globalchange  > 气候变化事实与影响
DOI: 10.5194/hess-20-3527-2016
Scopus记录号: 2-s2.0-84986294651
论文题名:
The transformed-stationary approach: A generic and simplified methodology for non-stationary extreme value analysis
作者: Mentaschi L; , Vousdoukas M; , Voukouvalas E; , Sartini L; , Feyen L; , Besio G; , Alfieri L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期:9
起始页码: 3527
结束页码: 3547
语种: 英语
Scopus关键词: Climate change ; Degrees of freedom (mechanics) ; HTTP ; Pareto principle ; Time series ; Time series analysis ; Value engineering ; Water levels ; Environmental variables ; Extreme value analysis ; Extreme value distributions ; Generalized extreme value ; Generalized Pareto distribution ; Non-stationary time series ; Significant wave height ; Statistical techniques ; Low pass filters ; climate change ; extreme event ; methodology ; river discharge ; seasonal variation ; software ; temporal variation ; time series ; time series analysis ; water level ; wave height
英文摘要: Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016). © Author(s) 2016.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78746
Appears in Collections:气候变化事实与影响

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作者单位: European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability (IES), Climate Risk Management Unit, via Enrico Fermi 2749, Ispra, Italy; Università di Genova, Dipartimento di Ingegneria Chimica, Civile Ed Ambientale, via Montallegro 1, Genova, Italy; Ifremer, Unité de Recherche Recherches et Développements Technologiques, Laboratoire Comportement des Structures en Mer (CSM), Pointe du Diable, Plouzané, France; Department of Marine Sciences, University of the Aegean, University Hill, Mytilene, Lesbos, Greece

Recommended Citation:
Mentaschi L,, Vousdoukas M,, Voukouvalas E,et al. The transformed-stationary approach: A generic and simplified methodology for non-stationary extreme value analysis[J]. Hydrology and Earth System Sciences,2016-01-01,20(9)
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