Assessing uncertainty in stormwater quality modelling

Designing effective stormwater pollution mitigation strategies is a challenge in urban stormwater management. This is primarily due to the limited reliability of catchment scale stormwater quality modelling tools. As such, assessing the uncertainty associated with the information generated by stormwater quality models is important for informed decision making. Quantitative assessment of buildup and wash-off process uncertainty, which arises from the variability associated with these processes, is a major concern as typical uncertainty assessment approaches do not adequately account for process uncertainty. The research study undertaken found that the variability of build-up and wash-off processes for different particle size ranges leads to processes uncertainty. After variability and resulting process uncertainties are accurately characterised, they can be incorporated into catchment stormwater quality predictions. Accounting of process uncertainty influences the uncertainty limits associated with predicted stormwater quality. The impact of build-up process uncertainty on stormwater quality predictions is greater than that of wash-off process uncertainty. Accordingly, decision making should facilitate the designing of mitigation strategies which specifically addresses variations in load and composition of pollutants accumulated during dry weather periods. Moreover, the study outcomes found that the influence of process uncertainty is different for stormwater quality predictions corresponding to storm events with different intensity, duration and runoff volume generated. These storm events were also found to be significantly different in terms of the Runoff-Catchment Area ratio. As such, the selection of storm events in the context of designing stormwater pollution mitigation strategies needs to take into consideration not only the storm event characteristics, but also the influence of process uncertainty on stormwater quality predictions. (C) 2016 Elsevier Ltd. All rights reserved.