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Application of Nested Copulas for Flood Frequency Analysis (Case Study: Dez Basin, Iran) | ||
| Water Harvesting Research | ||
| دوره 9، شماره 1، 2026، صفحه 57-70 اصل مقاله (869.19 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2026.11256.1205 | ||
| نویسندگان | ||
| Alireza Amirrezaeieh1؛ Rasoul Mirabbasi* 2؛ Ali Saremi1 | ||
| 1Department of Agricultural Systems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. | ||
| 2Department of Water Engineering, Shahrekord University, Shahrekord, Iran./Water Sciences and Hydroinformatics Research Center, Khazar University, Mahsati str. 41, AZ 1096, Baku, Azerbaijan | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Archimedean؛ copula؛ joint analysis؛ marginal distribution؛ power law | ||
| مراجع | ||
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Ahmadi, F., Radmaneh, F., Parham, G., & Mirabbasi Najafabadi, R. (2017). Application of Archimedean Copula Functions in Flood Frequency Analysis (Case Study: Dez Basin). Iranian Journal of Soil and Water Research, 48(3), 477-489.
Balistrocchi, M., & Bacchi, B. (2017). Derivation of flood frequency curves through a bivariate rainfall distribution based on copula functions: application to an urban catchment in northern Italy's climate. Hydrology Research, 1-10.
Dastourani, M., & Nazeri Tahroudi, M. (2022). Toward coupling of groundwater drawdown and pumping time in a constant discharge. Applied Water Science, 12(4), 1-13.
Hosking, J. R. M. & Wallis, J. R. (1988). The effect of intersite dependence on regional flood frequency analysis. Water Resources Research, 24(4), 588-600.
Joe, H. (1997). Multivariate models and multivariate dependence concepts. London: Chapman & Hall, 399 pp.
Karmakar, S., & Simonovic, S. P. (2009). Bivariate flood frequency analysis. Part 2: a copula‐based approach with mixed marginal distributions. Journal of Flood Risk Management, 2(1), 32-44.
Khashei‐Siuki, A., Shahidi, A., Ramezani, Y., & Nazeri Tahroudi, M. (2021). Simulation of potential evapotranspiration values based on vine copula. Meteorological Applications, 28(5), e2027.
Ming, X. Xu, W. Li, Y. Du, J. Liu, B. & Shi, P. (2015). Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period. Stochastic environmental research and risk assessment, 29(1), 35-44.
Mukherjee, A., et al. (2025). Assessing compound flood drivers in Peninsular India: Multivariate copula‑based approach. Journal of Environmental Management, 395, 127875. https://doi.org/10.1016/j.jenvman.2025.127875
Nazeri Tahroudi, M. (2025). Comprehensive global assessment of precipitation trend and pattern variability considering their distribution dynamics. Sci Rep 15, 22458. https://doi.org/10.1038/s41598-025-06050-5
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2022). Trivariate joint frequency analysis of water resources deficiency signatures using vine copulas. Applied Water Science, 12(4), 1-15.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2021a). Flood routing via a copula-based approach. Hydrology Research, 52(6), 1294-1308.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2021b). Multivariate analysis of rainfall and its deficiency signatures using vine copulas. International Journal of Climatology. 42(4): 2005-2018.
Nelsen. R. B. (2006). An introduction to copulas. Springer, New York, 269p.
Raji, M., Tahroudi, M. N., Ye, F., & Dutta, J. (2022). Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models. Journal of Environmental Management, 307, 114518.
Reddy, M. J., & Ganguli, P. (2012). Bivariate flood frequency analysis of Upper Godavari River flows using Archimedean copulas. Water Resources Management, 26(14), 3995-4018.
Rehamnia, I., Nazeri Tahroudi, M. & Saeidinia, M. Evaluating the role of dimensionality and complexity structure in time series models for precipitation simulation. Model. Earth Syst. Environ. 12, 4 (2026). https://doi.org/10.1007/s40808-025-02649-9
Saad, C. El Adlouni, S. St-Hilaire, A. & Gachon, P. (2015). A nested multivariate copula approach to hydrometeorological simulations of spring floods: the case of the Richelieu River (Québec, Canada) record flood. Stochastic Environmental Research and Risk Assessment, 29(1), 275-294.
Saeidinia, M., Haghiabi, A.H., Nazeri Tahroudi, M. et al. 2025. Deep learning and vine copula-based sequencing: approaches under investigation for forecasting soil temperature dynamics. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-025-03061-6
Salvadori, G. and De Michele, C. (2007). On the use of copulas in hydrology: theory and practice. Journal of Hydrologic Engineering, 12(4), 369-380.
Sklar A (1959) Fonctions de Repartition and Dimensions et LeursMarges. Publications de L’Institute de Statistique, Universite’ de Paris, Paris, 8: 229–231.
Snyder, W. M. (1962). Some possibilities for multivariate analysis in hydrologic studies. Journal of geophysical research, 67(2), 721-729.
Soltaninia, S., & Eskandaripour, M. (2025). Trivariate flood frequency analysis using copula functions: Assessing non‑stationary hydrological risks under climate change. Journal of Water and Climate Change, 16(10), 3130–3156. https://doi.org/10.2166/wcc.2025.068
Sraj, M., Bezak, N., & Brilly, M. (2015). Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River. Hydrological Processes, 29(2), 225-238.
Sun, B., Liu, X., Wang, G., Miao, P., Xie, K., & Ma, H. (2026). Multivariate joint risk assessment of small‑ and medium‑sized river flood in arid and semi‑arid regions based on vine copula. Water, 18(9), 1098. https://doi.org/10.3390/w18091098
Wong, S. T. (1963). A multivariate statistical model for predicting mean annual flood in New England. Annals of the Association of American Geographers, 53(3), 298-311.
Xiao, Y., Guo, S., Liu, P., & Fang, B. (2008). A new design flood hydrograph method based on bivariate joint distribution. IAHS Publications-Series of Proceedings and Reports, 319, 75-82.
Yue, S. & Rasmussen, P. (2002). Bivariate frequency analysis: discussion of some useful concepts in hydrological application. Hydrological Processes, 16(14), 2881-2898.
Yue, S. Ouarda, T. B. M. J. & Bobée, B. (2001). A review of bivariate gamma distributions for hydrological application. Journal of Hydrology, 246(1), 1-18.
Zhang, L. & Singh, V. P. (2006). Bivariate flood frequency analysis using the copula method. Journal of Hydrologic Engineering, 11(2), 150-164. | ||
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