TY - JOUR
T1 - Modelling guidelines––terminology and guiding principles
AU - Refsgaard, Jens Christian
AU - Henriksen, Hans Jørgen
N1 - Funding Information:
The present work was carried out within the Project ‘Harmonising Quality Assurance in model based catchments and river basin management (HarmoniQuA)’, which is partly funded by the EC Energy, Environment and Sustainable Development programme (Contract EVK2-CT2001-00097). The constructive comments and suggestions to the manuscript by the HarmoniQuA project team and by our colleague William (Bill) G. Harrar are acknowledged. Finally, the constructive criticisms by Keith Beven, University of Lancaster; Rodger Grayson, University of Melbourne and a third, anonymous referee helped to improve the manuscript significantly.
PY - 2004/1
Y1 - 2004/1
N2 - 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 pre-specified 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.
AB - 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 pre-specified 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.
KW - Confirmation
KW - Domain of applicability
KW - Model guidelines
KW - Scientific philosophy
KW - Uncertainty
KW - Validation
KW - Verification
UR - http://www.scopus.com/inward/record.url?scp=0348011425&partnerID=8YFLogxK
U2 - 10.1016/j.advwatres.2003.08.006
DO - 10.1016/j.advwatres.2003.08.006
M3 - Article
SN - 0309-1708
VL - 27
SP - 71
EP - 82
JO - Advances in Water Resources
JF - Advances in Water Resources
IS - 1
ER -