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. Increasingly, these datasets, methods and models are being applied in collaboration with stakeholders during the decision-making process. As many natural hazard processes, particularly hydrological ones such as floods and droughts, cross administrative and national borders, risk assessment and management increasingly require consideration of transboundary systems, upstream–downstream interactions, and cross‑border cooperation.
We invite contributions related to all aspects of natural hazard risk assessment at the continental to global scale, focussing on:
- single hazards, multi- or compound hazards;
- all facets of risk, including hazard, exposure and vulnerability;
- risk mitigation under current and future conditions (climate & socio-economic), including nature-based solutions;
- case studies showcasing appropriate use of continental to global risk assessment data in risk management practice, including in transboundary contexts where shared basins, aquifers, or other cross‑border systems require coordinated action;
- novel globally-applicable approaches for leveraging global datasets and models to inform local risk assessment.
- challenges and opportunities in governance and integrated water resources management for transboundary aquifers and river basins;
Wildfires are no longer isolated environmental events: they are a defining global risk, shaped by interacting climate, ecological and socioeconomic drivers, and capable of cascading impacts across ecosystems, infrastructure and societies. Reducing wildfire risk therefore requires more than local analyses—it demands a coherent, global perspective on hazard, exposure, vulnerability and future change.
The State of Wildfires project responds to this challenge by delivering an annual, globally consistent assessment of wildfire activity, impacts, drivers, attribution and future risk. Now in its third year, the project brings together an international, multidisciplinary collaboration to synthesise the latest science and data, with the explicit aim of supporting both fundamental understanding and practical risk management for preparedness and adaptation. For example, the report could be used to inform integrated fire management, climate negotiations, Loss & Damage and adaptation finance, land-based mitigation solutions, and asset exposure for insurance, urban planning and public health.
In this invited talk, I will present key insights from the first two State of Wildfires reports 2023-2025, alongside early results from the forthcoming edition. A central feature of the project is its event-based structure, using four major wildfire events each year to connect global drivers with local consequences. Examples include the Canadian and Greek wildfires of 2023-2024, and the Los Angeles and South American fires of 2024-2025. These case studies provide a framework for integrating observations, the latest scientific modelling and novel analysis, in a way that is both scientifically robust and decision-relevant.
More broadly, the State of Wildfires project demonstrates how globally applicable datasets, shared methodologies and scenario-based projections can be used to address natural hazards holistically—bridging scales from the continental to the local. Part of the strength of this project is the large international team and regional expertise, helping to push the frontiers of wildfire science while openly confronting the uncertainties that define wildfire risk in the decades ahead.
How to cite: Burton, C., Di Giuseppe, F., Jones, M., and Kelley, D. and the State of Wildfires report co-authors: Understanding natural hazards holistically: The State of Wildfires Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18245, https://doi.org/10.5194/egusphere-egu26-18245, 2026.
By August 2022, drought in the Greater Horn of Africa had resulted in 3.6 million livestock deaths and left 28 million people highly food insecure, urgently requiring humanitarian assistance. Pastoralist communities, whose livelihoods depend on the availability of pasturelands, are particularly vulnerable to the impacts of drought.
Operational drought Early Warning Systems and Early Action Protocols in the region predominantly rely on real time observations and precipitation forecasts. However, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI), provide a more direct measure of pasture conditions. Incorporating vegetation forecasts into these systems could shift the focus toward impact-based forecasting, offering a more accurate basis for early action.
Numerous statistical and machine learning approaches have been developed to forecast vegetation conditions using satellite-derived vegetation indicators, often in combination with hydroclimatic and land surface variables. Despite this, a gap remains between academic research and the methods currently applied in operational settings.
Here, we conduct a benchmarking analysis of existing statistical and machine learning models that forecast vegetation indices (NDVI and VCI) to provide decision-makers with an informed overview of the range of available solutions.
We evaluate four types of models: autoregressive models, Gaussian processes, convolutional long short-term memory neural networks, and transformers, assessing their ability to forecast vegetation indices across different spatial resolutions: VIIRS (500 m) and Sentinel-2 (20 m). We also examine model performance during documented extreme drought events in cross-border arid and semi-arid pastoralist regions of the Greater Horn of Africa. Our analysis highlights the relative strengths and limitations of these models, providing guidance for integrating vegetation-based forecasts into operational early warning systems to better support drought-affected pastoralist communities.
How to cite: Hopling, C., Robin, C., Benson, V., Zehner, M., Weynants, M., Rowhani, P., Muthoka, J., Memarian-Sorkhabi, O., and Reichstein, M.: Benchmarking Vegetation Forecasts for Drought Early Warning in Eastern Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20907, https://doi.org/10.5194/egusphere-egu26-20907, 2026.
Environmental change and rapid population growth are altering the impacts of floods, landslides and flash floods. The Global South is disproportionally affected by these changes, resulting into an uneven impact of these geo-hydrological hazards compared to the Global North. Comprehensive global documentation of geo-hydrological hazards is needed to improve our understanding of these hazards, yet this remains challenging. Existing data collection approaches—such as remote sensing, empirical news article screening; and field-based surveys—have limitations, constraining our ability to accurately analyze distribution, impacts and trends in geo-hydrological hazard occurrence. Moreover, most global datasets suffer from various geographical, linguistic and socio-economical biases.
To further address these challenges, we introduce a new global dataset documenting geo-hydrological hazards automatically extracted from online news articles by a large language model-based text mining algorithm, called HazMiner. A total of 6 366 905 news articles published in 58 languages from 2017 until 2025 were analyzed. The resulting dataset includes the location, timing and impact of 21 411 floods, 7 659 landslides and 3 606 flash floods. Compared to EM-DAT, a well-established global disaster dataset, our dataset documents 31 150 more geo-hydrological hazard events over the same period. Among these, 784 events resulted in at least one but fewer than ten fatalities and therefore do not meet one of EM-DAT inclusion criteria, collectively accounting for 3,578 fatalities.
Spatially, these impactful hazards occur in densely populated areas and with floods primarily located along rivers, and landslides and flash floods concentrated in mountainous regions. Temporally, floods and flash floods show seasonal trends for both hemispheres. Furthermore, 30 810 geo-hydrological hazard events do not report any fatalities, providing a broader interpretation of these hazards at the global scale compared to existing global disaster datasets. This dataset offers a new detailed global view of the hazards and has the potential to improve our understanding of their spatial-temporal occurrence and their associated impacts and risks.
How to cite: Valkenborg, B., Dewitte, O., and Smets, B.: A new global dataset of geo-hydrological hazards and their impacts automatically extracted from online news articles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4873, https://doi.org/10.5194/egusphere-egu26-4873, 2026.
Tourism is among the sectors most affected by climate change and natural disasters. Impacts range from direct damage to infrastructure and supply chains to long-term business interruption. Moreover, depending on tourism typology, the consequences of specific hazards can differ substantially: coastal destinations face different challenges than mountain sites. Yet, at the global scale, it remains difficult to identify where climate risks threaten tourism most, and which types of destinations are exposed to which hazards. Here, we present Global Tourism Climate Exposure Layer (G-TCEL), the first disaggregated global tourism impact screening at destination level, designed to reveal climate-risk hotspots for tourism across different tourism landscapes.
The assessment builds on three primary components: (1) a global tourism landscape disaggregation to differentiate between modes of tourism, (2) a global tourism density index, and (3) a collection of global climate and disaster risk indicators.
The global tourism landscape disaggregation uses topographic, land cover, and demographic data to identify key typologies of tourism activity. We distinguish, for example, between coastal tourism along oceans and lakes, mountain tourism, and urban tourism. These tourism landscapes represent distinct categories of tourism business activity with unique requirements and vulnerabilities. For each square kilometer of the Earth’s surface, weights are assigned to each tourism landscape. To link landscapes with tourism activity, we compiled a tourism density index using global datasets on accommodations, activities, and points of interest. Finally, using e.g. the latest CMIP6 climate model projections, we tailored a suite of global climate risk indicators to the specific vulnerabilities of each tourism landscape.
Applying an exposure-at-risk methodology, G-TCEL is a screening in which the tourism landscape at a given location provides weights for the relevance of different climate risks, while tourism density identifies where tourism activity is concentrated. This approach enables us to map and compare destination-level climate exposure for tourism worldwide, highlighting hotspots across coastal, mountain, and urban tourism landscapes. Results are aggregated globally at administrative level 2 and disaggregated by tourism landscape.
This work is the companion abstract to Daniell et al. 2026 which provides a subnational global tourism statistics database at multiple levels, part of which is used to estimate tourism density within this screening approach.
How to cite: Schäfer, A., Daniell, J., Khazai, B., Maier, A., Brand, J., and Girard, T.: The Global Tourism Climate Exposure Layer (G-TCEL): Revealing the world’s tourism climate-risk hotspots, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14491, https://doi.org/10.5194/egusphere-egu26-14491, 2026.
As climate change and urbanization in low-lying areas increase flood risk, accelerated flood adaptation by households is urgently required. Households may flood proof or elevate their homes, in addition to flood protection by the government and insurance that covers residual risk. However, there are limits to the adaptability of societies (Aerts et al. 2024). For instance, social vulnerability factors such as low income or high age may reduce households’ adaptation efforts, leading to higher physical vulnerability of their homes. When societies stop implementing additional adaptation, risk may become ‘intolerable’, and people may have no other option than to leave the area. At this point, ‘adaptation limits’ are reached. We will present a concept of how to quantitatively assess where and when the limits of flood adaptation are reached and how these are shaped by vulnerability dynamics. This concept will be implemented in the framework of the ERC Synergy grant LIMIT2ADAPT by developing a global model for simulating flood adaptation limits by integrating a flood-risk model with an adaptation decision agent-based model and by assessing historical time series of social and physical vulnerability and flood risk for four selected river basins, using exposure-, survey- and census data. This information is crucial for prioritizing adaptation.
Reference
Aerts, J. C. J. H., Bates, P. D., Botzen, W. J. W., de Bruijn, J., Hall, J. W., van den Hurk, B., Kreibich, H., Merz, B., Muis, S., Mysiak, J., Tate, E., Berkhout, F. (2024): Exploring the limits and gaps of flood adaptation. - Nature Water, 2, 719-728. https://doi.org/10.1038/s44221-024-00274-x
How to cite: Kreibich, H., Aerts, J., Tate, E., and Bates, P.: Assessing the limits of adaptation to riverine flood risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13102, https://doi.org/10.5194/egusphere-egu26-13102, 2026.
Flooding is Europe’s most costly natural hazard, with economic losses almost ten times higher than in the 1990s, and riverine floods alone accounting for more than one third of all disaster-related damages. Despite its growing importance, flood risk management strategies remains highly fragmented, which is particularly evident in the case of insurance. European countries have in fact adopted diverse insurance market structures, ranging from solidaristic, highly regulated systems with broad coverage, to voluntary, risk-based markets with low penetration and strong dependence on post-disaster public aid. This fragmentation raises concerns about the sustainability, fairness and efficiency of current approaches in a changing climate, and have prompted growing interest in more coordinated European approaches.
In this study, we develop an integrated model to assess flood insurance market reforms and their interaction with optimized public investments in structural flood protections under current risk conditions, estimated through EFAS data. We simulate three insurance market configurations —fully risk-based, solidaristic, and public–private partnership— implemented either nationally or through a single EU-wide pool, where insurance take-up is modelled via an affordability threshold calibrated to reproduce observed penetration rates under current conditions.
Our results indicate that countries with low insurance penetration incur high costs when acting in isolation. In contrast, EU-level cooperation, through cross-border insurance pooling and coordinated investments in flood protection, substantially increases insurance coverage at lower overall costs, while also improving equity across countries. Notably, an integrated and moderately solidaristic EU-wide insurance scheme could already ensure affordable residential flood insurance for all exposed households, significantly reducing reliance on post-disaster public aid.
Overall, jointly designed insurance reforms and coordinated flood protection strategies offer strong potential to enhance financial risk sharing and support a more cohesive and climate-resilient Europe.
How to cite: Scarpellini, L., Ficchì, A., Riedel, L., Bresch, D. N., and Castelletti, A.: Strengthening flood resilience in Europe: modelling joint insurance reforms and coordinated protection strategies to improve flood risk management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6456, https://doi.org/10.5194/egusphere-egu26-6456, 2026.
Floods are among the most damaging natural hazards worldwide, with impacts expected to intensify due to climate change and increasing exposure in flood-prone regions. Recent large river floods, such as the July 2021 event in the Meuse basin affecting Belgium, Germany, and the Netherlands, have led to new interest in Nature-based Solutions (NBS) to manage floods by using natural processes in river systems. NBS for example, include reforestation and flood plain restoration in downstream areas. However, robust evidence of the effectiveness of NBS across spatial scales, hydroclimatic conditions, and flood magnitudes remains limited, particularly for large river basins and transboundary settings.
In this study, we evaluate the effectiveness of NBS for fluvial flood hazard reduction, applying the hydrological–hydrodynamic GEB modelling framework. Basin-scale hourly hydrological simulations are dynamically linked to the 2D hydrodynamic model SFINCS, to simulate flood hazard dynamics on a 10m resolution. Next, we include two NBS measures in the model: upstream reforestation and downstream in-channel (floodplain restoration). We schematize these measures into our modeling framework and simulate their effectiveness for lowering flood peaks as individual measures and in combination. Reforestation is implemented within the hydrological model by altering land cover, soil, vegetation, and Manning's roughness parameter in designated upstream zones. Floodplain restoration is represented in the hydrodynamic model by modifying topography and hydraulic parameters along the main river channel in downstream areas.
We test the NBS across basins with different hydroclimatic conditions and spanning multiple countries. We selected three similarly sized catchments (~30,000 km²) across different Köppen climate zones: the transboundary Meuse basin in Western Europe, the Upper Paraná River basin in Brazil, and the Krishna River sub-basin (Tungabhadra) in India. The model has been validated against satellite-observed flood extents from Copernicus Emergency Management Service products, showing good agreement for the 2021 Meuse flood (Critical Success Index = 0.75). For each of the three basins, we select multiple flood peaks with different timings and magnitudes. Using boundary conditions for these different events as input, flood extents are simulated before and after NBS implementation and evaluated by comparing baseline and intervention scenarios. We evaluate differences by (1) quantifying basin-scale changes in peak discharge, (2) inundation extent, and (3) average water depth.
The novelty of this research lies in its comparison of NBS across multiple river basins with different climates and geographic settings. By testing the same NBS measures in the Meuse, Upper Paraná, and Krishna basin, this study assesses whether their effects on flood peaks and inundation patterns are consistent across regions and flood events. This provides much-needed evidence on the conditions under which NBS are (or are not) effective in reducing flood hazards in large river systems.
How to cite: Sadana, T., C.J.H. Aerts, J., Busker, T., and De Bruijn, J.: Cross-Scale Evaluation of Nature-Based Solutions for fluvial flood hazard reduction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18095, https://doi.org/10.5194/egusphere-egu26-18095, 2026.
Floods constitute the most costly and prevalent climate-sensitive acute natural hazard, posing increasing risks to global communities and critical infrastructure. We introduce a novel, high-resolution global flood modeling framework engineered for cloud-scalable execution, leveraging the US Army Corps of Engineers’ HEC-RAS 2D hydraulic engine. This system performs physics-based simulation of fluvial, pluvial, and coastal flood hazards under both baseline and future CMIP6 climate projections. The framework integrates advanced climate forcings, including high-resolution gridded precipitation, dynamically downscaled streamflow, and sea-level projections derived from a combination of regional and global datasets. Fluvial boundary conditions are synthesized via a hybrid approach combining regional frequency analysis with machine learning–based hydrograph generation. Pluvial and coastal components explicitly incorporate extreme rainfall statistics and cyclonic surge dynamics, respectively. Simulations are conducted across multiple Shared Socioeconomic Pathways (SSPs) and time horizons, resulting in flood inundation layers for different return periods. Flood depth layers are derived by projecting surface water elevation onto a newly developed high-resolution digital terrain model (DTM). This downscaling process rigorously maintains the hydrodynamic fidelity of the HEC-RAS 2D model, thereby enabling granular, asset-level flood exposure and risk assessments. By seamlessly integrating physically-based hydrodynamics with a globally scalable computational architecture, this framework significantly advances quantitative flood risk assessment, supporting rigorous, climate-informed decision-making for applications spanning insurance, engineering design, and long-term resilience planning.
How to cite: Gomez, M., Kim, J., Maneta, M., Lammers, M., Hsieh, H., Bryant, K., Pouyaei, A., Liang, C., Hsieh, T.-L., Flaming, Z., Amodeo, M., and Kearns, E.: Cloud-scalable Global Climate-driven Flood Modeling using the HEC-RAS 2D Engine., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14018, https://doi.org/10.5194/egusphere-egu26-14018, 2026.
Global flood losses have exceeded one trillion dollars this century, driven by the twin forces of climate change and floodplain urbanisation. However, our collective understanding of these risks remains fundamentally constrained by the observational record, which is too short to adequately sample extremes, and by existing global models that fail to account for the spatial dependence of hazard events. In this study, we present the first high-resolution global catastrophe model for fluvial, pluvial, and coastal flooding, offering a comprehensive quantification of property flood risk that alleviates the limitations of historical data and existing models.
Our framework couples existing 30 m global flood hazard maps – simulated using a 1D/2D inertial formulation of the shallow water equations – with a stochastic event set representing 10,000 years of plausible climate conditions. By using a conditional extremes statistical model trained on ERA5 reanalysis data, we generated 9.7 million synthetic global flood events that capture the complex spatio-temporal and multi-peril dependencies often ignored in traditional assessments. This hazard event set is intersected with a spatially granular global economic exposure database, where regional capital stock models for residential, commercial, and industrial assets are downscaled onto the Global Human Settlement Layer and assessed using engineering-based vulnerability functions.
We estimate that the global Annual Average Loss (AAL) from flooding is $144 billion under current climate conditions. Crucially, the model reveals that in a 1-in-250-year extreme year (0.4% annual exceedance probability), global direct losses reach $531 billion: equivalent to 0.5% of global GDP and approximately 2.6 times the economic losses of Hurricane Katrina. The analysis highlights large risk inequities: while North America and East Asia face the highest absolute losses (~$350 billion in extreme years), the mega-deltas of Southeast Asia emerge as critical hotspots where losses are highest relative to capital stock.
Furthermore, we demonstrate the high volatility of flood losses, showing that even (a purely theoretical) 50 years of stationary observations cannot constrain national-scale AAL estimates to within a factor of three. These results underscore the necessity of synthetic event-based modelling for robust resilience planning, justifying large-scale mitigation investments, and helping to bridge the disaster insurance protection gap.
How to cite: Wing, O., Lamb, C., Haylock, M., Quinn, N., Bates, P., and Daniell, J.: A comprehensive event-based quantification of global flood risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17374, https://doi.org/10.5194/egusphere-egu26-17374, 2026.
TERIA, the Taiwan Earthquake Impact Research and Information Application Platform, is a grid-based framework designed to assess earthquake impacts using diverse inventory databases collected from government agencies. Building on previous developments, TERIA integrates scenario-based simulations, enhanced impact analysis modules, and interactive visualization tools to support disaster risk management and preparedness planning. The platform provides quantitative, spatially explicit assessments of seismic ground motion, casualties, and infrastructure damage—including buildings, roads, bridges, water supply systems, and power networks—presented through interactive 500 m × 500 m grid maps.
TERIA has been widely applied in national and regional earthquake drills, including scenario-based simulations of major events such as the 2017 magnitude 6.6 Shanjiao Fault, the 2018 magnitude 8.0 Hualien Outer Sea, the 2021 magnitude 6.9 Zhongzhou Structure, the 2022 magnitude 6.9 Hualien Hsincheng Fault, the 2023 magnitude 7.3 Chiayi Frontal Structure and Meishan Fault, and the 2024 magnitude 8.5 Ryukyu Trench earthquakes. By expanding Taiwan’s seismic databases, improving analysis modules, and enhancing system usability, TERIA provides a standardized, automated environment for earthquake impact assessment, information sharing, and disaster risk management, thereby supporting enhanced earthquake resilience across Taiwan.
How to cite: Liu, S.-Y., Huang, M.-W., Ke, S.-S., and Wu, B.-R.: TERIA: A Grid-Based Framework for Earthquake Impact Assessment and Disaster Risk Management in Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2970, https://doi.org/10.5194/egusphere-egu26-2970, 2026.
Volcanic hazards remain an overlooked risk for many organizations. International companies with globally distributed assets easily underestimate their exposure because volcanic risk is less visible compared to other natural hazards, especially to asset managers in non-volcanic countries. Accurate risk assessments require high-resolution catastrophe model runs for every volcano near their assets, which can be resource-intensive and often impractical. Most companies will shy away from this investment, leaving a critical gap in risk awareness and preparedness.
To address this challenge, we present the methodology for a global volcanic hazard scanning tool designed for rapid, high-level risk assessment. The tool provides a comparative ranking of an organization’s asset portfolio without requiring complex probabilistic simulations. Instead, it leverages key parameters such as proximity to active volcanoes and eruption frequency to generate a risk profile.
Once areas of high risk are identified, we run high-resolution catastrophe models for the relevant volcanoes and integrate these results into a combined, volcano-agnostic risk assessment for each asset. This hierarchical approach—first global scanning, then targeted high-resolution modeling—enables efficient resource allocation while capturing cumulative risk from multiple volcanoes affecting the same asset.
By offering a practical and cost-effective solution, the tool helps organizations strengthen resilience without the need for exhaustive modeling across all sites, while still providing robust insights where they matter most.
How to cite: Baladima, F. and Strehlow, K.: VolcanoScan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20969, https://doi.org/10.5194/egusphere-egu26-20969, 2026.
Agriculture employs more than 1 in 4 people globally and is adversely affected by many factors including natural perils including drought, storms, hail, floods, earthquakes and volcanic eruptions.
In most countries in Europe, the agriculture industry contributes less than 3% of GDP, with around 1.5% total contribution to GDP across the EU. However, it employs around 4% of the population. In the East Asia-Pacific, there exists great variability, with countries like Australia and Japan having a relatively low contribution in the order of 1-2% of GDP, however certain nations are towards 30% of GDP, with an average of 5.5% of GDP. When excluding high income nations this increases to 8%. In terms of employment, at least 1 in 5 jobs are associated with agriculture across the EAP.
Beyond the employment and productivity issues with climate perils for agriculture, the irrigation, equipment, buildings and systems associated with agriculture as well as the connections to the tourism, finance and general commercial services sector are important to assess. This capital stock is characterized for Europe and East Asia-Pacific as part of this work.
The study of the European Investment Bank (fi-compass, 2025) in Europe analysed drought, hail, rain and frost damages across the EU agriculture industry with estimates around 28 billion euros per year in terms of overall damages (ca. 6% of annual crop and livestock production). Comparisons are made to existing studies in the East Asia-Pacific as well as other studies in Europe to gauge the extent of the potential exposure at risk for agriculture.
Preliminary numbers of employment, capital and production exposure from agriculture, and their relationship to damages and losses seen in past years in these two regions are produced in this abstract and act as a stepping stone to global modelling which can facilitate insurance solutions and risk sharing such as Pelaez et al. (2023). This abstract is a companion to the EGU abstracts of Maier et al. (2026) on an agritourism modelling and exposure framework and Daniell et al. (2026) on tourism region statistics globally.
fi-compass (2025) Insurance and Risk Management Tools for Agriculture in the EU - https://www.fi-compass.eu/library/market-analysis/insurance-and-risk-management-tools-agriculture-eu
Pelaez, A. G., Daniell, J.E., Douglas, R., Langdale, C., Krishnan, A.N. (2023): University of Cambridge Institute for Sustainability Leadership (CISL). (2023). Risk sharing for Loss and Damage: Scaling up protection for the Global South. Cambridge, UK: University of Cambridge Institute for Sustainability Leadership - Position Paper for COP28.
How to cite: Brand, J., Daniell, J., Maier, A., Schaefer, A., Romero, R., and Claassen, J.: Assessing the exposure to natural perils of the agriculture industry: a comparison of Europe vs. East Asia-Pacific, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15930, https://doi.org/10.5194/egusphere-egu26-15930, 2026.
Central Asia is highly vulnerable to increasing drought frequency and intensity due to climate change, strong dependence on irrigated agriculture, and complex transboundary water systems. Effective drought risk management in the Aral Sea Basin (ASB), therefore, requires timely, spatially explicit, and policy-relevant information that can be accessed and interpreted by water managers, environmental experts, and hydrometeorological services. In this study, we present Droughtmap-ASB, an operational, Earth observation–based drought monitoring and decision support tool designed to support drought assessment, near real time warning, and policy-relevant planning across multiple spatial and temporal scales.
Droughtmap-ASB integrates satellite-derived vegetation and evaporative stress indicators with climate reanalysis data to provide a comprehensive characterization of agricultural and meteorological drought. The core framework combines Sentinel-3–based NDVI and land surface temperature-dependent Evaporative Stress Index (ESI) with a dynamic ten-year baseline to compute a Drought Severity Index (DSI), capturing drought onset, duration, and intensity. In addition, the system implements SPI, SPEI, and the Hydrothermal Coefficient (HTC) derived from ERA5 reanalysis data, enabling consistent assessment of meteorological drought conditions. Drought conditions are classified into eight standardized drought classes ranging from initial mild to long-term severe drought.
A key strength of Droughtmap-ASB is its multi-scale spatial design, which allows analyses at resolutions of 5 × 5 km grids up to rayon, oblast, national, and basin-wide levels, ensuring compatibility with both operational water management and policy frameworks. The web-based dashboard provides interactive visualization, while an automated bulletin module generates bi-weekly, monthly, and seasonal drought reports, supporting routine information dissemination for end-users.
By translating complex Earth observation data into actionable indicators, standardized drought classes, and policy-ready bulletins, Droughtmap-ASB bridges the gap between scientific monitoring and decision-making. The tool supports evidence-based water allocation, agricultural risk management, and climate adaptation planning, contributing to improved drought preparedness and resilience in Central Asia.
Keywords: Agricultural drought; Meteorological drought; Web-based drought monitoring; Earth observation data; Drought bulletins; Central Asia
How to cite: Usman, M., Voelkel, M., and Conrad, C.: Droughtmap-ASB: An Integrated Earth Observation–Based Drought Monitoring and Decision Support System for Water and Environmental Management in Central Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4865, https://doi.org/10.5194/egusphere-egu26-4865, 2026.
Developing a scalable, out-of-the-box flood-modelling framework that performs quickly and reliably across diverse hydrological and geomorphological contexts remains a major challenge in large-scale flood risk assessment. Global flood models increasingly aim to reduce dependence on locally available datasets, yet the limited availability of high-resolution data constrains the reproducibility and transferability of existing modelling approaches.
In this study, we explore and evaluate an out-of-the-box flood-modelling approach using the FastFlood tool. FastFlood is designed for rapid fluvial and pluvial flood assessment and is supported by globally available datasets for topography, land cover, and soil parameters, enabling flood simulations to be initialized even where local inputs are sparse or absent. We implement a structured, multi-scenario framework that investigates performance across low-detail, globally parametrized runs to high-detail, calibrated configurations.
We will present this new approach through a series of case study applications illustrating its performance across varying levels of detail and contrasting hydrological conditions. These cases demonstrate the method’s potential for deployment in flood-prone regions facing data limitations, supporting advances in global early warning, rapid impact assessment, and anticipatory action. A detailed case study is presented with focus on the Ontario province in Canada, with validation of the model at several levels of detail with historic and reference simulations using HEC-RAS, obtaining extent-wise similarity with the FastFlood output of 98.5 percent over the entire Trent river course.
How to cite: van Roon, K., Kolaparambil, F. J., van den Bout, B., and Meijvogel, D.: A rapid, out-of-the-box, regional flood modelling framework using FastFlood for Canadian case studies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11238, https://doi.org/10.5194/egusphere-egu26-11238, 2026.
How to cite: Zoccatelli, D., Dewals, B., Kwadijk, J., Macdonald, E., Merz, B., Pfister, L., Slager, K., Willems, P., and Courtois, S.: Event-Based Design-Flood estimation in small catchments: how catchment storage controls sensitivity to antecedent moisture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21939, https://doi.org/10.5194/egusphere-egu26-21939, 2026.
Rapid urbanisation and climate change are increasing flood exposure in cities, but global assessments commonly analyse infrequent, high-magnitude events such as the 1 in 100-year flood. This focus can underestimate the cumulative impacts of chronic flood hazards. In this study, we provide the first global analysis of cumulative chronic flood exposure for the world’s cities. By incorporating high-resolution flood hazard data, with population and gross domestic product (GDP) per capita estimates, we compare cumulative chronic flood exposure across spatial scales and against more extreme flood events. Our definition of ‘chronic flooding’ combines the 1 in 5-year river, coastal, and rainfall flood events. We quantify ‘cumulative flooding’ by scaling chronic-event exposures to align with the probability of occurrence associated with rarer, high-magnitude flooding. We find that globally, 170 million people, representing a combined GDP of US$1.69 trillion, are exposed to cumulative chronic flooding events. Within this total, low and lower-middle-income regions experience disproportionate exposure density. This is particularly apparent in Sub-Saharan Africa, where exposure totals from frequent, low-magnitude flooding exceed those of higher-magnitude events. We identify that patterns of inequality also extend downward to city size. Cumulative exposure to chronic flooding is disproportionately concentrated in cities with fewer than 1 million inhabitants. These smaller cities account for 121 million exposed people, constituting a combined GDP of US$1.1 trillion at risk from cumulative chronic flooding. Collectively, smaller cities represent over 80% of the exposure estimates for more extreme flood events. We discover that wider regional trends of inequality also manifest and intensify across city sizes. Exposure in smaller cities is weighted towards low- and lower-middle-income countries in Sub-Saharan Africa and Eastern and South-Eastern Asia, regions where cities are among the world’s fastest growing but often have limited resources and infrastructure. Our analysis shows that, when aggregated to comparable occurrence likelihoods, exposure to cumulative chronic flooding can approach and exceed exposure estimates to more extreme events. In highlighting unequal exposure burdens across scales and magnitudes, these findings can complement prevailing approaches to flood risk.
How to cite: Bean, C., Bates, P., Collins, B., Hawker, L., Jjemba, E., and Fox, S.: Chronic flooding drives cumulative exposure inequalities across global cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20307, https://doi.org/10.5194/egusphere-egu26-20307, 2026.
Global assessments of socio-economic impacts and risks from extreme weather events are often constrained by fragmented and insufficient data. The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a comprehensive framework for impact attribution and projection across sectors, including hazard indicators for extreme events. However, for sound impact assessments, model simulations need to be properly validated against observational data. Currently, this is challenging because impact observations and records suitable for validation exercises are fragmented and often need to be collected from different sources, complicating the development of impact functions and the attribution of longer event time series. While socio-economic impacts such as fatalities, damages, and displacement are documented in global databases such as EM-DAT and the Global Internal Displacement Database (GIDD) of the Internal Displacement Monitoring Centre (IDMC), the spatial extents of affected areas are typically derived from separate remote-sensing initiatives, including the Global Flood Database (GFD) and the World Fire Atlas. To address this fragmentation, we compiled a multi-source, event-based catalogue, initially focusing on floods and tropical cyclones, with the framework designed to be extensible to additional hazards. We integrated socio-economic impact records from EM-DAT, GIDD and the Dartmouth Flood Observatory (DFO) with observational data on spatial event extents as well as simulated hazard data developed for ISIMIP. Here, we focus on exploring different data products providing spatially explicit gridded flood extents based on satellite imagery to assess their suitability for an inclusion into such an event catalogue. In particular, we test the Copernicus Global Flood Monitoring (GFM) service that leverages Sentinel‑1 SAR imagery to generate a global ~20 m ensemble flood product from three independent water detection algorithms (2015-2025). We present a comparative assessment of GFM against established datasets, such as the MODIS‑based GFD and the RAPID SAR flood mapping system and investigate the suitability of GFM for automated global risk pipelines by addressing the distinct limitations. The GFD is a valuable historical archive already linked to DFO impact records, yet its reliance on optical imagery makes it vulnerable to cloud cover and darkness. This limits effective temporal resolution, thereby increasing the likelihood that short-lived flood peaks are missed. RAPID provides high‑resolution SAR mapping, but depends on trigger‑based tasking that can miss events when hydrological or rainfall‑based triggers fail. In contrast, GFM offers systematic, near‑global, all‑weather processing of available Sentinel‑1 acquisitions. Detecting inundation in urban areas remains a persistent challenge across remote sensing products due to signal blockage—a critical limitation given that socio-economic assets are concentrated in these zones. While GFM cannot fully resolve this physical constraint, it mitigates the ambiguity by explicitly delineating structurally non-observable pixels in its Exclusion Layer, ensuring that users distinguish 'unobserved' areas from 'non-flooded' conditions. Unlike GFD or RAPID, GFM explicitly distinguishes structurally non‑observable pixels (e.g., urban areas, dense vegetation) from actual water and flags low‑confidence conditions. We showcase a workflow for spatio‑temporal matching of these footprints with reported disaster events and propose options to combine these products into a comprehensive event catalogue
How to cite: Shirvani, Z., Novak, L., Frieler, K., and Sauer, I. J.: Exploring the Copernicus Global Flood Monitoring system for the development of a global impact attribution and validation dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20728, https://doi.org/10.5194/egusphere-egu26-20728, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 3
EGU26-1600 | ECS | Posters virtual | VPS13
Nationwide Multi-Scenario GLOF Hazard Mapping in Nepal Using Remote Sensing and Hydrodynamic ModellingWed, 06 May, 14:00–14:03 (CEST) vPoster spot 3
Rapid glacier retreat in the Nepal Himalaya has accelerated the formation and expansion of glacial lakes, increasing the likelihood of glacial lake outburst floods (GLOFs) with potentially severe downstream consequences. Existing GLOF studies in Nepal are largely site-specific and lack national-scale consistency, limiting their utility for systematic hazard planning. Here, we present a comprehensive, multi-scenario GLOF hazard assessment for Nepal based on three decades of satellite observations (1990–2023) and large-scale hydrodynamic modelling. Using multi-temporal remote sensing, we mapped 1,232 glacial lakes, including 265 newly formed lakes, and estimated lake volumes and peak discharges using established empirical relationships. Downstream flood propagation was simulated using the LISFLOOD-FP hydrodynamic model, enabling consistent, high-resolution inundation mapping across the country. To examine plausible future conditions under continued glacier retreat, we implemented scenario-based lake-volume increases of 10–50%, representing optimistic, intermediate, and pessimistic states. Results indicate a ~26.9% increase in total glacial lake area since 1990, with the most pronounced expansion in the Koshi and Karnali provinces. Modelled inundation extents and flood depths, particularly exceeding 3.5 m, increase substantially under higher-volume scenarios. Koshi and Karnali consistently emerge as the most exposed regions, with heightened impacts on settlements, hydropower infrastructure, and transport networks. The resulting national-scale GLOF hazard atlas provides a coherent framework for visualising present and future flood hazards and offers a practical basis for climate adaptation planning and disaster risk reduction in high-mountain regions.
How to cite: Saha, S., Singh, H., and Mohanty, M. P.: Nationwide Multi-Scenario GLOF Hazard Mapping in Nepal Using Remote Sensing and Hydrodynamic Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1600, https://doi.org/10.5194/egusphere-egu26-1600, 2026.
EGU26-22076 | ECS | Posters virtual | VPS13
Ethical AI for Disaster Resilience: Centering Frontline CommunitiesWed, 06 May, 14:03–14:06 (CEST) vPoster spot 3
Our dependency on artificial intelligence (AI) is increasing gradually for predicting disaster, allocating resources, emergency response systems, and calculating the impact of the disaster. These new technologies undoubtedly offer unparalleled opportunities to enhance resilience, but their implementation without ethical safety measures could multiply the existing inequalities with humanity. This study makes the case for a paradigm shift in humanitarian engineering toward human-centered AI, with a focus on prioritizing the requirements of frontline communities that are most impacted by climatic extremes. To investigate how design decisions affect equity results, this analysis draws on current developments in climate-resilient infrastructure and AI-driven catastrophe management. Using a policy-oriented perspective, this paper identifies three actionable strategies: mandating equity impact assessments for AI applications in disaster contexts, establishing governance frameworks that include community representation, and incorporating ethical AI training into engineering and public administration curricula. These ideas intend to bring about a convergence of scientific advancement and social justice, with the goal of ensuring that AI enhances human agency rather than diminishing it. Through the incorporation of frontline communities into the process of developing and deploying AI systems, this study will contribute to an approach to catastrophe resilience that is more accountable and inclusive. In conclusion, the article emphasizes the importance of interdisciplinary collaboration among engineers, policymakers, and affected people to develop AI solutions that are not only effective but also compassionate and egalitarian.
How to cite: Monalisa, S. and Mostafiz, R. B.: Ethical AI for Disaster Resilience: Centering Frontline Communities , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22076, https://doi.org/10.5194/egusphere-egu26-22076, 2026.
EGU26-16469 | ECS | Posters virtual | VPS13
Seismic Risk Assessment in Italy through Probabilistic Hazard Analysis and Integrated Exposure–Vulnerability ModellingWed, 06 May, 14:06–14:09 (CEST) vPoster spot 3
Seismic risk represents a major concern for densely populated urban areas, particularly in regions characterized by persistent volcanic and tectonic unrest. The city of Naples, southern Italy, is currently affected by an ongoing bradyseism crisis associated with the Campi Flegrei caldera, which has resulted in frequent low-to-moderate magnitude earthquakes (M 2–3+) over recent months. In this context, this study presents an integrated, data-driven framework for urban-scale earthquake risk mapping that combines probabilistic seismic hazard assessment with exposure and vulnerability modelling using convolutional neural networks (CNNs) and GIS techniques. Seismic hazard was quantified using earthquake records spanning 1990–2024 and modelled through six conditioning factors: elevation, slope, earthquake magnitude density, epicentral density, distance to epicentres, and peak ground acceleration. These spatial layers were integrated using a CNN architecture to generate a probabilistic hazard map representing the likelihood of earthquakes with magnitudes ≥3.5. Human exposure was subsequently assessed by integrating gridded population datasets with building footprints and parcel-level spatial data where available. Structural vulnerability was estimated through the fusion of land use/land cover information and recent building height data, both reclassified into susceptibility scores reflecting potential earthquake damage. The combined vulnerability index was categorized into five classes, with higher values corresponding to dense urban areas and taller building stock. The final seismic risk map was produced by integrating hazard, exposure, and vulnerability layers. Results highlight that areas characterized by high population density and intensive urban development sexhibit the highest seismic risk, consistent with observed urban patterns. The proposed methodology offers a transferable and automated approach for urban seismic risk assessment and can support risk-informed planning and disaster mitigation strategies in seismically active metropolitan regions.
How to cite: Sonowal, S., Amitrano, D., Pascarella, A. E., Kumar, R., and Gaicco, G.: Seismic Risk Assessment in Italy through Probabilistic Hazard Analysis and Integrated Exposure–Vulnerability Modelling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16469, https://doi.org/10.5194/egusphere-egu26-16469, 2026.
EGU26-643 | ECS | Posters virtual | VPS13
Global Synchronization of Compound Drought and Hot ExtremesWed, 06 May, 14:42–14:45 (CEST) vPoster spot 3
Compound drought–heatwave events (CDHEs) are becoming more frequent across several regions at the same time, heightening global climate risks, yet the processes that lead to their synchronized emergence remain poorly understood. Further, to assess how the governing drivers of synchrony have evolved, we employ statistical approaches to quantify the relative contributions of climatic oscillations and anthropogenic warming to CDHE occurrences. In this study, CDHEs are detected using the Blended Dry and Hot Index (BDHI), and their co-occurrence patterns are analyzed through a global complex-network approach that identifies statistically significant teleconnections. Complex network analysis reveals persistent synchronization hubs in the Amazon, West Africa, the Mediterranean, Southeast Asia, and northern Eurasia, highlighting regions where hot–dry extremes tend to cluster in time. Results also indicate that, although ENSO has historically played a major role in widespread CDHE clusters, its influence has weakened considerably in recent decades. In contrast, anthropogenic warming exhibits a consistently increasing and statistically significant effect, elevating the baseline probability of CDHEs even during weak or neutral ENSO conditions. Overall, our findings demonstrate a climate-system shift toward warming-dominated synchronization dynamics, in which background warming increasingly overrides natural variability. This transition heightens the risks of simultaneous climate shocks across continents, with major implications for disaster preparedness and global food–water security.
How to cite: Varghese, F. C., Saminathan, S., and Mitra, S.: Global Synchronization of Compound Drought and Hot Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-643, https://doi.org/10.5194/egusphere-egu26-643, 2026.
