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Quantile-based spatiotemporal risk assessment of exceedances

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Abstract

Structural characteristics of random field excursion sets defined by threshold exceedances provide meaningful indicators for the description of extremal behaviour in the spatiotemporal dynamics of environmental systems, and for risk assessment. In this paper a conditional approach for analysis at global and regional scales is introduced, performed by implementation of risk measures under proper model-based integration of available knowledge. Specifically, quantile-based measures, such as Value-at-Risk and Average Value-at-Risk, are applied based on the empirical distributions derived from conditional simulation for different threshold exceedance indicators, allowing the construction of meaningful dynamic risk maps. Significant aspects of the application of this methodology, regarding the nature and the properties (e.g. local variability, dependence range, marginal distributions) of the underlying random field, as well as in relation to the increasing value of the reference threshold, are discussed and illustrated based on simulation under a variety of scenarios.

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Acknowledgements

The authors are particularly grateful to the Associate Editor and the Reviewers for their detailed comments and constructive suggestions, which have led to significant improvements in the final version of the manuscript. This work has been partially supported by Grants MTM2012-32666 and MTM2015-70840-P of Spanish MINECO/FEDER, EU.

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Authors and Affiliations

  1. Department of Statistics and Operations Research, University of Granada, Campus Fuente Nueva s/n, 18071, Granada, Spain

    J. L. Romero & J. M. Angulo

  2. Department of Sciences and Informatics, University Centre of Defence at the Spanish Air Force Academy, MDE-UPCT, C/ Coronel López Peña s/n, 30720, Santiago de la Ribera, Murcia, Spain

    A. E. Madrid

Authors
  1. J. L. Romero
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  2. A. E. Madrid
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  3. J. M. Angulo
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Correspondence to J. M. Angulo.

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Romero, J.L., Madrid, A.E. & Angulo, J.M. Quantile-based spatiotemporal risk assessment of exceedances. Stoch Environ Res Risk Assess 32, 2275–2291 (2018). https://doi.org/10.1007/s00477-018-1562-9

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