The need for estimating micropollutants fluxes in stormwater systems increases the role of stormwater quality models as support for urban water managers, although the application of such models is affected by high uncertainty. This study presents a procedure for identifying the major sources of uncertainty in a conceptual lumped dynamic stormwater runoff quality model that is used in a study catchment to estimate (i) copper loads, (ii) compliance with dissolved Cu concentration limits on stormwater discharge and (iii) the fraction of Cu loads potentially intercepted by a planned treatment facility. The analysis is based on the combination of variance-decomposition Global Sensitivity Analysis (GSA) with the Generalized Likelihood Uncertainty Estimation (GLUE) technique. The GSA-GLUE approach highlights the correlation between the model factors defining the mass of pollutant in the system and the importance of considering hydrological parameters as source of uncertainty when calculating Cu loads and concentrations due to their influence. The influence of hydrological parameters on simulated concentrations changes during rain events. Four informal likelihood measures are used to quantify model prediction bounds. The width of the uncertainty bounds depends on the likelihood measure, with the inverse variance based likelihood more suitable for covering measured pollutographs. Uncertainty for simulated concentration is higher than for Cu loads, which again shows lower uncertainty compared to studies neglecting the hydrological submodel as source of uncertainty. A combined likelihood measure ensuring both good predictions in flow and concentration is used to identify the parameter sets used for long time simulations. These results provide a basis for reliable application of models as support in the development of strategies aiming to reduce discharge of stormwater micropollutants to the aquatic environment.
- Stormwater micropollutants
- Pollution control strategies
- Sobol’ sensitivity indices
- GLUE-GSA approach
- Generalized Likelihood Uncertainty