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

Abstract:

Pbkp_redictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical pbkp_redictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve pbkp_rediction. 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 pbkp_redictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve pbkp_rediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved pbkp_rediction over the space-only models. We pbkp_redicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal pbkp_redictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.