Potential Groundwater Dependent Ecosystem (GDE) - Species Tolerance Grid

dataset: GDE_TERR_SPECIES_TOLR
Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. The output was a species tolerance grid for Victoria. Each GDE map unit has been assigned a species tolerance value based on the median of it's contributing grid cells. Refer to the Potential Terrestrial Groundwater Dependent Ecosystems metadata record for each CMA for a full acount of the data product.
 
Citation proposal Citation proposal
(2021)

Potential Groundwater Dependent Ecosystem (GDE) - Species Tolerance Grid

Department of Environment, Land, Water & Planning
 

Simple

 
 

Description

Title
Potential Groundwater Dependent Ecosystem (GDE) - Species Tolerance Grid 
Alternate title
GDE_TERR_SPECIES_TOLR 
Supplemental Information
Related Documents: None For further information, see: Dresel, P. E., White, M. and Griffioen, P. (2011). Evaluation of Potenital Groundwater Dependent Ecosystems in Victoria - Revision 1. Department of Primary Industries, Bendigo, Vic. 
Status
Completed  
 
 

Temporal

 
 

Spatial

Code
4283 
 
 

Maintenance

Maintenance and update frequency
Irregular  
 
 

Format

Title
DIGITAL: ESRI GRID, ESRI ASCII GRID, JPEG2000 2 
 
 

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Keywords

Topic category
  • Inland waters
  • Biota
  • Geoscientific information
 
 

Resource Constraints

Use limitation
General 
Classification
Unclassified  
 
 

Lineage

Statement
Dataset Source: Species tolerance attribution has been added using a model developed by Arthur Rylah Institute. For further information see report prepared by Dresel, P. E. et. al. (2011). Dataset Originality: Derived 
Description
Source description not available 
 
 

Metadata Constraints

Classification
Unclassified  
 
 

Quality

Attribute Quality
Comments
Not known 
Positional Accuracy
Comments
As accurate as the source data 
Conceptual Consistency
Comments
Not known 
Missing Data
Comments
Victoria 
Excess Data
 
 

Acquisition Info

 
 

Raster Data Details

 
 

Point Cloud Data Details

 
 

Contour Data Details

 
 

Survey Details

 
 

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