Method of last resort (MOLR) long term average annual recharge estimates

dataset: MOLR_RECHARGE
This dataset (and the derivative 95% confidence interval for upper and lower datasets) were created by CSIRO and provides a spatial coverage of estimates of the long term average annual recharge across Victoria. It is based upon regression equations between soil order, vegetation type and long term average annual rainfall. More details on the method used to estimate this dataset are provided in the report: Leaney et al (2011) Recharge and discharge estimation in data poor areas: Scientific reference guide. CSIRO: Water for a Healthy Country National Research Flagship.
 
Citation proposal Citation proposal

(2013)

Method of last resort (MOLR) long term average annual recharge estimates

Commonwealth Scientific and Industrial Research Organisation

Description

Title
Method of last resort (MOLR) long term average annual recharge estimates 
Alternate title
MOLR_RECHARGE 
Supplemental Information
Related Documents: None See Geoscience Australia metadata record at: https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&catno=71607 
Status
Completed 
 
 

Spatial

Code
4283 
 
 

Maintenance

Maintenance and update frequency
Not planned 
 
 

Format

Title
DIGITAL: ESRI GRID, ESRI Shapefile 3 
 
 

Contacts

  Point of contact

Cited responsible party

No information provided.

Cited responsible party

No information provided.

Cited responsible party

No information provided.

Cited responsible party

No information provided.
 
 

Keywords

Topic category
  • Inland waters
  • Geoscientific information
 
 

Constraints

Use limitation
General 
Classification
Unclassified 
License type
DELWP Data License 
Website
 

License Text

License type
Restricted 
 
 

Lineage

Statement
Dataset Source: This dataset has been extracted for Victoria from a database of approx. 4400 recharge and/or deep drainage estimates collated from 172 studies. Crosbie et al. (2010B) used a sub-set of data from this database to determine whether simple empirical relationships could be found that relate groundwater recharge to nationally available datasets and hence whether they can be used to estimate recharge in data poor areas in a scientifically defensible way. It was found that vegetation and soil type were critical determinants in forming relationships between average annual rainfall and average annual recharge, whereas climate zones (Köppen-Geiger climate classification and aridity index) and surface geology (lithology) were not found to be significant determinants. The MOLR further simplified the relationships developed by Crosbie et al. (2010B) by combining the perennial and tree vegetation types due to a lack of data under these vegetation types. The soils groupings used by Crosbie et al. (2010B) have been retained for the MOLR, these are: Vertosols (VE); Calcarosols (CA), Chromosols (CH), Kurosols (KU) and Sodosols (SO); Podosols (PO); Rudosols (RU), Kandasols (KA) and Tenosols (TE); Ferrosols (FE), Dermosols (DE), Hydrosols (HY) and Organosols (OR). No estimate of recharge is possible using the MOLR from the last soils group (FE,DE,HY,OR) due to a lack of field studies required to develop the relationships. The relationships that were developed between recharge and mean annual rainfall, soil order and vegetation type used a two parameter regression model R =10^aP+b where a and b are the fitting parameters from a least squares regression between annual average rainfall (P) and the logarithm of annual average recharge (R). For more information on the relationship observed in the outputs from average annual rainfall and average annual recharge for the combination of soil and vegetation groups, the reader is referred to Section 2.3.3 in the document: http://www.clw.csiro.au/publications/waterforahealthycountry/2011/wfhc-recharge-discharge-scientific-guide.pdf. Dataset Originality: Derived 
Description
Collection Method: See above 
Description
Source description not available 
Description
See above 
 
 

Metadata Constraints Metadata Constraints

Classification
Unclassified 
 
 

Quality

Attribute Quality
Comments
Not Known 
 
Positional Accuracy
Comments
Not Known 
 
Conceptual Consistency
Comments
Not Known 
 
Missing Data
Comments
Victoria 
 
Excess Data
 
 

Overviews

Graphic Overview of Data Footprint


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