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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 23, issue 4
Hydrol. Earth Syst. Sci., 23, 2147–2172, 2019
https://doi.org/10.5194/hess-23-2147-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 2147–2172, 2019
https://doi.org/10.5194/hess-23-2147-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Apr 2019

Research article | 30 Apr 2019

A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation

Lorenz Ammann et al.

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Cited articles

Baker, D. B., Richards, R. P., Loftus, T. T., and Kramer, J. W.: A new flashiness index: characteristics and applications to midwestern rivers and streams, J. Am. Water Resour. As., 40, 503–522, https://doi.org/10.1111/j.1752-1688.2004.tb01046.x, 2004.
Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, https://doi.org/10.5194/hess-12-77-2008, 2008.
Bates, B. C. and Campbell, E. P.: A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall-runoff modeling, Water Resour. Res., 37, 937–947, https://doi.org/10.1029/2000wr900363, 2001.
Bertuzzo, E., Thomet, M., Botter, G., and Rinaldo, A.: Catchment-scale herbicides transport: Theory and application, Adv. Water Resour., 52, 232–242, https://doi.org/10.1016/j.advwatres.2012.11.007, 2013.
Beven, K. and Westerberg, I.: On red herrings and real herrings: disinformation and information in hydrological inference, Hydrol. Process., 25, 1676–1680, https://doi.org/10.1002/hyp.7963, 2011.
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Short summary
The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
The uncertainty of hydrological models can be substantial, and its quantification and realistic...
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