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Towards a methodology for testing models as hypotheses in the inexact sciences

This paper reviews the issues involved in treating hydrology as an example of an inexact science faced with significant epistemic uncertainties. It proposes a novel method for developing limits of acceptability for testing hydrological models as hypotheses about how a catchment hydrological system m...

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Detalles Bibliográficos
Autor principal: Beven, Keith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501651/
https://www.ncbi.nlm.nih.gov/pubmed/31105459
http://dx.doi.org/10.1098/rspa.2018.0862
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author Beven, Keith
author_facet Beven, Keith
author_sort Beven, Keith
collection PubMed
description This paper reviews the issues involved in treating hydrology as an example of an inexact science faced with significant epistemic uncertainties. It proposes a novel method for developing limits of acceptability for testing hydrological models as hypotheses about how a catchment hydrological system might function. The approach is based only on an analysis of the available observations and the consideration of event mass balance for successive rainfall-runoff events. It is shown that there are many events that are subject to epistemic uncertainties in the input data so that mass balance is not satisfied. The proposed approach allows taking these epistemic uncertainties into account in a pragmatic way before any model runs are made. It is an approach that might be applicable in other areas of environmental science where similar basic principles are fundamental to models, but which might not be satisfied by the observations that are used for model evaluation.
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spelling pubmed-65016512019-05-18 Towards a methodology for testing models as hypotheses in the inexact sciences Beven, Keith Proc Math Phys Eng Sci Perspectives This paper reviews the issues involved in treating hydrology as an example of an inexact science faced with significant epistemic uncertainties. It proposes a novel method for developing limits of acceptability for testing hydrological models as hypotheses about how a catchment hydrological system might function. The approach is based only on an analysis of the available observations and the consideration of event mass balance for successive rainfall-runoff events. It is shown that there are many events that are subject to epistemic uncertainties in the input data so that mass balance is not satisfied. The proposed approach allows taking these epistemic uncertainties into account in a pragmatic way before any model runs are made. It is an approach that might be applicable in other areas of environmental science where similar basic principles are fundamental to models, but which might not be satisfied by the observations that are used for model evaluation. The Royal Society Publishing 2019-04 2019-04-24 /pmc/articles/PMC6501651/ /pubmed/31105459 http://dx.doi.org/10.1098/rspa.2018.0862 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Perspectives
Beven, Keith
Towards a methodology for testing models as hypotheses in the inexact sciences
title Towards a methodology for testing models as hypotheses in the inexact sciences
title_full Towards a methodology for testing models as hypotheses in the inexact sciences
title_fullStr Towards a methodology for testing models as hypotheses in the inexact sciences
title_full_unstemmed Towards a methodology for testing models as hypotheses in the inexact sciences
title_short Towards a methodology for testing models as hypotheses in the inexact sciences
title_sort towards a methodology for testing models as hypotheses in the inexact sciences
topic Perspectives
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501651/
https://www.ncbi.nlm.nih.gov/pubmed/31105459
http://dx.doi.org/10.1098/rspa.2018.0862
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