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.

Stormwater quality modeling remains one of the most challenging issues in urban hydrology today. The processes involved in contaminant generation and transport are very complex, with many associated uncertainties, including uncertainty arising from process variability. In this study, the spatio-temporal variability of build-up/wash-off processes in a heterogeneous urban catchment within the Parisian region is assessed based on three stochastic modeling approaches integrated into the physically based distributed hydrological model, the Urban Runoff Branching Structure (URBS) model. Results demonstrate that accounting for process variability at the scale of a hydrological element is important for analyzing the contamination recorded at the catchment outlet. The intra-event dynamics of total suspended solids (TSS) were most accurately selected for the stochastic exponential SWMM model, as this model succeeded not only in simulating the general trend of TSS concentrations fluctuations but also in replicating multiple peaks observed in pollutographs. The advantage of this approach is that it captures the stochastic nature of the processes with minimal prior knowledge and without extensive calibration, though further enhancement is necessary for it to become a useful tool to support decision making.