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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 8 | Copyright

Special issue: Statistical methods for hydrological applications

Hydrol. Earth Syst. Sci., 17, 3171-3187, 2013
https://doi.org/10.5194/hess-17-3171-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Aug 2013

Research article | 06 Aug 2013

Data compression to define information content of hydrological time series

S. V. Weijs1, N. van de Giesen2, and M. B. Parlange1 S. V. Weijs et al.
  • 1School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Station 2, 1015 Lausanne, Switzerland
  • 2Water resources management, Delft University of Technology, Stevinweg 1, P.O. Box 5048, 2600 GA Delft, The Netherlands

Abstract. When inferring models from hydrological data or calibrating hydrological models, we are interested in the information content of those data to quantify how much can potentially be learned from them. In this work we take a perspective from (algorithmic) information theory, (A)IT, to discuss some underlying issues regarding this question. In the information-theoretical framework, there is a strong link between information content and data compression. We exploit this by using data compression performance as a time series analysis tool and highlight the analogy to information content, prediction and learning (understanding is compression). The analysis is performed on time series of a set of catchments.

We discuss both the deeper foundation from algorithmic information theory, some practical results and the inherent difficulties in answering the following question: "How much information is contained in this data set?".

The conclusion is that the answer to this question can only be given once the following counter-questions have been answered: (1) information about which unknown quantities? and (2) what is your current state of knowledge/beliefs about those quantities?

Quantifying information content of hydrological data is closely linked to the question of separating aleatoric and epistemic uncertainty and quantifying maximum possible model performance, as addressed in the current hydrological literature. The AIT perspective teaches us that it is impossible to answer this question objectively without specifying prior beliefs.

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