Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling |
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Authors: | Dotto Cintia B S Mannina Giorgio Kleidorfer Manfred Vezzaro Luca Henrichs Malte McCarthy David T Freni Gabriele Rauch Wolfgang Deletic Ana |
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Affiliation: | a Centre for Water Sensitive Cities, Department of Civil Engineering, Monash University, Australia b Dipartimento di Ingegneria Civile, Ambientale e Aerospaziale, University of Palermo, Italy c Unit for Environmental Engineering, University of Innsbruck, Austria d Department of Environmental Engineering (DTU Environment), Technical University of Denmark, Denmark e Muenster University of Applied Sciences, Department of Civil Engineering, Laboratory of Water Resources Management, Germany f Facoltà di Ingegneria ed Architettura, Kore di Enna University, Italy |
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Abstract: | Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the model’s structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study. |
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Keywords: | Urban drainage models Uncertainties Parameter probability distributions Bayesian inference GLUE SCEM-UA MICA AMALGAM MCMC Multi-objective auto-calibration |
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