IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v56y2007i4p395-414.html
   My bibliography  Save this article

Trend estimation in extremes of synthetic North Sea surges

Author

Listed:
  • Adam Butler
  • Janet E. Heffernan
  • Jonathan A. Tawn
  • Roger A. Flather

Abstract

Summary. Mechanistic models for complex atmospheric and hydrological processes are often used to simulate extreme natural events, usually to quantify the risks that are associated with these events. We use novel extreme value methods to analyse the statistical properties of output from a numerical storm surge model for the North Sea. The ‘model data’ constitute a reconstruction of the storm surge climate for the period 1955–2000 based on a high quality meteorological data set and constitute the only available source of information on surge elevations at offshore and unmonitored coastal locations over this period. Previous studies have used extreme value methods to analyse storm surge characteristics, but we can extend and improve on these analyses by using a local likelihood approach to provide a non‐parametric description of temporal and spatial variations in the magnitude and frequency of storm surge events.

Suggested Citation

  • Adam Butler & Janet E. Heffernan & Jonathan A. Tawn & Roger A. Flather, 2007. "Trend estimation in extremes of synthetic North Sea surges," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(4), pages 395-414, August.
    • Handle: RePEc:bla:jorssc:v:56:y:2007:i:4:p:395-414
    DOI: 10.1111/j.1467-9876.2007.00583.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2007.00583.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9876.2007.00583.x?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. V. Chavez‐Demoulin & A. C. Davison, 2005. "Generalized additive modelling of sample extremes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 207-222, January.
    2. Francesco Pauli & Stuart Coles, 2001. "Penalized likelihood inference in extreme value analyses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(5), pages 547-560.
    3. A. C. Davison & N. I. Ramesh, 2000. "Local likelihood smoothing of sample extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 191-208.
    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. Cristina Izaguirre & Fernando Méndez & Antonio Espejo & Inigo Losada & Borja Reguero, 2013. "Extreme wave climate changes in Central-South America," Climatic Change, Springer, vol. 119(2), pages 277-290, July.
    2. 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.

    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. Laurini, Fabrizio & Pauli, Francesco, 2009. "Smoothing sample extremes: The mixed model approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3842-3854, September.
    2. Padoan, S.A. & Wand, M.P., 2008. "Mixed model-based additive models for sample extremes," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2850-2858, December.
    3. Ahmad Aboubacrène Ag & Deme El Hadji & Diop Aliou & Girard Stéphane, 2019. "Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions," Dependence Modeling, De Gruyter, vol. 7(1), pages 394-417, January.
    4. Daouia, Abdelaati & Gardes, Laurent & Girard, Stephane, 2011. "On kernel smoothing for extremal quantile regression," LIDAM Discussion Papers ISBA 2011031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    6. Gardes, Laurent & Girard, Stéphane & Lekina, Alexandre, 2010. "Functional nonparametric estimation of conditional extreme quantiles," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 419-433, February.
    7. Abdelaati Daouia & Laurent Gardes & Stéphane Girard & Alexandre Lekina, 2011. "Kernel estimators of extreme level curves," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 311-333, August.
    8. Tong Siu Tung Wong & Wai Keung Li, 2015. "Extreme values identification in regression using a peaks-over-threshold approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 566-576, March.
    9. Emma F. Eastoe & Jonathan A. Tawn, 2009. "Modelling non‐stationary extremes with application to surface level ozone," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 25-45, February.
    10. Chiara Bocci & Enrica Caporali & Alessandra Petrucci, 2013. "Geoadditive modeling for extreme rainfall data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 181-193, April.
    11. M. de Carvalho & K. F. Turkman & A. Rua, 2013. "Dynamic threshold modelling and the US business cycle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 535-550, August.
    12. Zhang, Qingzhao & Li, Deyuan & Wang, Hansheng, 2013. "A note on tail dependence regression," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 163-172.
    13. Yaolan Ma & Bo Wei & Wei Huang, 2020. "A nonparametric estimator for the conditional tail index of Pareto-type distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(1), pages 17-44, January.
    14. Gardes, Laurent & Girard, Stéphane, 2008. "A moving window approach for nonparametric estimation of the conditional tail index," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2368-2388, November.
    15. António Rua & Miguel de Carvalho, 2010. "Nonstationary Extremes and the US Business Cycle," Working Papers w201003, Banco de Portugal, Economics and Research Department.
    16. Yun Feng & Weijie Hou & Yuping Song, 2024. "Tail risk forecasting and its application to margin requirements in the commodity futures market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1513-1529, August.
    17. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    18. Ying Hung & Li‐Hsiang Lin & C. F. Jeff Wu, 2022. "Varying coefficient frailty models with applications in single molecular experiments," Biometrics, The International Biometric Society, vol. 78(2), pages 474-486, June.
    19. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
    20. 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.

    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:bla:jorssc:v:56:y:2007:i:4:p:395-414. 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://edirc.repec.org/data/rssssea.html .

    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.