Using generalized additive modelling to understand the drivers of long-term nutrient dynamics in the Broadwater Estuary (a subtropical estuary), Gold Coast, Australia

Richards, Russell, Chaloupka, Milani, Strauss, Darrell and Tomlinson, Rodger (2014) Using generalized additive modelling to understand the drivers of long-term nutrient dynamics in the Broadwater Estuary (a subtropical estuary), Gold Coast, Australia. Journal of Coastal Research, 30 6: 1321-1329. doi:10.2112/JCOASTRES-D-12-00190.1


Author Richards, Russell
Chaloupka, Milani
Strauss, Darrell
Tomlinson, Rodger
Title Using generalized additive modelling to understand the drivers of long-term nutrient dynamics in the Broadwater Estuary (a subtropical estuary), Gold Coast, Australia
Journal name Journal of Coastal Research   Check publisher's open access policy
ISSN 0749-0208
1551-5036
Publication date 2014-11-01
Year available 2014
Sub-type Article (original research)
DOI 10.2112/JCOASTRES-D-12-00190.1
Volume 30
Issue 6
Start page 1321
End page 1329
Total pages 9
Place of publication Coconut Creek, FL United States
Publisher Coastal Education & Research Foundation
Collection year 2015
Language eng
Formatted abstract
Conclusions drawn from comparing short-term monitoring data with a baseline data set and water-quality guidelines need to be viewed in the context of numerous physical and biogeochemical mechanisms controlling nutrient concentrations within a system over long timescales. This paper highlights the use of generalized additive models (GAMs) to explore the functional relationships between four commonly used water-quality indicators (total nitrogen, total phosphorous, ammonia, nitrate) and a range of drivers including catchment inflow, wind speed, and tidal current. The results of this GAM assessment highlighted that nutrient concentrations within a subtropical estuary (Broadwater, Australia) is most dependent on catchment inflow. In particular, this assessment indicated the apparent importance of the Nerang River as a determinant of the nutrient concentrations observed in the Broadwater compared with the role of other tributaries, even though these other rivers provide the bulk of the freshwater flow into the system. This assessment also highlighted that the potential effects of monitoring location, tides, wind, and monitoring year need to be accounted for when framing the results of short-term data.
Keyword Water quality
Monitoring
Statistical modelling
Predictor and response variables
Quality guidelines
Water
Sediment
Nitrogen
Impact
Resuspension
Environment
Oysters
Metals
River
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Biological Sciences Publications
 
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