Flood risk analysis based on nested copula structure in Armand Basin, Iran

• Autorzy: Amini Sasan , Bidaki Rafat Zare , Mirabbasi Rasoul , Shafaei Maryam

Identyfikatory

DOI

10.1007/s11600-022-00766-y

• Języki publikacji: EN

Abstrakty

EN

Merging different food characteristics in a distribution function is provided by copula structures. In this study, the nested copula structure was used to construct a triradiate distribution of food duration (D), peak (P), and volume (V). The required data were obtained by screening the food events recorded at Armand Gauging Station, Iran. The characteristics of selected 63 food events (1993–2018) were extracted and the best marginal distribution function of each was determined by Kolmogorov–Smirnov test. Then the fitness of six different copula functions (Frank, Clayton, Joe, Gumbel–Hougaard, Gaussian and Student’s t were examined for creating the joint distribution function. The best fitted marginal distribution is Johnson SB, for food duration, and Lognormal (3p), for food peak and food volume. The best-fitted function for creating bivariate and trivariate distributions of food characteristics in Armand Basin is Frank copula. In the next phase, the bivariate and trivariate joint return periods (at two states of AND, OR), Kendall return period and conditional return periods were calculated. The results revealed that the conditional return period of one food variable given two other food variables is greater than the corresponding values for the conditional return period of two food variables given the third food variable.

Słowa kluczowe

EN

copula; nested structure; joints distribution; multivariate food analysis; marginal distribution; Armand Basin

PL

kopuła; struktura zagnieżdżona; rozkład spękań; wielowymiarowa analiza żywności; rozkład brzegowy

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 3
• Strony: 1385--1399
• Opis fizyczny: Bibliogr. 48 poz.

Twórcy

autor

Amini Sasan

• Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran

autor

Bidaki Rafat Zare

• Department of Natural Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran

• https://orcid.org/0000-0003-2242-2821

autor

Mirabbasi Rasoul

• Department of Water Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

autor

Shafaei Maryam

• Water Resources Allocation Expert in Ministry of Energy, Tehran, Iran

Bibliografia

• 1. Abdi A, Hassanzadeh Y, Talatahari S et al (2017) Parameter estimation of copula functions using an optimization-based method. Theor Appl Climatol 129:21–32
• 2. Abdollahi S, Akhoond-Ali AM, Mirabbasi R, Adamowski JF (2019) Probabilistic event based rainfall-runoff modeling using copula functions. Water Resour Manag 33:3799–3814
• 3. Ahmadi F, Radmanesh F, Sharifi MR, Mirabbasi R (2018) Bivariate frequency analysis of low flow using copula functions (Case study: Dez River Basin, Iran). Environ Earth Sci. https://doi.org/10.1007/s12665-018-7819-2
• 4. Amarasinghe U, Amarnath G, Alahacoon N, Ghosh S (2020) How do floods and drought impact economic growth and human development at the sub-national level in India? Climate 8(11):123
• 5. Ashkar F, Rousselle J (1981) Design discharge as a random variable: a risk study. Water Resour Res 17:577–591. https://doi.org/10.1029/WR017i003p00577
• 6. Ayantobo OO, Li Y, Song S (2019) Multivariate drought frequency analysis using four-variate symmetric and asymmetric Archimedean copula functions. Water Resour Manag 33:103–127
• 7. Bacchi B, Becciu G, Kottegoda NT (1994) Bivariate exponential model applied to intensities and duration of extreme rainfall. J Hydrol 155:225–236
• 8. Bezak N, Mikoš M, Šraj M (2014) Trivariate frequency analyses of peak discharge, hydrograph volume and suspended sediment concentration data using copulas. Water Resour Manag 28:2195–2212
• 9. Chowdhary H, Singh (2009) Copula Approach for Reducing Uncertainty in Design Flood Estimates in Insufficient Data Situations. In: World Environmental and Water Resources Congress 2009: Great Rivers © 2009 ASCE 4679. pp 4679–4688
• 10. De Martonne E (1926) Aréisme et indice artidite. Comptes Rendus de l’Académie Des Sciences. Paris 182:1395–1398
• 11. Fan Y (2015) Uncertainty quantification of hydrologic predictions and risk analysis. 328
• 12. Ganguli P, Reddy MJ (2013) Probabilistic assessment of flood risks using trivariate copulas. Theor Appl Climatol 111:341–360. https://doi.org/10.1007/s00704-012-0664-4
• 13. Gao Y, Wang D, Zhang Z et al (2018) Analysis of flood risk of urban agglomeration polders using multivariate copula. Water 10:1470. https://doi.org/10.3390/w10101470
• 14. Genest C, Rivest LP (1993) Statistical inference procedures for bivariate Archimedean copulas. J Am Stat Assoc 88:1034–1043
• 15. Gr̈aler B, Van Den Berg MJ, Vandenberghe S, et al (2013) Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrol Earth Syst Sci 17:1281–1296https://doi.org/10.5194/hess-17-1281-2013
• 16. Guégan D, Hassani BK (2019) Risk measurement. Springer
• 17. Guo A, Chang J, Wang Y et al (2017) Maximum entropy-copula method for hydrological risk analysis under uncertainty: a case study on the Loess Plateau, China. Entropy. https://doi.org/10.3390/e19110609
• 18. Honarbakhsh A, Chogan M, Zare Bidaki R, Pazhuhesh M (2019) Regional frequency analysis of low flow in parts of the northern Karun river basin in chaharmahal and Bakhtiari province. Environ Water Eng 4(4):274–285
• 19. Jiang C, Xiong L, Yan L et al (2019) Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice. Hydrol Earth Syst Sci 23:1683–1704
• 20. Joe H (1997) Multivariate models and multivariate dependence concepts. CRC Press
• 21. Kristanovic PF, Singh VP (1987) A multivariate stochastic flood analysis using entropy. In: International Symposium on Flood Frequency and Risk Analyses. pp 515–540
• 22. Latif S, Mustafa F (2020) Trivariate distribution modelling of flood characteristics using copula function—a case study for Kelantan River basin in Malaysia. AIMS Geosci 6:92–130. https://doi.org/10.3934/geosci.2020007
• 23. Markiewicz I, Strupczewski WG, Kochanek K (2010) On accuracy of upper quantiles estimation. Hydrol Earth Syst Sci 14(11):2167–2175. https://doi.org/10.5194/hess-14-2167-2010
• 24. Mitková VB, Halmová D (2014) Joint modeling of flood peak discharges, volume and duration: a case study of the Danube River in Bratislava. J Hydrol Hydromech 62:186–196. https://doi.org/10.2478/johh-2014-0026
• 25. Mohammdpour O, Hassanzadeh Y, Khodadadi A, Saghafian B (2017) Probabilistic risk analysis of flood events using trivariate copulas. J Civ Environ Eng 46:63–75
• 26. Mohd Lokoman R, Yusof F (2019) Parametric estimation methods for bivariate copula in rainfall application. J Teknol 81:1–10. https://doi.org/10.11113/jt.v81.12059
• 27. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290
• 28. Nazeri Tahroudi M, Ramezani Y, De Michele C, Mirabbasi R (2021) Multivariate analysis of rainfall and its deficiency signatures using Vine Copulas. Int J Clim. https://doi.org/10.1002/joc.7349
• 29. Nazeri Tahroudi M, Ramezani Y, De Michele C, Mirabbasi R (2022) Bivariate simulation of potential evapotranspiration using Copula-GARCH model. Water Resour Manage. https://doi.org/10.1007/s11269-022-03065-9
• 30. Nelsen RB (2006) An introduction to copulas. Springer
• 31. Renard B, Lang M (2007) Use of a gaussian copula for multivariate extreme value analysis: some case studies in hydrology. Adv Water Resour 30:897–912. https://doi.org/10.1016/j.advwatres.2006.08.001
• 32. Rizwan M, Guo S, Yin J, Xiong F (2019) Deriving design flood hydrographs based on copula function: a case study in Pakistan. Water (switzerl). https://doi.org/10.3390/w11081531
• 33. 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. Stoch Environ Res Risk Assess 29:275–294
• 34. Sackl B, Bergmann H (1987) A Bivariate flood model and its application. In: International Symposium on Flood Frequency and Risk Analyses. pp 571–582
• 35. Salleh N, Yusof F, Yusop Z (2016) Bivariate copulas functions for flood frequency analysis. In: AIP Conference Proceedings
• 36. Salvadori G, De Michele C (2004) Frequency analysis via copulas: theoretical aspects and applications to hydrological events. Water Resour Res 40:1–17. https://doi.org/10.1029/2004WR003133
• 37. Sandoval CE, Raynal-Villaseñor J (2008) Trivariate generalized extreme value distribution in flood frequency analysis. Hydrol Sci J 53:550–567. https://doi.org/10.1623/hysj.53.3.550
• 38. Schweizer B, Wolff EF (1981) On nonparamentric measures of dependence for random variables. Ann Stat 9:879–885
• 39. Serinaldi F, Grimaldi S (2007) Fully nested 3-Copula: procedure and application on hydrological data. J Hydrol Eng 12(4):420–430
• 40. Shafaei M, Fakheri-Fard A, Dinpashoh Y et al (2017) Modeling flood event characteristics using D-vine structures. Theor Appl Climatol 130:713–724. https://doi.org/10.1007/s00704-016-1911-x
• 41. Sharifi A, Dinpashoh Y, Mirabbasi R (2017) Daily runoff prediction using the linear and non-linear models. Water Sci Technol 76:793–805
• 42. 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. Hydrol Process 29:225–238. https://doi.org/10.1002/hyp.10145
• 43. Stedinger JR, Vogel RM, Foufoula-Georgiou E (1993) Frequency analysis of extreme events. Handb Hydrol 18:1
• 44. Todorovic P (1978) Stochastic models of floods. Water Resour Res 14:345–356
• 45. Xu Y, Huang G, Fan Y (2017) Multivariate flood risk analysis for Wei River. Stoch Environ Res Risk Assess 31:225–242. https://doi.org/10.1007/s00477-015-1196-0
• 46. Yue S, Ouarda TBMJ, Bobée B et al (1999) The Gumbel mixed model for flood frequency analysis. J Hydrol 226:88–100. https://doi.org/10.1016/S0022-1694(99)00168-7
• 47. Zhang L, Singh VP (2006) Bivariate flood frequency analysis using the copula method. J Hydrol Eng 11(2):150–164
• 48. Zhang L, Singh VP (2007) Trivariate flood frequency analysis using the Gumbel-Hougaard copula. J Hydrol Eng 12:431–439

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).