| Title: |
Modelling guidelines - terminology and guiding principles
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| Resource Type: |
document --> technical publication --> journal article
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| Country: |
EU Projects
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| Year: |
2004 |
| Availability: |
Refsgaard, J. C., Henriksen, H.C. (2004) Modelling guidelines - terminology and guiding principles Advances in Water Resources 27: 71-82
http://harmoniqua.wau.nl/public/Product/papers.htm
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| Author 1/Producer: |
Refsgaard, J. C.
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| Author / Producer Type: |
EC Project
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EUGRIS Keyword(s): |
Contaminated land-->Soil and groundwater processes-->Modelling
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| Short description: |
Some scientists argue, with reference to Popper’s scientific philosophical school, that models cannot be verified or validated. Other scientists and many practitioners nevertheless use these terms, but with very different meanings. As a result of an increasing number of examples of model malpractice and mistrust to the credibility of models, several modelling guidelines are being elaborated in recent years with the aim of improving the quality of modelling studies. This gap between the views and the lack of consensus experienced in the scientific community and the strongly perceived need for commonly agreed modelling guidelines is constraining the optimal use and benefits of models. This paper proposes a framework for quality assurance guidelines, including a consistent terminology and a foundation for a methodology bridging the gap between scientific philosophy and pragmatic modelling. A distinction is made between the conceptual model, the model code and the-site specific model. A conceptual model is subject to confirmation or falsification like scientific theories. A model code may be verified within given ranges of applicability and ranges of accuracy, but it can never be universally verified. Similarly, a model may be validated, but only with reference to site-specific applications and to prespecified performance (accuracy) criteria. Thus, a model’s validity will always be limited in terms of space, time, boundary conditions and types of application. This implies a continuous interaction between manager and modeller in order to establish suitable accuracy criteria and predictions associated with uncertainty analysis.
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Submitted By:
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Dr Stefan Gödeke WhoDoesWhat?
Last update: 14/02/2006
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