Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Dotto, Cintia Brum Siqueira; Mannina, Giorgio; Kleidorfer, Manfred; Vezzaro, Luca; Henrichs, Malte; McCarthy, David Thomas; Freni, Gabriele; Rauch, Wolfgang; Deletić, Ana

doi:10.1016/j.watres.2012.02.009

Cited by 163 publications

(91 citation statements)

References 40 publications

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“…The comparison presented in Dotto et al (2012) showed that this approach provides equivalent results to other uncertainty estimation methods with lower computational costs.…”

Section: Uncertainty Calibrationmentioning

confidence: 82%

Evaluation of stormwater micropollutant source control and end-of-pipe control strategies using an uncertainty-calibrated integrated dynamic simulation model

Vezzaro

Sharma

Ledin

et al. 2015

Journal of Environmental Management

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“…The comparison presented in Dotto et al (2012) showed that this approach provides equivalent results to other uncertainty estimation methods with lower computational costs.…”

Section: Uncertainty Calibrationmentioning

confidence: 82%

Evaluation of stormwater micropollutant source control and end-of-pipe control strategies using an uncertainty-calibrated integrated dynamic simulation model

Vezzaro

Sharma

Ledin

et al. 2015

Journal of Environmental Management

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“…They found that the maximum surface specific sulphide formation rate represented the most important parameter for predicting both sulphide-related processes and concrete corrosion rate. Moreover, in the urban drainage modelling field, some applications of GSA aimed at uncertainty analysis have been found (Freni et al, 2009a,b;Mannina and Viviani, 2010;Dotto et al, 2012;Vezzaro and Mikkelsen, 2012). Dotto et al (2012) compared different uncertainty analysis techniques in the urban drainage field using RSA with the GLUE approach.…”

Section: Gsa In Urban Drainagementioning

confidence: 99%

“…Moreover, in the urban drainage modelling field, some applications of GSA aimed at uncertainty analysis have been found (Freni et al, 2009a,b;Mannina and Viviani, 2010;Dotto et al, 2012;Vezzaro and Mikkelsen, 2012). Dotto et al (2012) compared different uncertainty analysis techniques in the urban drainage field using RSA with the GLUE approach. Freni et al (2009a,b) analysed the parametric and structural uncertainty of an urban drainage integrated model by using RSA with the GLUE methodology.…”

Section: Gsa In Urban Drainagementioning

confidence: 99%

“…the extreme spatial and temporal variability of the water quality-quantity characteristics or the lack of distributed field data, which consequently forces modellers to impose a considerable number of assumptions. Due to these assumptions their predictions are characterised by high uncertainty (Beck, 1987;Ashley et al, 2005;Deletic et al, 2012;Dotto et al, 2012;Mannina and Viviani, 2010). One may ask whether and how these model assumptions influence the output of the model.…”

Section: Introductionmentioning

confidence: 99%

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Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods

Vanrolleghem

Mannina

Cosenza

et al. 2015

Journal of Hydrology

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a b s t r a c tSensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important (factor prioritisation) and non-influential (factor fixing) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality-quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiarities, applicability, and reliability of the three methods is presented. Moreover, a graphical Venn diagram based classification scheme and a precise terminology for better identifying important, interacting and non-influential factors for each method is proposed. In terms of convergence, it was shown that sensitivity indices related to factors of the quantity model achieve convergence faster. Results for the Morris screening method deviated considerably from the other methods. Factors related to the quality model require a much higher number of simulations than the number suggested in literature for achieving convergence with this method. In fact, the results have shown that the term ''screening'' is improperly used as the method may exclude important factors from further analysis. Moreover, for the presented application the convergence analysis shows more stable sensitivity coefficients for the Extended-FAST method compared to SRC and Morris screening. Substantial agreement in terms of factor fixing was found between the Morris screening and Extended FAST methods. In general, the water quality related factors exhibited more important interactions than factors related to water quantity. Furthermore, in contrast to water quantity model outputs, water quality model outputs were found to be characterised by high non-linearity.

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“…Several GLUE applications have also been seen within urban drainage water quantity and quality modelling, (Aronica et al, 2005;Lindblom et al, 2007;Freni et al, 2008Freni et al, , 2009bMannina and Viviani, 2010;Lindblom et al, 2011), but GLUE, as well as Bayesian inverse methods (e.g. Dotto et al, 2009;Kleidorfer et al, 2009;Dotto et al, 2010;Freni and Mannina, 2010;Dotto et al, 2012), have so far mostly been applied to tailor-made models for relatively simple, well-defined urban drainage systems or in combination with high-quality data generated in research projects. Within flow modelling uncertainty is introduced from unreliable/inaccurate level or flow meters (Bertrand-Krajewski et al, 2003), inadequate rain gauge coverage (Willems, 2001;Vaes et al, 2005;Pedersen et al, 2010), and/or unreliable/inaccurate rain gauge measurements (input errors) (Barbera et al, 2002;Molini and Barbera, 2005;Shedekar et al, 2009).…”

mentioning

confidence: 99%

Informal uncertainty analysis (GLUE) of continuous flow simulation in a hybrid sewer system with infiltration inflow – consistency of containment ratios in calibration and validation?

Breinholt

Grum

Madsen

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Monitoring of flows in sewer systems is increasingly applied to calibrate urban drainage models used for long-term simulation. However, most often models are calibrated without considering the uncertainties. The generalized likelihood uncertainty estimation (GLUE) methodology is here applied to assess parameter and flow simulation uncertainty using a simplified lumped sewer model that accounts for three separate flow contributions: wastewater, fast runoff from paved areas, and slow infiltrating water from permeable areas. Recently GLUE methodology has been critisised for generating prediction limits without statistical coherence and consistency and for the subjectivity in the choice of a threshold value to distinguish "behavioural" from "nonbehavioural" parameter sets. In this paper we examine how well the GLUE methodology performs when the behavioural parameter sets deduced from a calibration period are applied to generate prediction bounds in validation periods. By retaining an increasing number of parameter sets we aim at obtaining consistency between the GLUE generated 90 % prediction limits and the actual containment ratio (CR) in calibration. Due to the large uncertainties related to spatiotemporal rain variability during heavy convective rain events, flow measurement errors, possible model deficiencies as well as epistemic uncertainties, it was not possible to obtain an overall CR of more than 80 %. However, the GLUE generated prediction limits still proved rather consistent, since the overall CRs obtained in calibration corresponded well with the overall CRs obtained in validation periods for all proportions of retained parameter sets evaluated. When focusing on wet and dry weather periods separately, some inconsistencies were however found between calibration and validation and we address here some of the reasons why we should not expect the coverage of the prediction limits to be identical in calibration and validation periods in real-world applications. The large uncertainties result in wide posterior parameter limits, that cannot be used for interpretation of, for example, the relative size of paved area vs. the size of infiltrating area. We should therefore try to learn from the significant discrepancies between model and observations from this study, possibly by using some form of non-stationary error correction procedure, but it seems crucial to obtain more representative rain inputs and more accurate flow observations to reduce parameter and model simulation uncertainty.

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Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Cited by 163 publications

References 40 publications

Evaluation of stormwater micropollutant source control and end-of-pipe control strategies using an uncertainty-calibrated integrated dynamic simulation model

Evaluation of stormwater micropollutant source control and end-of-pipe control strategies using an uncertainty-calibrated integrated dynamic simulation model

Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods

Informal uncertainty analysis (GLUE) of continuous flow simulation in a hybrid sewer system with infiltration inflow – consistency of containment ratios in calibration and validation?

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