A NONLINEAR DISAGGREGATION METHOD WITH A REDUCED PARAMETER SET FOR SIMULATION OF HYDROLOGIC SERIES

KOUTSOYIANNIS, D

dc.contributor.author	KOUTSOYIANNIS, D	en
dc.date.accessioned	2014-03-01T01:08:38Z
dc.date.available	2014-03-01T01:08:38Z
dc.date.issued	1992	en
dc.identifier.issn	0043-1397	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/10616
dc.subject	Seasonality	en
dc.subject	Second Order Statistics	en
dc.subject	Stochastic Simulation	en
dc.subject	Markov Model	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Limnology	en
dc.subject.classification	Water Resources	en
dc.subject.other	LOG NORMAL-DISTRIBUTIONS	en
dc.subject.other	MODELS	en
dc.title	A NONLINEAR DISAGGREGATION METHOD WITH A REDUCED PARAMETER SET FOR SIMULATION OF HYDROLOGIC SERIES	en
heal.type	journalArticle	en
heal.identifier.primary	10.1029/92WR01299	en
heal.identifier.secondary	http://dx.doi.org/10.1029/92WR01299	en
heal.language	English	en
heal.publicationDate	1992	en
heal.abstract	A multivariate dynamic disaggregation model is developed as a stepwise approach to stochastic disaggregation problems, oriented toward hydrologic applications. The general idea of the approach is the conversion of a sequential stochastic simulation model, such as a seasonal AR(1), into a disaggregation model. Its structure includes two separate parts, a linear step-by-step moments determination procedure, based on the associated sequential model, and an independent nonlinear bivariate generation procedure (partition procedure). The model assures the preservation of the additive property of the actual (not transformed) variables. Its modular structure allows for various model configurations. Two different configurations (PAR(1) and PARX(1)), both associated with the sequential Markov model, are studied. Like the sequential Markov model, both configurations utilize the minimum set of second-order statistics and the marginal means and third moments of the lower-level variables. All these statistics are approximated by the model with the use of explicit relations. Both configurations perform well with regard to the correlation of consecutive lower-level variables each located in consecutive higher-level time steps. The PARX(1) configuration exhibits better behavior with regard to the correlation properties of lower-level variables with lagged higher-level variables.	en
heal.publisher	AMER GEOPHYSICAL UNION	en
heal.journalName	WATER RESOURCES RESEARCH	en
dc.identifier.doi	10.1029/92WR01299	en
dc.identifier.isi	ISI:A1992KD06700010	en
dc.identifier.volume	28	en
dc.identifier.issue	12	en
dc.identifier.spage	3175	en
dc.identifier.epage	3191	en