Estimating the Probability of Compound Discharge/surge Events in a Complex Estuarine System Under Data Constraints

crossref(2021)

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摘要
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. Estuarine environments are particularly prone to compound flooding due to the interplay between coastal storm surge and river discharge processes, both often being driven by the same storm event. A detailed understanding of compounding mechanisms, including the dependence between flooding drivers, is necessary to avoid flood risk miscalculations when building/upgrading flood defences to mitigate risks associated with high impact events. Here, we use statistical methods to assess compound flooding potential in Sabine Lake, TX. Sabine Lake receives discharge from two rivers and is connected to the Gulf of Mexico coast through Sabine Pass. These geographic characteristics make it susceptible to compound flooding. We employ several trivariate statistical models (and simplified bivariate models for comparison) to examine the sensitivity of results to the choice of data pre-processing steps, statistical model setup, and outlier removal. We define a response function that represents water levels resulting from the interaction between discharge and storm surge inside Sabine Lake, and explore how the water level response 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. This highlights the need for testing various statistical modelling approaches in order to reliably capture potential compounding effects, especially under data constraints.
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