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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #237140
Title: Impact of sampling techniques on measured stormwater quality data for small streams

Author
item Harmel, Daren
item SLADE, RAYMOND - RETIRED--USGS
item Haney, Richard

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/20/2010
Publication Date: 9/1/2010
Citation: Harmel, R.D., Slade, R.M., Haney, R.L. 2010. Impact of sampling techniques on measured stormwater quality data for small streams. Journal of Environmental Quality. 39(5):1734-1742.

Interpretive Summary: Science-based sampling methods are needed to improve water quality assessment for characterization for developing Total Maximum Daily Loads (TMDLs), water quality standards, and nonpoint source pollution management. Storm event sampling, which is vital for adequate assessment of water quality in small (wadeable) streams, is typically conducted in a single location by automated samplers or manual grab techniques. Although it is typically assumed that samples from a single location adequately represent mean cross-sectional concentrations as determined by integrated sampling techniques, especially for dissolved constituents, this assumption of well-mixed conditions has received limited evaluation. Thus, this study evaluated concentration variability by comparing storm water samples collected with several techniques in various cross-sectional locations in three small streams. These streams were concluded to be well-mixed (less than or equal to 5 percent concentration variability) based on the USGS rule of thumb, which utilizes four-parameter probe readings. In contrast, grab sample results often indicated substantial variability in both dissolved and particulate constituent concentrations. Based on these results, storm water quality in small streams cannot be adequately sampled by a single grab sample collected at a single location in the cross-section because of variability over time and within the channel. However, frequent grab or automated sample collection can adequately capture this variability, especially if these single location results are corrected to represent mean cross-sectional concentrations. Repeated within-event integrated sampling can also capture concentration variability, but logistical constraints will limit application of this method.

Technical Abstract: Science-based sampling methodologies are needed to enhance water quality characterization for developing Total Maximum Daily Loads (TMDLs), setting appropriate water quality standards, and managing nonpoint source pollution. Storm event sampling, which is vital for adequate assessment of water quality in small (wadeable) streams, is typically conducted at a single point by an automated sampler or with a manual grab technique. Although it is typically assumed that samples from a single point adequately represent mean cross-sectional concentrations as determined by depth and width integrated sampling techniques, especially for dissolved constituents, this assumption of well-mixed conditions has received limited evaluation. Thus, this study quantified concentration variability by comparing storm water samples collected with several techniques in various points in three small stream cross-sections. These streams were concluded to be well-mixed (less than or equal to 5 percent concentration variability) based on the USGS 'rule of thumb,' which utilizes multiple four-parameter probe readings within each cross-section. In contrast, grab sample results often indicated substantial variability in both dissolved and particulate constituent concentrations based on percent difference results and statistical evaluation. These results indicate that storm water constituent concentrations in small streams cannot be adequately determined by a grab sample collected at a single point in the flow because of cross-sectional and temporal concentration variability. Frequent grab or automated sample collection better captures this variability, especially if the single point concentrations are mathematically adjusted to represent mean cross-sectional concentrations. Repeated within-event integrated sampling also can capture concentration variability, but logistical constraints limit application of this method.