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

• Published in: Natural hazards and earth system sciences Vol. 20; no. 10; pp. 2681 - 2699
• Main Authors: Jane, Robert, Cadavid, Luis, Obeysekera, Jayantha, Wahl, Thomas
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 10.10.2020; Copernicus Publications
• Subjects: Analysis; Aquifers; Canals; Climate change; Coastal structures; Coastal waters; Coasts; Dependence; Dimensional analysis; Environmental risk; Flood control; Flood management; Flood protection; Flood risk; Flooding; Floods; Groundwater; Groundwater levels; Human factors; Hydraulics; Hydrology; Mathematical analysis; Mathematical models; Motor vehicle drivers; Rain; Rainfall; Rivers; Safety factors; Sea level; Sea level rise; Statistical models; Surface water; Tidal waves; Two dimensional analysis; Water control; Water levels; Water shortages; South Florida; Florida; United States > US; Miami-Dade County Florida
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-20-2681-2020

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. In Miami-Dade County water control structures were designed assuming full dependence between rainfall and Ocean-side Water Level (O-sWL), 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. A two-dimensional analysis of rainfall and O-sWL showed that the magnitude of the conservative assumption in the original design is highly sensitive to the regional sea level rise projection considered. 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. The work represents a first step towards the development of a new framework capable of capturing dependencies between different flood drivers that could potentially be incorporated into future Flood Protection Level of Service (FPLOS) assessments for coastal water control structures.