Our tools that integrate measurements with models are helping resource managers make important investment and environmental management decisions.

The Challenge

Sediments entering the reef

Australia’s World Heritage-listed Great Barrier Reef is under threat from declining water quality. The main cause is pollutants - sediments, nutrients and pesticides - in waters that flow from agricultural lands adjacent to the Reef.

In response to declining water quality, the Australian Federal and Queensland State Governments created the Great Barrier Reef Water Quality Protection Plan. The Reef Plan aims to reduce pollutants flowing into the lagoon and includes targets such as a minimum 20 per cent reduction in sediment load by 2020.

Aerial view of the Great Barrier Reef © Mark Evans

Our Response

Improving water quality

We are working in the Great Barrier Reef Catchment to improve water quality, wetland integrity and fisheries while supporting and enhancing agricultural productivity.

We are developing statistical tools that will quantify the standard error associated with current sediment load estimates, helping land managers to know whether they have achieved their targets and whether changes they have made to land management practices are effective.

Our research in estimating sediment loads in the reef is investigating:

  • the sources of sediments, such as whether they are mainly generated by gullies or by hill slopes
  • where the priority areas are that may require more intensive monitoring of sediments to protect the reef
  • areas of the catchment where information is lacking, meaning we need to invest more effort in understanding sediments.

The Results

Estimating sediments for reef management

We have produced a framework that will help land managers identify the major sources of uncertainty in sediment load estimates and their associated magnitude. 

This framework quantifies estimates of constituents such as total suspended sediment, flows and loads. It blends a model with real observations to produce an estimate of the confidence in modelled predictions, and more importantly, it yields the “best estimate” of the state of the system.

For example, instead of a result showing a sediment load of 170 tonnes + or – 10 per cent, the framework will instead show there is a sediment load of 170 tonnes with a likelihood of 50 per cent that the figure will be exceeded. This would be a trigger for managers to look more closely at a specific area of the catchment or undertake additional monitoring.

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