Simple disaggregation by accurate adjusting procedures

Koutsoyiannis, D; Manetas, A

dc.contributor.author	Koutsoyiannis, D	en
dc.contributor.author	Manetas, A	en
dc.date.accessioned	2014-03-01T01:12:16Z
dc.date.available	2014-03-01T01:12:16Z
dc.date.issued	1996	en
dc.identifier.issn	0043-1397	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12043
dc.subject	Model Performance	en
dc.subject	Probability Distribution	en
dc.subject	Stochastic Model	en
dc.subject	Stochastic Simulation	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Limnology	en
dc.subject.classification	Water Resources	en
dc.subject.other	rain	en
dc.subject.other	runoff	en
dc.subject.other	model	en
dc.subject.other	probability	en
dc.subject.other	sampling	en
dc.subject.other	simulation	en
dc.subject.other	statistics	en
dc.subject.other	stochastic model	en
dc.subject.other	Rickettsia sp. PAR	en
dc.subject.other	hydrological modelling	en
dc.subject.other	rainfall/runoff model	en
dc.subject.other	stochastic simulation	en
dc.title	Simple disaggregation by accurate adjusting procedures	en
heal.type	journalArticle	en
heal.identifier.primary	10.1029/96WR00488	en
heal.identifier.secondary	http://dx.doi.org/10.1029/96WR00488	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	A multivariate disaggregation method is developed for stochastic simulation of hydrologic series. The method is based on three simple ideas that have been proven effective. First, it starts using directly a typical PAR(l) model and keeps its formalism and parameter set, which is the most parsimonious among linear stochastic models. This model is run for the lower- level variables without any reference to the known higher-level variables. Second, it uses accurate adjusting procedures to allocate the error in the additive property, i.e., the departure of the sum of lower-level variables within a period from the corresponding higher-level variable. They are accurate in the sense that they preserve explicitly certain statistics or even the complete distribution of lower-level variables. Three such procedures have been developed and studied in this paper, both theoretically and empirically. Third, it uses repetitive sampling in order to improve the approximations of statistics that are not explicitly preserved by the adjusting procedures. The model, owing to the wide range of probability distributions it can handle (from bell-shaped to J-shaped) and to its multivariate framework, is useful for a plethora of hydrologic applications such as disaggregation of annual rainfall or runoff into monthly or weekly amounts, and disaggregation of event rainfall depths into partial amounts of hourly or even less duration. Such real-world hydrologic applications have been explored in this study to test the model performance, which has proven very satisfactory.	en
heal.publisher	AMER GEOPHYSICAL UNION	en
heal.journalName	Water Resources Research	en
dc.identifier.doi	10.1029/96WR00488	en
dc.identifier.isi	ISI:A1996UV61300019	en
dc.identifier.volume	32	en
dc.identifier.issue	7	en
dc.identifier.spage	2105	en
dc.identifier.epage	2117	en