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1 November 2014 Using Generalized Additive Modelling to Understand the Drivers of Long-Term Nutrient Dynamics in the Broadwater Estuary (a Subtropical Estuary), Gold Coast, Australia
Russell Richards, Milani Chaloupka, Darrell Strauss, Rodger Tomlinson
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Abstract

Richards, R.; Chaloupka, M.; Strauss, D., and Tomlinson, R., 2014. Using generalized additive modelling to understand the drivers of long-term nutrient dynamics in the Broadwater Estuary (a subtropical estuary), Gold Coast, Australia.

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.

Russell Richards, Milani Chaloupka, Darrell Strauss, and Rodger Tomlinson "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, (1 November 2014). https://doi.org/10.2112/JCOASTRES-D-12-00190.1
Received: 24 September 2012; Accepted: 11 February 2013; Published: 1 November 2014
KEYWORDS
monitoring
predictor and response variables
statistical modelling
water quality
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