ABSTRACT
Hydrologic-hydraulic modelling of urban catchment is an asset for land managers to simulate Sustainable Urban Drainage Systems (SUDS) implementation to fulfil combined sewer overflow (CSO) regulations. This review aims to assess the current practices in modelling SUDS scenarios at large scale for CSO mitigation encompassing every stage of the modelling process from the choice of the equation to the validation of the initial state of the urban system, right through to the elaboration, modelling, and selection of SUDS scenarios to evaluate their performance on CSO. Through a quantitative and qualitative analysis of 50 published studies, we found a diversity of choices when modelling the status quo of the urban system. Authors generally do not explain the modelling processes of slow components (deep infiltration, groundwater infiltration) and interconnexion between SUDS and the sewer system. In addition, only a few authors explain how CSO structures are modelled. Furthermore, the modelling of SUDS implementation at catchment scale is highlighted in the 50 studies retrieved with three different approaches going from simplified to detailed. SUDS modelling choices seem to be consistent with the objectives: studies focusing on dealing with several objectives at the time typically opt for a complex system configuration that includes the surface processes, network, CSO, SUDS, and often the soil and/or groundwater components. Conversely, authors who have selected a basic configuration generally aim to address a single, straightforward question (e.g., which type of SUDS). However, elaboration and selection of scenarios for CSO mitigation is mainly based on local constraints, which does not allow hydrological performance to be directly optimised. In conclusion, to improve current practices in modelling SUDS scenarios at large scale for CSO mitigation, authors suggest to: (i) improve clear practices of CSO modelling, calibration and validation at the urban catchment scale, (ii) develop methods to optimize the performance of scenarios for CSO mitigation using hydrological drivers, and (iii) improve parsimonious and user-friendly models to simulate SUDS scenarios in a context of data scarcity.
