Predicting water quality characteristics in a timely and accurate manner is an essential ingredient of proactive and informed decision making against risks related to degradation in water quality, like algal blooms. To keep track of current and future changes in water quality for large areas, it is necessary to adapt monitoring, modelling and forecasting capabilities of water quality using advanced technology. This combines new in-situ sensor technology with large scale monitoring capabilities of Earth observation systems and dynamic system modelling. The CSIRO AquaWatch Mission will take a significant step forward in establishing a pathway to combine ground sensor data, satellite imagery and hydrodynamic models on a regional to continental scale to produce a range of value-added data products, delivered through online AquaWatch web services for use by water managers.
Following an introduction to the Aquawatch system, we will share our learnings from two Australian case studies on modelling and forecasting algal blooms using combined hyperspectral monitoring and process models: Lake Hume, a large reservoir often plagued with frequent harmful algal bloom outbreaks, and the Melbourne Water wastewater lagoon system with regular cyanobacteria blooms. To test a monitoring and forecasting service of toxic algal blooms, we collected bio-optical properties, stratification, and cyanobacteria bloom formation by combining grab sampling, continuous in-situ measurements, remote sensing technology and modelling (hydrodynamic and algal growth). We used a permanently installed hyperspectral camera system, which exploits the spectral reflectance signals emanating from algal-dominated waters corresponding to Chlorophyll-a concentration and cell counts via a band algorithm. Based on the continuous monitoring (15-minute interval) we then developed a short-term forecasting system for bloom dynamics based on a daily cyanobacteria index.