PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Joint frequency analysis of river flow rate and suspended sediment load using conditional density of copula functions

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, the river flow rate and suspended sediment load at Deh Molla hydrometric station in Zohreh River in the period of 1983–2018 were used for copula-based joint frequency. The Frank copula was selected as the best copula. The joint frequency analysis of the studied variables was performed based on the conditional density of the copula functions, which leads to the conditional occurrence probability of the variables. The results showed that with increasing river flow rate and suspended sediment load, the joint return period increases. Also, the joint return period for the "and" state is longer than the joint return period for the "or" state. When the river flow rate exceeds the threshold of 100 m3 /s and suspended sediment load exceeds the threshold of 2.7× 104 ton/day, the joint return periods for the "and" state and for the "or" state, are about 11 and 4 years, respectively.
Czasopismo
Rocznik
Strony
489--501
Opis fizyczny
Bibliogr. 47 poz.
Twórcy
  • Department of Water Engineering, University of Birjand, Birjand, Iran
  • Department of Water Engineering, University of Birjand, Birjand, Iran
  • Department of Water Engineering, University of Birjand, Birjand, Iran
  • Khuzestan Water and Power Authority (KWPA), Ahvaz, Iran
Bibliografia
  • 1. Abdous B, Genest C, Rémillard B (2004) Dependence properties of meta-elliptical distributions. In: Duchesne P, Rémillard B (eds) Statistical modeling and analysis for complex data problems. Kluwer, Dordrecht, pp 1–15
  • 2. Ayantobo OO, Li Y, Song S, Javed T, Yao N (2018) Probabilistic modelling of drought events in China via 2-dimensional joint copula. J Hydrol 559:373–391
  • 3. Bezak N, Mikos M, Sraj M (2014) Trivariate frequency analyses of peak discharge, hydrograph volume and suspended sediment concentration data using copulas. Water Resour Manage 28(8):2195–2212
  • 4. Bezak N, Rusjan S, Kramar Fijavz M, Mikos M, Sraj M (2017) Estimation of suspended sediment loads using copula functions. Water 9(8):1–23
  • 5. Blanco MLR, Castro MMT, Palleiro L, Castro MTT (2010) Temporal changes in suspended sediment transport in an Atlantic catchment, NW Spain. Geomorphology 123:181–188
  • 6. Bushra N, Trepanier JC, Rohli RV (2019) Joint probability risk modelling of storm surge and cyclone wind along the coast of Bay of Bengal using a statistical copula. Int J Climatol 39(11):4206–4217
  • 7. Capéraà P, Fougères AL, Genest C (2000) Bivariate distributions with given extreme value attractor. J Multivar Anal 72:30–49
  • 8. Cech C (2006) Copula-based top-down approaches in financial risk aggregation. Available at SSRN 953888
  • 9. Dastourani M, Nazeri Tahroudi M (2022) Toward coupling of groundwater drawdown and pumping time in a constant discharge. Appl Water Sci 12(4):1–13
  • 10. De Michele C, Salvadori G (2003) A generalized Pareto intensity‐duration model of storm rainfall exploiting 2‐copulas. J Geophys Res Atmos, 108(D2)
  • 11. Emami S, Parsa J (2021) Comparising performance of meta-heuristic algorithms with the sediment rate curve (case study: Zarrineh Rood River). J Watershed Eng Manag 13(1):43–54 ([In Persian])
  • 12. Fang HB, Fang KT, Kotz S (2002) The meta-elliptical distributions with given marginals. J Multivar Anal 82:1–16
  • 13. Favre AC, El Adlouni S, Perreault L, Thiemonge N, Bobee B (2004) Multivariate hydrologicalfrequency analysis using copulas. Water Resour Res 40(1):22–34
  • 14. Forbes C, Evans M, Hastings N, Peacock B (2011) Statistical distributions. John Wiley & Sons, Hoboken
  • 15. Genest C, Favre AC (2007) Everything you always wanted to know about copula modeling but were afraid to ask. J Hydrol Eng 12(4):347–368
  • 16. Heng S, Suetsugi T (2013) Using artificial neural network to estimate sediment load in ungauged catchments of the Tonle Sap River Basin, Cambodia. J Water Resour Prot 5:111–123
  • 17. Horowitz AJ (2003) An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. Hydrol Process 17:3387–3409
  • 18. Hui- Mean F, Yusof F, Yusop Z, Suhaila J (2019) Trivariate copula in drought analysis: a case study in peninsular Malaysia. Theoret Appl Climatol 138(1):657–671
  • 19. Joe H (1997) Multivariate models and multivariate dependence concepts. Chapman & Hall, London, p 399
  • 20. Keihani A, Akhondali AM, Fathian H (2021) Multivariate frequency analysis of peak discharge and suspended and bed sediment load in Karaj Basin, Iran. Water Resour Res 17(1):47–67 (in Persian)
  • 21. Khashei-Siuki A, Shahidi A, Ramezani Y, Nazeri Tahroudi M (2021) Simulation of potential evapotranspiration values based on vine copula. Meteorol Appl 28(5):e2027
  • 22. Laux P, Wagner S, Wagner A, Jacobeit J, Bardossy A, Kunstmann H (2009) Modelling daily precipitation features in the Volta Basin of West Africa. Int J Climatol 29(7):937–954
  • 23. Li T, Wang S, Fu B, Feng X (2020) Frequency analyses of peak discharge and suspended sediment concentration in the United States. J Soils Sediments 20:1157–1168
  • 24. Luo J (2011) Stepwise estimation of D-Vines with arbitrary specified copula pairs and EDA Tools
  • 25. Luo Y, Dong Z, Liu Y, Zhong D, Jiang F, Wang X (2021) Safety design for water-carrying lake flood control based on copula function: a case study of the Hongze Lake, China. J Hydrol 597:126188
  • 26. Miraboulghasemi H, Morid S (1997) Investigation of hydrological methods for estimating suspended load of rivers. J Water Dev 35:95–116 (in Persian)
  • 27. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):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 Climatol. https://doi.org/10.1002/joc.7349
  • 29. Nazeri Tahroudi M, Ramezani Y, De Michele C, Mirabbasi R (2022) Trivariate joint frequency analysis of water resources deficiency signatures using vine copulas. Appl Water Sci 12(4):1–15
  • 30. Nelsen RB (2006) An introduction to copulas, ser. Springer Science & Business Media
  • 31. Peng Y, Shi Y, Yan H, Zhang J (2020) Multivariate frequency analysis of annual maxima suspended sediment concentrations and floods in the Jinsha River, China. J Hydrol Eng 25(9):05020029
  • 32. Peng Y, Zhang J, Shi Y, Zhao X (2019) A copula-based method for stochastic simulation of daily suspended sediment concentration. J World Environ Water Resour Congr
  • 33. Pham MT, Vernieuwe H, De Baets B, Willems P, Verhoest NE (2016) Stochastic simulation of precipitation-consistent daily reference evapotranspiration using vine copulas. Stoch Environ Res Risk Assess 30:2197–2214. https://doi.org/10.1007/s00477-015-1181-7
  • 34. Reddy MJ, Ganguli P (2012) Bivariate flood frequency analysis of upper Godavari River flows using Archimedean copulas. Water Resour Manag 26(14):3995–4018
  • 35. Salvadori G, De Michele C (2004) Analytical calculation of storm volume statistics involving Pareto‐like intensity‐duration marginals. Geophys Res Lett 31(4)
  • 36. Salvadori G, De Michele C, Kottegoda NT, Rosso R (2007) Extremes in nature: an approach using copulas. Springer Science & Business Media, Dordrecht
  • 37. Sandercock PA (2015) Short history of confidence intervals: or, don’t ask “does the treatment work?” but “how sure are you that it works?” Stroke 46(8):184–187
  • 38. Shiau JT (2006) Fitting drought duration and severity with two-dimensional copulas. Water Resour Manag 20(5):795–815
  • 39. Shiau JT, Lien YC (2021) Copula-based infilling methods for daily suspended sediment loads. Water 13(12):1701
  • 40. Sklar A (1959) Fonctions de repartition and dimensions et leursmarges. Publications de L’Institute de Statistique. Universite’ De Paris 8:229–231
  • 41. Tahroudi MN, Mirabbasi R, Ramezani Y, Ahmadi F (2022) Probabilistic assessment of monthly river flow discharge using copula and OSVR approaches. Water Resour Manag 36:2027–2043. https://doi.org/10.1007/s11269-022-03125-0
  • 42. Tanim AH, Mullick RA, Sikdar S (2021) Evaluation of spatial rainfall products in sparsely gauged using copula uncertainty modeling with triple collocation. J Hydrol Eng 26 (4)
  • 43. Wang X, Gebremichael M, Yan J (2010) Weighted likelihood copula modeling of extreme rainfall events in Connecticut. J Hydrol 390(1–2):108–115
  • 44. Wong G, Lambert MF, Leonard M, Metcalfe AV (2010) Drought analysis using trivariate copulas conditional on climatic states. J Hydrol Eng 15(2):129–141
  • 45. Xu W, Hou Y, Hung Y, Zou Y (2010) Comparison of Spearman’s rho and Kendall’s tau in Normal and Contaminated Normal Models. Signal Process. https://doi.org/10.1016/j.sigpro.2012.08.005
  • 46. Yue S, Rasmussen P (2002) Bivariate frequency analysis: discussion of some useful concepts in hydrological application. J Hydrol Process 16(4):2881–2889
  • 47. Zhong M, Wang J, Jiang T, Huang Z, Chen X, Hong Y (2020) Using the apriori algorithm and copula function for the bivariate analysis of flash flood risk. Water. https://doi.org/10.3390/w12082223
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-480725eb-e2b6-4dd5-9b49-ac7cd8ac85c7
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.