A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour

Langousis, A; Koutsoyiannis, D

dc.contributor.author	Langousis, A	en
dc.contributor.author	Koutsoyiannis, D	en
dc.date.accessioned	2014-03-01T01:23:31Z
dc.date.available	2014-03-01T01:23:31Z
dc.date.issued	2006	en
dc.identifier.issn	00221694	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16994
dc.subject	Hurst phenomenon	en
dc.subject	Scaling	en
dc.subject	Stochastic hydrology	en
dc.subject	Stochastic models	en
dc.subject.other	Correlation methods	en
dc.subject.other	Mathematical models	en
dc.subject.other	Statistical methods	en
dc.subject.other	Stochastic programming	en
dc.subject.other	Time series analysis	en
dc.subject.other	Hurst phenomenon	en
dc.subject.other	Scaling	en
dc.subject.other	Stochastic hydrology	en
dc.subject.other	Stochastic models	en
dc.subject.other	Hydrology	en
dc.subject.other	Correlation methods	en
dc.subject.other	Hydrology	en
dc.subject.other	Mathematical models	en
dc.subject.other	Statistical methods	en
dc.subject.other	Stochastic programming	en
dc.subject.other	Time series analysis	en
dc.subject.other	behavioral ecology	en
dc.subject.other	methodology	en
dc.subject.other	seasonal variation	en
dc.subject.other	stochasticity	en
dc.title	A stochastic methodology for generation of seasonal time series reproducing overyear scaling behaviour	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jhydrol.2005.02.037	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jhydrol.2005.02.037	en
heal.publicationDate	2006	en
heal.abstract	In generating synthetic time series of hydrological processes at sub-annual scales, it is important to preserve seasonal characteristics and short-term persistence. At the same time, it is equally important to preserve annual characteristics and overyear scaling behaviour. This scaling behaviour, which is equivalent to the Hurst phenomenon and has been interpreted by many as nonstationarity of processes, has been detected in a large number of hydroclimatic series and has important effects on the planning and design of hydrosystems. However, when seasonal models are used the preservation of annual characteristics and overyear scaling is a difficult task and is often ignored unless disaggregation techniques are applied, which, however, involve several difficulties (e.g. in parameter estimation) and inaccuracies. As an alternative, a new methodology is proposed that directly operates on seasonal time scale, avoiding disaggregation, and that simultaneously preserves annual statistics and the scaling properties on overyear time scales. Two specific stochastic models are proposed, a simple widely used seasonal model with short memory to which long-term persistence is imposed using a linear filter, and a combination of two sub-models, a stationary one with long memory and a cyclostationary one with short memory. Both models are capable of generating spatially correlated synthetic time series for more than one location simultaneously. The models are tested in a real world case and found to be accurate in reproducing all the desired statistical properties and virtually equivalent from an operational point of view. © 2005 Elsevier B.V. All rights reserved.	en
heal.journalName	Journal of Hydrology	en
dc.identifier.doi	10.1016/j.jhydrol.2005.02.037	en
dc.identifier.volume	322	en
dc.identifier.issue	1-4	en
dc.identifier.spage	138	en
dc.identifier.epage	154	en