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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #221238
Title: Estimating Storm Discharge and Water Quality Data Uncertainty: A Software Tool for Monitoring and Modeling Applications

Author
item Harmel, Daren
item Smith, Douglas
item King, Kevin
item SLADE, RAYMOND - RETIRED

Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2008
Publication Date: 1/12/2009
Citation: Harmel, R.D., Smith, D.R., King, K.W., Slade, R.M. 2009. Estimating Storm Discharge and Water Quality Data Uncertainty: A Software Tool for Monitoring and Modeling Applications. Journal of Environmental Modelling and Software. 24(7):832-842.

Interpretive Summary: The uncertainty in hydrologic and water quality data has numerous economic, societal, and environmental implications; therefore, scientists can no longer ignore measurement uncertainty when collecting and presenting these data. Reporting uncertainty estimates with measured hydrologic and water quality data provides multiple benefits including improved monitoring design, enhanced decision-making, and improved model application and understanding. With these benefits in mind, we used an existing uncertainty estimation framework to develop a software tool to make it straightforward task to estimate and report the uncertainty in measured streamflow and water quality (chemical constituent) data. This tool uses published and/or user-input information to estimate the uncertainty within each monitoring procedural category and in resulting discharge, concentration, and load data. We then applied a spreadsheet version to estimate uncertainty in data collected from various hydro-climatic and watershed management conditions for storm events and baseflow. Results indicated that each procedural category can contribute substantial uncertainty; therefore, quality assurance protocols should address discharge measurement and sample collection as well as sample preservation/storage and laboratory analysis procedures, which are traditionally emphasized. The uncertainty in concentration and load data was typically least for discharge, more for sediment and dissolved N and P, and greatest for total N and P; however, high uncertainty was possible for all constituents data when data were missing. We hope this tool will eventually make uncertainty estimation a routine step in collecting and reporting hydrologic and water quality data.

Technical Abstract: Uncertainty inherent in hydrologic and water quality data has numerous economic, societal, and environmental implications; therefore, scientists can no longer ignore measurement uncertainty when collecting and presenting these data. Reporting uncertainty estimates with measured hydrologic and water quality data provides multiple benefits including improved monitoring design, enhanced decision-making, and improved model application and understanding. With these benefits in mind, we used an existing uncertainty estimation framework to develop a software tool to facilitate estimation and reporting of uncertainty in measured discharge and water quality (chemical constituent) data. This tool uses published and/or user-input information to estimate the uncertainty within each monitoring procedural category and in resulting discharge, concentration, and load data. We then applied a spreadsheet version to estimate uncertainty in data collected from various hydro-climatic and watershed management conditions for storm events and baseflow. Results indicated that each procedural category can contribute substantial uncertainty; therefore, quality assurance protocols should address discharge measurement and sample collection as well as sample preservation/storage and laboratory analysis procedures, which are traditionally emphasized. The uncertainty in concentration and load data was typically least for discharge, more for sediment and dissolved N and P, and greatest for total N and P; however, high uncertainty was possible for all constituents data when data were missing. We hope this tool will eventually make uncertainty estimation a routine step in collecting and reporting hydrologic and water quality data.