Extreme weather events like tropical cyclones and floods severely impact economies, causing growth losses, tax revenue declines, and increased government debt due to short-term deficit financing. This challenge is particularly acute for countries with existing debt issues, which often rely on slow and uncertain foreign aid whose terms are typically agreed upon only ex-post. In contrast, ex-ante financial instruments, such as insurance and sovereign catastrophe risk pools, offer faster, more predictable funding while encouraging risk reduction and adaptation investments.
Sovereign risk pools, such as the Caribbean Catastrophe Risk Insurance Facility (CCRIF), African Risk Capacity (ARC), and Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI), have proven valuable. However, they may not fully realize their financial resilience potential, as pooling within the same region can limit risk diversification. This presentation will introduce a method to design risk pools by maximizing diversification across countries regardless of region. Results show this approach consistently enhances risk diversification, more evenly distributes risk shares within the pool, and increases the number of benefiting countries.
Related publication:
Ciullo, A., Strobl, E., Meiler, S. et al. Increasing countries’ financial resilience through global catastrophe risk pooling. Nat Commun 14, 922 (2023). https://doi.org/10.1038/s41467-023-36539-4
How to cite: Ciullo, A., Strobl, E., Meiler, S., Martius, O., and Bresch, D. N.: Increasing countries’ financial resilience through global catastrophe risk pooling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7211, https://doi.org/10.5194/egusphere-egu25-7211, 2025.
Historically, much of natural disaster impact modelling has focussed on the damage to private assets. But to a government decision-maker it is not always clear how impacts to individual assets translate into a cost to the national economy. Understanding this important for adaptation decision-making: which communities are resilient enough to withstand and recover from disasters? When is a disaster large enough to have regional or national knock-on effects? What is the long-term, compounding economic cost of inaction?
In collaboration with the Thai and Egyptian governments, we have prototyped a coupling between an open-source probabilistic disaster impact model (CLIMADA) and an open-source macroeconomic model (DGE-CRED). We present a modelling framework and codebase designed for more data-scarce environments, where data and modelling can be collected and iterated on in the space of weeks or months.
The coupled model starts with publicly available, open-source data (with their known limitations). Data and insights from local partners are then critical to calibrate and enhance the data. The model creates thousands of plausible future timelines of shocks from natural disasters (fluvial flood, heatwave and drought), models their impacts on the economic sectors of most interest to our partners (agriculture, manufacturing, energy, tourism/services), and simulates their short- and long-term macroeconomic impacts (on e.g. GDP, employment rates, prices, well-being indicators) and, guided by local knowledge, the benefits of different adaptation measures.
How to cite: Fairless, C., Daou, D., and Mohammadiamanab, N.: Climate adaptation for politicians: shocking a macroeconomic model with stochastic natural disaster impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20714, https://doi.org/10.5194/egusphere-egu25-20714, 2025.
One of the bottlenecks in global risk assessment studies is the lack of global sub-national socio-economic datasets spanning the past decades. To bridge this gap, we have compiled 12 global sub-national socio-economic datasets covering cultural diversity, economic conditions, demographics, equity, governance, health, and social well-being. These datasets form a harmonised global socio-economic data cube with annual data for 1990-2021. The data is with either a gridded or sub-national level resolution, except for political stability, which is available only at the national level.
We further introduce 'humanscapes,' a novel concept designed to capture complex socio-economic realities at a sub-national level. Humanscapes reflect the interplay of these different datasets, covering over 28,000 administrative units, and are analysed using self-organising maps (SOM) to highlight unique sub-national characteristics. Humanscapes offer a refined method for understanding and mapping societal changes.
Our socio-economic data cube enhances precision in global and continental risk assessments by providing comprehensive socio-economic contexts previously unavailable. It thus opens new possibilities in assessing vulnerability to natural hazards on a global scale, aligning with frameworks like the Sendai Framework and the Paris Agreement.
How to cite: Kummu, M., Huggins, X., Chrisendo, D., Niva, V., Saarenheimo, V., Sandström, V., and Masoumzadeh Sayyar, S.: Unveiling global humanscapes: harmonised subnational socio-economic datasets for understanding societal changes and enhancing risk assessments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21201, https://doi.org/10.5194/egusphere-egu25-21201, 2025.
Understanding the drivers of disaster outcomes and identifying hotspots of social vulnerability requires datasets that integrate societal impacts, physical hazards, and human exposure. However, widely used international disaster databases, such as the Emergency Events Database (EM-DAT), often lack detailed information on hazard characteristics and population exposure. This limits their utility for comprehensive risk assessments and interdisciplinary research.
We present SHEDIS, an open-access family of datasets addressing this gap by linking disaster impact records from EM-DAT with subnational data on hazard metrics, human exposure, and disaster locations. The first module, SHEDIS-Temperature, focuses on temperature-related disasters occurring from 1979 to 2018, encompassing 382 events across 2,836 subnational locations in 71 countries. This dataset provides high-resolution hazard metrics derived from 0.1°, 3-hourly meteorological data, including absolute indicators such as apparent temperature (accounting for humidity and wind) and percentile-based thresholds to identify extreme temperature events. Population exposure is quantified using annually interpolated population maps, with metrics such as person-days of exposure to hazardous temperatures. Outputs are aggregated at both the impact record-level and administrative unit-level, offering flexibility for varied analytical needs.
Future expansions of SHEDIS will incorporate additional hazard types, further supporting global-scale risk assessments and practical applications. By providing detailed, subnational hazard and exposure data linked to disaster impacts, SHEDIS enables more nuanced analyses to advance international disaster science, inform resilience strategies, and contribute to disaster risk reduction.
How to cite: Lindersson, S. and Messori, G.: SHEDIS: Linking Subnational Hazard and Exposure information with DISaster impact records for international risk analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11199, https://doi.org/10.5194/egusphere-egu25-11199, 2025.
Climate risks are increasing globally due to climate change, driven by intensifying climate hazards (e.g. storms, floods) and changes in socioeconomic conditions that drive exposure and vulnerability. Climate Risk Assessments (CRAs) constitute a tool to understand such risks under current and future conditions, based on the analysis of geospatial datasets. However, CRA data are often scattered across different data platforms, therefore inhibiting their Findability, Accessibility, Interoperability, and Reusability (FAIR). Consequently, selecting appropriate datasets for the CRA at hand can be a daunting and time-consuming task.
To make CRA data FAIR, we develop Climate Risk STAC, a living metadata catalog of open-access geospatial datasets that is hosted in a collaborative environment for further development. Climate Risk STAC (version 0.1) includes 214 data entries of 84 global-scale datasets from nine different hazards, five types of exposed elements, and seven vulnerability categories. All data entries can be explored in a user-friendly browser which eases selection of suitable data. We further encourage contributions of new datasets, thereby facilitating a continuously growing, community-led catalog that reflects the current state-of-the-art in CRA concepts and data. Version 0.1 currently focuses on global-scale geospatial data. Due to its flexible and collaborative design, the catalog can easily be extended to accommodate datasets from other domains and at other spatial scales. Climate Risk STAC is available at https://doi.org/10.5281/zenodo.14018438.
How to cite: Reimann, L., Eilander, D., Tiggeloven, T., Vuckovic, M., Kummu, M., Vajda, A., Pal, J. S., Mazzoleni, M., Wetterhal, F., and Aerts, J. C. J. H.: A living, community-led metadata catalog of geospatial data for climate risk assessments: an introduction to Climate Risk STAC, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5981, https://doi.org/10.5194/egusphere-egu25-5981, 2025.
In the context of climate change, the combined effects of coastal land subsidence and sea level rise exacerbate coastal flood risks by altering relative sea levels. This study leverages high-resolution land subsidence rate data obtained from Interferometric Synthetic Aperture Radar (InSAR) and employs the LISFLOOD-FP two-dimensional hydrodynamic model to simulate coastal flooding for 43 coastal mega-cities globally. Our findings indicate that, when considering subsidence, over 76% of these cities experience an expansion in inundation areas under both Baseline and SSP5-8.5 scenarios. Furthermore, we conduct a quantitative assessment of the relative contributions of land subsidence and climate change to coastal flood inundation, identifying 19 cities where land subsidence plays a dominant role.
Moreover, the impact of urban expansion on coastal flood risk cannot be underestimated, particularly in coastal cities that experience rapid urbanization and extensive coastal reclamation activities. By incorporating annual data on the expansion of settlements, reclaimed coastal areas, and urban built-up areas, we evaluate the dynamic changes in coastal flood exposure and uncover a long-term trend of increasing potential impacts of coastal flooding in mainland China's coastal regions, which is at a continental scale. Specifically, the area of settlements located in coastal flood hazard zones has grown to 6.5 times its original size, while the area of reclaimed land within these zones has expanded to 26.3 times its original extent.
The insights from this study provide a valuable reference for sustainable development strategies and measures to address the escalating coastal flood hazards in coastal cities worldwide.
How to cite: Fang, J., Bian, W., Xia, H., and Li, Y.: Coastal flood risk assessment for global coastal cities: integrating land subsidence, climate change and urban expansions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2767, https://doi.org/10.5194/egusphere-egu25-2767, 2025.
Given the commitment to sea level rise, massive and costly coastal adaptation is essential to reduce flood risks. Yet, the economically optimal timing of adaptation and adaptation tipping points remain unexplored on global and continental scale in coastal impact assessments. In this study, we model efficient adaptation pathways for 41,327 individual coastal floodplains along Europe's coastline through 2150. We consider three disaster risk reduction measures as potential adaptation options: protection, retreat and accommodation. Our assessment identifies the economically optimal timing for implementing these options, as well as the associated adaptation tipping points.
Using the state-of-the-art COASTPROS-EU dataset to model current coastal protection levels, we estimate that expected annual flood damages currently total USD 182 billion (2024 value). Immediate adaptation investments could drastically reduce these damages to USD 4 billion. For 95% of coastal floodplains requiring (additional) adaptation, the optimal timing for initial adaptation investments is now. We attribute this urgency to the vulnerability and exposure of coastal floodplains, which are already locked-in into existing conditions and are economically under-protected.
Adaptation tipping points, i.e. critical thresholds that require a shift from one adaptation option to another, are most prevalent along the Mediterranean coastline. In these regions, accommodation eventually becomes insufficient, requiring a switch to either protection or retreat to maintain efficient flood risk mitigation. These adaptation tipping points are driven by committed sea level rise due to past emissions, with their timing influenced by the rate of future climate change. On average, tipping points occur 29 years earlier under higher climate change scenarios (SSP5-8.5) compared to lower ones (SSP1-2.6).
How to cite: Völz, V., Hinkel, J., Lincke, D., Honsel, L., Nicholls, R., Thiéblemont, R., Le Cozannet, G., and Sayers, P.: Adaptation pathways highlight urgent economic need to reduce flood risks in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1489, https://doi.org/10.5194/egusphere-egu25-1489, 2025.
Insurers and risk managers for critical infrastructure such as transport of power networks typically do not account for flooding and extreme winds happening at the same time in their quantitative risk assessments. We explore this potentially critical underestimation of risk from these co-occurring hazards through studying events using the regional 12 km resolution UK Climate Projections for a 1981-1999 baseline and projections of 2061-2079 (RCP8.5). We create a new wintertime (Oct-Mar) set of 3,427 wind events to match an existing set of fluvial flow extremes and design innovative multi-event episodes (Δt of 1-180 days long) that reflect how periods of adverse weather affect society (e.g. through damage). We show that the probability of co-occurring wind-flow episodes in Great Britain (GB) is underestimated 2-4 times if events are assumed independent. Significantly, this underestimation is greater both as severity increases and episode length reduces, highlighting the importance of considering risk from closely consecutive (Δt 3 days) and the most severe storms. In the future (2061-2079), joint wind-flow extremes are twice as likely as during 1981-1999. Statistical modelling demonstrates that changes may significantly exceed thermodynamic expectations of higher river flows in a wetter future climate. The largest co-occurrence increases happen in mid-winter (DJF) with changes in the north Atlantic jet stream an important driver; we find the jet is strengthened and squeezed into a southward-shifted latitude window (45-50°N) giving typical future conditions that match instances of high flows and joint extremes impacting GB today. This strongly implies that the driving large-scale driving conditions (e.g. jet stream state) for a multi-impact ‘perfect storm’ will vary by country; understanding regional drivers of weather hazards over climate timescales is vital to inform risk mitigation and planning (e.g. diversification, mutual aid across Europe).
How to cite: Hillier, J. K., Bloomfield, H., Manning, C., Garry, F., Shaffrey, L., Bates, P., and Khumar, D.: Increasingly seasonal jet stream enhances joint wind-flood risk in Great Britain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-290, https://doi.org/10.5194/egusphere-egu25-290, 2025.
Despite growing evidence that climate extreme events significantly affect local economies, the implications of cross-regional and planetary-scale dependencies in climate extremes remain inadequately understood. This study demonstrates the importance of linking the projected increase in spatially compounding hot, wet, and dry extremes to their economic impacts. Utilizing Earth System Model projections from the 6th phase of the Coupled Model Intercomparison Project, we analyse how planetary-scale and cross-regional dependencies amplify regional disparities in economic value under enhanced global warming. Regions with lower present-day economic wealth are disproportionately exposed to extreme events occurring concurrently with other areas, heightening threats to economic stability. This research illustrates how spatially compounding climate extremes can amplify global and regional consequences, with enhanced greenhouse gas forcing exacerbating regional disparities in economic inequalities.
The study underscores the necessity of considering climate extremes' impacts beyond local scales, requiring an assessment of cross-regional exposures and a deeper understanding of the links between localized impacts and global economic dynamics. Enhanced global warming impacts the association of events across regions, challenging traditional risk diversification strategies. Global catastrophe pooling has been suggested as a means to improve financial resilience; however, intercontinental concurrent exposure, especially to heavy precipitation events in low- to middle-income regions, may limit its effectiveness. Supra-continental economic exposure to climate extremes is also projected to rise, emphasizing the need to evaluate which regions could be included in effective pooling mechanisms. Policy coordination and international cooperation are vital, as spatially compounding climate extreme events demand joint recovery efforts, resource sharing, and comprehensive contingency planning. It is therefore critical that investors and insurers consider the likelihood of concurrent events across multiple regions to manage risks effectively and ensure financial stability.
How to cite: Biess, B., Gudmundsson, L., and Seneviratne, S. I.: Amplified potential global economic impacts from climate change due to spatially compounding climate extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10655, https://doi.org/10.5194/egusphere-egu25-10655, 2025.
Earthquake building exposure models are descriptions of the type, monetary values and inhabitants existing in a determined geographical area. Building stock models, or aggregated exposure models, summarize these key values on a regional level and are an established part of the risk assessment chain. They exist on a continental (e.g. ESRM20 in Europe; SARA in South America) or a global scale (e.g. GEM Global Exposure Model; PAGER; GED4GEM). Such models are created from a combination of cadaster information, national statistics, built area proxies, census data and/or local expert knowledge. In each country the processing method (therefore the model) differs, based on the level and type of information available. The input information for aggregated exposure models may be outdated for regions that experience rapid developments, as national data collection as censuses take a large amount of effort and are only conducted every five to ten years.
The advent of global building footprint models through artificial intelligence (Open Buildings; Global ML Building Footprints) and the slow but steady increase of building footprint coverage in OpenStreetMap have provided opportunities to model key values from bottom-up. Such model is able to keep the global scale, but considers individual buildings rather than district totals. For each building, the maximum amount of information is gathered, based on the dataset itself and other global datasets containing relevant values (such as height or occupancy type). Structural, monetary and population values can be added based on the relative occurence of building types in the aggregated models. An example is the ’model of European buildings’.
Inevitably, a switch from a top-down to a bottom-up approach to exposure modeling brings advantages and disadvantages, apart from the obvious increase of resolution to the individual building scale that comes with building-by-building models. We have taken three case studies and compared the strengths and weaknesses of each of the approaches, such as building (type), population and monetary value distribution, recentness of the data and total floor space size. The findings help to identify future directions for exposure modeling and aim to find the best approach to capture the dynamic nature of the built environment.
How to cite: Oostwegel, L. J. N., Evaz Zadeh, T., Schorlemmer, D., and Gueguen, P.: Top-down or bottom-up in earthquake exposure modeling: a comparison of aggregated and building-by-building models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6138, https://doi.org/10.5194/egusphere-egu25-6138, 2025.
Mud volcanism is the unique global geological phenomenon generally expresses in transportation of clayey masses and rock fragments from the deep underground to the day surface via the feeder channels, mostly developed within the Alpine-Himalayan (Mediterranean) and the Pacific Ocean folded belts. Azerbaijan is considered as the world most concentration province hosting over 350 onshore and offshore mud volcanoes. Some of them can fall into the category of hazardous natural objects and characterized by expressive and catastrophic eruptions with belch, ground subsidence, cracks and faults formation and extensive flows of liquid mud and leading to destructive consequences.
According to The Federal Emergency Management Agency the natural hazards (earthquakes, floods, avalanches, landslides, tornados, tropical cyclones, etc.) represent the environmental phenomena potentially affecting the various societies and human life and property in particular, causing loss of lives and properties damage. The National Risk Index includes 18 types of the natural hazards, including magmatic volcanic activity. Unfortunately, mud volcanoes are not considered as the natural hazard in spite of recorded historical and modern evidences.
One of the most remarkable and destructive mud eruptions had occurred in 2006 in Java (Indonesia), known at present as "Lusi". The result of eruption were the mudflows eventually buried dwelling houses, private businesses, roads, communications and forced nearly 60,000 people to leave their homes. Another recent eruption had taken place in the southern Taiwan in 2024 in Wandan mud volcano with some flames of about 30 m and 50 m high that damaged nearby power cables so the electricity had been cut to prevent the further crucial problems in power system.
In Azerbaijan, a fair number of mud volcanoes erupting with gas ignition, great belches and thick mud flows. However, for the present study four remarkable mud volcanoes had been selected as the potential sources of the natural hazard affecting the environment and human life and activities. These mud volcanoes are Lokbatan, Shikhzarli, Kechaldag and Keyreki. As well as being often-erupting volcanoes (except Kechaldag) they locate in specific areas. So, Lokbatan locates within the operating oil field with relevant infrastructure while Shikhzarli lies in the vicinity of the village. Both of them erupt with gas ignition and great belch. The only eruption of Kechaldag mud volcano had affected the hydraulic constriction since it locates at the shore of Jeyranbatan water reservoir. Keyreki mud volcano is surrounded by dense development is unsafe to urban houses located in extreme vicinity
The mentioned cases in Azerbaijan and beyond demonstrate destructive and catastrophic nature of the geological phenomenon expressed in fire, thick mud flows, volcanic bombs, ground cracks, landslides and soil subsidence. All these concomitant effects can affect and damage the nearby territories. The chaotic residential development nearby these natural objects increases by several times the risk of negative effects and impacts upon the human in case of eruption. A special attention should be paid to infrastructure (residential and industrial) as well as the various types of communications laid and running at a short distance from a mud volcano.
How to cite: Rashidov, T., Huseynov, D., and Mamishova, T.: Natural hazards from mud volcanoes: importance of understanding and acceptance by example of Azerbaijan , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12674, https://doi.org/10.5194/egusphere-egu25-12674, 2025.
Global hydrological models are valuable tools to predict flood hazard across data-scarce regions and future climate scenarios. Their ability to create spatially coherent projections means their results are broadly used for scientific analysis and policy planning. However, the complexity of the models, coupled with the high volume of data they generate, poses significant challenges in evaluating the process representation contained within the models. Existing analysis show, how a model transfers input into output varies strongly between global water models in a long-term analysis. Yet, flood event prediction needs to take place at daily or higher temporal resolution. Are global hydrological models able to accurately represent flood generation? And do they accurately combine different flood-generating processes, such as extreme rainfall, snowmelt, or wet antecedent conditions, into extreme flows?
In this analysis, we compare simulations from five global hydrological models. The models are part of the global water sector within the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). In ISIMIP, all models are run with the same forcing data, on a daily resolution from 1901 to 2019. We extract and compare runoff time series across the 67400 land cells. For each cell, a threshold-based flood event extraction allows calculation of flood duration, magnitude, number of extreme events, etc. Additionally, we use the extracted events to compare model inputs, such as precipitation, or model fluxes, such as snowmelt, that contribute to high-flow generation.
Five models (CWatM, H08, LPJmL, ORCHIDEE, WaterGAP2), with four input variables and fluxes (precipitation, runoff, soil moisture, and snowmelt) at daily resolution over 67400 land cells results in 58 billion data points to analyse. Extracting this process-based statistical information from the model data reduces the dimensionality and scope of the high-resolution data to a form where comparison between models is possible. How do high flow statistics compare between models? Does the same extreme rainfall result in extreme flow across all models? What role does snowmelt and soil moisture play in runoff generation between models? These questions support an evaluation of flood events within global models through process-based model intercomparison.
How to cite: Kularathne, N., Wagener, T., Reinecke, R., Tarasova, L., Schmied, H. M., and Stein, L.: Process-based evaluation of flood events across global water models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8694, https://doi.org/10.5194/egusphere-egu25-8694, 2025.
Flooding poses significant risks to human population, particularly in vulnerable regions such as Southeast Asia. However, there is limited understanding of how flood exposure varies across different population profiles, despite its critical importance in risk adaption and mitigation. This study addresses this gap by assessing flood exposure in Myanmar, Thailand, Laos, Cambodia, and Vietnam, with a specific focus on population distribution by gender and age groups. Our analysis reveals that while the absolute number of older adults exposed to flooding is relatively low compared to other age groups, the proportion of older adults affected is significantly higher. Overall, approximately 39% of individuals aged 65 and above are exposed to flood hazards, compared to 37% for total population. Gender differences in exposure are also observed, with women aged 80 and above exhibits the highest exposure percentage 42%. Furthermore, this study highlights the limitations of national-scale assessments in capturing localized disparities in flood exposure. For instance, while the overall exposure in Thailand may appear moderate at 40%, five provinces show disproportionally high exposure rates that exceed 95%, and Gini coefficients therein are also higher than national average, suggesting a larger disparity in flood exposure across demographic groups. These findings underscore the importance of subnational analyses in identifying vulnerable population and informing targeted adaptation strategies that address the specific vulnerabilities of older adults and other at-risk groups.
How to cite: Li, M. and Du, S.: Disparities of flood exposure across population profiles in Southeast Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14847, https://doi.org/10.5194/egusphere-egu25-14847, 2025.
Coastal flooding, both current and future, is a significant concern for Europe due to sea level rise, storms, and the exposure of critical infrastructure in low-lying coastal zones. To support adaptation efforts, it is essential to have information on future risks, including people and infrastructure at risks and potential economic damages. One of the objectives of the CoCliCo project is to address this need by providing new coastal risks assessments in Europe using state of the art coastal hazard, exposure and vulnerability datasets and information, including dynamic flood hazard assessment and new maps of infrastructures at risk.
This study first presents the risk assessment methodology used for the CoCliCo platform, which is divided into two parts. The first part focuses on physical risks, evaluating the number and area of infrastructure exposed to coastal flooding, as well as the potential costs of these damages. Cost calculations are based on vulnerability curves that take water depths into account, to accurately estimate damage for each infrastructure type. The second part concerns the assessment of the number of people exposed to coastal flooding, based on downscaled demographic projections. This study is conducted at the European scale, using simulations of coastal flooding for events with annual, centennial and millenial return periods, at various time points and under different socio-economic scenarios.
Preliminary results indicate that e.g. around 200,000 persons and 1.2 Billion euros are exposed to centennial flood events along the coasts of Europe (preliminary results based on the analysis of around 60% of the European coastal flood plains). In a virtual scenario in which current coastal protection would be suddenly removed, these figures increase by a factor of 50 to 100. Without further adaptation, people exposed to a centennial storm are projected to increase by 400% in 2050 while assets at risks increase by about 250%. Beyond 2050, results depend on future land use planning decisions and relocations within and outside the low elevation coastal zone. Despite their uncertainties due to e.g. the 25m resolution digital elevation model used to perform coastal flood simulations and the lack of precise and site specific information on coastal protection, these preliminary results remind the benefits of adaptation, the importance of maintaining current defenses to prevent large disasters and the need for further coastal adaptation decisions (including protection, accommodation and relocation, including with nature based solutions) in the coming years and decades. The results will be made available on the CoCliCo platform.
How to cite: Bascoul, V., Thiéblemont, R., Rohmer, J., Koks, E., De Plaen, J., Lincke, D., Bonatz, H., T. Vafeidis, A., Sayers, P., J. Nicholls, R., Toimil, A., and Le Cozannet, G.: Coastal Risk Management in Europe: Methods and preliminary results of the CoCliCo Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16071, https://doi.org/10.5194/egusphere-egu25-16071, 2025.
Droughts are among the costliest natural hazards, ranked third after storms and floods globally. Furthermore, they often cause enormous indirect impacts such as famines. Understanding the occurrence of economic drought impacts is a challenging task due to their slow onset, vast spatial extent and long duration. Besides meteorological conditions, drought occurrence strongly depends upon local human water management interventions such as irrigation and water withdrawal altering vulnerability. Additionally, identifying drought vulnerable assets and their temporal development presents a major challenge as they strongly depend on the regional socio-economic structure. In order to attribute historical drought damage and to project future drought risk, a deeper understanding of changes in drought exposure, vulnerability, and the damage-intensity relationship is required. Previous damage functions neglect that intense physical drought conditions do not always translate into a damage event. Therefore, we develop a two-step approach that i) estimates the likelihood of event occurrence from the physical conditions and ii) establishes a damage-intensity relationship. We test the explanatory power of common drought indicators such as the standardized precipitation-evapotransporation index (SPEI), soil moisture, and low river flow to reconstruct historical time series of drought damage reported by EM-DAT and NatCatSERVICE, globally. The drought indicators are derived from the Inter-Sectoral Impact Model Intercomparison Project round 3a and vary in their modeling complexity. While SPEI is based on mere climate reanalysis data, soil moisture is derived from global hydrological models and low river flow from their output coupled with the hydrodynamic model CaMa-Flood. We find that the suitability of drought indicators for damage reconstruction varies regionally. While low river flow may be applied in Europe for damage reconstruction, SPEI and soil moisture are more reliable predictors for most world regions. The explanatory power of the model shows strong regional variations, depending also on the quality of observational data. Observed damage can be well reproduced in regions such as Latin America and South East-Asia, but the model fails to reproduce damage time series in North Africa and Central Asia. We show that both modeling steps are necessary to reproduce observed drought damage and that the likelihood of event occurrence as well as the damage ratio increase under more intense physical drought conditions. Omitting the likelihood-intensity relationship may lead to an overestimation of historical drought damage, which is used as a reference in attribution and projection studies. As reproducing observed damage is indispensable for sound attribution studies, the two-step approach may allow us to better account for non-linear changes in drought impacts under climate change.
How to cite: Sauer, I., Günther, A., Frieler, K., Zimmermann, S., and Otto, C.: A global model to explain drought occurrence and damage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12825, https://doi.org/10.5194/egusphere-egu25-12825, 2025.
The tourism and travel industry is one of the key economic sectors across Europe, contributing ca.10% GDP yearly (with indirect and induced effects) equating to just under 2 trillion EUR. During COVID-19, the major negative effects on domestic and international tourism were a wake-up call to hotels, hospitality and the destinations to become more resilient to not only biological shocks but all manner of disasters in the wake of climate change and increasing climatic peril effects in many locations.
As part of the Hotel Resilient Initiative and in the MYRIAD-EU project, extensive analysis of the tourism sector has been undertaken for Europe in order to characterise the locations, values, and types of assets at risk for the tourism sector in spatial and temporal systems.
An analysis is made in this study for hotels and their destinations in Europe, to examine the sectoral risk to natural (geophysical, hydrological, and meteorological) and human-made perils in order to examine which locations are most at risk of financial direct damage now, and in 2050 for selected perils. Quantitative outputs are produced showing the most at risk locations in each country and across Europe.
In addition, where quantitative metrics could not be produced with great certainty, a tool has been produced giving a multi-risk vulnerability index in order to view and adjust the importance of different tourism indicators such as domestic and international expenditure, employment, tourism stays, attractions at a NUTS-3 EU level. The evaluation of the disaster types affecting it allows for a semi-quantitative view of the impacting factors on the locations, giving additional insights into the effects for the tourism industry.
It is found that hydro-meteorological perils have an increasing influence throughout Eastern Europe with the effects of climate change with yearly damages often exceeding 1 bn EUR. Geophysical perils such as earthquakes cause major singular shocks to locations, often taking years for the tourism industry to recover, especially across the Mediterranean and Eastern Europe. Drought, heat and water stress however is starting to cause major issues to the industry as seen in Spain last year.
The loss outputs from this study will support further development of the Hotel and Destination Resilient Scorecards being produced in various locations across Europe.
How to cite: Daniell, J., Schaefer, A., Brand, J., Daniell, J., Maier, A., Khazai, B., Girard, T., Romero, R., Claassen, J., Strelkovskii, N., Blanz, B., Ascherl, J., Mardell, C., and Michalke, S.: A Europe-wide Tourism Destination Socioeconomic Risk Model for Natural and Human-made Perils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14214, https://doi.org/10.5194/egusphere-egu25-14214, 2025.
Risk drivers, are non-static, including long-term trends as well as short-term changes. These can, for example, arise due to interactions from multiple hazards or as side-effects of risk reduction measures that address one hazard but neglect others. While dynamics of hazard and exposure and are increasingly being recognised and incorporated into (large scale) risk modelling, evidence and approaches for vulnerability dynamics are still lacking.
Within the MYRIAD-EU project we have collected empirical evidence of dynamics of vulnerability and other risk drivers, accounting for a multi-hazard setting, and developed methods to represent them in forward-looking risk models. Here, we present a new open online database that structures this information and aims to provide a comprehensive overview of (openly available) data and methods for both researchers and practitioners. The database is designed to include a diverse range of data types and methods including qualitative as well as quantitative approaches and ranging from local to global scale. To keep the database updated and comprehensive, it has been designed as a living catalogue and invites community contributions.
We welcome feedback on the database and invite participants to suggest other datasets and methods that could be included.
How to cite: Ward, P., Jäger, W., Stolte, T., de Rutier, M., Tiggeloven, T., De Polt, K., Buijs, S., Claassen, J., van Maanen, N., Ferreira, D., Nguyen, N. D., Dal Barco, M. K., Schlumberger, J., Torressan, S., Orth, R., Daniell, J., Duncan, M., and Smale, L.: Towards an Open Online Database of Empirical Evidence of Multi-Hazard Vulnerabilty and Risk Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15348, https://doi.org/10.5194/egusphere-egu25-15348, 2025.
Multi-hazard global disaster and impact datasets are often biased towards the Global North, resulting in significant data gaps for developing countries. To address this imbalance, we developed a new dataset by automatically analyzing the reports from the International Federation of Red Cross and Red Crescent Societies (IFRC). These reports document immediate aid, recovery, and resilience-building in the aftermath of disasters, targeting mainly countries in the Global South. From the 1,664 reports spanning 1996 and 2024 years, we identified 620 unique disasters affecting 143 different locations (39% in Asia, 16% in Africa, 18% in the Americas, 7% in Europe, 4% in Oceania). Using natural language processing, large language models, and machine learning, we extracted structured information on (i) the direct and indirect societal and environmental impacts and (ii) the response measures taken to address these disasters. Our approach captures a broad range of impacts, from traditional metrics like fatalities and economic losses to displacement, health, and well-being. Using guided topic modelling, we developed a typology of response measures, categorized into ten main classes (e.g., Healthcare and Medical Response, Shelter and Infrastructure Support, and Community Engagement and Communication). Our results show that hazard impacts in the Global South are much more diverse than previously reported in global databases. Moreover, preliminary results on the response measures characterization reveal notable geographical and hazard-specific biases. Our approach bridges critical data gaps, providing a more nuanced understanding of disaster impacts and responses, which is particularly valuable for informing and enhancing disaster risk reduction efforts in the Global South.
How to cite: Madruga de Brito, M., Rotaru, A. M., Wang, J., Gesualdo, G., Hasbini, L., Severino, L., and Nunes Carvalho, T. M.: Developing a multi-hazard impact and response dataset for the Global South, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17817, https://doi.org/10.5194/egusphere-egu25-17817, 2025.
It is widely acknowledged that the majority of regions worldwide are susceptible to a range of potentially catastrophic natural hazards. Achieving a comprehensive estimation of the aggregate losses incurred by these diverse hazards necessitates the implementation of a multifaceted, tiered risk assessment approach, underpinned by harmonised methodologies, in accordance with the provisions outlined in the Sendai Framework for Disaster Risk Reduction (SFDRR). This methodological framework facilitates the direct comparability of risk estimates, thereby providing a foundational basis for the formulation of decisions concerning balanced and cost-effective mitigation and preparedness strategies that adequately address risk prioritisation. The Central Asian region, which has a documented history of seismic activity, fluvial flooding and landslides, is a pertinent case study. In an effort to support the process of risk mitigation in this region, the European Union, in collaboration with the World Bank Group (WBG) and the Global Facility for Disaster Reduction and Recovery (GFDRR), launched the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) programme, targeting the countries of Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan.
In this context, the present work delineates the methodological framework and presents the results of the multi-hazard risk assessment carried out in the Central Asian region. These results are expressed in the form of probabilistic metrics pertaining to earthquake and flood loss estimates, including annual average losses, loss exceedance curves and return period specific losses. These metrics represent the basis for further technical recommendations, which are designed to support future disaster risk management (DRM) and disaster risk financing and insurance (DRFI) strategies in the region.
How to cite: Ceresa, P., Bazzurro, P., Parolai, S., Poggi, V., Scaini, C., Bussi, G., Fagà, E., Coccia, G., Peresan, A., Luna, D., Rubio, G., Ordaz, M., Salgado G., M. A., Avelar, C., and Tyagunov, S.: Strengthening financial resilience and accelerating risk reduction for natural hazards in Central Asia: methodological framework and results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18044, https://doi.org/10.5194/egusphere-egu25-18044, 2025.
