A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Global Sensitivity and GLUE-Based Uncertainty Analysis of a 2D-1D Dual Urban Drainage Model
This paper presents a global sensitivity and uncertainty analysis of a dual drainage model applied to a (4.9-ha) urban catchment based on generalized likelihood uncertainty estimation (GLUE). The model solves the nonlinear bidimensional (2D) and one-dimensional (1D) Saint-Venant equations to compute respectively the surface runoff and the flow in the sewer network. The sensitivity of the outlet hydrograph to the input parameters of the model was determined by using variance-based Sobol sensitivity indices. These indices highlight the great effect of the overland flow parameters on the model output, in contrast with the limited effect of the sewer-network parameters. The Manning coefficient of the impervious terrains was identified as the most influential model parameter. The effect on model calibration of two subjective factors of the GLUE methodology (the acceptability threshold and the shaping factor of the likelihood function) was analyzed. From this analysis, a NSE acceptability threshold of 0.6 and a shaping factor of 1 were adopted for model calibration. Greater values of these parameters resulted in very narrow uncertainty bounds and therefore in low coverage ratios. The results obtained after calibration and validation show a satisfactory performance of the model, with NSE values within 0.78 and 0.98 and coverage ratios of the 95% confidence intervals in the range 66–91%. The worst performances are obtained in the events with lowest rainfall depths, since in those cases the overland flow in the whole catchment is extremely shallow and the runoff propagation is not well captured by the model.
Global Sensitivity and GLUE-Based Uncertainty Analysis of a 2D-1D Dual Urban Drainage Model
This paper presents a global sensitivity and uncertainty analysis of a dual drainage model applied to a (4.9-ha) urban catchment based on generalized likelihood uncertainty estimation (GLUE). The model solves the nonlinear bidimensional (2D) and one-dimensional (1D) Saint-Venant equations to compute respectively the surface runoff and the flow in the sewer network. The sensitivity of the outlet hydrograph to the input parameters of the model was determined by using variance-based Sobol sensitivity indices. These indices highlight the great effect of the overland flow parameters on the model output, in contrast with the limited effect of the sewer-network parameters. The Manning coefficient of the impervious terrains was identified as the most influential model parameter. The effect on model calibration of two subjective factors of the GLUE methodology (the acceptability threshold and the shaping factor of the likelihood function) was analyzed. From this analysis, a NSE acceptability threshold of 0.6 and a shaping factor of 1 were adopted for model calibration. Greater values of these parameters resulted in very narrow uncertainty bounds and therefore in low coverage ratios. The results obtained after calibration and validation show a satisfactory performance of the model, with NSE values within 0.78 and 0.98 and coverage ratios of the 95% confidence intervals in the range 66–91%. The worst performances are obtained in the events with lowest rainfall depths, since in those cases the overland flow in the whole catchment is extremely shallow and the runoff propagation is not well captured by the model.
Global Sensitivity and GLUE-Based Uncertainty Analysis of a 2D-1D Dual Urban Drainage Model
Fraga, Ignacio (author) / Cea, Luis (author) / Puertas, Jerónimo (author) / Suárez, Joaquín (author) / Jiménez, Vicente (author) / Jácome, Alfredo (author)
2016-01-28
Article (Journal)
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Global Sensitivity and GLUE-Based Uncertainty Analysis of a 2D-1D Dual Urban Drainage Model
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