FN Archimer Export Format
PT J
TI The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis
BT 
AF MENTASCHI, Lorenzo
   VOUSDOUKAS, Michalis
   VOUKOUVALAS, Evangelos
   SARTINI, Ludovica
   FEYEN, Luc
   BESIO, Giovanni
   ALFIERI, Lorenzo
AS 1:1,2;2:1,4;3:1;4:2,3;5:1;6:2;7:1;
FF 1:;2:;3:;4:PDG-REM-RDT-LCSM;5:;6:;7:;
C1 European Commiss, JRC, IES, Climate Risk Management Unit, Via Enrico Fermi 2749, I-21027 Ispra, Italy.
   Univ Genoa, Dipartimento Ingn Chim Civile & Ambientale, Via Montallegro 1, I-16145 Genoa, Italy.
   IFREMER, Unite Rech Rech & Dev Technol, Lab Comportement Struct Mer CSM, F-29280 Plouzane, France.
   Univ Aegean, Dept Marine Sci, Univ Hill, Mitilini 81100, Lesbos, Greece.
C2 JRC, ITALY
   UNIV GENOA, ITALY
   IFREMER, FRANCE
   UNIV AEGEAN, GREECE
SI BREST
SE PDG-REM-RDT-LCSM
IN WOS Ifremer jusqu'en 2018
   DOAJ
   copubli-europe
IF 4.437
TC 45
UR https://archimer.ifremer.fr/doc/00354/46490/46266.pdf
   https://archimer.ifremer.fr/doc/00354/46490/47920.pdf
LA English
DT Article
AB 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 MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).
PY 2016
PD SEP
SO Hydrology And Earth System Sciences
SN 1027-5606
PU Copernicus Gesellschaft Mbh
VL 20
IS 8
UT 000383894400001
BP 3527
EP 3547
DI 10.5194/hess-20-3527-2016
ID 46490
ER

EF