%0 Journal Article %1 JAWR:JAWR544 %A Ouarda, T.B.M.J. %A Adlouni, S. El %D 2011 %I Blackwell Publishing Ltd %J JAWRA Journal of the American Water Resources Association %K Bayesian Nonstationarity %N 3 %P 496--505 %R 10.1111/j.1752-1688.2011.00544.x %T Bayesian Nonstationary Frequency Analysis of Hydrological Variables1 %U http://dx.doi.org/10.1111/j.1752-1688.2011.00544.x %V 47 %X Ouarda, T.B.M.J. and S. El-Adlouni, 2011. Bayesian Nonstationary Frequency Analysis of Hydrological Variables. Journal of the American Water Resources Association (JAWRA) 47(3):496-505. DOI: 10.1111/j.1752-1688.2011.00544.xAbstract:  The present paper provides a discussion of nonstationary frequency analysis models in hydrology with a focus on the Bayesian approach. The Bayesian model provides an efficient estimation framework of hydrological quantiles in the presence of nonstationarity. In nonstationary frequency analysis models, the parameters are functions of covariates, allowing for dependent parameters and trends. The use of the nonstationary Generalized Maximum Likelihood Estimation method in hydrologic frequency analysis is discussed. This model allows using prior information concerning the variables under study and considering a number of models (linear, quadratic, etc.) of the dependence of the parameters on covariates. A discussion is also provided concerning the use of the reversible jump Monte Carlo Markov Chain procedure which allows carrying out the estimation of the posterior distributions of the parameters and the selection of the Bayesian model at the same time. An application to a case study is presented to illustrate the potential of the model.

@article{JAWR:JAWR544, abstract = {Ouarda, T.B.M.J. and S. El-Adlouni, 2011. Bayesian Nonstationary Frequency Analysis of Hydrological Variables. Journal of the American Water Resources Association (JAWRA) 47(3):496-505. DOI: 10.1111/j.1752-1688.2011.00544.xAbstract:  The present paper provides a discussion of nonstationary frequency analysis models in hydrology with a focus on the Bayesian approach. The Bayesian model provides an efficient estimation framework of hydrological quantiles in the presence of nonstationarity. In nonstationary frequency analysis models, the parameters are functions of covariates, allowing for dependent parameters and trends. The use of the nonstationary Generalized Maximum Likelihood Estimation method in hydrologic frequency analysis is discussed. This model allows using prior information concerning the variables under study and considering a number of models (linear, quadratic, etc.) of the dependence of the parameters on covariates. A discussion is also provided concerning the use of the reversible jump Monte Carlo Markov Chain procedure which allows carrying out the estimation of the posterior distributions of the parameters and the selection of the Bayesian model at the same time. An application to a case study is presented to illustrate the potential of the model.}, added-at = {2011-06-07T10:54:55.000+0200}, author = {Ouarda, T.B.M.J. and Adlouni, S. El}, biburl = {https://www.bibsonomy.org/bibtex/2aa0363664be12b79018eb6a52b07f703/marsianus}, description = {Bayesian Nonstationary Frequency Analysis of Hydrological Variables1 - Ouarda - 2011 - JAWRA Journal of the American Water Resources Association - Wiley Online Library}, doi = {10.1111/j.1752-1688.2011.00544.x}, interhash = {b6cbbf3d9fbf728f97f19ced8b1a4a53}, intrahash = {aa0363664be12b79018eb6a52b07f703}, issn = {1752-1688}, journal = {JAWRA Journal of the American Water Resources Association}, keywords = {Bayesian Nonstationarity}, number = 3, pages = {496--505}, publisher = {Blackwell Publishing Ltd}, timestamp = {2013-11-18T15:47:52.000+0100}, title = {Bayesian Nonstationary Frequency Analysis of Hydrological Variables1}, url = {http://dx.doi.org/10.1111/j.1752-1688.2011.00544.x}, volume = 47, year = 2011 }