Stormwater Detention System Parameter Sensitivity and Uncertainty Analysis Using SWMM
Publication: Journal of Hydrologic Engineering
Volume 21, Issue 8
Abstract
A U.S. EPA (EPA) model was developed for the Cathedral Run stormwater wetland (Philadelphia, Pennsylvania). This research presents a formal sensitivity analysis of hydraulic and hydrologic model parameters contributing uncertainty with the multiobjective generalized sensitivity analysis (MOGSA) algorithm. The parameters identified as significant include: percent routed (), subcatchment soils, subcatchment width, wetland soils, and the flood weir coefficient. These results suggest that this model is well parameterized for detailed simulations of stormwater control installations, and contests the existence of a globally sensitive set of parameters. This research demonstrates that detailed models of stormwater control installations are significantly affected by uncertainty related to parameters beyond traditional calibration (i.e., runoff generation) parameters. The authors present a monitoring design based on wetland water surface elevation. The simplified monitoring scheme obtained statistically significant calibration data as determined through MOGSA. The generalized likelihood uncertainty estimation (GLUE) algorithm was then applied to develop marginal posterior model parameter distributions and two-dimensional (2D) probability spaces using a formal Bayesian likelihood function. The GLUE results demonstrate the importance of uncertainty and equifinality within the context of stormwater wetland modeling.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors acknowledge Jason Cruz and Chris Bergerson (PWD) for wetland instrumentation. The authors also thank four anonymous referees for their thoughtful comments and suggestions on this research.
References
Bachland, P., Bachland, S., Fleck, J., Anderson, F., and Windham-Meyers, L. (2013). “Differentiating transpiration from evaporation in seasonal agricultural wetlands and the link to advective fluxes in the root zone.” Sci. Total Environ., 484, 232–248.
Balascio, C., Palmeri, D., and Gao, H. (1998). “Use of a genetic algorithm and multi-objective programming for calibration of a hydrologic model.” Trans. ASAE, 41(3), 615–619.
Barco, J., Wong, K., and Stenstrom, M. (2008). “Automatic calibration of the U.S. EPA SWMM model for a large urban catchment.” J. Hydraul. Eng., 466–474.
Bastidas, L. A., Gupta, H. V., Sorooshian, S., Shuttleworth, W. J., and Yang, Z. L. (1999). “Sensitivity analysis of a land surface scheme using multi-criteria methods.” J. Geophys. Res., 104(D16), 19–481.
Bastidas, L. A., Hogue, T. S., Sorooshian, S., Gupta, H. V., and Shuttleworth, W. J. (2006). “Parameter sensitivity analysis for different complexity land surface models using multicriteria methods.” J. Geophys. Res.- Atmos., 111(D20).
Beling, F., Garcia, J., Paiva, E., Bastos, G., Paiva, J. (2011). “Analysis of the SWMM model parameters for runoff evaluation in Periurban basins from southern Brazil.” 12th Int. Conf. on Urban Drainage, International Water Association, London, 11–16.
Beven, K. (2006). “A manifesto for the equifinality thesis.” J. Hydrol., 320(1-2), 18–36.
Beven, K., and Binley, A. (1992). “The future of distributed models: Model calibration and uncertainty prediction.” Hydrol. Process., 6(3), 279–298.
Beven, K., and Binley, A. (2014). “GLUE: 20 years on.” Hydrol. Process., 28(24), 5897–5918.
Clark, M. P., Kavetski, D., and Fenicia, F. (2011). “Pursuing the method of multiple working hypotheses for hydrological modeling.” Water Resour. Res., 47(9).
Clemen, R. (1997). Making hard decisions: An introduction to decision analysis (Business Statistics), Duxbury, Pacific Grove, CA.
Clulow, A. D., et al. (2012). “Measurement and modelling of evaporation from a coastal wetland in Maputaland, South Africa.” Hydrol. Earth Syst. Sci., 16(9), 3233–3247.
Coenders-Gerrits, A. M. J., et al. (2014). “Uncertainties in transpiration estimates.” Nature, 506(7487), E1–E2.
Dotto, C., Kleidorfer, M., Rauch, W., and McCarthy, D. (2014). “Impacts of measured data uncertainty on urban stormwater models.” J. Hydrol., 508, 28–42.
Drexler, J. Z., Snyder, R. L., Spano, D., Paw, U., and Tha, K. (2004). “A review of models and micrometeorological methods used to estimate wetland evapotranspiration.” Hydrol. Process., 18(11), 2071–2101.
Duan, Q., et al. (2006). “Model parameter estimation experiment MOPEX: An overview of science strategy and major results from the second and third workshops.” J. Hydrol., 320(1-2), 3–17.
EPA. (1991). “Summary of the clean water act.” 〈http://www.epa.gov/laws-regulations/summary-clean-water-act〉 (Feb. 5, 2015).
EPA. (2003). “Total maximum daily load for sediment and nutrients wissahickon creek watershed.” Washington, DC.
EPA. (2015). “Storm water management model SWMM.” 〈http://www2.epa.gov/water-research/storm-water-management-model-swmm〉 (Jul. 1, 2015).
Gülbaz, S., and Kazezyılmaz-Alhan, C. (2013). “Calibrated hydrodynamic model for Sazlidere watershed in Istanbul and investigation of urbanization effects.” J. Hydrol. Eng., 75–84.
Hach. (2008). “Sigma flow meter models 910 and 920 user manual, edition 11.” 〈http://www.hachflow.com/pdf/4975_910-920manual.pdf〉 (Jul. 1, 2015).
He, J., Jones, J. W., Graham, W. D., and Dukes, M. D. (2010). “Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method.” Agric. Syst., 103(5), 256–264.
Hunt, W., Davis, A., and Traver, R. (2012). “Meeting hydrologic and water quality goals through targeted bioretention design.” J. Environ. Eng., 698–707.
Jain, G. V., et al. (2015). “Estimation of sub-catchment area parameters for storm water management model (SWMM) using geo-informatics.” Geocarto Int., 31(4), 462–476.
James, W., Rossman, L., and James, R. (2011). User’s guide to SWMM5, 13th Ed., CHI Press, Toronto.
Jung, Y., et al. (2014). “Sensitivity of subjective decisions in the GLUE methodology for quantifying the uncertainty in the flood inundation map for Seymour reach in Indiana, USA.” Water, 6(7), 2104–2126.
Khu, S. T., and Werner, M. G. (2003). “Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling.” Hydrol. Earth System Sci., 7(5), 680–692.
Knighton, J., White, E., Lennon, E., and Rajan, R. (2014). “Development of probability distributions for urban hydrologic model parameters and a Monte Carlo analysis of model sensitivity.” Hydrol. Process., 28(19), 5131–5139.
Maidment, D. R. (1993). Handbook of hydrology, McGraw-Hill, New York.
Maimone, M., O’Rourke, D. E., Knighton, J. O., and Thomas, C. P. (2011). “Potential impacts of extensive stormwater infiltration in Philadelphia.” Environ. Eng. Appl. Res. Pract., 14, 1–12.
Mancipe -Munoz, N., Buchberger, S., Suidan, M., and Lu, T. (2014). “Calibration of rainfall-runoff model in urban watersheds for stormwater management assessment.” J. Water Resour. Plann. Manage., 05014001.
Mantovan, P., and Todini, E. (2006). “Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology.” J. Hydrol., 330(1-2), 368–381.
Mantovan, P., Todini, E., and Martina, M. L. (2007). “Reply to comment by Keith Beven, Paul Smith and Jim Freer on Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology.” J. Hydrol., 338(3-4), 319–324.
Min, J. H., and Wise, W. R. (2010). “Depth-averaged, spatially distributed flow dynamic and solute transport modelling of a large-scaled, subtropical constructed wetland.” Hydrol. Processes, 24(19), 2724–2737.
Mogavero, K., Jones, G., and Wadzuk, B. (2009). “Developing a water budget for a constructed stormwater wetland.” World Environmental and Water Resources Congress, Environmental and Water Resources Institute ASCE, Reston, VA.
Moussa, R., and Chahinian, N. (2009). “Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events.” Hydrol. Earth Syst. Sci., 13(4), 519–535.
NOAA. (2015). “State annual and seasonal time series.” 〈https://www.ncdc.noaa.gov/temp-and-precip/state-temps/〉 (Feb. 5, 2016).
PWD (Philadelphia Water Department). (2007). “Wissahickon creek watershed comprehensive characterization report.” 〈http://www.phillywatersheds.org/doc/Wissahickon_CCR.pdf〉 (Jul. 1, 2015).
PWD (Philadelphia Water Department). (2011). “Green city clean waters.” 〈http://www.phillywatersheds.org/ltcpu/LTCPU_Complete.pdf〉 (Jul. 1, 2015).
Romanowicz, R., Beven, K. J., and Tawn, J. (1994). “Evaluation of predictive uncertainty in nonlinear hydrological models using a Bayesian approach.” Stat. Environ., 2, 297–317.
Rossman, L. (2010). “Storm water management model user’s manual version 5.0.” 〈http://nepis.epa.gov/Adobe/PDF/P100ERK4.pdf〉 (Jul. 1, 2015).
Sangal, S. K., and Bonema, S. R. (1994). “A methodology for calibrating SWMM models.” Current practices in modelling the management of stormwater impacts, W. James, ed., Lewis, Ann Arbor, MI.
Schaake, J. C., Cong, S., and Duan, Q. (2006). “The U.S. MOPEX data set.” IAHS Publ., 307, 9–28.
Schaefli, B., Talamba, D. B., and Musy, A. (2007). “Quantifying hydrological modeling errors through a mixture of normal distributions.” J. Hydrol., 332(3-4), 303–315.
Schoups, G., and Vrugt, J. A. (2010). “A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors.” Water Resour. Res., 46(10).
Stedinger, J. R., Vogel, R. M., Lee, S. U., and Batchelder, R. (2008). “Appraisal of the generalized likelihood uncertainty estimation GLUE method.” Water Resour. Res., 441(12).
Sun, N., Hall, M., Hong, B., and Zhang, L. (2012). “Impact of SWMM catchment discretization: Case study in Syracuse, New York.” J. Hydrol. Eng., 223–234.
Sun, N., Hong, B., and Hall, M. (2014). “Assessment of the SWMM model uncertainties within the generalized likelihood uncertainty estimation GLUE framework for a high-resolution urban sewershed.” Hydrol. Proces., 286(6), 3018–3034.
USDA Natural Resources Conservation Service. (2015). “Web soil survey.” 〈http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm〉 (Jul. 1, 2015).
Vrugt, J. A., Bouten, W., Gupta, H. V., and Sorooshian, S. (2002). “Toward improved identifiability of hydrologic model parameters: The information content of experimental data.” Water Resour. Res., 381(12), 48-1–48-13.
Vrugt, J. A., and Sadegh, M. (2013). “Toward diagnostic model calibration and evaluation: Approximate Bayesian computation.” Water Resour. Res., 49(7), 4335–4345.
Wan, B., and James, W. (2002). “SWMM calibration using genetic algorithms.” Proc., 9 th Int. Conf. Urban Drainage–Global Solutions for Urban Drainage, ASCE, Portland, OR.
Wang-Erlandsson, L., van der Ent, R. J., Gordon, L. J., and Savenije, H. H. G. (2014). “Contrasting roles of interception and transpiration in the hydrological cycle. 1: Temporal characteristics over land.” Earth Syst. Dyn., 5(2), 441–469.
Weijs, S. V., van de Giesen, N., and Parlange, M. B. (2013a). “HydroZIP: How hydrological knowledge can be used to improve compression of hydrological data.” Entropy 15(4), 1289–1310.
Weijs, S. V., Van De Giesen, N., and Parlange, M. B. (2013b). “Data compression to define information content of hydrological time series.” Hydrol. Earth Syst. Sci., 17(8), 3171–3187.
Yu, P. S., and Yang, T. C. (2000). “Fuzzy multi-objective function for rainfall-runoff model calibration.” J. Hydrol., 238(1-2), 1–14.
Zhang, W., Li, T., and Dai, M. (2015). “Uncertainty assessment of water quality modeling for a small-scale urban catchment using the GLUE methodology: A case study in Shanghai, China.” Environ. Sci. Pollut. Res., 22(12), 1–9.
Zheng, Y., and Keller, A. (2006). “Understanding parameter sensitivity and its management implications in watershed-scale water quality modeling.” Water Resour. Res., 425(5), 1–14.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 21 • Issue 8 • August 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Jul 22, 2015
Accepted: Jan 8, 2016
Published online: Mar 28, 2016
Published in print: Aug 1, 2016
Discussion open until: Aug 28, 2016
Authors
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.