Can a simple stochastic model generate rich patterns of rainfall events?

Papalexiou, S-M; Koutsoyiannis, D; Montanari, A

dc.contributor.author	Papalexiou, S-M	en
dc.contributor.author	Koutsoyiannis, D	en
dc.contributor.author	Montanari, A	en
dc.date.accessioned	2014-03-01T02:01:47Z
dc.date.available	2014-03-01T02:01:47Z
dc.date.issued	2011	en
dc.identifier.issn	00221694	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29254
dc.subject	Long-term persistence	en
dc.subject	Power-type tails	en
dc.subject	Rainfall modelling	en
dc.subject.other	Autocorrelation functions	en
dc.subject.other	Data sets	en
dc.subject.other	Different time scale	en
dc.subject.other	Long-term persistence	en
dc.subject.other	Power-type tails	en
dc.subject.other	Predictive capacity	en
dc.subject.other	Rainfall event	en
dc.subject.other	Rainfall modelling	en
dc.subject.other	Rainfall models	en
dc.subject.other	Rainfall patterns	en
dc.subject.other	Scaling behavior	en
dc.subject.other	Simple stochastic	en
dc.subject.other	Stochastic analysis	en
dc.subject.other	Storm events	en
dc.subject.other	Temporal resolution	en
dc.subject.other	Time-scales	en
dc.subject.other	Uncertain parameters	en
dc.subject.other	University of Iowa	en
dc.subject.other	Autocorrelation	en
dc.subject.other	Optical systems	en
dc.subject.other	Rain	en
dc.subject.other	Regression analysis	en
dc.subject.other	Stochastic systems	en
dc.subject.other	Uncertainty analysis	en
dc.subject.other	Stochastic models	en
dc.subject.other	autocorrelation	en
dc.subject.other	climate classification	en
dc.subject.other	data set	en
dc.subject.other	hydrological modeling	en
dc.subject.other	numerical model	en
dc.subject.other	parameterization	en
dc.subject.other	parsimony analysis	en
dc.subject.other	persistence	en
dc.subject.other	precipitation intensity	en
dc.subject.other	prediction	en
dc.subject.other	stochasticity	en
dc.subject.other	temporal analysis	en
dc.subject.other	trend analysis	en
dc.subject.other	uncertainty analysis	en
dc.title	Can a simple stochastic model generate rich patterns of rainfall events?	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jhydrol.2011.10.008	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jhydrol.2011.10.008	en
heal.publicationDate	2011	en
heal.abstract	Several of the existing rainfall models involve diverse assumptions, a variety of uncertain parameters, complicated mechanistic structures, use of different model schemes for different time scales, and possibly classifications of rainfall patterns into different types. However, the parsimony of a model is recognized as an important desideratum as it improves its comprehensiveness, its applicability and possibly its predictive capacity. To investigate the question if a single and simple stochastic model can generate a plethora of temporal rainfall patterns, as well as to detect the major characteristics of such a model (if it exists), a data set with very fine timescale rainfall is used. This is the well-known data set of the University of Iowa comprising measurements of seven storm events at a temporal resolution of 5-10. s. Even though only seven such events have been observed, their diversity can help investigate these issues. An evident characteristic resulting from the stochastic analysis of the events is the scaling behaviors both in state and in time. Utilizing these behaviors, a stochastic model is constructed which can represent all rainfall events and all rich patterns, thus suggesting a positive reply to the above question. In addition, it seems that the most important characteristics of such a model are a power-type distribution tail and an asymptotic power-type autocorrelation function. Both power-type distribution tails and autocorrelation functions can be viewed as properties enhancing randomness and uncertainty, or entropy. © 2011 Elsevier B.V.	en
heal.journalName	Journal of Hydrology	en
dc.identifier.doi	10.1016/j.jhydrol.2011.10.008	en
dc.identifier.volume	411	en
dc.identifier.issue	3-4	en
dc.identifier.spage	279	en
dc.identifier.epage	289	en