This report highlights that floods have led to more than 185 million internal displacements, or forced movements within one’s country, globally since 2008. Africa experienced around 29 million flood- induced displacements between 2008 and 2022, ranking as the second most affected region. Focusing on the Horn of Africa, particularly Sudan, Ethiopia and Somalia, this study introduces a novel flood displacement risk model. This model, developed under Work Package 3 of the HABITABLE project, aims to provide calibrated estimations of future movements, supporting the formulation of effective policies. The methodology incorporates a unique vulnerability assessment, considering factors often omitted in standard risk models, such as direct impacts on houses and livelihoods, and indirect impacts on critical facilities and services. The assessment uses a probabilistic approach, integrating climatic, hydrological and hydraulic modelling to estimate the impacts triggering displacement.
Using the latest technologies and a novel vulnerability assessment method, the study expresses displacement risk through average annual displacement (AAD) and probable maximum displacement. The results, evaluated under current and future climate conditions with optimistic and pessimistic scenarios, indicate a potential increase in AAD of two to four times compared with current conditions, and even higher risks for pessimistic scenarios, such as a ninefold increase for Sudan.
The outputs can inform national and subnational disaster risk reduction measures, helping identify areas prone to large-scale displacements. Decision-makers can use this information for risk-informed efforts to prevent and mitigate the impacts of displacement. The report concludes with recommendations for comprehensive policies and strategies to address flood-induced displacement risks and protect affected populations.
HABITABLE aims to significantly advance our understanding of the current interlinkages between climate impacts and migration and displacement patterns, in order to better anticipate their future evolutions.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 869395. The content reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.
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