In some contexts, particularly fragile and humanitarian settings where exposure, vulnerability, and institutional capacity are constrained, extreme weather events may interact with societal stressors such as conflict or political instability, producing compound and cascading risks. These dynamics pose particular challenges for risk assessment, forecasting, and anticipatory action. Addressing them requires closer integration of natural and social sciences, combining advances in hazard assessment and forecasting with insights into societal exposure, vulnerability, behaviour, mobility, and decision-making.
Yet only few studies are currently harnessing the full potential of interdisciplinary collaborations in this space, and several challenges pertaining to the choice of methods and the scale of analysis (e.g., regional, national) remain underexplored. This session provides a platform for interdisciplinary contributions that bridge natural and social sciences to better understand societal impacts of, and responses to, extreme weather events and related compound hazards.
We invite contributions including, but not limited to, studies of:
- Migration and displacement due to extreme events
- Environmental attitudes and behaviors influenced by extreme events
- Health and wellbeing effects of climate change and extreme events
- Food production and security in relation to extreme weather
- The interplay between climate change, environment, and conflict
- Anticipatory action and risk-informed decision-making for humanitarian preparedness and response;
- Methodological challenges to interdisciplinary collaborations
Orals: Fri, 8 May, 14:00–18:00 | Room 2.24
As climate change increases the frequency, intensity, and duration of many types of extreme weather events, scientists and advocates frequently point to these events as potential “teachable moments” for climate action. Although extreme weather often has significant social, economic, and health impacts, there is mixed evidence on whether experiencing or observing such events shifts climate-related attitudes, risk perceptions, or behaviors. Communication scholars and practitioners are therefore increasingly examining how to effectively communicate climate change–extreme weather links to help galvanize climate action at individual and policy levels. In this presentation, I will discuss what is known about effectively communicating links between climate change and extreme weather events, as well as current strengths, limitations, and gaps in the literature. Evidence-based communication strategies include clearly and accessibly explaining relevant climate science such as extreme event attribution studies; using storytelling to make impacts more concrete, emotionally engaging, and tangible; and leveraging trusted messengers such as weathercasters and health professionals. Limitations include a lack of longitudinal studies with repeated message exposures; geographic bias toward Global North countries; and a stronger focus on attitudes and beliefs than behaviors. I conclude by outlining promising topics for future research to help guide impactful communication strategies that promote climate action both during and after extreme weather events.
How to cite: Ettinger, J.: Communicating links between extreme weather events and climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3572, https://doi.org/10.5194/egusphere-egu26-3572, 2026.
In 2023 and 2024, Italy experienced severe flooding events with substantial environmental and socio-economic consequences. As climate change increases the frequency and intensity of extreme weather events, understanding individuals’ flood risk perceptions, preparedness, and responses to risk communication is crucial for effective climate adaptation and mitigation policies. In this work we assess preparedness against floods and risk perceptions, and examines whether targeted flood risk information can enhance risk awareness, pro-environmental behavior, and support for climate policies. To this aim, an original survey instrument was designed and administered to a representative sample of 3,423 residents in Emilia-Romagna and Tuscany in July 2024, following the 2023 flood events. The survey collected detailed information on socio-demographic characteristics, flood risk perceptions, preparedness and mitigation measures, awareness of municipal response strategies, information sources, and policy expectations. A key contribution of the study is the integration of survey responses with official flood hazard data, enabling a comparison between perceived and actual flood risk exposure. In December 2024, after new devastating floods in Italy, we conducted a follow-up survey, to allow us examining changes in preparedness and perceptions over time.
Across both surveys, we implemented pre-registered randomized experiments to assess the causal impact of flood risk communication. In the first survey, treated respondents received municipality-specific flood risk information after reporting their place of residence. In the second one, treated respondents watched a 75-second video explaining the causes, consequences, and dangers of floods. Results show that overall preparedness is low, with around 70% of respondents reporting no adaptive actions, but that targeted risk information delivered through effective visual messages significantly increases flood risk awareness, pro-environmental behavior, and support for climate-related policies. These findings highlight the importance of using direct, visually effective, and context-specific risk communication in fostering climate adaptation and public support for mitigation efforts.
How to cite: Ceola, S., Palazzoli, I., Puglisi, C., Binelli, C., and Muttarak, R.: Understanding Flood Preparedness and Risk Perception After Extreme Events: Survey and Experimental Evidence from Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11134, https://doi.org/10.5194/egusphere-egu26-11134, 2026.
An increasing number of studies address the exposure to extreme weather events as an influencing factor for people’s perception of climate change, environmental behavior, or policy preferences and voting intention. A crucial pre-requisite is the subjective perception of weather anomalies and extremes and translation into subjective risk perceptions. Generally, research has shown that humans can perceive weather anomalies, but studies yield mixed evidence depending on the specific context. So far, it is unclear under which conditions weather patterns are correctly perceived and which factors determine deviations in subjective perceptions from objective measurements. We contribute to this research gap by integrating novel georeferenced survey data on respondents’ subjective risk perceptions of weather extremes with spatially and temporally fine-grained Earth observation data. For this project, we have fielded a novel battery of survey items. These items were developed based on an extensive review of climate and environmental items from national and international survey programs. Our survey items are highly specific and capture respondents’ risk perceptions of 1. heatwaves, 2. heavy rainfall, 3. storms, 4. droughts as well as 5. floods. We aim to exploit the natural variation of weather patterns for these five weather types during the field period and in relation to respondent-specific baseline periods to analyze congruence and discrepancies between objective measurements and subjective perspectives. Our survey items have been fielded between November 2023 and January 2024 in a large probability-based panel program in Germany. By building on previous methodological work, we are able to link these data to highly customizable weather data from the European Union’s Earth observation program Copernicus and employ a range of robustness checks by varying spatial buffers and temporal reference periods.
How to cite: Abel, D., Jünger, S., and Quoß, F.: Bridging the Gap Between Extreme Weather Risk Perceptions and Objective Measurement - Evidence from Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20248, https://doi.org/10.5194/egusphere-egu26-20248, 2026.
Around 735 million people, or 9% of the world’s population, are currently exposed to chronic hunger. Recent stocktaking of Sustainable Development Goal (SDG) 2 “Zero hunger” highlights violent conflict, adverse climate and weather impacts, and poor economic performance as major barriers to progress. Assessments of possible future changes to the state of food security therefore should account for plausible developments in climatic, socioeconomic, and political conditions around the world. Here, we present a global study of how national institutional characteristics (democracy) and the breakdown of peace (conflict-related fatalities) affect the prevalence of undernourishment (PoU), over and beyond socioeconomic and agroclimatic drivers. Drawing on a statistical prediction framework trained and calibrated on more than half a century of empirical data, we simulate and assess future changes in country-level PoU until 2050. Projections are generated along alternative scenarios for climate change and socioeconomic development, along with new political development pathways that quantify future changes in democracy and conflict risk. Results demonstrate that while no scenario achieves SDG 2 within 2050, future progress in reducing chronic hunger will depend fundamentally on reducing conflict risk. We find comparatively weaker effect of agroclimatic heat exposure on projected PoU.
How to cite: Buhaug, H., Hermansen, G. H., Vesco, P., and Vestby, J.: Global hunger risk in alternative climate change and socio-political scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8274, https://doi.org/10.5194/egusphere-egu26-8274, 2026.
Extreme weather events like droughts can compromise food security, which can in turn trigger cascading impacts, such as increased risks of violent conflicts, particularly in vulnerable regions. While drought risk assessments are typically done at a domestic level, a considerable share of consumed food globally is obtained through international trade, which is often neglected.
This study integrates drought risk data with agricultural trade data to understand how drought risk propagates through the global food system. We focus on conflict-affected countries due to their particular vulnerability to extreme weather impacts and reliance on food imports. Specifically, we develop a framework to quantify drought risk associated with domestic production and crop imports, which we define as composite drought risk. This is done combining gridded drought risk data with crop production and trade for 23 countries.
Our findings reveal that while most conflict-affected countries face drought risk primarily through domestic production, incorporating trade networks substantially alters their risk profiles (>10% change in 13 countries, reaching 40%–50% in some cases). Import-related drought risk contributes over 10% of high drought exposure in 21 countries, reaching 80% in the most trade-dependent nations. We also identify critical trade dependencies that concentrate drought risk from specific partners.
Our approach demonstrates the added value of accounting for both direct climate hazards and socioeconomic pathways (represented by the international crop trade network) when assessing drought impacts on food security. Based on that, we suggest potential strategies considering domestic and trade measures tailored to countries’ composite drought risk profiles to improve food security.
How to cite: Moreno Dumont Goulart, H., Hamed, R., Hogeboom, R., Meijer, K., and Dahm, R.: Effects of international crop trade on drought risk of conflict-affected countries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14503, https://doi.org/10.5194/egusphere-egu26-14503, 2026.
The global supply chains for major food staples, including wheat, rice, soy, and maize, are significantly reliant on few chokepoints, predominantly situated within the maritime network. Grains traded at international markets are produced in a small number of breadbasket regions. This geographical production concentration has a substantial impact on the degree of reliance on these maritime chokepoints. It has been demonstrated on multiple occasions in preceding years that ports and shipping routes are susceptible to disruption as a result of extreme weather or political conflicts.
Here, we analyse short-term risks to global and regional food security arising from chokepoint disruptions. To this end, we have developed a model to construct global supply chain networks, incorporating different types of roads, inland waterways, railways, and maritime shipping lanes, with different ship types. Additionally, the model accounts for different types of logistic infrastructure that are important for crop transportation, like ports and railway stations and borders, with their subsequent costs and waiting time. The resulting networks are validated by different explicit examples of known crop transport routes. For the impact modelling, first, a transport cost matrix is calculated within the network, from and to every global Admin-1 region, which is done with a lowest-cost Dijkstra algorithm. Secondly, a crop trade matrix from and to every Admin-1 region is calculated. This is done by aggregating real-world trade data from country-level to Admin-1 level with a cost-based gravity-model that includes different types of consumption and the transport cost matrix. Thirdly, the lowest-cost-path between all regions that trade with each other is calculated, through the same Dijkstra algorithm as in step 1, and multiplied with the amount of trade from the trade matrix.
We assess risks to food security arising from factual as well as counterfactual scenarios, including single and multi-chokepoint disruptions. The outcomes of the different scenarios are compared with the baseline scenario, in which no chokepoint is deactivated. The study quantifies (i) how many people are affected by, and (ii) how much additional transport costs arise from alternative routes due to a disruption of a chokepoint, per crop.
The implemented supply chain network model provides a basis for understanding the implications of disruptions to global food security caused by chokepoint disruptions, highlights strongly affected ‘hotspot’ countries, and establishes the foundation for dynamic modelling of food insecurities. The model is developed for a fast computation of disruption analyses in big networks and will be available freely after final development.
How to cite: Kinkel, Y., Kuhla, K., and Otto, C.: Food security impacts of chokepoint disruptions in global crop supply chains, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18448, https://doi.org/10.5194/egusphere-egu26-18448, 2026.
Climate risks are increasing globally due to climate change and socio-economic development. Societies must implement adaptation measures today despite deep uncertainty regarding future climate trajectories, socio-economic pathways, and intervention effectiveness. Because no single strategy performs equally well across all impacts, for instance, protecting infrastructure versus saving lives, decisions depend on which outcomes are prioritized.
Most assessments focus on a single criterion, most often the cost to benefit ratio of measures, overlooking other trade-offs and risking maladaptation. Multi-criteria decision analysis (MCDA) addresses this by explicitly evaluating and weighting multiple objectives. When coupled with probabilistic risk modeling and uncertainty quantification, MCDA can identify strategies that are robust across various futures and stakeholder priorities.
In this project, we develop and test an integrated framework by coupling the new MCDA module of the open-source platform CLIMADA with its uncertainty and sensitivity quantification engine. Using a stylized case study from the Economics of Climate Adaptation (ECA), we explore how methodological and normative choices shape adaptation outcomes through three primary research questions:
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How do different impact units influence the prioritization of adaptation measures? We systematically compare rankings derived from multiple types of impact (e.g., population affected, economic losses, infrastructure exposure) to identify measures that perform consistently well across criteria versus those that are context-specific.
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How does the choice of risk metric affect the evaluation of adaptation measures? We quantify how rankings vary when using expected annual impact versus tail-risk metrics (high-impact, low-likelihood events), clarifying the normative implications of how "risk" is formulated.
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How sensitive and robust are MCDA-derived rankings to the weighting of decision criteria? We explore how results shift when assigning equal weights versus emphasizing specific priorities, making explicit how the assignment of preferences affects evaluations.
Across these questions, we perform an uncertainty and sensitivity analysis that propagates uncertainty through all model components. This allows for a quantitative assessment of decision robustness and identifies the assumptions to which results are most sensitive.
The key contributions of this work include the integration of MCDA with uncertainty analysis in a global modeling platform (CLIMADA); a systematic exploration of how normative modeling choices affect adaptation prioritizations; and a transparent, reproducible workflow for more integrated and value-aware climate-adaptation assessments.
How to cite: Juhel, S., Meiler, S., Hülsen, S., Kobler, E., McCaughey, J., Kropf, C., and Bresch, D. N.: Integrating Multi-Criteria Decision Analysis and Uncertainty Quantification for Climate Adaptation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17247, https://doi.org/10.5194/egusphere-egu26-17247, 2026.
This study presents an innovative methodological and interdisciplinary approach to addressing cascading climate risks in the housing finance sector. In collaboration with a large bank, the research team comprising behavioural, climate, and finance experts developed a decision-making framework to help anticipate and respond to future physical climate risks driven by the increasing frequency and intensity of extreme weather events. We used a qualitative-interview based approach with key decision-makers in the bank to identify six interconnected risk types: household, insurance, measurement, reputational, regulatory and credit loss. Then, building on two complementary methodologies – Storylines and Dynamic Adaptive Policy Pathways – we constructed plausible trajectories, integrating climate risk information, to facilitate development and implementation of risk-mitigation strategies by the bank. The study highlights a potential method for anticipating and preparing for climate-related financial vulnerabilities, especially in real estate markets where people may be unwilling (or unable) to move to new locations.
How to cite: Newell, B., Fiedler, T., Trezise, M., and Pitman, A.: Cascading Climate Risks: An adaptive decision-making framework for anticipating climate risks to mortgage providers and homeowners., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6073, https://doi.org/10.5194/egusphere-egu26-6073, 2026.
Recent events, such as the COVID-19 pandemic, underscore how localised disruptions can trigger far-reaching economic impacts through supply chain dependencies, extending indirect economic and social damages well beyond affected areas. Despite the growing recognition for the role of interdependencies on shock propagation, current models lack the granularity needed to understand and mitigate the propagation of climate shocks through interconnected supply networks.
Against this backdrop, our study proposes a firm-level climate stress-testing methodology for forecasting indirect social and economic damages arising from disruptions in production networks.
We first develop a firm-level agent-based model to simulate climate risk contagion within national supply chains. The model represents inter-firm production linkages and allows for heterogeneous behavioural responses under alternative assumptions regarding firm-level recovery dynamics, input specificity, and substitution possibilities following climate shocks. We then evaluate our model performance by comparing simulated impacts with observed indirect economic damages associated with the July 2021 and October 2022 flood events in Costa Rica. Using comprehensive administrative data from the Central Bank of Costa Rica’s electronic invoicing system, we reconstruct inter-firm transaction volumes and generate a detailed representation of the national production network. The resulting dataset is uniquely granular, combining full firm coverage (all firms being legally required to issue electronic invoices) with high temporal resolution based on monthly aggregation, allowing us to compare the model performance both at the regional and national level as well as the firm-level.
Our contribution is twofold. First, by conducting multiple simulations under alternative assumptions for a given climate extreme scenario, we explicitly account for uncertainty in the estimation of indirect economic impacts. This scenario-based approach allows us to assess the sensitivity of indirect damage estimates to key modeling assumptions. Second, by quantifying indirect impacts at the firm level and enabling aggregation at the city, district, and regional scales, the model delivers a high degree of spatial and economic granularity. The exceptional resolution of the underlying dataset allows policymakers to identify regions, firms, and communities that are most vulnerable to indirect damages associated with extreme weather events, thereby supporting more targeted and effective adaptation and risk-management strategies.
How to cite: Bossut, M., Juhel, S., Sandoval, C., Quiros, A., and Bresch, D.: A climate stress-testing methodology for climate extreme events -related systemic risks in national production networks., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22031, https://doi.org/10.5194/egusphere-egu26-22031, 2026.
Climate projections suggest greatly increased exposure to heat, and they have recently been outpaced by record-shattering heat events. Not all physical mechanisms are understood, and many open questions remain on the coordinated and uncoordinated human responses to record-breaking events. Insights into societal reactions to such outlier records are important for designing adaptation strategies, and for anticipating societal dynamics.
We hypothesize heat extremes trigger societal response. Therefore, we design a statistical framework to explore heat record exceedance in recent decades and combine it with socioeconomic impact and response data to elucidate event-response relationships. More specifically, we assess air conditioning uptake in Europe and heat-health impacts. As meteorological baseline we use daily maximum temperature and compare it with annual air-conditioning data at country-level, global burden of disease reports, and socio-economic variables. We validate our hypothesis using both fixed effects regression models, and event coincidence analysis. We first find that while temperature records show a strong upward trend in entire Europe, the occurrence of large temperature record exceedance is spatially heterogeneous. Fixed effects analyses show a statistically significant effect of highest temperature and gross-domestic product on air-conditioning uptake. They also highlight the importance of a one-year time lag between highest temperature and the air-conditioning data. Further, event coincidence analysis points at an impact of single heat events on air-conditioning uptake.
Overall, our results show promising insights into an issue that is of urgent societal importance in the face of new records. Insights into the driving role of single record-breaking events are very valuable for informing adaptation measures, wider policies, but also early warning systems and approaches related to anticipatory action.
How to cite: Lohrey, S., Falchetta, G., and Kornhuber, K.: Assessing Societal Response to Extreme Temperature Shocks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-987, https://doi.org/10.5194/egusphere-egu26-987, 2026.
Modelling future disaster risk is a critical component of disaster risk management. This is particularly the case in conflict-affected regions where overlapping crises amplify the challenges of disasters. Idlib - a city in northwestern Syria near the Turkish border – illustrates these challenges. Decades of authoritarian governance, armed conflict, displacement, and infrastructural degradation have compounded its vulnerability to seismic hazards. The February 2023 Turkey–Syria earthquake underscored these vulnerabilities, revealing both the city’s structural fragility and the political obstacles that undermine effective emergency response and recovery. With large-scale return migration and reconstruction now underway following Syria’s transition to a post-conflict government, understanding how risk may evolve in Idlib has become urgent.
We address this need by integrating quantitative risk modelling with qualitative insights from local stakeholders to assess potential future earthquake risk in Idlib. The analysis includes a new high-resolution building- and household-level exposure model of Idlib developed from various open data sources, including those of OpenStreetMap and the Global Earthquake Model, and population information from the International Organisation for Migration. The exposure model incorporates structural typology and building occupancy data – used to assign relevant physical vulnerability models from the Global Earthquake Model - and spatialised household information. Future projections of this exposure are then approximated based on urban development trend information obtained from local stakeholders and other relevant data sources, including UN Refugee Agency survey results about refugee return intention. Hazard characterisation leverages local ground-shaking data from the 2023 earthquake sequence.
The risk assessments quantify potential future losses in people-centred terms (e.g., potential earthquake-induced population displacement) rather than exclusively financial impacts. We use the assessments to evaluate the effectiveness of hypothetical policy interventions aimed at reducing building seismic vulnerability – such as introducing new construction techniques or enforcing stringent building codes- guided by stakeholder input. Comparative analysis of these hypothetical interventions highlights trade-offs between their cost/feasibility and the resulting risk reduction benefits. Beyond its case-study relevance, the study demonstrates the value of combining technical risk assessments with important contextual local knowledge in fragile settings.
How to cite: Heffer, A. and Cremen, G.: Future Earthquake Risk in Fragile Contexts: A Stakeholder-Oriented, People-Centred Assessment for Idlib, Syria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9378, https://doi.org/10.5194/egusphere-egu26-9378, 2026.
Anticipatory Action is increasingly put forward as a key approach to managing the emerging risks of climate change, by using forecasts to deliver vital resources to communities before disaster strikes. However, with climate change driving unprecedented weather extremes, how are anticipatory triggers, actions and implementation plans being designed to effectively prepare for and manage changing and emerging risks?
In this research we identify examples of existing good practice, potential obstacles to progress, and ways in which weather and climate science can be better harnessed to strengthen anticipatory action as a climate adaptation tool. We use a mixed-methods approach, combining literature reviews, key informant interviews and stakeholder workshops.
We find that while anticipatory action programming is usually informed by analysis of past events, there are emerging examples of good practice. These include addressing changing patterns of risk, undertaking scenario planning and simulation exercises, adapting triggers to account for upward trends in event frequency, and working to address the dangers of emerging risks such as heat waves and glacial lake outburst floods. However, in complex settings, for example in 'temporary' displacement camps, there is a need for longer-term thinking supported by integrated anticipatory action and resilience programming.
How to cite: Stephens, L., Young, A., Heinrich, D., Zeilstra, M.-A., Amuron, I., Bailey, M., Bahadur, A., and Coughlan de Perez, E.: Anticipatory action as a climate adaptation tool: an analysis of current practice, obstacles and opportunities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14218, https://doi.org/10.5194/egusphere-egu26-14218, 2026.
Sudan is increasingly exposed to compound risks from floods and droughts, amplified by conflict, climate variability, fragile infrastructures, and weakened institutional capacities. The APIS (Early Warning and Civil Protection for Floods and Droughts in Sudan) project has developed and tested a set of methodologies to strengthen multi-hazard risk assessment and early warning, tailored to contexts marked by fragility and data scarcity. At the core of this approach is the enhancement of the national early warning system through decision support tools for rain and flood forecasting and drought monitoring, strengthened by the effective use of information provided in operational bulletins disseminated through established procedures. The development of Impact-Based Forecasting (IBF) methodologies, built upon regional-level research and operational experiences, ensured the transfer and contextualization of established practices to the Sudanese domain.
Complementing this framework, a high-resolution forecasting chain based on the Weather Research and Forecasting (WRF) model was operationalized, delivering 3 km spatial resolution and 72-hour lead times for key weather variables to support IBF applications and assessing populations potentially affected by severe weather, including extreme rainfall, strong winds, and heatwaves. This system supports IBF applications and the assessment of populations potentially affected by severe weather, including extreme rainfall, strong winds, and heatwaves. The system was further reinforced through the rehabilitation and integration of meteorological and hydrological monitoring stations, enhancing the reliability of real-time observations. A national drought monitoring framework was also established to detect emerging stress conditions and assess related impacts on priority assets.
By combining hazard simulations with exposure and vulnerability information, the methodologies demonstrated consistency in generating tailored, real-time early warning products for disaster management authorities and humanitarian partners. A pivotal achievement included the establishment of a joint inter-sectoral operations room, which laid the foundation for sustained collaboration among relevant institutions. This forum fostered a sequential and multi-stakeholder forecasting process, with each member contributing their expertise, significantly enhancing the final product and ensuring its operational viability.
Current and future efforts will focus on tailoring impact-based forecasting products for distinct user groups by translating decision-maker–oriented outputs into simplified, community-accessible formats using clear language and intuitive icons to strengthen last-mile early-warning engagement.
Case studies from 2024 and 2025 illustrate the effectiveness of this approach, where daily monitoring and forecasting facilitated coordination and reduced the impacts of significant flood events. The Sudan experience underscores the value of regional collaboration in sustaining critical services and embedding multi-risk approaches into both scientific practice and governance frameworks for disaster risk reduction in humanitarian settings.
How to cite: Musa, A., Al Sheake, M., Homoudi, D., Khogly, H., Adam, E. A. N., Abohassabo, M. I., Abdella, A. I., Abkar, M., Omer, S., Testa, N., Gabellani, S., Masoero, A., Cremonese, E., Libertino, A., and Parodi, A.: Advancing Multi-Risk Early Warning in Fragile Contexts: Methodological Insights from Sudan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1267, https://doi.org/10.5194/egusphere-egu26-1267, 2026.
Extreme precipitation is a major trigger of urban and pluvial flooding and frequently acts as a primary or compounding hazard in humanitarian emergencies, triggering and exacerbating displacement, infrastructure damage, and vulnerability in already fragile contexts. Despite advances in disaster preparedness, anticipating the impacts of intense, localised rainfall remains challenging due to forecast biases and uncertainty, as well as the limited integration of hazard information with exposure and vulnerability. These limitations reduce the operational value of existing products for rapid, impact-oriented decision-making, particularly under the compressed timelines that characterise emergency response and anticipatory action.
Here, we present an easy-to-understand, actionable risk index for extreme precipitation that predicts impactful events up to 3 days ahead. The proposed index combines probabilistic estimates of extreme precipitation likelihood with potential impacts, derived from return-period-based forecasts that correct for systematic model biases, to estimate risk. Spatial forecast uncertainty is addressed through a fuzzy neighbourhood approach that accounts for displacement errors as a function of lead time. The resulting risk index is designed for straightforward integration with exposure and contextual information, such as population distribution or critical infrastructure, enabling the identification of regions and populations at risk from extreme precipitation within the forecast horizon. Using activations from the Rapid Mapping (RM) component of the Copernicus Emergency Management Service (CEMS) since 2024, we demonstrate that the index supports the anticipatory pre-tasking of satellite acquisitions for rapid mapping and facilitates timely, targeted emergency response by highlighting where high-impact precipitation is most likely to occur.
How to cite: Keune, J., Di Giuseppe, F., Barnard, C., and Wetterhall, F.: Anticipating impact: Forecasting the risk of extreme precipitation for emergency mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6399, https://doi.org/10.5194/egusphere-egu26-6399, 2026.
Global climate projections become increasingly pessimistic as the world suffers from a lack in consensus about rapid reductions in greenhouse gases emissions. This fact puts a huge pressure not only on the natural environment in which we live, but also on our societies and economies. Climate change will cause significant damages to many aspects of economic activity in multiple areas of the world through diminishing productivity, destroying local amenities and reducing life quality. Millions of people will experience income losses and poverty, some of whom will decide to move over short or long distances to flee the hazardous areas. In this paper, we develop a theoretical model of the world economy that projects economic and demographic variables until 2090 and quantifies the impact that future climate change has on the global economy, the spatial allocation of people and human migration movements at various spatial scales. The main findings of this exercise lead to a pessimistic conclusion that within current strict barriers to migrate, migration of people is not a plausible solution to upcoming climate challenges. In contrast, climate immobility of people generates huge economic losses and pushes millions into extreme poverty.
How to cite: Burzynski, M.: Projecting Climate-Induced Migration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1532, https://doi.org/10.5194/egusphere-egu26-1532, 2026.
Accelerating glacier melt and increasing climatic extremes are transforming mountain environments, heightening exposure to hazards such as glacial lake outburst floods, debris flows, and landslides. In the Hindu Kush Himalaya, where communities often inhabit multi-hazard landscapes, these environmental changes intensify livelihood insecurities and challenge local adaptive capacities. This study focuses on human mobility and immobility in response to such climate risks, which have received increasing attention in the last decade, but are still often framed as a binary. Drawing on qualitative fieldwork in Nepal’s Bhote Koshi Valley, we show that this framing obscures more intricate and differentiated ways human im/mobility is shaped by high-risk environments. Instead, we demonstrate that im/mobilities are spatio-temporally differentiated, deeply entangled and unequally distributed across social groups. A key finding of this study is the phenomenon of ‘monsoon mobilities’: a circular, annual and short- to medium-distance movement of people in anticipation of monsoon-induced risks. These mobilities take place in a context of fragile road infrastructure, where residents are at risk of temporary entrapment. At the same time, they depend on the movement of goods and people (e.g. trade and tourism) for their livelihoods, illustrating that monsoon mobilities function not only as an immediate safety response but also as a livelihood adaptation strategy– unequally accessible within the community. By showing how seasonal risks, fragile infrastructure, mobility-dependent livelihoods and social inequality co-produce differentiated mobility patterns, this study advances a nuanced understanding of climate-related im/mobility in mountain contexts, crucial to addressing specific mobility needs of risk-exposed communities.
How to cite: Abbing, R., Sterly, H., and Maharjan, A.: ‘Monsoon mobilities’: moving beyond the binary of migration and ‘trapped populations’ in a vulnerable mountain community in Nepal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4420, https://doi.org/10.5194/egusphere-egu26-4420, 2026.
Flooding is the world’s most pervasive natural hazard and is projected to intensify with ongoing socio-environmental change. Beyond the immediate damage they cause to infrastructure and livelihoods, floods can prompt disruptive short- and long-term population movements. This study quantifies and characterises population mobility in response to severe floods in Bihar, India. Bihar is a flood-prone and socio-economically vulnerable locale that experiences recurrent monsoon flooding affecting millions annually. We estimate the proportion of the population that responds to flooding events, examine the spatial and temporal characteristics of mobility (including distance travelled and timing relative to flood onset), and assess heterogeneity in responses across demographic groups (gender and age) and settlement types (urban, suburban, and rural).
We adopt a data-driven, multi-source geospatial approach centred on gridded user-count data from Meta’s Data for Good programme, which provides high-frequency proxies for population presence based on aggregated Facebook user activity. This Facebook data offers a rich source for tracking migration and displacement in response to crises such as disease outbreaks, flooding, and tropical cyclones across the globe, particularly in low- and middle-income countries where alternative mobility data are sparse. These data are integrated with complementary datasets, including night-time lights as a proxy for electricity access and economic activity, daily river-discharge records to capture hydrological extremes, WorldPop population surfaces, Global Human Settlement Layer – Degree of urbanisation (GHSL-SMOD), and satellite-derived flood extent maps. The combined framework enables identification of both spatial and temporal mobility responses to flooding while accounting for variations in urbanisation and infrastructure.
Our results show that active Facebook user counts decline by approximately 35% during flood periods. This reduction likely reflects a combination of factors, including power and connectivity outages, evacuation and displacement, and reduced access to mobile devices. We find that the correspondence between Facebook user counts and underlying population increases monotonically with the degree of urbanisation, suggesting greater data reliability in more urban contexts. Analysis of movement flows indicates that mobility during flooding is dominated by urban-to-urban movements, followed by urban-to-suburban transitions, with comparatively limited rural outflows. Demographic analysis further reveals differential impacts across gender and age cohorts, indicating uneven exposure and adaptive capacity within affected populations. Overall, this study demonstrates the value of integrating social-media-derived mobility data with remote sensing and hydrological information to generate timely, granular insights into flood-induced population dynamics. Such evidence can support more targeted humanitarian response, infrastructure planning, and long-term resilience-building efforts in flood-prone, data-scarce regions.
How to cite: Aggarwal, E., Darby, S., Tellman, B., Cheng, Z., Tatem, A. J., and Lai, S.: Detecting Flood-Induced Population Mobility Using Social Media and Satellite Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13759, https://doi.org/10.5194/egusphere-egu26-13759, 2026.
Climate change has emerged as a significant driver of forced displacement, particularly in vulnerable places such as small island nations, Sub-Saharan Africa, and some countries in South and Southeast Asia, yet the relationships between extreme weather events, displacement, mortality, and contextual factors remain poorly understood. We examine global patterns of climate-driven internal displacement using data from the Internal Displacement Monitoring Centre (IDMC) combined with mortality records from EM-DAT (2013-2023). We address three critical questions: (1) how displacement and mortality vary across extreme weather events (floods, storms, landslides, and wildfires); (2) whether trends in displacement and mortality differ over time by type of extreme weather event; and (3) how contextual factors—conflict, wealth distribution, and infrastructure accessibility—moderate displacement and mortality.
We create spatial hazard footprints for each extreme weather event by integrating satellite-based data sources—DLR Global WaterPack for floods, LHASA for landslides, GlobFire for wildfires, and IBTrACS for storms—with IDMC displacement event records. Then we overlay these footprints with human settlement data to calculate total population exposure for each event. This method helps us distinguish between total population exposure within mapped extreme weather event footprints and the actual proportion of exposed populations who become internally displaced persons. We link displacement events to mortality data through spatiotemporal matching and incorporate contextual factors including ACLED conflict data, gridded global GDP per capita, and ND-GAIN infrastructure indicators (paved roads, electricity access, ICT, and medical personnel). We use quantile regression models to estimate displacement and mortality ratios while controlling for hazard type, temporal trends, and interactions between extreme weather event type, contextual factors, and time.
Our analysis shows that displacement and mortality differ in both magnitude and variability across extreme weather event types. Floods and storms exhibit highly variable impacts, while landslides remain consistently low and wildfires show moderate variability. Over time, temporal trends diverge by disaster type, revealing heterogeneous vulnerability trajectories across hazard types. Contextual factors amplify disaster impacts, with particularly pronounced effects for floods. Wealth distribution (GDP per capita) exhibits nonlinear effects that we will explore further in ongoing analysis. These findings indicate that there is a need for disaster-specific adaptation strategies that account for contextual factors and temporal dynamics. Here, we present not only original footprints for historical extreme weather events and internal displacement, but also how these data can improve our responses to a changing climate.
How to cite: Kurli, V., Carrico, A., and Mehrabi, Z.: Mapping Climate-Driven Internal Displacement and Effect of Contextual Factors Globally , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22173, https://doi.org/10.5194/egusphere-egu26-22173, 2026.
Every year millions of people are displaced by extreme events around the world. The factors that cause someone to leave their home during a disaster are complex and interacting, and they are different between countries, cultures and socioeconomic groups.
However, the data on events and displacement can be noisy and uncertain, and building any kind of global model of disaster displacement is a challenge, although a necessary one. In this work we use theory from migration and displacement studies, both quantitative and qualitative, to constrain and guide the design of improved global displacement risk models for earthquakes and tropical cyclones. The model describes population displacement as a process driven by regionally-varying socioeconomic factors, not just loss of physical housing.
This work builds on an existing global probabilistic displacement risk model built by our consortium. We identify the most relevant drivers of displacement by modelling historic displacement events and selecting from a larger set of socioeconomic drivers of vulnerability. Our dimensional reduction process optimises explanatory power while ensuring that we stay consistent with theoretical frameworks of population displacement. Our modelling uses the CLIMADA platform and IDMC displacement data and we plan to expand to additional hazards.
Our work that informs strategic risk assessments for international aid organisations, global early warning systems, and provides a robust framework for individual countries and actors to train models with their own data and context. All our work is open source and we invite and support you to adapt this work for your own needs.
How to cite: Fairless, C., Paul, N., Oakes, R., Peter, M., Ponserre, S., and Souvignet, M.: An open population displacement risk model built on physical and socioeconomic drivers of displacement, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21585, https://doi.org/10.5194/egusphere-egu26-21585, 2026.
Understanding how climate variability shapes mobility in the Sahel and around the world requires tools that integrate environmental and social data across coherent spatial and temporal scales. Yet most empirical studies rely on single indicators such as SPEI or NDVI and operate within administrative boundaries that rarely align with ecological processes or mobility pathways. These constraints limit the capacity of social-science research to capture the multi-dimensional nature of climate stress and its influence on population movements. In this research work, the focus has been given to the Sahel region in Africa. This research presents the Sahel Cube, inspired by EUMETSAT’s D&V cube that uses EUMETSAT’s archive data and other environmental datasets. The cube unifies decades of climate, vegetation, and hydrometeorological information into a reproducible spatial–temporal architecture that supports cross-disciplinary analyses. As one of the use cases, we integrate Call Detail Record (CDR) based mobility trends to examine how, when, and where climate stress corresponds with observed mobility patterns. A core innovation of the cube is its capacity to generate geons, data-driven spatial units that reflect environmentally coherent regions rather than political borders. These geons improve the alignment between environmental dynamics and social processes, strengthening the evidence base for climate–mobility studies and broader nexus research.
How to cite: Zakir, K., Lang, S., Borderon, M., and Bircan, T.: Sahel Cube (Space-Time Data Cube) for Climate-Mobility and Interdisciplinary Nexus Research , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1033, https://doi.org/10.5194/egusphere-egu26-1033, 2026.
Flood risk has intensified globally due to climate change and has become a major driver of human displacement, with Africa being particularly vulnerable. Limited access to high-resolution, long-term flood observations has constrained understanding of displacement dynamics across the continent, where adaptive capacity remains low. Here, we integrate four decades (1984–2024) of monthly satellite-derived flood observations from Landsat and Sentinel-2 with subnational displacement records from the Internal Displacement Monitoring Centre (IDMC) and socio-economic indicators such as GDP per capita and urbanization from the Global Human Settlement Layer (GHSL) across Africa. Results reveal a marked expansion of flooded areas across Western and Central Africa. In the Niger, Congo, and Benue basins, flood extent has increased by 4.02 km²yr-1, while country-level trends are steepest in Mali (+6.08 km² yr-1), Nigeria (+4.43 km² yr-1), and the D.R. Congo (+4.11 km² yr-1). To quantify the probability that floods trigger displacement and the magnitude of displacement conditional on occurrence, a hurdle modeling framework has been adopted. Using a hurdle modeling framework, we separately quantify the probability that floods trigger displacement and the magnitude of displacement conditional on occurrence. Displacement responses exhibit strong spatial heterogeneity. Conditional on displacement, a one standard deviation increase in flood severity is associated with an approximately 27% increase in displacement magnitude, with hotspots in the Sahel, Southern Africa, and the Horn of Africa. This flood–displacement sensitivity is amplified in more urbanized areas and dampened in higher-income areas. The expansion of flood extent across major African basins, coupled with socio-economic vulnerabilities, signals escalating displacement risk and underscores the need for locally tailored adaptation strategies that integrate flood preparedness with displacement-sensitive disaster risk management.
Acknowledgment: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (RS-2021-NR055516, RS-2025-02312954).
How to cite: Nguyen, H.-M.-T., Hoffmann, R., Foreman, T., Lee, H., Omer, A., Yamazaki, D., and Kim, H.: Intensifying Flood Extent and Human Displacement Risk Across Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16224, https://doi.org/10.5194/egusphere-egu26-16224, 2026.
Linking satellite-derived environmental indicators to human mobility outcomes requires bridging remote sensing, climate science, and migration research. In the Central Sahel, where drought increasingly threatens livelihoods, understanding how climate stress translates into population movement remains complicated by a fundamental scale-of-analysis problem: patterns visible at national levels may obscure, or even reverse, at regional scales. This study traces the climate-migration signal across Burkina Faso, Chad, Mali, and Niger, quantifying drought's contribution to internal migration while examining how spatial heterogeneity shapes the relationship.
This study analyzed 77,783 internal migration flows (2005–2010) from a derived gravity model, linking them to drought severity measured via the Soil Moisture Agricultural Drought Index (SMADI) which is a composite satellite indicator integrating soil moisture, temperature, and vegetation health. A symmetric push-pull framework treated origin and destination conditions identically, addressing methodological critiques of traditional asymmetric gravity models. Machine learning algorithms (Random Forest, XGBoost) captured non-linear relationships, with climate attribution quantified through five complementary methods including SHAP value decomposition.
The results reveal that scale of analysis fundamentally shapes conclusions about climate-migration relationships. In three countries, drought contributed modestly but consistently to migration prediction: Chad (5–9% of model explanatory power), Burkina Faso (6–18%), and Niger (4–38% depending on attribution method). Mali, however, showed negative climate attribution (−23%), i.e., adding drought variables degraded predictive accuracy. This counterintuitive finding traces to within-country heterogeneity: the Mopti region exhibited an inverse drought-migration relationship (r = −0.22), likely reflecting the Inner Niger Delta's flood-pulse ecology where drought improves rather than undermines local livelihoods. Aggregating across regions with opposing signals cancels the climate effect and introduces prediction error.
Despite this heterogeneity, robust patterns emerged across all four countries. Push factors at origin dominated predictions (>99% of importance), while destination pull factors contributed negligibly, suggesting Sahelian migration functions primarily as stress response rather than opportunity-seeking behaviour. Rural-origin corridors showed 2–2.5 times higher climate sensitivity than urban-origin flows. Critically, partial dependence analysis revealed non-linear drought-migration relationships with plateaus at extreme drought severity, consistent with the immobility hypothesis wherein severe stress erodes the resources necessary for movement, potentially trapping vulnerable populations in place.
These findings carry two implications for interdisciplinary climate-mobility research. First, national-level analyses risk masking or misrepresenting climate signals when subnational regions exhibit opposing relationships, regional stratification is not merely preferable but essential for valid inference. Second, the transition from mobility to immobility at extreme drought levels suggests that climate adaptation policy must address both displaced populations and those trapped by insufficient resources to move. Bridging satellite drought monitoring with migration outcomes is methodologically feasible, but the bridge must be built at appropriate spatial scales.
How to cite: Lopes Jacob, A.: From satellite drought indices to migration flows: tracing climate signals across the Sahel, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19576, https://doi.org/10.5194/egusphere-egu26-19576, 2026.
On the night of March 31, 2017, the city of Mocoa, Colombia, suffered a series of landslides and debris flows triggered by extreme rainfall. Despite the existence of prior warnings of possible landslides, the event unfortunately resulted in 332 deaths, 398 injuries, and affected more than 7,700 families. Mocoa has long received populations displaced by armed conflict over recent decades, a process that has contributed to the rapid and informal urban expansion along river corridors and unstable slopes, increasing exposure to hydroclimatic hazards.
This study examines the disaster through an integrated disaster risk perspective, asking how the event was shaped by the conjunction of multiple factors, including the conflict-driven displacement, land governance, together with hydroclimatic extremes and limited monitoring capacity. We based our findings on a document review of planning instruments, available hazard mapping, documentation on early-warning arrangements, and the hydrometeorological context, complemented by GIS-based spatial analysis of affected areas in relation to mapped hazard zones and municipal-level conflict/displacement indicators.
The results of the Mocoa case illustrate how structural risk conditions associated with forced displacement and governance challenges persist. Post-2017 investments have improved warning systems and local monitoring, but underlying risk drivers, including displacement, governance limitations, and inadequate planning tools, remain unaddressed.
With this study, rather than proposing a solution, we discuss the implications for disaster risk management and anticipatory action in a humanitarian context, including integrating displacement dynamics into multi-risk assessments, designing response protocols that account for unequal capacity to act, and aligning land governance and early warning to mitigate the impact on populations already affected by violence and displacement.
How to cite: Yanalá-Bravo, J. C., Urueña-Ramirez, D. A., Márquez-Arévalo, S. M., and Ávila-Guzmán, M. P.: Compounding risk at the climate–conflict interface: forced displacement and informal urbanisation in the 2017 Mocoa debris-flow disaster (Colombia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20915, https://doi.org/10.5194/egusphere-egu26-20915, 2026.
Extreme weather events are increasingly compounding with conflict, severely limiting the ability of vulnerable communities to cope with their impacts. Both conflict and climate-related hazards can lead to displacement, which in turn heightens exposure and vulnerability to social and hydroclimatic shocks. As hydrometeorological hazards are projected to intensify under climate change, alongside increasing trends in conflict, it becomes paramount to better understand the links between conflict, displacement, and climate-related hazards. Yet, these interactions remain poorly understood in the context of Somalia.
This study investigates how conflict, climate-related hazards, and their compound effects influence patterns of internal displacement in Somalia. It integrates multiple datasets including hydrometeorological variables (e.g., precipitation, temperature), conflict event records, flood data and displacement records, aggregated at a monthly temporal scale and regional spatial level. The analysis applies monthly descriptive and spatial-temporal association methods by harmonizing conflict, climate, flood, and displacement datasets to a common administrative level and attributing displacement events based on threshold-based co-occurrence of hazards and conflict. The focus is on two critical years, 2022 and 2023, selected due to the concurrent intensification of drought, flooding and conflict, providing a unique opportunity to examine their cascading effects on internal displacement in Somalia. Displacement events were then categorized in relation to four drivers: conflict-related, drought-related, flood-related, and compound causes (i.e., conflict occurring alongside climate hazards).
Initial results indicated that in 2022, drought was the primary driver of displacement in central regions such as Bakool and Hiraan, while conflict alone triggered significant displacement in areas like Bay. Notably, compound displacement linked to both conflict and drought was detected in Lower Juba and Lower Shabelle. In 2023, displacement peaked during flood events in the rainy seasons, particularly in Hiraan, Gedo, and Lower Juba, often intersecting with ongoing conflicts. The study finds that while monthly, regional-scale aggregation provides a consistent basis for attributing displacement events, it may obscure short-term or highly localised dynamics.
This work contributes to a better understanding of how overlapping cascading hazards shape displacement patterns in Somalia. It shows the importance of spatial and temporal disaggregation in displacement attribution studies and emphasizes the importance and need to improve how displacement data are generated, accessed, and used in conflict contexts. In doing so, the research identifies critical gaps in current displacement modelling, including the need to harmonise trigger methodologies used across agencies and datasets. Building on this work, future work will further explore patterns of immobility under hazard and conflict stress.
How to cite: Abdillahi, O., van den Homberg, M., Ettema, J., and Matanó, A.: Relationships between hazard, conflict, and displacement for the 2022 flood and 2023 drought events in Somalia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23029, https://doi.org/10.5194/egusphere-egu26-23029, 2026.
On 6 February 2023, two major earthquakes (Mw 7.7 and Mw 7.6) struck southeastern Türkiye and northern Syria, causing widespread destruction across multiple provinces. Severe winter conditions, damaged transport infrastructure, and continuous aftershocks created extraordinary pressure on humanitarian logistics—especially warehousing, transport planning, and last-mile distribution. In this context, volunteer logisticians became a critical force for moving life-saving relief items quickly and fairly.
In Türkiye, AFAD led overall coordination in collaboration with municipalities, NGOs, and international partners. The early response demonstrated that logistics performance depends not only on the volume of aid, but on how well flows are organized. Road damage, congestion on key corridors, limited fuel and vehicle availability, and insufficient last-mile capacity meant that poorly coordinated movements sometimes increased bottlenecks rather than reducing them.
A major challenge was spontaneous volunteer convergence. When volunteer logisticians arrived without registration, tasking, or a clear chain of command, the result could be duplication (multiple teams doing the same sorting), competition for trucks and forklifts, inconsistent documentation, and unsafe work practices in unstable environments. These issues can reduce throughput, compromise accountability, and delay delivery to the highest-need locations.
Key lessons for volunteer logisticians in large-scale disasters include:
- Work within the coordination system: Register with a recognized organization and follow assigned tasks, reporting lines, and dispatch rules (who moves what, where, and when).
- Protect the flow, not the stockpile: Prioritize throughput—fast receiving, sorting, and dispatch—over hoarding or over-accumulating items at a single hub.
- Inventory discipline is non-negotiable: Use simple, consistent tracking (receiving logs, bin locations, dispatch notes, and delivery confirmation) to avoid loss, duplication, and inequity.
- Last-mile distribution is the hardest mile: Plan for small vehicles, short-haul shuttles, and flexible delivery points; match loads to real needs and local access conditions.
- Safety and standards first: Apply basic warehouse safety (PPE, lifting rules, traffic lanes, shift rotation) and protect volunteers from aftershock and weather risks.
- Data is logistics power: Share daily situation updates—stock levels, bottlenecks, fleet status, unmet needs, and delivery performance—to support prioritization and prevent congestion.
For future mega-disasters, structured volunteer logistics systems—pre-registration, rapid onboarding, role-based training, and standardized reporting—are essential. When volunteer logisticians are integrated into coordinated supply chains, they increase speed, transparency, and equity of distribution, turning solidarity into reliable operational capacity.
How to cite: isik, Z.: Volunteer Logistics in Mega-Disasters: Lessons from the 6 February 2023 Earthquakes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3428, https://doi.org/10.5194/egusphere-egu26-3428, 2026.
The extreme events caused by global warming have had profound impacts on natural ecosystems and socio- economic structures. We aim to introduce the impacts of climate change into Computable General Equilibrium (CGE) model in the form of loss functions. To more accurately assess the impact of extreme events on economic losses, we selected the extreme precipitation and temperature index and the Standardized Precipitation Evapotranspiration Index (SPEI), to explore their nonlinear relationships with direct economic losses from different disasters using MLP neural networks and three ensemble learning algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). The results show that the LightGBM algorithm performs the best, with R ^ 2 over 92 % and MAPE dropping below 10 %, and the level of economic development is the dominant factor in regional disaster losses. In the last four years, China has not experienced fluctuation in economic losses caused by serious extreme events, the disaster prevention and reduction work has achieved great results. The affected areas tend to be concentrated as a whole, with certain spatial heterogeneity.
How to cite: Chou, J. M. and Wang, Y. Q.: Exploring the economic loss characteristics of meteorological disasters in China based on CGE model improved loss function, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4668, https://doi.org/10.5194/egusphere-egu26-4668, 2026.
Temperature extremes are a deadly natural hazard and heavily affect socio-economic and natural systems. Several metrics have been developed to characterize the risk of temperature extremes to human health. At the national or subnational level, ad-hoc indicators are commonly implemented by civil protection authorities, meteorological services and other entities, and are often used to issue warnings and define reactive measures during emergencies. International standards dedicated to monitoring and anticipatory action, as well as for aggregating data for retrospective analysis or research, are not available. Similarly, models for the temperature-mortality risk are available only in some countries, mostly high-income ones, but not elsewhere.
With ERA5 as data source (ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present), we use a combination of temperature anomalies and feels-like temperature indicator (Universal Thermal Climate Index - UTCI) to define events of relevance, particularly for the humanitarian community and the civil protection community. Population and urbanisation data are employed to pinpoint locations with significant potential impacts, thus informative for preparedness and response analysis. The prospective use of discrete events as defining entity, together with vulnerability and exposure mapping, facilitates the tracking of the events and the identification of more specific areas of interest, thus helping to characterize impact before, during and after extreme temperature events.
We assess and validate the analysis based on a dataset of past impactful events, and propose a synthetic classification to highlight the level of awareness needed for the humanitarian community, in line with the impact severity. The resulting product is suitable for monitoring temperature extremes at global level in multi-hazard early warning systems, like the Global Disaster Awareness and Coordination System (GDACS).
How to cite: Masante, D., Acosta Navarro, J. C., Mastronunzio, M., Fioravanti, G., Hrast Essenfelder, A., Toreti, A., and Santini, M.: Monitoring Temperature Extremes, a Framework for Global Early Warning Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19374, https://doi.org/10.5194/egusphere-egu26-19374, 2026.
The rising mortality risk associated with global warming has emerged as a critical threat to public health landscape. The minimum mortality temperature (MMT) indicates the optimal temperature with the lowest mortality risk under long-term climate stress normally considered a proxy for adaption capacity. This study uses the MMT to analyze social factors in shaping temperature adaptation across Taiwan.
To derive the MMT, this study uses daily mortality data for non-accidental causes across gender and all age groups in Taiwan from 2008 to 2023, together with ambient temperature data, while controlling relative humidity, wind speed, and air pollution. Distributed Lag Non-linear Model (DLNM) combined with meta-regression are applied to analyze the temperature–mortality relationship to derive regional MMT in Taiwan.
The results show significant differences adaptation in the patterns of MMTs between special municipalities and non-metropolitan counties. Marked variation are also observed between gender and disease groups, showing difference adaptation conditions across Taiwan. These findings have important implications for public health planning and climate adaptation strategies.
How to cite: Li, S.-F., Chen, Z.-F., and Weng, W.: Adaptation to warming climate: analyzing minimum mortality temperature in Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19441, https://doi.org/10.5194/egusphere-egu26-19441, 2026.
Northeastern Nigeria faces a compounding crisis driven by conflict-induced displacement and intensifying climate hazards. In Dikwa, Borno State, Internally Displaced Persons (IDPs) occupy flood-prone sites with inadequate infrastructure, exacerbating their vulnerability. Humanitarian operations in these data-scarce settings often lack the detailed flood risk information necessary for effective mitigation. This study presents an integrative flood modelling framework that couples global datasets with participatory local data to assess flood risks and evaluate adaptation strategies across 17 IDP camps. We developed a coupled hydrological-hydrodynamic model (Wflow and Delft3D FM) using global open-access data as a baseline. To address the limitations of global models, we integrated local meteorological records and participatory data collected via KoBoToolbox, including drainage characteristics and historical flood marks. Results indicate that relying solely on global datasets underestimated flood hazards and diverged from local observations. Integrating local data significantly improved model validity. We utilized the validated model to assess shelter-level exposure under various return periods (T2 to T100) and simulated the efficacy of a conceptual drainage network. The proposed interventions reduced the total population at risk by approximately 50% across all return periods. However, the analysis revealed trade-offs, where drainage diverted water effectively from major settlements but increased risk in specific localized areas. This research demonstrates that while global data enables initial assessments, local verification is essential for operational relevance. The findings provide a reproducible workflow for quantifying flood hazards and designing adaptation measures in complex humanitarian emergencies.
How to cite: Ogunwumi, T., Hartgring, S., Moreno Dumont Goulart, H., Wanke, S., and Dahm, R.: Integrating global and local data for flood adaptation in IDP camps near Dikwa, Nigeria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20554, https://doi.org/10.5194/egusphere-egu26-20554, 2026.
Food supply shocks, characterized by sharp declines in food availability, threaten global food security, particularly as supply chains become increasingly interdependent. While globally integrated trade networks in food, fertilizers, and agricultural inputs can buffer localized shortages from natural hazards, this interconnectedness creates structural vulnerabilities: when key trading partners withdraw or critical supply routes close due to conflict, political instability, or infrastructure collapse, dependent countries face abrupt supply disruptions with limited alternatives. Rising geopolitical tensions - from armed conflicts to trade wars and the formation of political blocs - are progressively fragmenting global food trade networks. Countries are increasingly restricting exports to secure domestic supplies, impairing trade infrastructure, and imposing trade barriers, creating compounding and cascading disruptions that extend far beyond direct conflict zones.
This study employs a global three-stage cascade network model to quantify food security vulnerabilities for eleven critical staple crops across countries and political-military-economic blocs. We model sequential disruptions in natural gas trade, the key pre-cursor for nitrogen fertilizer production, trade in fertilizer which in turn reduces crop production capacity, and trade in food products. Using spatially explicit shock response coefficients, we calculate production losses at each cascade stage and aggregate results by country and defined blocs.
Our findings reveal pronounced regional disparities in agronomic supply chain dependency and vulnerability. The Persian Gulf region depends almost exclusively on crop imports, while the Global South relies on crops and potassium fertilizers. The EU and G7 face primary vulnerability to natural gas supply disruptions, whereas Latin America is critically dependent on nitrogen fertilizer imports. African nations are exposed to both direct food import disruptions and potassium fertilizer scarcity. Simulated trade disruptions project regional crop availability losses ranging from 0 to 70 percent, with severe humanitarian implications. We find that in a fragmented world, countries are generally better off participating in alliances where trade supposedly persists and where there is more support from other members in case of an emergency. Critically, no country is immune to food security collapse regardless of development status; already vulnerable countries with existing food insecurities will be disproportionately affected, creating humanitarian emergencies requiring coordinated anticipatory response with long-term consequences for global stability.
How to cite: Kiparisov, P. and Folberth, C.: Quantifying global and regional food crises through cascade modeling of supply fragmentation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21269, https://doi.org/10.5194/egusphere-egu26-21269, 2026.
Cities are increasingly at the forefront of climate change impacts, particularly as extreme heat intensifies and spreads across the globe. At the same time, transnational city networks such as ICLEI have emerged as key actors in urban climate governance, yet it remains unclear how environmental risk, economic capacity, and historical connectivity shape participation in these networks. We start from an in-depth investigation of the development of extreme hot summers in different regions of the world, the geographic spread of the world’s population, the localization of populated and urban regions, and the membership of city governments in ICLEI. Utilizing observations of extreme hot summers from 1990-2020, this study provides a large-scale, long-term retrospective on city engagement in transnational climate governance and contributes to discussions on how climate extremes shape the global development of urban climate networks. Using ERA5 reanalysis data on hot summer extremes alongside contextualizing social data on the global population density, ICLEI member city locations, and World Bank GDP data, we analyze spatial and temporal patterns of network developments.
We find that early ICLEI membership was concentrated in economically resourced and historically connected cities in Europe and North America, while later expansion increasingly reached cities in regions experiencing high absolute and intensifying hot summer extremes, including parts of West and Southern Africa. Our results further show that regional clustering and local diffusion play a central role in network expansion, with membership often spreading from early adopters to neighboring cities. Overall, the findings highlight how transnational urban climate governance emerges at the intersection of climate exposure, economic resources, and existing relationships.
The contribution bridges geoscience and social sciences by mapping geospatial and temporal climate data and data on the ICLEI network, contextualized with economic data. Importantly, our approach transcends outsourcing climate observation and reanalysis by engaging in deep interdisciplinary collaboration to gauge how changes in the network are aligned with climate extremes. It aims to take up geoscientific contributions into the theoretic thought of social scientific thought, providing a basis for an assessment that recognizes the natural environment as a factor in the social, economic, and political developments – such as the management of sustainability-oriented networks.
How to cite: Feuerlein, L., Gotthardt, D., Borchert, L., Wallenhorst, H., Wolf, L., Sillmann, J., and Oberg, A.: When do city networks cover regions prone to hot summer extremes? The ICLEI network case, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21914, https://doi.org/10.5194/egusphere-egu26-21914, 2026.
Flood impacts are increasing globally due to growing exposure and climate variability, placing pressure on traditional disaster risk reduction (DRR) approaches such as structural flood protection and post-event humanitarian response. In this context, disaster risk finance (DRF) instruments, including parametric insurance and catastrophe bonds, are increasingly explored as complementary tools to support rapid response and recovery.
While parametric approaches have gained traction for hazards such as earthquakes and tropical cyclones, flooding poses particular challenges for DRR applications due to its spatial heterogeneity and the complex relationship between rainfall, inundation, and impacts. These challenges are often expressed through basis risk, where modelled triggers do not align with experienced losses, undermining trust and effectiveness.
Drawing on more than five years of applied work supporting DRF initiatives, we will reflect on practical lessons from developing flood parametric insurance solutions under data-sparse conditions. Using a rainfall-based parametric insurance scheme for Pacific Island nations as a case study, we will examine the end-to-end workflow linking hazard data, event set generation, trigger definition, and payment certification, with particular attention paid to how uncertainty is managed and communicated.
The case study illustrates how choices around input datasets, spatial scale, exposure representation, and local climate characteristics shape basis risk, and how these trade-offs can be made explicit to stakeholders. We will show that, while flood parametric insurance remains challenging, advances in hazard modelling and analytical workflows are improving its viability as a DRR instrument when designed with an explicit focus on uncertainty and user needs.
We will conclude by discussing how insights from frontline implementation can inform the design of parametric instruments that support disaster preparedness, response, and climate adaptation
How to cite: Maisey, P. and Bast, H.: Opportunities and challenges in developing flood parametric insurance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5600, https://doi.org/10.5194/egusphere-egu26-5600, 2026.
Rapid structural damage assessment is critical for life-saving decision-making in the first hours following sudden-onset disasters, yet operational Urban Search and Rescue (USAR) teams must act under severe constraints: limited ground truth, disrupted connectivity, evolving situational awareness, and the need to justify prioritisation decisions in real time. In parallel, the remote sensing community has been a key part to providing initial information for early decisions. There is a rapidly expanding ecosystem of damage-mapping methods, including deep learning approaches and foundation models providing new opportunities. Their operational suitability for humanitarian response in terms of speed, uncertainty communication, and incremental updating still needs assessment and development for many of these methods.
We present an operationally driven evaluation and system design for post-disaster structural damage assessment using multimodal information streams. The study leverages building-level damage assessment datasets collected across multiple disasters and contexts, including the Beirut explosion (2020), Haiti earthquake (2021), Türkiye-Syria earthquake (2023), and the Myanmar-Thailand earthquake (2025). We compare and integrate methods spanning classical change detection, learning-based approaches, and multimodal fusion, with a focus on workflows that can ingest heterogeneous evidence (optical imagery, SAR products, and in-situ observations) and update outputs as new information becomes available during response.
Our proposed system is designed around the realities of humanitarian operations: generating actionable outputs at the speed required for USAR sectorisation and reconnaissance planning, while explicitly representing uncertainty to support accountable decision-making. We demonstrate how combining remote sensing modalities with sparse on-the-ground observations improves the timeliness and reliability of damage estimates. The results highlight that operational performance depends not only on predictive accuracy, but also on robustness under label scarcity, interpretability for non-specialist users, and the ability to revise assessments as the response evolves.
How to cite: Selvakumaran, S., Ma, W., Lammoglia Cobo, M. F., Thomas, D., He, N., and Marinoni, A.: Multimodal, uncertainty-aware structural damage assessment for post-disaster Urban Search and Rescue (USAR) decision-making , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21653, https://doi.org/10.5194/egusphere-egu26-21653, 2026.
