This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted.@en

Urban drainage modelling typically requires development of highly detailed simulators due to the nature of various underlying surface and drainage processes, which makes them computationally too expensive. Application of such simulators is still challenging in activities such as real-time control (RTC), uncertainty quantification analysis or model calibration in which numerous simulations are required. The focus of this paper is to present a rather simple hybrid surrogate modelling (or emulation) strategy to simplify and accelerate a detailed urban drainage simulator (UDS). The proposed surrogate modelling strategy includes: a) identification of the variables to be emulated; b) development of a simplified conceptual model in which every component contributing to the variables identified in step (a) is replaced by a function; c) definition of these functions, either based on knowledge about the mechanisms of the simulator, or based on the data produced by the simulator; and finally, d) validation of the results produced by the surrogate model in comparison with the original detailed simulator. Herein, a detailed InfoWorks ICM simulator was selected for surrogate modelling. The case study area was a small urban drainage network in Luxembourg. An emulator was developed to map the rainfall time series, as input, to a storage tank volume and combined sewer overflow (CSO) in the case study network. The results showed that the introduced strategy provides a reliable method to simplify the simulator and reduce its run time significantly. For the specific case study, the emulator was approximately 1300 times faster than the original detailed simulator. For quantification of the emulation error, an ensemble of 500 rainfall scenarios with 1 month duration was generated by application of a multivariate autoregressive model for conditional simulation of rainfall time series. The results produced by the emulator were compared to the ones produced by the simulator. Finally, as an indicator of the emulation error, distributions of Nash-Sutcliffe efficiency (NSE) between the emulator and simulator results for prediction of storage tank volume and CSO flow time series were presented.@en

A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) derivation of a training data set; (c) development of a data-driven surrogate model; (d) use of a forward uncertainty propagation scheme. The simplification comprises the following steps: (a) unit hydrograph derivation using a 2D hydrodynamic model; (b) calculation of the losses in order to determine the effective rainfall depth; (c) flood event simulation using the principle of the proportionality and superposition. The above methodology was implemented in an urban catchment located in the city of Athens, Greece. The model used for the first step of the simplification was FLOW-R2D, whereas the well-known SWMM software (US Environmental Protection Agency, Washington, DC, USA) was used for the second step of the simplification. For the training data set derivation, an ensemble of 100 Unit Hydrographs was derived with the FLOW-R2D model. The parameters which were modified in order to produce this ensemble were the Manning coefficients in the two friction zones (residential and urban open space areas). The surrogate model used to replicate the unit hydrograph derivation, using the Manning coefficients as an input, was based on the Polynomial Chaos Expansion technique. It was found that, although the uncertainties in the derived results have to be taken into account, the proposed methodology can be a fast and efficient way to cope with dynamic flood simulation in an urban catchment.@en