Vulnerability assessment of dam water supply capacity based on bivariate frequency analysis using copula

Chulsang Yoo, Eunsaem Cho

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The vulnerability of the water supply capacity of a dam is defined as the expected deficit volume from a typical water deficit event. In this study, a water deficit event was assumed to be a rectangle composed of the deficit duration and deficit intensity whose occurrence probability was then estimated by the bivariate frequency analysis based on the copula method. This approach is different from the conventional one based on the assumption of the same occurrence probability for all events. This proposed method was applied to the Namgang dam in Korea as an example and the resulting estimate of the vulnerability was compared with the conventional method. First, the 'OR' concept was found to be better than the 'AND' concept in the calculation of the occurrence probability. Additionally, based on the consideration of multicollinearity, it could be concluded that the occurrence probability should be estimated by considering the water deficit intensity and duration. For the Namgang dam, the vulnerability was determined to be 9.11 × 106 m3, which is about 3% of the total storage capacity. This estimated vulnerability is also about 70% of the amount estimated by applying the conventional method with the same occurrence probability for all water deficit events.

Original languageEnglish
Article number1113
JournalWater (Switzerland)
Issue number9
Publication statusPublished - 2018 Aug 21

Bibliographical note

Publisher Copyright:
© 2018 by the authors.


  • Bivariate frequency analysis
  • Copula model
  • Vulnerability assessment
  • Water deficit event
  • Water supply capacity

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology


Dive into the research topics of 'Vulnerability assessment of dam water supply capacity based on bivariate frequency analysis using copula'. Together they form a unique fingerprint.

Cite this