Water quality predictions support better catchment management

Water data collected over more than 20 years has been used to understand the key drivers of changes in water quality across over 100 Australian catchments. We used the results to develop predictive models that will help natural resource managers address declining water quality in Australia’s streams, lakes, estuaries and coastal seas.

Poor water quality is a national and international issue. In Australia, water quality pollution often comes from diffuse sources, from industries such as agriculture and grazing. These pollutants are then transported from streams into other receiving waters.

Research from the Environmental Hydrology and Water Resources group has created a new form of “environmental intelligence” to better understand the key drivers of stream water quality, and to quantify their effects.

The project used statistical modelling of long-term large-scale water quality data from the state of Victoria, and the Great Barrier Reef region in Queensland.

This allowed researchers to develop models to explain changes in water quality caused by changes in things like land use, vegetation cover, river flow and climate variability.

In Victoria, the Department of Environment, Land, Water and Planning (DELWP) provided data from its Water Measurement Information System. The project focused on data from 102 monitoring sites that had continuous data available from 1994 and 2015.

In Queensland, event-based water quality sample data from the Great Barrier Reef catchment were provided from the Paddock to Reef Integrated Monitoring, Modelling and Reporting Program. This included data for 32 stream monitoring sites between 2006 and 2016.

Professor Andrew Western has led the research team in analysing a range of key water quality indicators in Australian rivers, including sediments, nutrients and salt pollutants.

We found that water quality variation across the landscape was related to catchment characteristics. Some of these are natural, such as climate and topography, and some are the result of human influences, such as land use. Water quality variation over time was most influenced by streamflow, he says.

Soil moisture, recent streamflow, vegetation cover and water temperature, were also important drivers of water quality over time. The effects of these drivers on water quality across different time periods varied with catchment characteristics such as topography, climate and land-use.

Combining this understanding of key drivers across both space and time, the research team has developed a model for stream water quality to predict variations at different scales, ranging from highly localised flooding to the long term regional impacts of climate change.

Professor Western says the database and the model developed are available to project partners as well as general public via an online interface, which will support decision-making by catchment managers.

It will enhance their predictive capabilities and improve their ability to examine emerging water quality issues through scenario analysis. This, in turn, supports more sustainable systems – which is a key focus of our environmental engineering research program, he says.


Lintern, A., Webb, J. A., Ryu, D., Liu, S., Bende-Michl, U., Waters, D., Western, A. W. (2018). Key factors influencing differences in stream water quality across space. Wiley Interdisciplinary Reviews: Water, 5(1), e1260. doi:10.1002/wat2.1260

Lintern, A., Webb, J. A., Ryu, D., Liu, S., Waters, D., Leahy, P., et al. (2018). What are the key catchment characteristics affecting spatial differences in riverine water quality? Water Resources Research, 54, 7252–7272. doi.org/10.1029/2017WR022172

Guo, D., Lintern, A., Webb, J. A., Ryu, D., Liu, S., Bende‚ÄźMichl, U., et al (2018). Key Factors Affecting Temporal Variability in Stream Water Quality. Water Resources Research, 54. doi.org/10.1029/2018WR023370

Liu, S., Ryu, D., Webb, J., Lintern, A., Waters, D., Guo, D., & Western, A. (2018). Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis. Marine Pollution Bulletin, 137, 137–151. doi.org/10.1016/j.marpolbul.2018.10.019

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