Knowledge-theoretic models in hydrology

Détails

ID Serval
serval:BIB_772B276E400D
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Knowledge-theoretic models in hydrology
Périodique
PROGRESS IN PHYSICAL GEOGRAPHY
Auteur⸱e⸱s
Odoni Nicholas A., Lane Stuart N.
ISSN
0309-1333
Statut éditorial
Publié
Date de publication
04/2010
Volume
34
Numéro
2
Pages
151-171
Notes
ISI:000276157000002
Résumé
The rate of progress in quantitative modelling since the 1950s has been
such that application of sophisticated computer models to a wide range
of geoscientific problems is now routine. It is generally held that by
making such models more physically (physics) based, their explanatory
power and predictive reliability are enhanced. This formulation, a
model-theoretic approach, assumes accurate knowledge of the properties,
states and relationships between all of the objects that are known to
matter within the system of interest but, simultaneously, an incomplete
understanding of the totality that this knowledge creates. In
hydrological modelling, this translates into a severe dependence upon
the data models that are needed to make a hydrological model work. The
opposite extreme is a model-data approach in which measurements become
the basis of generic relationships. Even in the most heavily
data-derived cases (eg, neural network forecasting of river flows)
these data models can be shown implicitly to have a theoretical
content. Thus, both model-theoretic and model-data approaches sit
within a general class of modelling, best labelled as `data-theoretic'.
Here, we illustrate this point and advocate an approach that is
knowledge-theoretic rather than data-theoretic, to capture the much
richer sources of knowledge available to the modeller. These sources
include third-party reports, personal recollections and diaries, old
photographs and press articles, opinions, etc, which are, by
convention, either excluded from analysis, or simply added into
descriptions of model results at the point of dissemination and
consultation of model findings. We conclude by noting that this
approach to hydrological modelling fits into current thinking that the
process by which publics engage with knowledge must be moved upstream.
Here, the production of scientific knowledge comes to include not just
scientists and specialists, but also those people for whom model
predictions make a material difference.
Web of science
Création de la notice
03/02/2011 14:40
Dernière modification de la notice
20/08/2019 14:34
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