This service provides generated summaries (daily, monthly, annual) of the eReefs CSIRO biogeochemistry model v3.1 ( https://research.csiro.au/ereefs ) outputs at 4km resolution as a Thredds data service. This provides the data as an OPeNDAP service and as NetCDF files ( https://thredds.ereefs.aims.gov.au/thredds/ ). The processing is provided by the AIMS eReefs Platform ( https://ereefs.aims.gov.au/ereefs-aims ). These summaries are derived from the original daily model outputs available via the National Computing Infrastructure (NCI) ( https://dapds00.nci.org.au/thredds/catalogs/fx3/catalog.html ), and have been re-gridded from the original curvilinear grid used by the eReefs model into a regular grid so that the data files can be easily loaded into standard GIS software.

Notice: In the second half of 2025 there will be an updated hindcast of the BGC model (v4.0). This dataset and Thredds service will be updated when the raw model data becomes available on NCI available. The v3.1 data will then be deprecated and may become unavailable as it is too expensive to maintain both datasets.

The GBR4 BioGeoChemical (BGC) model builds on the GBR4 hydrodynamic model by modelling the water quality (nutrients and suspended sediment) and key ecological processes (coral, seagrass, plankton) that drive the water chemistry. This model allows us to better understand how water quality is affected by land runoff. 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).

This data service provides a regridded version of the daily BGC model. The source raw eReefs BGC model from CSIRO is a daily snapshot, and so the regridded version is also a snapshot, not a temporal aggregate. The monthly and annual products correspond to the average of the daily values of the aggregation period.

The original model output data set contains three (3) scenarios, each of which have an equivalent set of summaries in this data set:

Baseline (GBR4_H2p0_B3p1_Cq3b_Dhnd):

Paddock to Reef SOURCE Catchments with 2019 catchment condition from Dec 1, 2010 – Jun 30, 2018 (used for GBR Report Card 8 published in 2019), Empirical SOURCE with 2019 catchment condition, Jul 1, 2018 – April 30, 2019. This scenario most closely corresponds to historic BioGeoChemical conditions of the reef (see limitation).

Pre-industrial (GBR4_H2p0_B3p1_Cq3P_Dhnd):

Paddock to Reef SOURCE Catchments with Pre-Industrial catchment condition from Dec 1, 2010 – Jun 30, 2018 (used for GBR Report Card 8 published in 2019), Empirical SOURCE with Pre-Industrial catchment, Jul 1, 2018 – April 30, 2019.

Reduced (GBR4_H2p0_B3p1_Cq3R_Dhnd):

SOURCE Catchments 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 the Baseline scenario (q3b).

For more information about the biogeochemical model naming protocol, see https://research.csiro.au/ereefs/models/models-about/biogeochemical-simulation-naming-protocol/

Method:

A description of the processing, especially aggregation and regridding, is available in the "Technical Guide to Derived Products from CSIRO eReefs Models" document ( https://nextcloud.eatlas.org.au/apps/sharealias/a/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf ).

Data Dictionary:

This dataset contains a subset of the original BGC model variables. This subset was chosen based on those variables that are most likely to have utility. Additional information about these variables can be found using the OPeNDAP browser via the AIMS eReefs THREDDS server ( https://thredds.ereefs.aims.gov.au/thredds/ ).

alk: [mmol m-3] Total alkalinity

BOD: [mg O m-3] Biochemical Oxygen Demand

CH_N: [g N m-2] Coral host N

Chl_a_sum: [mg Chl m-3] Total Chlorophyll

CO32: [mmol m-3] Carbonate

CS_bleach: [d-1] Coral bleach rate

CS_Chl: [mg Chl m-2] Coral symbiont Chl

CS_N: [mg N m-2] Coral symbiont N

DIC: [mg C m-3] Dissolved Inorganic Carbon

DIN: [mg N m-3] Dissolved Inorganic Nitrogen

DIP: [mg P m-3] Dissolved Inorganic Phosphorus

DOR_C: [mg C m-3] Dissolved Organic Carbon

DOR_N: [mg N m-3] Dissolved Organic Nitrogen

DOR_P: [mg P m-3] Dissolved Organic Phosphorus

Dust: [kg m-3] Dust

EFI: [kg m-3] Ecology Fine Inorganics

EpiPAR_sg: [mol photon m-2 d-1] Light intensity above seagrass

eta: [metre] Surface Elevation

FineSed: [kg m-3] FineSed

Fluorescence: [mg chla m-3] Simulated Fluorescence

HCO3: [mmol m-3] Bicarbonate

Kd_490: [m-1] Vert. att. at 490 nm

MA_N: [g N m-2] Macroalgae N

MA_N_pr: [mg N m-2 d-1] Macroalgae net production

month_EpiPAR_sg: [mol photon m-2] Monthly dose light above seagrass

MPB_Chl: [mg Chl m-3 ] Microphytobenthos chlorophyll

MPB_N: [mg N m-3] Microphytobenthos N

Mud-carbonate: [kg m-3] Mud-carbonate

Mud-mineral: [kg m-3] Mud-mineral

Nfix: [mg N m-3 s-1] N2 fixation

NH4: [mg N m-3] Ammonia

NO3: [mg N m-3] Nitrate

omega_ar: [nil] Aragonite saturation state

Oxy_sat: [%] Oxygen saturation percent

Oxygen: [mg O m-3] Dissolved Oxygen

P_Prod: [mg C m-3 d-1] Phytoplankton total productivity

PAR: [mol photon m-2 s-1] Av. PAR in layer

PAR_z: [mol photon m-2 s-1] Downwelling PAR at top of layer

pco2surf: [ppmv] oceanic pCO2 (ppmv)

PH: [log(mM)] PH

PhyL_Chl: [mg Chl m-3 ] Large Phytoplankton chlorophyll

PhyL_N: [mg N m-3] Large Phytoplankton N

PhyS_Chl: [mg Chl m-3 ] Small Phytoplankton chlorophyll

PhyS_N: [mg N m-3] Small Phytoplankton N

PhyS_NR: [mg N m-3] Small Phytoplankton N reserve

PIP: [mg P m-3] Particulate Inorganic Phosphorus

R_400: [sr-1] Remote-sensing reflectance @ 400 nm

R_410: [sr-1] Remote-sensing reflectance @ 410 nm

R_412: [sr-1] Remote-sensing reflectance @ 412 nm

R_443: [sr-1] Remote-sensing reflectance @ 443 nm

R_470: [sr-1] Remote-sensing reflectance @ 470 nm

R_486: [sr-1] Remote-sensing reflectance @ 486 nm

R_488: [sr-1] Remote-sensing reflectance @ 488 nm

R_490: [sr-1] Remote-sensing reflectance @ 490 nm

R_510: [sr-1] Remote-sensing reflectance @ 510 nm

R_531: [sr-1] Remote-sensing reflectance @ 531 nm

R_547: [sr-1] Remote-sensing reflectance @ 547 nm

R_551: [sr-1] Remote-sensing reflectance @ 551 nm

R_555: [sr-1] Remote-sensing reflectance @ 555 nm

R_560: [sr-1] Remote-sensing reflectance @ 560 nm

R_590: [sr-1] Remote-sensing reflectance @ 590 nm

R_620: [sr-1] Remote-sensing reflectance @ 620 nm

R_640: [sr-1] Remote-sensing reflectance @ 640 nm

R_645: [sr-1] Remote-sensing reflectance @ 645 nm

R_665: [sr-1] Remote-sensing reflectance @ 665 nm

R_667: [sr-1] Remote-sensing reflectance @ 667 nm

R_671: [sr-1] Remote-sensing reflectance @ 671 nm

R_673: [sr-1] Remote-sensing reflectance @ 673 nm

R_678: [sr-1] Remote-sensing reflectance @ 678 nm

R_681: [sr-1] Remote-sensing reflectance @ 681 nm

R_709: [sr-1] Remote-sensing reflectance @ 709 nm

R_745: [sr-1] Remote-sensing reflectance @ 745 nm

R_748: [sr-1] Remote-sensing reflectance @ 748 nm

R_754: [sr-1] Remote-sensing reflectance @ 754 nm

R_761: [sr-1] Remote-sensing reflectance @ 761 nm

R_764: [sr-1] Remote-sensing reflectance @ 764 nm

R_767: [sr-1] Remote-sensing reflectance @ 767 nm

R_778: [sr-1] Remote-sensing reflectance @ 778 nm

salt: [PSU] Salinity

Secchi: [m] Secchi from 488 nm

SG_N: [g N m-2] Seagrass N

SG_N_pr: [mg N m-2 d-1] Seagrass net production

SG_shear_mort: [g N m-2 d-1] Seagrass shear stress mort

SGD_N: [g N m-2] Deep seagrass N

SGD_N_pr: [mg N m-2 d-1] Deep seagrass net production

SGD_shear_mort: [g N m-2 d-1] Deep seagrass shear stress mort

SGH_N: [g N m-2] Halophila N

SGH_N_pr: [mg N m-2 d-1] Halophila net production

SGHROOT_N: [g N m-2] Halophila root N

SGROOT_N: [g N m-2] Seagrass root N

TC: [mg C m-3] Total C

temp: [degrees C] Temperature

TN: [mg N m-3] Total N

TP: [mg P m-3] Total P

Tricho_Chl: [mg Chl m-3] Trichodesmium chlorophyll

Tricho_N: [mg N m-3] Trichodesmium Nitrogen

TSSM: [g TSS m-3] TSS from 645 nm (Petus et al., 2014)

Z_grazing: [mg C m-3 d-1] Zooplankton total grazing

Zenith2D: [rad] Solar zenith

ZooL_N: [mg N m-3] Large Zooplankton N

ZooS_N: [mg N m-3] Small Zooplankton N

z: [m] Z coordinate (depth)

time: [days since 1990-01-01 00:00:00 +10] Time

latitude: [degrees_north] Latitude (geographic projection)

longitude: [degrees_east] Longitude (geographic projection)

Depths:

This data set contains a subset of the depths available in the source data set. The depth is represented by the 'k' dimension. The following table shows the depths associated with each 'k' value.

k, depth

16, -0.5,

15, -1.5,

14, -3.0,

13, -5.55,

12, -8.8,

11, -12.75,

10, -17.75,

9, -23.75,

8, -31.0,

7, -39.5,

6, -49.0,

5, -60.0,

4, -73.0,

3, -88.0,

2, -103.0,

1, -120.0,

0, -145.0.

Limitations:

This dataset is based on a spatial and temporal model and as such is an estimate of the environmental conditions. It is not based on in-water measurements.

A technical assessment of the skill level of the BGC version 3.1 model (see links) shows that the absolute accuracy of the BGC model varies significantly with variable and location. As a result care should be taken to ensure the model is fit-for-purpose and in general BGC results should used in combination with second sources of information for making recommendations.

The modelled scenarios run for version 3.1 of the BGC model were developed for the purpose of comparing catchment run off effect comparison. As such they were driven with historic weather and river flow boundary conditions, but the sediment and nutrient loads were based on the results of the 2019 Source Catchment modelling. In this catchment modelling the land use is static over the simulation run. This means that for the 'Baseline' scenario this uses estimated land use from 2019 applied over all years (2010 - 2019). As a result improvements in land practices are effectively back dated to start of the simulation (2010). This results in early years in the simulation having slightly lower nutrient and sediment loads then actually happened. The BGC modelling team indicated this approach is likely to introduce small additional errors in places where the land practices have improved significantly, but are likely to be smaller than the inherent errors in the model. These errors only apply if the Baseline model data is interpreted as an estimate of historic conditions, rather than the original intended purpose of the scenario comparison.

References:

Reef 2050 Water Quality Improvement Plan (WQIP) 2017-2022. https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0017/46115/reef-2050-water-quality-improvement-plan-2017-22.pdf

Change Log:

2020-08-18 (Version 1): Initial version of the system and dataset. Dataset and service covers GBR4 BGC v3.1 from 2010 - 2019.

2025-05-16 (Version 1): Improved the metadata title and abstract to better represent the dataset. Also minted a DOI and added how the dataset should be cited.