Monte Carlo studies of sampling strategies for estimating tributary loads

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.