Management of Agricultural Non-point Source Pollution – A Case Study on Yarra River

The development and use of any specific model depend on the availability of data and the hydrological settings of a country. Because of data limitations (especially water quality and land management data), the water quality models developed for Australian catchments are lumped/semi-distributed conceptual models. Even within these modelling frameworks, water quality component is empirical or generation rates-based. In this context, developing an effective water quality management plan in the data-poor conditions of Australia still remains as a major challenge for water catchment managers, despite huge investment on river health improvement programs. Physics-based distributed water quality models such as SWAT are most suitable for agricultural non-point source pollution studies. However, because of high data requirement and processing, the applications of these models are limited in many datapoor catchments. In this study, relevant input data sources and analysis techniques were addressed especially for sparsely available water quality data to assemble, and to rigorously calibrate and validate the SWAT based Middle Yarra Water Quality Model (MYWQM) for the case study area - Middle Yarra Catchment (MYC) of Victoria, Australia. The regression based model LOADEST was used for estimating sediment, and nutrient observed loads from monthly water quality grab sample data. The MYWQM was then used to develop a water quality management plan for agricultural non-point source pollution in the MYC. In general, the MYWQM was found capable of predicting streamflow, sediment and nutrient loads in the MYC. The model was also found effective for simulating individual and integrated effects of several Best Management Practices (BMPs) in the MYC. Moreover, the model showed that the in-stream processes if not considered can result in incorrect estimates when simulating BMPs in the model. Overall, the performance of the MYWQM on evaluating the BMPs in the MYC demonstrated that data-intensive physics-based models can be applied in the data-poor conditions of Australia