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Activity Number: 474
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305139
Title: Quantifying the Uncertainty of Pollutant Loads in Great Barrier Reef Catchments: A Case Study Using Weany Creek
Author(s): Petra Kuhnert*+ and Rebecca Bartley and Erin Peterson and Scott Wilkinson and Christopher Wikle
Companies: CSIRO Mathematics, Informatics, and Statistics and CSIRO Land and Water and CSIRO Mathematics, Informatics, and Statistics and CSIRO Land and Water and University of Missouri
Address: Private Bag 2, Glen Osmond, _, 5064, Australia
Keywords: Data assimilation ; Bayesian hierarchical modelling ; Uncertainty ; Hydrology ; Pollutant loads

The export of pollutants from coastal catchments within Australia has important implications for the health of the Great Barrier Reef. As a result, there is a strong need to identify appropriate statistical methods for reliably estimating annual pollutant loads (with some measure of uncertainty) based on monitoring data and knowledge of catchment processes to assess progress towards defined loads targets. Current methods for estimating loads either rely solely on monitoring data, where a statistical framework is developed to account for key sources of uncertainty related to concentration and flow. Alternatively, a catchment model is used to calculate loads, which consists of deterministic relationships underpinned by the physical processes of the system. In this type of modelling framework, the model is calibrated with event monitoring data but no measure of uncertainty is estimated. We outline a Bayesian hierarchical modelling framework for quantifying loads and uncertainties using the Weany Creek site in the Burdekin catchment and outline extensions of this model for subcatchments and catchments that incorporate space and time.

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