Setting ecologically relevant targets for river pollutant loads to meet marine water quality requirements for the Great Barrier Reef, Australia: a preliminary methodology and analysis
Brodie, Jon E., Lewis, Stephen E., Collier, Catherine J., Wooldridge, Scott, Bainbridge, Zoe, Waterhouse, Jane, Rasheed, Michael, Honchin, Carol, Holmes, Glen, and Fabricius, Katharina (2017) Setting ecologically relevant targets for river pollutant loads to meet marine water quality requirements for the Great Barrier Reef, Australia: a preliminary methodology and analysis. Ocean & Coastal Management, 143. pp. 136-147.
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Abstract
Loads of suspended sediment, nutrients and pesticides discharged to the Great Barrier Reef (GBR) have increased greatly due to agricultural and urban development of the GBR catchment. As a result, in association with climate change impacts, the ecosystems of the GBR have degraded greatly in recent decades, and the decline in ecosystem health continues. Improved agricultural management practices are now being funded to reverse the decline. However the quantum of reduction in contaminant loads from individual rivers needed to reverse the decline is not known. To estimate load reductions that would reverse the decline we developed what we term Ecologically Relevant Targets (ERTs). These targets are the load reductions of sediment, nutrients and pesticides needed such that GBR lagoonal waters meet the relevant water quality guidelines for the region. The methodology proposed for setting the ERTs is described and examples of ERTs set for the Burdekin Region of the GBR are given.
Item ID: | 46345 |
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Item Type: | Article (Research - C1) |
ISSN: | 1873-524X |
Keywords: | Great Barrier Reef, rivers, pollutant loads, targets, ecology |
Date Deposited: | 15 Nov 2016 05:07 |
FoR Codes: | 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management @ 100% |
SEO Codes: | 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960503 Ecosystem Assessment and Management of Coastal and Estuarine Environments @ 100% |
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