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The Influence of Statistical Uncertainty in the Hydraulic Boundary Conditions on the Probabilistically Computed High Water Level Frequency Curve in the Rhine Delta

机译:水力边界条件下的统计不确定性对莱茵河三角洲概率计算的高水位频率曲线的影响

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The hydrodynamic characteristics of a delta or estuary are mainly governed by discharges of rivers and water level at the sea (or lake) boundaries. A joint probability approach is widely applied to quantify the high water level frequency in deltas. In the approach the relevant hydrodynamic loading variables, namely the astronomical tides, the wind induced storm surge and the river flows, are jointly investigated. The joint probability distribution is used to generate a large number of scenarios of boundary conditions which can drive a deterministic model to derive the water levels at locations of interest. The resulting water levels as well as their associated joint probabilities can be inverted to the high water level frequency curve. However, in the joint probability distribution, marginal distributions may contain large statistical uncertainties due to their relevant parameters being estimated from a limited length of data. In the case of the Rhine Delta, a nonparametric bootstrap method is applied to quantify the statistical uncertainties in three critical marginal distributions: wind induced storm surge peak level, wind induced storm surge duration and River Rhine discharge. The uncertainties are incorporated into the marginal distributions with a Monte Carlo integration method. Further the uncertainty-incorporated marginal distributions are used for the high water level frequency assessment. Compared to previous studies, water levels for given return periods are much higher. The uncertainty differs in each marginal distribution and its impact on the high water level frequency curve also varies.
机译:三角洲或河口的水动力特性主要由河流(或湖泊)边界的流量和水位控制。联合概率方法被广泛应用于量化三角洲中的高水位频率。在该方法中,共同研究了相关的水动力载荷变量,即天文潮汐,风引发的风暴潮和河流流量。联合概率分布用于生成大量边界条件场景,这些场景可以驱动确定性模型来推导感兴趣位置的水位。所产生的水位及其相关的联合概率可以反转为高水位频率曲线。但是,在联合概率分布中,由于从有限的数据长度中估计了它们的相关参数,因此边际分布可能包含较大的统计不确定性。对于莱茵河三角洲,采用非参数自举法来量化三个临界边际分布中的统计不确定性:风诱发的风暴潮峰值水平,风诱发的风暴潮持续时间和莱茵河流量。使用蒙特卡洛积分方法将不确定性纳入边际分布。此外,将不确定性合并的边际分布用于高水位频率评估。与以前的研究相比,给定返回期的水位要高得多。每个边际分布的不确定性都不同,其对高水位频率曲线的影响也不同。

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