Namoi Ecological expert elicitation and receptor impact models v01

## **Abstract** \n\nThe dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.\n\n\t\n\nReceptor impact models (RIMs) use inputs from surface water and groundwater models. For a given node, there is a value for each combination of hydrological response variable, future, and replicate or run number. RIMs are developed for specific landscape classes. The hydrological response variables that a RIM within a landscape class requires are organised by the R script RIM_Prediction_CreateArray.R into an array. The formatted data is available as an R data file format called RDS and can be read directly into R. The R script IMIA_NAM_RIM_predictions.R applies the receptor model functions (RDS object as part of Data set 1: Ecological expert elicitation and receptor impact models for the NAM subregion) to the HRV array for each landscape class (or landscape group) to make predictions of receptor impact varibles (RIVs). Predictions of a receptor impact from a RIM for a landscape class are summarised at relevant AUIDs by the 5th through to the 95th percentiles (in 5% increments) for baseline and CRDP futures. These are available in the NAM_RIV_quantiles_IMIA.csv data set. RIV predictions are further summarised and compared as boxplots (using the R script boxplotsbyfutureperiod.R) and as (aggregated) spatial risk maps using GIS.\n\n## **Dataset History** \n\nReceptor impact models (RIMs) are developed for specific landscape classes. The hydrological response variables that a RIM within a landscape class requires are organised by the R script RIM_Prediction_CreateArray.R into an array. The formatted data is available as an R data file format called RDS and can be read directly into R. \n\nThe R script IMIA_NAM_RIM_predictions.R applies the receptor model functions (RDS object as part of Data set 1: Ecological expert elicitation and receptor impact models for the NAM subregion) to the HRV array for each landscape class (or landscape group) to make predictions of receptor impact varibles (RIVs). Predictions of a receptor impact from a RIM for a landscape class are summarised at relevant AUIDs by the 5th through to the 95th percentiles (in 5% increments) for baseline and CRDP futures. These are available in the NAM_RIV_quantiles_IMIA.csv data set. RIV predictions are further summarised and compared as boxplots (using the R script boxplotsbyfutureperiod.R) and as (aggregated) spatial risk maps using GIS.\n\n## **Dataset Citation** \n\nBioregional Assessment Programme (2018) Namoi Ecological expert elicitation and receptor impact models v01. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/487a471c-7fa3-4313-871d-e048b4f4c2b4.\n\n## **Dataset Ancestors** \n\n* **Derived From** [Landscape classification of the Namoi preliminary assessment extent](https://data.gov.au/data/dataset/360c39e5-1225-401d-930b-f5462fdb8005)\n\n* **Derived From** [Namoi CMA Groundwater Dependent Ecosystems](https://data.gov.au/data/dataset/a3e21ec4-ae53-4222-b06c-0dc2ad9838a8)\n\n* **Derived From** [National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)](https://data.gov.au/data/dataset/6dbaee0d-8813-46b1-9c13-1b796e7ed3bf)\n\n* **Derived From** [Border Rivers Gwydir / Namoi Regional Native Vegetation Map Version 2.0. VIS_ID 4204](https://data.gov.au/data/dataset/b3ca03dc-ed6e-4fdd-82ca-e9406a6ad74a)\n\n* **Derived From** [Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014](https://data.gov.au/data/dataset/6f72f73c-8a61-4ae9-b8b5-3f67ec918826)\n\n* **Derived From** [Murray-Darling Basin Aquatic Ecosystem Classification](https://data.gov.au/data/dataset/a854a25c-8820-455c-9462-8bd39ca8b9d6)\n\n## **摘要** 本数据集由生物区域评估计划（Bioregional Assessment Programme）基于多源数据集衍生而来，本元数据声明的“谱系（Lineage）”字段已标注所用源数据集，生成该衍生数据集的具体流程则记载于本元数据声明的“历史（History）”字段中。 受体影响模型（Receptor Impact Models, RIMs）需依赖地表水与地下水模型的输入数据。对于指定节点，水文响应变量、未来情景与重复试验/运行编号的每一种组合均对应一个数值。RIMs针对特定景观类别开发，某一景观类别下的RIM所需的水文响应变量（hydrological response variable, HRV）由R脚本`RIM_Prediction_CreateArray.R`整理为数组。格式化后的数据以名为RDS的R数据文件格式存储，可直接读取至R环境中。R脚本`IMIA_NAM_RIM_predictions.R`将受体模型函数（RDS对象隶属于数据集1：NAM亚区生态专家征询与受体影响模型）应用于每个景观类别（或景观组）的HRV数组，以生成受体影响变量（receptor impact variables, RIVs）的预测结果。针对某一景观类别的RIM所得到的受体影响预测结果，会基于基准情景与CRDP未来情景，在对应的AUID处按5%递增区间从第5百分位至第95百分位进行汇总。此类汇总结果存储于`NAM_RIV_quantiles_IMIA.csv`数据集中。RIV预测结果还会进一步被汇总并以箱线图（通过R脚本`boxplotsbyfutureperiod.R`生成）以及地理信息系统（GIS）绘制的（聚合式）空间风险地图的形式进行对比展示。 ## **数据集历史** 受体影响模型（Receptor Impact Models, RIMs）针对特定景观类别开发。某一景观类别下的RIM所需的水文响应变量（hydrological response variable, HRV）由R脚本`RIM_Prediction_CreateArray.R`整理为数组。格式化后的数据以名为RDS的R数据文件格式存储，可直接读取至R环境中。 R脚本`IMIA_NAM_RIM_predictions.R`将受体模型函数（RDS对象隶属于数据集1：NAM亚区生态专家征询与受体影响模型）应用于每个景观类别（或景观组）的HRV数组，以生成受体影响变量（receptor impact variables, RIVs）的预测结果。针对某一景观类别的RIM所得到的受体影响预测结果，会基于基准情景与CRDP未来情景，在对应的AUID处按5%递增区间从第5百分位至第95百分位进行汇总。此类汇总结果存储于`NAM_RIV_quantiles_IMIA.csv`数据集中。RIV预测结果还会进一步被汇总并以箱线图（通过R脚本`boxplotsbyfutureperiod.R`生成）以及地理信息系统（GIS）绘制的（聚合式）空间风险地图的形式进行对比展示。 ## **数据集引用** 生物区域评估计划（2018）《Namoi生态专家征询与受体影响模型 v01》，生物区域评估衍生数据集。查阅日期：2018年12月11日，http://data.bioregionalassessments.gov.au/dataset/487a471c-7fa3-4313-871d-e048b4f4c2b4. ## **数据集溯源** * **衍生自** [Namoi初步评估范围景观分类](https://data.gov.au/data/dataset/360c39e5-1225-401d-930b-f5462fdb8005) * **衍生自** [Namoi CMA地下水依赖生态系统](https://data.gov.au/data/dataset/a3e21ec4-ae53-4222-b06c-0dc2ad9838a8) * **衍生自** [国家地下水依赖生态系统（Groundwater Dependent Ecosystems, GDE）图集（含西澳地区）](https://data.gov.au/data/dataset/6dbaee0d-8813-46b1-9c13-1b796e7ed3bf) * **衍生自** [Border Rivers吉尔德/纳莫伊区域原生植被地图 2.0版 VIS_ID 4204](https://data.gov.au/data/dataset/b3ca03dc-ed6e-4fdd-82ca-e9406a6ad74a) * **衍生自** [生物区域评估计划 澳大利亚集水区尺度土地利用 - 2014](https://data.gov.au/data/dataset/6f72f73c-8a61-4ae9-b8b5-3f67ec918826) * **衍生自** [墨累-达令盆地水生生态系统分类](https://data.gov.au/data/dataset/a854a25c-8820-455c-9462-8bd39ca8b9d6)