Modelling the impact of land use and catchment characteristics on stream water quality using a Bayesian hierarchical modelling approach in the Great Barrier Reef catchments

S. Liu, D. Ryu, A. Western, A. Webb, A. Lintern, D. Waters, B Thomson

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)

Abstract

The near-shore ocean ecosystem is influenced by catchment runoff. The Great Barrier Reef has been experiencing significant water quality deterioration over the past 150 years, due in part to agricultural intensification and urban settlement in adjacent catchments (Thorburn et al., 2013). There is a need for us to understand the influences of catchment characteristics on stream water quality, with an aim to mitigate and manage the water quality issue in the terrestrial stream runoff derived from the adjacent Great Barrier Reef catchments. The event-based water quality monitoring data set from the Paddock to Reef Integrated Monitoring Program across six Natural Management Regions provides a potential opportunity to develop a data-driven understanding of catchment characteristics affecting water quality at the catchment scale. This requires a robust and reliable modelling tool to relate the monitoring data to anthropogenic and natural processes. In this study, monitoring data of Total Suspended Solids (TSS) and dissolved Oxidised Nitrogen (NOX) from 32 sites across the Great Barrier Reef catchments are selected as case study constituents due to the high risk they pose to reef health when exported from catchments to the receiving marine environment. Also, these two constituents have distinct biogeochemical processes in catchments. Specifically, TSS is typically conserved (although mobilized and deposited) while travelling through river systems; while NOX can be potentially processed and removed from the system altogether. A Bayesian hierarchical linear modelling framework is adopted, due to its ability to borrow strength among sites, allowing information to be transferred across space, and due to its ability to provide uncertainty of the predictions. The Bayesian hierarchical linear regression model in this study is developed to evaluate the significance of various catchment characteristics (e.g., land uses, catchment topography and geology) on spatial variation in water quality. The main findings of this study are listed as follows, • Sites located in the Burdekin and Fitzroy Natural Management Regions tend to have greater TSS concentrations, illustrated by the modelled site-specific spatial random effects (deviation from the overall average concentration). Additionally, grazing and dry land agriculture land uses are positively correlated with the spatial random effect on TSS. The complexity of interaction between different catchment characteristics (e.g., land use and topography) can potentially result in a negative spatial random effect on TSS, which is reduced in the relatively steeper Great Barrier Reef catchments with a denser stream network. • Sugar cane is one of the most significant NOX contributors according to the modelling results, likely partially due to the excessive application of fertilizers; however, conservation land use has limited effect on NOX removal, indicating denitrification process alone may not be sufficient to remove NOX. The modelling results demonstrate different land management strategies are required for the purpose of reducing different constituents. This work will provide scientific insights for water quality management at the catchment scale.

Original languageEnglish
Title of host publicationProceedings - 22nd International Congress on Modelling and Simulation, MODSIM 2017
EditorsGeoff Syme, Darla Hatton MacDonald, Beth Fulton, Julia Piantadosi
PublisherModelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ)
Pages1948-1954
Number of pages7
ISBN (Electronic)9780987214379
Publication statusPublished - 2017
Externally publishedYes
EventInternational Congress on Modelling and Simulation 2017: Managing cumulative risks through model-based processes - The Hotel Grand Chancellor, Hobart, Australia
Duration: 3 Dec 20178 Dec 2017
Conference number: 22nd
https://www.mssanz.org.au/modsim2017/index.html

Conference

ConferenceInternational Congress on Modelling and Simulation 2017
Abbreviated titleMODSIM 2017
CountryAustralia
CityHobart
Period3/12/178/12/17
OtherThe 22nd International Congress on Modelling and Simulation (MODSIM2017) will be held at The Hotel Grand Chancellor Hobart, Tasmania, Australia from Sunday 3 to Friday 8 December 2017.

ASOR will be joining us again, for the 25th National Conference of the Australian Society for Operations Research as will the DST Group led Defence Operations Research Symposium (DORS 2017).
Internet address

Keywords

  • Bayesian hierarchical modelling
  • Land use
  • Oxidised Nitrogen
  • Suspended Solids
  • Water quality

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