Author(s) :
Weijs, S. V.
;
Giesen, N. van de
;
Parlange, 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
see more details from
hydrological datahydrological dataSubject Category: Miscellaneous
see more details 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
see more details. We exploit this by using data compression performance as a
time seriestime seriesSubject Category: Miscellaneous
see more details analysisanalysisSubject Category: Techniques, Methodologies and Equipment
see more details 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
see more details and quantifying maximum possible model performance, as addressed in the current hydrological
literatureliteratureSubject Category: Publications
see more details. 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
see more details, catchment hydrologycatchment hydrologySubject Category: Miscellaneous
see more details, compressioncompressionSubject Category: Techniques, Methodologies and Equipment
see more details, hydrological datahydrological dataSubject Category: Miscellaneous
see more details, hydrologyhydrologySubject Category: Miscellaneous
see more details, literatureliteratureSubject Category: Publications
see more details, modelsmodelsSubject Category: Techniques, Methodologies and Equipment
see more details, time seriestime seriesSubject Category: Miscellaneous
see more details, uncertaintyuncertaintySubject Category: Properties
see more details, watershedswatershedsSubject Category: Topographic Features
see more details
Identifier(s) :
catchment areas, uncertainties