PRACTICAL GUIDANCE FOR DISCHARGE AND WATER QUALITY DATA COLLECTION ON SMALL WATERSHEDS

Abstract

Many sampling projects have been initiated or modified in recent years to quantify the effects of water qualityprotection and enhancement programs. Although comprehensive references on the theory and procedures related to dischargedata collection have been published, similar guides to water quality sampling are not available. Several sources providegeneral guidance on sampling project design and on manual sampling procedures, but only recently has detailed informationon automated storm water quality sampling been developed. As a result, a compilation of available information on the designof water quality sampling projects is needed to support sound decision-making regarding data collection resources andprocedural alternatives. Thus, the objective of this article is to compile and present practical guidance for collection ofdischarge and water quality constituent data at the field and small watershed scale. The guidelines included are meant toincrease the likelihood of project success, specifically accurate characterization of water quality within project resourceconstraints. Although many considerations are involved in establishing a successful sampling project, the followingrecommendations are generally applicable to field and small watershed studies: (1) consider wet-weather access, travel time,equipment costs, and sample collection method in the selection of sampling site numbers and locations; (2) commit adequateresources for equipment maintenance and repair; (3) assemble a well-trained, on-call field staff able to make frequent sitevisits; (4) establish reliable stage-discharge relationships for accurate discharge measurement; (5) use periodic manual grabsample collection with adequate frequency to characterize baseflow water quality; (6) use flow-interval or time-interval stormsampling with adequate frequency to characterize storm water quality; and (7) use composite sampling to manage samplenumbers without substantial increases in uncertainty.