2023
DOI: 10.22541/au.168105782.23479951/v1
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Benchmarking Hydrological Models for an Uncertain Future

Abstract: This commentary discusses a framework for the benchmarking of hydrological models for different purposes when the datasets for different catchments might involve epistemic uncertainties. The approach might be expected to result in an ensemble of models that might be used in prediction (including models of different types) but also provides for model rejection to be the start of a learning process to improve understanding.

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Cited by 2 publications
(2 citation statements)
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“…A detailed overview of the limitations of aspects of the national scale models is given in Hannaford et al (2023). More philosophical issues such as model calibration equifinality (multiple models or parameter sets that yield equally acceptable validation results (Beven, 2008)) and treatment of uncertainty are less easily resolved. • Stochastic datasets not covering all system-critical droughts for some areas or potentially not providing a sufficient set of diverse future droughts appropriate for long-term strategic planning.…”
Section: Barriers To Progressmentioning
confidence: 99%
“…A detailed overview of the limitations of aspects of the national scale models is given in Hannaford et al (2023). More philosophical issues such as model calibration equifinality (multiple models or parameter sets that yield equally acceptable validation results (Beven, 2008)) and treatment of uncertainty are less easily resolved. • Stochastic datasets not covering all system-critical droughts for some areas or potentially not providing a sufficient set of diverse future droughts appropriate for long-term strategic planning.…”
Section: Barriers To Progressmentioning
confidence: 99%
“…Beven 2009) that underlies any numerical model. Depending on the process, the mental model is not, or less, influenced by limitations posed by data and technology and is more of an 'ideal type' than an actual model, thoughKrueger et al (2016) argue that technological possibilities of what can be modelled may already co-shape what can be imagined.…”
mentioning
confidence: 99%