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