Bainbridge, Zoe, Olley, Jon, Wilkinson, Scott, Bartley, Rebecca, Lewis, Stephen, Dougall, Cameron, Khan, Sana, Kuhnert, Petra, and Burton, Joanne (2023) Refining fine sediment source identification through integration of spatial modelling, concentration monitoring and source tracing: A case study in the Great Barrier Reef catchments. Science of the Total Environment, 10. 164731.

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Abstract

Excess fine sediment delivery is a major contributor to the declining health of the Great Barrier Reef and identifying the dominant source areas of fine sediment has been critical to prioritising erosion remediation programs. The Bowen River catchment within the Burdekin Basin has been recognised as a major contributor and hence received considerable research investment over the last two decades. This study adopts a novel approach to integrate three independently derived sediment budgets produced from a catchment scale sediment budget model (Dynamic SedNet), targeted tributary water quality monitoring and geochemical sediment source tracing to refine and map the sediment source zones within the Bowen catchment. A four year study of water quality monitoring combined with modelled discharge estimates and geochemical source tracing both identified that the Little Bowen River and Rosella Creek were the largest sources of sediment in the Bowen River catchment. Both data sets contradicted initial synoptic sediment budget model predictions due to inadequate representation of hillslope and gully erosion. Recent improvements in model inputs have resulted in predictions that are consistent with the field data and are of finer resolution within the identified source areas. Priorities for further investigation of erosion processes are also revealed. Examining the benefits and limitations of each method indicates that these are complimentary methods which can effectively be used as multiple lines of evidence. An integrated dataset such as this provides a higher level of certainty in the prediction of fine sediment sources than a single line of evidence dataset or model. The use of high quality, integrated datasets to inform catchment management prioritisation will provide greater confidence for decision makers when investing in catchment management.

Item ID:	79366
Item Type:	Article (Research - C1)
ISSN:	1879-1026
Keywords:	Catchment rehabilitation, Sediment fingerprinting, Sediment yield, Catchment models, Water quality, Fine sediment
Copyright Information:	© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Date Deposited:	20 Jul 2023 01:44
FoR Codes:	41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 35% 41 ENVIRONMENTAL SCIENCES > 4105 Pollution and contamination > 410504 Surface water quality processes and contaminated sediment assessment @ 35% 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410405 Environmental rehabilitation and restoration @ 30%
SEO Codes:	18 ENVIRONMENTAL MANAGEMENT > 1803 Fresh, ground and surface water systems and management > 180306 Measurement and assessment of freshwater quality (incl. physical and chemical conditions of water) @ 35% 18 ENVIRONMENTAL MANAGEMENT > 1802 Coastal and estuarine systems and management > 180202 Coastal erosion @ 35% 18 ENVIRONMENTAL MANAGEMENT > 1802 Coastal and estuarine systems and management > 180206 Rehabilitation or conservation of coastal or estuarine environments @ 30%
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