Microbial contamination of surface waters, a substantial public health concern throughout the world, is typically identified by fecal indicator bacteria such as E. coli. Thus, monitoring E. coli concentrations is critical to evaluate current conditions, determine restoration effectiveness, and inform model development and calibration. An often overlooked component of these monitoring and modeling activities is understanding the inherent random and systematic uncertainty present in measured data. In this research, a review and subsequent analysis was performed to identify, document, and analyze measurement uncertainty of E. coli data collected in stream flow and stormwater runoff as individual discrete samples or throughout a single runoff event. Data on the uncertainty contributed by sample collection, sample preservation/storage, and laboratory analysis in measured E. coli concentrations were compiled and analyzed, and differences in sampling method and data quality scenarios were compared. The analysis showed that: 1) manual integrated sampling produced the lowest random and systematic uncertainty in individual samples, but automated sampling typically produced the lowest uncertainty when sampling throughout runoff events; 2) sample collection procedures often contributed the highest amount of uncertainty, although laboratory analysis introduced substantial random uncertainty and preservation/storage introduced substantial systematic uncertainty under some scenarios; and 3) the uncertainty in measured E. coli concentrations was greater than that of sediment and nutrients, but the difference was not as great as may be assumed. This comprehensive analysis of uncertainty in E. coli concentrations measured in streamflow and runoff should provide valuable insight for designing E. coli monitoring projects, reducing uncertainty in quality assurance efforts, regulatory and policy decision making, and fate and transport modeling.

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The Leon River basin was selected as a benchmark watershed for the Conservation Effects Assessment Project to complement the historical USDA Agricultural Research Service experimental watersheds near Riesel, Texas. Excessive nutrient and bacteria concentrations contributed by agricultural, urban, and natural sources are the primary water quality concerns. Modeling and field evaluations of the hydrologic impact and soil and water quality response to tillage and nutrient management practices are the primary research themes of this project. Water quality data from 15 Leon River watersheds (0.3 ha [0.75 ac] to 6,070 km 2 [2,340 mi 2 ]) and 13 Riesel watersheds (1.2 ha [3.0 ac] to 70.4 ha [174 ac]) has improved modeling of phosphorus transformation and transport routines. Modeling research also coupled field-and farm-scale model output to improve the basin-scale Soil and Water Assessment Tool (SWAT) for the national assessment of conservation practices. Additional key products of Conservation Effects Assessment Project research include innovative erosion control methods on military lands, enhanced carbon sequestration estimates for various agricultural land uses, and improved understanding of environmental and economic impacts of organic fertilizer application.

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