Missing chaos in global climate change data interpreting?

0458834 - ÚVGZ 2017 RIV NL eng J - Journal Article
Stehlík, M. - Dušek, Jiří - Kiselák, J.
Missing chaos in global climate change data interpreting?
Ecological Complexity. Roč. 25, mar (2016), s. 53-59. ISSN 1476-945X. E-ISSN 1476-9840
R&D Projects: GA MŠMT(CZ) ED1.1.00/02.0073; GA ČR(CZ) GAP504/11/1151; GA MŠMT(CZ) LM2010007
Research Infrastructure: CzeCOS II - 90061
Institutional support: RVO:67179843
Keywords : Stochasticity * Determinism * Entropy * Chaos * Wetland ecosystem * Kullback–Leibler (KL) divergence
Subject RIV: EH - Ecology, Behaviour
Impact factor: 1.784, year: 2016

The main problem of ecological data modeling is their interpretation and its correct understanding. This problem cannot be solved solely by a big data collection. To sufficiently understand ecosystems we need
to know how these processes behave and how they respond to internal and external factors. Similarly,we need to know the behavior of processes that are involved in the climate system and the biosphere of
the earth. In order to characterize precisely the behavior of individual elements and ecosystems we need to use deterministic, stochastic and chaotic behavior. Unfortunately, the chaotic part of systems is
typically completely ignored in almost all approaches. Ignoring of chaotical part leads to many biasedoutcomes. To overcome this gap we model chaotic system behavior by random iterated function system
which provides a generic guideline for such data management. This also allows to replicate a complexity and chaos of ecosystem.
Permanent Link: http://hdl.handle.net/11104/0259065