EGU 2019: Continuing the
Flood risk conversation

Academic conferences play an important role in furthering the understanding of natural hazards, including flood. They enable individuals with common interests to come together in discussion, debate and learning, providing a fruitful ground for sharing and developing new ideas and strategies around managing these hazards.

In April 2019, I attended the European Geophysical Union (EGU) General Assembly in Vienna alongside three of my JBA colleagues. The EGU is one of the largest academic conferences in the world; this year, there were over 16,000 scientists from 113 countries attending the conference, alongside professionals across a variety of industries, including re/insurance. The conference focuses on the geosciences, covering everything from tectonics and glaciology to meteorology and hydraulic modelling.

We presented on various projects that JBA has undertaken in the field of flood risk, covering our flood mapping and catastrophe modelling from across the globe. Some of the key messages from these presentations are below.

Catastrophe modelling in developing markets

Two of JBA’s presentations explored the value of probabilistic catastrophe models in representing flood risk in emerging markets. Flood models have applications in re/insurance, finance, NGO projects, disaster risk reduction and more, enabling users to better understand, manage and quantify flood risk.

My colleague, Barbara Nix, presented on our Continental China Flood Model. The model is the first fully probabilistic flood model for the country, enabling the assessment of river and surface flood, as well as tropical cyclone and non-tropical cyclone events, on a national scale. Barbara explored the challenges in fully representing flood risk in China, including the lack of access to good quality exposure data, the difficulty in validating against limited claims data and the geographical size of China in modelling flood, and how these challenges were overcome in building the model.

Barbara also explained how the model improves the ability of re/insurers to close the large protection gap in China, improving access to adequate insurance cover and creating a more resilient society in the face of the significant threat from flood.

Our second presentation, by Ian Millinship, introduced a collaborative project between JBA and Arup (on behalf of the World Bank), modelling flood-related damage to property and life in Sierra Leone. Sierra Leone is vulnerable to flood; the country experiences monsoonal flooding which affects thousands of people each year, especially in informal developments. In a similar situation to China, the presentation explored how a lack of adequate data (including rainfall and river flow observations and elevation data) along with real world complexities (such as insufficient drainage and limited building codes) provides a challenge in modelling and assessing flood risk in the country.

Ian demonstrated how JBA used event-based catastrophe modelling to overcome these challenges to assess the socioeconomic impact of flooding in the country. The project modelled three cities in Sierra Leone using statistical and physical modelling methods to estimate the number of people affected, number of fatalities and economic property loss associated with the range of flood events within the flood event set, helping to inform urban planning and disaster risk recovery efforts to mitigate the effect of flooding.

Flood modelling to account for climate change

Later in the week, Sarah Jones had the opportunity to discuss how catastrophe models can assist in assessing long term financial impacts of flood risk and how JBA is incorporating climate change information into the insurance industry in the Costs of Natural Hazards poster session.

Our UK Flood Climate Change Model is the first of its kind in the UK, enabling re/insurers to understand, quantify and mitigate changing flood risk. The poster explored how the model helps to assess how insured losses may change by 2040 under a realistic warming scenario, with initial results suggesting that there may be a 25-30% increase in Annual Average Loss across the UK by 2040.

The poster demonstrated how the model provides users with a better understanding of future flood risk, including helping to identify areas that are more or less susceptible to changes to flood risk, enabling better long-term planning around re/insurance portfolios and mortgage decisions, as well as flood mitigation and damage reduction by government bodies.

Mapping hurricane-prone areas

Finally, I discussed the importance of mapping hurricane-induced storm surge and river flooding concurrently in US hurricane-prone areas, in order to fully account for the flood risk.

After comparing preliminary model outputs for Florida to the flood extent of Hurricane Irma during the development of the JBA Florida Flood Map, we found that many areas known to have flooded during the hurricane event were not flooded in the modelled inundation. My poster explored how analysis of the saline gauges along the St John River, Florida, found that salinity within the river peaked six hours prior to the maximum water level being reached, suggesting that the flooding experienced around the river can be attributed to a combination of both storm surge and rainfall runoff.

The interaction between these two hazards is not well studied or understood; as a result, the poster explored the JBA project undertaken to model these two hazards concurrently within a compound flood event. The project used a fluvial model with a hydrograph that peaked in 24 hours or less with the resulting flood map validated against the flood extents experienced during Hurricane Irma. The flood map was shown to have captured a greater number of flood locations during the event, demonstrating the importance of considering and modelling compound flood events when assessing flood risk.

For further information on any of our presentations or to understand more about how our catastrophe modelling and flood mapping capabilities can help you, please get in touch.

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