J. Bernier, Statistical Detection of Changes in Geophysical Series, Engineering Risk in Natural Resources Management, vol.275, pp.159-176, 1994.
DOI : 10.1007/978-94-015-8271-1_9

P. Bortot, S. Coles, and J. A. Tawn, The multivariate Gaussian tail model: an application to oceanographic data, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.49, issue.1, pp.31-49, 2000.
DOI : 10.1111/1467-9876.00177

T. A. Buishand, Extreme rainfall estimation by combining data from several sites, Hydrological Sciences Journal, vol.26, issue.4, pp.345-365, 1991.
DOI : 10.1080/02626668609491051

S. Chib, Marginal Likelihood from the Gibbs Output, Journal of the American Statistical Association, vol.92, issue.432, pp.1313-1321, 1995.
DOI : 10.1080/01621459.1995.10476591

S. Coles, An Introduction to Statistical Modeling of Extreme Values, 2001.
DOI : 10.1007/978-1-4471-3675-0

S. Coles and L. Pericchi, Anticipating catastrophes through extreme value modelling, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.4, issue.4, pp.405-416, 2003.
DOI : 10.1111/1467-9876.00413

S. Coles, L. R. Pericchi, and S. Sisson, A fully probabilistic approach to extreme rainfall modeling, Journal of Hydrology, vol.273, issue.1-4, pp.35-50, 2003.
DOI : 10.1016/S0022-1694(02)00353-0

S. Coles and J. A. Tawn, Statistics of Coastal Flood Prevention, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.332, issue.1627, pp.457-476, 1990.
DOI : 10.1098/rsta.1990.0126

S. G. Coles and J. A. Tawn, A Bayesian Analysis of Extreme Rainfall Data, Applied Statistics, vol.45, issue.4, pp.463-478, 1996.
DOI : 10.2307/2986068

D. Cooley, Statistical Analysis of Extremes Motivated by Weather and Climate Studies: Applied and Theoretical Advances, 2005.

T. Dalrymple, Flood frequency analyses. in Water-supply paper 1553-A, 1960.

L. De-haan and T. T. Pereira, Spatial extremes: Models for the stationary case, The Annals of Statistics, vol.34, issue.1, pp.146-168, 2006.
DOI : 10.1214/009053605000000886

E. Adlouni, S. , A. C. Favre, and B. Bobee, Comparison of methodologies to assess the convergence of Markov chain Monte Carlo methods. Computational Statistics and Data Analysis, 2006.

A. C. Favre, S. Adlouni, L. Perreault, N. Thiemonge, and B. Bobee, Multivariate hydrological frequency analysis using copulas, Water Resources Research, vol.246, issue.1, 2004.
DOI : 10.1029/2003WR002456

R. A. Fisher and L. H. Tippett, Limiting forms of the frequency distribution of the largest or smallest member of a sample, Mathematical Proceedings of the Cambridge Philosophical Society, vol.24, issue.02, 1928.
DOI : 10.1017/S0305004100015681

A. E. Gelfand and A. F. Smith, Sampling-Based Approaches to Calculating Marginal Densities, Journal of the American Statistical Association, vol.4, issue.410, pp.398-409, 1990.
DOI : 10.1080/01621459.1986.10478240

A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian data analysis, C. Hall, 1995.

A. Gelman, G. O. Roberts, and W. R. Gilks, Efficient Metropolis Jumping Rules. Pages 599-607 in Bayesian Statistics 5, 1996.

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, pp.721-741, 1984.

W. R. Gilks, N. G. Best, and K. K. Tan, Adaptive Rejection Metropolis Sampling within Gibbs Sampling, Applied Statistics, vol.44, issue.4, pp.455-472, 1995.
DOI : 10.2307/2986138

W. R. Gilks and P. Wild, Adaptive Rejection Sampling for Gibbs Sampling, Applied Statistics, vol.41, issue.2, pp.337-348, 1992.
DOI : 10.2307/2347565

H. Haario, E. Saksman, and J. Tamminen, An Adaptive Metropolis Algorithm, Bernoulli, vol.7, issue.2, pp.223-242, 2001.
DOI : 10.2307/3318737

H. Haario, E. Saksman, and J. Tamminen, Componentwise adaptation for high dimensional MCMC, Computational Statistics, vol.21, issue.2, pp.265-273, 2005.
DOI : 10.1007/BF02789703

W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97

J. A. Hoeting, D. Madigan, A. E. Raftery, and C. T. Volinsky, Bayesian model averaging: A tutorial, Statistical Science, vol.14, pp.382-401, 1999.

J. R. Hosking and J. R. Wallis, The effect of intersite dependence on regional flood frequency analysis, Water Resources Research, vol.31, issue.4, pp.588-600, 1988.
DOI : 10.1029/WR024i004p00588

J. R. Hosking and J. R. Wallis, Regional Frequency Analysis: an approach based on L-Moments. 226 p, 1997.

A. F. Jenkinson, The frequency distribution of the annual maximum (or minimum) values of meteorological elements, Quarterly Journal of the Royal Meteorological Society, vol.17, issue.348, pp.158-171, 1955.
DOI : 10.1002/qj.49708134804

R. E. Kass and A. E. Raftery, Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995.
DOI : 10.1080/01621459.1995.10476572

R. W. Katz, M. B. Parlange, and P. Naveau, Statistics of extremes in hydrology, Advances in Water Resources, vol.25, issue.8-12, pp.1287-1304, 2002.
DOI : 10.1016/S0309-1708(02)00056-8

H. Khodja, H. Lubès-niel, J. M. Sabatier, E. Servat, and J. E. , Analyse spatiotemporelle de données pluviométriques en Afrique de l'Ouest. Recherche d'une rupture en moyenne. Une alternative intéressante: les tests de permutations, pp.95-110, 1998.

A. F. Lee and S. M. Heghinian, A shift of the mean level in a sequence of independant normal random variables -a Bayesian approach, Technometrics, vol.19, pp.503-506, 1977.

Z. Q. Lu and L. M. Berliner, Markov switching time series models with application to a daily runoff series, Water Resources Research, vol.147, issue.2, pp.523-534, 1999.
DOI : 10.1029/98WR02686

H. Madsen and D. Rosbjerg, The partial duration series method in regional index-flood modeling, Water Resources Research, vol.30, issue.1, pp.737-746, 1997.
DOI : 10.1029/96WR03847

L. Marshall, D. Nott, and A. Sharma, A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling, Water Resources Research, vol.181, issue.3, 2004.
DOI : 10.1029/2003WR002378

E. S. Martins and J. R. Stedinger, Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data, Water Resources Research, vol.32, issue.12, pp.737-744, 2000.
DOI : 10.1029/1999WR900330

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1092, 1953.
DOI : 10.1063/1.1699114

N. Metropolis and S. Ulam, The Monte Carlo Method, Journal of the American Statistical Association, vol.44, issue.247, pp.335-341, 1949.
DOI : 10.1080/01621459.1949.10483310

R. A. Moyeed and R. T. Clarke, The use of Bayesian methods for fitting rating curves, with case studies, Advances in Water Resources, vol.28, issue.8, pp.807-818, 2005.
DOI : 10.1016/j.advwatres.2005.02.005

P. Naveau, D. Cooley, and P. Poncet, Spatial extremes analysis in climate studies. in. Extreme Value Analysis, 2005.

T. B. Ouarda, M. Lang, B. Bobee, J. Bernier, and P. Bois, Analysis of regional flood models utilized in France and Quebec, pp.155-182, 1999.

E. Parent and J. Bernier, Encoding prior experts judgments to improve risk analysis of extreme hydrological events via POT modeling, Journal of Hydrology, vol.283, issue.1-4, pp.1-18, 2003.
DOI : 10.1016/S0022-1694(03)00080-5

L. Perreault, Analyse bayésienne rétrospective d'une rupture dans les séquences de variables aléatoires hydrologiques, Thesis. ENGREF, INRS-Eau, vol.200, 2000.

L. Perreault, J. Bernier, B. Bobee, and E. Parent, Bayesian change-point analysis in hydrometeorological time series. Part 1. The normal model revisited, Journal of Hydrology, vol.235, issue.3-4, pp.221-241, 2000.
DOI : 10.1016/S0022-1694(00)00270-5

L. Perreault, J. Bernier, B. Bobee, and E. Parent, Bayesian change-point analysis in hydrometeorological time series. Part 2. Comparison of change-point models and forecasting, Journal of Hydrology, vol.235, issue.3-4, pp.242-263, 2000.
DOI : 10.1016/S0022-1694(00)00271-7

L. Perreault and V. Fortin, Mixture and Hidden Markov models for peak flow analysis, Seizièmes entretiens du centre Jacques Cartier, 2003.

L. Perreault, M. Hache, M. Slivitzky, and B. Bobee, Detection of changes in precipitation and runoff over eastern Canada and U.S. using a Bayesian approach, Stochastic Environmental Research and Risk Assessment (SERRA), vol.13, issue.3, pp.201-216, 1999.
DOI : 10.1007/s004770050039

L. Perreault, E. Parent, J. Bernier, B. Bobee, and M. Slivitzky, Retrospective multivariate Bayesian change-point analysis: A simultaneous single change in the mean of several hydrological sequences, Stochastic Environmental Research and Risk Assessment, vol.14, issue.4, pp.243-261, 2000.
DOI : 10.1007/s004770000051

D. S. Reis and J. R. Stedinger, Bayesian MCMC flood frequency analysis with historical information, Journal of Hydrology, vol.313, issue.1-2, pp.97-116, 2005.
DOI : 10.1016/j.jhydrol.2005.02.028

B. Renard, M. Lang, and P. Bois, Statistical analysis of extreme events in a nonstationary context via a Bayesian framework. Stochastic Environmental Research and Risk Assessment, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00452224

M. Ribatet, E. Sauquet, J. M. Gresillon, and T. B. Ouarda, A regional Bayesian POT model for flood frequency analysis. Stochastic Environmental Research and Risk Assessment, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00232788

C. Ritter and M. A. Tanner, Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy-Gibbs Sampler, Journal of the American Statistical Association, vol.85, issue.419, pp.861-868, 1992.
DOI : 10.1080/01621459.1991.10475006

G. O. Roberts and W. R. Gilks, Convergence of Adaptive Direction Sampling, Journal of Multivariate Analysis, vol.49, issue.2, pp.287-298, 1994.
DOI : 10.1006/jmva.1994.1028

M. Schalther and J. A. Tawn, A dependence measure for multivariate and spatial extreme values: Properties and inference, Biometrika, vol.90, issue.1, pp.139-156, 2003.
DOI : 10.1093/biomet/90.1.139

J. R. Stedinger, Estimating a regional flood frequency distribution, Water Resources Research, vol.11, issue.4, pp.503-510, 1983.
DOI : 10.1029/WR019i002p00503

M. A. Tanner, Tools for Statistical Inference, 1996.

D. Tapsoba, M. Hache, L. Perreault, and B. Bobee, Bayesian Rainfall Variability Analysis in West Africa along Cross Sections in Space???Time Grid Boxes, Journal of Climate, vol.17, issue.5, pp.1069-1082, 2004.
DOI : 10.1175/1520-0442(2004)017<1069:BRVAIW>2.0.CO;2

M. Thyer and G. Kuczera, Modeling long-term persistence in hydroclimatic time series using a hidden state Markov Model, Water Resources Research, vol.27, issue.8, pp.3301-3310, 2000.
DOI : 10.1029/2000WR900157

M. Thyer and G. Kuczera, A hidden Markov model for modelling long-term persistence in multi-site rainfall time series 1. Model calibration using a Bayesian approach, Journal of Hydrology, vol.275, issue.1-2, pp.12-26, 2003.
DOI : 10.1016/S0022-1694(02)00412-2

M. Thyer and G. Kuczera, A hidden Markov model for modelling long-term persistence in multi-site rainfall time series. 2. Real data analysis, Journal of Hydrology, vol.275, issue.1-2, pp.27-48, 2003.
DOI : 10.1016/S0022-1694(02)00411-0

M. Thyer, G. Kuczera, and Q. J. Wang, Quantifying parameter uncertainty in stochastic models using the Box???Cox transformation, Journal of Hydrology, vol.265, issue.1-4, pp.246-257, 2002.
DOI : 10.1016/S0022-1694(02)00113-0

L. Tierney, Markov Chains for Exploring Posterior Distributions, The Annals of Statistics, vol.22, issue.4, pp.1701-1728, 1994.
DOI : 10.1214/aos/1176325750

L. Tierney, Rejoinder: Markov Chains for Exploring Posterior Distributions, The Annals of Statistics, vol.22, issue.4, pp.1758-1762, 1994.
DOI : 10.1214/aos/1176325755

L. Tierney and A. Mira, Some adaptive Monte Carlo methods for Bayesian inference, Statistics in Medicine, vol.43, issue.17-18, pp.2507-2515, 1999.
DOI : 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2507::AID-SIM272>3.0.CO;2-J

Q. J. Wang, A Bayesian Joint Probability Approach for flood record augmentation, Water Resources Research, vol.21, issue.5, pp.1707-1712, 2001.
DOI : 10.1029/2000WR900401