Home > Research > Publications & Outputs > On (in)validating environmental models. 2. Impl...

Electronic data

  • Hydrological Processes - 2022 - Beven - On in validating environmental models 2 Implementation of a Turing‐like Test to

    Rights statement: 12m

    Accepted author manuscript, 1.57 MB, PDF document

    Embargo ends: 1/01/50

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

On (in)validating environmental models. 2. Implementation of a Turing‐like Test to modelling hydrological processes

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Forthcoming
<mark>Journal publication date</mark>19/09/2022
<mark>Journal</mark>Hydrological Processes
Publication StatusAccepted/In press
<mark>Original language</mark>English

Abstract

Part 1 of this study discussed the concept of using a form of Turing-like Test for model evaluation, together with eight principles for implementing such an approach. In this part, the framing of fitness-for-purpose as a Turing-like Test is discussed, together with an example application of trying to assess whether a rainfall-runoff model might be an adequate representation of the discharge response in a catchment for predicting future natural flood management scenarios. It is shown that the variation between event runoff coefficients in the record can be used to create some limits of acceptability that implicitly take some account of the epistemic uncertainties arising from lack of knowledge about errors in rainfall and discharge observations. In the case study it is demonstrated that the model used cannot be validated in this way across all the range of observed discharges, but that behavioural models can be found for the peak flows that are the subject of interest in the application. Thinking in terms of the Turing-like Test focusses attention on the critical observations needed to test whether streamflow is being produced in the right way so that a model is considered as fit-for-purpose in predicting the impacts of future change scenarios. As is the case for uncertainty estimation in general, it is argued that the assumptions made in setting behavioural limits of acceptability should be stated explicitly to leave an audit trail in any application that can be reviewed by users of the model outputs.