Flood Risk in Sierra Leone: How can insurance industry methods benefit disaster risk reduction?

Sierra Leone has been greatly impacted by natural disasters in recent decades. Of the total number of people affected by natural disasters in Sierra Leone in the last 30 years, 90% were affected by flooding (UNDP, 2012). These floods occur frequently and lead to loss of life, damage to property and disease outbreak. Vulnerability to flooding is particularly high due to construction of homes on floodplains and in (temporarily) dry riverbeds.

Organisations in the disaster risk reduction (DRR) community are seeking to improve management of flood risk around the world. In 2017, the World Bank funded a project to improve understanding of the impacts of flooding and landslides in three cities in Sierra Leone: Freetown, Makeni and Bo. JBA worked alongside Arup to estimate the number of people affected by flood events and the economic costs incurred on average per year, as well as in particularly extreme years.

Using catastrophe modelling in DRR

Catastrophe models have been widely used in the re/insurance industry for many years to estimate potential future financial losses. Whilst they are a mature product in this industry, application to DRR is less well developed. For quantifying the effect of flooding on the population of Sierra Leone, JBA borrowed methods from the re/insurance industry to design and build a bespoke catastrophe model to help the World Bank identify the areas with the largest risk to life and property from flooding. Tens of thousands of simulated future flood events were generated and the potential effect on life and property was quantified.

What were the results used for?

Areas at high risk to flood were identified to enable improved flood risk management. The district of Regent in the capital city of Freetown showed the highest estimated number of fatalities by area per year. The results also included an estimate of the number of people affected or displaced, and economic loss due to flood events as an average per year, and for more extreme years.

Figure 1: Buildings at risk (dark dots) from a 100-year river (light blue) and surface water (light purple) flood in Freetown.

The results of this study enable more effective management of flood by the government and other organisations in Sierra Leone. Results can be used to identify areas most likely to flood and therefore areas in which to avoid building homes and other structures to minimise the population at risk and further economic loss. The results also enable disaster planning and response, allowing organisations to direct more resources to flood-prone areas both before and after a flood event occurs.

August 2017 landslide and mudflow

As the results of this study were being finalised in August 2017, a large landslide occurred on the slopes of Sugar Mountain above the district of Regent in the capital city of Freetown. Torrential rainfall over three days destabilised the slope and the landslide mixed with the already full rivers to become a mudflow that propagated down through the residential areas of Regent and Lumley. Over 1,000 people lost their lives and many more were left homeless along the path of the mudflow.

Figure 2: Heavy rainfall triggered a landslide on the slopes above Regent, Freetown. The landslide mixed with floodwater to create a mudflow which caused the deaths of over 1,000 people in Regent and in downstream Lumley (source: UM news).

Although the main aim of the project was to provide a static view of risk that could be used for planning, immediately after this event occurred there was an urgent requirement to provide more information to inform the recovery effort. As the model was complete at this point, the JBA team extracted events from the simulated catalogue that were similar to the unfolding event in terms of meteorological conditions and geographic extent. These events were used to estimate the potential impact in terms of the number of people and properties affected by the August 2017 landslide and mudflow.

The district most severely affected by the mudflow was Regent, which had been identified as having the highest risk to life from flooding from the catastrophe model. While the exact location of extreme rainfall and landslide is difficult to predict, this event shows the potential of the results to be used in improving disaster preparedness. This type of information enables a rapid assessment of the potential scale and impacts of a disaster, allowing authorities to scale their response appropriately.

Expanding the use of cat models in DRR

The impact of the August 2017 floods and landslide highlighted the urgent need for improved flood information and flood risk management in Sierra Leone using sophisticated tools such as catastrophe models.

Although primarily used in the re/insurance industry, catastrophe models are invaluable for in DRR sector, helping to build resilience, improve land-use and disaster planning, and inform disaster recovery efforts across Sierra Leone and similar low income countries.