Estimating Uncertainty in Measured Discharge and Water Quality Data (and the Benefits that Result).

Water resource decision-making continues to rely on measured data, for which an understanding of measurement uncertainty is important but often ignored.  Thus, we initiated research to improve the understanding of the uncertainty inherent in measured water quality data.  In 2006, we published an uncertainty estimation framework and produced the first cumulative uncertainty estimates for measured water quality data (Harmel et al., 2006).  From this framework, the Data Uncertainty Estimation Tool for Hydrology and Water Quality (DUET-H/WQ) was developed (Harmel et al., 2009).  Both the software and its framework-basis utilize the root mean square error propagation methodology to provide uncertainty estimates.  DUET-H/WQ lists published uncertainty information for data collection procedures to assist the user in assigning appropriate data-specific uncertainty estimates and then calculates the uncertainty for individual discharge, concentration, and load values.

 

Results of DUET-H/WQ application to several real-world data sets indicated that substantial uncertainty can be contributed by each procedural category (discharge measurement, sample collection, sample preservation/storage, laboratory analysis, and data processing and management).  For storm loads, the uncertainty was typically least for discharge (±7-23%), higher for sediment (±16-27%) and dissolved N and P (±14-31%) loads, and higher yet for total N and P (±18-36%).  When these uncertainty estimates for individual values were aggregated within study periods (i.e. total discharge, average concentration, total load), uncertainties followed the same pattern (Q < TSS < dissolved N and P < total N and P).

 

Uncertainty estimates corresponding to measured discharge and water quality data can contribute to improved monitoring design, decision-making, model application, and regulatory formulation.  It is our hope this research contributes to making uncertainty estimation a routine data collection and reporting procedure and thus enhances environmental monitoring, modeling, and decision-making.  These data are too important to continue to ignore the inherent uncertainty.

 

 References
Harmel, R.D., R.J. Cooper, R.M. Slade, R.L. Haney, and J.G Arnold. 2006. Cumulative uncertainty in measured streamflow and water quality data for small watersheds. Trans. ASABE 49(3): 689-701.
Harmel, R.D., D.R. Smith, K.W. King, and R.M. Slade. 2009. Estimating storm discharge and water quality data uncertainty:  A software tool for monitoring and modeling applications. Environ. Modelling Software 24(7): 832-842.