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. 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.

Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine-copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre-processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.

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Two-sided extreme conditional sampling is regularly coupled with copula theory to assess the dependence between flood-risk drivers such as extreme precipitation or river discharge, and storm surge. The approach involves many subjective choices. Choices include sampling techniques used to identify extreme events (Block maxima or Peaks-over-Threshold; POT), whether to account for the fit of marginal distributions, and time-lags considered between the two drivers. In this study, estimates of the potential for compound events at three sites along the Texan Gulf Coast, where the U.S. Army Corps of Engineers (USACE) is undertaking Coastal Storm Risk Management (CSRM) projects, are shown to be highly sensitivity to model set-up. A pragmatic approach accounting for marginal fit in a POT framework is proposed and shown to 2 provide stable estimates of the compounding potential for high discharge and storm surge events. -We also explore the effect of using precipitation as a proxy for discharge in the absence of sufficiently long discharge records.Keywords (up to 8 alphabetical order): Compound flooding, Coastal flood risk, Copulas, Extreme value analysis, Gulf of Mexico, Multivariate statistical modelling, Sensitivity analysis.