Articles | Volume 4, issue 1/2
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, 2018
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, 2018
06 Dec 2018
06 Dec 2018

An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging

Tony E. Wong

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Cited articles

Arns, A., Wahl, T., Haigh, I. D., Jensen, J., and Pattiaratchi, C.: Estimating extreme water level probabilities: A comparison of the direct methods and recommendations for best practise, Coast. Eng., 81, 51–66,, 2013. 
Buchanan, M. K., Oppenheimer, M., and Kopp, R. E.: Amplification of flood frequencies with local sea level rise and emerging flood regimes, Environ. Res. Lett., 12, 064009,, 2017. 
Bulteau, T., Idier, D., Lambert, J., and Garcin, M.: How historical information can improve estimation and prediction of extreme coastal water levels: application to the Xynthia event at La Rochelle (France), Nat. Hazards Earth Syst. Sci., 15, 1135–1147,, 2015. 
Caldwell, P. C., Merrfield, M. A., and Thompson, P. R.: Sea level measured by tide gauges from global oceans – the Joint Archive for Sea Level holdings (NCEI Accession 0019568), Version 5.5, NOAA Natl. Centers Environ. Information, Dataset,, 2015. 
Ceres, R., Forest, C. E., and Keller, K.: Understanding the detectability of potential changes to the 100-year peak storm surge, Clim. Change, 145, 221–235,, 2017. 
Short summary
Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.