eReefs AIMS-CSIRO Aggregations of biogeochemistry model outputs

This generated data set contains summaries (daily, monthly, annual) of the eReefs CSIRO biogeochemistry model v3.1 (https://research.csiro.au/ereefs) outputs at 4km resolution, generated 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/t...alog.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. These summaries are updated in near-real time (daily) and are made available via a THREDDS server (http://thredds.ereefs.aims.g...alog.html) in NetCDF format. The GBR4 BioGeoChemical (GBR) 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). 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 – 30/6/2018 (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 – 30/6/2018 (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). 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://eatlas.org.au/pydio/...odels-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 (http://thredds.ereefs.aims.g...alog.html). 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 the following depths, which are a subset of the depths available in the source data set: -0.5, -1.5, -3.0, -5.55, -8.8, -12.75, -17.75, -23.75, -31.0, -39.5, -49.0, -60.0, -73.0, -88.0, -103.0, -120.0, -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...17-22.pdf

Processor
Knowledge Systems Team
Point Of Contact
eAtlas Data Manager Australian Institute of Marine Science (AIMS) e-atlas@aims.gov.au

Data collected from 01 Dec 2010 until 30 Apr 2019


Data Usage Constraints
  • Attribution 3.0 Australia