Predicting water quality at the catchment scale: learning from two decades of monitoring
Grant number: LP140100495 | Funding period: 2015 - 2018
Poor water quality affects many rivers and receiving waters such as the Great Barrier Reef and Gippsland Lakes. This project aims to use Bayesian hierarchical models of statewide water quality data to quantify the effects of a range of factors on stream water quality including climate, land use, river flow, vegetation cover, etcetera. The analysis intends to extract information from the entire data set rather than concentrating on individual sites. It intends to underpin a new predictive capacity including response to land use and management changes and climatic variations based on long-term data sets, as well as a water quality prediction capability. It is intended that the models developed..View full description
Related publications (15)
A predictive model for spatio-temporal variability in stream water quality
Danlu Guo, Anna Lintern, J Angus Webb, Dongryeol Ryu, Ulrike Bende-Michl, Shuci Liu, Andrew William Western
<jats:p>Abstract. Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream wa..
A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments
Danlu Guo, Anna Lintern, Angus Webb, Dongryeol Ryu, Ulrike Bende-Michl, Shuci Liu, Andrew Western
Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To ..
Future Water: Comparing and contrasting approaches to predicting water quality
D Guo, A Lintern, V Prodanovic, M Kuller, PM Bach, A Deletic, B Shi, D McCarthy, D Ryu, JA Webb, S Liu, AW Western
Globally, surface water quality deterioration is an important issue exacerbated by increasing urbanisation, intensified agricultur..
Understanding the spatial variability in catchment dynamics: a case study of 107 stream catchments in Victoria
A Lintern, J Webb, null ryu, S liu, U Bende-Michl, M Watson, D Waters, P Leahy, P Wilson, A Western
Rivers and streams around the world are being affected by declining water quality. When designing remediation strategies, we must ..
Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis
S Liu, D Ryu, JA Webb, A Lintern, D Waters, D Guo, AW Western
Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improv..
What Are the Key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality?
A Lintern, JA Webb, D Ryu, S Liu, D Waters, P Leahy, U Bende-Michl, AW Western
This study uses water-quality data collected over 20 years, from 102 predominantly rural sites across Victoria, Australia, to furt..
A web-based interface to visualize and model spatio-temporal variability of stream water quality
Danlu Guo, anna Lintern, James Webb, dongryeol Ryu, shuci Liu, Ulrike Bende-Michl, Paul Leahy, David Waters, Malcolm Watson, Paul Wilson, Andrew Western
Understanding the spatio-temporal variability in stream water quality is critical for designing effective water quality management..
Modelling the impact of land use and catchment characteristics on stream water quality using a Bayesian hierarchical modelling approach in the Great Barrier Reef catchments
S Liu, D Ryu, A Western, JA Webb, A Lintern, D Waters, B Thomson
The near-shore ocean ecosystem is influenced by catchment runoff. The Great Barrier Reef has been experiencing significant water q..