Flood modelling for urban resilience and disaster risk reduction in Sierra Leone
Working as part of a global consortium we developed a multi-city probabilistic flood model, enabling city councils to identify priorities for disaster risk management investments and better understand flood risk and urban resilience.
- Client: World Bank Global Facility for Disaster Reduction and Recovery (GFDRR)
- Location: Sierra Leone
- Partners: Arup, British Geological Survey (BGS), Geo-information and Environmental Management Services (INTEGEMS)
Under ACP-EU funding, the GFDRR aimed to support the development of new hazard and risk information in Sierra Leone. This targeted three major cities and outcomes would be used to identify priorities for disaster risk management investments for the Sierra Leone Urban Resilience project.
Flooding in Sierra Leone is largely caused by intense, localized monsoon rainfall and, although flood waters generally recede within days, there have been events lasting up to a month. It poses a significant and pervasive risk to urban communities in the country – between 1980 and 2010, over 220,000 people in Sierra Leone were affected by flooding according to data from EM-DAT. Kroo Bay, one of the largest informal housing settlements in the capital city of Freetown, has flooded almost every year since 2008 due to heavy rains. This is exacerbated by urban expansion onto floodplains and areas at risk from landslides, with floods regularly damaging infrastructure, housing and crops, and causing loss of life and livestock.
Working as part of a consortium led by Arup, we developed a multi-city probabilistic flood model for the cities of Freetown, Makeni and Bo and their surrounding areas. The model was developed using globally and nationally available datasets and proven JBA flood modelling methodologies, involving the generation of an extensive multi-peril event set, hazard maps, and bespoke vulnerability functions. The results from the model enabled the quantification of river and surface water flood risk in these urban areas and provided the basis for landslide risk modelling.
Our probabilistic flood modelling enabled the city councils, alongside the World Bank, to better understand the scale of flood-related risk. Outputs can be used to make recommendations for risk reduction, including informing future strategies around urban planning and disaster preparedness, and highlighting key areas at risk – as the project was nearing completion, a major landslide occurred in an area that had been identified as at risk in the outputs of the model.
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