Articles | Volume 23, issue 4
https://doi.org/10.5194/hess-23-2147-2019
https://doi.org/10.5194/hess-23-2147-2019
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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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.