Technical Papers
Aug 11, 2014

Uncertainty Assessment in Reservoir Water Quality Modeling: Implication for Model Improvement

Publication: Journal of Environmental Engineering
Volume 141, Issue 1

Abstract

As reservoir simulation models become more widely used, there is greater need for uncertainty assessment in water quality modeling. In comparison with the modeling of quantity phenomena, such as hydrological modeling, water quality modeling involves additional uncertainties in the modeling of pollutant loadings and the transport and fate of contaminants in receiving waters. In this work, a general and flexible method based on generalized likelihood uncertainty estimation (GLUE) is used to estimate the uncertainty in reservoir water quality modeling that arises from parameter uncertainty and error in model inputs. A one-dimensional model which was set up to simulate the hydrothermal and water quality of Pepacton Reservoir, part of the New York City water supply system, was used to demonstrate the method. Obtained results show that most model parameters and inputs follow wide non-Gaussian distributions, indicating they are of high uncertainty. The results also show that uncertainty is low for the simulated water temperatures of the epilimnion and hypolimnion, and dissolved oxygen (DO) of the epilimnion. Unfortunately, the simulation uncertainty for total phosphorus and chlorophyll a of the epilimnion and hypolimnion, and DO of the hypolimnion is high, especially at peak concentrations. These results can be helpful in understanding and improving the model.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

This work was supported financially by the New York City Department of Environmental Protection (NYCDEP). The authors are grateful to the editors and the anonymous reviewers for their insightful comments.

References

Beven, K., and Binley, A. (1992). “The future of distributed models—Model calibration and uncertainty prediction.” Hydrol. Processes, 6(3), 279–298.
Beven, K., and Freer, J. (2001). “Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology.” J. Hydrol., 249(1–4), 11–29.
Blasone, R. S., Madsen, H., and Rosbjerg, D. (2008a). “Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling.” J. Hydrol., 353(1–2), 18–32.
Blasone, R. S., Vrugt, J. A., Madsen, H., Rosbjerg, D., Robinson, B. A., and Zyvoloski, G. A. (2008b). “Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling.” Adv. Water Resour., 31(4), 630–648.
DeChant, C. M., and Moradkhani, H. (2012). “Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting.” Water Resour. Res., 48(4), W04518.
Doerr, S. M., Owens, E. M., Gelda, R. K., Auer, M. T., and Effler, S. W. (1998). “Development and testing of a nutrient-phytoplankton model for Cannonsville reservoir.” Lake Reservoir Manage., 14(2–3), 301–321.
Dotto, C. B. S., et al. (2012). “Comparison of different uncertainty techniques in urban stormwater quantity and quality modeling.” Water Res., 46(8), 2545–2558.
Elsawwaf, M., Willems, P., and Feyen, J. (2010). “Assessment of the sensitivity and prediction uncertainty of evaporation models applied to Nasser Lake, Egypt.” J. Hydrol., 395(1–2), 10–22.
Franceschini, S., and Tsai, C. W. (2010). “Assessment of uncertainty sources in water quality modeling in the Niagara River.” Adv. Water Resour., 33(4), 493–503.
Freer, J., Beven, K., and Ambroise, B. (1996). “Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach.” Water Resour. Res., 32(7), 2161–2173.
Freni, G., and Mannina, G. (2010a). “Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution.” J. Hydrol., 392(1–2), 31–39.
Freni, G., and Mannina, G. (2010b). “Uncertainty in water quality modelling: The applicability of variance decomposition approach.” J. Hydrol., 394(3–4), 324–333.
Freni, G., and Mannina, G. (2012). “Uncertainty estimation of a complex water quality model: The influence of Box–Cox transformation on Bayesian approaches and comparison with a non-Bayesian method.” Phys. Chem. Earth, 42–44, 31–41.
Freni, G., Mannina, G., and Viviani, G. (2008). “Uncertainty in urban stormwater quality modelling: The effect of acceptability threshold in the GLUE methodology.” Water Res., 42(8–9), 2061–2072.
Freni, G., Mannina, G., and Viviani, G. (2009). “Urban runoff modelling uncertainty: Comparison among Bayesian and pseudo-Bayesian methods.” Environ. Modell. Softw., 24(9), 1100–1111.
Harleman, D. R. (1982). “Hydrothermal analysis of lakes and reservoirs.” J. Hydraul. Eng., 108, 302–325.
Huang, Y. (2014). “Multi-objective calibration of a reservoir water quality model in aggregation and non-dominated sorting approaches.” J. Hydrol., 510, 280–292.
Huang, Y., and Liu, L. (2008). “A hybrid perturbation and Morris approach for identifying sensitive parameters in surface water quality models.” J. Environ. Inf., 12(2), 150–159.
Huang, Y., and Liu, L. (2010). “Multiobjective water quality model calibration using a hybrid genetic algorithm and neural network–based approach.” J. Environ. Eng., 1020–1031.
Huang, Y., and Pierson, D. (2012). “Identifying parameter sensitivity in a water quality model of a reservoir.” Water Qual. Res. J. Canada, 47(3–4), 451–462.
Janse, J. H., Scheffer, M., Lijklema, L., Van Liere, L., Sloot, J. S., and Mooij, W. M. (2010). “Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: Sensitivity, calibration and uncertainty.” Ecol. Modell., 221(4), 654–665.
Jin, X., Xu, C., Zhang, Q., and Singh, V. P. (2010). “Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model.” J. Hydrol., 383(3–4), 147–155.
Kavetski, D., Kuczera, G., and Franks, S. W. (2006a). “Bayesian analysis of input uncertainty in hydrological modeling: 2. Application.” Water Resour. Res., 42(3), W03408.
Kavetski, D., Kuczera, G., and Franks, S. W. (2006b). “Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory.” Water Resour. Res., 42(3), W03407.
Lamb, R., Beven, K., and Myrabø, S. (1998). “Use of spatially distributed water table observations to constrain uncertainty in a rainfallrunoff model.” Adv. Water Resour., 22(4), 305–317.
Lindenschmidt, K. E., Fleischbein, K., and Baborowski, M. (2007). “Structural uncertainty in a river water quality modelling system.” Ecol. Modell., 204(3–4), 289–300.
Liu, Y., Yang, P., Hu, C., and Guo, H. (2008). “Water quality modeling for load reduction under uncertainty: A Bayesian approach.” Water Res., 42(13), 3305–3314.
Mannina, G., and Viviani, G. (2010). “An urban drainage stormwater quality model: Model development and uncertainty quantification.” J. Hydrol., 381(3–4), 248–265.
McKay, M. D., Beckman, R. J., and Conover, W. J. (1979). “A comparison of three methods for selecting values of input variables in the analysis of output from a computer code.” Technometrics, 21(2), 239–245.
McMichael, C. E., Hope, A. S., and Loaiciga, H. A. (2006). “Distributed hydrological modeling in California semi-arid shrublands: MIKESHE model calibration and uncertainty estimation.” J. Hydrol., 317(3–4), 307–324.
Mertens, J., Madsen, H., Feyen, L., Jacques, D., and Feyen, D. (2004). “Including prior information in the estimation of effective soil parameters in unsaturated zone modeling.” J. Hydrol., 294(4), 251–269.
Montanari, A. (2005). “Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall-runoff simulations.” Water Resour. Res., 41(8), W08406.
Morris, M. D. (1991). “Factorial sampling plans for preliminary computational experiments.” Technometrics, 33(2), 161–174.
Naji, A., Cheng, A. H. D., and Ouazar, D. (1998). “Analytical stochastic solutions of saltwater/freshwater interface in coastal aquifers.” Stochastic Hydrol. Hydraul., 12(6), 413–430.
Omlin, M., Brun, R., and Reichert, P. (2001). “Biogeochemical model of Lake Zu¨rich: sensitivity, identifiability and uncertainty analysis.” Ecol. Modell., 141(1–3), 105–123.
Owens, E. M. (1998). “Development and testing of a one-dimensional hydrothermal models of Cannonsville reservoir.” Lake Reservoir Manage., 14(2–3), 172–185.
Pastres, R., and Ciavatta, S. (2005). “A comparison between the uncertainties in model parameters and in forcing functions: Its application to a 3D water-quality model.” Environ. Modell. Softw., 20(8), 981–989.
Rigosi, A., and Rueda, F. J. (2012). “Propagation of uncertainty in ecological models of reservoirs: From physical to population dynamic predictions.” Ecol. Modell., 247, 199–209.
Rojas, R., Feyen, L., and Dassargues, A. (2008). “Conceptual model uncertainty in groundwater modeling: Combining generalized likelihood uncertainty estimation and Bayesian model averaging.” Water Resour. Res., 44(12), W12418.
Rojas, R., Kahunde, S., Peeters, L., Batelaan, O., Feyen, L., and Dassargues, A. (2010). “Application of a multimodel approach to account for conceptual model and scenario uncertainties in groundwater modeling.” J. Hydrol., 394(3–4), 416–435.
Rosenblueth, E. (1975). “Point estimates for probability moments.” Proc. Natl. Acad. Sci. U.S.A., 72, 3812–3814.
Rueda, F. J., Fleenor, W. E., and Vicente, I. (2007). “Pathways of river nutrients towards the euphotic zone in a deep-reservoir of small size: Uncertainty analysis.” Ecol. Modell., 202(3–4), 345–361.
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, R. (2004). Sensitivity analysis in practice: A guide to assessing scientific model, Wiley, Chichester, U.K.
Schneiderman, E. M., et al. (2007). “Incorporating variable source area hydrology into a curve-number-based watershed model.” Hydrol. Processes, 21(25), 3420–3430.
Schneiderman, E. M., Pierson, D. C., Lounsbury, D. G., and Zion, M. S. (2002). “Modeling the hydrochemistry of the Cannonsville watershed with GWLF.” J. Am. Water Resour. Assoc., 38(5), 1323–1347.
Thorndahl, S., and Willems, P. (2008). “Probabilistic modelling of overflow, surcharge and flooding in urban drainage using the first-order reliability method and parameterization of local rain series.” Water Res., 42(1–2), 455–466.
Upstate Freshwater Institute (UFI). (2001). “Calibration, verification of a one-dimensional hydrothermal and eutrophication model for catskill/delaware reservoirs.”, NYCDEP, Vahalla, NY.
Willems, P. (2008). “Quantification and relative comparison of different types of uncertainties in sewer water quality modeling.” Water Res., 42(13), 3539–3551.
Wöhling, T., and Vrugt, J. A. (2011). “Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data.” Water Resour. Res., 47(4), W04510.
Zheng, Y., and Keller, A. A. (2007a). “Uncertainty assessment in watershed-scale water quality modeling and management: 1. Framework and application of generalized likelihood uncertainty estimation (GLUE) approach.” Water Resour. Res., 43(8), W08407.
Zheng, Y., and Keller, A. A. (2007b). “Uncertainty assessment in watershed-scale water quality modeling and management: 2. Management objectives constrained analysis of uncertainty (MOCAU).” Water Resour. Res., 43(8), W08408.
Zheng, Y., Wang, W., Han, F., and Ping, J. (2011). “Uncertainty assessment for watershed water quality modeling: A probabilistic collocation method based approach.” Adv. Water Resour., 34(7), 887–898.

Information & Authors

Information

Published In

Go to Journal of Environmental Engineering
Journal of Environmental Engineering
Volume 141Issue 1January 2015

History

Received: Jan 10, 2014
Accepted: Jul 11, 2014
Published online: Aug 11, 2014
Published in print: Jan 1, 2015
Discussion open until: Jan 11, 2015

Permissions

Request permissions for this article.

Authors

Affiliations

Yongtai Huang, Aff.M.ASCE [email protected]
Research Associate, CUNY Institute for Sustainable Cities, Hunter College, City Univ. of New York, New York 10065. E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share