EGU24-9197, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

A global stochastic flood risk model for any climate scenario

Oliver Wing1,2, Niall Quinn1, Malcolm Haylock1, Conor Lamb1, Rhianwen Davies1, Nick Sampson1, Izzy Probyn1, James Daniell3, Florian Elmer3, Johannes Brand3, and Paul Bates1,2
Oliver Wing et al.
  • 1Fathom, Bristol, UK (
  • 2School of Geographical Sciences, University of Bristol, Bristol, UK
  • 3Risklayer, Karlsruhe, Germany

Modelling flood hazards at large scales – both uniform frequency hazard maps and event simulations whose frequency varies in space – is a relatively new scientific endeavour. Data and computation constraints have historically necessitated either a more local focus to modelling efforts, or the building of proof-of-concept global-scale models whose fidelity inhibits most practical applications.

Here, we present a global climate-conditioned flood catastrophe model; the culmination of decades of research into scaling inundation modelling, the incorporation of climate change, and synthetic event generation. 30 m resolution global maps representing fluvial, pluvial, and coastal flooding for given return periods were simulated using a hydrodynamic model with sub-grid channels whose inputs were defined using regional flood frequency analyses. Change factors from climate model cascades were flexibly used to perturb the local flood frequency a given flood map represents. Separately, a 10,000-year-long set of synthetic events were simulated using a conditional multivariate statistical model fitted to global fluvial-pluvial-coastal reanalysis data. The empirical return period of a given event is used to sample the corresponding flood map return period in order to build a long synthetic series of floods.

With a global exposure model built using a top-down approach – downscaling capital stock models to high-resolution satellite-derived land-use and building height data – and a global vulnerability model derived from an extensive review of modelling and engineering literature, we demonstrate the calibration and validation of the global risk model. We also show the software challenges overcome to run this model, as well as to enable end-users to flexibly calculate the flood risk of their own exposures in the Oasis Loss Modelling Framework.

How to cite: Wing, O., Quinn, N., Haylock, M., Lamb, C., Davies, R., Sampson, N., Probyn, I., Daniell, J., Elmer, F., Brand, J., and Bates, P.: A global stochastic flood risk model for any climate scenario, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9197,, 2024.