Application of Nested Copulas for Flood Frequency Analysis (Case Study: Dez Basin, Iran)

Document Type : Research Paper

Authors

1 Department of Agricultural Systems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Water Engineering, Shahrekord University, Shahrekord, Iran./Water Sciences and Hydroinformatics Research Center, Khazar University, Mahsati str. 41, AZ 1096, Baku, Azerbaijan

10.22077/jwhr.2026.11256.1205

Abstract

One of the hydrological phenomena with a very complex nature is the flood phenomenon, which causes a lot of damage if it occurs. In this study, the joint frequency analysis of flood characteristics at the Sepid Dasht-Zaz station in the Dez basin, Iran, over a 30-year period was investigated using nested copula functions. For this purpose, the flood characteristics such as peak flow, flood volume and base time of the flood were used. By fitting 11 different distribution functions to the studied series, finally, based on statistical tests, the generalized extreme value distribution (GEV) was chosen as a suitable marginal distribution for the studied variables. After selecting the marginal distribution, Archimedean family copulas (including Frank, Ali-Mikhail-Haq, and Clayton) were used for joint frequency analysis of the paired flood characteristics (peak flow and flood volume, peak flow and time base and flood volume and time base). The results showed that Frank and Clayton for the mentioned pair-variables are most consistent with the empirical copulas. The joint return period was used to investigate the trivariate return period of events. The results of joint analysis of flood characteristics in the study area resulted in the typical return period curves for Sepid Dasht-Zaz, which provides the return period of each flood characteristic with different probabilities and given by other flood characteristics. Since the characteristics of floods are different in each flood event, univariate flood analysis does not take into account all characteristics, and hence the use of nested copulas by increasing dimension of analysis can provide more realistic results. 

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References

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Water Harvesting Research
Volume 9, Issue 1
Issue in Progress
2026
Pages 57-70

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