On the quest for chaotic attractors in hydrological processes

Koutsoyiannis, D

dc.contributor.author	Koutsoyiannis, D	en
dc.date.accessioned	2014-03-01T01:24:48Z
dc.date.available	2014-03-01T01:24:48Z
dc.date.issued	2006	en
dc.identifier.issn	0262-6667	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17440
dc.subject	Attractors	en
dc.subject	Capacity dimension	en
dc.subject	Chaos	en
dc.subject	Chaotic dynamics	en
dc.subject	Correlation dimension	en
dc.subject	Entropy	en
dc.subject	Hydrological processes	en
dc.subject	Nonlinear analysis	en
dc.subject	Rainfall	en
dc.subject	Runoff	en
dc.subject	Stochastic processes	en
dc.subject	Time series analysis	en
dc.subject.classification	Water Resources	en
dc.subject.other	Chaos theory	en
dc.subject.other	Estimation	en
dc.subject.other	Rain	en
dc.subject.other	Runoff	en
dc.subject.other	Time series analysis	en
dc.subject.other	Chaotic analysis	en
dc.subject.other	Hydrological process	en
dc.subject.other	Hydrometeorological time series	en
dc.subject.other	Hydrology	en
dc.subject.other	chaos theory	en
dc.subject.other	correlation	en
dc.subject.other	hydrology	en
dc.subject.other	hydrometeorology	en
dc.subject.other	nonlinearity	en
dc.subject.other	rainfall-runoff modeling	en
dc.subject.other	stochasticity	en
dc.subject.other	time series analysis	en
dc.title	On the quest for chaotic attractors in hydrological processes	en
heal.type	journalArticle	en
heal.identifier.primary	10.1623/hysj.51.6.1065	en
heal.identifier.secondary	http://dx.doi.org/10.1623/hysj.51.6.1065	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	In the last two decades, several researchers have claimed to have discovered low-dimensional determinism in hydrological processes, such as rainfall and runoff, using methods of chaotic analysis. However, such results have been criticized by others. In an attempt to offer additional insights into this discussion, it is shown here that, in some cases, merely the careful application of concepts of dynamical systems, without doing any calculation, provides strong indications that hydrological processes cannot be (low-dimensional) deterministic chaotic. Furthermore, it is shown that specific peculiarities of hydrological processes on fine time scales, such as asymmetric, J-shaped distribution functions, intermittency, and high autocorrelations, are synergistic factors that can lead to misleading conclusions regarding the presence of (low-dimensional) deterministic chaos. In addition, the recovery of a hypothetical attractor from a time series is put as a statistical estimation problem whose study allows, among others, quantification of the required sample size; this appears to be so huge that it prohibits any accurate estimation, even with the largest available hydrological records. All these arguments are demonstrated using appropriately synthesized theoretical examples. Finally, in light of the theoretical analyses and arguments, typical real-world hydrometeorological time series, such as relative humidity, rainfall, and runoff, are explored and none of them is found to indicate the presence of chaos. Copyright © 2006 IAHS Press.	en
heal.publisher	IAHS PRESS, INST HYDROLOGY	en
heal.journalName	Hydrological Sciences Journal	en
dc.identifier.doi	10.1623/hysj.51.6.1065	en
dc.identifier.isi	ISI:000242939300006	en
dc.identifier.volume	51	en
dc.identifier.issue	6	en
dc.identifier.spage	1065	en
dc.identifier.epage	1091	en