Flood hazard and risk maps form the evidence base for decision-marking regarding issues such as land use planning, insurance and capital provision, emergency response and disaster preparedness. None of these essential activities could be planned properly without such data and this is recognised by high level policy such as the EU Floods Directive, the Sendai framework and the flood and water management act in the UK. However, across most of sub-Saharan Africa such data are absent posing a huge challenge to disaster risk managers. The high cost and expertise needed to create flood hazard maps is a barrier to their provision in many sub-Saharan countries meaning that innovation low cost solutions are needed if the provision of such maps and associated benefits for risk management are to become universal. One solution is to use data from global flood models, which have emerged in the last five years, to fill the numerous gaps in coverage. These models make predictions everywhere based on techniques for hydrological prediction in ungauged basins combined with remotely sensed data sets on catchment topography and river size and location. Unfortunately, all global flood models have substantial limitations, such that, the data they produce are usually only considered accurate enough for high level national and transnational risk assessment. This hampers their ability to support a wide range of disaster risk management activities. A second generation of global flood models is therefore needed with sufficient predictive skill and quantification of uncertainty to discriminate risk levels at regional or even community scales. Only with such an advancements will it be possible to transform our understanding of risk and to identify risk hotspots where regional and community level risk reduction efforts would be best focused. HYFLOOD will improve our understanding of the occurrence, location and intensity of flooding with unprecedented detail by building on an existing global flood model to develop regional to community scale flood hazard maps. We will do this by using the remotely sensed data record on flood occurrence for several satellites to disaggregate river reaches into those that we think go overbank more or less often. This information will be used to locally change the river channel characteristics that will then influence the simulated flood inundation extents, depth and duration for extreme events. By overlaying information on population and land use we will make improved estimates of who and what is exposed to flooding. We will trial our approach with end-users in the Democratic Republic of the Congo via an existing collaboration between the University of Bristol and the University of Kinshasa who host the Congo Basin Network for Research and Capacity Development in Water Resources. The outcome of the project will be an improved flood hazard map for the African continent that for the first time can include local scale variability in river characteristics and a quantification of prediction uncertainty. This will be accompanied by the first estimate of river bathymetry at continental scale that can be used by other flood hazard and risk modelling groups. Therefore, HYFLOOD will improve our understanding of the hydrological and morphological factors that determine the occurrence, duration and impact of floods.

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.

Recent devastating floods across the UK and Europe have highlighted the need to make society more resilient to flooding. This need is even more urgent given that flooding is predicted to become more frequent and destructive with climate change. However, current estimates of future flood risk in the UK and elsewhere are unreliable as they are typically based on coarse resolution climate models, which are unable to capture the short-duration rainfall extremes responsible for flooding. They also do not capture critical changes in the spatial extent and temporal clustering of rainfall events and neglect key physical processes in changing rainfall and river flow patterns. FUTURE-FLOOD is an ambitious project to advance our understanding of future inland flood risk and provide new flood estimates across the UK that are fit for purpose. It will bring together internationally state-of-the-art high resolution climate projections with advanced flood modelling capability. We will exploit a new set of continuous 100yr climate projections that provide rainfall data hour by hour, for every 2.2km grid box across the UK, for 1981-2080 for twelve ensemble members. This is like starting twelve weather forecasts and running each for 100yrs. These projections (an extension of "UKCP Local" only available for three 20yr periods) are unique in their spatial and temporal coverage. They will be exploited to gain new understanding of changes in rainfall over the coming years and decades, including changes in temporal clustering, antecedent conditions and spatial extent of events. Such changes are not well understood but are likely to be critically important for flood risk. The 100yr UKCP Local projections will be used to drive hydrological and flood models, providing a complete UK-wide assessment of changes in the frequency and severity of compound pluvial and fluvial flooding for the first time. UKCP Local rainfall data will be used directly such that complex changes to rainfall patterns and intensity distributions are captured in the simulated river flows and flood levels. A recent pilot study (carried out by the project team) showed that using the full UKCP Local space-time varying precipitation fields to drive flood models can lead to radically different estimates of future flood risks to those contained in current guidance based on simplified uplift methods. This pilot study did not capture compound effects and was limited to only one pluvial site (Bristol) and two fluvial catchments (Thet and Dyfi) but demonstrated the need for the national-scale study proposed here. We will compare results with flooding simulated using standard approaches and coarser resolution climate model data, assessing the reliability of existing flood predictions. Additional flood modelling experiments will allow us to identify the physical controls on flooding and its change through time, including the role of changes in the space-time variability of rainfall and its interactions with the landscape. This understanding will be key to identifying improved uplift approaches commonly used by practitioners for future flood risk assessment. Providing flood projections continuously over 100yrs is a major step forward and will enable us to interpret individual observed flood events in the context of climate change and translate results to changes for specific policy-relevant global warming levels. We will combine the new flood hazard information with estimates of exposure and vulnerability to estimate flood risk (e.g. properties flooded, damage to critical infrastructure, monetary loss). This estimation will include projections of socio-economic change. We will demonstrate the use of this new information in decision-making at national scale. We will also co-develop a local-scale demonstrator (initially for Bristol) with city decision-makers to take the new flood information through to improving city resilience, assessing the scope for and benefits of adaptive action.

Climate and environmental (CE) risks (CER) to our economy and society are accelerating. CER include climate-related physical risks such as floods, storms, or changing growing seasons; climate-related transition risks such as carbon pricing and climate litigation; and environmental risks such as biodiversity loss. It is now well accepted that CER can impact asset values across multiple sectors and pose a threat to the solvency of financial institutions (FIs). This can cause cascading effects with the potential to undermine financial stability. The adoption of CER analytics will ensure that CE risks can be properly measured, priced, and managed by individual FIs and across the financial system. This is also a necessary condition to ensure that capital is allocated by FIs towards technologies, infrastructure, and business models that lower CER, which are also those required to deliver the net zero carbon transition, climate resilience, and sustainable development. These twin tracks - greening finance and financing green - are both enabled by CER analytics being appropriately used by FIs. The UK is a world-leader in Green Finance (GF). UK FIs have played a key role in GF innovation. Yet, despite these advances and leadership in almost every aspect of GF, UK FIs cannot secure the data and analytics needed to properly measure and manage their exposures to CER. While the last decade has seen the exponential growth of CE data, as well as improved analytics and methods, often produced by world-leading UK science, the vast majority of this has not found its way into FI decision-making. Our vision for CERAF is to establish a new national centre to resolve this disconnect. CERAF aims to enable a step-change in the provision and accessibility of data, analytics, and guidance and accelerate the integration of CER into products and decisions by FIs to manage CER risks and drive efficient and sustainable investment decisions, thereby delivering the following impacts: - Enhance the solvency of individual FIs in the UK and globally and so contribute to the resilience of the global financial system as a whole for all, as well the efficient pricing and reallocation of capital away from assets at risk to those that are more resilient. - Underpin the development and the growth of UK GF-related products and services. - Enable a vibrant ecosystem of UK enterprises providing CER analytics and realise the opportunity for UK plc of being a world-leader in the creation and provision of CER services. Our vision is that CERAF will be the nucleus of a new national centre established to deliver world-leading research, information, and innovation to systematically accelerate the adoption and use of CER data and analytics by FIs and to unlock opportunities for the UK to lead internationally in delivering CER services to support advancements in greening finance and financing green globally It aims to overcome the following barriers: 1) Making existing data on hazards, vulnerabilities, and exposures more accessible and useable for FIs, with clearly communicated confidence and with analytics that does not yet exist being secured; 2) Consistency and standards to reduce fragmentation, facilitate innovative products and enable the efficient flow and use of data; 3) Assurance and suitability are needed to understand which CER analytics are best suited for particular uses and provide transparency into underlying data and methodologies, so that CER analytics can be trusted and used; 4) Unlocking innovation through supporting FIs to test new approaches in a lower-risk way; and 5) Building capability, knowledge, and skills within FIs to analyse and interpret CER data. Resolving these barriers is a necessary condition for repricing capital and avoiding its misallocation, and achieving the UK's ambitions on GF.