Consideration of sample size for estimating contaminant load reductions using load duration curves

In Total Maximum Daily Load (TMDL) programs, load duration curves are often used to estimate reduction of contaminant loads in a watershed. A popular method for calculating these load reductions involves estimation of the 90th percentiles of monitored contaminant concentrations during different hydrologic conditions. However, water quality monitoring is expensive and can pose major limitations in collecting enough data. Availability of scarce water quality data can, therefore, deteriorate the precision in the estimates of the 90th percentiles, which, in turn, affects the accuracy of estimated load reductions. This paper proposes an adaptive sampling strategy that the data collection agencies can use for not only optimizing their collection of new samples across different hydrologic conditions, but also ensuring that newly collected samples provide opportunity for best possible improvements in the precision of the estimated 90th percentile with minimum sampling costs. The sampling strategy was used to propose sampling plans for Escherichia coli monitoring in an actual stream and different sampling procedures of the strategy were tested for hypothetical stream data. Results showed that improvement in precision using the proposed distributed sampling procedure is much better and faster than that attained via the lumped sampling procedure, for the same sampling cost. Hence, it is recommended that when agencies have a fixed sampling budget, they should collect samples in consecutive monitoring cycles as proposed by the distributed sampling procedure, rather than investing all their resources in only one monitoring cycle.