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Smooth copula‐based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada

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  • Fatima Palacios‐Rodriguez
  • Elena Di Bernardino
  • Melina Mailhot

Abstract

This paper proposes a smooth copula‐based Generalized Extreme Value (GEV) model to map and predict extreme rainfall in Central Eastern Canada. The considered data contains a large portion of missing values, and one observes several nonconcomitant record periods at different stations. The proposed two‐step approach combines GEV parameters' smooth functions in space through the use of spatial covariates and a flexible hierarchical copula‐based model to take into account dependence between the recording stations. The hierarchical copula structure is detected via a clustering algorithm implemented with an adapted version of the copula‐based dissimilarity measure recently introduced in the literature. Finally, we compare the classical GEV parameter interpolation approaches with the proposed smooth copula‐based GEV modeling approach.

Suggested Citation

  • Fatima Palacios‐Rodriguez & Elena Di Bernardino & Melina Mailhot, 2023. "Smooth copula‐based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada," Environmetrics, John Wiley & Sons, Ltd., vol. 34(3), May.
    • Handle: RePEc:wly:envmet:v:34:y:2023:i:3:n:e2795
    DOI: 10.1002/env.2795
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    References listed on IDEAS

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    1. Hofert, Marius & Pham, David, 2013. "Densities of nested Archimedean copulas," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 37-52.
    2. Segers, Johan, 2015. "Hybrid copula estimators," LIDAM Reprints ISBA 2015005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Marx, Brian D. & Eilers, Paul H. C., 1998. "Direct generalized additive modeling with penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 28(2), pages 193-209, August.
    4. Eric A. Lehmann & Aloke Phatak & Alec Stephenson & Rex Lau, 2016. "Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change," Environmetrics, John Wiley & Sons, Ltd., vol. 27(4), pages 239-251, June.
    5. Cooley, Daniel & Nychka, Douglas & Naveau, Philippe, 2007. "Bayesian Spatial Modeling of Extreme Precipitation Return Levels," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 824-840, September.
    6. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173, March.
    7. Jonathan Jalbert & Christian Genest & Luc Perreault, 2022. "Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve Construction at Unmonitored Locations," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 461-486, September.
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