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Determination of water level design for an estuarine city

  • Baiyu Chen
  • Guilin LiuEmail author
  • Liping WangEmail author
  • Kuangyuan Zhang
  • Shuaifang Zhang
Article
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Abstract

Based on the extreme value theory, self–affinity, and scale invariance, we studied the temporal and spatial relationship and the variation of water level and established a model of Gumbel–Pareto distribution for designed flood calculation. The model includes the previous extreme value models, the over–threshold data, and the fractal features shared by previous extreme value models. The model was simplified into a logarithmic normal distribution and a Pareto distribution for specific parameter values, and was used to calculate the designed flood values for the Shanghai Wusong Station in 100– and 1 000–year return periods. The calculated results show that the value of the designed flood height calculated in the Gumbel–Pareto distribution is between those in the Gumbel and Pearson–III distributions. The designed flood values in the 100–and 1 000–year return periods of the model were 0.03% and 0.11% lower, respectively, than the Gumbel distribution and 0.06% and 1.54% higher, respectively, than the Pearson–III distribution. Compared to the traditional model based solely on extreme probability, the Gumbel–Pareto distribution model could better describe the probabilistic characteristics of extreme marine elements and better use the data.

Keyword

self–affinity scale invariance extreme value distribution 

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Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of EngineeringUniversity of California BerkeleyBerkeleyUSA
  2. 2.College of EngineeringOcean University of ChinaQingdaoChina
  3. 3.College of Mathematical ScienceOcean University of ChinaQingdaoChina
  4. 4.Department of EconomicsPenn State UniversityState CollegeUSA
  5. 5.Department of Mechanical EngineeringUniversity of FloridaGainesvilleUSA

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