Multivariate statistical modelling of compound flooding events for southern African coasts

Kelln, Jessica; Hirt, Matthias; Dangendorf, Sönke; Arns, Arne; Schwarzkopf, Franziska U.; Aguilar, Sara Santamaria; Jensen, Jürgen

doi:10.1002/essoar.10501757.1

Cited by 1 publication

(1 citation statement)

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“…To address this issue, a detailed understanding of potential compounding effects is necessary when building/upgrading flood risk reduction measures or performing risk analyses to improve resilience against these high-impact compound coastal and riverine flooding events. Global and regional assessments provide insights into compound flooding potential over larger spatial scales (Couasnon et al, 2020;Eilander et al, 2020;Hendry et al, 2019;Kelln et al, 2020;Marcos et al, 2019;Ward et al, 2018), sometimes also analysing past trends (Wahl et al, 2015), or predicting future variability (Bevacqua et al, 2019;Moftakhari et al, 2017). However, every estuarine system is unique and local assessments of the interaction between flooding drivers provide a deeper understanding of the underlying processes driving compounding effects at a specific location (e.g., Bender et al, 2016;Bevacqua et al, 2017;Couasnon et al, 2018;Jane et al, 2020;Moftakhari et al, 2019;Serafin et al, 2019).…”

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confidence: 99%

Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints

Santos

Wahl

Jane

et al. 2021

J Flood Risk Management

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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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