Towards quantification of uncertainty in predicting water quality failures in integrated catchment model studies

This paper describes the development and application of a method for estimating uncertainty in the prediction of sewer flow quantity and quality and how this may impact on the prediction of water quality failures in integrated catchment modelling (ICM) studies. The method is generic and readily adaptable for use with different flow quality prediction models that are used in ICM studies. Use is made of the elicitation concept, whereby expert knowledge combined with a limited amount of data are translated into probability distributions describing the level of uncertainty of various input and model variables. This type of approach can be used even if little or no site specific data is available. Integrated catchment modelling studies often use complex deterministic models. To apply the results of elicitation in a case study, a computational reduction method has been developed in order to determine levels of uncertainty in model outputs with a reasonably practical level of computational effort. This approach was applied to determine the level of uncertainty in the number of water quality failures predicted by an ICM study, due to uncertainty associated with input and model parameters of the urban drainage model component of the ICM. For a small case study catchment in the UK, it was shown that the predicted number of water quality failures in the receiving water could vary by around 45% of the number predicted without consideration of model uncertainty for dissolved oxygen and around 32% for unionised ammonia. It was concluded that the potential overall levels of uncertainty in the ICM outputs could be significant. Any solutions designed using modelling approaches that do not consider uncertainty associated with model input and model parameters may be significantly over-dimensioned or under-dimensioned. With changing external inputs, such as rainfall and river flows due to climate change, better accounting for uncertainty is required.