Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1290 km watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest that SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.

Ten low-order streams draining headwater catchments within the East Fork Little Miami Watershed were evaluated throughout one year for the presence of six steroidal hormones, the antibiotic sulfamethoxazole, the antimicrobials triclosan and triclocarban, and the artificial sweetener sucralose. The wastewater management practices in the catchments included septic systems, sanitary sewers, a combination of both, and a parkland with no treatment systems. The concentrations and detection frequencies of sucralose showed a significant positive correlation with the septic density in each catchment. A similar relationship was found for sulfamethoxazole. Both sucralose and sulfamethoxazole are hydrophilic and unlikely to be removed effectively by sorption during septic treatment. The concentrations and detection frequencies of the antimicrobials were also positively correlated with septic density. The presence of the antimicrobials in the streams indicates that although they are hydrophobic, removal during septic treatment was incomplete. The target analytes that correlated with septic density were also detected in stream samples collected below a wastewater treatment plant located within the same watershed. The steroidal hormone estrone was the most frequently detected analyte at all sites. However, the estrone concentrations and detection frequencies did not correlate with the septic density due to multiple non-point sources.

Condition assessment modeling is drawing increasing interest as a technique that can assist in managing drinking water infrastructure. This article develops a Cox proportional hazard (PH)/ shared frailty model and applies it to the problem of investment in the repair and replacement of drinking water distribution system components. The model has been applied to a pipe break database collected by the city of Laramie (Wyo.) Utility Division to show how such models can help managers understand the factors affecting the survival of drinking water pipe sections or entire pipelines or pipe runs. Shared frailty represents unobserved external factors, known to be important, which vary randomly and are more consistent among sections in the same pipe run than among sections in different pipe runs. Results from the model developed in this article indicate that metal pipe has fewer breaks on average, than polyvinyl chloride pipe but that it is more subject to undefined random factors. The Cox PH model is used to develop expected piperun break curves. In addition the Cox PH/shared frailty model is used within the inspection value method to assist in making improved repair, replacement, and rehabilitation decisions for selected drinking water distribution system pipes.

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