Articles | Volume 23, issue 4
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, Fabrizio Fenicia, and Peter Reichert

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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,, 2004.
Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89,, 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,, 2001.
Bertuzzo, E., Thomet, M., Botter, G., and Rinaldo, A.: Catchment-scale herbicides transport: Theory and application, Adv. Water Resour., 52, 232–242,, 2013.
Beven, K. and Westerberg, I.: On red herrings and real herrings: disinformation and information in hydrological inference, Hydrol. Process., 25, 1676–1680,, 2011.
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.