Statistical modelling and power analysis for detecting trends in total suspended sediment loads

Wang, You-Gan, Wang, Shen S. J. and Dunlop, Jason (2015) Statistical modelling and power analysis for detecting trends in total suspended sediment loads. Journal of Hydrology, 520 439-447. doi:10.1016/j.jhydrol.2014.10.062

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ347933_OA.pdf Full text (open access) application/pdf 514.70KB 15

Author Wang, You-Gan
Wang, Shen S. J.
Dunlop, Jason
Title Statistical modelling and power analysis for detecting trends in total suspended sediment loads
Journal name Journal of Hydrology   Check publisher's open access policy
ISSN 0022-1694
Publication date 2015-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.jhydrol.2014.10.062
Open Access Status File (Author Post-print)
Volume 520
Start page 439
End page 447
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2015
Language eng
Abstract The export of sediments from coastal catchments can have detrimental impacts on estuaries and near shore reef ecosystems such as the Great Barrier Reef. Catchment management approaches aimed at reducing sediment loads require monitoring to evaluate their effectiveness in reducing loads over time. However, load estimation is not a trivial task due to the complex behaviour of constituents in natural streams, the variability of water flows and often a limited amount of data. Regression is commonly used for load estimation and provides a fundamental tool for trend estimation by standardising the other time specific covariates such as flow. This study investigates whether load estimates and resultant power to detect trends can be enhanced by (i) modelling the error structure so that temporal correlation can be better quantified, (ii) making use of predictive variables, and (iii) by identifying an efficient and feasible sampling strategy that may be used to reduce sampling error. To achieve this, we propose a new regression model that includes an innovative compounding errors model structure and uses two additional predictive variables (average discounted flow and turbidity). By combining this modelling approach with a new, regularly optimised, sampling strategy, which adds uniformity to the event sampling strategy, the predictive power was increased to 90%. Using the enhanced regression model proposed here, it was possible to detect a trend of 20% over 20 years. This result is in stark contrast to previous conclusions presented in the literature.
Keyword Environmental monitoring
Trend detection
Pollutant loads
Suspended sediment
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 5 Nov 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 23 Dec 2014, 00:19:16 EST by System User on behalf of School of Mathematics & Physics