The Australian National Groundwater Data Transfer Standard

The Australian National Groundwater Data Transfer Standard

In the past year, a working group has been drafting national data standards for core groundwater data. The objective of the project is to design a set of consistent data standards and conventions to facilitate the transfer of groundwater data in the country. Work has concentrated on developing data structures to accommodate the types of groundwater data that are commonly collected in the field, such as water levels, pumping rates, geological and geophysical logging, water chemistry and construction details.

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The following agencies are represented in the working group:

The project is funded by the participating agencies as well as from the National Landcare Program.

Why define Standards?

A common language

Each of the government agencies that maintain groundwater data in Australia has independently established database structures, suited to their particular functions and priorities. Likewise, users have set up their own ways of dealing with groundwater data. A generic data transfer standard would bridge the gap between user and provider.

Less time wasted

From a user perspective, such a standard would greatly reduce the time presently required to reformat data. Instead of dealing with the multitude of formats currently output from suppliers, the user only has to deal with a single standard format. Considering that many users obtain their data from many databases this will significantly boost productivity. You can spend less time getting the data into your database and more time using the data.

What does this mean?

The possibility of misinterpretation by users would decrease if data is uniformly structured, defined and documented. For example, using a standard coordinate system for the transfer of spatial data avoids confusion in terms of the projection details, such as the central meridian, standard parallels and spheroid. Establishing conventions in data management (eg negative values for artesian head measurements, positive for water levels below the ground surface) avoids misinterpretation of data. Proper metadata procedures give the data supplier an opportunity to describe the reliability and limitations placed on the dataset.


Financial savings can be realised when common data structures allow organisations to share the development costs of support software. Data custodians and users alike will benefit from the experience and contributions of others in developing software and hardware configurations to support a standard data structure. The pooling of resources will allow the current rapid technological advances to be realised by a larger audience. Software developers can target products within a broader and better-defined market.

What is in the Standards

The data standards have a number of components, namely:

  1. A data model that describes data and how it is organised. The model consists of defining the ‘things’ (termed entities) that data is collected about, the named properties (termed attributes) of these entities and the relationships between these entities.
  2. Attribute domains that are the set of all possible values of an attribute. In the data model, attributes are classified into broad data types such as character fields, floating point numbers, integers or codes. Code-type attributes can only be populated using a domain of codes specified by the standards. This is one way of enabling the consistent and efficient transfer of data, and the generation of domains of allowable codes has been a significant part of the work to date. The standards have comprehensive lists of codes and their description for parameters such as chemical analytes, lithologies, minerals, colour and construction material.
  3. Data conventions are established for how certain data is represented. The classic quandary is whether standing water levels above the measuring point (eg artesian heads) should be reported as positive or negative values.
  4. Standard units of measurement have been defined for many parameters that are measured eg metres for depth, degrees Celsius for temperature. Multiplication factors have been compiled to provide consistency in how data is converted from other units of measurement.
  5. Feedback from users confirms the importance of data quality indicators with the dataset, to judge its appropriateness and how it should be analysed. The data model allows the opportunity for basic field measurements to be accompanied with information such as the instrumentation used, the nominal error margins, any correction factors etc.
  6. Many entities have attributes that help define the data source, so that the data user can investigate the data further.

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