Stormwater quality modeling has arisen as a promising tool to develop mitigation strategies. The aim of this paper is to assess the build-up and wash-off processes and investigate the capacity of several water quality models to accurately simulate and predict the temporal variability of suspended solids concentrations in runoff, based on a long-term data set. A Markov Chain Monte-Carlo (MCMC) technique is applied to calibrate the models and analyze the parameter’s uncertainty. The short-term predictive capacity of the models is assessed based on inter- and intra-event approaches. Results suggest that the performance of the wash-off model is related to the dynamic of pollutant transport where the best fit is recorded for first flush events. Assessment of SWMM (Storm Water Management Model) exponential build-up model reveals that better performance is obtained on short periods and that build-up models relying only on the antecedent dry weather period as an explanatory variable, cannot predict satisfactorily the accumulated mass on the surface. The predictive inter-event capacity of SWMM exponential model proves its inability to predict the pollutograph while the intra-event approach based on data assimilation proves its efficiency for first flush events only. This method is very interesting for management practices because of its simplicity and easy implementation.

Effort to narrow the gap between the production and use of scientific knowledge for environmental decision-making is gaining traction, yet in practice, supply and demand remains largely unbalanced. A qualitative study based on empirical analysis offers a novel approach to exploring key factors, focussing on seven water models in the context of two organisations at the science-policy interface: the PIREN-Seine in France and the CRC for Water Sensitive Cities in Australia. Tentative linkages drawn from these examples identify: (1) objective and expertise; (2) knowledge and tools; and (3) support structures as main drivers influencing the production of scientific knowledge which, in turn, affect the use and utility of modelling tools. Further insight is gained by highlighting the wide spectrum of uses and utilities existing in practice, suggesting that such ‘boundary organisations’ facilitate interactions and exchanges that give added value to scientific knowledge. Coordinated strategies that integrate inter-, extra-, and intra-boundary activities, framed through collaborative scenario building and the use of interactive modelling platforms, may offer ways to enhance the use and utility of scientific knowledge (and its tools) to better support water resources management, policy and planning decisions, thus promoting a more cohesive relationship between science and policy.

Remote sensing (RS) and geographic information system (GIS) data is increasingly used in urban stormwater modeling. The undirected use of such data may waste economic and human resources. In order to provide guidance for practitioners to efficiently use different data collection resources, as well as give a reference for future works, this paper aims to assess the effects of using free access GIS data and ad hoc RS data on urban 2D–1D stormwater modeling. The 2D-surface Two-dimensional Runoff, Erosion, and Export model (TREX) model was published in Science of the Total Environment in 2008. The 1D-sewer CANOE (Logiciel intégré de conception et de diagnostic des réseaux d’assainissement) model was published in Journal of Hydrology in 2004. The two models are integrated in the TRENOE (TREX-CANOE) platform. The modeling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km²). Simulation results reveal that the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations. Nevertheless, using the very high resolution LiDAR (light detection and ranging) data is not equally significant for the water flow simulations at sewage outlets. Finally, we suggest that using the free access GIS data accompanying the urban sewer network design might be an acceptable low-cost solution for accurate urban 2D–1D stormwater modeling during moderate rainfall events. Further studies of urban stormwater modeling could focus on the development of “suitable” models with “enough” input data, depending on the management/research objectives.

The widespread use of separate stormwater systems requires better understanding of the interactions between urban landscapes and drainage systems. This paper describes a novel attempt of developing urban 2D-surface and 1D-drainage model “TRENOE” for urban stormwater quantity and quality modelling. The physically-based TREX model and the conceptual CANOE model are integrated into the TRENOE platform, highlighting that the roofs of buildings are represented separately from the surface model, but simulated as virtual “sub-basins” in the CANOE model. The modelling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km²). Simulation scenarios are developed for assessing the influences of different “internal” (model structure, numerical issues) and “external” (parameters, input data) factors on model performance. The adequate numerical precision and the detailed information of land use data are identified as crucial elements of water quantity modelling. Contrarily, the high-resolution topographic data and the common variations of the water flow parameters are not equally significant at the scale of a small urban catchment. Concerning water quality modelling, particle size distribution is revealed to be an important factor, while the empirical USLE equations need to be completed by a raindrop detachment process.