Load estimation with uncertainties from opportunistic sampling data - a semiparametric approach

Wang, You-Gan, Keuhnert, Petra and Henderson, Brent (2011) Load estimation with uncertainties from opportunistic sampling data - a semiparametric approach. Journal of Hydrology, 396 1-2: 148-157. doi:10.1016/j.jhydrol.2010.11.003

Author Wang, You-Gan
Keuhnert, Petra
Henderson, Brent
Title Load estimation with uncertainties from opportunistic sampling data - a semiparametric approach
Journal name Journal of Hydrology   Check publisher's open access policy
ISSN 0022-1694
Publication date 2011-01-05
Year available 2010
Sub-type Article (original research)
DOI 10.1016/j.jhydrol.2010.11.003
Volume 396
Issue 1-2
Start page 148
End page 157
Total pages 10
Place of publication The Netherlands
Publisher Elsevier
Collection year 2011
Language eng
Formatted abstract
We consider estimating the total load from frequent flow data but less frequent concentration data. There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates that minimizes the biases and makes use of informative predictive variables. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized rating-curve approach with additional predictors that capture unique features in the flow data, such as the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and the discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. Forming this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach for two rivers delivering to the Great Barrier Reef, Queensland, Australia. One is a data set from the Burdekin River, and consists of the total suspended sediment (TSS) and nitrogen oxide (NO. x) and gauged flow for 1997. The other dataset is from the Tully River, for the period of July 2000 to June 2008. For NO. x Burdekin, the new estimates are very similar to the ratio estimates even when there is no relationship between the concentration and the flow. However, for the Tully dataset, by incorporating the additional predictive variables namely the discounted flow and flow phases (rising or recessing), we substantially improved the model fit, and thus the certainty with which the load is estimated. © 2010.
Keyword Biased sampling
Load estimation
Rating curve
Standard error
Suspended sediment
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 11 November, 2010.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2011 Collection
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Citation counts: TR Web of Science Citation Count  Cited 20 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 27 times in Scopus Article | Citations
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Created: Fri, 11 Feb 2011, 14:15:05 EST by Kay Mackie on behalf of School of Mathematics & Physics