Articles | Volume 15, issue 10
https://doi.org/10.5194/hess-15-3123-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-15-3123-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
On the colour and spin of epistemic error (and what we might do about it)
K. Beven
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Department of Earth Sciences, Geocentrum, Uppsala University, Uppsala, 75236, Sweden
Centre for the Analysis of Time Series (CATS), London School of Economics, London, UK
P. J. Smith
Lancaster Environment Centre, Lancaster University, Lancaster, UK
A. Wood
Lancaster Environment Centre, Lancaster University, Lancaster, UK
JBA Consulting, Warrington, UK
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- Debates—The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph J. McDonnell & K. Beven 10.1002/2013WR015141
- A metric for attributing variability in modelled streamflows S. Shoaib et al. 10.1016/j.jhydrol.2016.08.050
- Stable isotope characterization of the Vermigliana catchment G. Chiogna et al. 10.1016/j.jhydrol.2013.11.052
- On (in)validating environmental models. 1. Principles for formulating a Turing‐like Test for determining when a model is fit‐for purpose K. Beven & S. Lane 10.1002/hyp.14704
- Testing probabilistic adaptive real‐time flood forecasting models P. Smith et al. 10.1111/jfr3.12055
- Creativity, Uncertainty, and Automated Model Building L. Marshall 10.1111/gwat.12552
- An epistemically uncertain walk through the rather fuzzy subject of observation and model uncertainties1 K. Beven 10.1002/hyp.14012
- Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication K. Beven 10.1080/02626667.2015.1031761
- On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes K. Beven et al. 10.1002/hyp.14703
- Separating aleatory and epistemic uncertainties: Probabilistic sewer flooding evaluation using probability box S. Sun et al. 10.1016/j.jhydrol.2011.12.027
- Diagnostic evaluation of multiple hypotheses of hydrological behaviour in a limits-of-acceptability framework for 24 UK catchments G. Coxon et al. 10.1002/hyp.10096
- What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
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