Journal
WATER RESOURCES MANAGEMENT
Volume 29, Issue 6, Pages 2059-2072Publisher
SPRINGER
DOI: 10.1007/s11269-015-0928-8
Keywords
Combined sewer system; Objective function; Acceptability threshold; Uncertainty analysis; Stormwater management model (SWMM); Generalized likelihood uncertainty estimation (GLUE)
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Funding
- National water pollution control and management technology major projects [2011ZX07303-002]
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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.
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