Evaluating the Use of Event Mean Concentration Models for the Management of Urban Drainage Systems

This thesis has investigated the potential for simple pollution description techniques to be used within integrated Catchment Models. The thesis proposes the use of an event mean concentration (EMC) as a measure which could be used to improve the assessment and design of solutions to manage the impacts of pollution on receiving water courses.

Processes and models proposed by previous research which predict storm water TSS concentrations in urban catchments have been presented and discussed. The most important considerations when developing a simple transferable TSS EMC storm water model have been identified as the inclusion of components which account for the build-up and wash-off processes which can be conceptualized using explanatory variables. In this respect, following analyses of a comprehensive TSS storm water quality data set collected in Australia, a new TSS EMC model which uses climatic and rainfall characteristic variables has been developed. Analysis of the model’s calibration and validation results were compared with those made by existing TSS EMC models and showed that the model had significant predictive efficiency.

To understand the potential and practical application of the model to catchments other than where it was developed, the model has been calibrated and validated to a water quality data set generated by a complex deterministic sewer quality model, subsequently, it has been used to estimate observed TSS EMC’s recorded at this catchment. Model calibration and validation results suggest that TSS EMC model accurately ‘mimics’ some of the water quality processes described by the complex model.

The simple EMC approach and associated uncertainty method presented in this work could be used to improve the application of the ICM process by offering practitioners and decision makers a new planning dimension; the interpretation of probabilistic results which could be used to improve the application and understanding associated with the ICM approach.