Coupling stochastic models of different timescales

Koutsoyiannis, D

dc.contributor.author	Koutsoyiannis, D	en
dc.date.accessioned	2014-03-01T01:16:15Z
dc.date.available	2014-03-01T01:16:15Z
dc.date.issued	2001	en
dc.identifier.issn	0043-1397	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14003
dc.subject	Stochastic Model	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Limnology	en
dc.subject.classification	Water Resources	en
dc.subject.other	DISAGGREGATION PROCEDURES	en
dc.subject.other	HYDROLOGY	en
dc.subject.other	SIMULATION	en
dc.subject.other	SERIES	en
dc.title	Coupling stochastic models of different timescales	en
heal.type	journalArticle	en
heal.identifier.primary	10.1029/2000WR900200	en
heal.identifier.secondary	http://dx.doi.org/10.1029/2000WR900200	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	A methodology is proposed for coupling stochastic models of hydrologic processes applying to different timescales so that time series generated by the different models be consistent. Given two multivariate time series, generated by two separate (unrelated) stochastic models of the same hydrologic process, each applying to a different timescale, a transformation is developed (referred to as a coupling transformation) that appropriately modifies the time series of the lower-level (finer) timescale so that this series becomes consistent with the time series of the higher-level (coarser) timescale without affecting the second-order stochastic structure of the former and also establishes appropriate correlations between the two time series. The coupling transformation is based on a developed generalized mathematical proposition, which ensures preservation of marginal and joint second-order statistics and of linear relationships between lower- and higher-level processes. Several specific forms of the coupling transformation are studied, from the simplest single variate to the full multivariate. In addition, techniques for evaluating parameters of the coupling transformation based on second-order moments of the lower-level process are studied. Furthermore, two methods are proposed to enable preservation of the skewness of the processes in addition to that of second-order statistics. The overall methodology can be applied to problems involving disaggregation of annual to seasonal and seasonal to subseasonal timescales, as well as problems involving finer timescales (e.g., daily to hourly), with the only requirement that a specific stochastic model is available for each involved timescale. The performance of the methodology is demonstrated by means of a detailed numerical example.	en
heal.publisher	AMER GEOPHYSICAL UNION	en
heal.journalName	Water Resources Research	en
dc.identifier.doi	10.1029/2000WR900200	en
dc.identifier.isi	ISI:000166582800018	en
dc.identifier.volume	37	en
dc.identifier.issue	2	en
dc.identifier.spage	379	en
dc.identifier.epage	391	en