EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

End-to-end modelling of flood risk and impact for climate change resilience

Anne Jones1, Andrew Taylor2, Junaid Butt3, Blair Edwards3, Jorge Luis Guevara Diaz4, and Priscilla Barreria Avegliano4
Anne Jones et al.
  • 1IBM Research Europe, Daresbury, UK (
  • 2STFC Hartree Centre, STFC, UK
  • 3IBM Research Europe, UK
  • 4IBM Research Brazil, Sao Paulo, Brazil

Climate change is driving increased urgency for better quantification of climate hazards and their impacts for stakeholders across multiple economic sectors. Flooding has been highlighted as one of the most significant climates risk to UK economic infrastructure, with costs expected to increase with climate-driven changes to rainfall, such as increased intensity of summer storms. To accelerate climate change adaptation and enable economic resilience to climate change impacts, close collaboration is needed between climate scientists, impact modellers, and stakeholders, and technology advances can support this by enabling and streamlining the process of developing and deploying climate impact modelling workflows to translate complex datasets and scientific models into actionable information.

In this presentation, we describe the application of such a technology for the case of pluvial flooding, undertaken as part of the IBM Research and Science and Technology Facilities Research Council partnership, the Hartree National Centre for Digital Innovation (HNCDI), a 5-year programme established to develop and apply new technology to key economic challenges in the UK. Here, we model pluvial flood hazard for a case study region in northeastern England, using a 2-d physical simulation model of flood inundation, driven by open-access geospatial and climate datasets. Flood hazard maps are translated to impact using open asset location data and damage functions.

We consider the sensitivity and scalability (in terms of computational cost) of the hazard and impact predictions to multiple factors, including (1) DEM/DSM representation of land surface (2) soil and land use parameterisation, and (3) model spatial resolution. We also contrast the use of drivers in the form of extreme rainfall scenarios created using a traditional design storm approach, and ensembles of synthetic storms from a stochastic weather generator, both derived from hourly 1km gridded rainfall observations. Finally, we reflect on key gaps to be addressed in the models, data and technology to meaningfully inform climate adaptation across industry sectors.

How to cite: Jones, A., Taylor, A., Butt, J., Edwards, B., Diaz, J. L. G., and Avegliano, P. B.: End-to-end modelling of flood risk and impact for climate change resilience, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7346,, 2023.