Catastrophe models are fundamental tools in the quantification of risk for the (re)insurance industry. When it comes to European earthquake risk modelling, the most widely used vendor models available in the industry have not been updated since 2011 and are largely out of date with respect to the latest scientific findings and data from the recently released pan-European earthquake hazard (ESHM20) and risk (ESRM20) models (e.g. Danciu et al., 2021, Crowley et al., 2021). This presentation aims to showcase our work to incorporate the latest pan-European hazard and risk research within the catastrophe modelling framework, using a largely consistent methodology across the continent. We will focus on the type of datasets that have been leveraged across hazard, exposure and vulnerability and present how these have been utilized for the validation of each component of the catastrophe model, from hazard to vulnerability and loss. We will subsequently demonstrate how we have adjusted the existing catastrophe models, as well as the challenges faced. Finally, we will then proceed to highlight the impact of incorporating such pan-European studies in our View of Risk for loss modelling across 12 different countries in Europe and what are the implications for reinsurance pricing and decision-making.  

How to cite: Papaspiliou, M., Petrone, C., Tomassetti, U., Kalita, P., and Panchireddi, B.: Pan-European earthquake risk modelling – leveraging the latest science in catastrophe modelling and implications for (re)insurance decision-making, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16648, https://doi.org/10.5194/egusphere-egu23-16648, 2023.

There are a number of European level datasets which have been produced over the last decade for natural perils to provide stochastic and probabilistic results at sites, or across the whole of Europe. As part of the MYRIAD-EU project, a key review of historical individual and multiple peril datasets has been made in order to create a compendium of useable results for regional level analysis in MYRIAD.

It uses datasets from SERA-EU, SHARE and ECA for earthquake, RAIN and PRIMAVERA for weather-related disasters such as storms, tornadoes and other events, historical volcanic eruption data from LAMEVE and VOGRIPA, hydrological data and past flood events from databases such as the work of DFO, MODIS, datasets from VU Amsterdam and other research institutions, and bushfire data from EFFIS and other local databases as well as heat wave and cold wave data from multiple datasets.

Where possible, stochastic event sets have been created in order to allow for concurrent and coinciding events to be identified. In many cases, stochastic event sets have not yet been able to be implemented and should be considered as a first step towards a fully event based process. As part of the scenario studies within MYRIAD-EU, probabilistic results will be turned into specific events in order to examine the risk and feedback loops associated with the different event combinations.

This effort has been placed on the MYRIAD-EU Zenodo, and provides the basis for studies into risk in terms of concurrent disasters.

How to cite: Schaefer, A., Daniell, J., Claassen, J., de Ruiter, M., and Brand, J.: An extended stochastic and probabilistic hazard event set for Europe for use in multi-hazard studies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8280, https://doi.org/10.5194/egusphere-egu23-8280, 2023.

The rapid and typically uncontrolled growth of many African cities leads to a plethora of problems and challenges. One of these is the formation and expansion of large urban gullies (UGs) in many (sub)tropical cities. UGs typically lead to the destruction of houses and other infrastructures, displace large numbers of people and often claim casualties. As the formation of such gullies is strongly linked to land use and rainfall intensity, the problems associated with UGs are likely to aggravate in the near future as a result of continued urban expansion and climate change. However, this newly emerging geo-hydrological hazard hitherto received very little research attention. Several studies report on the occurrence and impacts of UGs. Yet, they remain limited to specific local case studies. A clear understanding of the patterns, impacts and driving factors of UGs at larger scales is currently lacking. To address this gap, we aim to better understand the spatial patterns and UG occurrence at the scale of Africa.

In order to achieve this, we are documenting cases of UG occurrence across Africa through the visual analysis of very high spatial resolution satellite imagery. This mapping already allowed us to identify more than 3,500 UGs in 11 countries (mainly across D.R. Congo, Angola, Republic of the Congo, Nigeria and Mozambique). Using on this database, we develop a logistic regression model that accurately simulates the likelihood that UGs occur within (peri-)urban areas across Africa. Our preliminary results show that a combination of rainfall characteristics, topography, soil type and variables describing the land use/urban context can already robustly explain why certain cities are extremely susceptible to the problem and others not. Overall, our dataset and model are first crucial steps to better understand the current and future risks of UGs across Africa.

How to cite: Dujardin, E., Ilombe Mawe, G., Lutete Landu, E., Amery, A., Makanzu Imwangana, F., Hubert, A., Dewitte, O., and Vanmaercke, M.: Assessing urban gully occurrence at the scale of Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8728, https://doi.org/10.5194/egusphere-egu23-8728, 2023.

Electricity generation assets need to withstand climatological hazards all along their operating period. With the ongoing climate change, high temperature extremes are expected to increase, therefore, climate change needs to be accounted for in the estimations of extreme temperature levels at the design stage.

This study showcases a methodology designed to compute maps of daily maximum temperature return levels in summer over the continental USA by 2050 and the end of the century. The methodology first consists in building a variable whose extremes can be considered as stationary in order to then apply the statistical Extreme Value Theory to compute return levels. Previous studies (Parey et al., 2013) had shown that once the trends in mean and standard deviation are removed, the extremes of the reduced variable can be considered as stationary. The reduced variable is thus computed for daily maximum temperatures at each grid point across the continental USA in summer using the ERA5 reanalysis over the 1950-2014 period. Then, once the desired return level is estimated for this variable, temperature levels are obtained by re-introducing the removed information about the mean and the standard deviation of summer temperature at the desired horizon (Parey et al., 2013). To do so, a set of 9 CMIP6 climate models with 3 emission scenarios, SSP1-2.6, SSP2-4.5 and SSP3-7.0, is considered. For each time horizon, 27 extreme summer temperature maps are produced. Then, a criterium is designed to sum up the information and decide whether two different maps give significantly different results. Finally, once the criterium is applied to each pair of maps, either scenario by scenario or all scenarios together, a classification is applied to identify groups of statistically different maps.

References:

Parey S., Hoang TTH, Dacunha-Castelle D.: The importance of mean and variance in predicting changes in temperature extremes, Journal of Geophysical Research: Atmospheres, Vol 118, 1-12, 2013, doi:10.1002/jgrd.50629

Parey S., Hoang T.T.H., Dacunha-Castelle D.: Future high temperature extremes and stationarity, Natural Hazards, 2019, https://doi.org/10.1007/s11069-018-3499-1

How to cite: Parey, S., Collet, L., and Griffin, K.: Extreme high daily maximum temperature in the USA by the 2050 and 2100 horizons, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5005, https://doi.org/10.5194/egusphere-egu23-5005, 2023.

Slightly over half of the world’s human population lives in a river system shared by two or more countries. This transboundary aspect – caused by the utterly differing geographies of administrative country borders and river basins – adds to the intricacy of the global and continental-scale assessment of water-related risks. Whereas such assessments have started to evolve towards the inclusion of multiple hazards and stressors, vulnerabilities, exposures, and consequent risks, they have thus far been largely immune to the transboundary aspects of hydrology and water resources management. At the same time, the research on transboundary waters has its strongholds in matters such as risks related to conflicts or potential sources of conflicts, transboundary water agreements, and their diplomatic aspects, and other aspects related to water diplomacy, typically aiming at reducing political risks related to potential tensions and their mitigation between riparian countries. Bridges between these two strong research traditions are needed as international river systems are not immune to conventional water risks such as those related to hydrometeorology, contamination, or infrastructure deficiencies. We analyze spatially the exposure of the human population to ten major such water risks (due to interannual and seasonal variability; overuse; groundwater; coastal eutrophication; riverine and coastal floods; droughts, and water and sanitation services) in the major 310 international river systems of the planet. Our study approach (risk = stressor/hazard x exposure x vulnerability) aligns with that of the United Nations Sendai Framework and Intergovernmental Panel on Climate Change. Our results indicate that the lack of appropriate sanitation had globally the largest headcount, followed by riverine floods and lack of appropriate water supply. Each risk shows a specific pattern across the river systems, though. The largest human population at water risk was by far in the Ganges-Brahmaputra-Meghna system, followed by the Indus, Nile, Niger, Congo/Zaire, Rann of Kutsch, and Lake Chad Basin. Yet, many of these river systems have limited transboundary cooperation arrangements. The analysis outlines the importance of the transboundary aspect of water risks and their improved quantification in the pursuit of building up international cooperation and security through environmental management policies.

How to cite: Varis, O. and Keskinen, M.: Population at Water Risk in World’s International River Basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6341, https://doi.org/10.5194/egusphere-egu23-6341, 2023.

Africa’s disaster risk is fueled by vulnerability and lack of coping capacity factors, with specific components mostly missing in the literature. Having exceeded the midterm of the Sendai Framework for Disaster Risk Reduction (2015 to 2030), assessing the trend of disaster risk in Africa is necessary. This study answers two core questions: what are the disaster risk factors (and their interactions) in Africa? What trends and patterns have been observed in the last decade? Thus, this study determines the factors of disaster risk in Africa using random forest machine learning models and a Spatial Stratified Heterogeneity (SSH) technique using Geodetector software. Both analytical procedures gave rise to important factors (>10) of disaster risk in Africa. The interaction between these factors is also explored. Among the 22 variables included in the analyses, only one natural hazard (i.e., flood) is a significant factor, while current and projected violent conflicts are human-hazard factors of disaster risk in Africa. Additional results show the trend, pattern, and hotspots of African countries’ disaster risk in the last decade, based on the Index for Risk Management (INFORM) data. This study provides a broader understanding of disaster risk factors in Africa and their interactions, contributing to the foremost priority of the Sendai Framework for Disaster Risk Reduction. Furthermore, the trends, patterns and hotspots identified in this study show countries that should be prioritised for urgent actions.

Keywords: Africa, disaster risk factors, disaster risk reduction, Random Forest, Sendai framework, Spatial Stratified Heterogeneity

How to cite: Eze, E. and Siegmund, A.: Disaster risk factors and spatiotemporal trends in Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8307, https://doi.org/10.5194/egusphere-egu23-8307, 2023.

River bifurcations, multi-thread rivers and artificial channels are not commonly included in global river networks, as they defy the gravity flow assumption elevation-derived networks are based on (e.g. HydroSheds, MERIT Hydro). Yet, these natural and artificial river divergences are important features of the global river drainage system and matter greatly at local to regional scales for various riverine risk assessments. For example, large river deltas are often highly populated regions, in part because of the rivers’ many distributaries. Representing these diverging flows in global river networks will greatly improve the accuracy of many river-based geoscience applications, such as flood forecasting, water availability and quality simulations, or riverine habitat mapping. We developed a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. We achieve this by merging a 30m, Landsat-based river mask with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, this is the first global hydrography derived from a global 30m digital terrain model (FABDEM, based on Copernicus DEM) that shows greater accuracy over the traditionally used SRTM derivatives. OpenStreetMap river centrelines are used to increase the accuracy of the network outside of the river mask. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, reference river networks and randomised manual checks. The new network represents ~18 million km of streams and rivers with drainage areas greater than 50km² and includes ~58 thousand. bifurcations in rivers wider than 30m. The hydrography includes vector river segments, sub-1km reaches and catchments as well as 30m flow direction and accumulation rasters. With the advent of hyper-resolution modelling in the geosciences at the regional and global scale, we expect this river network to be relevant to a broad range of applications in flood protection, hydrology, ecology, fluvial geomorphology and others. The network has been developed as part of the NERC-funded EvoFlood project and will be used to improve global flood models.

How to cite: Wortmann, M., Slater, L., Hawker, L., and Neal, J.: A global 30m bifurcating river network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11336, https://doi.org/10.5194/egusphere-egu23-11336, 2023.

Floods are among the costliest and deadliest natural hazards. Flood risk assessments are required to better manage risk associated with floods. Nowadays, numerous flood risk models are available at various scales, from catchment to regional or even global scale. These models estimate risk (usually expressed in terms of the probability of flood loss) as the product of the hazard, exposure and vulnerability. Flood risk models are affected by numerous uncertainties that propagate through the model and contribute to the final uncertainty in risk estimates. Knowing which uncertainty sources mostly control risk estimates is essential to guide efforts for model improvement, as well as to help risk managers make better decisions. Past efforts to quantify and attribute the output uncertainty of risk models have reached conflicting conclusions. This may be because these studies used different risk models and different uncertainty and sensitivity analysis approaches; or, that they were conducted at relatively small (catchment and/or city) scale, in places with different climatic, hydrological, and socio-economic characteristics.

In this project, we investigate dominant uncertainties of a flood risk model across a much larger scale, namely the entire Rhine River basin, and explore whether dominant uncertainties at specific places can be linked to their physical or socio-economic characteristics. In particular, we analyse two model outputs: the Average Annual Losses (AAL) and Loss Exceedance Curves (LECs). For each output, we first identify the dominant input uncertainties (among uncertainty in the flood depth estimates, vulnerability curves and exposure dataset) in each spatial unit of the modelled domain; and second, we link those dominant input uncertainties to the characteristics of the spatial units.

We find that uncertainties in the vulnerability component dominate the AAL. The dominant uncertainties for the LECs change with the return period of loss, with vulnerability becoming increasingly important with increasing return period. Topography (flat versus steep terrains), degree of urbanization and economic value of the buildings are key characteristics for determining how dominant uncertainties change spatially within our study domain.

How to cite: Sarailidis, G., Pianosi, F., Wagener, T., Styles, K., Lamb, R., and Hutchings, S.: What controls uncertainty in flood risk estimates? An analysis across the Rhine River basin., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3836, https://doi.org/10.5194/egusphere-egu23-3836, 2023.

Hydraulic modelling is used to accurately model extreme flood events but comes with high computational costs, significant data requirements, and long simulation times. Increasing computational resources and higher-resolution data with more spatial coverage means that global-scale flood risk modelling capabilities are constantly evolving. Taking a nested approach, we used the HAND-SRC methodology to develop flood risk data at a continent-scale level and identify areas that would benefit from hydraulic modelling at a more granular level.

Height Above Nearest Drainage (HAND) is a simplified method used for flood zoning and identifying areas at risk of flooding using a Digital Elevation Model (DEM), and drainage network – which can be derived from the DEM. The HAND-SRC method uses channel geometry estimates, obtained from the DEM, and the Manning’s equation, to develop synthetic rating curves (SRC) which allow the conversion of flood discharges to a water height. The flood height can then be combined with a HAND model to produce a flood map. Existing applications of HAND SRC include Central and Eastern Canada (Scriven et al. 2021), and rivers in Texas and North Carolina (Zheng et al. 2018), using national datasets.

 We applied the HAND-SRC methodology using Python and open-source global datasets, to create continental-scale flood risk maps for Europe and the US.  The use of open-source global datasets and Python means the method has the potential to be applied anywhere globally.  

How to cite: Ramsamy, L., Brennan, J., Burke, C., Reveley, G., and Woodhouse, S.: A Simplified Conceptual Model Using Global Open-Source Datasets to Provide Continental and Global Scale Fluvial Flood Risk., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13658, https://doi.org/10.5194/egusphere-egu23-13658, 2023.

The past decade has seen considerable advances in the field of global flood modelling. In the 2010s, it began as a niche academic endeavour building models of the order 10³ m horizontal resolution. In the 2020s, it is maturing into an established scientific discipline and yields profitable commercial ventures, with global models emerging of the order 10¹ m resolution.

Building on the original 10² resolution global inland flood model of Sampson et al. (2015) – with a hydraulic engine based on the sub-grid version of the LISFLOOD-FP local inertial formulation of the shallow water equations (Bates et al., 2010; Neal et al., 2012) – we present the critical advances required to create a ~30 m resolution model of considerably greater fidelity and functionality:

• Using FABDEM as the underlying elevation grid, a machine-learning correction of the Copernicus global digital surface model to a digital terrain model (Hawker et al., 2022).
• Representing river hydrography with MERIT-Hydro (Yamazaki et al., 2019), ensuring the correct alignment of river channels with valley bottoms.
• Estimating river bathymetry prior to inundation modelling with a gradually varied flow solver (Neal et al., 2021).
• Updating boundary condition generation models with new hydrometric datasets and machine-learning hydrologic regionalization techniques (e.g. Zhao et al., 2021).
• Driving a global coastal flood model with a tide–surge–wave regional frequency analysis using tide gauges and reanalyses (Sweet et al., 2020).
• Implementing known and estimated flood protection measures as a rapid and adaptable post-process.
• Generating global climate change factors for fluvial, pluvial, and coastal floods for any plausible 21st century climate state.
• Applying climate change factors as a tractable post-process to a set of multi-frequency flood maps.

These updates form the third version of Fathom's global flood maps. We show that these herald a new era of global flood modelling precision and accuracy, with additional utility wrought from linking climate projections to high-resolution true hydrodynamic models at the global scale for the first time. We also chart the road ahead for global flood modelling: outlining the significant data and modelling challenges our community must address to continue on this unprecedented development trajectory.
 
References:
Bates, P., et al. (2010) https://doi.org/10.1016/j.jhydrol.2010.03.027
Hawker, L. & Uhe, P., et al. (2022) https://doi.org/10.1088/1748-9326/ac4d4f
Neal, J., et al. (2012) https://doi.org/10.1029/2012WR012514
Neal, J., et al. (2021) https://doi.org/10.1029/2020WR028301
Sampson, C., et al. (2015) https://doi.org/10.1002/2015WR016954
Sweet, W., et al. (2020) https://doi.org/10.3389/fmars.2020.581769
Yamazaki, D., et al. (2019) https://doi.org/10.1029/2019WR024873
Zhao, G., et al. (2021) https://doi.org/10.5194/hess-25-5981-2021

How to cite: Wing, O., Quinn, N., Uhe, P., Savage, J., Sampson, C., Addor, N., Lord, N., Collings, T., Hatchard, S., Hoch, J., Smith, A., Cooper, A., Bates, J., Wilkinson, H., Himsworth, S., Probyn, I., Haigh, I., Neal, J., and Bates, P.: A 30 m resolution global fluvial–pluvial–coastal flood inundation model for any climate scenario, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6383, https://doi.org/10.5194/egusphere-egu23-6383, 2023.

In the coming century, people in low-lying coastal urban areas are projected to face an increase in coastal flood risk due to increases in, for example, urban development, sea-level rise, subsidence, and degradation of foreshore vegetation. To implement and raise awareness of coastal climate change adaptation, it is important to better understand the effectiveness of coastal flood risk adaptation strategies, such as Nature-based Solutions and hybrid strategies. Nature-based adaptation in coastal areas, such as vegetation on the foreshore, is showing potential to mitigate the impacts of climate change. Unlike previous studies of Nature-based Solutions, we provide a quantitative assessment of the benefits of combining Nature-based Solutions and structural measures, so-called hybrid solutions, in terms of reduced economic damage, exposed population, and social vulnerability indicators such as poverty dynamics. We show that including hybrid solutions in coastal management strategies benefits people living in poverty more than other people, because the former group are often more prone to coastal flooding. As such, Nature-based and hybrid solutions in lower and middle income countries could contribute to the resilience of people in poverty.

How to cite: Tiggeloven, T., de Moel, H., and Ward, P.: Towards holistic global coastal flood risk assessments including Nature-based Solutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17430, https://doi.org/10.5194/egusphere-egu23-17430, 2023.

Inundation from storm tides and ocean waves is one of the greatest threats coastal communities endure; a threat that is increasing with sea-level rise and changes in storminess. Stakeholders require high resolution hazard data to make informed decisions on how best to mitigate and adapt to coastal flooding. Using a synthesis of observational, hindcast and modelled data, we apply a regional frequency analysis (RFA) approach to characterise extreme water level exceedance probabilities across all global coastlines. This is the first time an RFA has been applied to coastal water levels on a global scale. Wave setup is included in regions which are considered exposed to onshore wave action. The RFA is shown to increase return levels in areas prone to tropical cyclones.  Using Cyclone Yasi as a case-study, we detail the RFA methodology and demonstrate how it uses information from rare, extreme events to better characterise return period water levels in areas which haven’t yet been impacted in the observational record, simply due to chance. The results are output at approximate 1km resolution along the entire global coastline (excluding Antarctica) and have been corrected for use with digital elevation models, for applications such as inundation modelling.

How to cite: Collings, T., Quinn, N., Haigh, I., Green, J., Probyn, I., and Wilkinson, H.: Application of a global coastal regional frequency analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8604, https://doi.org/10.5194/egusphere-egu23-8604, 2023.

Extreme coastal flood events can have devastating impacts in densely populated and low-lying coastal areas, affecting societies, economies, and the environment. Flood risk assessments play a key role in reducing the potential impacts of these events. At global scale, coastal flood risk assessments allow determining the prime price definition of (re-)insurance companies, establishing of climate adaptation and risk reduction measures and understanding flood hazard and risk in data-scarce regions.

Flood risk assessments at large to global scales, however, have generally been based on extreme sea levels estimated for specific return periods, combined with static flood modelling approaches. These traditional approaches are computationally efficient but at large scales they neglect the spatial patterns of flood events, leading to miss-estimation of the risk. Stochastic flood modelling approaches, instead, can become an alternative to capture the spatiotemporal dependency of events.

In this study we analyse the added value of a stochastic coastal flood modelling approach over a traditional return period-based approach for 1000 years of synthetic tropical cyclone events in the east coast of Africa. Synthetic tropical cyclone events from the Synthetic Tropical cyclOne geneRation Model (STORM) combined with the Global Tide and Surge Model (GTSM) will be used to simulate water level timeseries. The Super Fast INundation of CoastS (SFINCS) hydrodynamic flood model together with an impact model will be used to derive the flood risk.

How to cite: Benito Lazaro, I., Aerts, J. C. J. H., Ward, P. J., Eilander, D., and Muis, S.: Stochastic coastal flood risk modelling in the east coast of Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-544, https://doi.org/10.5194/egusphere-egu23-544, 2023.

We present a climate-conditioned catastrophe flood model for the UK that simulates pluvial, fluvial and coastal flood risks at 1 arc second spatial resolution (~20-25m). Hazard layers for ten different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UKCP18 climate simulations. From these, monetary losses are computed for Great Britain only for five specific global warming levels (0.6, 1.1, 1.8, 2.5 and 3.3°C). The analysis contains a greater level of detail and nuance compared to previous work and represents our current best understanding of the UK’s changing flood risk landscape. Validation against national return period flood maps yielded Critical Success Index values in the range 0.6 to 0.78, and maximum water levels for the Carlisle 2005 flood were replicated to an RMSE of 0.41m without calibration. This level of skill is similar to local modelling with site specific data. Expected Annual Damage in 2020 was £730M, which compares favourably to the observed value of £714M reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ~3x higher and lie ~6-7 standard deviations away from the mean of our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1% annual probability flood losses were ~6% greater in the average climate conditions of 2020 than for the period of historical river flow and rainfall observations (centred approximately on 1995) and can be kept to around ~8% if all countries’ COP26 2030 carbon emission reduction pledges and ‘net zero’ commitments are implemented in full. Implementing only the COP26 pledges increases UK 1% annual probability flood losses by ~23% above recent historical values, and potentially ~37% if climate sensitivity turns out to be higher than currently thought.

How to cite: Bates, P., Savage, J., Wing, O., Quinn, N., Sampson, C., Smith, A., and Neal, J.: A catastrophe risk model for current and future flooding in the UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3372, https://doi.org/10.5194/egusphere-egu23-3372, 2023.