Influence of various water quality sampling strategies on load estimates for small streams

Abstract

Extensive streamflow and water quality data from eight small streams were systematically subsampled to represent various water-quality sampling strategies. The subsampled data were then used to determine the accuracy and precision of annual load estimates generated by means of a regression approach (typically used for big rivers) and to determine the most effective sampling strategy for small streams. Estimation of annual loads by regression was imprecise regardless of the sampling strategy used; for the most effective strategy, median absolute errors were ~30% based on the load estimated with an integration method and all available data, if a regression approach is used with daily average streamflow. The most effective sampling strategy depends on the length of the study. For 1-year studies, fixed-period monthly sampling supplemented by storm chasing was the most effective strategy. For studies of 2 or more years, fixed-period semimonthly sampling resulted in not only the least biased but also the most precise loads. Additional high-flow samples, typically collected to help define the relation between high streamflow and high loads, result in imprecise, overestimated annual loads if these samples are consistently collected early in high-flow events.