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Nonparametric Approach to Copula Estimation in Compounding The Joint Impact of Storm Surge and Rainfall Events in Coastal Flood Analysis

Author

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  • Shahid Latif

    (Western University)

  • Slobodan P. Simonovic

    (Western University)

Abstract

The joint probability modelling of storm surges and rainfall events is the main task in assessing compound flood risk in low-lying coastal areas. These extreme or non-extreme events may not be dangerous if considered individually but can intensify flooding impact if they occur simultaneously or successively. Recently, the copula approach has been widely accepted in compound flooding but is often limited to parametric or semiparametric distribution settings in a limited number of cases. However, both parametric and semiparametric approaches assume the prior distribution type for univariate marginals and copula joint density. In that case, there is a high risk of misspecification if the underlying assumption is violated. In addition, both approaches suffer from a lack of flexibility. This study uses bivariate copula density in the nonparametric distribution setting. The joint copula structure is approximated nonparametrically by employing the Bernstein copula estimator and Beta kernel copula density, and their performances are also compared. The proposed model is tested with 46 years of rainfall and storm surge observations collected on Canada's west coast. The marginal distribution of the selected flood variables is modelled using nonparametric kernel density estimation (KDE). Based on the different model compatibility tests, the Bernstein copula with normal KDE margins defined the joint dependence structure well. The selected nonparametric copula model is further employed to estimate joint and conditional return periods. It is found that flood hazard characteristics occurrence simultaneously is less frequent in AND-joint cases than in OR-joint cases. Also, the derived model is further used to estimate failure probability (FP) statistics to assess the variation of bivariate hydrologic risk during the project lifetime. It is found that FP statistics could be underestimated when neglecting the compound effect of storm surge and rainfall in the coastal flood risk.

Suggested Citation

  • Shahid Latif & Slobodan P. Simonovic, 2022. "Nonparametric Approach to Copula Estimation in Compounding The Joint Impact of Storm Surge and Rainfall Events in Coastal Flood Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5599-5632, November.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:14:d:10.1007_s11269-022-03321-y
    DOI: 10.1007/s11269-022-03321-y
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    References listed on IDEAS

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    1. Bruce M. Brown & Song Xi Chen, 1999. "Beta‐Bernstein Smoothing for Regression Curves with Compact Support," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(1), pages 47-59, March.
    2. Thomas Wahl & Shaleen Jain & Jens Bender & Steven D. Meyers & Mark E. Luther, 2015. "Increasing risk of compound flooding from storm surge and rainfall for major US cities," Nature Climate Change, Nature, vol. 5(12), pages 1093-1097, December.
    3. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    4. Ummul Abdul Rauf & Panlop Zeephongsekul, 2014. "Analysis of Rainfall Severity and Duration in Victoria, Australia using Non-parametric Copulas and Marginal Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4835-4856, October.
    5. Su Chen, 2015. "Optimal Bandwidth Selection for Kernel Density Functionals Estimation," Journal of Probability and Statistics, Hindawi, vol. 2015, pages 1-21, August.
    6. Bouezmarni Taoufik & Ghouch El & Taamouti Abderrahim, 2013. "Bernstein estimator for unbounded copula densities," Statistics & Risk Modeling, De Gruyter, vol. 30(4), pages 343-360, December.
    7. Jakob Zscheischler & Seth Westra & Bart J. J. M. Hurk & Sonia I. Seneviratne & Philip J. Ward & Andy Pitman & Amir AghaKouchak & David N. Bresch & Michael Leonard & Thomas Wahl & Xuebin Zhang, 2018. "Future climate risk from compound events," Nature Climate Change, Nature, vol. 8(6), pages 469-477, June.
    8. Jakob Zscheischler & Seth Westra & Bart J. J. M. Hurk & Sonia I. Seneviratne & Philip J. Ward & Andy Pitman & Amir AghaKouchak & David N. Bresch & Michael Leonard & Thomas Wahl & Xuebin Zhang, 2018. "Author Correction: Future climate risk from compound events," Nature Climate Change, Nature, vol. 8(8), pages 750-750, August.
    9. Tomasz Kulpa, 1999. "On approximation of copulas," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 22, pages 1-11, January.
    10. Sancetta, Alessio & Satchell, Stephen, 2004. "The Bernstein Copula And Its Applications To Modeling And Approximations Of Multivariate Distributions," Econometric Theory, Cambridge University Press, vol. 20(3), pages 535-562, June.
    11. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
    12. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, September.
    13. 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.
    14. Gregor Wei{ss} & Marcus Scheffer, 2012. "Smooth Nonparametric Bernstein Vine Copulas," Papers 1210.2043, arXiv.org.
    15. Diers, Dorothea & Eling, Martin & Marek, Sebastian D., 2012. "Dependence modeling in non-life insurance using the Bernstein copula," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 430-436.
    16. Kui Xu & Chao Ma & Jijian Lian & Lingling Bin, 2014. "Joint Probability Analysis of Extreme Precipitation and Storm Tide in a Coastal City under Changing Environment," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-11, October.
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