A Bayesian hierarchical spatio-temporal model for extreme rainfall in Extremadura (Spain)

References

• Acero, F.J., et al., 2017. Non-stationary future return levels for extreme rainfall over Extremadura (SW Iberian Peninsula). Hydrological Sciences Journal, 62 (9), 1394–1411. doi:10.1080/02626667.2017.1328559
   Web of Science ®Google Scholar
• Acero, F.J., García, J.A., and Gallego, M.C., 2011. Peaks-over-threshold study of trends in extreme rainfall over the Iberian Peninsula. Journal of Climate, 24 (3), 1089–1105. doi:10.1175/2010JCLI3627.1
   Web of Science ®Google Scholar
• Aryal, S.K., et al., 2009. Characterizing and modeling temporal and spatial trends in rainfall extremes. Journal of Hydrometeorology, 10, 241–253. doi:10.1175/2008JHM1007.1
   Web of Science ®Google Scholar
• Banerjee, S., Carlin, B.E., and Gelfand, A.E., 2004. Hierarchical modeling and analysis for spatial data. New York: Chapman & Hall.
   Google Scholar
• Berliner, M.L., 1996. Hierarchical Bayesian time series. New York: Springer.
   Google Scholar
• Casanueva, A., et al., 2014. Variability of extreme precipitation over Europe and its relationships with teleconnection patterns. Hydrology and Earth System Sciences, 18, 709–725. doi:10.5194/hess-18-709-2014
   Web of Science ®Google Scholar
• Casson, E. and Coles, S., 1999. Spatial regression models for extremes. Extremes, 1 (4), 449–468. doi:10.1023/A:1009931222386
   Google Scholar
• Cooley, D., Nychka, D., and Naveau, P., 2007. Bayesian spatial modeling of extreme precipitation return levels. Journal of the American Statistical Association, 102 (479), 824–840. doi:10.1198/016214506000000780
   Web of Science ®Google Scholar
• Cowles, M.K. and Carlin, B.P., 1996. Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association, 91 (434), 883–904. doi:10.1080/01621459.1996.10476956
   Web of Science ®Google Scholar
• Cressie, N., and Wikle, C.K., 2011. Statistics for spatio-temporal data. New York: Wiley.
   Google Scholar
• Davison, A.C., Padoan, S.A., and Ribatet, M., 2012. Statistical modeling of spatial extremes. Statistical Science, 27 (2), 161–186. doi:10.1214/11-STS376
   Web of Science ®Google Scholar
• Dyrrdal, A.V., et al., 2015. Bayesian hierarchical modeling of extreme hourly precipitation in Norway. Environmetrics, 26 (2), 89–106. doi:10.1002/env.2301
   Web of Science ®Google Scholar
• Epstein, E.S., 1985. Statistical inference and prediction in climatology: a Bayesian approach. Boston, MA: American Meteorological Society, Meteorological Monographs 20.
   Google Scholar
• Gaetan, C. and Grigoletto, M., 2007. A hierarchical model for the analysis of spatial rainfall extremes. Journal of Agricultural, Biological and Environmental Statistics, 12 (4), 434–449. doi:10.1198/108571107X250193
   Web of Science ®Google Scholar
• Gallego, M.C., et al., 2011. Trends in frequency indices of daily precipitation over the Iberian Peninsula during the last century. Journal of Geophysical Research, 116, D02109. doi:10.1029/2010JD014255
   Web of Science ®Google Scholar
• Gallego, M.C., García, J.A., and Vaquero, J.M., 2005. The NAO signal in daily rainfall series over the Iberian Peninsula. Climate Research, 29, 103–109. doi:10.3354/cr029103
   Web of Science ®Google Scholar
• García, J.A., et al., 2007. Trends in block-seasonal extreme rainfall over the Iberian Peninsula in the second half of the twentieth century. Journal of Climate, 20, 113–130. doi:10.1175/JCLI3995.1
   Web of Science ®Google Scholar
• Gelman, A., Meng, X.L., and Stern, H., 1996. Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica – Journal, 6, 733–807.
   Web of Science ®Google Scholar
• Gilks, W.R., Richardson, S., and Spiegelhalter, D.J., 1996. Introducing Markov Chain Monte Carlo. In: Markov Chain Monte Carlo in practice. New York: Chapman & Hall.
   Google Scholar
• Goodess, C.M. and Jones, P.D., 2002. Links between circulations and changes in the characteristics of Iberian rainfall. International Journal of Climatology, 22, 1593–1612. doi:10.1002/joc.810
   Web of Science ®Google Scholar
• Greatbatch, R.J., 2000. The North Atlantic oscillation. Stochastic Environmental Research and Risk Assessment, 14, 213–242. doi:10.1007/s004770000047
   Web of Science ®Google Scholar
• Koutsoyiannis, D. and Montanari, A., 2015. Negligent killing of scientific concepts: the stationarity case. Hydrological Sciences Journal, 60, 1174–1183. doi:10.1080/02626667.2014.959959
   Web of Science ®Google Scholar
• Link, W.A., and Barker, R.J., 2010. Bayesian inference with ecological applications. San Diego, CA: Academic Press.
   Google Scholar
• Lynch, S.M. and Bruce, W., 2004. Bayesian posterior predictive checks for complex models. Sociological Methods & Research, 32, 301–335. doi:10.1177/0049124103257303
   Web of Science ®Google Scholar
• Milly, P.C.D., et al., 2008. Stationarity is dead: whither water management? Science, 319, 573–574. doi:10.1126/science.1151915
   PubMed Web of Science ®Google Scholar
• Min, S.-K., et al., 2011. Human contribution to more-intense precipitation extremes. Nature, 470, 378–381. doi:10.1038/nature09763
   PubMed Web of Science ®Google Scholar
• Montanari, A. and Koutsoyiannis, D., 2014. Modeling and mitigating natural hazards. Stationarity is Immortal! Water Resources Research, 50, 9748–9756. doi:10.1002/2014WR016092
   Web of Science ®Google Scholar
• Pall, P., et al., 2011. Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature, 470, 382–385. doi:10.1038/nature09762
   PubMed Web of Science ®Google Scholar
• Plummer, M., et al., 2006. CODA: convergence diagnosis and output analysis for MCMC. R News, 6, 7–11.
   Google Scholar
• Queralt, S., et al., 2009. North Atlantic oscillation influence and weather types associated with winter total and extreme precipitation events in Spain. Atmospheric Research, 94, 675–683. doi:10.1016/j.atmosres.2009.09.005
   Web of Science ®Google Scholar
• Ragulina, G. and Reitan, T., 2017. Generalized extreme value shape parameter and its nature for extreme precipitation using long time series and the Bayesian approach. Hydrological Sciences Journal, 62 (6), 863–879. doi:10.1080/02626667.2016.1260134
   Web of Science ®Google Scholar
• Renard, B., 2011. A Bayesian hierarchical approach to regional frequency analysis. Water Resources Research, 47, W11513. doi:10.1029/2010WR010089
   Web of Science ®Google Scholar
• Renard, B., Garreta, V., and Lang, M., 2006. An application of Bayesian analysis and Markov chain Monte Carlo methods to the estimation of a regional trend in annual maxima. Water Resources Research, 42, W12422. doi:10.1029/2005WR004591
   Web of Science ®Google Scholar
• Renard, B. and Lall, U., 2014. Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields. Water Resources Research, 50, 9536–9554. doi:10.1002/2014WR016277
   Web of Science ®Google Scholar
• Renard, B., Sun, X., and Lang, M., 2013. Bayesian methods for non-stationary extreme value analysis. In: A. AghaKouchak, et al., eds. Extremes in a changing climate: detection, analysis and uncertainty. Water Science and Technology Library. New York: Springer, 39–95.
   Google Scholar
• Ribatet, M., Dombry, C., and Oesting, M., 2016. Spatial extremes and max-stable processes. In: D.K. Dey and J. Jan, eds. Extreme value modeling risk analysis, methods and applications. New York: CRC Press, 179–194.
   Google Scholar
• Rodríguez-Puebla, C., et al., 1998. Spatial and temporal patterns of annual precipitation variability over the Iberian Peninsula. International Journal of Climatology, 18, 299–316. doi:10.1002/(SICI)1097-0088(19980315)18:3<299::AID-JOC247>3.0.CO;2-L
   Web of Science ®Google Scholar
• Sang, H. and Gelfand, A.E., 2009. Hierarchical modeling for extreme values observed over space and time. Environmental and Ecological Statistics, 16, 407–426. doi:10.1007/s10651-007-0078-0
   Web of Science ®Google Scholar
• Spiegelhalter, D.J., et al., 2014. The deviance information criterion: 12 years on. Journal of the Royal Statistical Society B, 76, 485–493. doi:10.1111/rssb.12062
   Google Scholar
• Sun, X., et al., 2014. A general regional frequency analysis framework for quantifying local-scale climate effects: a case study of ENSO effects on Southeast Queensland rainfall. Journal of Hydrology, 512, 53–68. doi:10.1016/j.jhydrol.2014.02.025
   Web of Science ®Google Scholar
• Trigo, R.M., et al., 2004. North Atlantic oscillation influence on the precipitation river flow and water resources in the Iberian Peninsula. International Journal of Climatology, 24, 925–944. doi:10.1002/joc.1048
   Web of Science ®Google Scholar
• Viglione, A., et al., 2013. Flood frequency hydrology: 3. A Bayesian analysis. Water Resources Research, 49 (2), 675–692. doi:10.1029/2011WR010782
   Web of Science ®Google Scholar
• Wikle, C.K. and Anderson, C.J., 2003. Climatological analysis of tornado report counts using a hierarchical Bayesian spatio-temporal model. Journal of Geophysical Research, 108, 9005. doi:10.1029/2002JD002806
   Web of Science ®Google Scholar
• Wikle, C.K., Berliner, M.L., and Cressie, N., 1998. Hierachical Bayesian space-time models. Environmental and Ecological Statistics, 5, 117–154. doi:10.1023/A:1009662704779
   Web of Science ®Google Scholar