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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #191934
Title: CUMULATIVE UNCERTAINTY IN MEASURED STREAMFLOW AND WATER QUALITY DATA FOR SMALL WATERSHEDS

Author
item Harmel, Daren
item COOPER, R - MACAULAY INSTITUTE
item SLADE, R - RETIRED
item Haney, Richard
item Arnold, Jeffrey

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2006
Publication Date: 6/15/2006
Citation: Harmel, R.D., Cooper, R.J., Slade, R.M., Haney, R.L., Arnold, J.G. 2006. Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Transactions of the ASABE. 49(3):689-701.

Interpretive Summary: In previous work, scientists have not established an adequate understanding of the uncertainty present in measured water quality data. This uncertainty is introduced by four major steps in data collection: streamflow measurement, sample collection, sample preservation/storage, and laboratory analysis. Although previous research has determined relative differences in data collection steps, little information is available on the overall uncertainty. As a result, uncertainty is typically either ignored or arbitrarily assigned even though policy makers need scientifically-defensible estimates. The specific objectives of this research were to: 1) document common sources of uncertainty, 2) compare the uncertainty introduced by each step, and 3) calculate the uncertainty in resulting measured streamflow, sediment, and nutrient data for small agricultural. Best case, typical, and worst case "data quality" scenarios were examined. The uncertainty for each step under average conditions was from 8-23% for streamflow measurement, 9-46% for sample collection, 0-38% for sample preservation/storage, and 1-57% for laboratory analysis. The overall uncertainty for resulting water quality data ranged from 50-83% for worst case scenarios to 13-23% in best case scenarios. These results provide introductory estimates needed in water quality litigation, policy, and regulation. They should also prove valuable for watershed modelers to use in examining the "quality" of calibration and evaluation data sets, determining model accuracy goals, and evaluating model output performance.

Technical Abstract: The scientific community has not established an adequate understanding of the uncertainty inherent in measured water quality data, which is introduced by four procedural categories: streamflow measurement, sample collection, sample preservation/storage, and laboratory analysis. Although previous research has produced valuable information on relative differences between procedures within these categories, little information is available on the cumulative uncertainty in resulting data. As a result, uncertainty is typically either ignored or accounted for with an arbitrary margin of safety, even though wise water resource management demands scientifically-defensible estimates of data uncertainty. The specific objectives of this research were to: 1) document common sources of errors, 2) compare the uncertainty introduced by each procedural category, and 3) calculate the uncertainty in resulting measured streamflow, sediment, and nutrient data for small rural watersheds. Best case, typical, and worst case "data quality" scenarios were examined. Across all constituents, the calculated probable error range (+/- %) contributed by each procedural category under average conditions ranged from 8% (best case) to 23% (worst case) for streamflow measurement, 9% to 46% for sample collection, 0% to 38% for sample preservation/storage, and 1% to 57% for laboratory analysis. The cumulative uncertainty was substantial when the probable errors from these procedural categories were propagated to the resulting water quality data. Under average conditions, errors in constituent loads ranged from 50% to 83% for worst case scenarios, from 17% to 31% for typical scenarios, and from 13% to 23% in best case scenarios. This information provides introductory scientific estimates of uncertainty in water quality data to satisfy demands in litigation, policy, and regulation. These results and procedures presented should also prove valuable for modelers in examining the "quality" of calibration and evaluation data sets, determining model accuracy goals, and evaluating model output performance.