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 (12)
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>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Degraded water quality in rivers and streams can h..
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 ..