The Influence of Objective Function and Acceptability Threshold on Uncertainty Assessment of an Urban Drainage Hydraulic Model with Generalized Likelihood Uncertainty Estimation Methodology

Urban drainage model is an important computer-aided tool in stormwater management and drainage planning and designing. A popular urban drainage hydraulic model, stormwater management model (SWMM), was applied in a pump lifting combined sewer system for a high-intensity urban catchment located in Shanghai, China. Uncertainty of SWMM water quantity parameters was assessed with generalized likelihood uncertainty estimation (GLUE) methodology. The sensitivity of parameters was discussed and compared based on the results of uncertainty analysis. To discuss the influence of the acceptability threshold on model parameter sensitivity and the margin of uncertainty band, the GLUE approach was applied several times varying acceptability threshold. The results indicated that a higher acceptability threshold value is contributed to achieve a stricter verification with a high confidence level, and the uncertainty analysis significant level can be featured by the value of acceptability threshold. The selection of acceptability threshold value can be regarded as a tradeoff process. Both reducing the low efficient simulation and reducing computation cost should be considered for the selection of acceptability threshold. Moreover, the GLUE approach was applied several times varying different objective functions with corresponding acceptability thresholds. The results indicated that some parameters may be sensitive to a specific objective function, and other parameters may be sensitive to another objective function. Some parameters cannot easily identified when a single objective function was used within the GLUE approach, and a multiple-objective function combined different objective functions requirements, may be a alternative approach to reduce the model prediction uncertainty.

[1]  Christopher Zoppou,et al.  Review of urban storm water models , 2001, Environ. Model. Softw..

[2]  Lei Wang,et al.  A Review of Modelling Tools for Implementation of the EU Water Framework Directive in Handling Diffuse Water Pollution , 2010 .

[3]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[4]  P. Willems Quantification and relative comparison of different types of uncertainties in sewer water quality modeling. , 2008, Water research.

[5]  Murugesu Sivapalan,et al.  Modelling runoff generation on small agricultural catchments: Can real world runoff responses be captured? , 1997 .

[6]  Sangho Lee,et al.  Modification of the SCE-UA to Include Constraints by Embedding an Adaptive Penalty Function and Application: Application Approach , 2014, Water Resources Management.

[7]  Michael K. Stenstrom,et al.  Automatic Calibration of the U.S. EPA SWMM Model for a Large Urban Catchment , 2008 .

[8]  Keith Beven,et al.  Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework , 2007 .

[9]  C. Perrin,et al.  Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments , 2001 .

[10]  Christine A. Shoemaker,et al.  Cannonsville Reservoir Watershed SWAT2000 model development, calibration and validation , 2007 .

[11]  A. H. Elliott,et al.  A review of models for low impact urban stormwater drainage , 2007, Environ. Model. Softw..

[12]  Søren Liedtke Thorndahl,et al.  Event based uncertainty assessment in urban drainage modelling applying the GLUE methodology , 2008 .

[13]  G. Freni,et al.  Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas. , 2012, Water science and technology : a journal of the International Association on Water Pollution Research.

[14]  Ning Sun,et al.  Assessment of the SWMM model uncertainties within the generalized likelihood uncertainty estimation (GLUE) framework for a high‐resolution urban sewershed , 2013 .

[15]  Mehdi Ghasemzadeh,et al.  Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model , 2010 .

[16]  Soroosh Sorooshian,et al.  Model Calibration in Watershed Hydrology , 2009 .

[17]  Teresa B. Culver,et al.  Uncertainty Analysis for Watershed Modeling Using Generalized Likelihood Uncertainty Estimation with Multiple Calibration Measures , 2008 .

[18]  Mazdak Arabi,et al.  A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practices , 2007 .

[19]  F. Périé,et al.  Calibration, validation and sensitivity analysis of an ecosystem model applied to artificial streams. , 2008, Water research.

[20]  Gabriele Freni,et al.  Impact of rainfall data resolution in time and space on the urban flooding evaluation. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[21]  Neil McIntyre,et al.  Impact of rainfall temporal resolution on urban water quality modelling performance and uncertainties. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[22]  H. Gao,et al.  USE OF A GENETIC ALGORITHM AND MULTI-OBJECTIVE PROGRAMMING FOR CALIBRATION OF A HYDROLOGIC MODEL , 1998 .

[23]  G. Freni,et al.  Uncertainty in urban stormwater quality modelling: the effect of acceptability threshold in the GLUE methodology. , 2008, Water research.

[24]  Weng Tat Chan,et al.  Knowledge-Based System for SWMM Runoff Component Calibration , 1991 .

[25]  Gabriele Freni,et al.  The influence of rainfall time resolution for urban water quality modelling. , 2010, Water science and technology : a journal of the International Association on Water Pollution Research.

[26]  J. Feyen,et al.  GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application , 2009 .