FN Archimer Export Format
PT J
TI Joint stochastic simulation of extreme coastal and offshore significant wave heights
BT 
AF Legrand, Juliette
   AILLIOT, Pierre
   Naveau, Philippe
   RAILLARD, Nicolas
AS 1:1;2:2;3:1;4:3;
FF 1:;2:;3:;4:PDG-REM-RDT-LHYMAR;
C1 Laboratoire des Sciences du Climat et de l’Environnement, UMR8212 CEA-CNRS-UVSQ, IPSL & Université Paris-Saclay, 91191 Gif-sur-Yvettes, France
   Laboratoire de Mathématiques de Bretagne Atlantique, Université de Bretagne Occidentale, 29200 Brest, France
   IFREMER, RDT, F-29280 Plouzané, france
C2 CNRS, FRANCE
   UBO, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-REM-RDT-LHYMAR
IN WOS Ifremer UPR
   copubli-france
   copubli-univ-france
IF 1.8
TC 0
UR https://archimer.ifremer.fr/doc/00834/94619/102014.pdf
LA English
DT Article
DE ;Bivariate extremes;multivariate generalised Pareto distribution;simulation of ex-tremes;nonstationarity;extended generalised Pareto distribution;covariate effects;significant wave heights
AB The characterisation of future extreme wave events is crucial because of their multiple impacts, covering a broad range of topics such as coastal flood hazard, coastal erosion, reliability of offshore and coastal structures. The main goal of this paper is to propose and study a stochastic simulator that, given offshore conditions (peak direction Dp, peak period Tp and moderately high significant wave heights Hs), produces jointly offshore and coastal extreme Hs, a quantity measuring the wave severity and which represent a key feature in coastal risk analysis. For this purpose, we rely on bivariate Peaks over Threshold and a nonparametric simulation scheme of bivariate GPD is developed. From this joint simulator, a second generator is derived, allowing for conditional simulations of extreme Hs. Finally, to take into account nonstationarities, the extended generalised Pareto model is also adapted, letting the parameters vary with specific sea state parameters Tp and Dp. The performances of the two proposed generators are illustrated on simulated data and then applied to the simulation of new extreme oceanographic conditions close to the French Brittany coast using hindcast sea state data. Results show that the proposed algorithms successfully simulate future extreme Hs near the coast in a nonparametric way, jointly or conditionally on sea state parameters from a coarser model. 
PY 2023
PD DEC
SO Annals Of Applied Statistics
SN 1932-6157
PU Institute of Mathematical Statistics
VL 17
IS 4
UT 001170176500041
BP 3363
EP 3383
DI 10.1214/23-AOAS1766
ID 94619
ER

EF