The interconnected risks of flooding
Source: gov.uk (2025-04-04)

This research was applied to give the government, flood risk management authorities and the insurance industry a better understanding of risk.

Transforming flood assessment at multiple scales through better statistical understanding of risk

Rob Lamb (https://www.jbatrust.org/about-the-jba-trust/the-jba-trust-team/) 1, Jonathan Tawn (https://www.lancaster.ac.uk/maths/people/jon-tawn) 2, Caroline Keef (https://www.jbaconsulting.com/) 3, Ross Towe (https://www.research.lancs.ac.uk/portal/en/people/ross-towe(5c3b5c28-8ca2-47f6-bdd2-2dccad73a9fe).html) 2, Sarah Warren (https://www.jbaconsulting.com/) 3

1 JBA Trust and Lancaster Environment Centre, Lancaster University, United Kingdom

2 Lancaster University, United Kingdom

3 JBA Consulting, United Kingdom

Research led by Lancaster University, JBA and the JBA Trust - conducted over a decade - has supported the government, flood management authorities and the insurance industry to have a better understanding of flood risk from local to national scales.

Historically, flood risk was often assessed in isolated terms. This meant the focus was on single locations or individual flood events, rather than accounting for how extreme weather patterns can co-occur across large areas. As a result, assessments could underestimate the broader, interconnected risks of flooding.

The research team addressed this gap by developing methods that model flood events as multivariate extremes. This allowed for a more realistic estimation of the likelihood of concurrent flooding across multiple locations. The approach enabled flood risk to be assessed at a national scale, informing decisions in the UK’s National Security Risk Assessment (NSRA) and aiding global reinsurance companies in risk evaluations.

Multivariate Extreme Value theory

The research breakthroughs were founded on multivariate extreme value theory. The theory addressed the probability of multiple extreme events occurring simultaneously. Prior to this research, methods were limited in scope, handling only a few variables or locations. While they were mathematically convenient, they didn’t align with real-world flood data, often leading to inaccurate risk estimates.

To overcome this, Lancaster University researchers developed a conditional probability model that could handle a large number of variables with varied dependencies. This model demonstrated that, contrary to traditional beliefs, the probability of seeing a 1 in 100-year flood somewhere in England and Wales annually is as high as 88%.This finding underscored the need to shift from isolated risk descriptions to a more holistic framework, and recognised that a seemingly rare event locally could be much more probable when considered across a broader scale.

Impact

The new approach proved influential during the UK’s 2016 National Flood Resilience Review (NFRR), which was prompted by severe flooding in 2013 to 2014 and 2015 to 2016.

UK Chief Scientific Adviser (2016) said:

There was pressure on Government to better understand the risks involved. … Your contribution to the review was very important. Ministers were determined to base the review’s conclusions and recommendations on sound evidence and analysis… Our advice had significant influence on both the evidence and the way in which it was communicated.

The government’s conclusions were heavily based on the research insights, which reshaped the understanding of flood risk. It also highlighted the urgency of comprehensive preparedness.

A direct outcome of the NFRR was the government’s £12.5 million investment in new mobile flood defences, quadrupling the number of units from 2015 levels. Furthermore, a commitment to an ongoing £2.3 billion capital investment plan was secured, aiming to protect 300,000 homes. This strategic shift—grounded in more realistic risk assessments—increased the resilience of both urban and rural communities against future floods.

Beyond the UK, these advancements have been influential globally, especially for the insurance and reinsurance sectors.

Working with Lancaster University and the Environment Agency, JBA further refined the methods to improve their scalability and efficiency, leading to the development of the Multivariate Event Modeller tool. This open-source tool allows for joint probability analysis, making it accessible for environmental scientists and risk managers who need to analyse complex, interconnected flood events.

The research has extended into ocean wave analysis, contributing to a better understanding of coastal extremes that compound flood risks, especially in coastal regions.

These tools and insights have led to more accurate, data-driven assessments that can guide infrastructure planning, inform policy, and support sustainable urban development.

Resources

BBC News Article. (2016). Hundreds of key sites in England at Risk of Floods, dated 8th September 2016 corroborating £12.5 million investment means four times as many temporary flood barriers than in 2015. Available at: https://www.bbc.co.uk/news/science-environment-37306094 (Accessed: 24 March 2025).

Environment Agency. (2017). Planning for the risk of widespread flooding: Project Summary SC140002/S. Available at https://assets.publishing.service.gov.uk (https://assets.publishing.service.gov.uk/media/60364248d3bf7f0ab50880a8/Planning_for_the_risk_of_widespread_flooding_-_summary.pdf) (Accessed: 24 March 2025).

Grainger, J., Sykulski, A., Jonathan, P., & Ewans, K. (2021). Estimating the parameters of ocean wave spectra. Ocean Engineering, 229, Article 108934. Available at: doi.org/10.1016/j.oceaneng.xx-xxxx-xxxx (https://www.sciencedirect.com/science/article/pii/S0029801821003693?via%3Dihub) (Accessed: 24 March 2025).

Grainger, J., Sykulski, A., Ewans, K., Hansen, H. F., Jonathan, P. (2023). A multivariate pseudo-likelihood approach to estimating directional ocean wave models, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 72, Issue 3. Available at: doi.org/10.1093/jrsssc/qlad006 (https://doi.org/10.1093/jrsssc/qlad006) (Accessed: 24 March 2025).

Heffernan, J. E. and Tawn, J. A. (2004). A conditional approach to modelling multivariate extreme values (with discussion), J. Roy. Statist. Soc., B, 66, 497-547. Available at: doi.org/10.1111/j.1467-9868.2004.02050.x (https://doi.org/10.1111/j.1467-9868.2004.02050.x) (Accessed: 24 March 2025).

HM Government. (2016). National Flood Resilience Review (NFRR). Available at: https://assets.publishing.service.gov.uk/ (https://assets.publishing.service.gov.uk/media/5a7f973de5274a2e8ab4d1af/national-flood-resilience-review.pdf) (Accessed: 24 March 2025).

JBA Trust. (2022). Improving statistical models of large scale flood events. Available at: https://www.jbatrust.org/ (https://www.jbatrust.org/research/rivers-and-coasts/improving-statistical-models-of-large-scale-flood-events/) (Accessed: 24 March 2025).

Keef, C., Tawn, J. A. and Lamb, R. (2013). Estimating the probability of widespread flood events. Environmetrics, 24, 13-21. Available at: doi.org/10.1002/env.2190 (https://doi.org/10.1002/env.2190) (Accessed: 24 March 2025).

Lamb, R., Keef, C., Tawn, J. A., Laeger, S., Meadowcroft, I., Surendran, S., Dunning, P. and Batstone, C. (2010). A new method to assess the risk of local and widespread flooding on rivers and coasts. Journal of Flood Risk Management, 3, 323-336. Available at: doi.org/10.1111/j.1753-318X.2010.01081.x (https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1753-318X.2010.01081.x) (Accessed: 24 March 2025).

Multivariate Event Modeller - Github. Available at: https://github.com/jbaconsulting/Multivariate-Event-Modeller (Accessed: 24 March 2025).

REF 2021 Impact Case Study: A step-change in the understanding and quantification of risk to improve resilience to flooding, Lancaster University, Unit of Assessment: 10, Mathematical Sciences. Available at: https://results2021.ref.ac.uk/impact/ (https://results2021.ref.ac.uk/impact/acf48714-b559-41b4-9567-6c7ee6eac503?page=1) (Accessed: 24 March 2025).

REF 2021 Impact Case Study: Transforming Government assessments of flood risk and resilience through improved understanding of uncertainties in flood risk modelling Lancaster University, Unit of Assessment: 7, Earth Systems and Environmental Sciences. Available at: https://results2021.ref.ac.uk/impact/ (https://results2021.ref.ac.uk/impact/48a7116b-0d07-4016-ac36-7da5d51ab689?page=1) (Accessed: 24 March 2025).

Tawn, J. A., Shooter, R., Towe, R. and Lamb, R. (2018). Modelling spatial extreme events with environmental applications. Spatial Statistics, 28, 39-58. Available at: doi.org/10.1016/j.spasta.2018.04.007 (https://doi.org/10.1016/j.spasta.2018.04.007) (Accessed: 24 March 2025).

Towe, R., Tawn, J. A. and Lamb, R. (2018). Why extreme floods are more common than you might think? Royal Statistical Society Journal, Significance, Vol. 15, No. 6, 16-21. Available at: doi.org/10.1111/j.1740-9713.2018.01209.x (https://academic.oup.com/jrssig/article/15/6/16/7037887) (Accessed: 24 March 2025).

UK Parliament Statement. Written Statement UIN HLWS139 on the National Flood Resilience Review made by Lord Gardiner, 8th September 2016. Corroborates £12.5 million of spending on new temporary flood defences and a £2.3 billion investment to better protect 300,000 homes.

Funder
- JBA Trust
- Natural Environment Research Council (NERC)
- Environment Agency
Collaborators
- Lancaster University
- JBA Trust
- JBA Consulting
- Environment Agency
- Shell Research
Research period
- 2004 to 2023
Impact period
- 2008 to 2017
Impact country

UK

Globally

Contributing towards the areas of research interest
- 1 - Understanding future flood and coastal erosion risk