eReefs BioGeoChemical model regridded temporal aggregation data service - daily, monthly, annual (AIMS, source: CSIRO)

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.

Organization: Australian Ocean Data Network