On hypothesis testing in hydrology : why falsification of models is still a really good idea

Beven, Keith John (2018) On hypothesis testing in hydrology : why falsification of models is still a really good idea. WIREs WATER, 5 (3): e1278. ISSN 2049-1948

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This opinion piece argues that in respect of testing models as hypotheses about how catchments function, there is no existing methodology that adequately deals with the potential for epistemic uncertainties about data and hydrological processes in the modelling processes. A rejectionist framework is suggested as a way ahead, wherein assessments of uncertainties in the input and evaluation data are used to define limits of acceptability prior to any model simulations being made. The limits of acceptability might also depend on the purpose of the modelling so that we can be more rigorous about whether a model is actually fit-for-purpose. Different model structures and parameter sets can be evaluated in this framework, albeit that subjective elements necessarily remain, given the epistemic nature of the uncertainties in the modelling process. One of the most effective ways of reducing the impacts of epistemic uncertainties, and allow more rigorous hypothesis testing, would be to commission better observational methods. Model rejection is a good thing in that it requires us to be better, resulting in advancement of the science.

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This is the peer reviewed version of the following article: Beven KJ. On hypothesis testing in hydrology: Why falsification of models is still a really good idea. WIREs Water. 2018;5:e1278. https://doi.org/10.1002/wat2.1278 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/wat2.1278 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords:
?? data uncertaintyexploratory hydrologymodel evaluationmodel rejection observation techniqueswater science and technologybiochemistryaquatic sciencegeography, planning and developmentdiscipline-based research ??
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23 Jan 2018 08:52
Last Modified:
19 Apr 2024 01:44