Analysis of return periods and return levels of Yearly July–September extreme droughts in the West African Sahel
- 154 Downloads
This paper aims to model the occurrence of Yearly July–September (YJAS) extreme droughts in the West African Sahel (WAS) and to estimate return periods and return levels of these events through stationary peaks-over-threshold model. For this purpose, the historical gridded monthly rainfall data from Climatic Research Unit for the period 1901–2009 were used. The results show that return levels of YJAS dry extremes have increased since 1970, implying that YJAS extreme droughts are consistently more severe after 1970 than they were before. Approximately \(62\%\) of the WAS area in the postchange period of 1971–2009 was dominated by dry spells not longer than 1 year. The dynamics of the YJAS extremes drying trend indicate that the changes at the tails of YJAS dry extreme distribution have contributed to the dry trend in mean YJAS rainfall in the WAS. The estimated 40-year return level of these events based on 1971–2009 period was less than the average of dry extremes of the same period, suggesting that droughts could intensify in the future even though with some amelioration. Such a finding could prove helpful in anticipation of climate risks in this region where adaptive capacities are very low.
KeywordsWest African Sahel Extreme droughts Peaks-over-threshold Return periods Return levels
The authors thank the National Center for Atmospheric Science British Atmospheric Data Centre for making their CRU’s rainfall data available to them.
- Janicot S (1992) Spatiotemporal variability of West African rainfall. Part I: regionalizations and typings. J Clim 5(5):489–497. https://doi.org/10.1175/1520-0442(1992)005%3c0489:svowar%3e2.0.co;2 CrossRefGoogle Scholar
- Monerie P-A, Fontaine B, Roucou P (2012) Expected future changes in the African monsoon between 2030 and 2070 using some CMIP3 and CMIP5 models under a medium-low RCP scenario. J Geophys Res Atmos. https://doi.org/10.1029/2012jd017510
- R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 16 Jan 2018
- Smith RL (2003) Statistics of extremes, with applications in environment, insurance, and finance. C&H/CRC Monogr Stat Appl Probab. https://doi.org/10.1201/9780203483350.ch1
- UNDP (2013) Fast facts: UNDP and poverty reduction in Africa. http://www.africa.undp.org/content/dam/rba/docs/Outreach. Accessed 16 Jan 2018
- Wilks DS (2011) Statistical methods in the atmospheric sciences. Int Geophys. https://doi.org/10.1016/c2010-0-65519-2