IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v38y2018i9p1847-1870.html
   My bibliography  Save this article

Exploring the Potential for Multivariate Fragility Representations to Alter Flood Risk Estimates

Author

Listed:
  • Robert A. Jane
  • David J. Simmonds
  • Ben P. Gouldby
  • Jonathan D. Simm
  • Luciana Dalla Valle
  • Alison C. Raby

Abstract

In flood risk analysis, limitations in the multivariate statistical models adopted to model the hydraulic load have restricted the probability of a defense suffering structural failure to be expressed conditionally on a single hydraulic loading variable. This is an issue at the coastal level where multiple loadings act on defenses with the exact combination of loadings dictating their failure probabilities. Recently, a methodology containing a multivariate statistical model with the flexibility to robustly capture the dependence structure between the individual loadings was used to derive extreme nearshore loading conditions. Its adoption will permit the incorporation of more precise representations of a structure's vulnerability in future analyses. In this article, a fragility representation of a shingle beach, where the failure probability is expressed over a three‐dimensional loading parameter space—water level, wave height, and period—is derived at two localities. Within the approach, a Gaussian copula is used to capture any dependencies between the simplified geometric parameters of a beach's shape. Beach profiles are simulated from the copula and the failure probability, given the hydraulic load, determined by the reformulated Bradbury barrier inertia parameter model. At one site, substantial differences in the annual failure probability distribution are observed between the new and existing approaches. At the other, the beach only becomes vulnerable after a significant reduction of the crest height with its mean annual failure probability close to that presently predicted. It is concluded that further application of multivariate approaches is likely to yield more effective flood risk management.

Suggested Citation

  • Robert A. Jane & David J. Simmonds & Ben P. Gouldby & Jonathan D. Simm & Luciana Dalla Valle & Alison C. Raby, 2018. "Exploring the Potential for Multivariate Fragility Representations to Alter Flood Risk Estimates," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1847-1870, September.
  • Handle: RePEc:wly:riskan:v:38:y:2018:i:9:p:1847-1870
    DOI: 10.1111/risa.13007
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.13007
    Download Restriction: no

    File URL: https://libkey.io/10.1111/risa.13007?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
    ---><---

    References listed on IDEAS

    as
    1. Jim Hall & Paul Sayers & Richard Dawson, 2005. "National-scale Assessment of Current and Future Flood Risk in England and Wales," 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. 36(1), pages 147-164, September.
    2. Janet E. Heffernan & Jonathan A. Tawn, 2004. "A conditional approach for multivariate extreme values (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 497-546, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Yaohan & Dong, You & Guo, Hongyuan, 2023. "Copula-based multivariate renewal model for life-cycle analysis of civil infrastructure considering multiple dependent deterioration processes," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Chengguang Lai & Xiaohong Chen & Zhaoli Wang & Haijun Yu & Xiaoyan Bai, 2020. "Flood Risk Assessment and Regionalization from Past and Future Perspectives at Basin Scale," Risk Analysis, John Wiley & Sons, vol. 40(7), pages 1399-1417, July.

    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. Giuliano Di Baldassarre & Attilio Castellarin & Alberto Montanari & Armando Brath, 2009. "Probability-weighted hazard maps for comparing different flood risk management strategies: a case study," 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. 50(3), pages 479-496, September.
    2. Refk Selmi & Christos Kollias & Stephanos Papadamou & Rangan Gupta, 2017. "A Copula-Based Quantile-on-Quantile Regression Approach to Modeling Dependence Structure between Stock and Bond Returns: Evidence from Historical Data of India, South Africa, UK and US," Working Papers 201747, University of Pretoria, Department of Economics.
    3. Dapeng Huang & Renhe Zhang & Zhiguo Huo & Fei Mao & Youhao E & Wei Zheng, 2012. "An assessment of multidimensional flood vulnerability at the provincial scale in China based on the DEA method," 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. 64(2), pages 1575-1586, November.
    4. Anne‐Laure Fougères & John P. Nolan & Holger Rootzén, 2009. "Models for Dependent Extremes Using Stable Mixtures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 42-59, March.
    5. Alexandra Ramos & Anthony Ledford, 2009. "A new class of models for bivariate joint tails," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 219-241, January.
    6. Ralf Merz & Günter Blöschl & Günter Humer, 2008. "National flood discharge mapping in Austria," 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. 46(1), pages 53-72, July.
    7. Thomas D. Pol & Ekko C. Ierland & Silke Gabbert, 2017. "Economic analysis of adaptive strategies for flood risk management under climate change," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(2), pages 267-285, February.
    8. Lee Fawcett & David Walshaw, 2014. "Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 959-976, May.
    9. Barme-Delcroix, Marie-Francoise & Gather, Ursula, 2007. "Limit laws for multidimensional extremes," Statistics & Probability Letters, Elsevier, vol. 77(18), pages 1750-1755, December.
    10. K. M. Bruijn & N. Lips & B. Gersonius & H. Middelkoop, 2016. "The storyline approach: a new way to analyse and improve flood event management," 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. 81(1), pages 99-121, March.
    11. Xi Hu & Jim W. Hall & Peijun Shi & Wee Ho Lim, 2016. "The spatial exposure of the Chinese infrastructure system to flooding and drought hazards," 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. 80(2), pages 1083-1118, January.
    12. Marmai, Nadin & Franco Villoria, Maria & Guerzoni, Marco, 2016. "How the Black Swan damages the harvest: statistical modelling of extreme events in weather and crop production in Africa, Asia, and Latin America," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201605, University of Turin.
    13. Sauer, Johannes & Finger, Robert, 2014. "Climate Risk Management Strategies in Agriculture – The Case of Flood Risk," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 172679, Agricultural and Applied Economics Association.
    14. Zbigniew Kundzewicz & Nicola Lugeri & Rutger Dankers & Yukiko Hirabayashi & Petra Döll & Iwona Pińskwar & Tomasz Dysarz & Stefan Hochrainer & Piotr Matczak, 2010. "Assessing river flood risk and adaptation in Europe—review of projections for the future," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(7), pages 641-656, October.
    15. Daniela Castro Camilo & Miguel de Carvalho & Jennifer Wadsworth, 2017. "Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets," Papers 1709.01198, arXiv.org.
    16. Balakrishnan, N. & Hashorva, E., 2011. "On Pearson-Kotz Dirichlet distributions," Journal of Multivariate Analysis, Elsevier, vol. 102(5), pages 948-957, May.
    17. Koen Koning & Tatiana Filatova & Okmyung Bin, 2018. "Improved Methods for Predicting Property Prices in Hazard Prone Dynamic Markets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 247-263, February.
    18. de Valk, Cees, 2016. "A large deviations approach to the statistics of extreme events," Other publications TiSEM 117b3ba0-0e40-4277-b25e-d, Tilburg University, School of Economics and Management.
    19. Panagiota Galiatsatou & Christos Makris & Panayotis Prinos & Dimitrios Kokkinos, 2019. "Nonstationary joint probability analysis of extreme marine variables to assess design water levels at the shoreline in a changing climate," 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. 98(3), pages 1051-1089, September.
    20. Hentschel, Manuel & Engelke, Sebastian & Segers, Johan, 2022. "Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions," LIDAM Discussion Papers ISBA 2022032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    More about this item

    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:wly:riskan:v:38:y:2018:i:9:p:1847-1870. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    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.