Website
http://dataportal.arc.gov.au/NCGP/API/grants/LP140100495
Funding Status
Closed
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Funded Activity Summary
Predicting water quality at the catchment scale: learning from two decades of monitoring. 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 will jointly model a range of inter-related water quality parameters.Funded Activity Details
Start Date: 06-2015
End Date: 12-2018
Funding Scheme: Linkage Projects
Funding Amount: $315,000.00
Funder: Australian Research Council
Research Topics
ANZSRC Field of Research (FoR)
Surfacewater Hydrology | Natural Resource Management | Environmental Monitoring | Environmental Science and Management |
ANZSRC Socio-Economic Objective (SEO)
Physical and Chemical Conditions of Water in Fresh, Ground and Surface Water Environments (excl. Urban and Industrial Use) | Rural Water Evaluation (incl. Water Quality)
