UQ4FM: Uncertainty Quantification for Flood Modelling

Started

Not Energy Related
Other Cross-Cutting Technologies or Research(Other Supporting Data)

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics)
ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences)

Not Cross-cutting

Professor L Beevers
Sch of Engineering and Electronics
University of Edinburgh

Standard

EPSRC

01 March 2024

28 February 2027

36 months

£636,121

Civil eng. & built environment

Scotland

NC : Engineering

Professor L Beevers, Sch of Engineering and Electronics, University of Edinburgh

Dr A Wilson, Sch of Mathematics, University of Edinburgh

Project Contact, Ove Arup & Partners Ltd
Project Contact, Scottish Environmental Protection Agency
Project Contact, Kaya Consulting Limited
Project Contact, Environmental Agency
Project Contact, Fathom

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.

20/12/23