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The current state of knowledge regarding uncertainties in urban drainage models is poor. This is in part due to the lack of clarity in the way model uncertainty analyses are conducted and how the results are presented and used. There is a need for a common terminology and a conceptual framework for describing and estimating uncertainties in urban drainage models. Practical tools for the assessment of model uncertainties for a range of urban drainage models are also required to be developed. This paper, produced by the International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, is a contribution to the development of a harmonised framework for defining and assessing uncertainties in the field of urban drainage modelling. The sources of uncertainties in urban drainage models and their links are initially mapped out. This is followed by an evaluation of each source, including a discussion of its definition and an evaluation of methods that could be used to assess its overall importance. Finally, an approach for a Global Assessment of Modelling Uncertainties (GAMU) is proposed, which presents a new framework for mapping and quantifying sources of uncertainty in urban drainage models.

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Stormwater management through Blue-Green Infrastructure (BGI) delivers multiple benefits across urban environments. However, current integrated modelling tools fail to provide a simplified way of assessing these benefits. In this study, we reflected upon the development of an interdisciplinary BGI planning-support tool, known as the Water Sensitive Cities Toolkit (the WSC Toolkit) and offer guidance for effective tool development going forward. Based on interdisciplinary research, the WSC Toolkit incorporates a suite of independent sub-modules but can be connected together to provide integrated assessment, allowing evidence-based quantification of multiple benefits associated with BGI, e.g., stormwater treatment and harvesting, stream hydrology, erosion, minor flooding, urban microclimate, etc. Distinguished from other larger complex models, the WSC Toolkit was characterised by its simplicity, modularity and extensibility, providing scenario-based integrated assessment of these benefits. Through case studies, we demonstrated how the WSC Toolkit can be used to support improved decision-making towards maximising the benefits of BGI. We also showed how it can act as a platform for practical application of latest research outcomes and meanwhile encouraging interdisciplinary collaboration. We reflect upon five key lessons that could guide future researchers in developing effective integrated assessment tools, particularly within highly interdisciplinary fields such as BGI.

Uncertainty is intrinsic to all monitoring programs and all models. It cannot realistically be eliminated, but it is necessary to understand the sources of uncertainty, and their consequences on models and decisions. The aim of this paper is to evaluate uncertainty in a flow and water quality stormwater model, due to the model parameters and the availability of data for calibration and validation of the flow model. The MUSIC model, widely used in Australian stormwater practice, has been investigated. Frequentist and Bayesian methods were used for calibration and sensitivity analysis, respectively. It was found that out of 13 calibration parameters of the rainfall/runoff model, only two matter (the model results were not sensitive to the other 11). This suggests that the model can be simplified without losing its accuracy. The evaluation of the water quality models proved to be much more difficult. For the specific catchment and model tested, we argue that for rainfall/runoff, 6 months of data for calibration and 6 months of data for validation are required to produce reliable predictions. Further work is needed to make similar recommendations for modelling water quality.

The complex nature of pollutant accumulation and washoff, along with high temporal and spatial variations, pose challenges for the development and establishment of accurate and reliable models of the pollution generation process in urban environments. Therefore, the search for reliable stormwater quality models remains an important area of research. Model calibration and sensitivity analysis of such models are essential in order to evaluate model performance; it is very unlikely that non-calibrated models will lead to reasonable results. This paper reports on the testing of three models which aim to represent pollutant generation from urban catchments. Assessment of the models was undertaken using a simplified Monte Carlo Markov Chain (MCMC) method. Results are presented in terms of performance, sensitivity to the parameters and correlation between these parameters. In general, it was suggested that the tested models poorly represent reality and result in a high level of uncertainty. The conclusions provide useful information for the improvement of existing models and insights for the development of new model formulations.

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