GBR4 BGC Scenario Comparison - Dissolved inorganic nitrogen (DIN)

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What do these visualisations show?

These maps compare the estimated current state of the GBR (baseline) with pre-industrial and water quality target conditions (see eReefs BGC Scenarios for more information). The “Baseline scenario” panel shows the best estimate for Dissolved Inorganic Nitrogen (DIN) for the time period from December 2010 to April 2019.

The “Pre-industrial” panel shows the difference between the baseline scenario and pre-industrial conditions. Areas in blue correspond to locations where the pre-industrial values are estimated to be better than the current baseline (lower DIN in the pre-industrial past). Red areas correspond to areas where the pre-industrial conditions would have been worse (higher DIN) than the current baseline. As this plot compares the current conditions with estimated pre-industrial conditions the difference represents the anthropogenic influence on DIN on the GBR. It shows that current DIN levels in the marine environment are higher than pre-industrial conditions along most of the coastline and is elevated most during flooding events. The raised DIN extends out to the mid-shelf reefs, particularly along the reefs north of Townsville where the width of the GBR is narrower.

The “WQIP-Targets” panel shows the effects of the planned targets if they were achieved. The blue area indicates the amount of reduction of DIN relative to the current baseline. Red areas correspond to an increase of DIN relative to the current baseline. The comparison between the baseline and reduced loads (WQIP-Targets) scenario shows that implementing the measures to lower DIN concentration would actually result in a reduced DIN concentration along most of the coast, with the exception of the Broad Sound area where it shows mostly positive values.

The “% River water in sea water” panel shows the total percentage of river water in sea water. It can be used to identify significant river discharges and flood plumes which can have a big impact on the DIN concentration.

eReefs BGC Scenarios

In 2016, Brodie et al. conducted a study utilising the eReefs 4 km coupled hydrodynamic - biogeochemical model and two GBR Dynamic SedNet catchment model scenarios to analyse the water quality in the Great Barrier Reef (GBR) (Brodie et al., 2017). The complex eReefs biogeochemical model allowed for the resolution of biogeochemical processes affecting water quality changes. The main objective was to identify specific load reduction targets for different basins within the GBR, aiming to minimize ecological impacts related to chlorophyll and suspended sediment concentrations, as well as bottom light thresholds.

In 2020 Baird et al. built upon the previous study and expanded it by extending the time period from 2011–2014 to 2011–2018 and analysing the water quality response of a new set of catchment loads scenarios (Baird et al., 2021). These new scenario runs used the updated eReefs coupled hydrodynamic-biogeochemical model with the model configuration GBR4_H2p0_B3p1_Cq3x_Dhnd. H2p0 is the configuration of the hydrodynamic model and has been operational since 2016. B3p1 is the configuration of the biogeochemical model, described in Baird et al. (2020). Cq3x refers to one of the catchment model configurations (q3x = q3b, q3p, q3O, q3R, q3A, q3B, q3C; see Baird et al., 2021, for more information) of the 2019 report card version of the GBR Dynamic SedNet used to deliver nutrient and sediment river loads. Dhnd describes the deployment of the model, in this case being a non-assimilating hindcast (Baird et al., 2021).

For this scenario comparison we focused on the model configurations q3b baseline, q3p pre-industrial, and q3R reduced loads (WQIP-Targets):

q3b Baseline

This scenario is used as the baseline representing the best estimate of the current state. It uses the Paddock to Reef Integrated Monitoring, Modelling and Reporting Program (P2R) GBR Dynamic SedNet with 2019 catchment condition from 1st of December 2010 to 30th of June 2018 (used for GBR Report Card published in 2019) and Empirical SedNet with 2019 catchment condition from 1st of July 2018 to 30th of April 2019.

q3p Pre-Industrial

This scenario uses a configuration to simulate pre-industrial conditions and shows the change in water quality measures from the removal of all anthropogenic loads. P2R GBR Dynamic SedNet with Pre-Industrial catchment condition from 1st of December 2010 to 30th of June 2018 (used for GBR Report Card published in 2019), Empirical SedNet with Pre-Industrial catchment from 1st of July 2018 to 30th of April 2019.

q3R Reduced loads (WQIP-Targets)

This scenario uses GBR Dynamic SedNet with 2019 catchment condition (q3b) with anthropogenic loads (q3b - q3p) reduced according to the percentage reductions of DIN, PN, PP and TSS specified in the Reef 2050 Water Quality Improvement Plan (WQIP) 2017–2022 as calculated in Brodie et al. (2017). Further, the reductions are adjusted to account for the cumulative reductions already achieved between 2014 and 2019 that will be reflected in the 2019 catchment condition used in q3b.

Model variables

Dissolved Inorganic Nitrogen (DIN)

Concentration of Dissolved Inorganic Nitrogen (DIN). DIN = [NH₄] + [NO₃].

Within the GBR lagoon the main source of DIN is from land run off during flooding events. These flood plumes generally stay close to the coast, pushed northwards by the wind. The nitrogen in the plumes leads to rapid growth of phytoplankton. This algal rich water absorbs light leading to less light reaching the seagrass meadows and inshore coral reefs (see Secchi depth, vertical attenuation at 490nm & light intensity above seagrass product) lowering their growth.

Model specifics

This visualisation shows the modelled concentration of DIN in mg N m-3. DIN is the sum of nitrate and ammonium concentrations, [NO3]+[NH4].

The model contains two forms of DIN used by photosynthetic organisms, dissolved ammonium (NH4) and dissolved nitrate (NO3). In the model, the ammonium component of the DIN pool is taken up first, followed by the nitrate, with the caveat that the uptake of ammonium is limited by diffusion.

For nitrogen the main sources are river inputs, the atmosphere by nitrogen fixing Trichodesmium cyanobacteria and from upwelling of deeper nutrient rich waters. Nitrogen fixation (conversion of nitrogen gas to ammonium) occurs by trichodesmium algae when DIN is low (4 to 20 mg N m−3; Robson et al., 2013) and carbon and phosphorus are available to support nitrogen uptake.

An assessment of the BGC model shows it has a skill of (bias ± Root Mean Square Error) of nitrate of −0.70±3.86 mg N m−3 and ammonium of −0.77±1.63 mg N m−3 (Baird e. al., 2020). This represents the difference between the model values and 36 long-term water quality monitoring sites along the Queensland coast.

Dissolved Inorganic Nitrogen pre-industrial minus baseline (DIN_pre-base)

This variable represents the difference in concentration of dissolved inorganic nitrogen in mg N m-3 between the pre-industrial scenario and the baseline scenario. This highlights the anthropogenic contribution.

DIN_pre-base = pre-industrial (q3p) DIN - baseline (q3b) DIN

Dissolved Inorganic Nitrogen reduced loads (WQIP-Targets) minus baseline (DIN_reduced-base)

This variable represents the difference in concentration of dissolved inorganic nitrogen in mg N m-3 between the reduced loads (WQIP-Targets) scenario and the baseline scenario. This highlights the alignment of the current state with the targets.

DIN_reduced-base = reduced loads (q3R) DIN - baseline (q3b) DIN

Aggregation of all rivers (all_rivers)

The GBR4 river tracer model output represents the river water concentration in sea water for the major rivers along the Queensland coastline flowing into the Great Barrier Reef Marine Park. The “all_rivers” product aggregates the output for each river into one variable to represent the total concentration of river water in the sea water.

In the model, tracers are released at the mouth into the surface flow of each river. These tracers move with the ocean currents, becoming more dilute as they spread out and mix with the ocean water, allowing the concentration of river water to be tracked over time. These tracers show the fraction of the water, at any given location, associated with each river.

The lowest threshold of river water concentration (1%) shown in the visualisation was chosen to align with the visible extent of flood plumes as seen in satellite imagery. At this concentration we can expect organisms on the sea floor to see raised nutrient levels, some fine sediment and a significant reduction in light.

See Flood plume extents for major rivers on GBR based on modelled river tracers for more information.

References

Brodie, J., Baird, M., Mongin, M., Skerratt, J., Robillot, C., Waterhouse, J., 2017. Pollutant target setting for the Great Barrier Reef: using the eReefs framework. In: Syme, G., Hatton MacDonald, D., Fulton, B., Piantadosi, J. (Eds.), MODSIM2017, 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2017, pp. 1913–1919.

Mark E. Baird, Mathieu Mongin, Jennifer Skerratt, Nugzar Margvelashvili, Sharon Tickell, Andrew D.L. Steven, Cedric Robillot, Robin Ellis, David Waters, Paulina Kaniewska, Jon Brodie, 2021. Impact of catchment-derived nutrients and sediments on marine water quality on the Great Barrier Reef: An application of the eReefs marine modelling system. https://doi.org/10.1016/j.marpolbul.2021.112297

M.E. Baird, K.A. Wild-Allen, J. Parslow, M. Mongin, B. Robson, J. Skerratt, F. Rizwi, M. Soja-Woźnaik, E. Jones, M. Herzfeld, N. Margvelashvili, J. Andrewartha, C. Langlais, M.P. Adams, N. Cherukuru, M. Gustafsson, S. Hadley, P.J. Ralph, U. Rosebrock, T. Schroeder, L. Laiolo, D. Harrison, A.D.L. Steven CSIRO Environmental Modelling Suite (EMS): scientific description of the optical and biogeochemical models (vB3p0) Geosci. Model Dev., 13 (2020), pp. 4503-4553

Robson, B. J., Baird, M. E., and Wild-Allen, K. A.: A physiological model for the marine cyanobacteria,Trichodesmium, in: MODSIM2013, 20th International Congress on Modelling and Simulation, edited by: Piantadosi, J. R. S. A. and Boland, J., Modelling and Simulation Society of Australia and New Zealand, 1652–1658, available at: https://www.mssanz.org.au/modsim2013/H3/robson.pdf (last access: 25 October 2023), 2013.

Source data

The videos/images on this page are based on the 4km eReefs BioGeoChemical model (v3.1) (GBR4_H2p0_B3p1_Cq3b_Dhnd, GBR4_H2p0_B3p1_Cq3p_Dhnd, GBR4_H2p0_B3p1_Cq3R_Dhnd) run with SOURCE Catchments using Baseline, Pre-Industrial, and reduced loads (WQIP-Targets) catchment conditions. Detailed information about the model can be found in the paper: CSIRO Environmental Modelling Suite (EMS): Scientific description of the optical and biogeochemical models (vB3p0).

The raw model data is available from the NCI THREDDS server (daily, in curvilinear NetCDF format):

Aggregate data is available from the AIMS eAtlas THREDDS server (daily, monthly, yearly, in regular rectangular grid NetCDF format):

Data span

These results are based on a fixed time period (Dec 2010 - Apr 2019) hind-cast analysis developed for comparing changes in land practices. The river run off used to drive the BGC model were provided by the SOURCE Catchments modelling.