Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
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DOI：
10.1111/jfr3.12749
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发表时间：
2021-08
影响因子：
4.1
通讯作者：
V. M. Santos;T. Wahl;R. Jane;S. Misra;K. White
中科院分区：
文献类型：
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作者：
V. M. Santos;T. Wahl;R. Jane;S. Misra;K. White
复合驱油可能是两个或两个以上的过程相互作用的结果，这些过程本身可能不是极端的，但结合起来会导致极端的影响。在这里，我们使用统计方法来评估风暴潮和多个河流排放萨宾湖，得克萨斯州的复合效应。我们采用了几个三变量统计模型，包括vine‐ copula和条件极值模型，以检查结果对数据预处理步骤，统计模型设置和离群值的选择的敏感性。我们定义了一个响应函数，表示水位产生的放电和浪涌过程之间的相互作用，在萨宾湖，并探讨它是如何影响包括或忽略之间的依赖关系的贡献洪水驱动程序。我们的研究结果表明，占的依赖性导致水位高达30厘米高的2%的年度发生概率（AEP）事件和高达35厘米高的1%的AEP事件，假设独立相比。我们还发现，在不同的抽样方案，多变量模型配置，和离群值去除的敏感性的结果显着的变化。在数据有限的情况下，这突出表明需要测试各种统计建模方法，而选择最佳方法仍然是主观的。
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.