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Water quality monitoring programs have escalated in an effort to quantify loadings to streams and lakes from various watershed activities and managements. At the core of monitoring programs are strategies or schemes that determine when and how samples are taken for estimating stream loadings. Quantification of the differences between these schemes has not been adequately documented. An analytic approach was used to evaluate 45 commonly used sampling strategies that included time (5, 10, 15, 30, 60, 120, 180, 300, and 360-min) and flow-stratified (2.5, 5.0, 7.5, 10.0, 12.5 and 15.0 mm) schemes using discrete and composite sampling approaches. 305 storm hydrographs from 87 different watersheds in the United States were coupled with 2 concentration graphs (a 100% positive correlation of concentration to flow and a 100% negative correlation to flow) to estimate average bias values for each sampling strategy. The mean bias values for time-based sampling always increased with an increase in sampling time interval. With respect to time-weighted sampling, a positive correlated concentration graph generally resulted in under-prediction (positive bias) from the true load, while a negative correlated concentration always resulted in over-prediction (negative bias). With respect to flow-stratified sampling, the direction of bias was reversed from the time-weighted case. Flow-stratified sampling at intervals > 7.5 mm does not adequately capture concentration points around the peak. Even at the lowest flow interval used in this study (2.5 mm), the bias associated with each correlation case was significantly different from zero (a=0.05). Time discrete sampling schemes 15-min provided the only bias values not significantly different from zero (a=0.05). The findings indicate that prior to water quality monitoring, careful consideration should be given to the sampling strategy and its overall impact on load estimates.