the Euclidean/Pipe-flow model. Our assessment found that 96 river miles were detected as being impaired according to the Euclidean/Gradual-flow method, which is more than twice the 39 river miles found according to the Euclidean estimate. These results demonstrate that the Euclidean/Gradual-flow model substantially increase the sensitivity in detecting fecal impairment, which provide critical new information for watershed management and public health protection measures.