Editorial Type:
Article
Article Type:
Research Article

Atmospheric Predictors for Annual Maximum Precipitation in North Africa

Bouchra Nasri Canada Research Chair on the Estimation of Hydrometeorological Variables, Eau Terre Environnement Research Centre, Institut National de la Recherche Scientifique, Québec, Québec, Canada

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Yves Tramblay Unité Mixte de Recherche Hydrosciences, Institut de Recherche pour le Développement, Montpellier, France

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Salaheddine El Adlouni Département de Mathématique et de Statistique, Université de Moncton, Moncton, New Brunswick, Canada

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Elke Hertig Institute of Geography, University of Augsburg, Augsburg, Germany

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Taha B. M. J. Ouarda Canada Research Chair on the Estimation of Hydrometeorological Variables, Eau Terre Environnement Research Centre, Institut National de la Recherche Scientifique, Québec, Québec, Canada, and Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates

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Print Publication:
01 Apr 2016
DOI:
https://doi.org/10.1175/JAMC-D-14-0122.1
Page(s):
1063–1076
Restricted access

Abstract

The high precipitation variability over North Africa presents a major challenge for the population and the infrastructure in the region. The last decades have seen many flood events caused by extreme precipitation in this area. There is a strong need to identify the most relevant atmospheric predictors to model these extreme events. In the present work, the effect of 14 different predictors calculated from NCEP–NCAR reanalysis, with daily to seasonal time steps, on the maximum annual precipitation (MAP) is evaluated at six coastal stations located in North Africa (Larache, Tangier, Melilla, Algiers, Tunis, and Gabès). The generalized extreme value (GEV) B-spline model was used to detect this influence. This model considers all continuous dependence forms (linear, quadratic, etc.) between the covariates and the variable of interest, thus providing a very flexible framework to evaluate the covariate effects on the GEV model parameters. Results show that no single set of covariates is valid for all stations. Overall, a strong dependence between the NCEP–NCAR predictors and MAP is detected, particularly with predictors describing large-scale circulation (geopotential height) or moisture (humidity). This study can therefore provide insights for developing extreme precipitation downscaling models that are tailored for North African conditions.

Corresponding author address: Bouchra Nasri, INRS-ETE, 490 Rue de la Couronne, Québec, QC G1K 9A9, Canada. E-mail: bouchra.nasri@ete.inrs.ca

Abstract

The high precipitation variability over North Africa presents a major challenge for the population and the infrastructure in the region. The last decades have seen many flood events caused by extreme precipitation in this area. There is a strong need to identify the most relevant atmospheric predictors to model these extreme events. In the present work, the effect of 14 different predictors calculated from NCEP–NCAR reanalysis, with daily to seasonal time steps, on the maximum annual precipitation (MAP) is evaluated at six coastal stations located in North Africa (Larache, Tangier, Melilla, Algiers, Tunis, and Gabès). The generalized extreme value (GEV) B-spline model was used to detect this influence. This model considers all continuous dependence forms (linear, quadratic, etc.) between the covariates and the variable of interest, thus providing a very flexible framework to evaluate the covariate effects on the GEV model parameters. Results show that no single set of covariates is valid for all stations. Overall, a strong dependence between the NCEP–NCAR predictors and MAP is detected, particularly with predictors describing large-scale circulation (geopotential height) or moisture (humidity). This study can therefore provide insights for developing extreme precipitation downscaling models that are tailored for North African conditions.

Corresponding author address: Bouchra Nasri, INRS-ETE, 490 Rue de la Couronne, Québec, QC G1K 9A9, Canada. E-mail: bouchra.nasri@ete.inrs.ca
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