Challenging the standard dike freeboard: Methods to quantify statistical uncertainties in river flood protection

References

• Baker, V. R. 2008. Paleoflood hydrology: Origin, progress, prospects. Geomorphology 101: 1–13.
   Web of Science ®Google Scholar
• Balkema, A. A., and L. de Haan. 1974. Residual lifetime at great age. The Annals of Probability 2: 792–804.
   Web of Science ®Google Scholar
• Behrens, C. N., H. F. Lopes, and D. Gamerman. 2004. Bayesian analysis of extreme events with threshold estimation. Statistical Modelling 4: 227–244.
   Web of Science ®Google Scholar
• Beirlant, J., Y. Goegebeur, J. Segers, and J. Teugels. 2004. Statistics of extremes Theory and applications. Wiley Series in Probability and Statistics.
   Google Scholar
• Brandimarte, L., and G. Di Baldassarre. 2012. Uncertainty in design flood profiles derived by hydraulic modelling. Hydrology Research 43: 753–761.
   Web of Science ®Google Scholar
• British Columbia Ministry of Water, Land and Air Protection. 2003. Dike design and construction guide. Best management practices for British Columbia. http://www.env.gov.bc.ca/wsd/publicsafety/flood/pdfsword/dikedesconsguidejuly-2011.pdf
   Google Scholar
• Coles, S. G. 2001. An introduction to statistical modelling of extreme values. Bristol: Springer Series in Statistics.
   Google Scholar
• Coles, S. G., and E. A. Powell. 1996. Bayesian methods in extreme value modelling: A review and new developments. International Statistical Review 64: 119–136.
   Web of Science ®Google Scholar
• Coles, S. G., and J. A. Tawn. 1996. A Bayesian analysis of extreme rainfall data. Journal of the Royal Statistical Society C 45: 463–478.
   Web of Science ®Google Scholar
• Davison, A. C., and R. L. Smith. 1990. Models for exceedances over high thresholds (with discussion). Journal of the Royal Statistical Society B 52: 393–442.
   Google Scholar
• Eastoe, E. F., and J. A. Tawn. 2009. Modelling non-stationary extremes with application to surface level ozone. Journal of the Royal Statistical Society C (Applied Statistics) 58: 25–45.
   Web of Science ®Google Scholar
• Environment Agency. 2000. Fluvial freeboard guidance note. R&D Technical Report W187.
   Google Scholar
• Ferro, C. A. T., and J. Segers. 2003. Inference for clusters of extreme values. Journal of the Royal Statistical Society B 65: 545–556.
   Google Scholar
• Jakob, M., and M. Church. 2011. The trouble with floods. Canadian Water Resources Journal 36: 287–292.
   Web of Science ®Google Scholar
• MacDonald, A., C. J. Scarrott, D. Lee, B. Darlow, M. Reale, and G. Russell. 2011. A flexible extreme value mixture model. Computational Statistics & Data Analysis 55: 2137–2157.
   Web of Science ®Google Scholar
• Madsen, H., P. F. Rasmussen, and D. Rosbjerg. 1997. Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events. Water Resources Research 33: 747–757.
   Web of Science ®Google Scholar
• Milly, P. C. D., J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer. 2008. Stationarity is dead: Wither water management? Science 319: 573–574.
   PubMed Web of Science ®Google Scholar
• MMM Group Ltd. 2014. National floodplain mapping assessment – Final report. Issued for Public Safety Canada. June.
   Google Scholar
• Neill, C. R., and W. E. Watt. 2001. Report on six case studies of flood frequency analyses. Technical report Alberta Transportation, Transportation and Civil Engineering Division, Civil Projects Branch.
   Google Scholar
• Natural Environment Research Council (NERC). 1975. Flood studies report. Technical Report 1, NERC, London.
   Google Scholar
• Nolde, N., and H. Joe. 2013. A Bayesian extreme value analysis of debris flow. Water Resources Research 49: 7009–7022.
   Web of Science ®Google Scholar
• O’Connell, D. R. H., D. A. Ostenaa, D. R. Levish, and R. E. Klinger. 2002. Bayesian flood frequency analysis with paleohydrologic bound data. Water Resources Research 38: 16-1–16–13.
   Google Scholar
• Önöz, B., and M. Bayazit. 1995. Best-fit distributions of largest available flood samples. Journal of Hydrology 167: 195–208.
   Web of Science ®Google Scholar
• Pickands, J. III. 1975. Statistical inference using extreme order statistics. The Annals of Statistics 3: 119–131.
   Web of Science ®Google Scholar
• R Core Team. 2013. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ ( accessed December, 2014).
   Google Scholar
• Smith, R. L. 1989. Extreme value analysis of environmental time series: An example based on ozone data (with discussion). Statistical Science 4: 367–393.
   Google Scholar
• Tancredi, A., C. Anderson, and A. O’Hagan. 2006. Accounting for threshold uncertainty in extreme value estimation. Extremes 9: 87–106.
   Google Scholar
• United States Association of Civil Engineers. 1996. EM 1110-2-1619, Engineering and Design – Risk-Based Analysis for Flood Damage Reduction Studies. Technical Report.
   Google Scholar
• United States Federal Emergency Management Agency (FEMA). 2006. The national levee challenge. levees and the FEMA flood map modernization initiative. Technical report, Interagency Levee Policy Review Committee, Washington, DC.
   Google Scholar
• United States Water Resources Council. 1967. A uniform technique for determining flood flow frequencies. Washington DC, 15.
   Google Scholar
• Wadsworth, J. L., and J. A. Tawn. 2012. Likelihood-based procedures for threshold diagnostics and uncertainty in extreme value modelling. Journal of the Royal Statistical Society B 74: 543–567.
   Google Scholar