Articles | Volume 20, issue 10
https://doi.org/10.5194/nhess-20-2681-2020
https://doi.org/10.5194/nhess-20-2681-2020
Research article
 | 
10 Oct 2020
Research article |  | 10 Oct 2020

Multivariate statistical modelling of the drivers of compound flood events in south Florida

Robert Jane, Luis Cadavid, Jayantha Obeysekera, and Thomas Wahl

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Cited articles

Aas, K. and Berg, D.: Models for construction of multivariate dependence – a comparison study, Eur. J. Financ., 15, 639–659, 2009. 
Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair copula constructions of multiple dependence, Insurance: Math. Econ., 44, 182–198, 2009. 
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. 
Bedford, T. and Cooke, R. M.: Probability density decomposition for conditionally dependent random variables modeled by vines, Ann. Math. Artif. Intel., 32, 245–268, 2001. 
Bedford, T. and Cooke, R. M.: Vines – a new graphical model for dependent random variables, Ann. Stat., 30, 1031–1068, 2002. 
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Short summary
Full dependence is assumed between drivers in flood protection assessments of coastal water control structures in south Florida. A 2-D analysis of rainfall and coastal water level showed that the magnitude of the conservative assumption in the original design is highly sensitive to the regional sea level rise projection considered. The vine copula and HT04 model outperformed five higher-dimensional copulas in capturing the dependence between rainfall, coastal water level, and groundwater level.
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