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Water Modelling-Modelled Data-Annual Permitted Take (APT)-Murrumbidgee

Data and Resources

Additional Info

Identifier
Description Annual permitted take (APT) is a critical component of sustainable resource management, balancing the need for water resource utilisation with the preservation of ecosystems. It is a crucial mechanism for ensuring the long-term annual sustainable diversion limits (SDLs) set under the Murray-Darling Basin Plan are not exceeded, and that enough water is available for the environment. APT is the maximum amount of water permitted to be taken for consumptive purposes each year, and has been enforced
S3 object key
Data Custodian NSW Department of Climate Change, Energy, the Environment and Water
Point of Contact water.dataproducts@dpie.nsw.gov.au
Access Rights Open
Security Classification UNOFFICIAL
Date Modified 2024-02-26T13:25:02.023909
Access URL https://datasets.seed.nsw.gov.au/dataset/water-modelling-modelled-data-annual-permitted-take-apt-murrumbidgee
Sensitive Data
File size
Legal Authority
Disposal
Publisher NSW Department of Climate Change, Energy, the Environment and Water
Keyword WATER,WATER-Surface
Resource Type Dataset
Temporal coverage from 1895-01-01
Temporal coverage to 2022-06-30
Update Frequency Annual
Publish Date Missing
Purpose The data set provided contains flows at several gauges in each river system, as simulated by the annually extended APT model. Notwithstanding the model’s inherent limitations, these are a fair representation of those we would expect under current conditions development and operation rules. They can be compared with flows simulated by other key scenario models, such as long-term average annual extraction limit (LTAAEL) model or without development (WOD) model.
Location non_geospatial
Language English
Licence Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Data Status
Data Source https://datasets.seed.nsw.gov.au/dataset/water-modelling-modelled-data-annual-permitted-take-apt-murrumbidgee
Dataset DOI
Spatial Coverage
syndicated_id 5849d185-0460-4d20-b9c6-42bd8a890278