用Copula模型确定德国波罗的海海岸防护结构的水动力设计参数、水位和波高

ce/papers Pub Date : 2025-09-05 DOI:10.1002/cepa.3359
Christian Kaehler, Fokke Saathoff
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引用次数: 0

摘要

海岸防护结构的设计要求设计参数能准确地反映海岸水动力条件。目前,这些输入变量都是基于单变量概率模型,没有考虑水位和波浪的联合概率。用Copula模型对概率进行二元建模提供了另一种选择。copula可以用来描述水位和波高之间的非线性依赖关系,并计算发生的联合概率。然而,这种方法的应用对基础数据提出了更高的要求。由于研究区域现有的数据不符合要求,因此使用统计方法生成数据。首先,各种copula适应了从风暴潮事件中提取的水位和波高的物理一致组合并进行了验证。接下来,copula用于计算选定返回间隔的设计水位和波高。以堤防上浪的简化设计为例,对二元设计参数与一元设计参数进行了比较。各种模型的验证表明,Frank Copula最能描述依赖结构。用相同的返回间隔确定的二元参数高度低于用单变量方法确定的参数。现有的数据只允许有限的copula应用于研究领域的设计问题。然而,copula有潜力取代单变量方法来确定设计参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of the hydrodynamic design parameters water level and wave height using Copula models for the design of coastal protection structures on the Baltic Sea of Germany

The design of coastal protection structures requires design parameters that accurately represent the hydrodynamic conditions along the coast. Currently, these input variables are based on univariate probability models, which do not take into account the joint probability of water levels and waves. Bivariate modeling of the probability with Copula models offers an alternative.

Copulas can be used to describe the non-linear dependencies between water level and wave height and to calculate joint probabilities of occurrence. However, the application of this methodology places greater demands on the underlying data. As the data available in the study area does not meet the requirements, statistical methods are used to generate the data. First, various Copulas are adapted to physically consistent combinations of water level and wave height extracted from storm surge events and validated. Next, the Copulas are used to calculate design water levels and wave heights for selected return intervals. The bivariate design parameters are compared with the univariate ones in a simplified design example for wave run-up on a dike.

The validation of various models shows that the Frank Copula best describes the dependency structure. The bivariate parameter heights determined with the same return intervals are lower than the parameters determined with the univariate method. The available data only allow a limited application of the Copulas for design issues in the study area. Nevertheless, Copulas have the potential to replace the univariate methods for determining the design parameters.

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