Revisiting Stormwater Quality Conceptual Models in a Large Urban Catchment
Total Suspended Solids (TSS) stormwater models in urban drainage systems are often required for scientific, legal, environmental and operational reasons. However, these TSS stormwater traditional model structures have been widely questioned, especially when reproducing data from online measurements at the outlet of large urban catchments. In this thesis, three potential limitations of traditional TSS stormwater models are analyzed in a 185 ha urban catchment (Chassieu, Lyon, France), by means 365 rainfall events monitored online: a) uncertainties in TSS data due to field conditions; b) uncertainties in hydrological models and rainfall measurements and c) uncertainties in the stormwater quality model structures. These aspects are investigated in six separate contributions, whose principal results can be summarized as follows: a) TSS data acquisition and validation: (i) four sampling strategies during rainfall events are simulated and evaluated by online TSS and flow rate measurements. Recommended sampling time intervals are of 5 min, with average sampling errors between 7 % and 20 % and uncertainties in sampling errors of about 5 %, depending on the sampling interval; (ii) the probability of underestimating the cross section mean TSS concentration is estimated by two methodologies. One method shows more realistic TSS underestimations (about 39 %) than the other (about 269 %). b) Hydrological models and rainfall measurements: (iii) a parameter estimation strategy is proposed for conceptual rainfall-runoff model by analyzing the variability of the optimal parameters obtained by single-event Bayesian calibrations, based on clusters and graphs representations. The new strategy shows more performant results in terms of accuracy and precision in validation; (iv) a methodology aimed to calculate "mean" areal rainfall estimation is proposed, based on the same hydrological model and flow rate data. Rainfall estimations by multiplying factors over constant-length time window and rainfall zero records filled with a reverse model show the most satisfactory results compared to further rainfall estimation models. c) Stormwater TSS pollutograph modelling: (v) the modelling performance of the traditional Rating Curve (RC) model is superior to different linear Transfer Function models (TFs), especially in terms of parsimony and precision of the simulations. No relation between the rainfall corrections or hydrological conditions defined in (iii) and (iv) with performances of RC and TFs could be established. Statistical tests strengthen that the occurrence of events not representable by the RC model in time is independent of antecedent dry weather conditions; (vi) a Bayesian reconstruction method of virtual state variables indicate that potential missing processes in the RC description are hardly interpretable as a unique state of virtual available mass over the catchment decreasing over time, as assumed by a great number of traditional models.