A quantitative assessment of the reliability and feasibility of process-based urban stormwater quality models: Towards new evaluation criteria

Hydrologic models have been increasingly used as a numerical tool to support urban stormwater management. Evaluation of modeling approaches helps identify the strength and weakness of a model to meet end-user requirements. However, traditional model evaluations only focus on the technical performance of a model, whereas very few studies have been conducted to quantitatively evaluate practical constraints for model applications. Therefore, this study proposed a quantitative model evaluation framework, to analyze tradeoffs between scientific reliability and practical feasibility of four process-based urban stormwater quality models. These models were based on different levels of spatial discretization, including lumped, sub-catchment, UHE and grid based approaches; test simulations were applied to an urban catchment near Paris. Six criteria were introduced to quantitatively assess the characteristics of modeling approaches, including (1) match to observation, (2) forecast accuracy, (3) forecast variability, (4) data accessibility, (5) computational costs, and (6) model reusability. The results showed that the lumped model was the best tradeoff between scientific reliability and practical feasibility for the study case. Moreover, the greater spatially distributed exponential build-up/wash-off processes from the lumped to sub-catchment based model could only improve the numerical approximation of simulations to observations at the outlet, but performed much less well in other scientific reliability aspects. Which implies that these processes may not properly represent mechanisms for stormwater quality dynamics at the catchment scale. In addition, it was suggested that complex grid based models should work together with advanced parameter calibration approaches, in order to achieve good scientific reliability for research purposes. In perspective, quantitative evaluation of the stakeholder participation throughout the modeling processes could help to improve model-based outcomes with more adaptive stakeholder engagement.