The composite method: an improved method for stream-water solute load estimation
Version of Record online: 16 MAY 2006
Copyright © 2006 John Wiley & Sons, Ltd.
Volume 20, Issue 14, pages 3029–3047, September 2006
How to Cite
Aulenbach, B. T. and Hooper, R. P. (2006), The composite method: an improved method for stream-water solute load estimation. Hydrol. Process., 20: 3029–3047. doi: 10.1002/hyp.6147
- Issue online: 22 AUG 2006
- Version of Record online: 16 MAY 2006
- Manuscript Accepted: 18 JUL 2005
- Manuscript Received: 24 SEP 2004
- stream-water solute loads;
- water quality;
- sample design;
- bootstrap experiment
The composite method is an alternative method for estimating stream-water solute loads, combining aspects of two commonly used methods: the regression-model method (which is used by the composite method to predict variations in concentrations between collected samples) and a period-weighted approach (which is used by the composite method to apply the residual concentrations from the regression model over time). The extensive dataset collected at the outlet of the Panola Mountain Research Watershed (PMRW) near Atlanta, Georgia, USA, was used in data analyses for illustrative purposes. A bootstrap (subsampling) experiment (using the composite method and the PMRW dataset along with various fixed-interval and large storm sampling schemes) obtained load estimates for the 8-year study period with a magnitude of the bias of less than 1%, even for estimates that included the fewest number of samples. Precisions were always <2% on a study period and annual basis, and <2% precisions were obtained for quarterly and monthly time intervals for estimates that had better sampling. The bias and precision of composite-method load estimates varies depending on the variability in the regression-model residuals, how residuals systematically deviated from the regression model over time, sampling design, and the time interval of the load estimate. The regression-model method did not estimate loads precisely during shorter time intervals, from annually to monthly, because the model could not explain short-term patterns in the observed concentrations. Load estimates using the period-weighted approach typically are biased as a result of sampling distribution and are accurate only with extensive sampling. The formulation of the composite method facilitates exploration of patterns (trends) contained in the unmodelled portion of the load. Copyright © 2006 John Wiley & Sons, Ltd.