Advanced Uncertainty Quantification for Flood Inundation Modelling

Abstract

Flood hazards present a significant risk to the UK, with homes, businesses and critical infrastructure exposed to a mixture of fluvial, surface water and coastal flooding. Climate change is expected to influence river flows, changing the frequency and magnitude of future flood events. Flood hazard assessments are used by decision-makers to implement policies and engineering interventions to reduce the impacts of these flood events. Probabilistic flood modelling can explore input and parameter uncertainties in flood models to fully quantify inundation uncertainty. However, probabilistic methods require large computational costs—limiting their application. This paper investigates a range of advanced uncertainty quantification methods (traditional Monte Carlo (FMC), Kriging and multi-fidelity Monte Carlo (MFMC)) to reduce the dichotomy between accuracy and costs. Results suggest that Kriging can reduce computational costs by 99.9% over FMC. The significantly increased efficiency has the potential to improve future policy and engineering decisions, reducing the impacts of future flood events.