A web-based platform for integrated groundwater data management
Integrated management of groundwater involves implementing policies that can achieve economic and ecologically sustainable outcomes. Developing integrated policies needs to be supported by long term reliable data of groundwater levels and factors that influence recharge and extraction rates. In Australia, hydrological and groundwater data are currently collected and managed by different agencies, and scattered in various formats and locations. The objective of the National Groundwater Information System (NGIS) project is to provide a unified framework for collecting and managing groundwater data. The implementation of the NGIS requires: (1) setting up mechanisms for acquiring and storing data, (2) developing methods and tools to examine and improve the quality of collected data, (3) developing analytical tools to support various research and management purposes, and (4) providing methods and protocols for data access and sharing among relevant stakeholders. In this paper, we present a web-based platform that aims to serve these requirements in six sites: Wellington (NSW), Namoi (NSW), Ti Tree (NT), Willunga (SA), North Stradbroke Island (QLD), and Ovens (Victoria). The platform presents an architecture (i.e. technologies and tools) and data model that can be transferred to other locations. The paper gives an overview of the system architecture and the implemented prototype version. It concludes by highlighting future directions to extend the capacity and use of the platform
S. El Sawah, A. Hicks, P. Manger, I. Athanasiadis, B. Croke, A. Jakeman, A web-based platform for integrated groundwater data management, 19th Intl Conf on Modelling and Simulation (MODSIM 2011), pg. 3141-3147, 2011, Modelling and Simulation Society of Australia and New Zealand.
You might also enjoy (View all publications)
- Introducing digital twins to agriculture
- Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate
- Machine learning for large-scale crop yield forecasting