Document Type : Research Paper

Authors

1 Shahrekord University

2 Assistant Professor, Department of Water Engineering, Shahrekord University

3 Water Resources Allocation Expert in Ministry of Energy, Tehran, Iran.

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 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.

Keywords

 [1] Aas, K., Czado, C., Frigessi, A. and Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44(2), 182–198.
[2] Ayantobo, O. O., Li, Y. and Song, S. (2019). Multivariate drought frequency analysis using four-variate symmetric and asymmetric Archimedean copula functions. Water Resources Management, 33(1), 103–127.
[3] Bedford, T. and Cooke, R. M. (2002). Vines: A new graphical model for dependent random variables. Annals of Statistics, 1031–1068.
[4] Dayal, K. S., Deo, R. C. and Apan, A. A. (2019). Development of copula-statistical drought prediction model using the standardized precipitation-evapotranspiration index. In Handbook of Probabilistic Models. Elsevier Inc.
[5] Favre, A. C., Adlouni, S. El, Perreault, L., Thiémonge, N. and Bobée, B. (2004). Multivariate hydrological frequency analysis using copulas. Water Resources Research, 40(1), 1–12.
[6] Grimaldi, S. and Serinaldi, F. (2006). Asymmetric copula in multivariate flood frequency analysis. Advances in Water Resources, 29(8), 1155–1167.
[7] Jiang,C.,Xiong,L.,Yan,L., Dong, J. and Xu, C.Y. (2019). Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice. Hydrology and Earth System Sciences, 23(3), 1683–1704.
[8] Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC Press.
[9] Latif, S. and Mustafa, F. (2020). Trivariate distribution modelling of flood characteristics using copula function—A case study for Kelantan River basin in Malaysia. AIMS Geosciences, 6(1), 92–130.
[10] Mirabbasi, R., Fakheri-Fard, A. and Dinpashoh, Y. (2012). Bivariate drought frequency analysis using the copula method. Theoretical and Applied Climatology, 108(1–2), 191–206.
[11] Nash, J. E. and Sutcliffe, J. V. (1970). ’ L ~ E Empirical or Analytical Approaeb. Journal of Hydrology, 10(3), 282–290.
[12] Nguyen-Huy, T., Deo, R. C., An-Vo, D. A., Mushtaq, S. and Khan, S. (2017). Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones. Agricultural Water Management, 191(September), 153–172.
[13] Pereira, G. and Veiga, Á. (2018). PAR(p)-vine copula based model for stochastic streamflow scenario generation. Stochastic Environmental Research and Risk Assessment, 32(3), 833–842.
[14] Salvadori, G. and De Michele, C. (2006). Statistical characterization of temporal structure of storms. Advances in Water Resources, 29(6), 827–842.
[15] Shafaei, M., Fakheri-Fard, A., Dinpashoh, Y., Mirabbasi, R. and De Michele, C. (2017). Modeling flood event characteristics using D-vine structures. Theoretical and Applied Climatology, 130(3–4), 713–724.
[16] Sklar, A., SKLAR, A. and Sklar, C. A. (1959). Fonctions de reprtition an dimensions et leursmarges.
[17] Snyder, W. M. (1962). Some possibilities for multivariate analysis in hydrologic studies. Journal of Geophysical Research, 67(2): 721–729.
[18] Wong, S. T., Gray, D. M. and Hydro-, D. (1958). Mean Annual Flood I N New England ’. 298–311.
[19] Vernieuwe, H., Vandenberghe, S., De Baets, B. and Verhoest, N. E. C. (2015). A continuous rainfall model based on vine copulas. Hydrology and Earth System Sciences, 19(6), 2685–2699.
[20] Zhang, L. and Singh, V. P. (2007). Trivariate flood frequency analysis using the Gumbel–Hougaard copula. Journal of Hydrologic Engineering, 12(4), 431–439.