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The use of copula functions for predictive analysis of correlations between extreme storm tides

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  • Domino, Krzysztof
  • Błachowicz, Tomasz
  • Ciupak, Maurycy

Abstract

In this paper we present a method used in quantitative description of weakly predictable hydrological, extreme events at inland sea. Investigations for correlations between variations of individual measuring points, employing combined statistical methods, were carried out. As a main tool for this analysis we used a two-dimensional copula function sensitive for correlated extreme effects. Additionally, a new proposed methodology, based on Detrended Fluctuations Analysis (DFA) and Anomalous Diffusion (AD), was used for the prediction of negative and positive auto-correlations and associated optimum choice of copula functions. As a practical example we analysed maximum storm tides data recorded at five spatially separated places at the Baltic Sea. For the analysis we used Gumbel, Clayton, and Frank copula functions and introduced the reversed Clayton copula. The application of our research model is associated with modelling the risk of high storm tides and possible storm flooding.

Suggested Citation

  • Domino, Krzysztof & Błachowicz, Tomasz & Ciupak, Maurycy, 2014. "The use of copula functions for predictive analysis of correlations between extreme storm tides," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 489-497.
    • Handle: RePEc:eee:phsmap:v:413:y:2014:i:c:p:489-497
    DOI: 10.1016/j.physa.2014.07.020
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    References listed on IDEAS

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    1. Domino, Krzysztof, 2011. "The use of the Hurst exponent to predict changes in trends on the Warsaw Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 98-109.
    2. Cong, Rong-Gang & Brady, Mark, 2012. "The Interdependence between Rainfall and Temperature: Copula Analyses," MPRA Paper 112149, University Library of Munich, Germany.
    3. Cayton, Peter Julian A. & Mapa, Dennis S., 2012. "Time-varying conditional Johnson SU density in value-at-risk (VaR) methodology," MPRA Paper 36206, University Library of Munich, Germany.
    4. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
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    Cited by:

    1. Antoine J.‐P. Tixier & Matthew R. Hallowell & Balaji Rajagopalan, 2017. "Construction Safety Risk Modeling and Simulation," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1917-1935, October.
    2. Huang, Xu & Maçaira, Paula Medina & Hassani, Hossein & Cyrino Oliveira, Fernando Luiz & Dhesi, Gurjeet, 2019. "Hydrological natural inflow and climate variables: Time and frequency causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 480-495.

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