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/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. These summaries are updated in near-real time (daily) and are made available via a THREDDS server (https://thredds.ereefs.aims.gov.au/thredds/ ) in NetCDF format.

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).

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

本生成数据集包含eReefs CSIRO生物地球化学模型v3.1（https://research.csiro.au/ereefs）分辨率为4km的输出结果的日、月、年三类汇总数据，该数据由AIMS eReefs平台（https://ereefs.aims.gov.au/ereefs-aims）生成。此类汇总数据源自国家计算基础设施（National Computing Infrastructure, NCI，https://dapds00.nci.org.au/thredds/catalogs/fx3/catalog.html）提供的原始日度模型输出，并已从eReefs模型使用的原始曲线网格重投影为规则网格，以便数据文件可轻松加载至标准地理信息系统（GIS, Geographic Information System）软件中。此类汇总数据以准实时（每日）频率更新，并通过THREDDS服务器（https://thredds.ereefs.aims.gov.au/thredds/）以NetCDF格式对外提供。

GBR4生物地球化学（BioGeoChemical, BGC）模型以GBR4水动力模型为基础，对驱动水体化学过程的水质（营养盐与悬浮沉积物）及关键生态过程（珊瑚、海草、浮游生物）进行模拟。该模型可助力我们更好地理解陆地径流对水质的影响。有关该模型的详细信息可参阅论文：《CSIRO环境建模套件（EMS, Environmental Modelling Suite）：光学与生物地球化学模型科学描述（vB3p0）》。

原始模型输出数据集包含三类情景，本数据集针对每类情景均提供了对应的汇总数据：

基准情景（GBR4_H2p0_B3p1_Cq3b_Dhnd）：
采用2019年流域条件的“牧场至珊瑚礁（Paddock to Reef）”源流域情景，时段为2010年12月1日至2018年6月30日（用于2019年发布的《大堡礁报告卡8》）；以及采用2019年流域条件的经验源情景，时段为2018年7月1日至2019年4月30日。该情景最贴近大堡礁的历史生物地球化学条件（详见局限性说明）。

工业化前情景（GBR4_H2p0_B3p1_Cq3P_Dhnd）：
采用工业化前流域条件的“牧场至珊瑚礁”源流域情景，时段为2010年12月1日至2018年6月30日（用于2019年发布的《大堡礁报告卡8》）；以及采用工业化前流域条件的经验源情景，时段为2018年7月1日至2019年4月30日。

减排情景（GBR4_H2p0_B3p1_Cq3R_Dhnd）：
采用2019年流域条件（q3b）的源流域情景，其中人为负荷（q3b - q3p）依据《大堡礁2050水质改善计划（WQIP, Water Quality Improvement Plan）2017-2022》中规定的溶解无机氮（DIN, Dissolved Inorganic Nitrogen）、颗粒氮（PN, Particulate Nitrogen）、颗粒磷（PP, Particulate Phosphorus）及总悬浮固体（TSS, Total Suspended Solids）削减比例进行削减，该削减比例由Brodie等人（2017）测算得出。此外，还针对2014年至2019年间已实现的累积削减量进行调整，该累积削减量将体现在基准情景（q3b）所采用的2019年流域条件中。

如需了解生物地球化学模型命名规范的更多信息，请参阅：https://research.csiro.au/ereefs/models/models-about/biogeochemical-simulation-naming-protocol/

处理方法：
有关数据处理（尤其是聚合与重投影步骤）的详细说明可参阅《CSIRO eReefs模型衍生产品技术指南》文档（https://nextcloud.eatlas.org.au/apps/sharealias/a/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf）。

数据字典：
本数据集包含原始BGC模型变量的子集，该子集基于实用性最高的变量筛选得出。如需了解这些变量的更多信息，可通过AIMS eReefs THREDDS服务器（https://thredds.ereefs.aims.gov.au/thredds/）上的OPeNDAP浏览器进行查询。

alk：总碱度 [mmol m-3]
BOD：生化需氧量 [mg O m-3]
CH_N：珊瑚宿主氮 [g N m-2]
Chl_a_sum：总叶绿素 [mg Chl m-3]
CO32：碳酸盐 [mmol m-3]
CS_bleach：珊瑚白化速率 [d-1]
CS_Chl：珊瑚共生体叶绿素 [mg Chl m-2]
CS_N：珊瑚共生体氮 [mg N m-2]
DIC：溶解无机碳 [mg C m-3]
DIN：溶解无机氮 [mg N m-3]
DIP：溶解无机磷 [mg P m-3]
DOR_C：溶解有机碳 [mg C m-3]
DOR_N：溶解有机氮 [mg N m-3]
DOR_P：溶解有机磷 [mg P m-3]
Dust：粉尘 [kg m-3]
EFI：生态细无机物 [kg m-3]
EpiPAR_sg：海草上方光照强度 [mol photon m-2 d-1]
eta：海面高度 [metre]
FineSed：细沉积物 [kg m-3]
Fluorescence：模拟荧光 [mg chla m-3]
HCO3：碳酸氢盐 [mmol m-3]
Kd_490：490nm垂向衰减系数 [m-1]
MA_N：大型藻类氮 [g N m-2]
MA_N_pr：大型藻类净生产力 [mg N m-2 d-1]
month_EpiPAR_sg：海草上方月度光照剂量 [mol photon m-2]
MPB_Chl：底栖微藻叶绿素 [mg Chl m-3]
MPB_N：底栖微藻氮 [mg N m-3]
Mud-carbonate：泥质碳酸盐 [kg m-3]
Mud-mineral：泥质矿物 [kg m-3]
Nfix：固氮速率 [mg N m-3 s-1]
NH4：氨氮 [mg N m-3]
NO3：硝酸盐 [mg N m-3]
omega_ar：文石饱和状态 [nil]
Oxy_sat：氧气饱和度百分比 [%]
Oxygen：溶解氧 [mg O m-3]
P_Prod：浮游植物总生产力 [mg C m-3 d-1]
PAR：层内平均光合有效辐射 [mol photon m-2 s-1]
PAR_z：层顶下行光合有效辐射 [mol photon m-2 s-1]
pco2surf：海洋表层pCO2 [ppmv]
PH：pH值 [log(mM)]
PhyL_Chl：大型浮游植物叶绿素 [mg Chl m-3]
PhyL_N：大型浮游植物氮 [mg N m-3]
PhyS_Chl：小型浮游植物叶绿素 [mg Chl m-3]
PhyS_N：小型浮游植物氮 [mg N m-3]
PhyS_NR：小型浮游植物氮储备 [mg N m-3]
PIP：颗粒无机磷 [mg P m-3]
R_400：400nm遥感反射率 [sr-1]
R_410：410nm遥感反射率 [sr-1]
R_412：412nm遥感反射率 [sr-1]
R_443：443nm遥感反射率 [sr-1]
R_470：470nm遥感反射率 [sr-1]
R_486：486nm遥感反射率 [sr-1]
R_488：488nm遥感反射率 [sr-1]
R_490：490nm遥感反射率 [sr-1]
R_510：510nm遥感反射率 [sr-1]
R_531：531nm遥感反射率 [sr-1]
R_547：547nm遥感反射率 [sr-1]
R_551：551nm遥感反射率 [sr-1]
R_555：555nm遥感反射率 [sr-1]
R_560：560nm遥感反射率 [sr-1]
R_590：590nm遥感反射率 [sr-1]
R_620：620nm遥感反射率 [sr-1]
R_640：640nm遥感反射率 [sr-1]
R_645：645nm遥感反射率 [sr-1]
R_665：665nm遥感反射率 [sr-1]
R_667：667nm遥感反射率 [sr-1]
R_671：671nm遥感反射率 [sr-1]
R_673：673nm遥感反射率 [sr-1]
R_678：678nm遥感反射率 [sr-1]
R_681：681nm遥感反射率 [sr-1]
R_709：709nm遥感反射率 [sr-1]
R_745：745nm遥感反射率 [sr-1]
R_748：748nm遥感反射率 [sr-1]
R_754：754nm遥感反射率 [sr-1]
R_761：761nm遥感反射率 [sr-1]
R_764：764nm遥感反射率 [sr-1]
R_767：767nm遥感反射率 [sr-1]
R_778：778nm遥感反射率 [sr-1]
salt：盐度 [PSU]
Secchi：赛氏盘透明度 [m]
SG_N：海草氮 [g N m-2]
SG_N_pr：海草净生产力 [mg N m-2 d-1]
SG_shear_mort：海草剪切死亡率 [g N m-2 d-1]
SGD_N：深层海草氮 [g N m-2]
SGD_N_pr：深层海草净生产力 [mg N m-2 d-1]
SGD_shear_mort：深层海草剪切死亡率 [g N m-2 d-1]
SGH_N：喜盐草氮 [g N m-2]
SGH_N_pr：喜盐草净生产力 [mg N m-2 d-1]
SGHROOT_N：喜盐草根氮 [g N m-2]
SGROOT_N：海草根氮 [g N m-2]
TC：总碳 [mg C m-3]
temp：水温 [degrees C]
TN：总氮 [mg N m-3]
TP：总磷 [mg P m-3]
Tricho_Chl：束毛藻叶绿素 [mg Chl m-3]
Tricho_N：束毛藻氮 [mg N m-3]
TSSM：645nm波长反演总悬浮固体 [g TSS m-3]（Petus et al., 2014）
Z_grazing：浮游动物总摄食率 [mg C m-3 d-1]
Zenith2D：太阳天顶角 [rad]
ZooL_N：大型浮游动物氮 [mg N m-3]
ZooS_N：小型浮游动物氮 [mg N m-3]
z：垂向坐标（深度） [m]
time：时间 [days since 1990-01-01 00:00:00 +10]
latitude：纬度 [degrees_north]
longitude：经度 [degrees_east]

深度信息：
本数据集包含源数据集中部分深度层级数据，深度由‘k’维度表示。下表列出了各‘k’值对应的深度：
k, 深度
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

局限性说明：
本数据集基于时空模型构建，仅为环境条件的估算结果，并非基于原位实测数据。
针对BGC v3.1模型的技术能力评估（详见相关链接）显示，该模型的绝对精度随变量与区域存在显著差异。因此，使用时需确保该模型符合应用场景需求，且总体而言，BGC模型结果应与其他来源的信息结合使用以形成决策建议。
BGC v3.1模型的模拟情景最初用于对比流域径流影响，因此其驱动条件采用历史气象与河流边界条件，但沉积物与营养盐负荷基于2019年源流域模型的测算结果。该流域模型中，模拟时段内土地利用保持静态。这意味着‘基准情景’采用2019年的估算土地利用覆盖2010-2019年全时段，从而将土地管理改进的时间节点回溯至模拟起始年（2010年），导致模拟早期的营养盐与沉积物负荷略低于实际情况。BGC模型团队指出，该方法可能在土地管理改进显著的区域引入小幅额外误差，但该误差通常小于模型本身的固有误差。此类误差仅在将基准情景模型数据解读为历史条件估算值时存在，而非该情景最初用于情景对比的预设目的。

参考文献：
《大堡礁2050水质改善计划（WQIP）2017-2022》，https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0017/46115/reef-2050-water-quality-improvement-plan-2017-22.pdf