Exploring Model Defects Using Linear Analysis: A GMDSI worked example report

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Date
2021-02
Authors
Nicol, Chris
Doherty, John
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Publisher
National Centre for Groundwater Research and Training, Flinders University
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© 2021 Flinders University
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Abstract
The Groundwater Modelling Decision Support Initiative (GMDSI) is an industry-funded and industry-aligned project focused on improving the role that groundwater modelling plays in supporting environmental management and decision-making. Over the life of the project, it will document a number of examples of decision-support groundwater modelling. These documented worked examples will attempt to demonstrate that by following the scientific method, and by employing modern, computer-based approaches to data assimilation, the uncertainties associated with groundwater model predictions can be both quantified and reduced. With realistic confidence intervals associated with predictions of management interest, the risks associated with different courses of management action can be properly assessed before critical decisions are made. In this GMDSI worked example report, we demonstrate how linear analysis can be used to explore whether a groundwater model can indeed be useful while being wrong, and under what circumstances it can actually be described as being "fit for purpose". However, before doing this, we explore the metrics on which these descriptions must rest.
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© 2021 Flinders University. This work is copyright. Apart from any use permitted under the Copyright Act 1968, no part may be reproduced by any process, nor may any other exclusive rights be exercised, without the permission of Flinders University, GPO Box 2100, Adelaide 5001, South Australia.
Keywords
Groundwater modelling, Data assimilation, Linear analysis, Aquifers, Groundwater management, Great Artesian Basin
Citation
Nicol, C. and Doherty J. (2021). Exploring Model Defects Using Linear Analysis: A GMDSI worked example report. National Centre for Groundwater Research and Training, Flinders University, South Australia