TY - JOUR

T1 - Data compression to define information content of hydrological time series

AU - Weijs, Steven V.

AU - Van De Giesen, N.

AU - Parlange, M. B.

PY - 2013/8/6

Y1 - 2013/8/6

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84897620442&partnerID=8YFLogxK

U2 - 10.5194/hess-17-3171-2013

DO - 10.5194/hess-17-3171-2013

M3 - Article

AN - SCOPUS:84897620442

VL - 17

SP - 3171

EP - 3187

JO - Hydrology and Earth System Sciences

JF - Hydrology and Earth System Sciences

SN - 1027-5606

IS - 8

ER -