A state-of-the-art river water diagnostic system to help monitor algal blooms has been proposed for the Swan and Canning estuaries by the head of the University of WA’s Centre for Water Research, Professor Jorg Imberger.
Details of the $2.5 million integrated system have been presented to several State Government ministers, including Environment Minister Dr Judy Edwards. Called the Water Column Diagnostic System (WCDS), if adopted its central monitoring station would be anchored in the main basin off Applecross at a point where the Swan is close to 20 metres deep.
“This instrument is controlled in real time from on-shore computers,” Professor Imberger said.
“Currently 15 locations around the world have it operating, including on Lake Como in Northern Italy, and Sydney’s water storage at Lake Burragorang and Prospect Reservoir.”
Consideration is being given to introducing WCDSs to San Francisco Bay, along the Los Angeles coast, Hong Kong harbour, the Venice lagoon, and Japan’s Lake Biwa.
“To help monitor oxygen cycling in the Swan-Canning estuary and remuneration rates of nutrients the system is also equipped with a set of oxygen sensors,” Professor Imberger said.
He said the system which had been developed by his campus centre monitored targeted water bodies by using computer models dubbed ELCOM and CAEDYM.
The system would also monitor all major inflows into the estuaries with the results able to be viewed on internet-connected computers by the public and scientists.
Professor Imberger said the WCDS would be a tool to help return the Swan and Canning to good ecological health by 2015 with little or no toxic algal blooms.
The integrated system includes a tidal gauge at Fremantle and up to a dozen inflow gauges of streams augmenting the Swan and Canning.
All calibrated information would be centrally analysed and be able to be viewed in the way television channels display weather conditions across Western Australia after evening news bulletins.
Scientists will be able to create simulation models using the WCDS-produced data and assess varying conditions to help predict algal blooms.
