Bainbridge, Z.T., Lewis, S.E., and Brodie, J.E. (2007) Sediment and nutrient exports for the Burdekin River catchment, north Queensland: a comparison of monitoring and modelling data. In: Proceedings of MODSIM07: the International Congress on Modelling and Simulation. pp. 874-880. From: MODSIM07: International Congress on Modelling and Simulation, 10-13 December 2007, Christchurch, New Zealand.

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Abstract

Several empirical models have been developed to estimate sediment and nutrient inputs into the Great Barrier Reef lagoon, although limited monitoring data are available for their validation. The lack of monitoring data for GBR catchments limits the refinement of these models, and particularly their assumptions of tropical landscape processes. Such limitations reduce the confidence of these models for application within the Reef Water Quality Protection Plan process, such as the setting of end-of-river load targets, and scenario load forecasting based on improved catchment condition from the adoption of best management practices. The benefits of a coupled monitoring and modelling approach have been demonstrated in a number of other north Queensland catchments, such as the Tully/Murray Water Quality Improvement Plan (WQIP) (Armour et al., 2007). Improved modelling estimates have been produced for these catchments due to their smaller size, resultant less variability and the use of higher resolution input data. The considerable size of the Burdekin makes similar efforts difficult; however, this is a first attempt at comparing available monitoring data with modelling efforts for this catchment. We present flow weighted sediment and nitrogen loads averaged over three wet seasons of monitoring data (2002/03, 2004/05 and 2005/06) from the major sub-catchments and river mouth of the Burdekin River to compare with recent SedNet and ANNEX model load estimates by CSIRO. CSIRO incorporated a number of refinements to the models based on issues identified during previous modelling runs. These refinements include improvements to the hillslope erosion component through better input data resolution (estimates of actual ground cover and improved spatial resolution of slope) and improved gully and river bank erosion components through the incorporation of field measurements from the Burdekin catchment. In most cases, monitored and modelled comparisons of TSS loads were reasonable, although poorer comparisons occurred for some sites, explainable by limited monitoring data or coarse resolution “blanket” model assumptions, such as riparian vegetation and dam trapping capacity. The dam trapping algorithm applied to the Burdekin Falls Dam (BFD) by the SedNet model appears to be overestimating the suspended sediment and particulate nutrient trapping capacity of this dam. A modified algorithm that accounts for the dry tropical hydrology (i.e. highly episodic flows with shorter residence times than assumed by SedNet) will allow for better load estimates at the mouth of the Burdekin River. Particulate nutrient comparisons were less satisfactory, with the model seemingly overestimating monitored PN exports; the ANNEX model estimates are based on the Australian Soil Resource Information System database of nutrient concentrations in soils. This database may need refinement for these highly weathered, nutrient-poor landscapes. Reasonable comparisons were found between the modelled and monitored loads of dissolved organic nitrogen, however, comparisons were only fair for dissolved inorganic nitrogen. Further investigation of the role of inorganic nitrogen in this tropical system is warranted, and particularly, the contributions from natural sources, such as rainfall or bedrock, compared to those from land uses including fertilisers and cow excreta. Additional monitoring data at different spatial and temporal scales are required to further test the accuracy of these models, particularly for the southern region of the Burdekin catchment, where below-average flow events have occurred within the timeframe of the monitoring project. This will allow further confidence in using modelled outputs to identify catchment point sources and consequent delivery to downstream environments, and for the setting of water quality targets.

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