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  • Data compression to define information content of hydrological time series.

    Author(s) : Weijs, S. V.Giesen, N. van deParlange, M. B.

    Author Affiliation : School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique F'ed'erale de Lausanne, Station 2, 1015 Lausanne, Switzerland.

    Author Email : steven.weijs@epfl.ch

    Journal article : Hydrology and Earth System Sciences 2013 Vol.17 No.8 pp.3171-3187 ref.48

    Abstract : When inferring modelsmodelsSubject Category: Techniques, Methodologies and Equipment
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    from hydrological datahydrological dataSubject Category: Miscellaneous
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    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 compressioncompressionSubject Category: Techniques, Methodologies and Equipment
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    . We exploit this by using data compression performance as a time seriestime seriesSubject Category: Miscellaneous
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    analysisanalysisSubject Category: Techniques, Methodologies and Equipment
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    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 uncertaintyuncertaintySubject Category: Properties
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    and quantifying maximum possible model performance, as addressed in the current hydrological literatureliteratureSubject Category: Publications
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    . The AIT perspective teaches us that it is impossible to answer this question objectively without specifying prior beliefs.

    ISSN : 1027-5606

    DOI : 10.5194/hess-17-3171-2013

    Record Number : 20133329278

    Publisher : Copernicus Gesellschaft mbH

    Location of publication : Gottingen

    Country of publication : Germany

    Language of text : English

    Indexing terms for this abstract:

    Descriptor(s) : analysisanalysisSubject Category: Techniques, Methodologies and Equipment
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    , catchment hydrologycatchment hydrologySubject Category: Miscellaneous
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    , compressioncompressionSubject Category: Techniques, Methodologies and Equipment
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    , hydrological datahydrological dataSubject Category: Miscellaneous
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    , hydrologyhydrologySubject Category: Miscellaneous
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    , literatureliteratureSubject Category: Publications
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    , modelsmodelsSubject Category: Techniques, Methodologies and Equipment
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    , time seriestime seriesSubject Category: Miscellaneous
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    , uncertaintyuncertaintySubject Category: Properties
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    , watershedswatershedsSubject Category: Topographic Features
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    Identifier(s) : catchment areas, uncertainties