A new approach of monitoring and physically-based modelling to investigate urban wash-off process on a road catchment near Paris

摘要

Nowadays, the increasing use of vehicles is causing contaminated stormwater runoff to drain from roads. The detailed understanding of urban wash-off processes is essential for addressing urban management issues. However, existing modelling approaches are rarely applied for these objectives due to the lack of realistic input data, unsuitability of physical descriptions, and inadequate documentation of model testing. In this context, we implement a method of coupling monitoring surveys with the physically based FullSWOF (Full Shallow Water equations for Overland Flow) model (Delestre et al., 2014) and the process-based H-R (Hairsine-Rose) model (Hairsine and Rose, 1992a, 1992b) to evaluate urban wash off process on a road catchment near Paris (Le Perreux sur Marne, Val de Marne, France, 2661 m(2)). This work is the first time that such an approach is applied for road wash-off modelling in the context of urban stormwater runoff. On-site experimental measurements have shown that only the finest particles of the road dry stocks could be transferred to the sewer inlet during rainfall events, and most Polycyclic Aromatic Hydrocarbons (PAHs) are found in the particulate phase. Simulations over different rainfall events represent promising results in reproducing the various dynamics of water flows and sediment transports at the road catchment scale. Elementary Effects method is applied for sensitivity analysis. It is confirmed that settling velocity (Vs) and initial dry stocks (S) are the most influential parameters in both overall and higher order effects. Furthermore, flow-driven detachment seems to be insignificant in our case study, while raindrop-driven detachment is shown to be the major force for detaching sediment from the studied urban surface. Finally, a multiple sediment classification regarding the Particle Size Distribution (PSD) can be suggested for improving the model performance for future studies. (C) 2016 Elsevier Ltd. All rights reserved.