Evaluation of the Performance and the Predictive Capacity of Build-Up and Wash-Off Models on Different Temporal Scales

Abstract

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.

Accounting for the Spatio-Temporal Variability of Pollutant Processes in Stormwater TSS Modeling Based on Stochastic Approaches

“…The ranges of the EMC for the roads were defined based on the minimal and maximal EMC values calculated over twelve samples collected at the boulevard Alsace Lorraine's inlet during the experimental campaign. Previous analysis of these values showed that they are higher than EMCs calculated for other road catchments due to the site's high traffic density [11]. However, some of the road segments in the catchment are not necessarily frequented at the same density as the boulevard; therefore, their loadings of TSS might be lower.…”

“…Even when parameters are optimized, a high level of uncertainty is associated with their calibrated values [13], revealing important challenges in defining sampling ranges for water quality parameters. The sampling ranges of the EMC for roads were defined by measurements collected at the inlet of the main boulevard within the catchment [11], while intervals for roof surfaces were determined by available data provided in the literature [3]. The boundaries of the sampling ranges for SWMM wash-off parameters C 1 and C 2 correspond to the lowest and highest values obtained from the best cases investigated in an earlier study conducted within the same catchment [11].…”

“…The sampling ranges of the EMC for roads were defined by measurements collected at the inlet of the main boulevard within the catchment [11], while intervals for roof surfaces were determined by available data provided in the literature [3]. The boundaries of the sampling ranges for SWMM wash-off parameters C 1 and C 2 correspond to the lowest and highest values obtained from the best cases investigated in an earlier study conducted within the same catchment [11]. As for the M(V) curve coefficient b, the boundaries are selected to replicate first flush and uniformly distributed events, since the occurrence of last flush events within the region was rare during the monitored period.…”

“…The performance of these models has constantly been put into question since, to date, no reliable stormwater quality model exists despite extensive research on the subject [7]. Conceptual models remain fairly common for stormwater quality modeling and have been integrated into software such as SWMM and Flupol [8,9], despite their limitations [10,11].…”

Rainfall Washoff of Spores From Concrete and Asphalt Surfaces

“…The results were summarized according to the median coefficient values because the Alternative Fit experienced two instances where k 1 was substantially higher than for the rest of the tests. Others have also observed convergence to optimum values that diverge substantially from the mean or have no clear physical significance (Al Ali et al., 2016). Additionally, the coefficient structure is computationally easier to fit using statistical software packages than the nested exponentials in the SWMM Fit and likely contributes to its higher AIC value.…”