This session invites interdisciplinary contributions, bringing together geoscientists, social scientists, economists, and policy experts to examine the complex and often compounding interactions between social inequalities and climate hazards such as floods, heatwaves, droughts, storms, landslides, and wildfires across different scales, including within countries, between countries, and across continents.
Topics of interest include (but are not limited to):
-Case studies illustrating how environmental and social inequalities intersect.
-Types of inequality: social, gender-based, infrastructural, recovery time, education, income source, wealth distribution, climate justice, food security
-Impacts of climate hazards: displacement, fatalities, psychological and physical health, developmental setbacks.
-Long-term recovery challenges: absence of recovery, prolonged recovery periods, slower developmental trajectories.
-Historical and political-ecological perspectives on disasters and their long-term societal impacts.
-Innovations in data, metrics, or methods (e.g., AI, remote sensing, socio-environmental modelling) for assessing inequality and disaster risk across spatial and temporal scales.
Orals: Mon, 4 May, 14:00–15:35 | Room -2.31
Water-related disasters not only directly lead to loss of life and property, but also entrench poverty, widen disparities, and hinder the accumulation of human capital such as education and health, posing a long-term threat to people's livelihoods. The impact is not uniform: the more vulnerable the group, the greater the damage and the slower the recovery. Those lacking assets and social capital, in particular, have been found to recover more slowly, even from disasters of the same scale. Ultimately, this leads to increased poverty and inequality.
In recent years, the number of water-related disasters worldwide has increased due to the impacts of climate change and other factors, accompanied by growing economic losses. Against this backdrop, there has been a growing trend to shift the focus of assessments from the traditional emphasis on 'costs of damage and loss' to 'restoring livelihood opportunities and socio-economic activities'. Therefore, it is essential to consider not only the direct damage and loss caused by water-related disasters, such as housing destruction, road inundation, farmland damage and asset loss, but also their medium- to long-term socioeconomic effects, such as widening inequality and the intergenerational entrenchment of poverty. New measurement and evaluation methodologies must also be pioneered.
We argue that 'water-related disasters cause direct damage and loss and can also contribute to the widening of socioeconomic inequality, particularly in lower-middle- and low-income countries'. Based on the hypothesis that 'appropriate flood protection investments as climate adaptation measures can contribute to mitigating damage and loss, as well as reducing disparities and strengthening social resilience', we have conducted extensive research and development. This presentation introduces the following: (1) an empirical analysis of the impact of floods on poverty and economic inequality; and (2) an evaluation of climate adaptation measures that enhance social resilience, with a focus on the long-term socioeconomic spillover effects of flood protection investments.
How to cite: Kawasaki, A.: Reducing poverty and inequality and enhancing social resilience through flood protection investment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3161, https://doi.org/10.5194/egusphere-egu26-3161, 2026.
Climate change is characterised by systemic differences between those who drive greenhouse gas emissions and those who experience the greatest impacts. These differences unfold across three interconnected dimensions: the sources of emissions, the unequal distribution of climate hazards, and the discrepancies in vulnerability of specific socioeconomic groups. While attribution science has traditionally linked cumulative anthropogenic emissions to changes in climate hazards, recent advances in source attribution and impact-oriented approaches are now connecting emissions from specific actors to particular hazards and, increasingly, to their associated societal consequences.
Here, we outline how computationally efficient climate modelling tools, such as emulators, expand the scope of source attribution by enabling the exploration of counterfactual climates at scale. This flexibility allows a systematic assessment of how normative assumptions shape attribution outcomes, for example by comparing multiple “emitter lenses” - such as consumption-based versus production-based accounting - each associated with distinct policy instruments and governance contexts.
We illustrate these perspectives using a recent work that attributes present-day extremely hot and dry months to 1990-2020 emissions by income groups, finding that high-income groups disproportionately contributed to the emergence of climate extremes worldwide [1], alongside a complementary study that attributes observed extremes to emissions from fossil fuel and cement producers using event attribution frameworks [2]. Together, these examples highlight how methodological choices and attribution lenses influence quantitative estimates, as well as the challenges associated with moving from carbon accounting to climate accountability.
Exploring the “multiverse” of counterfactual climates can enhance transparency in climate justice debates and support the integration of diverse socioeconomic perspectives into decision-making and legal processes.
[1] Schöngart, S., Nicholls, Z., Hoffmann, R., Pelz, S., & Schleussner, C. F. (2025). High-income groups disproportionately contribute to climate extremes worldwide. Nature Climate Change, 1-7.
[2] Quilcaille, Y., Gudmundsson, L., Schumacher, D. L., Gasser, T., Heede, R., Heri, C., ... & Seneviratne, S. I. (2025). Systematic attribution of heatwaves to the emissions of carbon majors. Nature, 645(8080), 392-398.
How to cite: Schöngart, S., Nicholls, Z., Hoffmann, R., Pelz, S., Quicaille, Y., and Schleussner, C.-F.: From carbon accounting to climate accountability: Navigating a multiverse of counterfactual climates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7383, https://doi.org/10.5194/egusphere-egu26-7383, 2026.
Direct and indirect impacts of floods on drinking water services threaten to increase risks of disease outbreaks and may lead to development setbacks in low-resource settings. Especially in complex-emergency settings where data collection remains challenging and infectious disease burdens high, urban flood vulnerabilities are still poorly understood. Our study contributes to addressing this gap by combining various data sources to assess vulnerabilities of the water supply system in the face of extreme flooding in the town of Uvira, located in South Kivu in the Democratic Republic of Congo. Uvira is a town of an estimated 280,000 inhabitants (in 2020), illustrative of a complex emergency setting with limited access to basic drinking water and sanitation and a high cholera disease burden. The town experiences distinct wet and dry periods and is situated on a hilly terrain along the shore of Lake Tanganyika with five rivers flowing through it. In April 2020 the city experienced a catastrophic flood event which affected around 80 000 people and destroyed critical water infrastructure. In this study we used three complementary approaches to study flood events and related drinking water service vulnerability in Uvira: (1) we mapped the extent of three flood events, including the April 2020 event, using high-resolution optical images and open-access optical and synthetic aperture radar (SAR) data from Sentinel-1 and Sentinel-2; (2) we overlaid maps of the water supply infrastructure to identify system exposures to flooding; (3) we carried out a survey-based rapid assessment of 148 households 12 weeks after the April 2020 flood focused on drinking water-related practices. Preliminary results from flood mapping and household survey analysis suggest that households were exposed to flooding in nine out of fourteen districts, mostly in the vicinity of rivers. Critical points of the piped drinking water system affected by the flood included the main water intake for the water supply network, located on the Mulongwe river, which was destroyed and led to a 6-week disruption of the entire drinking water supply service. Around half of the survey participants reported having changed their drinking water source after the April 2020 flood. Despite regular interruptions of water services, storage capacities within households were modest at the time of the survey (median =22L per person). Results from flood extend mapping leveraging open access satellite image data from Sentinel-1 and 2 as well as high resolution optical data before and after extreme flood events, will complement these findings by highlighting neighbourhoods and water collection points which were most severely exposed to the 2020 flood event as well as to two smaller flood events in December 2020 and April 2024 in Uvira. As such, our results demonstrate the feasibility of organising remote research in complex-emergency settings by leveraging electronic data collection tools and satellite data to gain insights into flood vulnerabilities of drinking water services in resource-limited settings. Our results may be used to inform measures for strengthening the resilience of drinking water services in low-resource, data-scarce urban communities in a global context of increasing exposure to extreme flooding.
How to cite: Greenwood, E. E., Isundwa, F. K., Mufitini Saidi, J., Shetebo, J., Azman, A., Cumming, O., and Gallandat, K.: Understanding vulnerabilities to extreme flooding along the drinking water supply chain in an urban, complex-emergency setting: Analyses of satellite imagery, water utility, and household survey data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17379, https://doi.org/10.5194/egusphere-egu26-17379, 2026.
Flood-risk assessments increasingly consider how flood risk is distributed across populations. However, future flood risk is subject to a number of uncertainties related to flood hazard, exposure, vulnerability, and human response, which are often not fully considered in such assessments. These uncertainties can be amplified by the finer scales required for distributional analyses. To better understand which uncertainties are relevant for distributional impacts, we perform a large-scale uncertainty characterization experiment using a calibrated agent-based model over the course of a multi-decadal simulation. We find that failing to account for key uncertainties, particularly related to flood damage estimation and human response, results in major biases in future flood losses and recovery. Furthermore, the relative importance of these uncertain factors vary depending on the population of interest. For example, we find that behavioral risk factors towards flooding are the most influential in shaping high-income population recovery, but factors related to housing preference and affordability are the most influential in shaping low-income recovery. Our results highlight the need to systematically account for multiple sources of uncertainty to better understand the distribution of flood risks.
How to cite: Bhaduri, P., Pollack, A., Daniel, B., and Srikrishnan, V.: Neglecting Human Response Leads to Biased Distributional Flood Risk Outcomes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14269, https://doi.org/10.5194/egusphere-egu26-14269, 2026.
Water security has emerged as a central challenge in the context of escalating climate change and rising socioeconomic inequalities. Its scope has expanded beyond the physical availability of freshwater incorporating multiple dimensions. River basins serve as fundamental natural units for understanding and managing these interconnected dimensions, offering a critical lens through which basin level vulnerabilities and inequalities can be assessed. However, sociohydrological perspectives that capture the interactions among these drivers remain underexplored, and traditional approaches that rely on static thresholds often fail to account for evolving climate induced and socioeconomic pressures. This study investigates the multidimensional nature of water security across major global river basins using an unsupervised machine learning framework. The framework classifies river basins into distinct spatial management units based on water security metrics. The resulting clusters reveal unique combinations of vulnerabilities, reflecting differences in exposure to climate hazards, ecological conditions, and socioeconomic inequalities. The findings highlight that global river basins experience disturbances and stressors at multiple levels, driven by both natural and human systems. The analysis uncovers spatial patterns of similarity and differences, demonstrating how multiple dimensions jointly shape basin level water security. These insights provide a basis for more targeted, equitable, and resilience focused water management strategies. The outcomes support policymakers and stakeholders in designing basin specific interventions that strengthen water security under increasing climate and societal pressures.
How to cite: Sreeshna, T. R., Wei, Y., Patil, R., Banad, S., and Dhanya, C. T.: Integrated Multidimensional Water Security Framework for Classifying Major Global River Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-563, https://doi.org/10.5194/egusphere-egu26-563, 2026.
Climate change has increasingly been recognized as deepening social inequality, as responsibility for greenhouse gas (GHG) emissions and vulnerability to climate-related impacts are unevenly distributed across populations. While recent research highlights the growing importance of intranational climate inequality, quantitative evidence remains limited in South Korea.
Climate inequality encompasses a broad range of interpretations. In this study, climate inequality refers to the disparity between climate change-induced risks and GHG emissions. Among various climate-related hazards, this study focuses on flood risk as a major and recurring urban threat in South Korea.
For flood risk assessment, the IPCC framework was applied. Indicators for vulnerability and sensitivity indices were selected through a review of prior studies and weighted using a combination of Principal Component Analysis (PCA) and the Entropy method. The hazard index was estimated from historical flood inundation maps, with vulnerability and sensitivity indices constructed using key socioeconomic, housing, and built-environment indicators.
For the assessment of GHG emissions, emission values at the individual building level were estimated using data from a limited number of buildings with available emission information. Ordinary Least Squares (OLS) regression was applied to estimate GHG emissions for the remaining residential buildings.
Flood risk and estimated GHG emissions were aggregated and compared at the administrative dong level—the smallest local administrative unit in South Korea—and the resulting gap was defined as climate inequality in this study. The results reveal a pattern of climate inequality within Seoul: socially vulnerable areas are more exposed to flood risks exacerbated by climate change, whereas wealthier areas contribute disproportionately to GHG emissions. By empirically demonstrating the existence of climate inequality in South Korea, this study provides a foundational framework for future research on climate inequality.
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime.", funded by Korea Ministry of Climate, Energy and Environment (MCEE) (RS-2022-KE002123).
How to cite: Kim, S. R., Kim, Y., Woo, C., Jang, Y., and Jeon, S. W.: Spatial Analysis of Climate Inequality in Seoul, South Korea: A Focus on the Disparity Between Urban Flood Risk and Greenhouse Gas Emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9065, https://doi.org/10.5194/egusphere-egu26-9065, 2026.
To systematically investigate exposure differences among social groups to urban flooding, this study focuses on the central urban area of Shanghai. Using the TELEMAC-2D two-dimensional hydrodynamic model, this study simulate flooding processes under rainfall events with return periods of 20, 50, and 100 years. We extract maximum water depth and flow velocity and combine these parameters with flood hazard indicators to delineate flood risk zones and examine the spatial expansion of flooding under different scenarios. Then 100-m resolution population grid data and residential property price information are integrated to quantitatively assess flood exposure from three perspectives: total population, the elderly population aged 65 and above, and socio-economic groups at different income levels. This analysis emphasizes the non-uniform distribution of social group exposure under extreme rainfall conditions. Furthermore, we construct an integrated vulnerability index by applying the entropy weight method to flood hazard intensity, elderly population exposure, and economic vulnerability. The index characterizes the spatial pattern of vulnerability risk and its dynamic evolution in response to increasing rainfall intensity. The results indicate that both the extent of high-risk areas and the size of the exposed population increase markedly with longer rainfall return periods. Elderly populations exhibit a pronounced amplification of exposure within high-risk zones. Under certain flooding scenarios, areas with relatively high economic status still display significant clustering of vulnerability risk. Overall, the findings demonstrate that urban flood risk is strongly differentiated across social groups. These results provide scientific support for equity-oriented urban flood risk management and targeted protection strategies for vulnerable populations.
How to cite: Ma, F.: Exposure Inequality and the Evolution of Social Vulnerability to Urban Flooding under Multiple Rainfall Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2421, https://doi.org/10.5194/egusphere-egu26-2421, 2026.
Recovery from extreme events remains poorly understood, yet it critically shapes long-term development opportunities. Especially, if the recovery from an extreme event is still ongoing when a subsequent disaster strikes, potentially causing poverty traps. This may become more likely with more intense and frequent extreme events under climate change. Unequal pre-disaster conditions may influence post-disaster recovery capacities and associated inequalities. In this work, we employ an agent-based model that explicitly resolves household-level recovery dynamics to assess the distributional effects of tropical cyclones under different warming scenarios, accounting for changes in cyclone intensity and frequency. The model is constrained using empirical insights from observed changes in nighttime light intensity after historical tropical cyclones, allowing us to link hazard intensity to recovery times across income groups. We drive the agent-based model with future tropical cyclone time series derived from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).We assess asset damage, consumption losses, and well-being losses across income groups and countries. Our results show that longer recovery times among low-income households amplify inequality, particularly in terms of well-being losses. Depending on national hazard and income distributions, patterns of poverty risk arising from incomplete recovery vary across countries and warming levels. Our observationally constrained modeling framework enables the explicit incorporation of recovery processes into both historical impact assessments and future risk analyses, resolving losses across different income groups. Moreover, the framework is transferable beyond tropical cyclones to other capital-destroying hazards, such as floods.
How to cite: Sauer, I., Zhang, Q., Quesada Chacón, D., and Otto, C.: Disparities in recovery capacity amplify inequality under consecutive extreme events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15131, https://doi.org/10.5194/egusphere-egu26-15131, 2026.
The Copenhagen Accord provided the first actionable construct to mobilize climate finance by providing a quantitative figure of USD 100 billion and delivery timeline of 2020 (later extended to 2025). The donor-pool claimed that the goal was met in 2022, however, the finance provision has been widely debated for its unsuitable quality that does not meet contextual needs. While any progress towards climate finance provision is praiseworthy, the recipients must assess the assistance received for its alignment with country’s communicated needs and key decisions on climate finance. This article explores the attributes of climate finance committed to Pakistan, a developing and climatically vulnerable economy, heavily reliant on international climate finance to meet its adaptation and mitigation targets. The study uses OECD data on climate finance owing to its comprehensive activity level donor-reporting, coverage of the entire delivery period, and widespread use within global reporting and scholarly investigations concerning climate finance. The assessment finds that USD 12.53 billion in climate finance were committed to Pakistan during 2010-2022, funneled majorly by multilateral institutions, showcasing significant yearly imbalances between adaptation and mitigation proportions, 83% extended as debt, and energy sector attracting most finance while other priority sectors of the country received lesser. The country's assessment highlight a broader pattern whereby climate finance extended is not only insufficient but also burdensome as well as misaligned with the charcteristics mentioned within negotiations. Therefore, inequalities faced in the global South worsen as the sources to build resilience are often lacking and a significant amount of resources repay the debts incurred, ironically, through the provision of climate finance. We argue, that Pakistan models the very recepient for whom climate finance is intended. The country experiences intensifying climatehazards, from floods to heatwaves - yet the resources meant to meet resilience needs are insufficient and contextually non-responsive to needs and priorities - highlighting a classic example of worsening inequalities
How to cite: Irfan, K., Khayyam, U., Hashmi, Z. U. R., and Saeed, F.: Climate Finance Committed to Pakistan Under the USD 100 Billion Goal of the Copenhagen Accord., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21540, https://doi.org/10.5194/egusphere-egu26-21540, 2026.
This study investigates how educational attainment influences temperature-related mortality among the elderly across Belgian provinces, addressing a critical gap in understanding climate-related health inequalities. While climate change poses a major global health threat, evidence on socioeconomic disparities in temperature-mortality associations remains limited. Educational attainment can shape vulnerability through multiple pathways: enhanced cognitive skills improve risk assessment and adaptation, and higher socioeconomic status enables protective investments. Prior research suggests that lower-educated populations face greater risks, though findings vary across contexts.
Previous research has shown that different regions in Belgium exhibit distinct mortality patterns, influenced partly by individual socioeconomic status but also by regional socioeconomic conditions and environmental factors. Using Belgian mortality data spanning from 2000 to 2019 and a two-stage meta-regression framework, we examine temperature-mortality relationships across two models: age and education stratification. The analysis focuses on individuals aged 65 and over across 11 provinces, distinguishing between low, secondary, and superior education levels. We use meta-predictors at the provincial level to identify underlying socioeconomic and environmental factors that drive geographic variations in temperature-mortality vulnerability, moving beyond individual-level characteristics to capture contextual determinants of climate health inequalities.
Results reveal strong age gradients consistent with existing literature, with adults aged over 85 experiencing substantially higher temperature-related mortality than younger elderly groups. Educational gradients are also observed as expected, with the lowest educated populations showing higher overall risk, though these effects remain statistically uncertain due to wide confidence intervals at the highest and lowest temperature percentiles. Given the temperature distribution, cold-related mortality predominates across all groups, though risk is higher at warmer temperatures. Regional patterns emerge in line with prior findings, with southern provinces generally showing higher excess mortality than northern areas, confirming the anticipated north-south divide in temperature-related mortality vulnerability.
The analysis will be extended by incorporating additional years of mortality data and additional metadata to better capture vulnerability factors. Furthermore, patterns will be examined at smaller geographical units to identify localized disparities in temperature-related mortality risk. To address the changing nature of education and potential cohort effects, educational attainment differentials will be used to ensure appropriate interpretation of educational disparities across cohorts. The measured relative risks will be used to project changes in mortality under different Shared Socioeconomic Pathways, enabling assessment of future climate change impacts on vulnerable populations.
How to cite: Kuijt, E.: Degrees of inequality: How educational attainment shapes mortality associated to non-optimal temperatures in different provinces of Belgium., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-535, https://doi.org/10.5194/egusphere-egu26-535, 2026.
Floods are the world’s most frequent and damaging natural hazard, and their impacts are projected to intensify under climate change. Yet the relationship between economic flood damage (EFD), greenhouse gas emissions, and economic development remains poorly quantified in global climate-justice debates. Here, we analyse 2,032 flood events across 132 countries (1990–2022) to assess disparities between direct tangible flood losses, historical CO₂ emissions, and GDP. We show that South and Southeast Asia experience a disproportionate share of global EFD, despite contributing minimally to cumulative emissions and having comparatively weak GDP, revealing pronounced inequities in the distribution of climate-related losses.
We evaluate inequality by linking EFD to the GINI index, finding that high-inequality regions (e.g., South America, Sub-Saharan Africa) consistently exhibit elevated EFD. Using negative binomial regression, we quantify the influence of CO₂ responsibility and economic capacity on flood losses. Building on the principle of Common But Differentiated Responsibilities and Respective Capabilities (CBDR-RC), we propose a dual-threshold framework based on (1) historical CO₂ emissions per capita and (2) average GDP per capita. This yields a transparent mechanism for a flood-focused Loss and Damage Fund (LDF).
Our results indicate that 59 countries should be eligible for LDF support, including 100% of LICs, and that 38 countries—primarily high-income and OECD members—should be prioritised as fund contributors. We identify an additional 35 “grey-zone” countries whose rising GDP and emissions challenge static interpretations of climate responsibility.
This study provides the first global, event-level assessment linking flood damages to equity and historical responsibility. It offers a reproducible methodology and a policy-ready framework to operationalise climate justice in loss-and-damage finance, strengthening the scientific basis for negotiations at COP and informing equitable global adaptation strategies.
How to cite: Eudaric, J., Camero, A., and Kreibich, H.: Climate justice and economic flood damage in the Anthropocene, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1523, https://doi.org/10.5194/egusphere-egu26-1523, 2026.
Ecosystem services (ES) are increasingly recognized as critical natural capital for achieving the United Nations Sustainable development goals (SDGs). However, a significant gap remains in translating the understanding of ES-SDG relationships into actionable, spatially explicit strategies, particularly at large scales and over extended periods. Addressing this gap, our study provides a long-term (2000-2020), high-resolution (1 km) national assessment for China, analyzing the interlinkages between five key ecosystem services and the progress of 16 SDGs (excluding SDG 14). We found a general upward trajectory in SDG achievement across China over the 21-year period, with ES demonstrating a significant positive influence on SDG progress. Notably, net primary productivity (NPP) and grain production were the ES with the strongest effects on SDG scores. While the local effect of ES on SDGs was predominantly positive, a spatial mismatch between the supply of and demand for ES was observed at broader scales, moderating these benefits. Our analysis further indicates that China's current ecological conservation zones do not sufficiently protect areas supplying high-value ES critical for SDG attainment. We propose a spatial optimization approach to identify these key zones, offering a strategy to enhance the effectiveness of ecological policy and resource allocation. This study underscores the necessity of integrating multi-scale ES assessments into sustainability planning to bridge the gap between ecological potential and development outcomes.
How to cite: Liu, F.: Ecosystem services are critical for advancing the progress of sustainable development goals in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3662, https://doi.org/10.5194/egusphere-egu26-3662, 2026.
Fragile and conflict-affected regions face overlapping shocks, from displacement and market instability to escalating climate extremes, that continue to deepen food and nutrition insecurity. The combined effects of protracted conflict, economic collapse, and the breakdown of essential services have intensified humanitarian needs while restricting access to those most affected. Addressing these challenges requires integrating innovative data sources and analytical tools, such as Earth Observation (EO) products, to fill critical information gaps and support evidence-based decision-making in fragile and hard-to-reach contexts.
On this premise, the European Space Agency’s European Resilience from Space (ERS) programme, through the Smart Connect project, supports UNICEF in developing a near-real-time, spatially detailed early warning system to monitor short-term malnutrition risk. The system produces monthly outputs in the form of Severity Nutrition Index (SNI) maps, including six consecutive one-month-ahead forecasts. Specifically, every month the SNI is calculated at the administrative level 2 for every subnational unit as a composite 0–1 score summarizing overall nutrition risk.
The main innovation of this work lies in the proposed risk-based methodology, that integrates large volumes of data from diverse sources to capture the key drivers and dynamics influencing nutritional conditions.
The model is organized into four thematic modules: Climate & Environmental, Socio-Economic, Conflict & Displacement, and Health & Nutrition. Each module is implemented through a multi-dimensional framework. For example, the Climate & Environmental module includes three dimensions: agriculture, livestock, and water availability. Within each dimension, the model calculates (1) a main factor representing the baseline condition, (2) an impact factor capturing stressors, (3) a temporal component reflecting the persistence of previous months, and (4) a dynamic weight that adjusts to emerging conditions. This hierarchical and modular architecture allows customized assessments across domains, ensuring coherence across diverse contexts. Moreover, its scalable design facilitates replication in other fragile settings.
The robustness of the approach is reflected in its use of reliable and accessible datasets, demonstrating how Earth Observation products can be effectively combined with socio-economic, conflict, health and basic nutrition data to produce simple 0–1 score at the subnational level, where higher values indicate worse conditions.
For testing and validating the result, Sudan was selected as the primary use case since recent reports are indicating that nearly half of the population is facing high levels of acute food insecurity.
For the Sudan use-case, the SNI has demonstrated its ability to highlight emerging malnutrition risk zones with sufficient lead time to inform early action and guide targeted assessments. Validation against available food security and nutrition datasets confirms its value as a relative early-warning measure, while recognizing that it is not an absolute prevalence indicator due to persistent data gaps and spatial inconsistencies. Despite these limitations, the Index offers a systematic, data-driven approach for monitoring nutrition risk in fragile and conflict-affected contexts and is designed to complement, rather than replace, existing analytical products and situation reports.
How to cite: Bellotto, K., Suskova, J., Bojor, A., Welscher, F., Panta, N., Mathieu, P. P., and Natali, S.: Early Warning Maps: Predicted Nutrition Severity in Fragile and Conflict-Affected Contexts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5700, https://doi.org/10.5194/egusphere-egu26-5700, 2026.
Flood risk disproportionately impacts socially and economically marginalized households, creating feedback loops that reinforce cycles of poverty and limit long-term resilience. Most flood risk management strategies have traditionally focused on understanding the physical drivers of flooding thereby limiting the risk mitigation to structural flood protection measures, which have in-turn resulted in unintended consequences like levee effect. While socio-hydrological assessments of risk and vulnerability indicators exist, most studies assume the exposed populations to be behaviourally homogeneous, thereby failing to explain how flood risk is persisted, redistributed, and entrenched across different sections of the society. The current study addresses this gap by simulating the migration decisions of nearly 100,000 households in an agent-based model, conditioning agent behaviour on socio-economic backgrounds to capture divergent pathways of migration, in-situ adaptation, and long-term risk persistence. The households are classified into four behavioural archetypes grounded in critical socio-economic indicators including social stratification, asset ownership, income source and literacy.
Our analysis reveals the ‘flood exposure trap’ is driven by the intersection of resource constraints and behavioural immobility. The least mobile groups remain critically exposed, experiencing prolonged entrapment in high-hazard zones for over a decade of repeated flood events. These households absorb cumulative losses that further erode their capacity to recover, effectively locking them into a cycle of poverty. In contrast, high-mobility groups successfully reduce their exposure under historical flood conditions by relocating; however they fail to prevent escalated flood exposure under unprecedented, climate change-driven extremes. Thus, proactive migrants eventually face renewed exposure as hazard magnitudes exceed historical precedents. The results indicate that long-term flood resilience is not merely a function of hazard intensity, but is fundamentally governed by social inequality and behavioural heterogeneity. Our work emphasizes the need for equity-sensitive flood risk management strategies that explicitly account for the heterogeneous behavioural constraints of vulnerable populations.
How to cite: Singh, A., Dawson, R., and Dhanya, C. T.: Socio-economic Inequality and Behaviour Heterogeneity Drive the Flood Exposure Trap, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7622, https://doi.org/10.5194/egusphere-egu26-7622, 2026.
Climate hazards systematically intersect with and amplify pre-existing socioeconomic inequalities, producing uneven patterns of exposure, impact, and recovery that undermine progress toward the Sustainable Development Goals (SDGs). This study presents a quantitative, geospatial assessment of climate–inequality interactions in the climate-sensitive districts of Gaya, Arwal, and Aurangabad in Bihar, India, where recurrent droughts, heat extremes, and episodic flooding disproportionately affect marginalized populations.
An integrated analytical framework combines long-term climate records (1981–2022), satellite-derived indicators (MODIS land surface temperature and NDVI, drought and flood exposure metrics), and disaggregated socioeconomic data capturing income source, landholding size, education, gender, infrastructure access, and food security. Climate hazard dynamics are quantified using standardized drought and heat indices and extreme-event frequency analysis, while multidimensional inequality is represented through a GIS-based Socio-Climate Vulnerability Index developed using multi-criteria decision analysis. Results show a statistically significant increase in drought frequency across all districts (Sen’s slope ≈ 0.02–0.03 yr⁻¹, p < 0.05) and a rise in mean growing-season land surface temperature of 0.9–1.3 °C. Spatial hotspot analysis indicates that 35–45% of high-exposure zones overlap with areas characterized by low income, small or landless holdings, and limited infrastructure.
Households in high-vulnerability clusters experience 20–30% lower yield stability, 15–25% higher food insecurity prevalence, and recovery periods that are on average 1.5–2 times longer than district means following major drought or heat events. Repeated exposure to climate extremes is associated with persistent developmental deficits, including reduced livelihood diversification and adverse health outcomes. By integrating remote sensing, spatial statistics, and socio-environmental modelling, this study provides novel, scalable metrics for quantifying climate justice and inequality. The findings underscore the urgency of equity-centered climate adaptation and disaster risk reduction strategies tailored to structurally disadvantaged regions.
How to cite: Kumar, A. and Pathak, K.: Quantifying Climate–Inequality Interactions under Recurrent Hazards: A Geospatial Assessment of Socio-Climate Vulnerability in Bihar, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7709, https://doi.org/10.5194/egusphere-egu26-7709, 2026.
Drought is a major natural hazard in arid and semi-arid regions, where strong dependence on rainfed agriculture amplifies socio-economic vulnerability and population exposure. Effective drought risk reduction requires an integrated assessment of hazard, vulnerability, and exposure. However, such comprehensive drought risk analyses remain limited for Iran.
In this study, we present a spatio-temporal drought risk evaluation across Iran for the period 2000–2019 using a multi-component natural hazard framework. Drought hazard is characterized using the Standardized Precipitation Evapotranspiration Index (SPEI), while drought vulnerability is quantified through integrating socio-economic and demographic indicators. The likelihood of drought has risen in 57% of Iran's territory, particularly in the northwest, west, and central areas, with an annual increase of up to 10%. In 21% of Iran's territory, the risk of drought has decreased by as much as 10% annually, mainly in the northern and southern parts of the Alborz Mountains, which include the provinces of Tehran, Gilan, Mazandaran, and Khorasan Razavi. Our findings indicate that the spatial distribution of drought risk varies throughout Iran and is influenced by the interplay of climatic and socioeconomic factors.
The findings of this study provide valuable insights that can inform the development of effective strategies for managing and mitigating drought risk in Iran.
How to cite: Rastgoo, P., Torkaman Pary, A., Moradi, A., Zeuss, D., and Alemayehu Abera, T.: Integrated approach for spatio-temporal drought risk evaluation in Iran, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10699, https://doi.org/10.5194/egusphere-egu26-10699, 2026.
Urban flooding is increasing worldwide due to the combined effects of climate change driven extreme precipitation and rapid urbanization. Flood impacts within cities exhibit strong spatial heterogeneity, yet most existing urban flood models remain highly complex and computationally demanding, limiting their applicability for targeted risk assessment and early warning in urban governance. In practice, decision-makers increasingly require refined simulations focusing on high-risk and high-impact scenarios, such as underpasses, residential communities, underground garages, metro systems, vulnerable buildings, and urban reservoirs.
To address this gap, we present the Urban Flood Intelligent Model (UFIM), a community-scale urban flood modelling software specifically designed for fine-scale flood simulation and early warning in critical urban environments (https://www.antmap.net/web/ufim-en/). UFIM integrates high-resolution topographic data with a dynamic real-time 1D-2D coupled hydrodynamic framework, explicitly accounting for drainage network and surface interactions and backflow processes. Flexible coupling strategies allow both loosely and tightly coupled configurations, enabling realistic representation of complex urban drainage and surface flow dynamics while maintaining computational efficiency. UFIM supports heterogeneous rainfall inputs, multiple infiltration schemes, diverse outlet boundary conditions, and grid-based surface roughness parameterization. The model is implemented with a user-oriented interface, predefined parameter sets, and advanced visualization tools, lowering the technical barrier for operational use. In addition, UFIM offers cross-platform compatibility (Windows/Linux), rapid deployment via Docker, seamless GIS integration, and AI-assisted diagnostics for model performance evaluation and optimization.
UFIM has been extensively tested across multiple urban scenarios, including residential communities, functional zones, and complex mixed-use areas, under both observed extreme rainfall events and design storms with different return periods. Validation results demonstrate stable long-term simulations and consistently high predictive performance, with inundation detection accuracies exceeding 85% across tested applications.
These results indicate that UFIM provides a robust and practical tool for community-scale flood risk assessment, scenario-based early warning, and resilient urban planning, bridging the gap between advanced hydrodynamic modelling and real-world urban flood governance needs.
How to cite: Chen, C., Li, Y., Wang, L., Wang, P., Zhang, Y., and Hu, T.: UFIM: A Community-Scale Urban Flood Intelligence Framework for Climate-Driven Extreme Rainfall, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21398, https://doi.org/10.5194/egusphere-egu26-21398, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot A
EGU26-1620 | Posters virtual | VPS31
Urban Renewal Makes Cities More Livable-An Empirical Study of Fuzhou City from the Perspective of Thermal EnvironmentMon, 04 May, 14:18–14:21 (CEST) vPoster spot A
Urban renewal is not only a transformation in urban development models but also a shift in urban governance approaches. Implementing urban renewal initiatives is a crucial component of the new urbanization strategy. After experiencing rapid urbanization characterized primarily by "extensive expansion," Chinese cities are gradually shifting toward "intensive development," entering a stage of optimizing existing urban stock through renewal. As a new engine for promoting high-quality urban development, urban renewal is increasingly becoming a key force in optimizing urban spaces and enhancing people's quality of life. It serves as a vital means to advance modernization and achieve the construction of livable cities. Clarifying the thermal environmental effects of urban renewal and their driving mechanisms can provide targeted management strategies for improving urban thermal environments and enhancing livability.
This study focuses on renewal areas within Fuzhou's built-up zones where significant changes have occurred in building structures while the underlying surfaces remain impervious. We analyzed the spatiotemporal distribution characteristics of heat island intensity at key time nodes and the changes in heat island patterns within the renewal area. Additionally, the differences in thermal environmental effects across different types of urban renewal areas at the block scale have been quantified. On this basis, we explored the driving mechanisms of these thermal environmental effects.
The main findings are as follows: (1) From 2000 to 2022, the urban renewal area of Fuzhou City covered approximately 67 km², with the renewal zone concentrated in the old urban area. Renewal during this period mainly focused on the transformation from high-density mid-to-low-rise buildings to low-density mid-to-high-rise buildings, as well as the transformation of industrial sites.
(2) The spatial distribution of changes in urban heat island intensity aligns closely with urban development types. Areas where heat island intensity weakens are mainly concentrated in urban renewal zones, while areas where it strengthens appear in urban expansion zones. The distribution of extremely strong heat islands shows a migration trend from northwest to southeast, consistent with Fuzhou’s urban development strategy.
(3) Overall, urban renewal has improved the thermal environment of Fuzhou. The average intensity of the urban heat island in the updated area decreased by 1.00°C. The primary change in heat island intensity was the transition from extremely strong heat islands to lower intensity categories, effectively mitigating extreme thermal risks.
(4) The analysis of driving mechanisms shows that the thermal environmental effects of urban renewal are driven by the interaction of the water vapor index (NDMI), vegetation index (NDVI), bare soil index (BSI), building coverage rate (BCR), building height (BH), POI mixture degree, and distance to adjacent green spaces and factories. Among these, BSI and BCR are the main driving forces for the increase in heat island intensity, while BH, POI mixture degree, and distance to adjacent factories are the primary factors driving the decrease in heat island intensity.
How to cite: Liu, Z.: Urban Renewal Makes Cities More Livable-An Empirical Study of Fuzhou City from the Perspective of Thermal Environment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1620, https://doi.org/10.5194/egusphere-egu26-1620, 2026.
EGU26-1736 | ECS | Posters virtual | VPS31
Assessing Socio-Economic Impacts of Climate Change in the Arctic through Geoinformatics: the contribution of EO-PERSIST projectMon, 04 May, 14:21–14:24 (CEST) vPoster spot A
In the context of a rapidly changing climate, there is a growing need to assess the impacts of climate change on natural systems, infrastructure, and human activities. Arctic regions are particularly vulnerable, as climate-driven changes extend beyond environmental degradation to significantly affect multiple socioeconomic dimensions. Therefore, there is an increasing need for holistic frameworks capable of capturing and analysing the socioeconomic impacts of climate change on local Arctic communities. In this regard, recent advances in geoinformation technologies - particularly Earth Observation (EO), cloud computing, Geographic Information Systems (GIS), and WebGIS platforms - offer unprecedented opportunities for Arctic climate change research. Nevertheless, a notable gap remains in existing methodological approaches for the effective integration of geoinformatics with socioeconomic studies. This study aims to provide an overview of the EO-PERSIST project, an EU-funded project under the MSCA Staff Exchanges scheme, which aims at developing a cloud-based geospatial platform for understanding the socioeconomic impacts of climate change on Arctic communities. In addition, this study presents the proposed methodological frameworks integrating socioeconomic and geoinformation data developed under EO-PERSIST project, alongside key results from the socioeconomic modeling and the project’s Use Cases. Overall, this work highlights the need for an interdisciplinary and integrated approach that combines EO data, geospatial technologies, and socioeconomic analysis to support informed decision-making in Arctic regions. The EO-PERSIST geospatial platform contributes to this effort by providing key research outputs and methodological approaches that support adaptation strategies and policy development, ultimately enhancing resilience in Arctic permafrost environments.
Keywords: GIS; Earth Observation; Geoinformatics; EO-PERSIST, Cloud Platform, Arctic, Socioeconomic Impact; Acknowledgement The present research study is supported by the project “EO-PERSIST”, funded by the European Union’s Horizon Europe research and innovation program (HORIZON-MSCA-2021-SE-01-01, under grant agreement no. 101086386
How to cite: Starakis, M., Myofa, N., Volianaki, E., Tselos, G. N., Petropoulou, K., Detsikas, S. E., Litke, A., and Petropoulos, G. P.: Assessing Socio-Economic Impacts of Climate Change in the Arctic through Geoinformatics: the contribution of EO-PERSIST project , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1736, https://doi.org/10.5194/egusphere-egu26-1736, 2026.
EGU26-15302 | ECS | Posters virtual | VPS31
Integrating Remote Sensing and Participatory Assessment Techniques to Map Multi-Hazard Vulnerability and Resource Gaps: A Geospatial Study of Socioeconomic Inequity of Coastal BangladeshMon, 04 May, 14:24–14:27 (CEST) vPoster spot A
Coastal communities in Bangladesh are increasingly exposed to a range of natural hazards due to their low elevation, the dynamic nature of river systems, and environmental changes driven by climate. This study presents an integrated geospatial framework for assessing multi-hazard vulnerability and mapping community resources in Dakhin Bedkashi Union, Koyra Upazila, a coastal administrative unit bordering the Sundarbans mangrove forest. The research addresses six key hazards that impact the region: riverbank erosion, cyclones, flooding and tidal surges, waterlogging and salinity intrusion, drought, and earthquakes. This study employed a mixed-methods approach combining remote sensing analysis, GIS-based spatial modeling, and participatory assessment techniques. Temporal analysis of riverbank erosion was conducted using Normalized Difference Water Index (NDWI) derived from Landsat imagery (1990–2022) processed in Google Earth Engine. Cyclone exposure was evaluated through historical track digitization (1990–2022) and network analysis to determine shelter accessibility within 500m, 1000m, and 1500m service areas. Flood susceptibility, earthquake risk zonation, and seasonal drought patterns were mapped using datasets from the Bangladesh Agricultural Research Council and Space Research (BRAC) and Space Research and Remote Sensing Organization (SPARRSO). Primary data collection included three Focus Group Discussions (n=47 participants), two Key Informant Interviews, and GPS-based ground truthing of critical infrastructure. Results indicate that river erosion and tidal flooding pose the highest risks to the study area, followed by cyclone exposure and waterlogging. The NDWI time-series reveals progressive land loss along the Kopotakkho River, exacerbated by inadequate embankment construction and proliferation of informal sluice gates for shrimp aquaculture. Network analysis demonstrates that residents in peripheral wards must travel over 45 minutes on foot to reach cyclone shelters, with accessibility further constrained by predominantly unpaved road networks. The area falls within earthquake Zone III (moderate risk) but remains vulnerable to potential tsunami-induced coastal inundation. Community consultations revealed that while cyclone impacts have decreased due to improved early warning systems, chronic hazards including erosion, salinity intrusion, and waterlogging increasingly threaten livelihoods and freshwater security. The resource mapping component identified critical gaps in disaster response infrastructure: only four cyclone shelters and one health facility serve a population exceeding 16,000. Housing vulnerability is acute, with 98% of structures classified as non-permanent (kaccha) construction. This research demonstrates how combining top-down remote sensing with bottom-up community knowledge can expose the hidden spatial dimensions of socioeconomic vulnerability in climate-threatened deltas.
How to cite: Rabbi, N. F., Tazwar, M., Mahmud, S. A., and Muna, T. S.: Integrating Remote Sensing and Participatory Assessment Techniques to Map Multi-Hazard Vulnerability and Resource Gaps: A Geospatial Study of Socioeconomic Inequity of Coastal Bangladesh, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15302, https://doi.org/10.5194/egusphere-egu26-15302, 2026.
