NH9.1 | Global and continental scale risk assessment for natural hazards: methods and practice
EDI
Global and continental scale risk assessment for natural hazards: methods and practice
Co-organized by GM6
Convener: Philip Ward | Co-conveners: Hessel Winsemius, Melanie J. Duncan, James Daniell, Susanna Jenkins
Orals
| Fri, 28 Apr, 14:00–15:45 (CEST), 16:15–18:00 (CEST)
 
Room 1.31/32
Posters on site
| Attendance Fri, 28 Apr, 08:30–10:15 (CEST)
 
Hall X4
Orals |
Fri, 14:00
Fri, 08:30
The purpose of this session is to: (1) showcase the current state-of-the-art in global and continental scale natural hazard risk science, assessment, and application; (2) foster broader exchange of knowledge, datasets, methods, models, and good practice between scientists and practitioners working on different natural hazards and across disciplines globally; and (3) collaboratively identify future research avenues.
Reducing natural hazard risk is high on the global political agenda. For example, it is at the heart of the Sendai Framework for Disaster Risk Reduction and the Paris Agreement. In response, the last decade has seen an explosion in the number of scientific datasets, methods, and models for assessing risk at the global and continental scale. More and more, these datasets, methods and models are being applied together with stakeholders in the decision decision-making process.
We invite contributions related to all aspects of natural hazard risk assessment at the continental to global scale, including contributions focusing on single hazards, multiple hazards, or a combination or cascade of hazards. We also encourage contributions examining the use of scientific methods in practice, and the appropriate use of continental to global risk assessment data in efforts to reduce risks. Furthermore, we encourage contributions focusing on globally applicable methods, such as novel methods for using globally available datasets and models to force more local models or inform more local risk assessment.

Orals: Fri, 28 Apr | Room 1.31/32

Chairpersons: Philip Ward, Susanna Jenkins, James Daniell
14:00–14:05
14:05–14:25
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EGU23-16648
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solicited
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On-site presentation
Myrto Papaspiliou, Crescenzo Petrone, Umberto Tomassetti, Pratim Kalita, and Bhaskara Panchireddi

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.

14:25–14:35
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EGU23-8280
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ECS
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On-site presentation
Andreas Schaefer, James Daniell, Judith Claassen, Marleen de Ruiter, and Johannes Brand

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.

14:35–14:45
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EGU23-8728
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ECS
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On-site presentation
Elise Dujardin, Guy Ilombe Mawe, Eric Lutete Landu, Arno Amery, Fils Makanzu Imwangana, Aurélia Hubert, Olivier Dewitte, and Matthias Vanmaercke

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.

14:45–14:55
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EGU23-5005
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On-site presentation
Sylvie Parey, Lila Collet, and Kristen Griffin

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.

14:55–15:05
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EGU23-6341
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On-site presentation
Olli Varis and Marko Keskinen

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.

15:05–15:15
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EGU23-8307
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ECS
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Virtual presentation
Emmanuel Eze and Alexander Siegmund

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.

15:15–15:25
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EGU23-11336
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ECS
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On-site presentation
Michel Wortmann, Louise Slater, Laurence Hawker, and Jeffrey Neal

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.

15:25–15:35
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EGU23-3836
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ECS
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On-site presentation
Georgios Sarailidis, Francesca Pianosi, Thorsten Wagener, Kirsty Styles, Rob Lamb, and Stephen Hutchings

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.

15:35–15:45
Coffee break
Chairpersons: Susanna Jenkins, James Daniell, Philip Ward
16:15–16:20
16:20–16:40
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EGU23-8017
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ECS
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solicited
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Virtual presentation
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Lukas Riedel, Thomas Röösli, Pamela Probst, Isabelle Bey, and David N. Bresch
River floods are amongst the most devastating natural hazards. Reliable information on impeding hydrometeorological events enables humanitarian agencies to take action and to support the efforts of authorities and affected residents. Forecasting the socioeconomic impacts of such events improves focused measures to protect livelihoods. At the Federal Office of Meteorology and Climatology MeteoSwiss, we develop a globally consistent river flood impact model based on river discharge forecasts by the Global Flood Awareness System (GloFAS) and river flood hazard maps to support the humanitarian community.
 
Daily probabilistic river discharge forecasts of GloFAS are released by the Copernicus Emergency Management Service and can be downloaded from the Copernicus Climate Data Store. Global river flood hazard maps for flood events of different magnitude are available from the Joint Research Centre Data Catalogue of the European Commission. Additionally, the global database of flood protection standards FLOPROS is freely accessible. These open data collections enable the computation of forecasted, globally consistent river flood hazard footprints considering regional protection standards. With these footprints, we compute timely socioeconomic impact forecasts using the open-source, probabilistic impact model CLIMADA.
 
In this presentation, we demonstrate the new river flood module implemented in CLIMADA, which automatically downloads GloFAS data, computes flood footprints, and calculates flood impacts. We further discuss the benefits of impact forecasts for anticipatory action and disaster relief efforts compared to forecasts based on physical hazards alone.

How to cite: Riedel, L., Röösli, T., Probst, P., Bey, I., and Bresch, D. N.: Globally consistent, open-source river flood impact model using open data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8017, https://doi.org/10.5194/egusphere-egu23-8017, 2023.

16:40–16:50
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EGU23-13658
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ECS
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On-site presentation
Laura Ramsamy, James Brennan, Claire Burke, Graham Reveley, and Sally Woodhouse

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.

16:50–17:00
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EGU23-6383
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ECS
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On-site presentation
Oliver Wing, Niall Quinn, Pete Uhe, James Savage, Chris Sampson, Nans Addor, Natalie Lord, Tom Collings, Simbi Hatchard, Jannis Hoch, Andy Smith, Anthony Cooper, Joe Bates, Hamish Wilkinson, Sam Himsworth, Izzy Probyn, Ivan Haigh, Jeff Neal, and Paul Bates

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.

17:00–17:10
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EGU23-17430
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ECS
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On-site presentation
Timothy Tiggeloven, Hans de Moel, and Philip Ward

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.

17:10–17:20
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EGU23-8604
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ECS
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On-site presentation
Thomas Collings, Niall Quinn, Ivan Haigh, Joshua Green, Izzy Probyn, and Hamish Wilkinson

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.

17:20–17:30
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EGU23-544
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ECS
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On-site presentation
Irene Benito Lazaro, Jeroen C.J.H. Aerts, Philip J. Ward, Dirk Eilander, and Sanne Muis

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.

17:30–17:40
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EGU23-3372
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On-site presentation
Paul Bates, James Savage, Ollie Wing, Niall Quinn, Christopher Sampson, Andrew Smith, and Jeff Neal

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.

17:40–18:00

Posters on site: Fri, 28 Apr, 08:30–10:15 | Hall X4

Chairpersons: James Daniell, Susanna Jenkins, Hessel Winsemius
X4.83
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EGU23-2584
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Hugo Rakotoarimanga, Rémi Meynadier, Anna Weisman, Oliver Wing, and Hessel Winsemius

AXA proposes a novel continental-scale generator of synthetic gridded rainfall daily timeseries (10km resolution) with applications to cross-country risk assessment under current and future climate scenarios. Europe serves as a case-study to demonstrate and assess its performance in terms of hazard modelling and extrapolation to unobserved extreme local and regional events. This generator belongs to the class of time and space reshuffling Stochastic Weather Generators (SWGs) and generates unobserved events by re-sequencing historical multisite timeseries (E-OBS). Consistency at continental scale is ensured by relying on weather regimes and atmospheric situations characterized from the ERA5 reanalysis over Europe. The use of atmospheric drivers and dry-wet alternating cycles allows for the determination of both precipitation-prone situations or on the contrary drier spells, while preserving the physics of the atmospheric water cycle. Spatial reshuffling is introduced by regional differentiation. Transitions between regimes can be either calibrated from the historical data or extrapolated to represent future states of the climate along with an appropriate uplifting of the humidity-related variables. This generator is operationally used at AXA as part of a European flood risk model and serves as the main input to an hydrological and hydraulic model.

How to cite: Rakotoarimanga, H., Meynadier, R., Weisman, A., Wing, O., and Winsemius, H.: A Continental Stochastic Precipitation Generator, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2584, https://doi.org/10.5194/egusphere-egu23-2584, 2023.

X4.84
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EGU23-8361
Dirk Eilander, Anaïs Couasnon, Frederiek Sperna Weilander, Hessel Winsemius, and Philip Ward

In low-lying coastal areas floods occur from (combinations of) fluvial, pluvial, and coastal drivers. If these drivers co-occur, they can cause or exacerbate flooding, and are referred to as compound flood events. Furthermore, if these flood drivers are statistically dependent, their joint likelihood might be misrepresented if dependence is not accounted for. However, most large-scale flood risk models do not account for the hydrodynamic interactions and statistical dependence between flood drivers. We present a globally-applicable framework for compound flood risk assessments using combined hydrodynamic, impact and statistical modeling. The framework broadly consists of three steps. First, a large stochastic event set is derived from reanalysis data, taking into account co-occurrence of, and dependence between all annual maxima flood drivers. Then, both flood hazard and impact are simulated for different combinations of drivers at non-flood and flood conditions. Finally, the impact of each stochastic event is interpolated from the simulated events to derive a complete flood risk profile. The framework has been applied to a case study in Mozambique where we found that if dependence between flood drivers is not accounted for, the impact of especially rare events is underestimated. In this contribution we discuss findings from the case study as well as challenges faced when upscaling the framework to for large-scale compound flood risk assessments.

How to cite: Eilander, D., Couasnon, A., Sperna Weilander, F., Winsemius, H., and Ward, P.: Towards large-scale compound flood risk modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8361, https://doi.org/10.5194/egusphere-egu23-8361, 2023.

X4.85
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EGU23-5998
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ECS
Leanne Archer, Jeffrey Neal, Paul Bates, Dereka Carroll, and Scott Weaver

Climate change is making rainfall associated with tropical cyclones more extreme. Some of the places most affected by tropical cyclones are small islands, such as Puerto Rico in the Caribbean which was severely impacted by Hurricane Maria in 2017. However, we know very little about how sensitive flooding in small islands is to changing rainfall characteristics, or how population exposure to flooding might change in the future. This is due to the limited data availability necessary to produce high-resolution flood hazard and population exposure estimates for a wide range of possible scenarios. Using an island-scale (~9000km2) event-based rainfall-driven hydrodynamic flood model at 20m resolution for the island of Puerto Rico, we simulate a range of observed rainfall grids from Hurricane Maria across time and space (such as IMERG and NCEP Stage IV). We assess how the current population exposure to rainfall-driven flooding changes across the range of observation rainfall footprints to determine how sensitive the flood extent and population exposure is to different rainfall inputs. We also compare these outputs to flood extents produced using an event set of synthetic hurricane rainfall events that share similar rainfall and track characteristics to Hurricane Maria under current and future climate scenarios (1.5°C and 2°C). Additionally, we utilise high-resolution (90m) gridded estimates of future population in Puerto Rico (FuturePop), to determine how an event with the same extreme magnitude as Hurricane Maria would impact population exposure to flooding under different future Shared Socioeconomic Pathway scenarios. The results of this analysis aim to improve understanding regarding the range of plausible estimates of current and future population exposure to flooding in Puerto Rico. These results will help inform adaptation to more extreme flood risk in Puerto Rico under current and future climate change.

How to cite: Archer, L., Neal, J., Bates, P., Carroll, D., and Weaver, S.: Population Exposure to Rainfall-Driven Flooding from Hurricane Maria in Puerto Rico, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5998, https://doi.org/10.5194/egusphere-egu23-5998, 2023.

X4.86
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EGU23-8874
Niall Quinn, Callum Murphy-Barltrop, and Izzy Probyn

Floods are one of the most common, costly, and deadly natural disasters in many regions of the world. Billions of dollars of damages are caused annually, while most studies predict a further worsening of impacts under a warming climate over the next century. To help mitigate the impacts it is important to understand where, when and the likely severity of flooding that might take place. Recently, the emergence of efficient hydraulic modeling frameworks, able to produce flood hazard maps over the entire world, have provided a vital tool that helps to provide this information to end users. However, these maps are typically ‘static’, offering no information about what a real flood event could look like. This is problematic to, for example, emergency planners who may need to know how large the worst case event might be, or those in the insurance sector who may be interested in estimating tail losses across asset portfolios spanning large spatial regions. To meet these requirements, it is important to consider the spatial dependencies in flood events, i.e., given there is flooding in one region, what is the likelihood we see flooding simultaneously in another. 

In this work we attempt to meet this need through the development of a modeling framework that enables the automated creation of thousands of years of synthetic flood footprints, representing pluvial, fluvial and coastal processes, anywhere in the world. We do this by obtaining global, freely available reanalysis products to use as training data to characterize the flood dependence structures within a multivariate extreme value model at selected locations. The dependence structures are then used to derive synthetic events, interpolated to create event surfaces, which are then used to sample from existing global static hazard layers. The output is a dataset containing thousands of years of synthetic multi-peril (pluvial, fluvial, coastal) flood event footprints around the world. This presentation outlines the key input datasets, methodological steps, and validation procedures implemented. We also highlight important limitations and plans for future development. 

How to cite: Quinn, N., Murphy-Barltrop, C., and Probyn, I.: A global synthetic multi-peril flood event set, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8874, https://doi.org/10.5194/egusphere-egu23-8874, 2023.

X4.87
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EGU23-12804
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Julien Cardinal and Rémi Meynadier

Severe convective storms are a common occurrence during spring and summer season in European countries. The damages caused by hail and wind gusts can be substantial to properties, especially on motor. The development of a convective storms hazard stochastic catalog is an important step for AXA to assess and mitigate this peril.
We propose a method to build a catalog of synthetic events based on multiple meteorological drivers from ECMWF-ERA5 and EUMETSAT-CMSAF. New atmospheric temporal sequences are created by reshuffling historical data, with constraints to keep physical consistency (identification of weather patterns and historical transition probabilities between them). The probability of hail occurrences is then assessed for each meteorological configuration, learning from in-situ reports (ESWD and Keraunos), with historical validation to ensure accuracy of the hail prediction. A catalog of new plausible scenarios for convective storm hazard is produced and crossed with exposure and vulnerability data to assess the subsequent risk.

How to cite: Cardinal, J. and Meynadier, R.: AXA probabilistic Severe Convective Storm model in western Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12804, https://doi.org/10.5194/egusphere-egu23-12804, 2023.

X4.88
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EGU23-13548
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ECS
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Elinor Meredith, Susanna Jenkins, Josh Hayes, David Lallemant, Natalia Deligne, and Rui Xue Natalie Teng

The destruction of thousands of homes by lava flows of Nyiragongo volcano, Democratic Republic of Congo, and La Palma, Canary Islands, in 2021 serve as a reminder of the devastating impact of lava flows. However, studies on lava flow impacts on the built environment are relatively rare. We reviewed literature to compile a global dataset of lava flow impacts to buildings and infrastructure from ~3500 BCE to 2022 CE, and use this to assess temporal and spatial trends of events. Our findings show a recent increase in recorded events, and that these occur more frequently than previously thought, with almost four impact events per decade in the past 100 years. This is likely from population expansion and reflecting a recent increase in recording. The majority of recorded events were in Italy, USA, and Réunion Island, France, with a rise in records in Africa since 1800 and the most impacted structures at Nyiragongo volcano, DRC. Impact records have developed from qualitative eruption reports to quantitative impact assessments, and the majority of studies report a binary impact on structures; with towns and/or structures stated as either destroyed or unaffected. However, several reports give specific details of damage indicating that lava flow impacts may not be binary. The dataset provides a baseline to assess past impacts, and be updated as future studies reveal past lava flow impact events, or when future lava impact events occur.

How to cite: Meredith, E., Jenkins, S., Hayes, J., Lallemant, D., Deligne, N., and Teng, R. X. N.: Assessing global trends in lava flow impact events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13548, https://doi.org/10.5194/egusphere-egu23-13548, 2023.

X4.89
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EGU23-15002
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ECS
Gaia Olcese, Paul Bates, Jeffrey Neal, Christopher Sampson, Oliver Wing, and Niall Quinn

Stochastic flood models can simulate synthetic flood events with a realistic spatial structure, unlike traditional flood models, which do not take into consideration the spatial dependency of flood events. This is particularly relevant to loss calculations at regional to continental scales. The development of large-scale stochastic flood models has been limited so far by the availability of gauge data, needed as a model input. Global hydrological models can provide simulated discharge hindcasts that have been used to drive stochastic flood modelling in data-rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally by simulating synthetic flood events in different regions of the world. The results (published in a recent paper in WRR) show a promising performance of the model-based approach, with errors comparable to those obtained over data-rich sites. This suggests that a network of synthetic gauge data derived from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data-scarce regions and loss exceedance curves where exposure data are available. As part of this research, we are currently working on the development of a stochastic flood model of Southeast Asia using discharge data from global hydrological models. 

How to cite: Olcese, G., Bates, P., Neal, J., Sampson, C., Wing, O., and Quinn, N.: Use of hydrological models in global stochastic flood modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15002, https://doi.org/10.5194/egusphere-egu23-15002, 2023.

X4.90
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EGU23-15051
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ECS
James Savage, Pete Uhe, Ollie Wing, Chris Sampson, Andy Smith, Natalie Lord, Nans Addor, Simbi Hatchard, Jannis Hoch, Joe Bates, Niall Quinn, Tom Collings, Izzy Probyn, Ivan Haigh, Joshua Green, Anthony Cooper, Hamish Wilkinson, and Sam Himsworth

In recent years there have been many new global datasets and methodological advancements that could be utilised by hydraulic models to help better understand global flood risk both in the present day and in the future. A major challenge facing modellers is how to incorporate these new datasets to improve the understanding of flood risk in both well, and less well, developed countries using a consistent approach, particularly as the latter of these contain increasingly larger exposures to floods.

This new framework presents a computationally efficient yet flexible approach that seeks to utilise new global datasets and allows flood hazard maps to be calculated anywhere in the world, for any event severity (within a pre-defined range) and for any future climate scenario. The framework can be applied to all three of the major flood perils; fluvial, pluvial and coastal.

At the heart of the framework is an efficient post-processing methodology that incorporates outputs from leading climate models, flood defence datasets and a baseline set of simulations spanning a range of evert severities. Furthermore, the flexible approach allows users to modify assumptions of flood defences and incorporate new climate simulations as and when they become available to quickly re-calculate flood hazard.

We present here the full modelling chain, from input data through to flood hazard outputs covering all aspects of modelling, from determining model boundary conditions and estimating channel bathymetry, to post-processing the presence of flood defences and interpolation to future climate scenarios. We show that such an approach is able to replicate explicitly modelling the scenarios required at a fraction of the computation cost and demonstrate how this is crucial to anyone wanting to understand how exposure to floods may change into the future.

How to cite: Savage, J., Uhe, P., Wing, O., Sampson, C., Smith, A., Lord, N., Addor, N., Hatchard, S., Hoch, J., Bates, J., Quinn, N., Collings, T., Probyn, I., Haigh, I., Green, J., Cooper, A., Wilkinson, H., and Himsworth, S.: A new framework for building global flood models for the present day and future climates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15051, https://doi.org/10.5194/egusphere-egu23-15051, 2023.

X4.91
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EGU23-17431
Philip Ward and the The MYRIAD-EU team

The MYRIAD-EU project sets out to catalyse a paradigm shift in how risks are currently assessed and managed. Instead of addressing risks and hazards one by one, we are co-developing the first harmonised framework for multi-hazard, multi-sector, and systemic risk management. The interlinkages between the different hazards, economic sectors, and regions are being studied in 5 pilots around the EU. In this presentation, highlights from across the project will be presented. These includes the first version of the overall framework, insights from its testing in practice, progress towards a first global multi-hazard dataset, and methods for developing multi-risk adaptive pathways.

How to cite: Ward, P. and the The MYRIAD-EU team: MYRIAD-EU: multi-hazard risk assessmnet and management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17431, https://doi.org/10.5194/egusphere-egu23-17431, 2023.

X4.92
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EGU23-15337
Anyssa Diouf, Tristan Perotin, Hugo Rakotoarimanga, and Madeleine-Sophie Déroche

European windstorms are powerful extratropical cyclones mostly taking place during the winter months, and are one of Europe’s costliest natural disasters. The close study and assessment of this risk has therefore been essential for the insurance industry concerned. Typically, insurers resort to physical natural catastrophe models developed by third-party companies to analyze the risk, as they capture its components of hazard (events frequency and severity), exposure (insured assets values), and vulnerability (assets' damageability to given hazard intensities). AXA proposes a modeling methodology to produce a hazard catalog of synthetic windstorm events, and a vulnerability module, built around publicly available, purchased, or internal data. The hazard catalog is created using a meteorological feature tracking algorithm to extract trajectories and footprints of European windstorms in CMIP6 and ECMWF-ERA5 data. The catalog is then enriched to become a 10,000-year stochastic catalog by physically resampling original events with a perturbation technique, and statistically downscaling them to a 4-km resolution. The vulnerability, that yields damage ratios from local windspeed intensities, predicts the expected probability of claim occurrence and a distribution of conditional damage ratios based on wind gust value and exposure risk drivers. The model shows good backtesting performances at continental scale on market and AXA exposure. It is fully integrated within AXA's modelling ecosytem and is operationnally used to assess one of the major risks faced by the Group. 

How to cite: Diouf, A., Perotin, T., Rakotoarimanga, H., and Déroche, M.-S.: Developing a natural catastrophe model for European winter windstorms, an insurer’s perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15337, https://doi.org/10.5194/egusphere-egu23-15337, 2023.

X4.93
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EGU23-13924
A modular Pan-European Flood Catastrophe model
(withdrawn)
Christopher Sampson, Hessel Winsemius, Oliver Wing, Remi Meynadier, Hugo Rakotoarimanga, Mark Hegnauer, Hélène Boisgontier, and Andy Smith