Identifying hot spots of security vulnerability associated with climate change in Africa

Abstract

Given its high dependence on rainfed agriculture and its comparatively low adaptive capacity, Africa is frequently invoked as especially vulnerable to climate change. Within Africa, there is likely to be considerable variation in vulnerability to climate change both between and within countries. This paper seeks to advance the agenda of identifying the hot spots of what we term “climate security” vulnerability, areas where the confluence of vulnerabilities could put large numbers of people at risk of death from climate-related hazards. This article blends the expertise of social scientists and climate scientists. It builds on a model of composite vulnerability that incorporates four “baskets” or processes that are thought to contribute to vulnerability including: (1) physical exposure, (2) population density, (3) household and community resilience, and (4) governance and political violence. Whereas previous iterations of the model relied on historical physical exposure data of natural hazards, this paper uses results from regional model simulations of African climate in the late 20th century and mid-21st century to develop measures of extreme weather events—dry days, heat wave events, and heavy rainfall days—coupled with an indicator of low-lying coastal elevation. For the late 20th century, this mapping process reveals the most vulnerable areas are concentrated in Chad, the Democratic Republic of the Congo, Niger, Somalia, Sudan, and South Sudan, with pockets in Burkina Faso, Ethiopia, Guinea, Mauritania, and Sierra Leone. The mid 21st century projection shows more extensive vulnerability throughout the Sahel, including Burkina Faso, Chad, Mali, northern Nigeria, Niger, and across Sudan.

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Notes

  1. 1.

    This model does not include indirect effects of climate-related extreme events on mortality through changes in diseases such as malaria or meningococcal meningitis.

  2. 2.

    The January 2012 special issue of the Journal of Peace Research is dedicated to assessing the links between climate change and conflict.

  3. 3.

    DARA Climate Vulnerability Monitor is found at http://daraint.org/. The ND-GAIN Index is available at http://index.gain.org. The Maplecroft index is available at http://maplecroft.com/. The OneWorld is found at http://www.oneworldgroup.co.za/. For a survey of hot spot climate mapping exercises, see (de Sherbinin 2014).

  4. 4.

    See http://ccaps.aiddata.org/climate

  5. 5.

    In earlier iterations, we relied on the Global Rural–urban Mapping Project (GRUMP). LandScan (2008)TM High Resolution global Population Data Set copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with the United States Department of Energy.

  6. 6.

    Sources include World Development Indicators, Columbia University’s Center for International Earth Science Information Network, and USAID Demographic and Health Surveys among others.

  7. 7.

    For the virtues of equal weights in composite indices, see (Stapleton and Garrod 2006, 2007). For the problems with equal weight-based indices, see (Chowdhury and Squire 2005).

  8. 8.

    The 9 AOGCMs used are CGCM3.1, CNRM-CM3, ECHAM/MPI-OM, GFDL-CM2.0, MIROC3.2 (medres), MRI-CGCM3.2, NCAR CCSM3, NCAR PCM, UKMO-HadCM3. The process is detailed in (Cook and Vizy 2012).

  9. 9.

    Climate-mode boundary conditions include seasonality, but filter out shorter timescales. This process has been shown to be an effective approach to understand climate variability over Africa in other studies (e.g., Vizy and Cook 2002; Patricola and Cook 2007, 2010). A detailed description on the climate-mode ensemble design methodology is provided in (Cook and Vizy 2012).

  10. 10.

    It is possible that some currently unpopulated areas will become habitable as a result of climate change.

  11. 11.

    This is based on the assumption that fewer dry days and heavy rainfall events would reduce exposure to extreme events. It is important to produce a more regionally focused assessment that better accounts for the direct and indirect consequences of these imperfect “disaster” proxies, as their impacts may vary considerably from region to region over continental Africa.

  12. 12.

    Wheeler includes windstorms, droughts, floods, wildfires, and extreme heat events in his measures of climate-related disasters.

  13. 13.

    In addition to the five measures Wheeler identified, we also include storms and mass movement wet landslides in our measures of climate-related disasters.

  14. 14.

    See http://stg.ccaps.aiddata.org.

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Acknowledgments

This material is based upon work supported by, or in part by, the U.S. Army Research Office contract/grant number W911NF-09-1-007 under the Minerva Initiative of the U.S. Department of Defense. We also acknowledge the AOGCM modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 and CMIP5 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. We thank Kaiba White for her contributions to the development of the maps in earlier iterations.

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Correspondence to Joshua W. Busby.

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Busby, J.W., Cook, K.H., Vizy, E.K. et al. Identifying hot spots of security vulnerability associated with climate change in Africa. Climatic Change 124, 717–731 (2014). https://doi.org/10.1007/s10584-014-1142-z

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Keywords

  • Regional Climate Model
  • Late 20th Century
  • Extreme Weather Event
  • Political Violence
  • Community Resilience