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 alkalinityBOD: [mg O m-3] Biochemical Oxygen DemandCH_N: [g N m-2] Coral host NChl_a_sum: [mg Chl m-3] Total ChlorophyllCO32: [mmol m-3] CarbonateCS_bleach: [d-1] Coral bleach rateCS_Chl: [mg Chl m-2] Coral symbiont ChlCS_N: [mg N m-2] Coral symbiont NDIC: [mg C m-3] Dissolved Inorganic CarbonDIN: [mg N m-3] Dissolved Inorganic NitrogenDIP: [mg P m-3] Dissolved Inorganic PhosphorusDOR_C: [mg C m-3] Dissolved Organic CarbonDOR_N: [mg N m-3] Dissolved Organic NitrogenDOR_P: [mg P m-3] Dissolved Organic PhosphorusDust: [kg m-3] DustEFI: [kg m-3] Ecology Fine InorganicsEpiPAR_sg: [mol photon m-2 d-1] Light intensity above seagrasseta: [metre] Surface ElevationFineSed: [kg m-3] FineSedFluorescence: [mg chla m-3] Simulated FluorescenceHCO3: [mmol m-3] BicarbonateKd_490: [m-1] Vert. att. at 490 nm MA_N: [g N m-2] Macroalgae NMA_N_pr: [mg N m-2 d-1] Macroalgae net productionmonth_EpiPAR_sg: [mol photon m-2] Monthly dose light above seagrassMPB_Chl: [mg Chl m-3 ] Microphytobenthos chlorophyllMPB_N: [mg N m-3] Microphytobenthos NMud-carbonate: [kg m-3] Mud-carbonateMud-mineral: [kg m-3] Mud-mineralNfix: [mg N m-3 s-1] N2 fixationNH4: [mg N m-3] AmmoniaNO3: [mg N m-3] Nitrateomega_ar: [nil] Aragonite saturation stateOxy_sat: [%] Oxygen saturation percentOxygen: [mg O m-3] Dissolved OxygenP_Prod: [mg C m-3 d-1] Phytoplankton total productivityPAR: [mol photon m-2 s-1] Av. PAR in layerPAR_z: [mol photon m-2 s-1] Downwelling PAR at top of layerpco2surf: [ppmv] oceanic pCO2 (ppmv)PH: [log(mM)] PHPhyL_Chl: [mg Chl m-3 ] Large Phytoplankton chlorophyllPhyL_N: [mg N m-3] Large Phytoplankton NPhyS_Chl: [mg Chl m-3 ] Small Phytoplankton chlorophyllPhyS_N: [mg N m-3] Small Phytoplankton NPhyS_NR: [mg N m-3] Small Phytoplankton N reservePIP: [mg P m-3] Particulate Inorganic PhosphorusR_400: [sr-1] Remote-sensing reflectance @ 400 nmR_410: [sr-1] Remote-sensing reflectance @ 410 nmR_412: [sr-1] Remote-sensing reflectance @ 412 nmR_443: [sr-1] Remote-sensing reflectance @ 443 nmR_470: [sr-1] Remote-sensing reflectance @ 470 nmR_486: [sr-1] Remote-sensing reflectance @ 486 nmR_488: [sr-1] Remote-sensing reflectance @ 488 nmR_490: [sr-1] Remote-sensing reflectance @ 490 nmR_510: [sr-1] Remote-sensing reflectance @ 510 nmR_531: [sr-1] Remote-sensing reflectance @ 531 nmR_547: [sr-1] Remote-sensing reflectance @ 547 nmR_551: [sr-1] Remote-sensing reflectance @ 551 nmR_555: [sr-1] Remote-sensing reflectance @ 555 nmR_560: [sr-1] Remote-sensing reflectance @ 560 nmR_590: [sr-1] Remote-sensing reflectance @ 590 nmR_620: [sr-1] Remote-sensing reflectance @ 620 nmR_640: [sr-1] Remote-sensing reflectance @ 640 nmR_645: [sr-1] Remote-sensing reflectance @ 645 nmR_665: [sr-1] Remote-sensing reflectance @ 665 nmR_667: [sr-1] Remote-sensing reflectance @ 667 nmR_671: [sr-1] Remote-sensing reflectance @ 671 nmR_673: [sr-1] Remote-sensing reflectance @ 673 nmR_678: [sr-1] Remote-sensing reflectance @ 678 nmR_681: [sr-1] Remote-sensing reflectance @ 681 nmR_709: [sr-1] Remote-sensing reflectance @ 709 nmR_745: [sr-1] Remote-sensing reflectance @ 745 nmR_748: [sr-1] Remote-sensing reflectance @ 748 nmR_754: [sr-1] Remote-sensing reflectance @ 754 nmR_761: [sr-1] Remote-sensing reflectance @ 761 nmR_764: [sr-1] Remote-sensing reflectance @ 764 nmR_767: [sr-1] Remote-sensing reflectance @ 767 nmR_778: [sr-1] Remote-sensing reflectance @ 778 nmsalt: [PSU] SalinitySecchi: [m] Secchi from 488 nmSG_N: [g N m-2] Seagrass NSG_N_pr: [mg N m-2 d-1] Seagrass net productionSG_shear_mort: [g N m-2 d-1] Seagrass shear stress mortSGD_N: [g N m-2] Deep seagrass NSGD_N_pr: [mg N m-2 d-1] Deep seagrass net productionSGD_shear_mort: [g N m-2 d-1] Deep seagrass shear stress mortSGH_N: [g N m-2] Halophila NSGH_N_pr: [mg N m-2 d-1] Halophila net productionSGHROOT_N: [g N m-2] Halophila root NSGROOT_N: [g N m-2] Seagrass root NTC: [mg C m-3] Total Ctemp: [degrees C] TemperatureTN: [mg N m-3] Total NTP: [mg P m-3] Total PTricho_Chl: [mg Chl m-3] Trichodesmium chlorophyllTricho_N: [mg N m-3] Trichodesmium NitrogenTSSM: [g TSS m-3] TSS from 645 nm (Petus et al., 2014)Z_grazing: [mg C m-3 d-1] Zooplankton total grazingZenith2D: [rad] Solar zenithZooL_N: [mg N m-3] Large Zooplankton NZooS_N: [mg N m-3] Small Zooplankton Nz: [m] Z coordinate (depth)time: [days since 1990-01-01 00:00:00 +10] Timelatitude: [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, depth16, -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.pdfChange 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.

本服务提供eReefs CSIRO生物地球化学模型v3.1（eReefs CSIRO biogeochemistry model v3.1，https://research.csiro.au/ereefs）输出结果的日、月、年度汇总数据，分辨率为4km，以Thredds数据服务（Thredds data service）形式提供。该服务以OPeNDAP服务（OPeNDAP）和NetCDF文件（NetCDF，https://thredds.ereefs.aims.gov.au/thredds/）两种形式分发数据，处理工作由澳大利亚海洋科学研究所eReefs平台（AIMS eReefs Platform，https://ereefs.aims.gov.au/ereefs-aims）完成。

此类汇总数据源自澳大利亚国家计算基础设施（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，已从eReefs模型使用的曲线网格重采样至规则网格，以便数据文件可直接加载至标准地理信息系统（GIS）软件中。

注意事项：2025年下半年将发布更新后的生物地球化学模型（v4.0）回溯模拟结果。待原始模型数据在NCI上线后，本数据集及Thredds服务将同步更新，原v3.1版本数据将被弃用，由于同时维护两套数据集成本过高，v3.1数据后续可能无法继续获取。

GBR4生物地球化学（BioGeoChemical, BGC）模型基于GBR4水动力模型构建，通过模拟水质（营养盐与悬浮沉积物）及驱动水化学变化的关键生态过程（珊瑚、海草、浮游生物），助力研究者更深入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）：光学与生物地球化学模型科学描述（vB3p0）》（CSIRO Environmental Modelling Suite (EMS): Scientific description of the optical and biogeochemical models (vB3p0)）。

本数据服务提供重采样后的日度BGC模型数据。CSIRO提供的原始eReefs BGC模型输出为日度快照，因此重采样后的数据同样为快照形式，而非时间聚合数据。月度与年度产品为对应统计周期内日度数值的平均值。

原始模型输出数据集包含3种情景，本数据集对应每种情景均提供一套等价的汇总数据：
1. 基准情景（Baseline，标识为GBR4_H2p0_B3p1_Cq3b_Dhnd）：采用2019年流域状态的“牧场到礁”（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》）；采用2019年流域状态的经验源流域情景，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历史生物地球化学条件（详见局限性说明）。
2. 工业前情景（Pre-industrial，标识为GBR4_H2p0_B3p1_Cq3P_Dhnd）：采用工业前流域状态的“牧场到礁”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》）；采用工业前流域状态的经验源流域情景，时段为2018年7月1日至2019年4月30日。
3. 减排情景（Reduced，标识为GBR4_H2p0_B3p1_Cq3R_Dhnd）：采用2019年流域状态（q3b），并根据《2017-2022年大堡礁2050水质改善计划》（Reef 2050 Water Quality Improvement Plan, WQIP）中规定的溶解无机氮（DIN）、颗粒氮（PN）、颗粒磷（PP）及总悬浮固体（TSS）削减比例，对人为负荷（q3b - q3p）进行削减，具体计算参见Brodie等人（2017）的研究。此外，削减量已针对2014-2019年间已实现的累积削减进行调整，该部分削减将体现在基准情景（q3b）采用的2019年流域状态中。

如需了解生物地球化学模型命名规范，可访问：https://research.csiro.au/ereefs/models/models-about/biogeochemical-simulation-naming-protocol/

处理方法：有关数据处理（尤其是聚合与重采样流程）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，选取依据为变量的实际应用价值。如需了解变量的更多信息，可通过澳大利亚海洋科学研究所eReefs Thredds服务器（https://thredds.ereefs.aims.gov.au/thredds/）的OPeNDAP浏览器查询。
以下为变量清单及说明：
alk: [毫摩尔/立方米] 总碱度
BOD: [毫克氧/立方米] 生化需氧量
CH_N: [克氮/平方米] 珊瑚宿主氮
Chl_a_sum: [毫克叶绿素/立方米] 总叶绿素
CO32: [毫摩尔/立方米] 碳酸根离子
CS_bleach: [每日⁻¹] 珊瑚白化速率
CS_Chl: [毫克叶绿素/平方米] 珊瑚共生体叶绿素
CS_N: [毫克氮/平方米] 珊瑚共生体氮
DIC: [毫克碳/立方米] 溶解无机碳
DIN: [毫克氮/立方米] 溶解无机氮
DIP: [毫克磷/立方米] 溶解无机磷
DOR_C: [毫克碳/立方米] 溶解有机碳
DOR_N: [毫克氮/立方米] 溶解有机氮
DOR_P: [毫克磷/立方米] 溶解有机磷
Dust: [千克/立方米] 沙尘
EFI: [千克/立方米] 生态细无机质
EpiPAR_sg: [摩尔光子/平方米·日] 海草上方光照强度
eta: [米] 海面高度
FineSed: [千克/立方米] 细颗粒沉积物
Fluorescence: [毫克叶绿素a/立方米] 模拟荧光值
HCO3: [毫摩尔/立方米] 碳酸氢根离子
Kd_490: [米⁻¹] 490nm处垂直衰减系数
MA_N: [克氮/平方米] 大型藻类氮
MA_N_pr: [毫克氮/平方米·日] 大型藻类净生产力
month_EpiPAR_sg: [摩尔光子/平方米] 海草上方月度累计光照剂量
MPB_Chl: [毫克叶绿素/立方米] 底栖微藻叶绿素
MPB_N: [毫克氮/立方米] 底栖微藻氮
Mud-carbonate: [千克/立方米] 碳酸盐泥
Mud-mineral: [千克/立方米] 矿物泥
Nfix: [毫克氮/立方米·秒⁻¹] 固氮速率
NH4: [毫克氮/立方米] 氨氮
NO3: [毫克氮/立方米] 硝酸盐
omega_ar: [无量纲] 文石饱和度
Oxy_sat: [%] 氧气饱和度百分比
Oxygen: [毫克氧/立方米] 溶解氧
P_Prod: [毫克碳/立方米·日] 浮游植物总生产力
PAR: [摩尔光子/平方米·秒] 层内平均光合有效辐射
PAR_z: [摩尔光子/平方米·秒] 层顶下行光合有效辐射
pco2surf: [ppmv] 海洋表层pCO2（ppmv）
PH: [log(毫摩尔)] 酸碱度
PhyL_Chl: [毫克叶绿素/立方米] 大型浮游植物叶绿素
PhyL_N: [毫克氮/立方米] 大型浮游植物氮
PhyS_Chl: [毫克叶绿素/立方米] 小型浮游植物叶绿素
PhyS_N: [毫克氮/立方米] 小型浮游植物氮
PhyS_NR: [毫克氮/立方米] 小型浮游植物氮储备
PIP: [毫克磷/立方米] 颗粒无机磷
R_400: [球面度⁻¹] 400nm处遥感反射率
R_410: [球面度⁻¹] 410nm处遥感反射率
R_412: [球面度⁻¹] 412nm处遥感反射率
R_443: [球面度⁻¹] 443nm处遥感反射率
R_470: [球面度⁻¹] 470nm处遥感反射率
R_486: [球面度⁻¹] 486nm处遥感反射率
R_488: [球面度⁻¹] 488nm处遥感反射率
R_490: [球面度⁻¹] 490nm处遥感反射率
R_510: [球面度⁻¹] 510nm处遥感反射率
R_531: [球面度⁻¹] 531nm处遥感反射率
R_547: [球面度⁻¹] 547nm处遥感反射率
R_551: [球面度⁻¹] 551nm处遥感反射率
R_555: [球面度⁻¹] 555nm处遥感反射率
R_560: [球面度⁻¹] 560nm处遥感反射率
R_590: [球面度⁻¹] 590nm处遥感反射率
R_620: [球面度⁻¹] 620nm处遥感反射率
R_640: [球面度⁻¹] 640nm处遥感反射率
R_645: [球面度⁻¹] 645nm处遥感反射率
R_665: [球面度⁻¹] 665nm处遥感反射率
R_667: [球面度⁻¹] 667nm处遥感反射率
R_671: [球面度⁻¹] 671nm处遥感反射率
R_673: [球面度⁻¹] 673nm处遥感反射率
R_678: [球面度⁻¹] 678nm处遥感反射率
R_681: [球面度⁻¹] 681nm处遥感反射率
R_709: [球面度⁻¹] 709nm处遥感反射率
R_745: [球面度⁻¹] 745nm处遥感反射率
R_748: [球面度⁻¹] 748nm处遥感反射率
R_754: [球面度⁻¹] 754nm处遥感反射率
R_761: [球面度⁻¹] 761nm处遥感反射率
R_764: [球面度⁻¹] 764nm处遥感反射率
R_767: [球面度⁻¹] 767nm处遥感反射率
R_778: [球面度⁻¹] 778nm处遥感反射率
salt: [PSU] 盐度
Secchi: [米] 基于488nm计算的赛氏盘深度
SG_N: [克氮/平方米] 海草氮
SG_N_pr: [毫克氮/平方米·日] 海草净生产力
SG_shear_mort: [克氮/平方米·日] 海草剪切死亡率
SGD_N: [克氮/平方米] 深水海草氮
SGD_N_pr: [毫克氮/平方米·日] 深水海草净生产力
SGD_shear_mort: [克氮/平方米·日] 深水海草剪切死亡率
SGH_N: [克氮/平方米] 喜盐草属氮
SGH_N_pr: [毫克氮/平方米·日] 喜盐草属净生产力
SGHROOT_N: [克氮/平方米] 喜盐草属根系氮
SGROOT_N: [克氮/平方米] 海草根系氮
TC: [毫克碳/立方米] 总碳
temp: [摄氏度] 水温
TN: [毫克氮/立方米] 总氮
TP: [毫克磷/立方米] 总磷
Tricho_Chl: [毫克叶绿素/立方米] 束毛藻叶绿素
Tricho_N: [毫克氮/立方米] 束毛藻氮
TSSM: [克总悬浮固体/立方米] 基于645nm计算的总悬浮固体（Petus等人，2014）
Z_grazing: [毫克碳/立方米·日] 浮游动物总摄食率
Zenith2D: [弧度] 太阳天顶角
ZooL_N: [毫克氮/立方米] 大型浮游动物氮
ZooS_N: [毫克氮/立方米] 小型浮游动物氮
z: [米] Z坐标（深度）
time: [自1990-01-01 00:00:00 +10起的日数] 时间
latitude: [北纬度数] 纬度（地理投影）
longitude: [东经度数] 经度（地理投影）

深度维度：
本数据集包含原始数据集的部分深度层级，深度由维度“k”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：
本数据集基于时空模型构建，仅为环境条件的估算值，未采用原位实测数据。针对BGC v3.1模型的技术评估显示，模型的绝对精度随变量与区域存在显著差异。因此，使用时需确保模型符合应用场景需求，且一般建议将BGC模型结果与其他数据源结合使用以形成决策建议。
本数据集针对BGC v3.1模型设计的情景，最初用于对比流域径流影响，模拟采用了历史气象与河流边界条件，但沉积物与营养盐负荷基于2019年源流域模型的结果。该流域模型中，模拟时段内土地利用状态保持静态。以基准情景为例，其采用2019年的土地利用估算值覆盖整个模拟时段（2010-2019年），这意味着土地管理措施的改善被回溯至模拟起始年份（2010年），导致模拟早期年份的营养盐与沉积物负荷略低于实际值。BGC模型团队指出，该处理方式在土地管理措施改善显著的区域可能引入小幅额外误差，但该误差小于模型本身的固有误差。上述误差仅在将基准情景数据解释为历史条件估算值时生效，而非该情景最初用于对比的初衷。

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

更新日志：
2020-08-18（版本1）：系统与数据集首次上线，数据集及服务覆盖2010-2019年的GBR4 BGC v3.1数据。
2025-05-16（版本1）：优化元数据标题与摘要以更精准描述数据集，新增数字对象标识符（DOI）及数据集引用规范。