Future drought risk in Africa: integrating vulnerability, climate change, and population growth
Abstract
Drought risk refers to the potential losses from hazard imposed by a drought event, and it is generally characterized as a function of vulnerability, hazard, and exposure. In this study, drought risk is assessed at a national level across Africa, and the impacts of climate change, population growth, and socioeconomic vulnerabilities on drought risk are investigated. A rigorous framework is implemented to quantify drought vulnerability considering various sectors including economy, energy and infrastructure, health, land use, society, and water resources. Multi-model and multi-scenario analyses are employed to quantify drought hazard using an ensemble of 10 regional climate models and a multi-scalar drought index. Drought risk is then assessed in each country for 2 climate emission pathways (RCP4.5 and RCP8.5), 3 population scenarios, and 3 vulnerability scenarios during three future periods between 2010 and 2100. Drought risk ratio is quantified, and the role of each component (i.e. hazard, vulnerability, and exposure) is identified, and the associated uncertainties are also characterized. Results show that drought risk is expected to increase in future across Africa with varied rates for different models and scenarios. Although northern African countries indicate aggravating drought hazard, drought risk ratio is found to be highest in central African countries as a consequent of vulnerability and population rise in that region. Results indicate that if no climate change adaptation is implemented, unprecedented drought hazard and risk will occur decades earlier. In addition, controlling population growth is found to be imperative for mitigating drought risk in Africa (even more effective than climate change mitigation), as it improves socioeconomic vulnerability and reduces potential exposure to drought.
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The significant climate sensitivity, exposure, and reduced adaptive capacities in Africa translate into major vulnerability.
“However, the significant climate sensitivity, exposure, and reduced adaptive capacities in Africa consequently translate into major vulnerability (Ahmadalipour et al., 2019; Leal Filho et al., 2021b).”
From Deploying artificial intelligence for climate change adaptation (2022) · cites this paper as Ahmadalipour et al. (2019)
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Deploying artificial intelligence for climate change adaptation
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“…expected to increase, and terms such as “flash drought” are now used to describe the sudden increase in the intensity of drought episodes observed (Challinor et al., 2014; Otkin et al., 2018; Xie et al., 2018; Ahmadalipour et al., 2019; Cohen et al., 2021; Otkin et al., 2021; Parker et al., 2021).”
- Authors
- Ahmadalipour, A., Moradkhani, H., Castelletti, A., & Magliocca, N.
- Affiliations
- University of Alabama (US)
- Politecnico di Milano (IT)
- Corresponding author
- Ahmadalipour, Ali
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 662
- Pages
- 672-686
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W2912730226
- Cited by
- 318
- References
- 93
- Citations by year
- 2026: 29 · 2025: 38 · 2024: 51 · 2023: 64 · 2022: 47 · 2021: 46 · 2020: 31 · 2019: 12
- FWCI
- 17.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Science of The Total Environment (publishedVersion)
- PubMed (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Climate change impacts on agriculture
- Social Sciences › Sociology and Political Science › Disaster Management and Resilience
- Keywords
- drought risk, drought vulnerability, climate change impacts, population growth, socioeconomic vulnerability, drought hazard, regional climate model, demographic scenarios, climate change adaptation, Africa
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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