Sasan Amini; Rafat Zare Bidaki; Rasoul Mirabbasi; Marym Shafaei
Abstract
In this study, we applied the vine copula structures for multivariate analysis of flood characteristics. For this purpose, the hydrographs of 98 flood events recorded at Landi station in Bazoft watershed, in Chaharmahal va Bakhtiari Province, were selected and the flood characteristics, including peak ...
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In this study, we applied the vine copula structures for multivariate analysis of flood characteristics. For this purpose, the hydrographs of 98 flood events recorded at Landi station in Bazoft watershed, in Chaharmahal va Bakhtiari Province, were selected and the flood characteristics, including peak flood (P), flood volume (V), flood duration (D) and time to peak (T) were extracted. Then, the best fitted distribution on each variable was selected by Kolmogorov-Smirnov test. In the next phase, the C-vine and D-vine structure were created considering three (P,V and T/D) and four variables (P,D,T and V) in changeable orders. In this way, the flood volume and peak were considered in a constant combination, and flood duration or the time to peak were consideredchangeable in tri-variate joints. In the four-variable joints, different combinations of all four variables were used. We used Gumbel, Frank, Joe, Clayton, Gaussian and t-student copula functions to combine these variables. The results obtained from the theoretical joint were compared with the experimental joint of that compound. Results showed that the best permutations of C-vine and D-vine copulas are similar in trivariate models TPV, (NSE=0.913), and the Gumbel and Gaussian copulas have selected as the best-fitted copula at the edges. In four-variate cases, the best C-vine and D-vine structures were PVTD and PTVD, (NSE=0.989) and the Gumbel and Gaussian were the abundant copulas in both of C-vine and D-vine models. The results indicated that the four-variate vine structures have higher concordance with the empirical copula than the tri-variate structures.
