Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2012
Forfattere
Hilde Karine WamSammendrag
Det er ikke registrert sammendrag
Forfattere
Hilde Karine WamSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Narten HøbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Tore SkrøppaSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Tore SkrøppaSammendrag
Det er ikke registrert sammendrag
Sammendrag
We exploit two recently developed informationtheoretic quantities (or measures) designed to quantify information content and complexity of ordered data (time series), respectively. Both are based on order statistics of given data sets, and probe into the shortterm structure of the data only due to finite length restrictions. Their usage requires fixing one parameter, word length or order depth D. The information measure is the orderbased Shannon entropy HS, and the complexity measures is the JensenShannon divergence CJS. The latter requires a chosen reference distribution, i.e. CJS represents a class of measures. Entropy HS and complexity CJS of data series may be represented against each other in a twodimensional diagram which we will refer to as ComplexityEntropy Causality Plane, or CECP. Very long realizations of classic stochastic processes and chaotic deterministic maps each obey one location in the CECP, specific for the process. This can be used to differentiate chaos from correlated noise (Rosso et al. 2007), which is notoriously difficult otherwise. For observed data, a mixture of deterministic (signal) and stochastic (noise) parts is to be expected. We use an ensemble of longterm river runoff time series as example, which are known to exhibit powerlaw decaying longrange correlations. We compare these data with a longrange correlated candidate process, the k noise, from the perspective of order statistics and the CECP. Although these processes resemble runoff series in their correlation behavior and may be even tuned to any runoff series by changing the value of k, the CECP locations and in particular the order pattern statistics reveals qualitative differences between them. We give a detailed account of these differences, and use them to conclude on the deterministic nature of the (shortterm) dynamics of the runoff time series. The proposed methodology also represents a stringent test bed for hydrological or other environmental models. http://dames.pik-potsdam.de/Abstracts.pdf