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An Extreme Value Mixture model to assess drought hazard in West Africa

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  • Abdoulaye Sy

    (CERDI - Centre d'Études et de Recherches sur le Développement International - IRD - Institut de Recherche pour le Développement - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne)

  • Catherine Araujo-Bonjean

    (CERDI - Centre d'Études et de Recherches sur le Développement International - IRD - Institut de Recherche pour le Développement - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne)

  • Marie-Eliette Dury

    (CERDI - Centre d'Études et de Recherches sur le Développement International - IRD - Institut de Recherche pour le Développement - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne)

  • Nourddine Azzaoui

    (LMBP - Laboratoire de Mathématiques Blaise Pascal - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne)

  • Arnaud Guillin

    (LMBP - Laboratoire de Mathématiques Blaise Pascal - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne)

Abstract

A critical stage in drought hazard assessment is the definition of a drought event, and the measure of its intensity. Actually, the classical approach imposes to all climatic region the same set of thresholds for drought severity classification, hence resulting in a loss of information on rare events in the distribution tails, which are precisely the most important to catch in risk analysis. In order to better assess extreme events, we resort to an extreme value mixture model with a normal distribution for the bulk and a Generalized Pareto distribution for the upper and lower tails, to estimate the intensity of extreme droughts and their occurrence probability. Compare to the standard approach to drought hazard, which relies on a standardized precipitation index and a classification of drought intensity established from the cumulative standard normal distribution function, our approach allows the drought threshold and the occurrence probability of drought to depend on the specific characteristics of each precipitation distribution. An application to the West Africa region shows that the accuracy of our mixture model is higher than that of the standard model. The mixture performs better at modelling the lowest percentiles and specifically the return level of the centennial drought, which is generally overestimated in the standard approach.

Suggested Citation

  • Abdoulaye Sy & Catherine Araujo-Bonjean & Marie-Eliette Dury & Nourddine Azzaoui & Arnaud Guillin, 2021. "An Extreme Value Mixture model to assess drought hazard in West Africa," CERDI Working papers hal-03297023, HAL.
  • Handle: RePEc:hal:cdiwps:hal-03297023
    Note: View the original document on HAL open archive server: https://uca.hal.science/hal-03297023
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    References listed on IDEAS

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    1. Donald Wilhite & Mark Svoboda & Michael Hayes, 2007. "Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 763-774, May.
    2. Bin He & Jianjun Wu & Aifeng Lü & Xuefeng Cui & Lei Zhou & Ming Liu & Lin Zhao, 2013. "Quantitative assessment and spatial characteristic analysis of agricultural drought risk in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 66(2), pages 155-166, March.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Maxx Dilley & Robert S. Chen & Uwe Deichmann & Arthur L. Lerner-Lam & Margaret Arnold, 2005. "Natural Disaster Hotspots: A Global Risk Analysis," World Bank Publications - Books, The World Bank Group, number 7376, December.
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    Keywords

    Mixture model; Generalized pareto distribution; Drought; Extreme value theory;
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