Articles | Volume 11, issue 9
Nat. Hazards Earth Syst. Sci., 11, 2463–2468, 2011
https://doi.org/10.5194/nhess-11-2463-2011

Special issue: 12th Plinius Conference on Mediterranean Storms

Nat. Hazards Earth Syst. Sci., 11, 2463–2468, 2011
https://doi.org/10.5194/nhess-11-2463-2011

Brief communication 15 Sep 2011

Brief communication | 15 Sep 2011

Brief communication "Climatic covariates for the frequency analysis of heavy rainfall in the Mediterranean region"

Y. Tramblay, L. Neppel, and J. Carreau Y. Tramblay et al.
  • Hydrosciences Montpellier, UMR5569, CNRS – IRD-UM1-UM2, Université Montpellier 2, Maison des Sciences de l'Eau, Place Eugène Bataillon, 34095 Montpellier Cedex 5, France

Abstract. In Mediterranean regions, climate studies indicate for the future a possible increase in the extreme rainfall events occurrence and intensity. To evaluate the future changes in the extreme event distribution, there is a need to provide non-stationary models taking into account the non-stationarity of climate. In this study, several climatic covariates are tested in a non-stationary peaks-over-threshold modeling approach for heavy rainfall events in Southern France. Results indicate that the introduction of climatic covariates could improve the statistical modeling of extreme events. In the case study, the frequency of southern synoptic circulation patterns is found to improve the occurrence process of extreme events modeled via a Poisson distribution, whereas for the magnitude of the events, the air temperature and sea level pressure appear as valid covariates for the Generalized Pareto distribution scale parameter. Covariates describing the humidity fluxes at monthly and seasonal time scales also provide significant model improvements for the occurrence and the magnitude of heavy rainfall events. With such models including climatic covariates, it becomes possible to asses the risk of extreme events given certain climatic conditions at monthly or seasonal timescales. The future changes in the heavy rainfall distribution can also be evaluated using covariates computed by climate models.

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