Monte Carlo techniques were used to evaluate the accuracy and precision of tributary load estimates, as these are affected by sampling frequency and pattern, calculation method, watershed size, and parameter behavior during storm runoff events. Simulated years consisting of 1460 observations were chosen at random with replacement from data sets of more than 4000 samples. Patterned subsampling of these simulated years produced data appropriate to each sampling frequency and pattern, from which load estimates were calculated. Thus results for all sampling strategies were based on the same series of simulated years. Sampling frequencies ranged from 12 to roughly 600 samples per year. Unstratified and flow-stratified sampling were examined, and loads were calculated with and without the use of the Beale Ratio Estimator. All loads were evaluated by comparison with loads calculated from all 1460 samples in the simulated year. Studies consisting of 1000 iterations were repeated twice for each of five parameters in each of three watersheds. The results show that bias and precision of loading estimates are affected not only by the frequency and pattern of sampling and the calculation approach used, but also by the watershed size and the behavior of the chemical species being monitored. Furthermore, considerable interaction exists between these factors. In every case, loads based on flow-stratified sampling and calculated using the Beale ratio estimator provided the best results among the strategies examined. Differences in bias and precision among watersheds and among transported materials are related to the variability of instantaneous fluxes in the systems being monitored. These differences are qualitatively predictable from knowledge of the time behavior of the material and hydrological systems involved. Attempts to derive quantitative relationships to predict the sampling effort required to achieve a specified level of precision have not been successful. INTRODUCTION During the last decade, extensive efforts have been made to identify and reduce sources of pollution to the Laurentian Great Lakes. An important component of this program has been monitoring efforts designed to measure the tributary loads of various pollutants. A substantial portion of the pollutant loads of many tributary loads is contributed by agricultural nonpoint sources. Monitoring programs have been implemented by many different state, provincial, and federal agencies in the United States and Canada and have involved a wide range of sampling frequencies, sampling designs, and load calculation methodologies.Several factors combine to make difficult the design of an adequate sampling program for a tributary when nonpoint sources are important contributors to pollutant loads. First, the time patterns of flow and pollutant concentration of the tributary are often poorly known during the design of the monitoring program. Second, many tributaries are characterized by highly skewed distributions of pollutant fluxes: commonly as much as 80% of the annual l...

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