Uncertainty assessment of future hydroclimatic predictions: A comparison of probabilistic and scenario-based approaches

Koutsoyiannis, D; Efstratiadis, A; Georgakakos, KP

dc.contributor.author	Koutsoyiannis, D	en
dc.contributor.author	Efstratiadis, A	en
dc.contributor.author	Georgakakos, KP	en
dc.date.accessioned	2014-03-01T01:27:32Z
dc.date.available	2014-03-01T01:27:32Z
dc.date.issued	2007	en
dc.identifier.issn	1525-755X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18493
dc.subject.classification	Meteorology & Atmospheric Sciences	en
dc.subject.other	climate modeling	en
dc.subject.other	climate prediction	en
dc.subject.other	climate variation	en
dc.subject.other	hydrological modeling	en
dc.subject.other	Monte Carlo analysis	en
dc.subject.other	probability	en
dc.subject.other	statistical analysis	en
dc.subject.other	stochasticity	en
dc.subject.other	uncertainty analysis	en
dc.subject.other	Eurasia	en
dc.subject.other	Europe	en
dc.subject.other	Greece	en
dc.subject.other	Southern Europe	en
dc.title	Uncertainty assessment of future hydroclimatic predictions: A comparison of probabilistic and scenario-based approaches	en
heal.type	journalArticle	en
heal.identifier.primary	10.1175/JHM576.1	en
heal.identifier.secondary	http://dx.doi.org/10.1175/JHM576.1	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	During the last decade, numerous studies have been carried out to predict future climate based on climatic models run on the global scale and fed by plausible scenarios about anthropogenic forcing to climate. Based on climatic model output, hydrologic models attempt then to predict future hydrologic regimes at regional scales. Much less systematic work has been done to estimate climatic uncertainty and to assess the climatic and hydrologic model outputs within an uncertainty perspective. In this study, a stochastic framework for future climatic uncertainty is proposed, based on the following lines: 1) climate is not constant but rather varying in time and expressed by the long-term (e.g., 30 yr) time average of a natural process, defined on a finescale; 2) the evolution of climate is represented as a stochastic process; 3) the distributional parameters of a process, marginal and dependence, are estimated from an available sample by statistical methods; 4) the climatic uncertainty is the result of at least two factors, the climatic variability and the uncertainty of parameter estimation; 5) a climatic process exhibits a scaling behavior, also known as long-range dependence or the Hurst phenomenon; and 6) because of this dependence, the uncertainty limits of the future are affected by the available observations of the past. The last two lines differ from classical statistical considerations and produce uncertainty limits that eventually are much wider than those of classical statistics. A combination of analytical and Monte Carlo methods is developed to determine uncertainty limits for the nontrivial scaling case. The framework developed is applied with temperature, rainfall, and runoff data from a catchment in Greece, for which data exist for about a century. The uncertainty limits are then superimposed onto deterministic projections up to 2050, obtained for several scenarios and climatic models combined with a hydrologic model. These projections indicate a significant increase of temperature in the future, beyond uncertainty bands, and no significant change of rainfall and runoff as they lie well within uncertainty limits. © 2007 American Meteorological Society.	en
heal.publisher	AMER METEOROLOGICAL SOC	en
heal.journalName	Journal of Hydrometeorology	en
dc.identifier.doi	10.1175/JHM576.1	en
dc.identifier.isi	ISI:000247619500001	en
dc.identifier.volume	8	en
dc.identifier.issue	3	en
dc.identifier.spage	261	en
dc.identifier.epage	281	en