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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/nhess-2020-82
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-82
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Apr 2020

Submitted as: research article | 02 Apr 2020

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This preprint is currently under review for the journal NHESS.

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

Robert Jane1, Luis Cadavid2, Jayantha Obeysekera3, and Thomas Wahl1 Robert Jane et al.
  • 1Civil, Environmental and Construction Engineering & National Center for Integrated Coastal Research, University of Central Florida, 12800 Pegasus Drive, Orlando, FL 32816, USA
  • 2Operational Hydraulics Unit – Applied Hydraulics Section, South Florida Water Management District, West Palm Beach, FL35406, USA
  • 3Sea Level Solutions Center, Florida International University, Miami, FL 33199, USA

Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea-level rise in the United States, due to a variety of natural and human factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbates impacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood risk and under-design of flood defence structures. At present, design assessments of flood defence structures in Miami-Dade County assume rainfall and Ocean-side Water Level (O-sWL) are fully dependent, a conservative assumption inducing large safety factors. Here, an analysis of the dependence between the principal flooding drivers over a range of lags at three locations across the county is carried out. The conservative nature of the existing structural design assessment is subsequently explored, by combining a two-dimensional analysis of rainfall and O-sWL with regional sea-level rise projections. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher dimensional copulas in capturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. This leads to recommendations for revised future design frameworks able to capture and represent dependencies between different flood drivers.

Robert Jane et al.

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Robert Jane et al.

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Latest update: 31 May 2020
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