Predictive Uncertainty in Water-Quality Modeling
Publication: Journal of Environmental Engineering
Volume 135, Issue 12
Abstract
A general and integrated approach to parameter identification, model calibration, and estimation of predictive uncertainty in water-quality models is proposed and validated. The proposed approach determines the maximal conditional likelihood functions of each of the model parameters using a transformation that forces the model errors to be normally distributed, with predictive uncertainty characterized by random normally distributed and homoscedastic model errors in the transform space. The proposed approach is demonstrated using a watershed-scale model to predict the fecal coliform levels in a third-order stream within the Little River Experimental Watershed in Georgia. Maximal conditional likelihood functions were identified for all parameters in the log, square root, and no-transformation cases. The key results are: (1) the number of sensitive parameters and the optimal parameter values can depend on the transformation; (2) only in the case of the log-transformation are the errors normally distributed and consistent with the assumed Gaussian likelihood function; (3) the standard error in the model is least for the no-transform case and highest for the log-transform case; and (4) the observed model errors are most predictable using the log-transform and least predictable using the no-transform approach.
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Acknowledgments
Stimulating discussions with Victor Pestien in the Department of Mathematics helped crystallize some of the ideas presented here. The contributions of David Bosch (USDA) and Paige Gay (University of Georgia) in providing data on the Little River Watershed were much appreciated.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 135 • Issue 12 • December 2009
Pages: 1315 - 1325
Copyright
© 2009 ASCE.
History
Received: Jul 8, 2008
Accepted: May 6, 2009
Published online: May 13, 2009
Published in print: Dec 2009
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