New, low-cost assistance may soon be on the way to help manage one of the Great Barrier Reef’s biggest threats.
This threat is land-based pollution caused by the many rivers and streams that flow along the reef into coastal waters.
The size of the reef stretching along the Queensland coast for 2,300 kilometers makes it extremely difficult to get an idea of what is going on in real time.
Researchers at the ARC Center of Excellence for Mathematical and Statistical Frontiers (ACEMS) have now developed statistical predictive tools in collaboration with scientists at the Queensland Department of Environment and Science, which could lead to the deployment of many more low-cost sensors in these rivers and streams.
There are currently fewer than 50 long-term river monitoring stations providing information that informs Great Barrier Reef protection programs. That means there are thousands of kilometers of coastal lands and waterways where we have limited information, Dr. Catherine Leigh, an ACEMS Associate Investigator with QUT’s Mathematical Science School, said.
There is an opportunity for lower cost sensors to be infilled at a finer scale. At this stage, however, low-cost sensors are still not capable of showing the two things that are most important in determining water quality. These are direct sediment and nutrient measurements. Plants and animals can be smothered by sediments. For life, nutrients are important, but an imbalance can cause a variety of problems. Turbidity and conductivity are what the low cost sensors measure. Turbidity is a water clarity measure, and conductivity reflects water levels of ions such as salt.
In research just published in PLOS ONE, the ACEMS team developed statistical tools to take that turbidity and conductivity data and predict levels of sediments and nutrients in the water.
“They are really the key things water agencies are looking for, both in what their values are and how they change over time,” Dr. Leigh said.
The sensor data was provided to the Queensland Department of Environment and Science by the Water Quality and Investigations (WQI) Team. Managers can look to automate the sensor process by predicting sediment and nutrient levels.
“Right now, somebody has to go to the monitoring station physically, get a sample, take it back to the laboratory and test it. If we can automate this process with the sensors, we can get much more frequent predictions of what is going on, said Dr. Sevvandi Kandanaarachchi, an ACEMS Associate Researcher at Monash University’s Department of Econometrics and Business Statistics.
It is important to predict these quantities because if they change suddenly, it is an indication that something with the system needs to be looked at.
Dr Leigh hopes that the project will result in many more low-cost sensors being deployed. She also says that they are looking to develop an app that can be used by farmers and other landowners.
“They are keen to ensure that they do not waste nutrients, that the plants on the ground take up what they use and do not end up in a stream,” Dr Leigh said. They are also interested in reducing land erosion.
The ACEMS and WQI teams showed how to detect anomalies in the sensor data in the work published earlier this year. They needed ways, in other words, to show if a sensor worked properly.
“You want to know that the data you gather is good before you go and predict anything else,” Dr Leigh said.
This new research will also help answer questions such as where to place the sensors, how many are needed in some places, and whether to move around them.
The big picture is to make sure that certain things can hurt the reef and that our rivers don’t end up in a stream. If they do, we can act timely to figure out what’s going on and why, Dr Leigh said.