Currently 6.4 million people, as well as critical infrastructure such as road, rail and power networks, are exposed to flood risk across the UK, and this is expected to rise to 10.8 million people and encompass further critical assets by 2080. The 2020 National Risk Register places flooding behind only pandemics and large-scale attacks as the most significant risks to the UK. Despite this, routine flood risk assessments for planning, development and adaptation purposes use deterministic methods to assess flood hazard, using hydro-dynamic process-based models which are computationally heavy (~hours to ~weeks run time). This established process fails to acknowledge, quantify and capture the cascading uncertainties inherent in the process, which manifest from a wide range of sources including climate scenarios, flow gauging, extreme value estimates and hydrological models. Under estimation of current and future flood hazard could lead to what the Government's Climate Change Risk Assessment (CCRA) terms 'lock-in' and under-engineered adaptation measures, whilst over-estimation could lead to financially non-viable schemes and inappropriate development. The flood analytics industry must urgently move towards probabilistic methods which acknowledge and quantify cascading uncertainties; but this requires yet-to-be developed algorithms which capture the critical uncertainties within the process and reduce the computational burden associated with forward Uncertainty Quantification (UQ). This project will deliver the speed up required to robustly assess flood hazard uncertainty through the development of novel and bespoke uncertainty quantification algorithms for inundation modelling; and by demonstrating their applicability to the prediction of current and future flood hazards at a range of scales, incorporating a wide range of uncertainties in the modelling chain. Success will deliver the step change needed by the flood analytics industry to embrace the necessary transition to UQ assessment, thus placing the UK at the forefront of flooding research, and future proofing climate change adaptation.

Floods are the most common and widely distributed natural risk to life and property worldwide, causing over £4.5B worth of damage to the UK since 2000. Managing flood risk therefore presents a substantial challenge to this and future governments. Arising from the requirements of the EU Floods Directive (2007/60/EC), flood hazard maps for the UK must be delivered by December 2013. Due to limitations in current methodologies these maps take a deterministic approach to mapping catchment scale flood hazard, and do not incorporate climate change projections. Climate projections are predicted to result in the increase of UK properties at risk from flooding and coastal erosion: understanding the uncertainty these bring to flood hazard is therefore of vital economic significance to the UK. Different methods to assess or determine flood hazards have evolved through research and practice. However, these do not allow for uncertainty estimates to be explicitly included within the process. While uncertainty analysis has been an area of research over a number of years, it has not yet achieved widespread implementation in flood modelling studies and decision making for a number of reasons. With developments in the field, such as improved computational power and newly available standardised climate datasets, incorporating uncertainty into assessments is becoming increasingly possible and indeed essential. It is clear that a gap currently exists in uncertainty estimation in flood hazard prediction, particularly in relation to climate change projections, and that this area of research is critical to current policy and operational drivers. This proposal has been developed to comprehensively address this gap. The project will develop a novel probabilistic modelling framework to assess the impact of uncertainty arising from climate change on flood hazard predictions, generate exemplar probabilistic flood hazard maps for selected case study catchments and attempt to quantify the change to flood hazard as a result of climate projections.