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Approaches to evaluate water quality model parameter uncertainty for adaptive TMDL implementation

Publication ,  Journal Article
Stow, CA; Reckhow, KH; Qian, SS; Lamon, EC; Arhonditsis, GB; Borsuk, ME; Seo, D
Published in: Journal of the American Water Resources Association
December 1, 2007

The National Research Council recommended Adaptive Total Maximum Daily Load implementation with the recognition that the predictive uncertainty of water quality models can be high. Quantifying predictive uncertainty provides important information for model selection and decision-making. We review five methods that have been used with water quality models to evaluate model parameter and predictive uncertainty. These methods (1) Regionalized Sensitivity Analysis, (2) Generalized Likelihood Uncertainty Estimation, (3) Bayesian Monte Carlo, (4) Importance Sampling, and (5) Markov Chain Monte Carlo (MCMC) are based on similar concepts; their development over time was facilitated by the increasing availability of fast, cheap computers. Using a Streeter-Phelps model as an example we show that, applied consistently, these methods give compatible results. Thus, all of these methods can, in principle, provide useful sets of parameter values that can be used to evaluate model predictive uncertainty, though, in practice, some are quickly limited by the "curse of dimensionality" or may have difficulty evaluating irregularly shaped parameter spaces. Adaptive implementation invites model updating, as new data become available reflecting water-body responses to pollutant load reductions, and a Bayesian approach using MCMC is particularly handy for that task. © 2007 American Water Resources Association.

Duke Scholars

Published In

Journal of the American Water Resources Association

DOI

ISSN

1093-474X

Publication Date

December 1, 2007

Volume

43

Issue

6

Start / End Page

1499 / 1507

Related Subject Headings

  • Environmental Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0907 Environmental Engineering
  • 0905 Civil Engineering
  • 0406 Physical Geography and Environmental Geoscience
 

Citation

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Stow, C. A., Reckhow, K. H., Qian, S. S., Lamon, E. C., Arhonditsis, G. B., Borsuk, M. E., & Seo, D. (2007). Approaches to evaluate water quality model parameter uncertainty for adaptive TMDL implementation. Journal of the American Water Resources Association, 43(6), 1499–1507. https://doi.org/10.1111/j.1752-1688.2007.00123.x
Stow, C. A., K. H. Reckhow, S. S. Qian, E. C. Lamon, G. B. Arhonditsis, M. E. Borsuk, and D. Seo. “Approaches to evaluate water quality model parameter uncertainty for adaptive TMDL implementation.” Journal of the American Water Resources Association 43, no. 6 (December 1, 2007): 1499–1507. https://doi.org/10.1111/j.1752-1688.2007.00123.x.
Stow CA, Reckhow KH, Qian SS, Lamon EC, Arhonditsis GB, Borsuk ME, et al. Approaches to evaluate water quality model parameter uncertainty for adaptive TMDL implementation. Journal of the American Water Resources Association. 2007 Dec 1;43(6):1499–507.
Stow, C. A., et al. “Approaches to evaluate water quality model parameter uncertainty for adaptive TMDL implementation.” Journal of the American Water Resources Association, vol. 43, no. 6, Dec. 2007, pp. 1499–507. Scopus, doi:10.1111/j.1752-1688.2007.00123.x.
Stow CA, Reckhow KH, Qian SS, Lamon EC, Arhonditsis GB, Borsuk ME, Seo D. Approaches to evaluate water quality model parameter uncertainty for adaptive TMDL implementation. Journal of the American Water Resources Association. 2007 Dec 1;43(6):1499–1507.
Journal cover image

Published In

Journal of the American Water Resources Association

DOI

ISSN

1093-474X

Publication Date

December 1, 2007

Volume

43

Issue

6

Start / End Page

1499 / 1507

Related Subject Headings

  • Environmental Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0907 Environmental Engineering
  • 0905 Civil Engineering
  • 0406 Physical Geography and Environmental Geoscience