IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v121y2025i4d10.1007_s11069-024-06966-7.html
   My bibliography  Save this article

Joint frequency analysis of streamflow and sediment amount with copula functions in the Kızlırmak Basin, Turkey

Author

Listed:
  • Tahsin Baykal

    (Kırıkkale University)

Abstract

The accurate determination of sediment amount is crucial for the design and operation of reservoirs. The sediment rating curve (SRC) is the most widely used method for determining sediment amount. The SRC was derived from streamflow and sediment amount measurements taken at hydrometric monitoring stations. However, when measurements cannot be made at these stations (such as flooding), sediment amount determination becomes difficult. Therefore, in recent years, researchers have used copula functions to determine the relationship between streamflow and sediment amount. In this study, the joint distribution functions of streamflow and sediment amount at five different stations (Avşar, Bulukabaşı, İnözü, Söğütlühan and Yamula) in the Kızılırmak Basin, one of the most important basins of Turkey, were determined. Initially, the relationship between streamflow and sediment amount was examined and a positive correlation was found between the parameters. Then, the marginal distributions of each dataset were determined and joint distributions were generated using the Burr, Roch-Alegre, BB1 and Tawn Copula functions. The root mean square error of the mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE) were used to select the optimal joint function. According to the optimal joint function determined for each station, the common return periods for both the AND and OR scenarios were calculated. When joint return periods were analyzed, the amount of sediment exceeded the average amount of sediment at all stations.

Suggested Citation

  • Tahsin Baykal, 2025. "Joint frequency analysis of streamflow and sediment amount with copula functions in the Kızlırmak Basin, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(4), pages 4219-4238, March.
  • Handle: RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06966-7
    DOI: 10.1007/s11069-024-06966-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-024-06966-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-024-06966-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Charpentier, Arthur & Segers, Johan, 2009. "Tails of multivariate Archimedean copulas," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1521-1537, August.
    2. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    3. Gregor Weiß, 2011. "Copula parameter estimation by maximum-likelihood and minimum-distance estimators: a simulation study," Computational Statistics, Springer, vol. 26(1), pages 31-54, March.
    4. Ali, Mir M. & Mikhail, N. N. & Haq, M. Safiul, 1978. "A class of bivariate distributions including the bivariate logistic," Journal of Multivariate Analysis, Elsevier, vol. 8(3), pages 405-412, September.
    5. Yang Peng & Xianliang Yu & Hongxiang Yan & Jipeng Zhang, 2020. "Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3913-3932, September.
    6. Edward Frees & Emiliano Valdez, 1998. "Understanding Relationships Using Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 2(1), pages 1-25.
    7. M. Reddy & Poulomi Ganguli, 2012. "Bivariate Flood Frequency Analysis of Upper Godavari River Flows Using Archimedean Copulas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 3995-4018, November.
    8. Justine Power & Marie-Pier Côté & Thierry Duchesne, 2024. "A Flexible Hierarchical Insurance Claims Model with Gradient Boosting and Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 28(4), pages 772-800, October.
    9. Eduardo F. L. de Melo & Beatriz Vaz de Melo Mendes, 2009. "Local Estimation of Copula Based Value-at-Risk," Brazilian Review of Finance, Brazilian Society of Finance, vol. 7(1), pages 29-50.
    10. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
    11. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hengxin Cui & Ken Seng Tan & Fan Yang, 2024. "Portfolio credit risk with Archimedean copulas: asymptotic analysis and efficient simulation," Annals of Operations Research, Springer, vol. 332(1), pages 55-84, January.
    2. Christian Genest & Johanna Nešlehová & Johanna Ziegel, 2011. "Inference in multivariate Archimedean copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 223-256, August.
    3. Elena Di Bernardino & Didier Rullière, 2016. "A note on upper-patched generators for Archimedean copulas," Working Papers hal-01347869, HAL.
    4. Weiß, Gregor N.F. & Scheffer, Marcus, 2015. "Mixture pair-copula-constructions," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 175-191.
    5. J. Rosco & Harry Joe, 2013. "Measures of tail asymmetry for bivariate copulas," Statistical Papers, Springer, vol. 54(3), pages 709-726, August.
    6. Ko, Vinnie & Hjort, Nils Lid, 2019. "Model robust inference with two-stage maximum likelihood estimation for copulas," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 362-381.
    7. Hua, Lei, 2017. "On a bivariate copula with both upper and lower full-range tail dependence," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 94-104.
    8. repec:hal:wpaper:hal-00834000 is not listed on IDEAS
    9. Xie, Jiehua & Lin, Feng & Yang, Jingping, 2017. "On a generalization of Archimedean copula family," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 121-129.
    10. repec:jss:jstsof:21:i04 is not listed on IDEAS
    11. Siburg, Karl Friedrich & Stoimenov, Pavel & Weiß, Gregor N.F., 2015. "Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 129-140.
    12. Moradian, Sogol & Olbert, Agnieszka I. & Gharbia, Salem & Iglesias, Gregorio, 2023. "Copula-based projections of wind power: Ireland as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    13. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    14. Zhang, Kong-Sheng & Lin, Jin-Guan & Xu, Pei-Rong, 2016. "A new class of copulas involving geometric distribution: Estimation and applications," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 1-10.
    15. Naifar, Nader, 2012. "Modeling the dependence structure between default risk premium, equity return volatility and the jump risk: Evidence from a financial crisis," Economic Modelling, Elsevier, vol. 29(2), pages 119-131.
    16. Zheng Wei & Seongyong Kim & Boseung Choi & Daeyoung Kim, 2019. "Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 365-387, January.
    17. Boris Brodsky & Henry Penikas & Irina Safaryan, 2009. "Detection of Structural Breaks in Copula Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 16(4), pages 3-15.
    18. Kojadinovic, Ivan & Yan, Jun, 2010. "Comparison of three semiparametric methods for estimating dependence parameters in copula models," Insurance: Mathematics and Economics, Elsevier, vol. 47(1), pages 52-63, August.
    19. Naifar, Nader & Hammoudeh, Shawkat & Al dohaiman, Mohamed S., 2016. "Dependence structure between sukuk (Islamic bonds) and stock market conditions: An empirical analysis with Archimedean copulas," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 148-165.
    20. Ehab M. Almetwally & Aisha Fayomi & Maha E. Qura, 2024. "Advanced Copula-Based Models for Type II Censored Data: Applications in Industrial and Medical Settings," Mathematics, MDPI, vol. 12(12), pages 1-35, June.
    21. Elena Di Bernardino & Didier Rullière, 2017. "A note on upper-patched generators for Archimedean copulas," Post-Print hal-01347869, HAL.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06966-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.