Flood risk emerges from dynamic interactions among climate extremes, human systems, and cascading impact pathways that extend into economic, social, and health domains. Traditional risk assessments often inadequately represent these interdependencies. Responding to emerging evidence that flood risk is shaped by interdependencies, health impacts, and evolving vulnerability, my research develops a suite of methodological approaches to advance systemic flood risk modelling. These include system dynamics modelling to capture feedback between hazard, exposure, vulnerability, and human adaptation; hierarchical Bayesian regression and multivariate statistical models to quantify cascading impacts across sectors and scales; and scenario-based simulations that explore how changes in drivers and adaptive responses modulate risk pathways. We further leverage longitudinal survey datasets, probabilistic methods, and open datasets to bridge local empirical findings with broader flood risk dynamics. By integrating health risk metrics which are often missing from conventional frameworks alongside economic and social outcomes, our methods aim to quantify the full cascade of flood impacts and support evidence-based adaptation strategies and inclusive disaster risk management that reflect the complex Human–Flood system.

How to cite: Sairam, N.: Quantifying Cascading Economic, Social, and Health Impacts of Flooding, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13128, https://doi.org/10.5194/egusphere-egu26-13128, 2026.

Growth in satellite observations and modeling capabilities has transformed drought monitoring by enabling near real-time situational awareness. Yet many operational efforts still emphasize hazards rather than impacts, and they often miss the compound and cascading risks that frequently accompany drought, including heatwaves, wildfires, floods, and debris flows. In this presentation, we first introduce a real-time drought monitoring and seasonal prediction system that integrates diverse data streams with AI-based algorithms for drought forecasting (https://drought.eng.uci.edu/). We then describe how drought information can be expanded beyond hazard metrics by incorporating impact and vulnerability data to support impact-based assessment of extremes and decision-relevant risk insights (https://water.eng.uci.edu/).  Using several examples, we argue for an impact-centered drought monitoring paradigm that links hydroclimate conditions to physical and societal outcomes, such as crop yield losses, food insecurity, energy production disruptions, and labor impacts. We also highlight key challenges that must be addressed to make this approach operational, including inconsistent and incomplete drought impact records, limited Information about local water management and human interventions (e.g., demand, intra- and inter-basin transfers, pumping, and withdrawals), and persistent gaps between impact models and existing drought monitoring workflows. Finally, we discuss anthropogenic drought as a framing concept and show how impact-based drought analysis can be strengthened by representing drought as a coupled climate–human phenomenon rather than a purely climatic hazard. 

How to cite: AghaKouchak, A., Nguyen, P., Ung, T., de Oliveira, D., Hjelmstad, A., Massing, J., Aljohani, A., Love, C., Mirchi, A., Feldman, D. L., Placht, D., and Najib, D.: From Hazard to Consequence: Impact-Based Drought Monitoring and Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13488, https://doi.org/10.5194/egusphere-egu26-13488, 2026.

The Santorini volcano, Greece, attracts global scientific interest and constitutes a top tourist destination. The 17th century BCE eruption (“Minoan event") was likely the largest ever experienced by humanity. It was associated with significant tephra falls, earthquakes, and tsunamis inundating the eastern Mediterranean basin. Global climate changes were attributed to the Minoan event. Geological and archaeological evidence supports that the Minoan event drastically influenced eastern Mediterranean civilizations. Minoan tephra layers formed key horizon markers driving revisions of the Mediterranean civilization chronology. Comparative studies indicate great similarity between Santorini and Krakatoa, but the Minoan eruption exceeded in size the 1883 CE Krakatoa eruption. During historical times the volcanic cycle in Santorini restarted with eruptions of smaller size and magma emplacement in the caldera, thus shaping the Kamenae (Burned) islands, exactly as happened with the post-1883 generation of the Anak (Child) island in the Krakatoa caldera. In 1650 CE, a violent eruption occurred at the submarine Kolumbo volcano, which is situated a few kilometers outside the Santorini caldera but very likely is fed by the same magmatic chamber. Further research is needed to understand if magma generation at depth is possibly controlled by the occurrence of large-magnitude intermediate-depth earthquakes. The 1650 CE eruption and associated strong earthquakes and tsunamis caused loss of life and significant destruction. After several small-to-medium eruptive episodes during the 18th-20th centuries, Santorini has remained dormant since 1950. However, on 9 July 1956, the area to the east of Santorini was ruptured by a magnitude 7.7 tectonic earthquake, which, along with its large tsunami, caused extensive loss of life and destruction in the entire southern Aegean Sea. Submarine surveys indicate that the 1956 rupture zone possibly belongs to the same NE-SW-trending fracture zone passing from the Kolumbo and Santorini volcanoes. There is no historical evidence for similar tectonic earthquakes occurring in the past. Data-driven probabilistic seismic hazard assessment utilizing incomplete and uncertain earthquake catalogues indicates that the 1956-type earthquakes may have very long repeat times. During 2025, an unusual cluster comprising thousands of earthquakes but with a maximum magnitude of only 5.3 and sources at distances of 20-40 km to the east of Santorini caused extensive social anxiety. This was magnified because of two reasons. First, preventive measures taken by civil protection authorities were unprecedented. Second, uncontrolled public statements were expressed by specialists and non-specialists about imminent eruptions and forthcoming large earthquakes, which raised important geoethical challenges. The seismic crisis received international attention because Santorini is a spot of worldwide tourist interest. More than 13,000 people evacuated voluntarily. For the interpretation of the cluster, the “seismic swarm” hypothesis appears more as a “deus ex machina” explanation than a convincing scientific result. The competing “foreshocks-mainshock-aftershocks” model fits the data better. Santorini is a key volcano offering results valuable for better understanding the behavior of many volcanoes around the globe, revealing global climate impacts of volcanic origin, deciphering unknown aspects regarding prehistoric civilizations in the Mediterranean, and providing important lessons learned for volcanic and other geohazard management.  

How to cite: Papadopoulos, G.: Large volcanic eruptions, earthquakes, and tsunamis in Santorini: a multi-hazard physical laboratory of global interest, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3519, https://doi.org/10.5194/egusphere-egu26-3519, 2026.

Geoid height values obtained in 1984, 1996 and 2008 were used to study the thermal evolution (considering density variations caused by temperature alterations) of a sedimentary basin located in the central eastern part of the Atlantic Ocean region. Historical and registered earthquakes have been detected in this basin.

The results obtained show a heterogeneous basin with increases/decreases in density (temperature) values occurring in the time interval between measurements and points with identical values in different years, separating regions of warming from regions of cooling. It is also observed that the maximum values obtained increase from 1984 to 1996 and 2008, occurring at different latitudes.

The minimum values obtained in 1984 are clearly higher than values obtained in 1996 and 2008 at same latitudes. The minimum values obtained in 2008 are higher than those of 1996 in latitudes between 35.8 and 36.2 N and also for latitudes equal to or greater than 36.6 N. At intermediate latitudes, the values obtained in 2008 are lower than those obtained in 1996.

Climate data presented on IPMA website show high values of precipitation data occurring in 1996 in months with lowest temperature values in Mainland Portugal, suggesting that the low values of temperature found may be related with infiltration of cold water and to an increase of water pressure in depth.

In the present work, special attention is given to the western boundary of the basin, where it is possible to observe high temperature values associated with lateral cooling of seamounts linked to cooling in the sedimentary basin, and a consequent increase in temperature in the inner part of the seamount. The location of 3 earthquakes recorded in May, July, and August 2005 showed  that they occurred near points without changes, separating a warming area (on the West side) from a cooling area (on the East side). The earthquakes are located in the warming area.

The analyzed data show that the region under study experienced warming in the past and is now in a heterogeneous cooling phase. Areas in a warming phase can be identified with the 2008 geoid height values, after been cooled in 1996.

Climate data was used to identify temporal relationships between geoid height values and precipitation and temperature values in mainland Portugal.

Earthquakes with magnitude greater than 4.0 were identified in the region in 2005. They are located close to the crossing points of geoid height values between 1996 and 2008, which separate areas under heating from areas under cooling, giving rise to different horizontal thermal and pressure gradients in the western and eastern side of the point with no changes in density (temperature) and possible contribution to the occurrence of earthquakes.

How to cite: Duque, M. R.:   Using geoid height changes to study the thermal evolution of a sedimentary basin and possible relation with earthquake occurrence in the region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1467, https://doi.org/10.5194/egusphere-egu26-1467, 2026.

A remote sensing index is often used to identify meteorological and agricultural droughts. Google Earth Engine analyzes CHIRPS data from 2015 to 2024 and Landsat-8/Sentinel-2 data from 2020 to 2024. The Vegetation Condition Index (VCI), Temperature Condition Index (TCI), composite Vegetation Health Index (VHI), and Standardized Precipitation Index (SPI) were calculated for four seasons using NDVI, EVI, LST, and CHIRPS precipitation data to explain specific spatiotemporal trends. Meteorological and agricultural droughts include precipitation deficits and vegetation stress. From the study, pre-monsoon analysis reveals significant intra-seasonal correlations between VCI and VHI (0.84) and TCI and VHI (0.75), indicating that moisture reserves and thermal stress influence vegetation health during arid periods. The VCI-VHI correlation (0.91) predominates during the monsoon season, indicating plant growth amidst substantial precipitation. As the season nears peak aridity, the correlations between post-monsoon and winter TCI-VHI increase (0.81 and 0.83), signifying thermal stress. A weak correlation (≤ 0.50) between SPI and vegetation indices across the seasons indicates that current precipitation does not succeed in reliably predicting vegetation stress, since vegetation depends on accumulated soil moisture rather than instantaneous rainfall. Vegetation indices exhibit substantial temporal persistence: Pre-monsoon VCI conditions are strong predictors of winter VCI (0.98), VHI forecasts winter VHI (0.92), and TCI predicts winter TCI (0.87), thereby enabling nine-month drought forecasting. The findings demonstrate that vegetation indices serve as drought indicators for seasonal water resource planning and agricultural vulnerability assessment in monsoon-affected nations.

How to cite: Thounaojam, L. and Oinam, B.: Spatio-temporal dynamics of meteorological and agricultural droughts: A multi-seasonal analysis of Vegetation Health and Climate Indices Using Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2205, https://doi.org/10.5194/egusphere-egu26-2205, 2026.

The objectives of the experiment were the following:

• Evaluation of continuous ground motion from satellite data (European Ground Motion Service for the period 2019-2023 and IRIDE for 2024);
• Analysis of different types of landslides (active landslides, dormant landslides, landslide-prone areas, subsidence);
• Identification of elements for the Emergency Limit Condition (CLE) analysis near areas affected by specific ground motions derived from satellite data in the period 2019-2024
• Identification of buildings in the municipality of Perugia near areas affected by specific ground motions derived from satellite data in the period 2019-2024
• Submission of the results to all regional offices that authorize, evaluate, design, or schedule interventions on the territory and to the regional civil protection agency.

QGIS version 3.42 software was used for the experiment.

The following databases were imported into QGIS:

• European Ground Motion Service satellite data.
• IRIDE satellite data – Cross Monitoring of Ground Motion and "Hot Spots" of Cover Change.
• PAI geomorphological landslide hazard maps.
• Local Seismic Hazard Map of the Umbria Region.
• Umbria Region Emergency Limit Condition Analysis (CLE) maps.
• Geological Map of the Umbria Region.
• Building database of the Umbria Region's land registry system for the Municipality of Perugia.
• Administrative Boundaries of the Umbria Region and base maps such as the Regional Technical Map and Google Satellite.

Spatial analyses were performed using the GIS on the collected data to homogenize and select specific information useful for subsequent processing.

Multiple analyses werw performed for 2 specific case studies.

All objectives were achieved: assessment of continuous ground motion from satellite data (two different databases: IRIDE for 2024 and EGMS 2019-2023); subsequent analysis using different types of landslides (active landslides, dormant landslides, landslide-prone areas, subsidence); subsequent assessment using the CLE (Emergency Limit Condition) elements; subsequent assessment using buildings in the Municipality of Perugia.

How to cite: Motti, A. and Natali, N.: Experimentation with the use of EGMS and IRIDE satellite data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3065, https://doi.org/10.5194/egusphere-egu26-3065, 2026.

Earthquake clusters can be broadly classified into two types: swarm-like sequences and mainshock–aftershock sequences. The spatial organization of the two types provides important insights into underlying tectonic processes and fluid migration in earthquake source regions. In this study, we apply the nearest-neighbor distance approach on the Southern California focal-mechanism earthquake catalog (the CNN_SoCal catalog) and introduce two new statistical indicators-skewness and kurtosis to distinguish between these two classes of earthquake clusters. We find that the square root of kurtosis and skewness provide effective and interpretable indicators for clusters classification. In the kurtosis–skewness diagram, swarm-like sequences and mainshock–aftershock sequences tend to occupy distinct regions, enabling a practical distinction between the two sequence types without relying on subjective inspection of individual clusters. Overall, the proposed approach offers an efficient way to differentiate swarm-like and mainshock–aftershock seismicity in large catalogs. The method is computationally light, easy to implement, and suitable for rapid screening of earthquake sequence types in high-resolution regional datasets.

How to cite: Fan, Y. and Hu, F.: A New Statistical Method to distinguish Different Earthquake Cluster Types, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4745, https://doi.org/10.5194/egusphere-egu26-4745, 2026.

The Himalayan river Basins frequently experience devastating floods. These river basins require accurate predictions and timely warnings to support effective flood risk management. While accurate prediction is crucial for saving lives, disaster managers often face a difficult trade-off between computational cost and warning lead time. High-fidelity physics-based models are precise but are computationally expensive for rapid decision-making, whereas low-fidelity geo-spatial models often lack accuracy in data-scarce regions. Our proposal is a framework to improve the flood inundation prediction in the Himalayan basin by combining the reliability of hydrodynamic modeling with the speed of machine learning.

In this study, we developed a 2D HEC-RAS model using a Rain-on-Grid approach to simulate the historical floods. We utilize the developed hydrodynamic model to generate a dataset of flood inundations that captures the basin's flow dynamics. These datasets will serve as the foundation for training advanced machine learning algorithms, including a Random Forest Regressor (RF) and a Convolutional Neural Network (CNN), to identify and predict flood patterns. Our model will integrate critical landscape features, including elevation, slope, land-use characteristics, the Normalized Difference Vegetation Index (NDVI), and satellite-derived rainfall data, to approximate the complex physical processes embedded in the hydrodynamic model. This allows the machine learning approach to achieve comparable predictive accuracy while reducing computational time. Through comprehensive validation against established benchmarks and real-world flood events, our research aims to deliver a scalable, computationally efficient, and highly accurate flood prediction tool. This framework has the potential to transform disaster preparedness and response capabilities in the Himalayan region by enabling timely, data-driven policy planning and proactive risk mitigation strategies.

How to cite: Duwal, S., Pradhan, P. M., Liu, D., and Bhattarai, Y.: Coupling Hydrodynamic Modeling with Machine Learning for Flood Risk Assessment in the Himalayan River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7810, https://doi.org/10.5194/egusphere-egu26-7810, 2026.

Remote sensing enables spatially continuous and timely monitoring of hydro-climatological extremes by capturing key land–atmosphere variables across large regions, including for data-scarce areas. The rising frequency of heat extremes across India in recent decades underscores the need for effective monitoring, especially in data-scarce regions. This study evaluates the potential of monitoring heat extremes over the Indian sub-continent using satellite based observations and data driven approaches. For this, MODIS land surface temperature (LST) along with NDVI, land use/land cover and elevation information is used with traditional machine learning models namely Random Forest (RF) and XGBoost. Subsequently, the performance of the two ML models in estimating maximum temperatures across the Indian subcontinent was evaluated and validated using in situ temperature observations from the Indian Meteorological Department. Heat extremes were identified using both absolute temperature percentile thresholds and Standardized Temperature Index based heat stress categories. The performance of ML models was evaluated using station‑wise categorical verification metrics such as hit rate, false alarm ratio, and critical success index. Results show that the ML models exhibit higher accuracy in predicting mean temperatures compared to extremes, and XGBoost outperforms the RF model with lower RMSE and higher R². The results further reveals that ML model prediction skill exhibits considerable geographic variability across the sub-continent, with reduced performance over mountainous areas. This study demonstrates that integrating satellite-based data with machine learning provides an effective approach for monitoring heat extremes across the Indian subcontinent, particularly in data-scarce environments.

How to cite: Remesh Ancy, A. and Mitra, S.: Monitoring Heat Extremes over India Using Earth Observations and Data Driven Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16372, https://doi.org/10.5194/egusphere-egu26-16372, 2026.

Land subsidence poses growing risks to urban infrastructure, water resources, and long-term resilience, requiring assessment frameworks that link present-day observations with planning-relevant forecasts. This study develops an integrated approach for land subsidence susceptibility mapping and trend forecasting over multi-year horizons. The analysis uses SBAS-InSAR deformation time series derived from Sentinel-1 observations from 2017 to 2025 to characterize subsidence patterns across East Baton Rouge Parish, Louisiana. Subsidence susceptibility is modeled using an ensemble machine-learning framework that combines Extra Trees and Random Forest regressors and incorporates geological, topographic, hydrological, land use, infrastructure, and climatic conditioning factors. The susceptibility results highlight the dominant influence of land use, elevation, proximity to faults and rivers, and terrain-hydrology interactions on subsidence patterns. To extend assessment beyond observation periods, a physics-informed long short-term memory (LSTM) ensemble is introduced for forecasting. The model integrates data-driven learning with physically motivated constraints to ensure stable and realistic deformation trajectories. The forecasts preserve observed spatial patterns while exhibiting physically consistent temporal evolution and quantified uncertainty. The results demonstrate that combining InSAR observations with physics-informed deep learning enables robust, planning-scale subsidence assessment and forecasting. The proposed framework is transferable to other urban settings where long-term subsidence poses increasing societal risk.

How to cite: Kangah, D. and Abdalla, A.: Near-Decadal Land Subsidence Susceptibility and Trends Using Physics-Informed LSTM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16451, https://doi.org/10.5194/egusphere-egu26-16451, 2026.

Earthquake catalogue declustering is a critical preprocessing step in time-dependent seismicity analyses (Gardner and Knopoff, 1974; Reasenberg, 1985), yet its systematic influence on conditional earthquake probability estimates remains insufficiently quantified, particularly in tectonically complex continental collision zones such as the Himalayas (Bungum et al., 2017). Renewal-based recurrence models typically assume that declustered catalogues isolate tectonically driven mainshock recurrence by removing dependent events. However, recent advances in declustering theory demonstrate that methodological choices, ranging from fixed spatio-temporal windows to adaptive and stochastic approaches, can substantially modify inter-event time statistics and inferred recurrence memory (Zaliapin et al., 2008; Zaliapin & Ben-Zion, 2020; Teng & Baker, 2019). Despite these developments, the implications of declustering-induced variability for time-dependent conditional probabilities remain underexplored in active orogenic belts.

In this study, we explicitly quantify how alternative declustering strategies influence time-dependent recurrence behavior and conditional rupture probabilities across selected Himalayan seismic source zones. Inter-event time series were constructed for moderate-to-large earthquakes (M ≥ 4.0) using both raw (non-declustered) and declustered catalogues derived from regional earthquake compilations. Declustering was performed using commonly applied fixed-window and adaptive approaches to capture epistemic variability associated with catalogue preprocessing. The resulting inter-event times were analyzed within renewal process models, including Brownian Passage Time (BPT), Lognormal, Weibull, and Gamma distributions, to estimate conditional probabilities as functions of elapsed time since the most recent major event.

Results show that declustered catalogues consistently yield smoother initial probability gradients and delayed probability peaks relative to raw catalogues, reflecting reduced short-term temporal clustering in inter-event time distributions. These shifts correspond to systematic changes in inferred renewal memory parameters, with declustering suppressing short-term contagion effects while largely preserving long-term mean recurrence intervals. In the Himalayas, collision-driven aftershock swarms and spatially heterogeneous fault interactions amplify these effects, introducing substantial epistemic uncertainty in early-time conditional probabilities, which can locally exceed factors of two to three depending on the declustering strategy employed. In contrast, long-term probability remains comparatively robust across declustering scenarios, consistent with steady-state tectonic strain accumulation.

These findings identify catalogue declustering as a dominant and often underappreciated source of uncertainty in time-dependent seismic probability modelling, reinforcing recent calls for ensemble-based and transparent pre-processing strategies in probabilistic seismic hazard workflows. This study advances a methodological framework for interpreting renewal-based conditional probabilities in clustered tectonic regimes. The Himalayas emerge as a natural laboratory where combined raw and declustered analyses can yield more resilient probabilistic interpretations.

How to cite: Pratap, B. and Sharma, M. L.:  Sensitivity of Time-Dependent Earthquake Conditional Probabilities to Catalogue Declustering in the Himalayas , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17842, https://doi.org/10.5194/egusphere-egu26-17842, 2026.

Landslides have become one of the most destructive geological hazards in the Himalayan region, exhibiting a significant increase in both occurrence and intensity in recent decades. This increasing trend poses serious threats to human life, infrastructure, and essential public assets, underscoring the need for comprehensive risk evaluation in these highly vulnerable mountainous terrains. The present study offers an extensive assessment of landslide hazard, vulnerability, and associated risk in the Darma Valley of the Kumaun Himalaya, India. Landslide susceptibility was modelled using a Multilayer Perceptron (MLP) neural network, and the model’s predictive performance was validated through ROC–AUC analysis. Vulnerability was quantified by integrating land-use/land-cover categories with their respective economic valuations. Furthermore, rainfall and seismic intensity maps were combined with the susceptibility outputs to derive a detailed landslide hazard map. The results indicate that roads are the most vulnerable elements, followed by settlements and dam infrastructures, largely due to their substantial reconstruction costs and higher exposure levels. The final risk map, produced by integrating hazard and vulnerability layers, reveals that approximately 9% of the study area falls within high to very high risk zones, 22% within moderate risk, 26% within low risk, and 43% within very low risk zones. These findings offer essential guidance for promoting sustainable development and supporting land-use planning that accounts for environmental risks. They also contribute to more informed and effective decision-making aimed at strengthening the resilience of the fragile and sensitive Himalayan landscape.

How to cite: Shawez, M., Kumar, S., Gupta, V., Kumar, P., and Rawat, G.: Landslide Hazard, Vulnerability, and Risk Analysis (HVRA) Using Machine Learning and AI: A Case Study of the Darma Valley, Kumaun Himalaya, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18022, https://doi.org/10.5194/egusphere-egu26-18022, 2026.

Slow-moving, deep-seated landslides represent a significantly underestimated geologic hazard, incurring huge economic loss and persistent long-term risk to communities annually. Further, they have the potential to evolve into catastrophic events, which necessitates continuous monitoring to better understand their dynamics, minimize potential losses, and implement appropriate mitigation measures. The present study aims at understanding the dynamics of the slow-moving slopes housing villages such as Bhatwari, Raithal, and Barsu in the Bhagirathi Valley, Uttarakhand Himalaya, by means of PS-InSAR techniques. A total of 129 ascending-pass and 114 descending-pass scenes of Sentinel-1, from January-2021 up to March-2025, have been utilized to estimate slope velocities along the radar line-of-sight (LOS) for each pass, using open-source tools such as ISCE and StaMPS.  Further, these LOS velocities were decomposed to obtain vertical (up-down) and horizontal (east-west) velocities. The results reveal that Raithal (elevation ~2150 m), on middle of the slope, is subsiding at ~3 mm/year with an eastward movement of ~5 mm/year. Bhatwari (1650 m), on the lower slope, shows eastward creep at ~4 mm/year and upliftment at ~2 mm/year, suggesting rotational landslide activity. Barsu (2262 m), situated at a slope ~3 km upstream, exhibits eastward movement at ~6 mm/year and subsidence at ~3 mm/year. Field investigations corroborate these findings, revealing features such as scarps, cracks, tilted structures, disrupted roads, and longitudinal and transverse ponds. The persistent creeping suggests the potential for sudden slope failure during heavy rainfall or earthquakes, which may dam the Bhagirathi River, and the impoundment may further trigger cascading downstream hazards. Therefore, there is a need for a comprehensive investigation integrating the PS results with the slope stability analysis that assesses the role of geology, rainfall, and earthquakes. This integration shall assist in estimating the risk posed by the failure and further help in mitigation planning.

How to cite: Gupta, A. K., Luirei, K., Gupta, V., and Shawez, M.: PS-InSAR based Slope Deformation Monitoring in the Bhagirathi Valley, Uttarakhand Himalaya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18221, https://doi.org/10.5194/egusphere-egu26-18221, 2026.

Chișinău, the capital of the Republic of Moldova, faces significant geohazard challenges due to its unique geological setting on loess-covered plateaus dissected by river valleys and ravines. Urban expansion and infrastructure development have intensified landslide susceptibility in this region, threatening residential areas, transportation networks, and critical infrastructure. This study presents a comprehensive analysis of urban landslides in Chișinău using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique applied to Sentinel-1 satellite data spanning the last five years (2019-2025).

The PS-InSAR methodology provides millimeter-level precision in detecting and monitoring ground deformation over time, making it particularly suitable for identifying slow-moving landslides and ground subsidence in urban environments. We processed ascending and descending Sentinel-1 SAR imagery to generate time-series deformation maps and identify persistent scatterers across the Chișinău metropolitan area. The analysis revealed multiple zones of significant ground displacement, with deformation rates ranging from -15 to +25 mm/year, concentrated primarily in areas with steep terrain, proximity to water courses, and urban development on historically unstable slopes.

The susceptibility map derived from our analysis indicates high-risk zones in the northern and western sectors of Chișinău, particularly around suburb localities Vatra, Ghidighici, and Durlești, where loesslike deposits on valley slopes are subjected to both natural erosion processes and anthropogenic pressures. The southeastern areas near locality Bubuieci also show elevated landslide susceptibility, correlating with urban expansion into previously undeveloped terrain. Integration of PS-InSAR results with geological maps, digital elevation models, and land-use data enabled the development of a comprehensive landslide susceptibility assessment framework.

Key findings reveal that ground deformation patterns in Chișinău exhibit strong seasonal variations, with accelerated movement during spring months corresponding to snowmelt and precipitation events. Urban infrastructure, including roads, buildings, and utilities, located within identified high-risk zones, shows structural damage consistent with slow-moving landslide activity. The study identifies critical infrastructure corridors, including major transportation routes (E583, E581) traversing the study area, that require enhanced monitoring and mitigation measures.

Acknowledgements: This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS – UEFISCDI, project number 40PCBROMD within PNCDI IV.

How to cite: Sandric, I., Nicoara, I., Spian, C., Tambur, A., Ilinca, V., Jeleapov, V., Irimia, R., Daia-Creinicean, T., and Alexandru, N.: Urban Landslide Monitoring Using PS-InSAR Sentinel-1 Data in Chișinău, Republic of Moldova (2019-2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19054, https://doi.org/10.5194/egusphere-egu26-19054, 2026.

Rapid glacier retreat in the Nepal Himalaya has accelerated the formation and expansion of glacial lakes, increasing the likelihood of glacial lake outburst floods (GLOFs) with potentially severe downstream consequences. Existing GLOF studies in Nepal are largely site-specific and lack national-scale consistency, limiting their utility for systematic hazard planning. Here, we present a comprehensive, multi-scenario GLOF hazard assessment for Nepal based on three decades of satellite observations (1990–2023) and large-scale hydrodynamic modelling. Using multi-temporal remote sensing, we mapped 1,232 glacial lakes, including 265 newly formed lakes, and estimated lake volumes and peak discharges using established empirical relationships. Downstream flood propagation was simulated using the LISFLOOD-FP hydrodynamic model, enabling consistent, high-resolution inundation mapping across the country. To examine plausible future conditions under continued glacier retreat, we implemented scenario-based lake-volume increases of 10–50%, representing optimistic, intermediate, and pessimistic states. Results indicate a ~26.9% increase in total glacial lake area since 1990, with the most pronounced expansion in the Koshi and Karnali provinces. Modelled inundation extents and flood depths, particularly exceeding 3.5 m, increase substantially under higher-volume scenarios. Koshi and Karnali consistently emerge as the most exposed regions, with heightened impacts on settlements, hydropower infrastructure, and transport networks. The resulting national-scale GLOF hazard atlas provides a coherent framework for visualising present and future flood hazards and offers a practical basis for climate adaptation planning and disaster risk reduction in high-mountain regions.

How to cite: Saha, S., Singh, H., and Mohanty, M. P.: Nationwide Multi-Scenario GLOF Hazard Mapping in Nepal Using Remote Sensing and Hydrodynamic Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1600, https://doi.org/10.5194/egusphere-egu26-1600, 2026.

Disasters triggered by natural hazards increasingly unfold as compound and cascading events, placing extraordinary demands on the institutions responsible for coordination and decision-making. Emergency Operations Centers (EOCs) sit at the nexus of these multi-hazard crises, linking infrastructures, agencies, and communities, yet their organizational design is rarely examined through a systemic resilience lens. This presentation contributes empirical insights into how EOCs enable—or constrain—resilience under escalating uncertainty.

Drawing on qualitative analysis of U.S. Federal and state EOC doctrine and training materials, this study conceptualizes EOCs as socio-technical systems operating along a continuum between mechanistic (hierarchical and rule-based) and organic (networked and adaptive) organizational structures. Findings reveal that while formal guidance emphasizes mechanistic control to ensure accountability and resource tracking, effective EOC performance during complex and cascading disasters depends heavily on organic processes such as lateral information sharing, informal coordination, and emergent problem-solving. These adaptive mechanisms—critical for responding to interacting hazards and rapidly shifting conditions—remain largely undocumented and are instead learned through experience and social networks.

The analysis further identifies predisaster networking among EOC participants as key enabling conditions for systemic resilience. Pre-established relationships enhance information flow, reduce coordination friction, and support adaptive decision-making when conventional procedures are strained by compound hazards. From a resilience perspective, EOCs function not merely as coordination hubs but as institutional platforms where resistance, recovery, adaptation, and potential transformation are negotiated in real time.

This presentation advances the disaster- and climate-resilience discourse by reframing EOC design as a resilience-building intervention. It offers actionable strategies for strengthening systemic resilience, including integrating organic coordination mechanisms into doctrine, redesigning training and exercises to emphasize adaptive capacity, and evaluating EOC performance beyond compliance metrics. By explicitly addressing institutional dynamics within multi-hazard contexts, this work bridges theory and practice in climate-resilient development.

How to cite: Chang, R.: Designing Institutional Resilience for Compound Disasters: EOC Structures, Networks, and Adaptive Operations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6236, https://doi.org/10.5194/egusphere-egu26-6236, 2026.

Urban fire risk in heritage cities threatens lives, livelihoods, and irreplaceable historical monuments. Nepal's heritage cities, rich in cultural landmarks, face acute vulnerability due to dense settlement patterns driven by uncontrolled urbanization. Fragmented data availability prevents stakeholders from implementing effective fire risk mitigation measures at the community level, which intensifies the existing vulnerabilities. In this study, we address this challenge by developing comprehensive data through collaborative public-private partnerships involving multiple stakeholder experts. We propose scalable interventions designed to reduce fire risk while strengthening community resilience in ways that align with heritage preservation objectives. This integrated approach ensures that safety measures protect both people and the cultural assets that define these historic urban centers.

Our study area is Bhaktapur Municipality, a UNESCO World Heritage site rich with traditional wooden architecture. Our approach combines municipal planning data, private building inventories, community knowledge, and emergency response databases for fire hazards. We integrate Analytical Hierarchy Process (AHP) with GIS technology across three domains: hazard factors, vulnerability indicators, and response capacity. We establish public-private partnerships to gain access to previously prepared fire incident datasets while we protect commercial interests. We establish multi-stakeholder data protocols and develop community-centered collection mechanisms that respect local knowledge systems. We leverage real field knowledge from community-level surveys to assess the present scenario and propose upgrades to current practices. We perform dynamic vulnerability assessments that support both emergency planning and heritage conservation. Through weighted overlay analysis, we determine optimized fire hydrant placement for narrow streets that existing firefighting services cannot access. This spatial analysis ensures that infrastructure improvements respect the historic urban fabric while they enhance emergency response capabilities.

We expect collaborative data partnerships to enhance decision-making through three key contributions: (i) bridge critical information gaps that have long hindered effective fire risk management, (ii) support sustainable development, cultural preservation, and community resilience as interconnected goals and (iii) offer scalable lessons for complex urban management challenges in resource-constrained environments. This integrated framework demonstrates how heritage cities can balance safety imperatives with conservation priorities through evidence-based interventions.

How to cite: Ghimire, S., Dangol, S., Khatri, S., Duwal, S., and Bhattarai, Y.: A Framework for Fire Risk Assessment in Heritage Cities through Multi-Stakeholder Data Integration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7909, https://doi.org/10.5194/egusphere-egu26-7909, 2026.

Early warning systems are fundamental to cyclone risk reduction, yet evacuation outcomes depend on whether warnings trigger timely household action and whether households can physically reach shelters. This study quantifies evacuation thresholds, time-to-action, and mobility constraints using georeferenced survey data from 1,126 households across two cyclone-prone coastal unions in Bangladesh. Using household-level GPS data, we measured distance to the nearest cyclone shelter for each household and analysed evacuation behaviour across spatial distance thresholds.

Early warning message (EWM) coverage was high, with 93.5% of households reporting receipt of warnings, yet only 80.8% evacuated, indicating a persistent warning–action gap. Logistic regression shows that households receiving EWMs had more than twice the odds of evacuation (OR = 2.13, 95% CI: 1.27–3.55, p < 0.01), although evacuation likelihood varied significantly by distance to shelters, and road conditions. Distance to shelters and road conditions were also significantly associated with evacuation outcomes (p < 0.001).

Time-to-action analysis indicates delayed mobilisation after warnings: only 39.8% of households began preparation within 1 hour, and 9.3% delayed action beyond 6 hours. Distance and road conditions compounded these delays: evacuation times rose sharply beyond 1 km and were significantly longer where roads were reported poor or waterlogged during the cyclone, suggesting that delayed mobilisation increases exposure to peak travel constraints.

Spatial constraints also explain non-evacuation among warned households. Among households (18%) that received warnings but did not evacuate, the dominant barriers were distance from shelters (50.0%), shelter overcrowding and lack of privacy or maternal facilities (48.9%), and lack of transportation (45.7%), alongside caregiving and health-related constraints. Only 2.4% cited lack of knowledge about shelter locations, indicating that non-evacuation reflects spatial and mobility exclusion rather than information failure.

These findings demonstrate that cyclone evacuation is a threshold-based and constrained mobility process, where warnings increase evacuation odds but do not guarantee timely action for households facing greater distance, degraded road conditions, and care burdens. Strengthening anticipatory action therefore requires addressing spatial inequalities in last-mile accessibility, reducing response delays, and improving shelter suitability for households with health and caregiving needs in high-risk coastal settings.

How to cite: Islam, M. R., Rahman, M. H., Ahmed, F. A., and Salehin, Dr. M.: Warning is not enough: time delays and spatial inequalities in household-scale cyclone evacuation in coastal Bangladesh, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10195, https://doi.org/10.5194/egusphere-egu26-10195, 2026.

Resilience and sustainability are widely recognized as desirable properties of infrastructure systems.  Although related, they can become conflicting objectives, especially when resources available to enhance them are limited, making trade-offs between short-term resilience and long-term sustainability inevitable. Despite growing needs of increasing both resilience and sustainability, systematic analyses of such trade-offs remain limited.  In this work, we address this gap by developing a stylized, minimalistic stochastic model of system functionality under a sequence of disruptions.  The results reveal the nature of the trade-offs between short-term resilience and long-term sustainability and show that, depending on the effectiveness of investments in each, sub-optimal allocations may arise and should be avoided.  The analysis establishes clear relationships demonstrating how physical system features and investment strategies interplay to influence the nature of such resilience-sustainability trade-offs.

How to cite: Muneepeerakul, R. and Lin, N.: Trade-off between short-term resilience and long-term sustainability in infrastructure systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13004, https://doi.org/10.5194/egusphere-egu26-13004, 2026.

Background: The Danube Delta, a UNESCO Biosphere Reserve and one of Europe's most important wetland ecosystems, faces increasing environmental pressures from climate change, including altered hydrological regimes, flooding patterns, and ecosystem degradation. Effective climate adaptation and nature-based solutions in such regions require not only hazard modeling but also robust tools for assessing how local communities perceive their environment and the governance structures meant to protect it. Understanding these perceptions helps designing risk communication strategies and fostering behavioral preparedness.

Methods: This study presents the development and psychometric validation of two scales measuring (1) perceptions of natural resources and (2) perceptions of local development and quality of life among Danube Delta inhabitants. A cross-sectional survey was conducted with 503 residents (76.3% female; M age = 24.8 years). Exploratory and confirmatory factor analyses were employed to establish the factorial structure and validity of both instruments.

Results: Descriptive findings revealed that residents perceive estate-level government engagement in ecosystem conservation as notably low (M = 46/100), significantly lower than local government engagement—a finding with direct implications for implementing top-down nature-based adaptation strategies. The Natural Resources Perception Scale yielded a 6-item, two-factor structure with excellent fit indices (CFI = .97, TLI = .96, RMSEA = .08): Factor 1 captures environmental quality (air, water, soil), while Factor 2 captures biodiversity (fish, birds, animals). The Local Development and Quality of Life Scale retained 12 items across two factors (CFI = .95, TLI = .94, RMSEA = .07): Factor 1 addresses tourism and infrastructure development, while Factor 2 encompasses governance engagement, ecosystem conservation mechanisms, and inhabitants' quality of life. Both scales demonstrated good internal consistency (α = .83 and α = .92, respectively).

Conclusion: These instruments offer researchers and practitioners standardized tools for assessing community perceptions in climate-vulnerable regions. Such assessments can inform the design of locally-relevant risk communication and identify gaps in perceived governance effectiveness. Future applications may include longitudinal tracking of perception changes following climate events or conservation interventions.

How to cite: Avram, E., Iacob, C. I., Ionescu, D., and Armas, I.: Development and validation of scales measuring natural resources and local development perceptions in the Danube Delta, a climate-vulnerable ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14542, https://doi.org/10.5194/egusphere-egu26-14542, 2026.

How to cite: Mayacela-Salazar, B. and Torres-Ramirez, R.: Linking paleochannel evidence and physical vulnerability to urban flooding: a spatial analysis in Ibarra, Ecuador , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15733, https://doi.org/10.5194/egusphere-egu26-15733, 2026.

This presentation outlines a multi-faceted framework for addressing climate change adaptation. The methodology is built upon key pillars, including the resilience and integrity of cultural heritage assets, responsible resource use, and effective mitigation of hazard impacts. The success of any adaptation initiative depends on a holistic evaluation that considers not only technical feasibility and cost, but also its broader societal, cultural, community, and economic impacts, including the project's carbon footprint and adherence to principles of the circular economy.
The management of cultural heritage threated by increasing climate change hazards needs a multi-criteria evaluation framework. A structured approach to decision-making ensures that all intervention strategies prioritize conservation, resilience, and long-term sustainability. Criteria and their respective measurement spaces include technical feasibility, cost (which may be incured looking towards the benefit of increasing climate resilience for culturally significant buildings and unbuilt spaces), adherence to regulatory compliance, as well as impact on society and culture. Authenticity, values, and integrity of the heritage building or unbuilt space must be kept. Thus framework emphasizes key climate-specific metrics: hazard impact mitigation, proactive adaptability (such as preventive retrofit), efficient resource use (including materials, time and workforce as they may be depicted in devices for costs calculation), minimizing carbon footprint, and aligning with the circular economy. Actually building retrofit by itself as a reuse strategy illustrates the principles of circular economy itself at its best in the built environment. Such a retrofit project must demonstrate community acceptance (for example by respecting the mental map of landmarks to be kept in case of reconstruction, following Kevin Lynch principles as well as a psychogeograhic parcours), offer educational value, and ensure positive economic impact. 
This strategic management model follows a four-level hierarchy, from the definition of the overarching mission and objectives (Level 1), over problem definition, diagnosis, and stakeholders analysis (Level 2) to defining the challenge and identifying opportunities (Level 3). The highest level of decision-making (Level 4) - implementation - involves setting evaluation criteria, criterion weighting, and decision rules to inform the final choice and design of the intervention. The outcome is an action plan comprising operational and communication (a higher level of participation) means, demonstrated in a model project aiming at long-term resilience and effective climate risk management.

An analysis of various climate change-related events (floods: Elbe/East Germany 2013, Passau 2013, Florence 1966, Bosnia Herzegovina 2025, and winter storms: Lothar 1999, and Atlantic storm in 2013) is included, detailing, along with articles and online exhibitions, for each event:

• Structures: Locations and specific areas affected, such as the Elbe and Bosna rivers, the Black Forest, inner-city forests in Karlsruhe, and the Pena Palace park in Sintra.
• Damage: The impacts range from common effects like flooding of streets, transport disruption, and damage to lower levels of buildings (incl. economic impacts and activity disruption) to specific damage like forest destruction and agricultural land saturation from freaic water.
• Intervention:
  • Short-term/Emergency: Early warning, sandbags, closing roads, removing fallen trees.
  • Long-term: Awareness campaigns, a flood museum (Passau), landscape solutions (river renaturation, changing vegetation to more storm-resilient species in affected forests)

How to cite: Bostenaru Dan, M. and the Climate Adaptation Working Group at ICOMOS Iscarsah: A Structured Framework for Climate-Adaptive Cultural Heritage Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15909, https://doi.org/10.5194/egusphere-egu26-15909, 2026.

In this study, a physics-based, performance-oriented framework to estimate the probability of failure of a cross-laminated timber (CLT) shear wall has been proposed. In low-lying coastal regions, residential buildings are becoming more exposed to both the pluvial and fluvial flooding. In previous studies, most structural analysis has been done emphasizing either solely on masonry-wall structures or entire building structures made of wood. In this study, the CLT shear wall has been subjected to flood-induced load. The wall demand is expressed in terms of a combination of hydrostatic and hydrodynamic forces, with the water depth acting as the intensity measure. Structural resistance has been computed at the component level by combining the in-plane and out-of-plane resistance models. Among the in-plane, bracket sliding, and rocking capacities, along with their combination has been considered. Whereas for out-of-plane bending resistance, the bending strength of CLT has been considered. Based on the demand and the resistance value, a limit state function has been formulated. Using a series of crude Monte Carlo simulations, the uncertainties in flood depth that lead to the damage state have been calculated. Overall, the results demonstrate that for all the given water depths, the CLT shear wall can withstand the load and avoid structural failure.

How to cite: Khan, N. M., Kameshwar, S., and Bin Mostafiz, R.: Physics-Based Flood Fragility Modeling of CLT Shear Walls , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16181, https://doi.org/10.5194/egusphere-egu26-16181, 2026.

Seismic risk represents a major concern for densely populated urban areas, particularly in regions characterized by persistent volcanic and tectonic unrest. The city of Naples, southern Italy, is currently affected by an ongoing bradyseism crisis associated with the Campi Flegrei caldera, which has resulted in frequent low-to-moderate magnitude earthquakes (M 2–3+) over recent months. In this context, this study presents an integrated, data-driven framework for urban-scale earthquake risk mapping that combines probabilistic seismic hazard assessment with exposure and vulnerability modelling using convolutional neural networks (CNNs) and GIS techniques. Seismic hazard was quantified using earthquake records spanning 1990–2024 and modelled through six conditioning factors: elevation, slope, earthquake magnitude density, epicentral density, distance to epicentres, and peak ground acceleration. These spatial layers were integrated using a CNN architecture to generate a probabilistic hazard map representing the likelihood of earthquakes with magnitudes ≥3.5. Human exposure was subsequently assessed by integrating gridded population datasets with building footprints and parcel-level spatial data where available. Structural vulnerability was estimated through the fusion of land use/land cover information and recent building height data, both reclassified into susceptibility scores reflecting potential earthquake damage. The combined vulnerability index was categorized into five classes, with higher values corresponding to dense urban areas and taller building stock. The final seismic risk map was produced by integrating hazard, exposure, and vulnerability layers. Results highlight that areas characterized by high population density and intensive urban development sexhibit the highest seismic risk, consistent with observed urban patterns. The proposed methodology offers a transferable and automated approach for urban seismic risk assessment and can support risk-informed planning and disaster mitigation strategies in seismically active metropolitan regions.

How to cite: Sonowal, S., Amitrano, D., Pascarella, A. E., Kumar, R., and Gaicco, G.: Seismic Risk Assessment in Italy through Probabilistic Hazard Analysis and Integrated Exposure–Vulnerability Modelling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16469, https://doi.org/10.5194/egusphere-egu26-16469, 2026.

Our dependency on artificial intelligence (AI) is increasing gradually for predicting disaster, allocating resources, emergency response systems, and calculating the impact of the disaster. These new technologies undoubtedly offer unparalleled opportunities to enhance resilience, but their implementation without ethical safety measures could multiply the existing inequalities with humanity. This study makes the case for a paradigm shift in humanitarian engineering toward human-centered AI, with a focus on prioritizing the requirements of frontline communities that are most impacted by climatic extremes. To investigate how design decisions affect equity results, this analysis draws on current developments in climate-resilient infrastructure and AI-driven catastrophe management. Using a policy-oriented perspective, this paper identifies three actionable strategies: mandating equity impact assessments for AI applications in disaster contexts, establishing governance frameworks that include community representation, and incorporating ethical AI training into engineering and public administration curricula. These ideas intend to bring about a convergence of scientific advancement and social justice, with the goal of ensuring that AI enhances human agency rather than diminishing it. Through the incorporation of frontline communities into the process of developing and deploying AI systems, this study will contribute to an approach to catastrophe resilience that is more accountable and inclusive. In conclusion, the article emphasizes the importance of interdisciplinary collaboration among engineers, policymakers, and affected people to develop AI solutions that are not only effective but also compassionate and egalitarian. 

How to cite: Monalisa, S. and Mostafiz, R. B.: Ethical AI for Disaster Resilience: Centering Frontline Communities , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22076, https://doi.org/10.5194/egusphere-egu26-22076, 2026.

Urban gullies are an emerging geo-hydrological hazard of the Anthropocene, particularly in rapidly urbanizing tropical cities. In the Democratic Republic of the Congo (DRC), intense rainfall, steep slopes, erodible soils, and uncontrolled urban expansion combine to create highly favorable conditions for the formation and rapid expansion of UGs. Recent national-scale inventories reveal that more than half of Congolese cities are significantly affected, with nearly 3,000 urban gullies mapped. These features can reach tens of meters in depth and width within a few years, causing widespread destruction of housing, roads, and infrastructure, and leading to population displacement, injuries, and fatalities.

Recent analyses estimate that approximately 118,000 people were displaced by urban gullies in the DRC between 2004 and 2023, with displacement rates more than doubling after 2020. Currently, about 3.2 million people live within potential gully expansion zones, a number expected to increase dramatically as urbanization continues. Despite this growing risk, major knowledge gaps persist regarding the socio-economic impacts, rainfall thresholds, and short-term dynamics controlling gully initiation and expansion, severely limiting disaster risk management and early warning capacities.

This project aims to address these gaps through the implementation of a Congolese observatory of urban gullies, focusing on the cities of Kinshasa and Bukavu. Building on previous achievements, the project combines geomorphological research, citizen science, and policy advocacy to provide a proof of concept for an operational observatory.

The project adopts a participatory citizen science approach, engaging at-risk communities as “citizen observers” to collect in-situ data on gully dynamics, rainfall events, and socio-economic impacts. Community information sessions support risk awareness, co-development of data collection tools, and validation of observations. Data are collected using mobile applications, complemented by high-resolution geomorphological monitoring through rain gauge networks, GPS surveys, and drone imagery. These datasets enable improved characterization of gully expansion processes and identification of rainfall thresholds associated with hazardous events.

Beyond data generation, the project emphasizes governance and advocacy by translating scientific results into policy briefs and stakeholder workshops involving communities, authorities, NGOs, and urban planners. The project ultimately seeks to strengthen disaster risk management, inform sustainable urban planning, and demonstrate the feasibility and necessity of a dedicated national observatory for urban gullies in the DRC.

How to cite: Ilombe Mawe, G., Lutete Landu, E., Mugaruka Bibentyo, T., Makanzu Imwangana, F., Nzolang, C., Poesen, J., Dewitte, O., Bielders, C., Vanmaercke, M., and Michellier, C.: Implementation of a Congolese observatory of urban gullies for research, governance, and early warning system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22962, https://doi.org/10.5194/egusphere-egu26-22962, 2026.

Urban gullies (UGs) are an increasingly urgent concern in many cities of the Global South. Rapid and largely unplanned urban expansion, combined with inadequate drainage infrastructure, erodible soils, and intense rainfall, have led to the formation of thousands of large UGs —often several tens of meters wide and deep and extending over hundreds of meters— in cities across the Democratic Republic of the Congo. These gullies cause loss of life, destroy housing and critical infrastructure, and further exacerbate the vulnerability of already marginalized populations. The situation is particularly severe in Kinshasa, where more than 800 UGs have already been recorded, threatening over one million people.

A wide range of initiatives has been implemented to stop UG expansion. These include large-scale engineering interventions led by the state or non-governmental organizations, such as concrete reinforcement of gully heads and canalizing the gully channel. However, many measures are expensive and/or often fail.

Nevertheless, emerging evidence highlights promising strategies for urban gully prevention and control. A key principle is to prevent rainwater from leaving individual parcels by installing water retention structures, as the accumulation of runoff along roads is a primary driver of gully initiation and expansion. A critical requirement for success is that a majority of households actively participate in such initiatives. Improving risk awareness and creating synergies between UG control and water accessibility will therefore be crucial to achieving this.

Here we aim to demonstrate the effectiveness of such a strategy. For this purpose, we installed water retention structures in a representative catchment in Kinshasa affected by UGs. This is done in close collaboration with local stakeholders. By monitoring and studying participation rates as well as the resulting hydrological effects (e.g., through the involvement of local students), we will develop actionable guidelines to this growing problem in Kinshasa and elsewhere, thereby enhancing both urban resilience and water security in vulnerable neighborhoods.

How to cite: Lutete Landu, E., Ilombe Mawe, G., Makanzu Imwangana, F., Makonga, L.-O., Lusolamo Nguizani, D., Luemba Luemba, R., Bielders, C., Michellier, C., Dewitte, O., Poesen, J., and Vanmaercke, M.: Stabilisation of urban gullies by managing rainwater at parcel scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23167, https://doi.org/10.5194/egusphere-egu26-23167, 2026.

Translating coarse-resolution climate projections into actionable, city-scale hazard information remains a critical challenge for coastal infrastructure planning worldwide. We present a transferable framework that combines adaptive-mesh numerical modeling with a physically consistent pseudo-global warming (PGW) methodology to generate high-resolution, climate-adjusted tropical cyclone (TC) scenarios. Here, we employ the CPAS (ClusterTech Platform for Atmospheric Simulation) model at variable resolutions (96-48-24-12-3 km), coupled with bias-corrected CMIP6 data under SSP5-8.5 forcing. Climate perturbations are applied using a physically consistent approach that also helps reduce model spin-up. The methodology incorporates a scale-aware physics scheme specifically validated for TCs. It bridges the scale gap between global climate models (~100 km) and decision-relevant hazard assessment (~1 km), offering a pathway applicable to coastal megacities globally. 

We demonstrate the framework using five representative TCs impacting the South China coast during 2008-2021, spanning a range of intensities, sizes, and approach characteristics. Historical control simulations accurately reproduce observed storm tracks and structures, establishing confidence in the climate-perturbed scenarios. Systematic climate change signals emerge across the event portfolio: (1) variable intensity amplification (3.1-8% °C⁻¹ climate sensitivity), dependent on storm structure, with the strongest storms exhibiting the largest response; (2) nonlinear precipitation enhancement, with median increases of 30-35% and amplification up to 50% at extreme percentiles; and (3) diverse structural responses, with some storms contracting while others expand their damaging wind field.

Event-to-event differences (e.g., initial intensity, storm size, track angle, and rapid intensification) drive diverse climate responses, making uniform adjustment factors potentially misleading. The framework provides physics-based, scenario-specific hazard simulations at 3 km resolution (extendable to < 1 km), directly linkable to exposure databases for “what-if” stress-testing of historical events under future climate conditions. Although demonstrated for TCs, the framework is transferable to other storm types and regions, with adaptive meshing enabling efficient, decision-relevant hazard modeling over complex coastal terrain.

How to cite: Zheng, Y., Tam, C., Cheung, C.-C., and Sze, W.-P.: Integrating Climate Models and Coastal Risk Assessment in relation to Tropical Cyclones using an Adaptive Mesh Framework , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4650, https://doi.org/10.5194/egusphere-egu26-4650, 2026.

Geotechnical centrifuge modeling provides an effective approach to reproduce prototype-relevant stress states for high-speed dry granular flows. Yet, in a rotating reference frame, the Coriolis acceleration induced by rapid granular motion can become comparable to the centrifugal acceleration, thereby markedly modifying run-out behavior and impact responses and complicating the interpretation of physical modeling results. This study integrates a suite of centrifuge model tests with discrete element method (DEM) simulations to systematically elucidate how Coriolis effects govern both the mobility of dry granular flows and their impact on rigid barriers. For run-out processes, a DEM framework incorporating both centrifugal and Coriolis accelerations is employed to compare granular mobility under three Coriolis configurations: dilative, compressive, and deflective conditions. The results indicate that the dilative Coriolis condition substantially enhances flow mobility and kinetic energy, whereas the compressive condition suppresses run-out and promotes flow densification. In contrast, under the deflective Coriolis condition, the sensitivity of the final run-out distance and overall flow scale to Coriolis effects is significantly reduced. This reduced sensitivity is attributed to two opposing deflection stages during propagation and deposition, suggesting a practical advantage for mitigating Coriolis-induced bias in centrifuge modeling. For impact processes, centrifuge experiments combined with DEM simulations are used to characterize granular impact behaviors on rigid barriers under different Coriolis conditions. The Coriolis effect has a limited influence on the peak magnitude of the total impact force, but it significantly alters the force time history and spatial distribution by modifying the velocity structure, flow thickness, and particle-scale momentum transfer. Notably, impact responses obtained under the dilative Coriolis condition are closer in force level to Coriolis-free reference cases, whereas the resultant force application point is comparatively insensitive to the Coriolis configuration. Overall, the results demonstrate that Coriolis effects should not be treated as a uniform experimental disturbance. Instead, they represent a key control factor whose influence depends on the specific quantities of interest. The findings provide methodological guidance for configuring centrifuge experiments and interpreting results in the modeling of high-speed dry granular flows, with explicit implications for both run-out and impact simulations. 

How to cite: Zhang, B.: Understanding Coriolis effects in centrifuge modeling of high-speed dry granular flows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7212, https://doi.org/10.5194/egusphere-egu26-7212, 2026.

Landslides are among the most destructive natural hazards in the Himalayan region, where steep terrain, complex lithology, heterogeneous soil cover, and intense hydro-meteorological forcing collectively govern slope instability. Despite growing recognition of ARs as major contributors to extreme rainfall, their explicit integration into physically informed slope stability assessments in the Himalayas remains limited. This research aims to investigate the impact of atmospheric-river-driven precipitation on slope stability across the Himalayan region by coupling landslide inventory data, soil characteristics, topographic controls, and slope stability theory. landslide occurrences are analyzed with respect to topographic parameters derived from digital elevation models, such as slope angle, elevation, and terrain morphology. Given the limited availability of site-specific geotechnical data over large mountainous regions, soil mechanical properties specifically cohesion and angle of internal friction are inferred from soil type and texture classes obtained from global soil databases. Representative ranges of shear strength parameters are assigned based on established values reported in the literature.

Temporal characteristics of AR events, including shape, movement, intensity, duration, and antecedent moisture conditions, are linked with observed landslide occurrences to identify critical thresholds associated with slope failure. Slope stability is evaluated using the factor of safety (FOS) concept derived from limit equilibrium principles for infinite and shallow slope conditions. The influence of atmospheric rivers is incorporated through rainfall-induced changes in pore-water pressure and effective stress, enabling assessment of strength reduction and progressive destabilization under extreme precipitation scenarios. The outcomes of this research are expected to quantify the degradation of slope stability associated with atmospheric-river-driven rainfall, identify soil and terrain combinations most susceptible to AR-induced failures, and provide a physically meaningful explanation for observed landslide spatial clustering during extreme precipitation events. By integrating atmospheric processes with geotechnical slope stability analysis, this study advances the understanding of hydro-geomorphic hazards in the Himalayas and contributes to improved landslide susceptibility assessment, risk mitigation, and climate-resilient land-use planning in mountainous regions.

How to cite: Raina, B. A. and Nayak, M. A.: Atmospheric Rivers as Triggers of Slope Instability and Landslides in the Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9527, https://doi.org/10.5194/egusphere-egu26-9527, 2026.

Landslides are among the natural hazards that significantly impact human life and infrastructure, making accurate landslide mapping essential for hazard assessment, risk reduction, and land use planning. However, mapping landslides, particularly in vegetated areas, remains challenging, as traditional field-based and manual mapping approaches are time-consuming and require substantial expert knowledge. Semi-automatic mapping methods based on high-resolution Digital Terrain Models (DTMs) have improved landslide inventory preparation; however, their transferability to larger and diverse environmental settings remains limited and require further assessment.  Therefore, this study aims to assess the transferability of a Geographic Object-Based Image Analysis (GEOBIA) landslide mapping approach using optimal moving window sizes, and to examine whether model performance varies across specific land use classes and improves with higher-quality DTM data.

A GEOBIA-based model, originally developed for forest covered landslides in the cuesta landscape of Jena region (Zangana et al., 2025), was transferred and applied to landslides at the Swabian Alb escarpment in south-western Germany, which are located not only in forests, but also in grasslands and settlements. The study area is characterized by Jurassic limestones overlying marls and clays. It is affected mainly by rotational slides, slump-earthflows, and translational landslides, some of which show repeated reactivation. The manually mapped landslide inventory was used for result validation and accuracy assessment. DTM derivatives (from the 2003 and 2023 data) were prepared using optimal moving window sizes following Zangana et al. (2025). The semi-automatic landslide detection workflow involved multi-resolution segmentation (MRS) and support vector machine (SVM) classification, followed by expert-based refinement and accuracy assessment against the reference map. Finally, transferability was further examined through land use class-based performance analysis and by evaluating the effect of higher-quality 2023 DTM data on model results.

The results indicate that the model developed for the Jena region is transferable to the Swabian Alb. When applied to the 2003 dataset, without differentiating between land use types, the model achieved a 75% detection rate for landslide body areas. Using the 2023 dataset increased detection accuracy to 86% compared to the 2003 data. The area-based detection accuracy in this study is approximately 30% higher than reported for the Jena region by Zangana et al. (2025). When considering only forested areas—for which the model was originally developed—the true positive rate increased by about 15%, while false positives decreased by a similar margin. Although the approach effectively identifies landslides, particularly in vegetated areas, it currently performs best for cuesta-related rotational slides. Further assessment and refinement are needed to extend its applicability to other landslide types. Nevertheless, the method shows strong potential for detecting landslides with distinct geomorphological signatures in high-resolution DTMs worldwide.

Reference: Zangana, I., Bell, R., Drăguţ, L., Sîrbu, F., and Schrott, L.: Mapping forest-covered landslides using Geographic Object-Based Image Analysis ( GEOBIA ), Jena region , Germany, Nat. Hazards Earth Syst. Sci., 25, 4787–4806, https://doi.org/10.5194/nhess-25-4787-2025, 2025.

How to cite: Zangana, I., Bell, R., Drăguţ, L., and Schrott, L.: Transferability of Semi-Automatic Landslide Mapping Approach Using High-Resolution DTMs: a Case Study from the Swabian Alb, Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11366, https://doi.org/10.5194/egusphere-egu26-11366, 2026.

Wildfires are increasingly destructive events, threatening ecosystems and human infrastructure while contributing significantly to carbon emissions. Accurate and timely burned area mapping is therefore essential for effective mitigation and recovery. Optical satellite sensors are often hindered by clouds and smoke, making Synthetic Aperture Radar (SAR) sensors like Sentinel-1, with their all-weather capability, a crucial tool for monitoring. However, SAR backscatter is significantly influenced by topography, which can distort signals and hinder accurate detection.

This study evaluates the impact of angular-based radiometric terrain normalization (RTN) on burned area mapping using Sentinel-1 SAR data and the Normalized Radar Burn Ratio (NRBR) index. We compare the performance of NRBR calculated with standard sigma nought (σ⁰) and with gamma nought (γ⁰) corrected via an angular-based RTN model implemented in Google Earth Engine. A U-Net deep learning model was used to delineate burned areas in Portugal and California. Results show that NRBR without RTN achieved better accuracy in Portugal, suggesting potential overcorrection effects in moderate terrain. In California, RTN slightly improved overall accuracy and reduced commission errors, although omission errors remained high. These findings indicate that while RTN enhances radiometric consistency, its impact on burned area detection with NRBR is limited, likely because the NRBR formulation itself already mitigates topographic effects through pre/post-fire ratios.

How to cite: Tarazona, Y. and Mantas, V.: The Impact of Radiometric Terrain Normalization (γ⁰) on Burned Area Mapping Accuracy Using Sentinel-1 data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15290, https://doi.org/10.5194/egusphere-egu26-15290, 2026.

Probabilistic precipitation, such as the 100-year rainfall, is widely used as the design storm for planning flood control structures. However, due to climate change, the return periods estimated 50 years ago are no longer valid. This shift necessitates a fundamental reconsideration of how we determine design levels for construction. In other words, there is an urgent need for more sophisticated methodologies capable of handling non-stationary precipitation data.

To address these challenges, we present two key topics:

• In Japan, the national and local governments have issued guidelines suggesting that future extreme rainfall intensities can be estimated by multiplying present-day values by a change factor of 1.1 to 1.15, assuming a 2.0°C increase in global temperature. While these guidelines tend to treat the change factor as largely uniform across regions for practical simplicity, we contend that it should be estimated with greater geographical precision. Consequently, we estimated the change factors specifically for Mie Prefecture in central Japan. Our results demonstrate that even within a single prefecture, the factor varies significantly depending on the specific location.

• We have been developing a novel approach to estimate future 100-year precipitation levels through multivariate analysis. The details of this methodology and our findings will be presented in our poster session.

How to cite: Kurata, M., Hasegawa, M., Mizuki, C., and Kuzuha, Y.: Estimation of Future 100-year Precipitation in Mie Prefecture, Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16126, https://doi.org/10.5194/egusphere-egu26-16126, 2026.

Despite urgent needs for adaptive coastal risk management, operational systems still rely heavily on static triggers and fragmented information that overlook interactions between evolving hazards and response actions. Building on a completed game-like deep reinforcement learning (DRL) testbed, we present a pathway toward operational coastal decision support, progressing toward real-world case studies such as Venice in Italy and South East Queensland in Australia.

In the first phase, we developed a controllable game-like scenario that captures the essential components of coastal emergency management: a simplified representation of coastal geography and built assets, dynamic multi-hazard drivers evolving over time, and an action space reflecting plausible operational interventions under constraints. Using this environment, we demonstrated that a PPO-based DRL agent can learn adaptive policies through repeated interactions, as we gained practical lessons on state representation, constraint handling, and reward design for safety-critical objectives.

We then focus on the transition from simulation to real-world settings by outlining a set of alternative state-representation options, spanning classical dimensionality reduction and feature engineering through to learned latent-state methods. We report results for selected approaches, using autoencoders as the primary entry point to compress high-dimensional spatio-temporal hazard and exposure information into compact variables that retain decision-relevant structure while improving training efficiency and robustness. This provides a practical interface to real-world, digital-twin style environments built from geospatial and socio-economic data and forecast inputs.

Finally, we propose an orchestration layer to reduce the risk of AI-driven decision making and improve usability. A large language model (LLM) ingests DRL outputs and contextualises recommendations via retrieval-augmented generation over plans, studies, and standard operating procedures, together with API calls to dynamic data feeds. The proposed orchestration layer is intended to translate DRL outputs into human-readable and auditable decision support for a human-in-the-loop operator, grounding recommendations in retrieved local documentation and live data feeds to strengthen transparency, uncertainty communication, and operational trust.

How to cite: Sano, M., Ferrario, D., Casagrande, S., Vascon, S., Torresan, S., and Critto, A.: Deep Reinforcement Learning for Operational Coastal Emergency Response With AI Agent Orchestration and Human Oversight, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17369, https://doi.org/10.5194/egusphere-egu26-17369, 2026.

This paper presents the development and pilot evaluation of Map@Me, an RPG-based serious game designed to improve understanding of hydrogeological risks and evacuation planning. Developed within the Motivation and Engagement in Disaster Mapping in Europe (MEDiME) Horizon Project, Map@Me targets a diverse audience and was tested in formal education settings, specifically middle and high schools.

The game integrates real local hazard maps, allowing players to explore their own environments and engage in realistic evacuation scenarios. With Map@Me, the player traces a realistic evacuation route that takes into account diverse mobility conditions, including disabilities, as well as advantageous and challenging factors, such as access to local knowledge and unfamiliarity with the area. Using a randomised system to determine the fictional character’s features in a real hazard map scenario, Map@Me represents a good example of how traditional disaster education can be supported by participatory methods of learning, whereby the agents can, in a controlled environment, experiment creatively with their behavioural choices and address their intrinsic biases.

During the presentation of the preliminary results from pilot sessions conducted with students, we will highlight both traditional learning outcomes—such as knowledge of evacuation sites and emergency preparedness measures—and “soft” learning outcomes, including cooperation, empathy, and collective responsibility.

The findings suggest that serious games such as Map@Me can enhance inclusive, place-based disaster preparedness, hazard map literacy and risk awareness, and overall contribute to a more socially aware approach to risk communication among younger audiences.

How to cite: Petraroli, I., Flacke, J., and Atun, F.: Motivation and Engagement in Disaster Mapping in Europe (MEDiME): Understanding hydrogeological risks and vulnerability through serious gaming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17956, https://doi.org/10.5194/egusphere-egu26-17956, 2026.

The increasing complexity of cascading and compounding effects, necessitates innovative tools for wide stakeholder engagement and decision-making, especially in uncertain situations. Risk communicators often struggle to successfully convey these complexities to diverse groups of actors. In the PARATUS project, we implemented a series of four serious games: High Water Pantano, Bucur Simulation, Saltum Montem, Paratus Systemic Risk Game; to address this gap through experiential process.

The structured, stakeholder-driven process used in the PARATUS project was grounded in the CompleCSUs framework and the design thinking methodologies. The development process included four phases, as follows: 1) Research and conceptualization, focused on the literature review and Miro app based mapping of stakeholder needs and PARATUs four case study areas (Caribbean, Bucharest, Istanbul, and the Alps); 2) Scenario and role design, focused on translating real-world impact chains co-developed with stakeholders into interactive storylines; 3) Prototyping and iterative testing, focused on stakeholders interacting with the prototypes and providing direct feedback to the tools; and 4) Implementation and evaluation, focused on the deployment of serious games in workshops and assessing their effectiveness.
Some benefits identified include increased transdisciplinary collaboration and the opportunity for stakeholder exploration of the results of inaction or certain decisions linked with the risk reduction, in  a safe, simulated environment. However, the four-phase serious games approach in the PARATUS also resulted in certain critical lessons for the future implementation of co-design processes. These included the need for more flexibility in formats of the tools (analog vs. digital) to accommodate technical and context-based limitations; the importance of understanding the institutional hierarchies and factoring them into the process activities, and the need for multilingual support, especially in the transboundary context, for increase of the accessibility of the tools and trust levels of the participants. Following such four-step process, scientific risk assessment can be transformed into a scalable, user-centered and engaging tool for fostering long-term resilience.

How to cite: Kulakowska, M., Atum, F., Koelle, B., and Magnueszewski, P.: A Four-Phase Serious Games Approach in the PARATUS Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20736, https://doi.org/10.5194/egusphere-egu26-20736, 2026.

Agricultural production remains central to food security and rural livelihoods, yet it is increasingly exposed to compound and cascading natural hazards under a changing climate. Drought, flooding, and extreme rainfall, heat stress, storms, and soil degradation do not operate in isolation. Their impacts often accumulate across the seasonal calendar and propagate beyond the field through labor, processing, storage, and distribution constraints. This contribution synthesizes evidence on how multi-hazard pressures disrupt agricultural productivity and stability, with attention to major staple and cash crops (for example, rice, wheat, maize, sugarcane, and soybean) and to vulnerability patterns that shape disproportionate impacts on resource-constrained and smallholder systems. We review and organize recent findings around three linked questions: (1) how hazard timing and co-occurrence influence crop sensitivity across key growth stages; (2) which biophysical and socioeconomic conditions amplify losses and slow recovery; and (3) which adaptation pathways show consistent promise under multi-hazard risk. A central focus is Climate-Smart Agriculture (CSA) as an integrated response, including practices that aim to improve productivity while strengthening resilience and reducing environmental tradeoffs. However, the review also highlights barriers that frequently limit CSA uptake in high-vulnerability settings, including institutional constraints, knowledge gaps, and financing limitations. By connecting hazard mechanisms to stage-specific crop impacts and to constraints along agricultural value chains, the synthesis supports more targeted adaptation planning and more realistic resilience strategies. The paper argues for context-specific, multi-stakeholder approaches that combine policy, technology, and farmer-centered implementation to address increasing climate and hazard uncertainty.

How to cite: Hossain, T. and Mostafiz, R.: Compound hazards, crop sensitivity, and climate-smart adaptation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22036, https://doi.org/10.5194/egusphere-egu26-22036, 2026.

The early decades of the 21st century have been marked by a profound and accelerating shift in the European hydrological cycle, demanding a fundamental re-evaluation of how areas susceptible to precipitation extremes are identified. This study presents a risk map for extreme precipitation events (EPEs), categorizing areas into four risk levels: from no risk to high risk. The study includes 70 years of historical data from E-OBS (1951-2020) and 13 bias-adjusted CORDEX models under two future scenarios, SSP2-4.5 and SSP5-8.5, for the period 2021-2100. To ensure reliable detection of long-term changes, the analysis employs the Mann-Kendall test with an iterative pre-whitening procedure.

The historical risk assessment derived from seven decades of E-OBS observational data shows a heterogeneous distribution across Europe. Elevated risk zones are predominantly concentrated along the western coastal regions of Scandinavia, particularly in Norway's Atlantic-facing territories. In contrast, large portions of the continental interior, including substantial areas of Poland, Germany, and the eastern Baltic States, exhibit medium to low risk levels. Under the SSP2-4.5 scenario, some areas may experience heightened risk of precipitation extremes, notably in Scandinavia, yet considerable uncertainty remains across models.

The SSP5-8.5 scenario presents a noticeable increase in risk levels, with widespread agreement among climate models. Almost the entire study domain transitions into medium to high-risk categories. This wholesale shift represents not merely an intensification of existing patterns but a fundamental reorganization of the region's extreme precipitation climatology. The changes are especially pronounced across the Scandinavian countries, with almost the entire region falling into the high-risk category.

The contrast between two emissions scenarios emphasizes the strong sensitivity of extreme precipitation patterns to greenhouse gas concentration pathways. These findings provide critical evidence for the necessity of urgent mitigation actions, and support adaptation planning in regions that may potentially experience heightened risk of extreme precipitation.

How to cite: Fakour, P., Ustrnul, Z., and Messori, G.: Climate Driven Risk Assessment: Identifying Susceptible Areas and Future Shifts in Extreme Precipitation Across North-Central Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-792, https://doi.org/10.5194/egusphere-egu26-792, 2026.

The magnificent but complex topography of the Alps makes them Europe's indispensable water tower, channeling vital precipitation into rivers that sustain communities, economies, and ecosystems downstream. Therefore, gaining an understanding of how precipitation behaves in this mountain region is crucial. These mountain regions are home to a great variety of climatic regimes, from Mediterranean and Atlantic maritime to strongly continental, and yet, hydro-meteorological extremes pose an increasingly disastrous threat, heightening pressure on water security, infrastructure resilience, and transboundary risk management.

There has been much research into precipitation extremes that has taken place over various parts of the Alps, bringing to light their intensity, frequency, and dynamics. However, these studies are confined to specific regions of the mountain range. The novelty of this work is threefold: a) It is based on a consistent, high-resolution, observation-based, recently published alpine-wide database of High-Impact Precipitation Events (HIPEs), observed for the period 1961-2022 and developed through a homogeneous transnational approach (Lemus-Canovas et al., 2025); b) It is based on a multi-metric analysis to draw a comprehensive picture of changes in the HIPE features, including the assessment of their spatial extent, which has not been addressed before; and c) The variations in the different characteristics of these events, as well as the responsible drivers, are addressed by considering the contribution of the annual frequency of weather types.

By combining Regression Analysis with the modeling of the Generalized Extreme Value distribution, the study was able to identify significant patterns of increase in HIPE occurrences in subregions of the Alps, along with different contributions from the main weather types when explaining the annual variability of the different characteristics of HIPEs. Such techniques offer a nuanced view of the contribution of atmospheric dynamics and local factors in determining the observed variability of precipitation extremes.

Overall, our results indicate that the emerging shifts in HIPE behavior will most likely result from a nonlinear interaction of thermodynamic intensification and dynamics of circulation. The findings are intended to emphasize the emerging regional vulnerabilities and provide science-based support for cross-border preparedness for flooding and adaptation to climate change in Alpine environments.

Keywords: High-Impact Precipitation Events (HIPEs), European Alps, trend analysis, Alpine-wide observations

Reference: Lemus-Canovas, M. (2025). High-Impact Precipitation Events in the European Alps (1961–2022) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17047822        

How to cite: Gagnani, B., Lemus i Cánovas, M., and Crespi, A.: Understanding the observed changes in High-Impact Precipitation Events across the European Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1064, https://doi.org/10.5194/egusphere-egu26-1064, 2026.

Flooding remains one of India’s most persistent hydroclimatic threats, driven by monsoon variability, rapid urbanisation, and chronic gaps in observational hydrometric data. These limitations affect both fast-growing cities and large river basins, where ungauged tributaries and limited socio-economic preparedness compound the overall risk. To address this challenge, we develop a holistic, multi-scale flood-risk assessment framework that integrates reanalysis-driven flood hazards with socio-hydrological vulnerability patterns across India. First, four leading Hydrological Reanalysis Datasets, ERA5, IMDAA, CFSR/CFSv2, and MERRA-2, are evaluated using three decades of rainfall. ERA5 emerges as the most reliable dataset across diverse hydro-climatic zones. Validation using CC, KGE, NSE, POD, FAR, EB, and CSI shows strong agreement with gauged data.  At the city scale, compound flood behaviour is quantified using bivariate and trivariate copulas linking long-duration rainfall (Rx1day), short cloudbursts (N25), and annual peak discharge (Q). River-connected cities exhibit pronounced upper-tail dependence under Gumbel–Hougaard structures, revealing synchronised extremes where intense rainfall and river overflow co-occur. Non-river cities show distinct rainfall-only compound signatures. These joint-probability structures provide realistic estimates of compound flood likelihoods in complex urban environments. At the basin scale, an extreme-value workflow using rolling annual maxima, KS-based distribution selection, and event-shape normalisation is applied to generate gridded 3-day, 5-day, and 7-day RP100 rainfall and discharge inputs. These time series force LISFLOOD-FP to produce representative design-flood hazard layers—depth, velocity, and depth×velocity—across India’s major basins. To capture the human dimension of flood impacts, a socio-hydrological module integrates 54 indicators of exposure, sensitivity, and adaptive capacity using a multi-criteria decision-making approach. District-level vulnerability scores and rankings reveal high- and very-high-vulnerability clusters across eastern, central, and northeastern India. These socio-economic patterns are analysed alongside hazard outputs, identifying critical “hotspot” regions where high exposure (population and area), weak coping capacity, and severe hydrodynamic hazards converge.

How to cite: Singh, H. and Mohanty, M. P.: Towards a Dual-Scale Flood Risk Assessment in India: Copula-Based Urban Extremes, Basin-Scale Design Flood Simulation, and Socio-Hydrological Vulnerability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1115, https://doi.org/10.5194/egusphere-egu26-1115, 2026.

In Central Europe, climate change contributes to an increasing frequency of devastating flood events, such as those observed in Western Europe in July 2021. While floods with return periods of up to 200 years have been relatively well studied, understanding and preparedness for more extreme, less frequent High-Impact-Low-Probability (HILP) floods remain limited. A key tool to assess the potential consequences of such events is the stress-test scenario. More specifically, these are hypothetical yet plausible simulations of a very low-likelihood flood events.

This review systematically analyses scientific studies that apply stress-testing approaches to HILP floods. The focus lies on research examining the impacts of very extreme pluvial and fluvial floods on humans, the built environment, and critical infrastructure. A systematic SCOPUS keyword search initially identified ~12,000 studies, which were reduced to 137 relevant publications using a filtering process assisted by a large language model. The selected studies are differentiated by how physical boundary conditions are derived, floods are modeled, and impacts are quantified.

The analysis shows that most studies use univariate statistical methods to derive hypothetical rainfall events, while more complex approaches such as climate model reforecasts or multivariate weather generators are employed far less frequently. A wide range of techniques is used to modify historical events to simulate unprecedented flooding. Counterfactual scenarios in flood modeling mainly investigate the effects of reservoirs and similar structures, whereas other simulations explore the potential of early-warning systems to reduce exposure. In terms of impact modeling, the reviewed literature examines a broad range of system components. About 60% of studies employ simple GIS overlays to assess the number of structures affected by floodwaters, while more advanced modeling tools include agent-based models, cascading impact models, network theory, and multi-criteria decision models. Only a few studies assess multi-sectoral impacts, and their analyses are often shallow or overly simplified. Future research should address this gap to achieve a more comprehensive understanding of the potential damages caused by HILP floods.

How to cite: Lennartz, M., Vorogushyn, S., and Merz, B.: Stress testing approaches for High-Impact-Low-Probability floods: A review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1846, https://doi.org/10.5194/egusphere-egu26-1846, 2026.

Over the past decade, hail has been responsible for most financial losses associated with severe convective storms, with costs steadily increasing. As the population grows and increasingly invests in vulnerable infrastructure, the risk of economic damage from large hail also rises. Accurate hazard assessment is therefore essential for effective mitigation.

Report-based hail climatologies are limited by observational biases, resulting in large uncertainties, particularly in sparsely populated areas. While recent machine learning approaches have enabled the development of hail climatologies for hazard assessment, many existing datasets remain limited by regional biases and coarse spatial and/or temporal resolution.

Here, we present a novel, high-resolution large hail (> 2.5 cm in diameter) hazard dataset on a 4 km × 4 km, half-hourly grid over the contiguous United States (CONUS), ranging from 2000 to 2022. This unprecedented spatiotemporal resolution is enabled by integrating hail reports with multiple high-resolution remotely sensed hail-proxy observations, including radar reflectivity, satellite-derived brightness temperature, and total ice water path, together with key hail-relevant environmental parameters from the ERA5 reanalysis. The large hail hazard model, which we trained to produce the dataset, is based on the gradient-boosted decision tree algorithm XGBoost and provides increased spatial and temporal detail relative to prior large hail climatologies.

When evaluated on held-out test data, the model accurately reproduces the interannual, seasonal, and diurnal cycles of large hail. It resolves fine-scale topographic influences and captures coherent hail tracks. Performance is strongest for typical events, while rarer, atypical cases present a trade-off between improved detection and increased false positives.

The resulting dataset provides a high-resolution basis for large hail risk assessment across the CONUS. Because it relies on globally available satellite observations and reanalysis, the framework is transferable to other regions and can be applied to kilometer-scale weather and climate model output.

How to cite: Koch, R., Prein, A., Lohmann, U., and Aellen, N.: A Machine Learning Based High-Resolution Large Hail Climatology for the Contiguous United States, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2305, https://doi.org/10.5194/egusphere-egu26-2305, 2026.

Extreme precipitation is a natural hazard with significant socioeconomic implications, namely for sectors such as agriculture, including viticulture. This study provides the first comprehensive analysis of extreme precipitation events in mainland Portugal, based on sub-hourly observations. Using 10-minute precipitation data from 71 weather stations for the period 2000 to 2022, we assess the spatial and temporal variability of these events, including their seasonality, diurnal cycle, and synoptic-scale drivers. The mean seasonal contribution of extreme precipitation to total annual precipitation, defined using thresholds of 10–20 mm h^-1 (yellow warnings) and >20 mm h^-1 (orange and red warnings) following the criteria of the Portuguese Weather Service, is highest in winter, indicating a stronger influence of intense precipitation on annual totals. This contribution decreases in autumn and spring, reaching its minimum in summer. Extreme precipitation events occur most frequently between September and December, with a secondary maximum in April and May, particularly in the Alentejo region. The diurnal cycle exhibits a pronounced afternoon peak, consistent with convectively driven thunderstorms. In spring and summer, extreme events tend to account for a larger fraction of daily precipitation totals. Two extreme events were selected not only as case studies of heavy precipitation, hail and lightning but also as examples of understanding the specific weather conditions and atmospheric dynamics associated with such severe weather patterns. In the first case, the event on 28 May 2018 in the Douro region was associated with a cut-off low, whereas in the second case, the event on 14 September 2021 in the Alentejo region was associated with a frontal system in the final phase of its life cycle. ERA5 instability indices show a good agreement with observed lightning patterns. These results, particularly at the regional scale, provide valuable insights for climate research and socio-economic sectors such as viticulture, where extreme precipitation and hailfall pose significant risks. Acknowledgements: Research funded by Vine & Wine Portugal–Driving Sustainable Growth Through Smart Innovation, PRR & NextGeneration EU, Agendas Mobilizadoras para a Reindustrialização, Contract Nb. C644866286-011. The authors acknowledge National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020).

How to cite: Cruz, J., Belo-Pereira, M., Fonseca, A., and Santos, J. A.: Characterising Hourly Extreme Precipitation in Portugal: Spatial–Temporal Variability and Case Studies in Two Wine Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3539, https://doi.org/10.5194/egusphere-egu26-3539, 2026.

Flood generation arises from complex, scale-dependent processes that vary across global river catchments. Understanding and predicting the controls on flood generation is key in obtaining robust estimations of flood frequency, particularly for ungauged basins. Indeed, reliable design-flood estimates are fundamental for flood risk assessment, infrastructure design as well as understanding riverine geomorphology and ecology. While recent advances have improved global flood estimation, driven largely by improved hydrological understanding and expanded data availability, key drivers of extreme floods, including compound processes and scale-dependent effects, are still insufficiently represented, and model performance has rarely been evaluated systematically across hydro-climatic regions and flood magnitudes. Here, we leverage recent advances in global river gauge records, high-resolution river hydrography, and comprehensive catchment attribute datasets in order to develop a machine learning model for estimating design floods in ungauged rivers worldwide. We generate a global design-flood dataset and conduct a systematic evaluation of model performance across hydro-climatic regions and flood magnitudes, benchmarking against existing global design-flood products. Using interpretable machine-learning techniques, we identify dominant controls on flood generation and demonstrate how basin classification can inform flood estimation from moderate to extreme events. Our results reveal strong region-specific controls on flood extremes and provide new insights for improving design-flood estimation frameworks worldwide.

How to cite: Liu, Y., Slater, L., Moulds, S., Wortmann, M., Zhang, B., Gu, X., and Parsons, D.: Controls on river flood generation: implications for design floods , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4055, https://doi.org/10.5194/egusphere-egu26-4055, 2026.

In June 2024, the Muhuri River in eastern Bangladesh reportedly rose by ~7 m within three days, exceeding danger levels by >3 m and affecting more than 3.6 million people across 11 districts. Severe monsoon flooding in August 2024 reinforced the vulnerability of transboundary, data-scarce catchments to extreme hydrological hazards. To assess future flash-flood risks in the Muhuri River Basin, a four-tank conceptual rainfall–runoff model was developed and calibrated against daily discharge at Parshuram station for 2010–2025 (KGE = 0.72; PBIAS = 5.78%; RMSE = 14.30). The calibrated model was forced with an ensemble of 15 bias-corrected CMIP6 General Circulation Models under SSP2-4.5 and SSP5-8.5. Bias correction used Empirical Quantile Mapping applied to precipitation, maximum temperature, and minimum temperature. The projections were analyzed for near- (2026–2050), mid- (2051–2075), and late-century (2076–2100) periods relative to a historical baseline (1985–2014). The results suggest intensification of high-flow hazards: the 95th-percentile daily high flow (Q95) increases by up to 20.7% under SSP5-8.5, and the frequency of Q95 exceedance events increases by 69%. It is also noted that October discharge (post-monsoon) increases by 28.6%, consistent with delayed recession and a higher likelihood of prolonged inundation. The extreme-event analysis further suggests that 100-year flood magnitudes may increase by up to 22%, with substantial inter-model spread. Our data further indicates measurable reorganization of seasonal flow regimes under future forcing, consistent with emerging non-stationary flood behavior. Overall, these findings support climate-informed, impact-based early warning and adaptation planning for vulnerable transboundary basins.

How to cite: Rokonuzzaman, M., Sadat Khan, N., and Tanim Siddiquie, K.: Climate-Driven Intensification of Flash-Flood Hazards in Transboundary Muhuri River basin: A CMIP6-Based Assessment Using the Tank Hydrological Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4504, https://doi.org/10.5194/egusphere-egu26-4504, 2026.

Climate change is expected to alter floods in complex ways. Understanding changes in flood frequency and driving processes under changing climatic conditions is urgently needed to develop sustainable adaptation measures to floods in rivers such as the Swiss section of the Rhône, which has been hit by a severe flood in June 2024. The investigation of future flood characteristics and driving processes is, however, challenging because these events exhibit large interannual variability and are, among other processes, caused by localized intense precipitation, which is not optimally represented in global climate model simulations. High-resolution initial condition large ensembles and convection-resolving climate simulations can be used to address these challenges. This work leverages these two types of climate ensembles and process-based hydrologic modeling to quantify long-term changes in peak flows in the Swiss section of the Rhône until the end of the century, and their uncertainty. We will use these simulations to investigate the driving processes underlying future floods and how these differ from those relevant for flood generation today. In doing so, we provide information crucial for decision-making related to future flood protection and adaptation.

How to cite: Elkouk, A., Astagneau, P. C., Wood, R. R., and Brunner, M. I.: Improving flood projections in the Rhône River using high-resolution initial condition large ensembles and convection-resolving climate simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4837, https://doi.org/10.5194/egusphere-egu26-4837, 2026.

Global flood reporting has generally improved over the last century, particularly as global disaster
databases have started archiving and aggregating flood events. While these disaster databases,
including EM-DAT, DFO, HANZE, and UNDRR, are helpful tools for flood analysis, they are
often incomplete in their reporting. Therefore, aggregating them to form a more complete database
is critical. It is equally important when training a disaster-level flood event model using these
databases to account for systemic inequality in flood reporting. Here, we present a three-phase
INLA-SPDE flood prediction model which determines relevant climate signals from documented
flood events, determines which countries report floods the most consistently relative to the climate
signals, and projects global latent flood risk on a monthly 0.25-degree scale using the best reporting
countries for calibration. The result is a spatial risk field which ranks relative risk of disaster-level
flood events based on climate conditions one month in advance (sub-seasonal timescale).
Predictions at the 0.25-degree level perform well at relative risk ranking (AUC = 81.7 %) but
returns many false positives due to the complexity and rarity of these events (Precision = 6.12 %).
However, when aggregating predictions to the country level, this issue is minimized (Precision =
49.0 %), meaning country level predictions may be used to determine monthly likelihood of a flood
event occurring, while cell level predictions may be used to determine high risk locations within
the country. Combined, this modeling methodology allows for prediction of floods beyond the
capabilities of normal disaster database models by separating flood reporting biases from latent
climate signals as much as possible.

How to cite: Magers, B., Najibi, N., and Devineni, N.: Employing integrated nested laplace approximation with stochastic partial differential equation (INLA-SPDE) modeling for sub-seasonal flood prediction using a globally aggregated disaster database, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5588, https://doi.org/10.5194/egusphere-egu26-5588, 2026.

Due to climate change, the quantity of extreme rainfall events caused by tropical cyclones in Beijing have exhibited a significant increasing trend, posing great challenge to disaster prevention and mitigation efforts in the city. To enhance Beijing's capacity to respond to tropical cyclone-induced precipitation, this study collects multi-source data on precipitation, tropical cyclones, and precipitable water vapor. It proposes a dual-scale (temporal and spatial) quantitative identification method for tropical cyclone precipitation, generates a tropical cyclone precipitation dataset for Beijing, and compiles a dataset of 20 typical tropical cyclones. Furthermore, the study elucidates the characteristics of tropical cyclones affecting Beijing. 24 indicators reflecting the characteristics of tropical cyclone were designed based on the how tropical cyclone forms rainfall. A quantitative study on the relationship between the characteristics of tropical cyclones and the precipitation characteristics in Beijing, (i.e. the total rainfall and the maximum 1-day rainfall) was conducted. The results indicates that: 1) A increasing trend was observed in the number of tropical cyclones impacting Beijing from the 1980s to the 2010s, with indications that this trend is likely to continue. Furthermore, statistical analysis confirms that tropical cyclones making landfall in Shandong and Liaoning provinces are more likely to trigger extreme rainfall events in Beijing; 2) the correlation between the total precipitation of the tropical cyclone and the distance from the tropical cyclone centre to the centroid of Beijing shows a trigonometric distribution, and the total precipitation generally reaches to maximum at the distance of 500 km, and to minimum at the distance of 400 and 700km; 3) the maximum 1-day precipitation of the tropical cyclone and the precipitable water vapor show a significant positive correlation which is affected by the distance from the tropical cyclone centre to the centroid of Beijing; specifically, the correlation coefficient up to 0.95 when the distance is less than 500km, whereas it decreases to 0.56 when the distance exceeds 500km; 4) Regression models were constructed to quantify the relationships between the 24 characteristic indicators and the precipitation characteristics in Beijing (i.e. the total rainfall and the maximum 1-day rainfall). The models demonstrated a strong goodness-of-fit, with Pearson correlation coefficients reaching 0.90 for total rainfall and 0.83 for maximum 1-day rainfall.

How to cite: Zhang, X., Li, Y., Chen, Y., Lu, Y., Xue, Z., and Hu, X.: The Influence of Tropical Cyclone Characteristics on Rainstorm Magnitude in Beijing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6101, https://doi.org/10.5194/egusphere-egu26-6101, 2026.

Hailstorms are a major source of insured losses in Europe, with unprecedented damage in France in 2022 exceeding €6 billion. Anticipating how hail frequency may evolve under climate change is therefore critical for insurance risk management. This study assesses future changes in atmospheric environments conducive to hail using EURO‑CORDEX regional projections based on CMIP5 simulations. Sixteen GCM–RCM pairs provide 6‑hourly atmospheric fields at 11 km resolution, enabling a temporal and spatial high‑resolution, multi‑model analysis. Following the approach of Rädler et al. (2018), we compute key convective indices - Lifted Index (LI), 0-6 km vertical wind shear, and accumulated precipitation - and flag hail‑conducive days as a proxy for hail occurrence. Over the historical baseline (1980–2005), France experienced approximately 75 hail-conducive days per season (April–October). By mid-century (2050), the multi-model mean projects ~95 days, corresponding to a ~26% increase, rising to ~40% by 2100 under RCP8.5. The upward trend is statistically significant; however, substantial spatial heterogeneity is observed across France and adjacent countries. These findings have direct implications for the insurance sector: increasing hail risk challenges current pricing models, portfolio management, reinsurance treaty, and underscores the necessity of integrating climate projections into catastrophe modeling frameworks.

Keywords: hailstorms; severe convective storms; EURO‑CORDEX; CMIP5; RCP8.5; Lifted Index; vertical wind shear (0–6 km); multi‑model ensemble; regional climate modeling; France; insurance risk.

How to cite: Romain, M., Gilles, A., and Blandine, L.: Hailstorms in a warming climate: What future for France and insurance sector?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6709, https://doi.org/10.5194/egusphere-egu26-6709, 2026.

Estimating sub-daily precipitation return values at the kilometer-scale is critical for climate-risk assessments. However, this remains a challenge due to the strong intermittency of extremes, heterogeneous observational networks, and the breakdown of stationarity assumptions under climate change. Furthermore, coarse-resolution global climate models (e.g., CMIP6) systematically underestimate local extremes by smoothing convective processes and orographic gradients, thereby blurring hotspots that are critical for impact modelling.

We present an innovative statistical downscaling method that repurposes Areal Reduction Factors (ARFs), traditionally used to relate point rainfall to areal averages, as a resolution-bridging tool for extreme precipitation. By comparing return values derived from high-resolution (COMEPHORE, ~1 km2) and coarse-resolution (CERRA, ~25 km2) reanalyses, we compute spatially varying ARF maps. These maps quantify the attenuation of extremes induced by coarse spatial resolution and serve as multiplicative scaling factors to translate coarse-resolution outputs into 1-km products while preserving the large-scale climate signal.

This framework is validated against independent rain-gauge observations across multiple return periods and seasons. Finally, we apply the method to CMIP6 simulations to generate 3-hourly, 1-km precipitation return values for both historical and future periods.

This approach provides a computationally efficient and climate-change-consistent pathway to generate high-resolution hazard metrics, without the prohibitive cost of convection-permitting regional climate simulations.

How to cite: Droin, C., Lambert, A., Terrier, M., and Troin, M.: An ARF-based method to downscale sub-daily extreme precipitation return values, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7863, https://doi.org/10.5194/egusphere-egu26-7863, 2026.

The Spanish Mediterranean region is frequently impacted by flash floods, driven by intense convective rainfall, the presence of torrential basins, and dense urbanization in flood-prone areas. This situation can be aggravated by climate change, as demonstrated in recent studies. In this context, one way to reduce risk is to decrease vulnerability by improving both early warning systems and public preparedness. The recently completed Next Generation Flood2Now project aimed to address these two points through an interdisciplinary approach that brought together experts in hydrology, meteorology, sociology, databases, and complex systems, complemented by the participation of a private company. Two basins that suffer frequent flooding were chosen for the project: the torrential basin of the Francolí River, which can be dry in some reaches and times of the year but often experiences catastrophic flash floods as a result of intense rainfall, and the Arga River basin, characterized by a permanent flow produced by snowmelt and rainfall.

The ultimate goal of the project was to develop a hydrometeorological prediction chain that can be used operationally to aid decision-making in the face of potential floods. This was achieved using the INUNGAMA and PIRAGUA_flood (Llasat et al., 2024) databases, which contain all recorded floods in Catalonia and the Pyrenees, respectively, between 1980 and 2020. Based on these databases, an analogue model was developed (Guzzón et al., 2025) that considers the geopotential fields of 1000 and 500 hPa and weather types classified according to the method of Beck et al. (2007). In parallel, the RIBS hydrological model (Garrote and Bras, 1995) was calibrated at a set of points in the respective basins, where streamflow data recorded at gauging station are available, and subsequently fed with rainfall fields corresponding to flood events and their analogues, generating a probabilistic flow output that allows estimating the uncertainty of a given flood event causing damage (Carril-Rojas et al., 2025). For this purpose, flood compensation payments were taken into account, based on information from the Insurance Compensation Consortium. The data generated by the analogues, as well as the predictions obtained from the GFS, constituted the input for the Delft-FEWS platform (https://oss.deltares.nl/web/delft-fews/), which generates a set of flow rates and an exceedance warning as output. To update the flood databases, an AI-based methodology was created that extracts information from the press, analyzes it, and inputs it into the database. At the same time, citizen science campaigns, workshops and exhibitions have been developed, both to raise awareness among the population in both basins and to obtain more real-time observations of the river level. The contribution presented here shows the methodological synthesis of the project and the main results.

Carril-Rojas, et al., 2025. A Flood Forecasting Method in the Francolí River Basin (Spain) Using a Distributed Hydrological Model and an Analog-Based Precipitation Forecast. Hydrology, https://doi.org/10.3390/hydrology12080220

Garrote, L.; Bras, R.L., 1995. A Distributed Model for Real-Time Flood Forecasting Using Digital Elevation Models. JoH, https://doi.org/10.1016/0022-1694(94)02592-Y

Llasat, M.C., et al., 2024. Floods in the Pyrenees: A global view through a regional database, NHESS, https://doi.org/10.5194/nhess-24-3423-2024

How to cite: Llasat, M.-C., Marcos Matamoros, R., Guzzon, C., Arbaizar, J., Cabañas, A., Cachay Melly, J., Carril-Rojas, D., Díaz Guilera, A., Fernández-Fidalgo, J., Garrote, L., Llasat-Botija, M., Marinelli, D., Mediero, L., and Varela, O.: Improvement of early warning systems for flood risk with a distributed hydrological model and an analog-based precipitation forecast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8029, https://doi.org/10.5194/egusphere-egu26-8029, 2026.

Precipitation whiplash, defined as a rapid transition between anomalously wet and anomalously dry conditions, has emerged as an important hydroclimatic extreme under climate change, with potential implications for wildfire activity. While previous studies have linked precipitation whiplash to wildfire regimes in arid and semi-arid regions, its occurrence and impacts in monsoon-dominated East Asia remain poorly understood. This study investigates the occurrence of precipitation whiplash across South Korea and examines its relationship with interannual variability in winter wildfire damage.

Using high-resolution (500m) daily precipitation data from the Korea Meteorological Administration, Standardised Precipitation Index (SPI) values were calculated at one-month (SPI1) and three-month (SPI3) timescales. Precipitation whiplash events were identified based on SPI-based thresholds, including moderate (±1) and extreme (±1.6) definitions. In addition, a persistence-weighted whiplash intensity metric was developed to integrate anomaly magnitude and temporal reinforcement. National wildfire-damaged area for January–March during 2001–2016 was used to assess climate–fire relationships.

Results show that precipitation whiplash occurred recurrently across South Korea, with substantial interannual variability in both spatial extent and intensity. Pearson correlation analysis revealed positive associations between winter wildfire-damaged area and whiplash indicators, with the strongest relationship observed for the spatial extent of extreme short-term whiplash (SPI1-whiplash1.6; r = 0.81). Moderate positive correlations were also found for SPI1-whiplash1, SPI3-whiplash1, and national mean persistence-weighted whiplash intensity.

These findings indicate that extreme, spatially extensive wet–to-dry transitions are closely linked to enhanced winter wildfire damage in South Korea, highlighting precipitation whiplash as a relevant climate-based indicator for wildfire risk assessment in monsoon-influenced regions.

How to cite: Lee, Y., Hwang, J. H., Yoo, Y.-J., and Jeon, S.: SPI-based Assessment of Precipitation Whiplash and Its Relationshipwith Winter Wildfire Damage Area in South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8877, https://doi.org/10.5194/egusphere-egu26-8877, 2026.

ABSTRACT

Floods are a major natural disaster that occurs suddenly, causing loss of life and significant socioeconomic damage, making proactive response through accurate forecasting essential. River water levels are a key indicator for determining flood occurrence, but forecasting accuracy varies depending on hydrological, meteorological, and watershed characteristics. In particular, small-scale rivers have higher water level variability compared to large-scale rivers, limiting their practical flood response capabilities. To overcome these limitations, this study aimed to develop an AI-based river water level forecasting model and improve its forecasting performance. The forecast model is configured to forecast river water levels three hours ahead using time-series data from the previous 24 hours as input, based on the forecast time point. Four locations among existing AI-forecast rivers where flood forecasting is difficult were selected, and input variables reflecting each location's hydrological, meteorological, and oceanographic characteristics were configured. As a result, this study confirmed a trend toward improved flood forecasting performance through the configuration of input variables tailored to each site's characteristics and the adoption of the latest AI models.

Acknowledgments

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).

How to cite: Jung, H., Kim, H., Lee, J., and Kim, S.: Development and Performance Improvement of a Time-Series-Based AI Model for Flood Forecasting and Warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9017, https://doi.org/10.5194/egusphere-egu26-9017, 2026.

ABSTRACT

Compound drought–heat events are increasing under climate change, yet quantitative assessment of how antecedent drought alters extreme heat risk remains limited. This study examines the relationship between antecedent moisture conditions and extreme summer temperatures in Korea by combining correlation analysis with copula-based probabilistic modeling. Spearman’s rank correlation analysis reveals a consistent negative association between antecedent Standardized Precipitation Index (SPI) and daily maximum temperature (Tmax), indicating that drier conditions are systematically linked to higher summer temperatures. Copula models are then used to characterize the joint dependence between SPI and Tmax and to estimate conditional exceedance probabilities and return periods under different moisture states. The results show that dry and extremely dry conditions increase the probability of exceeding high Tmax thresholds and substantially shorten return periods, whereas wet conditions suppress extreme heat risk. The influence of antecedent drought becomes more pronounced for longer return periods, highlighting enhanced sensitivity in the extreme tail of the temperature distribution. These findings suggest that extreme heat risk is dynamically conditioned by prior hydrological states, emphasizing the importance of accounting for antecedent drought in interpreting and anticipating high-impact temperature extremes.

Acknowledgments

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).

How to cite: Lee, J., Jung, H., Kim, H., and Kim, S.: Analysis of the Impact of Preceding Drought on Heatwave Extremes: Focusing on South Korea., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9019, https://doi.org/10.5194/egusphere-egu26-9019, 2026.

Abstract

Despite the rapid intensification of extreme rainfall due to recent climate change, the Probable Maximum Precipitations (PMPs), key design standards for hydraulic structures in Korea, have not been updated for a long period. This lack of updates raises concerns that the current standards fail to reflect actual climate risks. To address this, this study proposes a future PMPs estimation procedure tailored to Korean conditions based on the WMO (2009) hydro-meteorological method. The procedure was applied to the SSP2-4.5 and SSP5-8.5 scenarios of the EC-Earth3-Veg and HadGEM3-RA models to project future ensemble-based PMPs. In particular, conventional methods calculate the moisture maximization ratio by fixing the representative precipitable water to historical observed values. This approach tends to over-reflect the increase in future maximum precipitable water, leading to an overestimation of PMPs. To overcome this limitation, this study estimated future annual representative precipitable water based on a Copula model. By applying this to the moisture maximization ratio calculation, the issue of PMPs overestimation was effectively mitigated. However, since this study relies on the results of two climate models, inherent uncertainties exist. Therefore, future research needs to project future PMPs using a multi-model ensemble with a broader range of models to enhance reliability.

Acknowledgment

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).

How to cite: Kim, H., Lee, J., Jung, H., and KIm, S.: Projection of Future PMPs in Korea Using a Copula-Based Moisture Maximization Ratio, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9020, https://doi.org/10.5194/egusphere-egu26-9020, 2026.

Hailstorms are known to cause great damage to agriculture and infrastructure. However, understanding and identifying the conditions favorable to hail remains a major challenge due to the complex, multi-scale nature of deep convective processes. In this study, we investigate the use of a W-Net convolutional neural network (CNN), which proved successful in image segmentation tasks, to identify atmospheric environments susceptible to hail from high-resolution numerical weather prediction data. The NOAA High-resolution Rapid Refresh (HRRR) model explicitly resolves convective processes owing to its fine spatial (3 km) and temporal (hourly) resolution. We consider meteorological variables from HRRR outputs relevant to deep convection and hail as input features for the W-Net model. Together with hail reports across the United States for the past ten years, we construct a deep learning framework. The trained network learns spatial patterns associated with hail-prone environments and produces gridded probability maps of hail occurrence. This data-driven approach shows the potential of deep learning methods for identification of hazardous convective weather. Once trained, the model can be applied to other regions, provided that the sub-daily, high-resolution meteorological fields are available.

How to cite: Hercigonja, L., Aslam, Z., Ashfaq, M., and Telišman Prtenjak, M.: Detecting hail-prone environments using a W-Net convolutional neural network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9179, https://doi.org/10.5194/egusphere-egu26-9179, 2026.

On 29 October 2024, a cut-off low triggered an extreme rainfall event over eastern Spain, with daily accumulations exceeding annual precipitation in several areas. The impacts were particularly severe in the province of Valencia, where widespread totals above 300 mm were recorded and local maxima reached up to 771 mm, resulting in more than 200 fatalities. Given the magnitude of the disaster, the dynamical and thermodynamical drivers of the event, as well as the potential role of climate change, have already prompted extensive investigation. Several media reports and recent studies (Campos et al., 2025) have pointed to the presence of an upper-level tropospheric moisture plume resembling an atmospheric river (hereafter AR-like structure) connecting the Mediterranean region with the tropical Atlantic via North Africa, suggesting that it may have contributed to the event’s intensification. However, the quantitative contribution of this structure to the observed precipitation remains unclear.

Here we address this issue using the WRF model coupled with Water Vapor Tracers (WRF-WVTs), which allows tracking moisture from prescribed source regions to precipitation while fully resolving the event dynamics. Our results show that the Mediterranean Sea was the dominant direct moisture source, while moisture associated with the AR-like structure contributed approximately 20-30% of the total precipitation. To further assess the role of this remote moisture transport, we introduce a methodology to quantify its indirect impact through enhanced latent heat release and the resulting increase in atmospheric instability. We find that this indirect mechanism is substantially more important than the direct moisture contribution, highlighting the key role of the AR-like structure in intensifying the event.

Campos, D. A., Grayson, K., Saurral, R. I., Beyer, S., John, A., Olmo, M., and Doblas-Reyes, F.: The October 2024 Extreme Precipitation Event over Valencia: Storyline Attribution of the Synoptic-Scale Thermodynamic Drivers, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-5929, 2025.

How to cite: Crespo-Otero, A., Insua-Costa, D., and Míguez-Macho, G.: On the role of an AR-like moisture plume in the October 2024 Valencia extreme rainfall event, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9499, https://doi.org/10.5194/egusphere-egu26-9499, 2026.

This study investigates the relationship between Euro-Atlantic large-scale atmospheric circulation, characterized using year-round weather regimes (WRs), and major flood events in the Greater Alpine Region (GAR). With the goal of characterizing atmospheric conditions leading to major flood events and therefore gaining insights on their predictability, we identify the WR paths most commonly linked to these events in the GAR.

To this aim, we identify on average one major flood event per year over the GAR in the period 1951-2023 using discharge simulations from a regionalized rainfall-runoff model (a modified version of the TUW model) for the region. Of all the events identified, those selected were chosen based on three features: spatial extent, duration and intensity. These events cover large portions of the GAR, allowing the exploration of the role of large-scale atmospheric dynamics rather than local or convective processes.

WRs classification is based on daily geopotential height data at 500 hPa (Z500) from ERA5 reanalysis, following the methodology outlined by Grams et al. (2017), that enables the year-round characterization of atmospheric patterns.

Given the prevalence of regional floods during autumn, our analysis focuses on SON (September–October–November). We link flood events to their corresponding WRs at the time window included between peak day and two days before and compare their frequency of occurrence against the seasonal WRs climatology to isolate statistically significant associations. Results reveal that floods occur predominantly under Scandinavian Blocking (ScBL) and Scandinavian Trough (ScTr) regimes, which favor events of large-scale precipitation that can, in turn, lead to flood events during this season. Categorizing their magnitude using a quantile-based approach, we observe that precipitation events happening in proximity of floods associated with ScTr and ScBL regimes can be classified as extremes (magnitude exceeding 95th quantile). This result is also supported by the integrated water vapor (IVT) analysis, showing the presence of a larger south-north vapor transport from the Mediterranean basin towards the Alps during flood events when compared to the characteristic IVT patterns of ScBL and ScTr regimes.

Linking autumn floods over the GAR with specific large-scale atmospheric circulation regimes would support the potential use of WRs diagnostics as predictive tools to improve early warning systems for extreme hydrological events at present and, in principle, under future climate scenarios.

How to cite: Tessari, I., Kumar, V., Basso, A., Lombardo, L., Giuntoli, I., Corti, S., Arnone, E., and Viglione, A.: Linking Euro-Atlantic Weather Regimes to Precipitation Patterns and Major Regional Flood Events in the Greater Alpine Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9570, https://doi.org/10.5194/egusphere-egu26-9570, 2026.

Coastal megacities are increasingly exposed to estuarine saltwater intrusion (SWI) as a result of climate change and extreme hydro-meteorological events. Shanghai, a water-stressed megacity with a population exceeding 24 million, relies on the Yangtze Estuary for more than 80% of its freshwater supply, rendering it highly sensitive to  saltwater intrusion. In this study, we apply the three-dimensional hydrodynamic model UFDECOM-i, developed by our research group based on the ECOM-si. The model incorporates two-way nested unstructured quadrilateral grids, enabling an improved representation of complex estuarine bathymetry and hydrodynamic interactions among multiple bifurcated channels during extreme conditions.

We first simulate the extreme SWI event that occurred in the summer of 2022, driven by a basin-wide record-breaking megadrought. Our simulations show that the concurrence of exceptionally low river discharge and persistent northerly winds led to severe summer salinization in the estuary. As a consequence, the Qingcaosha Reservoir experienced 98 consecutive days during which water was unfit for intake, far beyond its designed operational resilience. Further diagnostic analysis reveals that reduced river discharge weakened the seaward freshwater flushing, while wind-induced landward Ekman transport generated an anomalous horizontal estuarine circulation. This circulation pattern is characterized by inflow through the North Channel and outflow through the South Channel, and it intensified the direct transport of high-salinity water toward critical water intake locations.

In addition, we quantitatively assess the influence of future sea-level rise, SLR, on SWI intensity. Simulations under a 1.17 m SLR scenario derived from RCP8.5 projections reveal a pronounced non-linear amplification of saltwater intrusion intensity. The sensitivity of estuarine salinity to river discharge reduction increases significantly. Notably, the impact of SLR on salinity enhancement is approximately twice that of discharge decline. Under the 1.17 m SLR scenario, the duration of water unfit for intake at the Chenhang Reservoir increases from 76.17 days to 115.49 days, corresponding to a 51.6% increase from present-day levels. These results provide a quantitative basis for assessing future freshwater security in the Yangtze Estuary and offer scientific support for adaptive water resource management and refined diversion strategies under a changing climate, aligning with the United Nations Early Warnings for All initiative aimed at strengthening climate-resilient water security.

How to cite: yining, W.: Extreme Saltwater Intrusion in the Yangtze Estuary under Compound Drought, Wind Forcing, and Sea-Level Rise, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10504, https://doi.org/10.5194/egusphere-egu26-10504, 2026.

To assess flash flood susceptibility, an analysis of physiographic parameters of catchments affected by flash floods in the past was conducted. The study focuses on 17 catchments, ranging from 9 to 80 km², with water gauge stations in the Czech Republic. Based on parameters such as catchment area, slope, elevation, Melton ratio, river length and slope, river network length and density, shape index, arable land proportion, curve number, and road network density, categorization into clusters I–III was performed using principal component analysis and k-medoids clustering. To evaluate the hydrological response in relation to these parameters, the flashiness index (quantifying the magnitude and timing of the flood wave) was calculated for events in which peak discharge exceeded the 1-year return period discharge. The results show that the highest flashiness values were recorded in a group of small, steep catchments characterized by high terrain roughness, maximum elevations, a dense river network, and compact shape.

How to cite: Honzíčková, D., Šulc Michalková, M., Borga, M., Brázdil, R., Štěpánek, P., Zahradníček, P., Coufal, P., Geršlová, Z., and Caletka, M.: Why do some catchments exhibit a more flashy response? Catchment parameters controlling flash flood generation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11176, https://doi.org/10.5194/egusphere-egu26-11176, 2026.

On 29 October 2024, an exceptional hydro-meteorological event impacted eastern Spain, producing extreme rainfall accumulations and widespread flash flooding, with the most severe consequences recorded in the Valencian Community. The event resulted in 238 fatalities nationwide, of which 230 occurred in the province of Valencia, and caused economic losses estimated at approximately €17 billion. More than 80% of the fatalities were concentrated in the southern Valencian Metropolitan Area, highlighting its extreme vulnerability to compound rainfall-runoff processes. This area is a highly urbanized Mediterranean lowland located south of the city of Valencia (Spain), draining an area of approximately 530 km² through a dense network of ephemeral streams. These catchments are characterized by short response times, steep upstream slopes, permeable lithologies, and limited natural floodplain storage. Downstream of natural flood-lamination areas such as the Pla de Quart, river channels intersect densely populated municipalities, where urban expansion has progressively encroached upon flood-prone areas, substantially increasing exposure and, consequently, flood risk.

This contribution presents a detailed reconstruction of this catastrophic flooding, integrating meteorological analysis, distributed hydrological modelling, and high-resolution hydraulic simulations. Rainfall reconstruction was performed using an extensive rain-gauge network across the Valencian Community, capturing the strong spatial variability and temporal clustering of the event. The storm evolved through two clearly differentiated phases: an initial morning rainfall episode that led to widespread soil saturation across the catchments, followed by an afternoon-evening phase characterized by extraordinary rainfall intensities and persistence.

Hydrological and sediment transport simulations were conducted to represent both the generation and propagation of runoff and solid load throughout the catchment, using the fully distributed eco-hydrological model TETIS. The modelling framework combined long-term daily simulations to establish realistic antecedent conditions with event-scale subdaily simulations at 10-minute resolution. This approach enabled the reconstruction of hydrographs and sediment fluxes for the main tributaries upstream of flood-lamination zones, prior to the occurrence of major overbank flooding. It also allowed the estimation of peak discharges that largely exceeded the instrumental record, highlighting the significant contribution of solid load to the total flood volume.

The resulting hydrographs (with a combined peak of 7,500 m³/s) were used as boundary conditions for two-dimensional hydraulic modelling, allowing the reproduction of flood propagation and inundation patterns in highly urbanized areas of southern Valencian Metropolitan Area. Results show that flood depths and impacts were strongly amplified by floodplain anthropization associated with urban expansion, leading to water levels substantially higher than those expected under more natural conditions.

This integrated reconstruction improves the understanding of the coupled meteorological and hydrological processes that controlled the October 2024 flood and provides a physically consistent basis for assessing flood hazards in highly urbanized Mediterranean catchments.

How to cite: Beneyto, C., Cachay-Melly, J. A., Aranda, J. Á., Eguibar, M. Á., and Francés, F.: Reconstruction of the October 2024 catastrophic flooding in the southern Valencia Metropolitan Area, Spain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11620, https://doi.org/10.5194/egusphere-egu26-11620, 2026.

An increasing frequency of severe weather events, including damaging winds and tornadoes, poses significant hazards to infrastructure, ecosystems, and society (Fischer et al., 2025). Accurate and rapid surveys of damage caused by severe convective wind events remain challenging, yet they are essential for understanding impacts, refining wind-damage classification schemes, and improving severe weather databases. This study demonstrates an integrated workflow that combines a GIS-based mobile field-mapping application with multi-scale remote sensing data (UAV imagery and high-resolution satellite observations) to document and assess severe wind damage using the new International Fujita Scale (ESSL 2023). The approach is demonstrated through a case study of a tornado and associated severe wind damage near Hažlín in eastern Slovakia. A configurable mobile mapping platform tailored for damage surveys was used by the field team to collect standardized, georeferenced damage observations, damage type and intensity, and photographic documentation. Complementing the field mapping, high-resolution UAV surveys and pre-/post-event satellite imagery supported detailed characterization of vegetation and structural damage patterns at spatial scales unattainable from ground surveys alone. By integrating standardized field observations with remote sensing, the proposed approach improves damage classification accuracy and contributes to severe-wind climatological databases.

ESSL: International Fujita (IF) Scale, https://www.essl.org/cms/research-projects/international-fujita-scale/ (last access: 12 January 2025), 2023.

Fischer, J., Groenemeijer, P., Holzer, A., Feldmann, M., Schröer, K., Battaglioli, F., Schielicke, L., Púčik, T., Antonescu, B., Gatzen, C., and TIM Partners: Invited perspectives: Thunderstorm intensification from mountains to plains, Nat. Hazards Earth Syst. Sci., 25, 2629–2656, https://doi.org/10.5194/nhess-25-2629-2025, 2025.

How to cite: Fedor, T., Nováková, M., and Onačillová, K.: Damage assessment of severe wind events using mobile field mapping and remote sensing: A case study of the Hažlín tornado, Slovakia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11984, https://doi.org/10.5194/egusphere-egu26-11984, 2026.

Floods can have enhanced impacts when they occur in close succession with streamflow droughts. Despite the increased likelihood of impacts during such drought-to-flood transition events, substantial gaps remain in the understanding of the hydrological processes and drivers leading to their development. Specifically, it remains unclear how changes in hydrological conditions (IHCs) after drought and meteorological forcings during the transition towards flood conditions propagate to flood characteristics, such as duration and peak flow. We address this research gap by running three stress test experiments at the event scale: (1) changes in IHC based on perturbations applied to the meteorological forcing (e.g., temperature and precipitation) during the drought; (2) perturbation of observed forcing during the transition; and (3) modifications to both IHC and forcings during the event (i.e., a combination of cases 1 and 2). To do so, we calibrate the bucket-type GR4J hydrological model across a quasi-global dataset encompassing 2003 catchments. Using the results of all synthetic experiments conducted across all catchments, we estimate the isolated and joint sensitivities of both transition and flood characteristics to changes in drought characteristics (e.g., duration, intensity, severity) and to meteorological forcings during the transition. By analyzing the joint sensitivity of a given attribute (e.g., peak flow) to IHCs and meteorological forcings, we estimate the relative importance of each in driving the attribute's overall sensitivity (i.e., total effect of all individual sensitivities). Our findings indicate that, regardless of whether the system is stressed in isolation (i.e., experiment 1 or 2) or jointly (i.e., experiment 3), meteorological forcings - primarily temperature - are the main drivers affecting changes in transition and flood characteristics across hydroclimatic regions. Overall, clear differences between sensitivities, e.g., of peak flow to precipitation during the transition, emerge when comparing snowmelt- and rainfall-influenced catchments. Furthermore, hydroclimatic and streamflow regimes, along with catchment storage dynamics, are the main proxies for understanding the sensitivity of transition attributes to changes in IHC and meteorological forcings. Additionally, we show that IHCs are more important than meteorological forcing in explaining the variability of transition characteristics. Consequently, although IHCs control the baseline state, small changes in variables such as temperature during transitions can significantly alter flood characteristics, largely independently of the IHCs. Ultimately, we show that warming, rather than variations in drought severity (i.e., antecedent conditions) or precipitation, is likely to shape future transitional floods.  Our results enhance our understanding of drought-to-flood transitions and their sensitivities to hydrometeorological conditions, thereby generating clearer insights into how droughts and meteorology influence floods and their impacts.

How to cite: Muñoz-Castro, E., Anderson, B. J., Swain, D. L., and Brunner, M. I.: How do drought conditions and meteorology shape subsequent floods? Insights from quasi-global stress-test experiments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13390, https://doi.org/10.5194/egusphere-egu26-13390, 2026.

Growth in satellite observations and modeling capabilities has transformed drought monitoring by enabling near real-time situational awareness. Yet many operational efforts still emphasize hazards rather than impacts, and they often miss the compound and cascading risks that frequently accompany drought, including heatwaves, wildfires, floods, and debris flows. In this presentation, we first introduce a real-time drought monitoring and seasonal prediction system that integrates diverse data streams with AI-based algorithms for drought forecasting (https://drought.eng.uci.edu/). We then describe how drought information can be expanded beyond hazard metrics by incorporating impact and vulnerability data to support impact-based assessment of extremes and decision-relevant risk insights (https://water.eng.uci.edu/).  Using several examples, we argue for an impact-centered drought monitoring paradigm that links hydroclimate conditions to physical and societal outcomes, such as crop yield losses, food insecurity, energy production disruptions, and labor impacts. We also highlight key challenges that must be addressed to make this approach operational, including inconsistent and incomplete drought impact records, limited Information about local water management and human interventions (e.g., demand, intra- and inter-basin transfers, pumping, and withdrawals), and persistent gaps between impact models and existing drought monitoring workflows. Finally, we discuss anthropogenic drought as a framing concept and show how impact-based drought analysis can be strengthened by representing drought as a coupled climate–human phenomenon rather than a purely climatic hazard. 

How to cite: AghaKouchak, A., Nguyen, P., Ung, T., de Oliveira, D., Hjelmstad, A., Massing, J., Aljohani, A., Love, C., Mirchi, A., Feldman, D. L., Placht, D., and Najib, D.: From Hazard to Consequence: Impact-Based Drought Monitoring and Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13488, https://doi.org/10.5194/egusphere-egu26-13488, 2026.

Poland’s location in the mid-latitudes determines a moderate influx of solar radiation and characteristic circulation conditions that strongly control weather variability. Of particular importance is the dominance of westerly circulation, enhanced by the zonal configuration of relief. The frequent passage of cyclones and atmospheric fronts, together with the advection of air masses with highly contrasting thermal properties, results in pronounced weather variability. Depending on the direction of air-mass advection, both extremely warm and extremely cold conditions may occur.

Since the late 20th century, the climate has been characterized by the predominance of anomalously warm months, seasons, and years. Notable examples include the exceptionally hot summer of 2003 in Western Europe and the summer of 2010 in Eastern Europe, both of which caused thousands of excess deaths among populations unaccustomed to prolonged heat stress. At the same time, extreme cold events still occur. Although they have become less frequent and less intense than during the 20th century, they continue to generate severe economic and biometeorological impacts. An example is January 2017 in the Balkan Peninsula, which was among the coldest and snowiest months on record in that region.

In the current year, a pronounced negative temperature anomaly was also observed: in May, snowfall and widespread frost occurred over large parts of Poland, as widely reported by the media. The aim of this study was therefore to assess how strongly thermal conditions in May 2025 deviated from the long-term climatological mean in Poland. Specifically, the study seeks to quantify the magnitude of the air-temperature anomaly and to identify the synoptic conditions responsible for the persistence of anomalously low temperatures.

The primary dataset consists of mean monthly air temperatures for May for the period 1951–2025 from 60 synoptic stations in Poland. These publicly available data were obtained from the database of the Polish national meteorological service (IMGW-PIB, https://danepubliczne.imgw.pl/). This dataset was used to calculate the magnitude of the temperature anomaly in May 2025. Anomalies were expressed both in absolute terms (°C) and in standardized units (multiples of the standard deviation, SD). A second dataset, also obtained from IMGW-PIB, comprises daily mean, maximum, and minimum air temperatures for May for the 75-year period 1951–2025 from selected stations. Synoptic conditions were analysed using surface and upper-air weather charts from the Polish (meteo.imgw.pl) and German (www.wetter3.de) meteorological services.

Preliminary results indicate that the cold conditions over Poland were primarily controlled by low-pressure systems that induced advection of cold air from the northern sector. Their persistence was favoured by a characteristic omega-blocking pattern over Western Europe and the presence of a deep upper-level trough over Central Europe. This configuration effectively inhibited the eastward progression of baric systems, allowing the prevailing weather regime to persist over the region for an extended period.

How to cite: Guzik, I. and Twardosz, R.: Global warming vs local reality: Poland's exceptionally cool May 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13998, https://doi.org/10.5194/egusphere-egu26-13998, 2026.

Climate change is altering the frequency, intensity, and spatial patterns of extreme weather events. Urban areas are particularly affected because population, critical infrastructure, and economic assets are concentrated in a relatively small area, which increases the potential impacts of extreme events. Climate risk depends not only on the hazard itself, but also on what is exposed and how sensitive it is to the damage. Understanding climate risk requires considering how hazards interact with exposure and vulnerability. For this reason, risk-based approaches are increasingly needed to support climate adaptation and decision-making. The increasing availability of open-source tools, including the CLIMADA model, has made climate risk assessment more accessible. This study applies the CLIMADA risk assessment framework to evaluate urban heat-stress risk in Vilnius, Lithuania.

The analysis focuses on heat stress as an illustrative case to examine potential impacts on human health under three levels of global warming: the recent past, +2 °C, and +4 °C relative to pre-industrial conditions. Climate hazards are characterised using projections from climate models, and uncertainty in future conditions is represented through an ensemble of simulations. This ensemble consists of five CMIP6 models: CanESM5, ACCESS CM2, GFDL-CM4, MPI-ESM1-2-LR, and NorESM2-MM. Heat-stress intensity affecting people is quantified using the Humidex index, which is calculated from gridded air temperature and relative humidity data and used as the hazard intensity in the risk assessment. Exposure is defined as the spatial distribution of people and relevant assets. Because this study focuses on heat-stress risks to human health in urban areas, exposure is characterised using a set of socio-demographic and urban indicators, including population distribution, age structure, the locations of critical infrastructure, and urban surface characteristics. Vulnerability describes how sensitive exposed people or assets are to the hazard and is represented through vulnerability functions. In this analysis, vulnerability is expressed through the mean damage degree and the percentage of affected assets, which are used together to estimate the mean damage ratio.

Preliminary results are presented as risk maps showing the spatial distribution of heat-related health risk in Vilnius under different climate change scenarios. This spatial information supports the identification of priority areas for adaptation planning and risk reduction.

How to cite: Kapilovaite, J.: Assessing Urban Heat-Stress Risk Under Future Climate Scenarios: A Case Study of Vilnius, Lithuania, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15466, https://doi.org/10.5194/egusphere-egu26-15466, 2026.

Flooding is one of the most severe natural hazards worldwide, causing substantial economic losses and catastrophic impacts. Although levees are widely implemented for flood mitigation, few global flood models explicitly incorporate their influence on flood routing and risk assessment. In our previous study (Zhao et al., 2025, doi: https://doi.org/10.1029/2024WR039790), we developed a levee module for the CaMa-Flood model and generated levee parameters for global ungauged rivers. Furthermore, recent GPU-based computational optimizations in CaMa-Flood model (Kang et al., 2026, doi: 10.22541/essoar.176442648.85093032/v3) now enable simulations of global flood dynamics at high spatial resolution within a feasible timeframe.

These advancements allow us to analyze long-term changes in flood hazards and risks while explicitly accounting for levee effects. In this study, we forced the CaMa-Flood model with ensemble CMIP6 runoff data to simulate historical and future river hydrodynamic changes at the daily scale, comparing scenarios with and without levee considerations. Our results reveal that levees significantly mitigate global flood hazard. Driven by this protection, urbanization rates within levee-protected areas have substantially outpaced those in unprotected regions over the past four decades. Conversely, flood hazard in unprotected river reaches increases due to the hydrodynamic effects induced by levee construction. Regarding flood exposure, multi-model estimates show that while levees have historically played a crucial role in shielding a substantial fraction of the floodplain population and slowing the growth rate of flood exposure, rapid population growth has largely offset these protective benefits. Consequently, the absolute flood-affected population under levee protection in the present day remains at a historically high level, comparable to that of previous decades without levees. Furthermore, projections indicate that flood exposure will continue to increase through the mid-21st century under changing climate conditions. Overall, this work provides new insights into global flood modeling and risk assessment and supports improved flood management and decision-making in levee-protected regions.

How to cite: Zhao, G., Yamazaki, D., Hirabayashi, Y., Khanh, D. N., and Kang, S.: New Insights Into Global Flood Hazard and Risk Considering Levees Under a Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15543, https://doi.org/10.5194/egusphere-egu26-15543, 2026.

Snow disasters, intensified by climate change, pose increasing threats to infrastructure and socio-economic systems. However, conventional risk assessments often rely heavily on meteorological hazards or static topographic factors, frequently overlooking the critical role of exposure in urban environments. This study introduces the Maximum Disaster Spatial Density (MDSD) method, a novel optimization framework designed to identify snow disaster-prone areas by integrating historical disaster records (2009–2018) with climatic, topographic, and social variables. Using datasets covering South Korea, including radar-based precipitation, temperature, MODIS-based Normalized Difference Snow Index (NDSI), elevation, and building density, the MDSD algorithm iteratively optimizes feature weights to maximize disaster density within high-risk clusters. Our analysis reveals that precipitation and building density are the dominant determinants of snow disaster vulnerability, whereas elevation and satellite-based snow cover duration show relatively lower importance. This finding challenges the traditional assumption that high-altitude mountainous regions are inherently more vulnerable, quantitatively demonstrating that disaster risk is driven by the intersection of extreme weather and built-environment exposure. To address model uncertainty, we applied an ensemble approach with 20 realizations, generating a probabilistic snow disaster risk map (0–1 scale). This map effectively highlights high-risk zones, including coastal urban areas, which were previously underestimated by topography-based assessments. Furthermore, we propose a dual-track disaster response strategy by integrating this static risk map as a priority filter into a real-time monitoring system. This framework enables decision-makers to prioritize resource allocation to high-exposure areas during extreme snow events, bridging the gap between scientific risk assessment and practical disaster management.

Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00518650).

How to cite: Park, J., Kim, S., Kim, D., Lee, J., and Bateni, S. M.: Development of a Probabilistic Snow Disaster Risk Assessment Framework Integrating Hazard and Exposure: The Maximum Disaster Spatial Density (MDSD) Optimization Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16332, https://doi.org/10.5194/egusphere-egu26-16332, 2026.

Climate change is expected to alter the frequency, magnitude, and temporal structure of flood waves, with direct implications for local scour development at bridge piers and, consequently, for bridge safety and management. While numerous studies have addressed individual methodological components of this problem - such as climate change projections, hydrological modelling, or scour estimation - the methodological links between climate change indicators, flood wave characteristics, and local scour processes remain fragmented and are often treated in isolation. This contribution presents preliminary insights from an ongoing systematic literature review that investigates how climate change signals are propagated through hydrological and hydraulic modelling chains and ultimately reflected in local scour assessments at bridge piers. The review focuses on peer-reviewed studies addressing (i) climate change modelling and approaches, (ii) hydrological representations of flood waves, including peak flow, volume, duration, and hydrograph shape, and (iii) deterministic and probabilistic methods for evaluating local scour. Attention is given to how uncertainties are treated across these methodological steps and to the extent to which flood wave characteristics beyond peak discharge are explicitly considered. The preliminary synthesis highlights recurring methodological patterns, key knowledge gaps, and inconsistencies in current practice, especially regarding the integration of climate projections with hydrological design events and scour modelling frameworks. The findings are organized into a structured classification of modelling approaches, input data requirements, and uncertainty treatment, providing a basis for further in-depth analysis. This contribution provides a structured synthesis of existing methodological approaches and identifying gaps relevant for future model development and application of climate change assessment on erosion processes around bridge piers.

Acknowledgment:

This work has been supported in part by the Croatian Science Foundation under the project SERIOUS – Synthetic dEsign hydrographs undeR current and future clImate for local bridge scOUr aSsessment (IP-2024-05-1497) and by the “Young Researchers’ Career Development Project – Training New Doctoral Students” (DOK-2020-01-5354).

How to cite: Potočki, K., Panchanathan, A., Lacko, M., and Bezak, N.: Climate Change, Flood Wave Characteristics and Local Scour: A review of Modelling Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16359, https://doi.org/10.5194/egusphere-egu26-16359, 2026.

Quantifying the extremeness of precipitation events in a spatio-temporally consistent manner, especially over a large river basin with hydro-climatic heterogeneity, poses a key challenge in flood risk assessment. The Mahanadi River basin (MRB) in India is located in the core monsoon region and often experiences tropical cyclone-induced severe rainfall. Extreme precipitation in the upstream sub-basins of MRB, leads to major flooding in the densely populated lower sub-basin (delta) region. Existing studies typically quantify precipitation extremes for the basin as a whole and thereby often overlook spatial heterogeneity, resulting in an underestimation of the variability and distribution of extreme rainfall across sub-basins. This study, therefore, applies the Weather Extremity Index (WEI) and its cross-scale extension (xWEI) at the sub-basin scale to further investigate flood-generating processes.

Both WEI and xWEI quantify extremeness as a function of the spatial extent and rarity (frequency) of precipitation events. However, WEI characterises events by their maximum extremeness at a specific duration of rainfall accumulation, whereas xWEI captures extremeness across the entire spatio-temporal scale. In this study, WEI is applied as an event-centred diagnostic, while xWEI is computed continuously in time for the sub-basins. Both indices are used to examine precipitation extremeness during six major flood events that occurred in the delta region since 2000.

The WEI time series shows systematically high values at medium to long accumulation durations (5–10 days) in the upstream sub-basins prior to flood events in the delta. An exception is observed for the 2006 flood, during which high WEI values occurred directly over the delta on the flood date. The absence of a consistent dominant duration in WEI over the delta suggests that local precipitation alone is not sufficient to explain flood occurrence. A similar pattern is observed in the xWEI time series, with higher values in the upstream catchments during the days preceding delta floods. In addition, xWEI highlights pronounced extremeness at short to medium durations (2–5 days) in the upstream basins. Across all six events, xWEI consistently reveals a clear upstream–downstream evolution of precipitation extremeness in the MRB, with maxima appearing in the upper sub-basin at 2–3 days before and in the middle sub-basin 1–2 days before any major flood event at delta.

How to cite: Karmakar, S., Voit, P., and Chatterjee, C.: Quantification of Precipitation Extremeness over a Large Indian River Basin using Weather Extremity Indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17294, https://doi.org/10.5194/egusphere-egu26-17294, 2026.

Future fluvial flood risk can be estimated by applying change factors (CFs) to present day fluvial flood hydrology. At the global scale, CFs can be derived from the outputs of GCM-GHM ensemble pairs, such as ISIMIP3b. CFs are often calculated based on the proportional change of an index flood (e.g. median annual flood) between the future and present day, and subsequently applied over all return periods. Whilst this approach is statistically robust given the limited number of samples in ISIMIP3b, it assumes that rare and regular floods change proportionally. Scientific literature disputes this, suggesting that future changes may vary significantly based on flood rarity. Calculation of return period specific change factors can be achieved with stationary extreme value analysis on baseline and future periods, however the limited samples from climate ensembles result in noisy change factors for extreme events. Moreover, the analysis applied using different ensemble members results in greater uncertainty. A potential method to increase sample size to reduce noise is to apply non-stationary extreme value analysis across an ensemble’s entire time series.

This work compares multiple approaches to derive return period dependent CFs, including stationary flood frequency analysis, and non-stationary GAMLSS, across multiple distributions and ensemble members. We demonstrate and assess the differences in CFs between 2y (regular) and 100y (rare) floods, using Europe as a test domain. We find that CFs can be modeled as return period dependent, with clear spatial patterns present, and significant differences in changes in rare and regular floods in many locations. The variation of CF with return period depends on region, driving GCM - GHM pair, and the selected distribution fitting approach. In some locations, GCM-GHM pairs largely agree on the degree and direction of change between rare and regular floods, yet in others, GCMs-GHM pairs can give very different estimates of this (including opposing directions). Stationary flood frequency analysis for CFs results in greater noise, and CFs with greater magnitude. The GAMLSS approach significantly mitigates spatial noise, but reduces the intensity of changes in CFs as a function of RP. Our study overall highlights the importance of considering how CFs vary by return period, and how this variation itself is critically dependent on the driving GCM-GHM ensemble used.

How to cite: Hatchard, S. and Addor, N.: Do Small and Large River Floods Change Differently under Future Climate Change? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17320, https://doi.org/10.5194/egusphere-egu26-17320, 2026.

As global temperatures rise, the spatial and temporal patterns of weather hazards are shifting, increasingly affecting industries vital to economic growth. Shipping, the backbone of global trade, is particularly susceptible, yet inland waterways—crucial for linking domestic regions to international markets—have received insufficient attention. This study presents the first quantitative assessment of how weather hazards impact inland waterway operations, focusing on China’s Yangtze River and Grand Canal system, the world’s busiest inland waterways. Between 2000 and 2024, these waterways averaged 301 suitable navigation days per year, marking an overall improvement over the preceding two decades, largely attributed to a 55% decline in strong wind events, from ~31 to ~14 days annually. However, low visibility remains the dominant constraint on navigability, causing ~44 days of disruption annually, with fog and haze emerging as the primary contributors. Heavy rainfall, intensifying with warming, leads to ~10 days of disruption, ranking as the third major hazard to navigation. Simulations suggest that improving navigation technologies, such as lowering visibility requirements from 2,000 meters to 1000 or 500 meters, could extend navigability on the Lower Yangtze River by 25 to 35 days annually, enhancing operational efficiency and fostering economic growth.

How to cite: Sun, S.: Weather Hazards Dynamically Reshape Navigability in China's Inland Waterways, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18834, https://doi.org/10.5194/egusphere-egu26-18834, 2026.

How to cite: Grieger, J., Ruff, F., Forster, C., Fauer, F., Feldmann, H., Fischer-Frenzel, P., Friederichs, P., Haufs, E., Lucio-Eceiza, E. E., Meredith, E. P., Merkes, S. T., Pinto, J. G., Schröter, J., Szemkus, S., Tonn, M., Ulbrich, U., Vlachopoulos, O., Voit, P., Vorogushyn, S., and Zimmermann, T.: ClimXtreme addressing heavy precipitation events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19066, https://doi.org/10.5194/egusphere-egu26-19066, 2026.

ClimXtreme is a research programme funded by the German Federal Ministry of Research, Technology and Space (BMFTR) that comprises 25 individual projects and aims to improve understanding of European extreme weather events and associated uncertainties under anthropogenic climate change. 

As part of the cross‑project collaboration, a coordinated approach is being established to initiate and sustain targeted stakeholder communication and support it throughout the project period. The aim is to develop information, data and tools targeted to stakeholder needs. To this end, Hazard‑specific Stakeholder Interaction (HaSSi) groups coordinate collaborative work on windstorms, heavy precipitation, and heat/drought. This contribution summarizes activities within the HaSSi Heat/Drought group and provides an overview of the activities and outcomes within the project. 

Our group brings together diverse projects that study heatwaves and droughts from multiple perspectives. These include their underlying large‑scale dynamics, their impacts (for example on crops and fire weather), and event‑based attribution methods. The combination of expertise from various fields gives us a multifaceted perspective on heat and drought events, which we use in two ways:

Communication with stakeholders is maintained through monthly online seminars, in which scientific findings and their relevance to stakeholders are discussed, thereby gathering valuable input from the stakeholders' perspectives. As valuable, cross-project output tailored to stakeholders, we also analyse current extreme events from the multifaceted view gained from the respective individual projects. We illustrate this approach using findings from the ClimXtreme report on the European Summer 2025.

How to cite: Lemburg, A., Szemkus, S., Buschow, S., Dietz, V., Dillerup, I., Feldmann, H., Fischer-Frenzel, P., Friederichs, P., Grieger, J., Kraulich, F., León-FonFay, D., Merkes, S. T., Pfleiderer, P., Pinto, J. G., Schröter, J., Sippel, S., Ulbrich, U., Vlachopoulos, O., and Zimmermann, T.: ClimXtreme addressing heat and drought events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19526, https://doi.org/10.5194/egusphere-egu26-19526, 2026.

In recent years, the impact of extreme weather events, such as droughts, has been reported more frequently in Central and Eastern European countries. Rising temperatures and increasingly variable precipitation patterns affect natural ecosystems and agricultural areas, which could impact water resources, agricultural productivity and livelihoods in the future.

This study analyses the Standardised Precipitation Index (SPI), derived from Climate Hazards Centre Infrared Precipitation with Stations (CHIRPS) data, over the last 20 years. The regional focus is on Central and Eastern European countries, with a spatial gradient from the Baltic Sea to the Adriatic Sea in order to cover several biogeographic regions.

The SPI is a widely used tool for assessing the severity and frequency of drought events, providing a standardised measure of precipitation anomalies over various temporal and spatial scales. This study uses SPI-1 and SPI-3 data (at 1- and 3-month scales) for Poland, the Czech Republic, Slovakia, Slovenia, Hungary and Croatia between 2005 and 2024. The aim of analysing these data is to initially identify spatial and temporal patterns in drought frequency, duration, and intensity for this period.

Our future collaborative work will assess the relationship between analysed precipitation patterns and other key climatic factors (e.g. temperature) and further environmental factors (e.g. soil, water and vegetation conditions). This can provide a deeper understanding of the spatial and temporal distribution of climatic extreme events in this region. Furthermore, SPI analyses should be complemented by climate projections to provide insights into potential future changes in precipitation patterns and the frequency of extreme events in Central and Eastern Europe.

How to cite: Huth, J., Hordofa, A. T., Kropacek, J., Nagy, A., Habel, M., Mukanov, B., Maerker, M., and Bachofer, F.: Spatiotemporal variability of precipitation in Central and Eastern European countries: A 20-year time series analysis of CHIRPS data for the detection of extreme events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19630, https://doi.org/10.5194/egusphere-egu26-19630, 2026.

ClimXtreme is a research programme funded by the German Federal Ministry of Research, Technology and Space (BMFTR) that comprises 25 individual projects and aims to improve understanding of European extreme weather events and associated uncertainties under anthropogenic climate change. As part of the cross‑project collaboration, a coordinated approach is being established to initiate and sustain targeted stakeholder communication and support it throughout the project period. The aim is to develop information, data and tools targeted to stakeholder needs. To this end, Hazard‑specific Stakeholder Interaction (HaSSI) groups coordinate collaborative work on windstorms, heavy precipitation, and heat/drought.

The HaSSI Wind group includes projects investigating physical mechanisms behind cyclones (CyclEx), applying newly developed statistical methods (CoDEx and SCaHA), analyzing storm-related impacts (COO, FORTEC and ECCES II), and developing methods for knowledge exchange (ClimXchange). With the pressure tendency equation, project CyclEx quantifies the influence of diabatic heating on cyclone intensification. Cyclones with a relatively large diabatic heating influence exhibit steeper deepening rates, more warm conveyor belt activity, increased precipitation, and stronger wind gusts compared to cyclones with a small diabatic influence. Project CoDEx develops statistical models to estimate extreme values in space and time. Project SCaHA focuses on statistical modeling of clustering and seasonality in vorticity extremes over the North Atlantic using the fractional compound Poisson process, and on identifying suitable models for different regions. As for the storm-related impacts, project COO uses a tracking methodology to assign a loss motivated storm severity index to each storm event and to perform a loss motivated ranking of historical and future events. Project FORTEC uses logistic regression models to identify relevant storm damage factors and model current and future storm damage risk for two damage types: vegetation damage along railway lines and building damage. Project ECCES II uses hydrodynamic tide-surge model and sensitivity experiments to evaluate the impact of regional sea level rise on storm surges in the North Sea with a focus on the nonlinear processes. With respect to stakeholder engagement, project ClimXchange strengthens climate research communication by providing hands-on guidance on suitable approaches, methods and communication skills. With joint efforts, we aim to provide a diverse view regarding storms in Europe and exchange actively with relevant stakeholders.

How to cite: Liu, X., Lorenz, R., Grieger, J., Ulbrich, U., Friederichs, P., Pinto, J. G., Christ, S., Fischer-Frenzel, P., Fried, R., Mendel, M., Merkes, S. T., Quinting, J., and Zimmermann, T.: ClimXtreme addressing (large scale winter) storm events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19825, https://doi.org/10.5194/egusphere-egu26-19825, 2026.

As climate change intensifies, shifts in temperature and precipitation extremes raise concerns about increasing river flood risks. Understanding and quantifying how climate change influences floods is relevant from both theoretical and practical perspectives. From a research standpoint, although flood trend detection has been widely investigated, attributing observed changes to specific drivers remains challenging. From a practical perspective, reliable methods are needed to incorporate evolving climate conditions into flood predictions—particularly in ungauged basins—since traditional flood frequency analysis assumes stationarity.
In this work, we present research conducted at Politecnico di Torino addressing these issues. The objectives are to investigate the relationship between floods and climate extremes at both local and large spatial scales, and to propose methods for linking changes in flood frequency curves to evolving precipitation and temperature patterns. The study area is the Great Alpine Region (GAR), including the Po River valley in Northern Italy. Both data-based and modelling approaches are employed.
The data-based approach relies on the derived flood frequency framework and estimates flood sensitivity to changes in precipitation extremes by linking flood frequency curves and Intensity–Duration–Frequency curves through quantile–quantile relationships at the local scale. The modelling approach is based on the application of a regionally distributed rainfall–runoff model forced by climate model outputs, allowing changes in flood event characteristics to be investigated at the regional scale.
This study is carried out within the Clim2FlEx project and the RETURN Extended Partnership and is funded by the Italian Ministry of Education, Universities and Research (MUR, PRIN project 2022AX3882) and the European Union NextGenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005 – Spoke VS1).

How to cite: Viglione, A., Lombardo, L., Cafiero, L., Basso, A., Mazzoglio, P., Ganora, D., Claps, P., and Laio, F.: Linking climate and rainfall extremes to flood changes: data-based and modelling approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19843, https://doi.org/10.5194/egusphere-egu26-19843, 2026.

The impact of a heavy precipitation event is determined not only by the total amount of precipitation but also by its spatial and temporal distribution. This study introduces a framework to quantify the key spatio-temporal properties of precipitation events - namely their characteristic time, length, and speed - using gridded datasets. 
To this end, we apply a spectral filtering approach based on wavelet decomposition. Wavelet decomposition has been proven to be highly effective in uncovering underlying frequency structures in time series and is well-suited for the analysis of two-dimensional spatial patterns. Previous applications to spatial precipitation fields (e.g., Buschow, 2024; Buschow & Friederichs, 2021) have demonstrated its potential to improve the understanding and description of precipitation events. We extend these methods to capture both spatial and temporal characteristics, providing for a comprehensive description of three-dimensional precipitation extremes.

Focusing on Germany, we analyze summer precipitation events using high-resolution datasets. These include the RadKlim dataset provided by the German Weather Service and a novel CPM ensemble, obtained from the NUKLEUS project. 
We assess the physical plausibility of the derived characteristics, examine their relationships to large-scale atmospheric dynamics, and also assess their changes with ongoing climate change. Our results reveal systematic patterns in the spatio-temporal organization of precipitation extremes. 
The framework presented here provides a robust tool for understanding extreme precipitation and offers potential for improved risk assessment and future climate studies. Our work is part of the BMFTR-funded ClimXtreme CoDEx project.

How to cite: Szemkus, S., Buschow, S., and Friederichs, P.: Describing the spatio-temporal structure of precipitation extremes using wavelet transformation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20065, https://doi.org/10.5194/egusphere-egu26-20065, 2026.

Most climate change impact assessments focusing on floods rely on daily resolution data. For snow-influenced catchments, these assessments typically project either decreases or no changes in flood magnitude and frequency because decreases in snowmelt can compensate for increases in extreme precipitation. Yet, sub-daily rainfall extremes will intensify more strongly than daily rainfall extremes under climate change. This suggests that a daily resolution may be insufficient for studying future flood responses to rainfall intensification. In this study, we show how moving from daily to hourly resolution data reshapes our understanding of how floods will change in a warming climate.

We find that, in more than 75% of the Alpine rivers studied, daily streamflow projections underestimate the magnitude and recurrence rate of the 100-yearly flood compared to hourly projections. While hourly projections show increases in flood magnitudes in strongly snow-influenced basins, daily projections point to decreasing flood magnitudes in the future. Under a high emission scenario, these differences in the sign of change between hourly and daily projections become significant before mid-century in more than half of the catchments. The flood seasonality signal also differs between daily and hourly projections for snow-influenced catchments: daily projections show a clearly earlier onset of floods compared to the historical period, whereas this signal is weak in hourly projections. While the daily perspective suggests a reduction in the magnitude of extreme floods due to a decrease in the magnitude of extreme snowmelt events in the future, the hourly perspective indicates that intensified sub-daily precipitation can compensate for snowmelt decline.

These results highlight that using daily instead of hourly projections may lead to wrong conclusions on changes in flooding in a warming climate in terms of the magnitude of change and, in snow-influenced catchments, even in terms of the sign of change. Using hourly resolution data is therefore good practice to guide adaptation strategies to flood response to climate warming.

How to cite: Astagneau, P. C., Wood, R. R., and Brunner, M. I.: Time resolution changes perspective on flood responses to climate warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20501, https://doi.org/10.5194/egusphere-egu26-20501, 2026.

This paper presents a comparison of three methods for determining cumulonimbus (Cb) cloud-top heights. Two methods are prognostic, based on the 1/4 CAPE and 1/2 CAPE approaches, while the third is a diagnostic polynomial method. The analysis was conducted for five convective events that developed under different synoptic and mesoscale conditions. Surface and upper-air synoptic charts, WRF simulations, radar, and radiosonde data, as well as observed lightning data are used to characterize the convective environments and evaluate the methods. The polynomial method, which approximates moist adiabats using a fifth- degree polynomial function, proved to be successful in diagnosing the Cb cloud-top heights at the observed locations. Therefore, cloud-top heights obtained from the polynomial method are used as a reference values for assessing the performance of the prognostic methods. Results show that, for most locations and events, both CAPE-based methods tend to overestimate Cb cloud-top heights. The 1/4 CAPE method generally exhibits smaller deviations from the observed values of Cb cloud-top heights than the 1/2 CAPE method. Nevertheless, the Cb cloud-top heights estimated using both CAPE methods exhibit a strong linear correlation with observed heights in four out of five analyzed events.

How to cite: Oštrić, S., Šoljan, V., Babić, K., and Telišman Prtenjak, M.: Comparison of Methods for EstimatingThunderstorm Cloud-Top Heights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20647, https://doi.org/10.5194/egusphere-egu26-20647, 2026.

Windstorm events are among the most damaging weather-related hazards in Europe, accounting for approximately 70% of the total insured losses. Existing research has primarily focused on storm climatology, hazard metrics, and loss-based assessments derived from insurance or damage data. While these approaches have advanced our understanding of windstorm dynamics and impacts, they provide limited insights into the spatial and quantitative characteristics of exposure.

Windstorm damage occurs where localised extreme wind gusts intersect with exposed socio-environmental assets. Exposure is therefore a central, yet still insufficient quantified, component of windstorm risk. In practice, exposure is often approximated through loss proxies or simplified indicators, largely due to persistent challenges in defining spatially coherent storm-affected areas from gust observations at the event scale.

This study develops an event-based windstorm exposure analysis for Sweden by linking observationally identified windstorm events with high-resolution national exposure datasets. Windstorms are identified from long-term station observations using local percentile exceedances combined with spatio-temporal clustering, resulting in a catalogue of extreme events. Storm-affected areas are identified using a single exceedance-based footprint approach applied to two wind data sources: interpolated station observations and ERA₅ reanalysis. In both cases, footprints are defined using locally derived percentile thresholds of wind gust intensity, capturing areas that experienced unusually strong winds relative to  local conditions.

For each event and footprint definition, exposure is quantified through the spatial intersection of extreme winds with datasets that describe population distribution, buildings, transportation, forest structure, and topography. Exposure metrics include population counts, infrastructure length, building numbers, and forest area.

A comparative analysis of exposure estimates across different footprint methodologies is conducted to assess the sensitivity of windstorm exposure to footprint definition. The resulting dataset provides an event-based representation of windstorm exposure in Sweden and establishes a foundation for improved attribution of impacts and future vulnerability and risk analyses.

How to cite: Georgali, E. and Karagiorgos, K.: Event-based windstorm exposure in Sweden using observational and reanalysis-derived storm footprints., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20762, https://doi.org/10.5194/egusphere-egu26-20762, 2026.

This study assesses the impacts of climate change on rainfall patterns in the North-Eastern (NE) Indian state of Tripura using historical rainfall observations from the Indian Meteorological Department (IMD) and future climate projections derived from CMIP6 General Circulation Models (GCMs). The analysis quantifies changes in rainfall patterns and updates the Intensity-Duration-Frequency (IDF) curves to incorporate the effects of projected climate variability. The performance of 13 CMIP6 GCMs is evaluated through statistical analysis to assess their ability to reproduce historical precipitation patterns over the period 1984–2014. Based on this evaluation, the most suitable models are selected for projecting future rainfall behavior. The results indicate a potential shift in future rainfall patterns, with Tripura expected to experience more frequent extreme rainfall events in the future, with daily rainfall of 100mm or more. The IDF curves for the future period (2025–2054) are developed using the selected CMIP6 model outputs and compared with IDF curves derived from IMD observed rainfall data. The updated IDF curves offer valuable insights into the evolution of rainfall extremes, enhancing our understanding of climate change's impacts on rainfall-induced natural hazards in Tripura.

How to cite: Kumar, S.: Assessment of Climate Change Impacts on Rainfall Extremes and Intensity–Duration–Frequency Curves in Tripura, Northeast India, Using CMIP6 Climate Models dataset, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20865, https://doi.org/10.5194/egusphere-egu26-20865, 2026.

The storm Hans struck Eastern Norway in August 2023 with two days of intense rain, triggering what may become the country’s most expensive weather-related disaster. Remarkably, the flooding affected large rivers that normally peak during the snowmelt season from April to June. We answer the two related research questions of a) could we have anticipated a storm of Hans’ magnitude, and b) could the storm have coincided with the April to June snowmelt, causing a compound event. We leveraged the UNSEEN methodology (UNprecedented Simulated Extremes using ENsembles), using 3.5 years of extended-range weather forecasts and their 20-year reforecasts from ECMWF as well as ERA5 reanalysis. UNSEEN uses large ensembles of model simulations to assess rare but plausible extremes, which enables the assessments of return periods not possible with the short historical record. We found that while Hans is at the high end of what could be expected, the larger sample reveals a continuum of weaker but still previously unprecedented events throughout the year that remain capable of causing severe impacts. The most extreme rainfall events are most probable between July and September, when snowmelt does not amplify flooding. However, two of the most extreme simulated events occurred in May, indicating that a physically consistent compound disaster — heavy rain coinciding with snowmelt — is possible under the right conditions. Together, these results show how data-driven counterfactuals can help anticipate unprecedented extremes. 

How to cite: Hellan, S. P., Dunn-Sigouin, E., Kolstad, E. W., Sauvat, E., Simpson, R., and Wulff, C. O. W.: How unlikely was the storm Hans? Reusing extended range forecasts to anticipate unprecedented extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22475, https://doi.org/10.5194/egusphere-egu26-22475, 2026.

Hydrological signatures (HS) have proven to be highly effective in calibrating physically-based hydrological models, enhancing their process consistency. However, their integration into parameter optimization for deep learning (DL)-based hydrological models has been limited. To address this gap, we propose a novel HS-informed framework that dynamically integrates hydrological signatures into DL parameterization through a multi-task learning approach. This study evaluates the impact of HS integration on model performance using a large-scale, global hydrological dataset. The HS-informed model achieved a significant performance improvement, with a median Nash-Sutcliffe Efficiency (NSE) of 0.739, compared to 0.666 for the baseline model across the test set. Notably, the most pronounced improvements in NSE were observed in hydrologically complex basins, including baseflow-dominated (+0.135), drought-prone (+0.148), and flood-prone basins (+0.159). Sensitivity analysis further revealed that the HS-informed model could leverage extended historical input data (over 120 days) to sustain robust performance (median NSE of 0.715) over a 30-day forecast period. Shapley Additive Explanations (SHAP) analysis highlighted two key mechanisms underlying these improvements: the enhanced recognition of long-term hydrological patterns through improved memory and a better representation of catchment heterogeneity by emphasizing non-climatic attributes. These findings demonstrate that integrating hydrological signatures offers a superior approach to traditional point-error-based calibration in AI-driven hydrological modeling.

How to cite: wang, Z., li, C., and cui, P.: A Novel Hydrological Signature-Informed Framework for Enhancing Extreme Streamflow Prediction Using Multi-Task Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2556, https://doi.org/10.5194/egusphere-egu26-2556, 2026.

The increasing frequency of extreme weather events is drawing attention to groundwater flooding, which is caused by rising groundwater levels and can result in significant damage to infrastructure, buildings, and the environment. Unlike fluvial or pluvial flooding, groundwater flooding is difficult to detect and not easily managed with traditional protective measures. Numerical models—particularly probabilistic approaches such as Bayesian inference—help to better quantify uncertainties in modeling and forecasting. Flood risk maps are essential for managing groundwater flooding; however, precise uncertainty analyses are often lacking. Citizen science and low-cost sensors can also contribute by bridging data gaps and encouraging public participation. This study presents a framework for assessing vulnerability to groundwater flooding that accounts for uncertainties and generates probabilistic maps. Using a case study from Garching in 2023, it demonstrates how modeling tools can be effectively utilized. Finally, the study suggests expanding monitoring tools and citizen engagement to strengthen risk communication, raise awareness, and better integrate groundwater flood protection measures.

How to cite: Chiogna, G. and Richieri, B.: Groundwater Flooding: Developing an approach to risk assessment and communication, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3383, https://doi.org/10.5194/egusphere-egu26-3383, 2026.

Soil moisture is a core element in shaping land–atmosphere interactions, playing a critical role in ecosystem functioning and sustaining water resources for human use. However, existing approaches, including numerical and AI-based methods, still suffer from notable limitations in soil moisture forecasting. In this study, we develop a novel AI-based soil moisture forecasting model (ASM), which is capable of providing low-resolution global forecasts and high-resolution regional forecasts of soil moisture at the subseasonal timescale. ASM consistently outperforms other representative state-of-the-art AI models across all forecast lead times. Compared with ECMWF, ASM is closer to the ground truth, and better preserve finer-scale spatial details. For regional predictions, ASM produces reliable high-resolution subseasonal soil moisture forecasts for two drought-prone regions selected as case studies: Southern Africa and Henan Province, China.

How to cite: Zhang, Q. and Huang, X.: Global–regional integrated subseasonal forecasts of soil moisture drought, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4172, https://doi.org/10.5194/egusphere-egu26-4172, 2026.

Global warming is altering snowmelt dynamics and flood generating mechanisms, yet their compound effects on cold-region floods remain unclear. Here, we investigate flood mechanism transitions and their drivers across 424 Northern Hemisphere snow-dominated catchments. Through comparative analysis, we pinpoint the specific impacts of these shifts on flood characteristics. Our results indicate that 48.3% of the catchments have undergone a snowmelt-to-rainfall transition in flood generating mechanisms. While this has not systematically altered long-term flood magnitude trends, it has significantly steepened the flood rising limb. Furthermore, although rising temperatures have advanced the timing of snowmelt and rain-on-snow floods, the shift toward rainfall dominance has largely offset this trend, leading to a stronger synchronization between flood timing and extreme precipitation. These findings offer critical insights for flood forecasting and water management in snow-dominated regions.

How to cite: Bai, X., Liu, W., Wang, H., Feng, Y., and Sun, F.: Changing flood-generating mechanisms and their impacts on flood characteristics in snow-dominated catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5267, https://doi.org/10.5194/egusphere-egu26-5267, 2026.

Floodplains attract disproportionate concentrations of population and economic activity globally, yet the systemic flood risks emerging from this uneven development remain poorly characterized. Through a global analysis spanning 2000-2020, we quantify floodplain development patterns and associated flood losses across nations with varying income levels and flood protection capacities. Our results reveal that floodplains have experienced faster growth than non-floodplains in both population density and GDP density. These trends diverge sharply by income and protection levels: floodplain population density growth rates in low- and lower-middle-income countries outpaced those in high-income nations by factors of 2.33 and 7.58, respectively. Similarly, due to levee effect, regions with flood protection capacity of 100 years or more experienced GDP density growth that was 4.51 times higher than in regions with less than 10-year protection. The heightened sensitivity of flood losses to socio-economic growth stems from uneven floodplain development. This creates a divergent risk pattern: wealthier, well-protected regions accumulate greater economic assets at risk, whereas poorer, under-protected areas face the compounded burden of exposure to both population and GDP risks. Our findings highlight the urgent need for flood risk adaptation strategies that explicitly consider and address underlying floodplain socio-economic inequalities in exposure and protection.

How to cite: Lan, Z., Liu, W., Wang, T., and Sun, F.: Mapping global floodplain development disparities highlights drivers underlying intensifying flood losses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5280, https://doi.org/10.5194/egusphere-egu26-5280, 2026.

Droughts are commonly classified into meteorological, agricultural, hydrological, and ecological types, yet how these categories interact dynamically and propagate across space and time at subseasonal scales remains poorly understood. Here we show that subseasonal droughts propagate as directional, cascading processes across the land-atmosphere system. We develop an event-based analytical framework using event coincidence analysis to identify subseasonal drought events as sustained extremes in precipitation-evapotranspiration balance, soil moisture, runoff, and vegetation condition across the contiguous United States from 1982 to 2025, using satellite observations and land data assimilation system simulations. We find robust lead-lag relationships and coherent propagation pathways in which meteorological droughts systematically precede agricultural, hydrological, and ecological droughts across space and time. Event coincidence analysis identifies statistically significant drought sources and sinks and their time-lagged directional dependencies, allowing directional propagation patterns to be traced across drought types and regions. We find consistent cross-type drought transitions in several climate-sensitive regions (for example, SPEI → soil moisture → NDVI in the Southern Plains), with meteorological droughts typically preceding agricultural and ecological impacts by several weeks and with variable amplification along the transition. Linking these propagation pathways to near-surface temperature, wind fields, and 850-hPa geopotential height shows that large-scale atmospheric circulation modulates timing and intensity of cross-type drought cascades. These findings show that subseasonal drought evolution is governed by directional temporal cascades and by coherent spatial propagation pathways across the land-atmosphere system, indicating non-local controls and distinct temporal signatures.

How to cite: Kumar, S. and Tian, D.: Cascading propagation of subseasonal droughts across the land-atmosphere system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5997, https://doi.org/10.5194/egusphere-egu26-5997, 2026.

Flash droughts (FDs) are characterized by their rapid onset, but their societal and agricultural impacts depend critically on the duration of anomalous moisture stress. While the land–atmosphere processes governing FD initiation have been widely studied, the role of subsurface water storage in regulating persistence and recovery remains poorly constrained. Groundwater depth serves as the primary regulator of drought propagation. Rather than treating groundwater as a passive reservoir, this research investigates its active role in the initiation and subsequent evolution of FDs. We investigate how groundwater storage either dampens flash-drought intensification via upward moisture flux or catalyzes the evolution of these events into major hydrological crises. Our approach determines the precise influence of the water table on the intensification and multi-seasonal persistence of FD events. We utilize groundwater-level observations from the Central Ground Water Board of India, spanning 1996 – 2023, to construct seasonal groundwater depth fields (0.25° resolution) for pre-monsoon, monsoon, and post-monsoon conditions. FD events are identified using a gridded catalog derived from the Standardized Evaporative Stress Ratio (SESR). Our analysis will employ contingency-based statistical tests (χ^²) and survival-type hazard analysis to quantify the probability of drought termination as a function of categorized water-table depths (shallow, intermediate, and deep). Spatial block-bootstrapping will be applied to account for regional spatial dependencies. We aim to identify critical groundwater depth thresholds beyond which the probability of flash-to-hydrological drought transition increases significantly. This work provides a new perspective on groundwater as a modulator of drought evolution in monsoon-dominated, groundwater-stressed environments.

How to cite: Vidushi, V. and Syed, T. H.: Groundwater Depth as a Control on Flash-Drought Dissipation Versus Hydrological-Drought Development in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6210, https://doi.org/10.5194/egusphere-egu26-6210, 2026.

Increasingly frequent and severe droughts pose substantial risks to agricultural water systems globally. Farmers can mitigate drought impacts through on-farm adaptation strategies, such as reducing drainage or increasing groundwater retention. However, the feedback between farmers’ adaptive behaviour and groundwater dynamics remains poorly understood. To address this gap, we developed an agent-based model to evaluate how individual farmers’ adaptation decisions influence local and regional groundwater systems. The model couples farmer decision-making, grounded in protection motivation theory, with the hydrological dynamics of the eastern Netherlands simulated using the WALRUS hydrological model. We ran scenarios based on different climate conditions and land use configurations to assess the effects of adaptation behaviour. Our findings show that farmers with adaptation measures experience substantially less drought damage associated with low groundwater levels during moderate droughts (65% reduction), but these measures are less effective during extreme droughts (13% reduction). Farmers who adopt these measures also experience slightly increased damage during wet periods, indicating a higher risk of waterlogging. Importantly, both benefits and drawbacks extend beyond the farm scale, affecting groundwater levels of both adapting and non-adapting farmers in the area. Ongoing work explores the spatial distribution of these effects in more detail to better understand the neighbourhood effects for both social and hydrological dynamics. The findings of our study can be used to support strategies that minimise trade-offs between groundwater extremes through both individual and collective adaptation. 

How to cite: De Graaff, L., Mazzoleni, M., Wens, M. L. K., Brauer, C. C., and Van Loon, A. F.: Exploring neighbourhood effects of farm-level drought adaptation on groundwater extremes with a coupled agent-based and hydrological model , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6318, https://doi.org/10.5194/egusphere-egu26-6318, 2026.

In recent years, the Netherlands has experienced extreme climatic events, including droughts in 2018 and 2022 and record-breaking wet years, such as 2024. These events are prompting a paradigm shift among water managers and users from rapidly draining water to holding it when possible to mitigate dry years, while maintaining the capacity to deal with floods. This research aims to examine how water users' decisions to adapt to drought can influence the water system, and vice versa, while accounting for trade-offs with flood risk. We address this research question using an agent-based model (ABM) based on a small agricultural catchment (Hupsel, ~1,400 ha) in which dairy farming is the predominant land use. The ABM has two main components: 1) the hydrological system; and 2) the human decision-making system. The hydrological system focuses on shallow groundwater and surface water, represented by a MODFLOW model that includes drainage and surface water networks, a single-layer sandy aquifer, and different land use types via the unsaturated zone flow (UZF) package. In the human decision-making system, farmers can decide among different adaptation options to drought based on the protection motivation theory: 1) adopt groundwater irrigation; 2) retain water in ditches to enhance recharge; 3) remove drains or ditches to enhance recharge further; and 4) change crops to less water-demanding ones. Results focused on irrigation indicate that consecutive years of drought lead to higher irrigation adoption, which, in turn, depletes the aquifer and makes the water system more sensitive to dry spells. ABMs are a valuable tool to explore the feedback between humans and the water system in a spatially explicit way, moving beyond the usual representation of anthropogenic interventions as model boundary conditions.

How to cite: Henao Casas, J. D., De Graaff, L., Van Huijgevoort, M., Van Der Velde, Y., and Van Loon, A.: Exploring human-groundwater feedbacks in the Dutch agricultural context under climate extremes using an agent-based model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6478, https://doi.org/10.5194/egusphere-egu26-6478, 2026.

Global warming has significantly altered the spatiotemporal distribution of floods, leading to substantial variations in human adaptation patterns. Identifying the potential drivers of these changes and the underlying mechanisms of disaster adaptation is essential for formulating effective flood risk strategies. Based on observed streamflow records from 9,531 hydrological stations and data from 910 major flood events worldwide, this study reveals that most regions globally exhibit synchronized trends in drought and flood flows, with 28.14% showing a simultaneous increase and 33.36% showing a simultaneous decrease. To mitigate flood risk, residents in 53% of countries—most notably in the Middle East—demonstrate a tendency to migrate away from flood-prone areas. This retreat has significantly reduced flood-related mortality and forced displacement. Conversely, in regions with robust flood protection infrastructure, residents tend to maintain shorter migration distances. Further analysis of the drivers behind floodplain migration indicates that in developing nations, flood-induced mortality and displacement are the primary catalysts for relocation. In these contexts, the psychological memory of destruction or the urgent need for resources often compels residents to either flee or, paradoxically, migrate toward flood-prone zones. Under climate-driven pressures, the extent of flood inundation is a more significant determinant of migration patterns in regions such as Australia. Notably, in countries like the Philippines and Kenya, the mitigation of compound drought-flood extremes has encouraged further settlement in flood-prone areas, highlighting the complexity of multi-hazard interactions. This study systematically deciphers the mechanisms underlying flood adaptation strategies and attributes their primary drivers, providing a robust scientific framework for enhancing flood risk management and regional resilience.

How to cite: Wang, N.: Resident Adaptation Patterns Under the Influence of Global Flood Evolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6507, https://doi.org/10.5194/egusphere-egu26-6507, 2026.

Intense rainfall events often trigger landslides and torrential flows, which are not only hazardous processes on their own, but can also generate cascading hazards through sudden and massive sediment delivery to river networks. Slope processes are therefore key drivers of geomorphic change in mountainous catchments, enhancing hillslope-channel connectivity and promoting rapid channel reorganisation. In light of the above, it is essential to characterise structural and functional connectivity (Heckmann et al., 2018), as well as geomorphic organisation before and after intense precipitation events, to better evaluate flood hazards and associated risks. Against this background, the January 2020 Gloria storm affected the Tordera River basin (Catalonia, NE Spain), where more than 480 mm of rainfall was recorded over 96 hours, with 24-hour accumulation over 200 mm, causing widespread sediment mobilisation and channel changes, as well as significant damage due to flooding and landslides.

In this study, we aim to evaluate: 1) how pre-event hillslope-channel connectivity influences the geomorphic response to extreme floods and the post-event geomorphic changes through an integrated analysis of the index of connectivity (IC); 2) the spatial distribution patterns of erosion and sedimentation through the geomorphic mapping of the active riverbed and sediment bars (both active and stable), before and after the Gloria storm, and the existing inventory of landslides caused by the event. High-resolution DTMs (Digital Terrain Models) were generated from airborne LiDAR surveys conducted in 2011, 2016, and 2023. The IC was derived from a pre-event DTM to characterise structural sediment connectivity following Cavalli et al. (2013), while erosion and sedimentation processes were quantified using the difference between DTMs (DTMs of Difference, DoDs) for pre-event (2016-2011) and post-event (2023-2016) periods. GIS-based geomorphic mapping of active channels and sediment bars before and after Gloria was used to assess event-scale channel reorganisation.

Preliminary results indicate a clear spatial correspondence between pre-event connectivity patterns and the magnitude of the geomorphic change observed during that extreme flood. Areas characterised by high pre-event erosion rates, identified from 2016-2011 DoDs, largely coincide with sectors where numerous landslides were triggered during the Gloria storm. High connectivity values also correspond to areas dominated by erosion and deposition in the 2016-2011 DoDs, highlighting the role of pre-event structural connectivity in conditioning sediment transfer pathways. Furthermore, active bars mapped after the event predominantly overlap with areas affected by pre-event erosion, whereas bars that remained stable during the storm are mainly associated with zones characterised by pre-event deposition. The active channel also experienced noticeable widening during the event, while the majority of vegetated bars that were stable before Gloria remained stable throughout the storm, reinforcing the link between pre-event geomorphic organisation and flood response. These findings highlight the importance of pre-event structural connectivity in controlling geomorphic response during extreme rainfall events, providing valuable insight for hazard assessment and river management.

Cavalli, M. et al.  (2013). Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. Geomorphology, 188, 31-41.

Heckmann, T. et al.  (2018). Indices of sediment connectivity: opportunities, challenges and limitations. Earth-Science Reviews, 187, 77-108.

How to cite: Jacobo-Quiñones, N., Guinau, M., Abancó, C., García-Sellés, D., López-Tarazón, J. A., Zapico, I., Tapia, M., González, M., and Pinyol, J.: Linking pre-event hillslope-channel connectivity to its geomorphic response during an extreme rainfall event: insights from the 2020 Gloria storm in the Tordera River basin (NE Spain), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7681, https://doi.org/10.5194/egusphere-egu26-7681, 2026.

Kerala, on the windward side of the Western Ghats in southern India, receives about 3000 mm of annual rainfall under a tropical monsoon climate, driven by orographic south-west monsoon rainfall. The state has a high population density of about 859 persons per square kilometre and a limited geographical extent, with settlements concentrated in river valleys and downstream of reservoirs. These physiographic and socio-hydrological conditions make flood events critically important from both hydrological and societal perspectives. The catastrophic flood of 2018 further emphasized the need for an updated hydrological reassessment of existing dams and their spillway performance, and reservoir rule curves in Kerala.
Kerala has 53 large dams, of which 30 dams distributed across nine river basins are analysed in this study. The selected catchments are characterized by short hydrological response lengths and steep terrain, with longitudinal bed slopes ranging from 20 to 80 m km⁻¹. The Western Ghats rise sharply from near sea level to elevations of approximately 2500 m, promoting intense orographic rainfall, short travel times, and rapid runoff concentration. For the 30 dam catchments, the Time of Concentration (Tc) varies between 0.7 and 5 h, indicating fast-rising floods with minimal natural attenuation. Several catchments exhibit high hydrological response, with specific flood exceeding 13 m³ s⁻¹ km⁻². Most dams are located within 100 km of the Arabian Sea coastline and occur in serial or cascade arrangements along the same river valleys, a configuration that is hydrologically relevant for upstream–downstream flood interactions.
The study reassesses the Inflow Design Flood (IDF) and spillway adequacy of the selected dams. Of the 30 projects, 20 dams were completed before 1985, before the Bureau of Indian Standards (BIS) issued Indian Standard IS 11223:1985, which formally introduced IDF categories such as the Probable Maximum Flood (PMF), Standard Project Flood (SPF), and 100-year flood. In projects commissioned before 1985, spillway capacities were generally fixed using prevailing hydrological practices, limited storm data, and engineering judgment.
In the present reassessment, IDF estimation is carried out in accordance with BIS guidelines using a hydro-meteorological approach, and unit hydrograph parameters are derived from the Flood Estimation Report. Storm parameters are derived from the Probable Maximum Precipitation (PMP) Atlas for the West-Flowing Rivers of the Western Ghats, published by the India Meteorological Department (IMD) and the Central Water Commission (CWC), which compiles major historical storm events from 1905 to 2010. The revised design floods are compared with existing spillway capacities, and the analysis also examines relationships with Tc, gross storage, specific flood, and year of dam completion.
Results indicate that 26 out of the 30 dams show spillway inadequacy under the revised IDF. In several projects, design flood exceedance exceeds 200%, and in some cases, reaches more than 300%. Spillway inadequacy is more frequent in short-response catchments with lower Tc and higher specific flood values. This study offers a comparative hydrological perspective for steep tropical catchments in Kerala. It may support an informed, evidence-based reassessment of existing dams using updated datasets and contemporary analytical practices for prioritization of dam safety.

How to cite: Issac, I., Sen, S., and Goel, N. K.: Design Flood Revisions and Spillway Adequacy in Steep Tropical Catchments: A Multi-Dam Reassessment from Kerala, India , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7809, https://doi.org/10.5194/egusphere-egu26-7809, 2026.

Surface water availability (WA), defined as precipitation minus evapotranspiration, is affected by changes in vegetation structure. These biophysical impacts can alter the distribution of water availability, shifting both its average and extreme values, while the divergence is not yet quantified. Using long-term remote sensing observations, our analysis reveals that increases in leaf area index (LAI) lead to a widespread decline in average water availability, with a global reduction of -2.11 mm/month m² m^-2. Additionally, we show that in humid regions, extreme water availability—represented by the 15th and 85th percentiles of water availability from 2001 to 2020—exhibits stronger sensitivity to LAI variations than average water availability. Overall, the fraction of variance in low water availability explained by greening is minimal (-2.7%), followed by average water availability (6.8%), while high water availability exhibits the largest fraction (-23.6%).

How to cite: Li, Z., Liu, W., Wang, T., and Sun, F.: Biophysical impacts of Earth greening modulate average and extreme water availability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8602, https://doi.org/10.5194/egusphere-egu26-8602, 2026.

Extreme precipitation events have caused obvious damage to human environments and socioeconomic systems. However, the changes in extreme precipitation and their underlying causes remain unclear. This study analyzed daily precipitation data from 2,254 meteorological stations across China from 1981 to 2018, focusing on two key extreme precipitation indicators: Max 1-day precipitation amount (Rx1day) and Max 5-day precipitation amount (Rx5day). Trend analysis was conducted for 17 river basin divisions using the Mann-Kendall method. We also applied the field significance test, a statistical method to evaluate whether a spatial pattern of locally significant results, to determine whether observed trends at individual stations were statistically significant or due to random variation. The results showed that 59.3% and 58.6% of the stations exhibited increasing trends in Rx1day and Rx5day, respectively, with significant trends identified at 5.4% and 4.1% of the stations. The field significance test revealed a significant increasing in Rx1day across China at the 5% significance level. Among the 17 sub-basins, significant increases in extreme precipitation were observed in the Inland rivers of Xinjiang and Northern Tibet. The result was consistent with the warming and humidification trends in northwest China. We further analyzed the relationship between urbanization and extreme precipitation by using population density to distinguish rural and urban stations. We found that the spatial distribution of urban stations closely overlapped with stations experiencing increased extreme precipitation, while rural stations corresponded with those showing a decrease. With the progress of urbanization, variations in the trends observed at urban and rural stations have emerged. Nevertheless, urban stations exerted a more pronounced influence on the increasing trend of extreme precipitation.

How to cite: Wu, L.: Urbanization influence on changes of extreme precipitation in mainland China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9087, https://doi.org/10.5194/egusphere-egu26-9087, 2026.

This research investigates the influence of the El Niño–Southern Oscillation (ENSO) on extreme flood events in the United States and its potential connection to flood insurance claims from the National Flood Insurance Program (NFIP). Given the recently observed increase in the frequency of extreme weather events, this study aims to quantify the correlation between ENSO indicators and recorded economic losses at state and county levels across the USA. Emphasis is particularly placed on the state of California, which is highly sensitive to El Niño events.

The methodology is based on the integration of multiple datasets, including ENSO indices from NOAA, US-CAMELS streamflow data, COBE sea surface temperature (SST), digital elevation models (DEM), National Hydrography Dataset (NHD), OpenStreetMap (OSM), and US Census data. From these datasets, geospatial and physical features were extracted, such as hydrographic and road network density, mean elevation, distance to the coastline, county centroid coordinates, and population. These features were analyzed using statistical tools, including the Pearson correlation coefficient and Threshold Exceedance Analysis, applied across multiple percentile showing thresholds (90–99%).

In addition, a machine learning model was developed to predict flood insurance claims per 100,000 residents. The results indicate that correlations between ENSO indices and streamflow data are significantly stronger than those between ENSO indices and insurance claim records, highlighting the substantial influence of socioeconomic factors on the insurance claim filing process. California exhibits the highest positive correlation between the maximum annual ENSO index and insurance claims (r ≈ 0.35). The developed CatBoost model can be used to predict a high percentage (>60%) of their variability, using both static and dynamic features.

The study concludes that ENSO indices can contribute meaningfully to flood risk prediction frameworks. Future work will focus on extending the analysis to additional states or the entire USA and incorporating new explanatory features to further improve model performance.

How to cite: Tsolakidis, K.-C., Papoulakos, K., Tepetidis, N., Iliopoulou, T., Dimitriadis, P., Tsaknias, D., and Koutsoyiannis, D.: ENSO impacts on flood risk and insurance claims in the United States: a machine learning approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9856, https://doi.org/10.5194/egusphere-egu26-9856, 2026.

Climate change and anthropogenic activities increasingly stress groundwater resources, even in generally water-rich areas like Germany, threatening socio-economic and ecological systems. Since the impacts of groundwater droughts often emerge slowly and implicitly, it remains unclear to what extent they are noticed and recognized by society.

We address this gap by linking hydrological observations of groundwater droughts in Germany with news-derived indicators of societal awareness at the national scale. We analyzed 30-year groundwater records from and 521 regions and 13,900 monitoring wells across aquifers of different depths, after quality control including outlier screening, level-shift detection, and linear interpolation of short gaps (≤1 month) to daily resolution. We then identified drought periods and quantified their duration and severity using the variable threshold method, and classified events by the strength of potential human influence. Drought events with strong human influence are defined as those for which the variability of the associated time series dominates more by long-term trend rather than by interannual variability, or the event itself is strongly affected by abrupt level shifts. Finally, drought periods with strong and weak human influence were linked to a multi-sector drought-impact dataset derived from German newspaper articles (2000–2024) to assess societal awareness of groundwater droughts nationwide.

We found at least one drought event in 89.4% of the time series. In regions, drought events with weak human influence lasted, on average, 127 days and had a mean severity (maximum deviation below the drought threshold) of 0.2 m. Societal awareness was generally highest during the early phases of groundwater droughts, prior to the maximum groundwater-level deviation. Strong human influence amplified drought conditions, increasing the number of events by 7.2% and their mean duration by 2.9% within each region, and also leading to much earlier societal awareness. However, awareness did not persist throughout the drought period: awareness strength declined much faster than the groundwater-level recovery rate, and no significant relationship was found between changes in awareness strength and groundwater levels in deep aquifers during drought periods. These findings suggest that "invisible" groundwater droughts, especially in deep aquifers, are not fully perceived by society and highlight the need for improved groundwater policy coordination at the national level.

How to cite: Wang, Z., Peña Guerrero, D., Sodoge, J., Ebeling, P., Zheng, Y., Siebert, C., Madruga de Brito, M., Merz, R., Stahl, K., and Tarasova, L.: Is society aware of “invisible” droughts? - a groundwater perspective , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9896, https://doi.org/10.5194/egusphere-egu26-9896, 2026.

Groundwater abstraction can substantially reshape how climatic drought propagates into aquifer storage in semiarid Mediterranean settings. Here we propose an attribution framework to separate hydroclimatic and anthropogenic controls on standardized groundwater anomalies in two hydraulically connected aquifers of Spain’s Ebro Basin: the Plio-Quaternary of Alfamén and the Miocene of Campo de Cariñena.

We first reconstruct temporally continuous groundwater-level series for 1980–2025 using transfer-function noise (TFN) models in Pastas. Models are driven by daily precipitation and Penman–Monteith potential evapotranspiration, and include reconstructed monthly abstraction stresses. From these reconstructions we compute monthly Standardized Groundwater Indices (SGI) under current, pumped conditions and compare them to multiscale Standardized Precipitation–Evapotranspiration Index (SPEI) to quantify climate–groundwater coupling and identify spatial response types. We then isolate the effect of abstraction by building counterfactual “no-pumping” simulations through linear decomposition of calibrated TFN models and removal of pumping contributions, enabling within-piezometer comparisons against a reference-consistent baseline. Focusing on 2010–2025, we evaluate how abstractions alters anomalies beyond frequency using an SGI < −1 threshold, including month-level reclassification, event structure, peak timing, exceedance probabilities, and the instantaneous abstraction effect defined as ΔSGI = SGI_pumped − SGI_nopump.

Under pumping, climate–groundwater coupling strengthens monotonically with climatic accumulation, mean SGI–SPEI correlations increase from ~0.07–0.10 at 1-month SPEI to ~0.43 (Alfamén) and ~0.52 (Cariñena) at 48-month SPEI scales. Long-window coupling and response types show coherent spatial organization across intensively cultivated areas, particularly along the valley floor and lower piedmont. Persistent SGI declines under pumping concentrate in the central parts of both aquifers, broadly coinciding with irrigation hotspots, whereas piezometers near aquifer margins more often exhibit transient or non-significant declines. A key exception occurs in the shallow Plio-Quaternary of Alfamén near ephemeral streams, where episodic focused infiltration can temporarily offset local drawdown. Removing abstraction fundamentally shifts the apparent drought timescale. SGI without pumping shows no declining trends and aligns most strongly with annual climate variability (around SPEI12), with correlation peaks up to ~0.8 and network means near ~0.45 in both aquifers, indicating that observed downward SGI trends largely reflect externally imposed abstraction.

Counterfactual diagnostics reveal temporal reorganization. Pumping produces longer, more persistent anomalies episodes and seasonally biased onsets (late autumn/early winter, plus a June onset cluster in Cariñena), while peak timing of the events remains partly climate-governed. Exceedance probabilities of crossing SGI < −1 are higher at every monitoring point under pumping; the largest increases appear in central sectors, where sustained pumping and thicker saturated zones amplify cumulative stress on storage, but elevated likelihoods and ΔSGI also extend beyond the main abstraction hotspots into areas without raw drawdown signals. Over the monitoring network, we observe that pumping increases the likelihood and persistence of moderate groundwater anomalies, delays recovery, and lengthens the effective memory of the system, implying that SGI derived from observed heads in heavily exploited aquifers reflects a compound climate–management signal and should be complemented with counterfactual baselines and month-resolved persistence metrics for attribution and management.

How to cite: Cid-Escobar, D., Limones, N., Fernández, M., and De Stefano, L.: Beyond climatic-driven groundwater drought: extracting anthropogenic signatures fromstandardized groundwater indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11457, https://doi.org/10.5194/egusphere-egu26-11457, 2026.

Chile has endured a decade-long “mega-drought,” yet it remains unclear whether this represents a temporary climate anomaly or the onset of long-term aridification. While droughts are typically temporary events, persistent or recurrent droughts can indicate a transition toward aridification, that is, a gradual shift to drier conditions. We assessed how temporal changes in water supply and demand at multiple time scales affect vegetation productivity and land cover changes in continental Chile to diagnose the region's climate trajectory from drought to aridification. Since 2000, much of the region has seen a continuous decrease in water supply alongside a rise in atmospheric water demand. Further, in water-limited ecoregions, evapotranspiration, likely reflecting reduced transpiration or vegetation cover, has declined over time, with this trend intensifying over longer time scales. A long-term decline in water availability and shifting demand have led to declining vegetation productivity, especially in the Chilean Matorral and the Patagonia Steppe ecoregions. We discovered a link between these declines and drought indices related to soil moisture and actual evapotranspiration at time scales of up to 12 months. Further, our results indicate that the trends in drought indices account for up to 78% of shrubland and 40% of forest area changes across all ecoregions. The most important variable explaining cropland changes is the burned area. Our findings suggest that Chile is undergoing a transition from episodic drought to aridification, underscoring the need for adaptation strategies aligned with this emerging baseline.

How to cite: Zambrano, F., Vrieling, A., Meza, F., Duran-Llacer, I., Fernández, F., Venegas-González, A., Raab, N., and Craven, D.: From Drought to Aridification: Land-Cover Fingerprints of a Drying Chile, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20700, https://doi.org/10.5194/egusphere-egu26-20700, 2026.

Floods account for more than 40% of recorded natural disasters in the last two decades (EM-DAT). Given the current climate dynamics, flooding is likely to increase in intensity with shorter return periods. Flood risk is highest in the Global South countries due to lack of adequate prevention, prediction and monitoring systems.

However, the evolution of earth observation technology over the last decade carries a great potential for accurate, near real-time flood extent mapping and prediction. While this favors synthetic aperture radar (SAR) based approaches due to its ‘all weather’, day or night and canopy penetrating capabilities, the flooded scene presents a complex environment characterized by mixed signal to surface interaction mechanisms that complicate SAR imagery analysis. This necessitates fusion with other remote sensing flood detection techniques.

This study aims to develop an earth observation data fusion model for near-real time flood detection, early warning and risk as well as damage assessment in data-limited areas that fall within the exclusion mask of the Copernicus Global Flood Awareness System (GloFAS). The area of study is Tana Delta Ramsar Site in Kenya, a productive ecosystem comprising of a unique mix of fresh water, floodplain, estuarine areas and beaches supporting several ecosystem services. This analysis focuses on the April 2024 flooding event.

The initial step involved comparative analysis of three Sentinel-1 (S1) flood detection techniques namely image segmentation (Otsu threshold), multi-temporal change detection (CD) and a hybrid (Otsu + CD) technique, post-processed for removal of permanent water as well as slope and spatial-context conditions. With the limitation of missing validation data, Sentinel-2 (S2) image classification was used albeit with a 3-day acquisition date misalignment between the post-flood S1 and S2 images thus assuming no significant land cover changes took place.

Preliminary results show higher flood areas detected by the VH vis-à-vis the VV channel. In particular, the Otsu detected 240.85 km² for VH and 196.98 km² for the VV while the CD returned 40.64 km² and 27.56 km² for the VH and VV respectively with a change threshold of 1.5. Lastly, the hybrid approach detected 143.66 km² for the VH and 47.33 km² for the VV against S2’s 194.29 km². This difference could be due to the depolarization of the VV backscatter in the vegetated areas.

The next steps will involve testing of operational workflows such as the Copernicus Global Flood Monitoring (GFM), UN-SPIDER recommended flood detection approach and the AUTOWADE 1.0 (AUTOmatic Water Areas DEtector) in a data limited context (Tana Delta, April 2024 flood event). Further, the Otsu threshold required initialization of a bimodal histogram for best performance while the CD results rely on the chosen change threshold hence are not suitable for automated inundation mapping. Consequently, the study will explore the use of AlphaEarth and Clay geo-foundation models for rapid flood mapping in complex land use/ land cover and data-limited areas.

Key words: SAR, Data fusion, Flood extent mapping, geo-foundational models

How to cite: Muriithi, D. M. and Vitti, P. A.: Earth Observation Data Fusion for rapid flood extent mapping in data-limited areas: A Case Study of Tana River Delta, Kenya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-110, https://doi.org/10.5194/egusphere-egu26-110, 2026.

India's low-lying floodplains and heavy monsoon rains cause frequent flooding in the Ghaghara River basin. Each year, these floods catastrophically damage infrastructure, agriculture, and human life. To effectively mitigate these impacts, understanding flood dynamics through precise and timely assessment techniques is crucial. Therefore, the study combines Machine Learning (ML) with hydrological and hydraulic models to create a strong modeling chain. Using historical hydrological and meteorological data, the LSTM model is trained to reconstruct continuous streamflow in an ungaged basin. The Soil and Water Assessment Tool (SWAT) then utilizes the ML-derived outflows to support model validation. The predicted runoff in SWAT is used in the HEC-RAS model to assess urban flood inundation and depth in the basin. The ML model achieved a good result for both training and testing. Similarly, the SWAT model demonstrated reliable performance, with a validation accuracy of 0.71 and a calibration accuracy of 0.75, making the model's results suitable for further analysis and interpretation. The available water level and flood depth were used to validate the HEC RAS flood result, which demonstrated satisfactory results. The hybrid ML, hydrological, and hydraulic approach effectively identifies vulnerable flood zones in the Ghaghara basin, thereby improving the accuracy of streamflow, runoff, and flood inundation predictions. The framework supports more efficient planning and mitigation efforts, offering a dependable method for flood assessment in areas with limited data.

How to cite: Bashir, M., Berama, S. M. A., and Ahmad, R.: Hybrid Machine Learning with Hydrological and Hydraulic Models for Runoff Prediction and Flood Risk Assessment in Ghaghara Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-646, https://doi.org/10.5194/egusphere-egu26-646, 2026.

Floods in large transboundary basins such as the Brahmaputra pose persistent threats to lives, livelihoods, and infrastructure. Flood event database is initially created utilizing gridded routed streamflow simulations. For each event occurrence, we extract a set of flood attributes including peak discharge, flood volume, duration metrices. These hydrologic characteristics are integrated with an inundation component, enabling the Flood Severity Index (FSI) to represent not only the intensity of flooding within the channel but also the amount and duration of inundation across the adjacent floodplain. Utilizing this index, we provide a data-driven machine learning framework to predict RFSI throughout the basin. Predictor variables include key hydro-climatic inputs such as temperature, precipitation, which collectively influence the generation and evolution of flood events. Multiple machine learning models were evaluated using performance metrics including R², RMSE, MAE, and cross-validation, all of which demonstrated strong predictive skill across diverse hydrologic regimes, establishing the proposed data-driven framework as a scalable and computationally efficient tool for forecasting flood severity. This approach offers a strong basis for evaluating future flood scenarios and understanding how climate change may alter flood risk, especially in large transboundary regions with limited observational data.

How to cite: Parashar, T., Chakma, S., and Saharia, M.: A Data-Driven Approach for Predicting Riverine Flood Severity Index in the Transboundary Brahmaputra River Sub- Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-754, https://doi.org/10.5194/egusphere-egu26-754, 2026.

Accurate flood inundation modelling in monsoon-dominated regions remains fundamentally limited by the scarcity of discharge observations, particularly along small tributaries that contribute substantially to flood peaks. Operational hydraulic models such as LISFLOOD-FP typically use boundary conditions derived from a small number of main-stem gauging stations. Consequently, floodplain dynamics in headwater and fringe zones are systematically underestimated, especially in peninsular India where ungauged tributaries and side valleys can supply 20–40% of peak flow during extreme rainfall events. This omission introduces major errors in hazard assessment and reduces the usefulness of model outputs for early warning and risk preparedness. This study presents a data-efficient geomorphic–recession-based method for reconstructing discharge hydrographs for ungauged tributaries without requiring additional gauge infrastructure. The approach integrates three components: (1) Recession constant estimation from CAMELS-IND catchments using baseflow separation and multi-event recession analysis; (2) Geomorphic prediction of tributary-specific recession behaviour based on drainage area, basin slope, and land-cover characteristics, enabling flow recession prediction where gauge data are unavailable; (3) Event-based hydrograph disaggregation and scaling, where synthetic hydrograph shapes are generated and apportioned across tributaries according to drainage area ratios and their predicted recession behaviour. Reconstructed tributary hydrographs are automatically introduced into LISFLOOD-FP as distributed lateral boundary conditions at tributary–floodplain junction nodes, enabling both main-stem and tributary-driven flood dynamics to be simulated simultaneously. The framework is tested on multiple monsoon flood events across peninsular India. LISFLOOD-FP simulations are conducted under two forcing scenarios: (i) conventional configuration using only main-stem discharge data, and (ii) the proposed distributed tributary inflow scheme. Model outputs are evaluated against Sentinel-1 SAR flood extent maps, which provide an independent, satellite-based benchmark of observed inundation patterns. Key performance metrics include spatial correspondence (F1-score, precision, recall), headwater and fringe-zone inundation extent, and the model's ability to capture compound flooding behaviours (e.g., tributary–main-stem surge interactions). This work provides the first operational framework for generating tributary-scale discharge inputs for flood inundation models in data-scarce monsoon basins using only regional hydrological signatures and topographic data. The method is computationally efficient, scalable across complex tributary networks, and requires no additional hydrometric infrastructure. By explicitly representing ungauged tributary forcing, the approach aims to substantially improve flood hazard mapping and forecasting in regions that are often highly exposed to tributary driven flooding yet poorly resolved in existing operational models. The framework offers a practical and transferable pathway for enhancing flood early warning systems in peninsular India and comparable monsoon-affected regions globally.

Keywords: CAMELS-IND; LISFLOOD-FP; recession analysis; Sentinel-1; ungauged tributaries.

How to cite: Tripathi, V., Singh, H., and Mohanty, M.: Ungauged Tributary Discharge Reconstruction for Monsoon Flood Inundation Modelling: A Geomorphic-Recession Approach Applied to Peninsular India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-815, https://doi.org/10.5194/egusphere-egu26-815, 2026.

Flooding is one of the most frequent and destructive natural disasters in Japan, particularly in river basins with high population density and urbanization. This study aimed to use a GIS-based probabilistic Certainty Factor (CF) model to evaluate flood susceptibility in the Arakawa River basin, Japan. Nineteen flood conditioning factors were incorporated: soil, land use/land cover (LULC), normalized difference built-up index (NDBI), normalized difference water index (NDWI), normalized difference vegetation index (NDVI), curvature, elevation, precipitation, slope, topographic position index (TPI), sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), drainage density (Dd), distance to streams, distance to roads, flow accumulation, population, and aspect were included to assess their impact on flood frequency. A flood susceptibility map (FSM) was generated by applying the Certainty Factor model. A total of 230 flood locations within the study area were examined and geostatistically processed in ArcGIS for model validation. The resulting FSM was categorized into five susceptibility classes: very low, low, moderate, high, and very high. The spatial distribution of these classes showed that 22.5% of the area falls under moderate susceptibility, 37.1% under high, and 22.6% under very high susceptibility. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), yielding an accuracy of approximately 71%. The results indicate that the most influential factors affecting flood susceptibility in the basin are elevation, stream power index (SPI), sediment transport index (STI), flow accumulation, and distance to roads. The suggested framework offers useful spatial insights that can assist in supporting decision-makers to reduce both economic losses and risks to human life.

How to cite: M. Elsadek, W., Ahmed, H. S., and Kanae, S.: Probabilistic flood susceptibility assessment using a GIS-based Certainty Factor approach in the Arakawa River basin, Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2015, https://doi.org/10.5194/egusphere-egu26-2015, 2026.

Due to climate change, previously safe buildings will face flood risk, while for others risk will grow. The trend can be inverted by retrofitting buildings to achieve low losses (e.g., economic loss, downtime). Retrofit should focus on property flood resilience (PFR) measures to restricting water entry (e.g., flood skirts) and/or reducing its impact (e.g., water-resistant plasters). Currently, PFRs are selected with prescriptive checklists, rather than based on an explicit model of building water ingress (only possible with research-oriented computational fluid dynamics). Consequently, there is no guarantee on the effectiveness of the selected PFRs, nor the trade-off between their cost and reduced future losses. This work shows the preliminary implementation of a simplified water ingress model overcoming this gap.

For a selected hydrograph (i.e., time-variant depth and velocity of the exterior water), water ingress through a building envelope is modelled with a 1D dynamic flow model, using a quasi-steady, fixed-step, explicit Euler scheme. Each ingress pathway is treated as an orifice-like opening, with flow regulated by both hydrostatic water head difference and a velocity-dependent correction to account for drag effects. Calibration of the opening areas and discharge coefficients is based on available experimental data. PFR measures are explicitly considered: for example, a waterproofing membrane renders inactive all orifices below a certain height, while causing a sudden influx if a calibrated pressure strength is exceeded. After aggregating the flows, the interior water height is calculated separately for the building and the basement using mass conservation.

The model is illustrated for an archetype consistent with a ~1980s terraced masonry building in the United Kingdom. Its materials, plan dimensions, height, and water entry points are characterised according to relevant statistics. Inventories of finishes and contents are derived using public commercial listings (e.g., Zoopla). Apart from the as-built configuration, three retrofit solutions are defined considering combinations of PFR measures (e.g., waterproofing membranes, self-closing airbricks, flood doors/windows, non-return valves, raising power sockets and contents). The flood hazard profile is characterised consistently with an ideal site exceeding 1% annual flood probability. Using several hydrographs, the probability distribution of peak interior water depth is computed. The results are used as inputs of an analytical, component-based flood vulnerability assessment. Expected annual economic losses are finally calculated and compared.

The preliminary results show that this approach is simple enough for the early design phase yet accurate enough to allow identifying the marginal benefit/cost of PFR measures. However, benchmarking against refined computational fluid dynamics models is identified as a required step to fully validate the approach and generalise its results.

How to cite: Gentile, R.: Modelling flood water ingress in buildings: towards a simplified, orifice-based hydraulic model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2984, https://doi.org/10.5194/egusphere-egu26-2984, 2026.

Fluvial floods are one of the most common types of flooding worldwide. Therefore, accurate flood prediction is essential for effective flood preparedness and risk management. This study investigates the prediction of fluvial flood extent under three flood scenarios (Q₁₀, Q₁₀₀, and Q₁₀₀₀) using deep learning (DL), in particular, the U-Net model. The U-Net model was trained on official flood maps, created as part of the second cycle of the EU Flood Directive (2007), along with seven high-resolution predictors derived from the LiDAR DEM (1 m resolution), orthophotos (20 cm resolution), and ZBGIS spatial database: slope, stream power index (SPI), topographic wetness index (TWI), height above the nearest drainage (HAND), distance from river, roughness, and normalized difference vegetation index (NDVI). Multicollinearity among predictors was tested using the Pearson correlation and Variance Inflation Factor (VIF) with thresholds for Pearson correlation ≤0.7 and VIF ≤5. The model performance was evaluated using three quantitative metrics (Recall, Precision, and F1-score) and training time. The study focused on four river sections in Slovakia (Kysuca, Gidra, Torysa, and Topľa). In each U-Net application, three sections were used for training and one for testing and performance evaluation. The results indicate that the highest model performance was achieved when predicting flood extents on river sections that were similar in width and length. This was particularly evident in the cases where the training/testing river sections included combinations Torysa/Kysuca, Topľa/Kysuca or Kysuca/Torysa. When DL models were trained on narrow/short river sections and then tested on wider/longer sections, the number of false negative (FN) pixels tends to be high. Conversely, when these models are trained on wider/longer river sections and tested on narrow/short ones, the number of false positive (FP) pixels increases. Based on these findings, we recommend avoiding both of these training/testing strategies when transferring the prediction of fluvial flood extent across distinct river sections. Furthermore, the optimized U-Net model showed relatively fast training times with the maximum equal to 15 minutes. The findings of this study suggest strong potential for near-real-time or even real-time flood mapping and operational use. Acknowledgment: Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V03-00085.

How to cite: Vojtek, M., Držík, D., Kapusta, J., and Vojteková, J.: Evaluating the river-to-river transferability of deep learning-based fluvial flood extent predictions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3447, https://doi.org/10.5194/egusphere-egu26-3447, 2026.

This study analyzes embankment breach outflows resulting from overtopping using probabilistic modeling. The effects of uncertainties in the inflow hydrograph and embankment breach parameters on the breach outflow were evaluated using a numerical model that considers (1) breach parameters, including final bottom elevation, width, side slopes, formation time, weir coefficient, and water surface elevation triggering the breach; and (2) inflow hydrograph parameters, such as peak flow rate and time to peak, as probabilistic variables. The Monte Carlo method was employed to conduct 10,000 simulations for each scenario. Histograms and exceedance probability curves of the resulting peak outflows were generated, and probability density functions were fitted and evaluated using the Chi-square goodness of fit test. It was found that both the range and type of the final bottom elevation distribution significantly influence the breach outflow, with observed values ranging from 353 to 2170 m³/s depending on the parameter combinations. Modeling the inflow as either deterministic or probabilistic did not significantly impact the discharge; however, a normal distribution is recommended for representation. The deterministic breach model yielded a peak outflow that was approximately 40% lower than the maximum value produced by the probabilistic simulations, underscoring the importance of incorporating uncertainty into breach analyses. 

How to cite: Gödek Hayal, N. P., Calamak, M., and Yanmaz, A. M.: Assessing the Uncertainty of Embankment Breach Outflow due to Overtopping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4042, https://doi.org/10.5194/egusphere-egu26-4042, 2026.

This study develops a high-resolution, grid-based explainable machine learning (XAI) framework to systematically identify dominant flood-influencing factors across Jeju Island, Korea, by integrating historical flood trace maps with multi-source spatial datasets. Flood occurrence was classified at a 100 m grid resolution using four state-of-the-art tree-based ensemble algorithms, enabling robust modeling of nonlinear interactions between hydro-meteorological, geomorphological, and infrastructural variables. Model performance was rigorously evaluated across multiple subregions to quantify spatial heterogeneity in predictive skill and controlling mechanisms. The models achieved moderate to high classification performance, with maximum recall and F1-scores reaching 0.81 and 0.75, respectively, demonstrating strong capability in detecting flood-prone conditions.
Explainability analyses based on feature-importance metrics consistently identified short- and long-duration extreme rainfall (3-hour and 12-hour maxima), 5-day antecedent precipitation, maximum wind speed, groundwater level, and proximity to detention facilities and river networks as the most influential predictors of flood occurrence. Notably, their relative contributions exhibited pronounced spatial variability. In inland and high-elevation basins, flood dynamics were primarily governed by rainfall persistence and subsurface hydrological responses, whereas in coastal and highly urbanized zones, flood occurrence was more strongly modulated by drainage connectivity and proximity to hydraulic infrastructure.
These spatially differentiated controls reflect the complex volcanic hydro-geomorphological setting of Jeju Island and highlight the limitations of uniform flood warning criteria. The findings underscore the necessity of region-specific, dynamically adaptive warning thresholds that explicitly account for local hydrological processes and infrastructure configurations.
Overall, this study demonstrates the methodological advantages of grid-based explainable machine learning for physically interpretable and spatially adaptive flood risk assessment. The proposed framework provides a transferable blueprint for data-driven disaster risk management in volcanic island environments and other hydrogeomorphologically complex regions under intensifying climate extremes.

Acknowledgements
The research for this paper was carried out under the KICT Research Program (Project no. 20260161-001, Development of Digital Urban Flood Control Technology for the Realization of Flood Safety City) funded by the Ministry of Science and ICT.

How to cite: Moon, H., Kim, K.-T., and Kim, G.: Identification of Dominant Flood Influencing Factors with Grid-Based Explainable Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4695, https://doi.org/10.5194/egusphere-egu26-4695, 2026.

As urbanization and climate change accelerate, extreme flood events in urban areas have significantly increased, posing a major threat to human and property safety. As an efficient research tool, numerical simulation technology has shown significant application value in mitigating the impacts of urban flood disasters. This study integrates multiple data sources, including Digital Elevation Models (DEM), topographical features, underground sewer systems, rainfall intensity, water level dynamics, and pump station operations, to construct an urban flood inundation simulation. Additionally, using the incipient velocity formula, the dynamic flood risk levels for humans and vehicles were quantitatively analyzed. The main results include: 1. The numerical model accurately simulated the hydraulic characteristics of flood events. The results indicating high model reliability and providing a solid foundation for subsequent risk assessments. 2. During peak rainfall periods, the risk level for humans and vehicles escalates significantly. After the peak, the slight risk for humans decreases, while the magnitude of extreme risks in later stages becomes more severe with larger rainfall return periods. Conversely, the flood risk for vehicles steadily increases, surpassing that of humans overall. 3. In the later stages of rainfall events, both humans and vehicles encounter extensive areas where water depths exceed danger thresholds, transforming them into extreme risk areas. The results obtained in this research contribute to enhancing public awareness of urban flood risks and revealing the spatiotemporal evolution of these risks. They also provide important theoretical support and practical guidance for enhancing urban resilience and promoting sustainable development.

How to cite: Zhu, W. and Xu, Z.: Risk Assessment of Human and Vehicle Stability in Extreme Weather Events in Coastal Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4726, https://doi.org/10.5194/egusphere-egu26-4726, 2026.

Dam breach flooding is low probability yet high consequence hazard particularly in arid and semi-arid regions where urban and rural population is deployed along the ephemeral streams. The study analyses the flood hazard and flood risk assessment of the Wadi Baysh Dam in southwestern part of Saudi Arabia, based on a dam breach analysis as a result of an observed extreme rainfall event happened in July 2011. The target is to evaluate the flood impacts on the downstream under various breach scenarios and to support risk informed mitigation and emergency planning.

Three dam breach scenarios were studied using HEC-RAS 2D hydrodynamic model simulations: (i) full breach (S1), (ii) half breach (S2), and (iii) one-third breach (S3). The 2D simulations produced spatially distributed inundation depth, maximum flow velocity and inundation duration, which were later used to derive composite dam-breach flood hazard maps. By integrating composite flood hazard and landuse map as a vulnerability measure (including urban areas, agricultural land, roads, rangeland, bare ground and tree cover classes), risk maps of dam-breach floods were created and finally exposure analysis was conducted.

Results show strong non-linear relationship between breach severity and downstream impacts. Under full-breach conditions, the total inundated area reaches approximately 371 km², with large portions classified as moderate to high flood hazard. Agricultural land and rangeland exhibit the greatest exposure, while urban areas, although spatially limited, experience locally elevated hazard levels due to high flow depths and velocities. Partial-breach scenarios substantially reduce inundation extents (~278 km² for half breach and ~32 km² for one-third breach); however, hazardous conditions persist along the main wadi channel and low-lying floodplain zones, indicating that partial structural failure still poses significant downstream risk.

Composite flood risk assessment shows that low-to-moderate risk dominates across all scenarios, yet localized high-risk zones emerge near critical infrastructure and densely cultivated areas, particularly under full- and half-breach conditions. The results further demonstrate that reductions in breach size do not translate linearly into risk reduction, underscoring the importance of explicitly considering multiple breach scenarios in dam-safety assessments.

The study highlights the value of scenario-based 2D dam-breach modelling for flood-risk assessment in arid environments, where observed extreme storms can act as credible compound triggers. The proposed framework supports advances in flood-risk modelling by integrating hazard characterization, land-use exposure, and risk classification, providing actionable insights for emergency action plans, evacuation zoning, and long-term risk mitigation strategies.

How to cite: Zaidi, S., Alomary, M., and Al-Gafari, Y. H.: Scenario-Based 2D Hydrodynamic Modelling of Dam-Breach Flood Hazard and Risk under an Extreme Storm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4751, https://doi.org/10.5194/egusphere-egu26-4751, 2026.

Accurate global flood forecasting, particularly in ungauged basins, remains a primary challenge for operational hydrology and disaster risk reduction. While data-driven approaches using Long Short-Term Memory (LSTM) networks have set new benchmarks in global streamflow prediction, limitations regarding forecast lead time, temporal consistency, and operational robustness to missing data persist.

In this work, we present the next-generation Google global hydrologic model, which introduces three major advancements over previous state-of-the-art systems. First, we integrated AI-based medium-range weather forecasts as additional meteorological forcing, alongside traditional deterministic products. Second, leveraging recent contributions to the Caravan community dataset, we expanded the training dataset three-fold to include nearly 16,000 streamflow gauges globally. Third, we implemented a novel masked mean embedding LSTM architecture. This design eliminates the traditional encoder-decoder state hand-off issue (which introduces temporal inconsistencies or forecast hairs) and enables the model to remain operational during weather data outages by dynamically averaging embeddings from available input sources.

Our results demonstrate a significant extension of the reliable forecast horizon: the new model achieves accuracy at a 7-day lead time comparable to the 5-day lead time performance of its predecessor. Furthermore, the model continues to outperform other global operational systems, such as GloFAS and GeoGlows, across both gauged and ungauged basins. These advancements represent a significant step toward providing more timely and reliable flood warnings in regions where traditional monitoring infrastructure is scarce.

In conjunction with this update, we released two new community resources. The Google Runoff Reanalysis & Reforecast (GRRR) dataset provides a comprehensive, multi-decade reforecast archive generated by the current operational global model. Additionally, we have launched the GoogleHydrology GitHub repository, which provides an open-source research implementation that closely approximates our operational environment. This release is intended to facilitate the reproduction of our findings and provide the scientific community with a robust baseline for future global hydrologic modeling research.

How to cite: Loike, G., Nearing, G., and Cohen, D. and the Google Research - Floods Forecasting: The Next-Generation Google Flood Forecasting Model & Community Resources, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6973, https://doi.org/10.5194/egusphere-egu26-6973, 2026.

The Danube River Basin in the heart of Europe covers an area of just over 800,000 km², and fully or partly overlaps the territories of 19 countries and the living places of approximately 79 million inhabitants. Many of these people live and work in areas threatened by river floods. Despite 170 years of international coordination by various Danube river commissions and a long, sad record of disasters there are hardly any joint efforts for dealing with this hazard across borders. Barriers to better coordination arise from at least eleven different languages spoken in the basin, political tensions between some of the countries, and the heterogeneity of economic conditions. More basin-wide research about actual and future river flood hazards could play an important role in raising common problem awareness and joint action.

Our contribution – funded by the EU HORIZON project DIRECTED (grant no. 101073978) – is the further development and application of PIK’s Danube Model. Basically a combination of the eco-hydrological model SWIM (similar to SWAT) and the hydrodynamical model CaMa-Flood, it is capable of calculating river water stages and flood heights for ten-thousands of subcatchments from daily weather inputs. Using bias-adjusted ISIMIP CMIP6 climate scenario data we produced respective flood projections under SSP 370 and 585 scenarios.

We are going to present maps of the river system with each river reach coloured after the average return intervals expected for floods at the end of the century which are currently assigned 100-year return periods. Both scenario maps for SSP 370 and 585, respectively, show shifts towards longer and shorter return periods with many extremes: Current 100-year floods are often projected to occur over three times more or less frequently. Trends of shortening return periods however dominate both scenarios – river flood hazards are likely to increase under climate change. The maps also show rather different spatial patterns which indicate the high uncertainty in the projections and in the estimation of extreme value distributions despite having used 600 model years for the analysis.

Besides discussing possible systematic errors of the return interval estimations caused by single extreme climate realizations, the importance of considering levees in river flood models is practically exemplified. Especially in level areas like the Pannonian Basin the simulated flood events show more than 1 m water level differences between model runs with and without levees.

How to cite: Conradt, T.: Expected changes of river flood hazards across the Danube Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7164, https://doi.org/10.5194/egusphere-egu26-7164, 2026.

Accounting for nearly 30% of all the losses due to natural disasters, flood emerges as one of the most havoc-creating extreme events in the world. Recently, as an adverse effect of climate change, the tropical river basins have witnessed recurring extreme flood events leading to significant devastation of agricultural production. We have studied the climate change-induced flood risk associated with crop damage in a paddy crop-dominated tropical river basin in India. We have considered a 50-year return period design flood and three (2010-2039, 2040-2069 and 2070-2099) future scenarios in the most extreme representative concentration pathway 8.5 conditions in a hydrodynamic modelling framework as the test case. Nine different Global Climate Models (GCMs) are used here. Analysis of the three best performing (HadGEM2-AO, IPSL-CM5A-MR and MIROC-ESM-CHEM) GCM data-driven flood inundation depth and extent, and the associated net loss/benefit from the cultivation of normal rice variety indicates increased flood risk in the projected scenarios as compared to the historical period. In contrast to high water level, occurrence of comparatively low inundation depth but for a longer period of time is found to increase the flood vulnerability of the paddy crops in the future projected time frames. As a significant alteration in the cultivation pattern is highly subjective on the adoption/willingness of the local farmers, we suggest an alternate rice planning, considering cultivation of an alternate rice variety as a probable adaptation strategy to minimize climate change induced flood risk. Considering the near-future period (2010s) and the MIROC-ESM-CHEM model, our study shows that cultivation of shallow, medium deep or deep water rice varieties in high flood inundation areas can reduce the very high flood risk from about 35% to 17%. The methodology adopted herein encourages the application of hydrodynamic modelling in analyzing projected flood-agriculture risk and paves avenues for more novel scientific research.

How to cite: Chatterjee, C., Khatun, A., and Sahoo, B.: Exploring possibilities to reduce climate change induced flood risk and crop production losses using hydrodynamic modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7289, https://doi.org/10.5194/egusphere-egu26-7289, 2026.

Earthen levees represent one of the primary structural measures for flood protection in floodplain areas. However, they can paradoxically increase hydraulic risk by fostering a false sense of security among the exposed population and urban planners (Castellarin et al., 2011). Moreover, these structures are susceptible to failure through various mechanisms triggered by physical processes during flood events, including overtopping and seepage or piping (Palladino et al., 2019).

Monitoring levee conditions using non-invasive geophysical techniques, such as Electrical Resistivity Tomography (ERT), is a highly effective tool for assessing internal structural integrity and detecting potential weaknesses. Such approaches can provide early warning of seepage or piping processes, thereby helping to prevent breach formation and enhance flood risk management (Dezert et al., 2019).

This work describes the results of an experimental monitoring system developed on an earthen levee along the Tatarena stream, central Italy.  In particular, the outcomes of an ERT monitoring system is used to collect geophysical parameters (i.e. electrical conductivity and permittivity) correlated to the main hydraulic characteristics of the investigated soil (levee body and foundation), such as porosity, water content, permeability. The observations are correlated with rainfall and groundwater measurements and used as a reference data to address numerical modelling of water infiltration process for seepage vulnerability assessment. The developed methodology, based on coupling experimental ERT monitoring and numerical modelling, can provide valuable insights into water infiltration processes, enabling the assessment of the hydraulic condition of levees, which is essential for identifying potential critical areas.

Castellarin, A., Di Baldassare, G., Brath. (2011). A floodplain management strategy for flood attenuation in the River Po. River Res. Appl. 27 (8), 1037–1047.

Dezert, T., Palma Lopes, S., Fargier, Y., Côte, P. (2019). Combination of geophysical and geotechnical data using belief functions: Assessment with numerical and laboratory data. Journal of Applied Geophysics, Vol. 170. https://doi.org/10.1016/j.jappgeo.2019.103824.

Palladino M.R., Barbetta S., Camici S., Claps P., Moramarco T. (2019). Impact of animal burrows on earthen levee body vulnerability to seepage, J Flood Risk Management.2020;13 (Suppl. 1): e12559, https://doi.org/10.1111/jfr3.12559.

How to cite: Bonaccorsi, B., Rizzo, E., Boldrin, P., Giampaolo, V., De Martino, G., Aronica, G. T., Dionigi, M., Ciabatta, L., Moramarco, T., and Barbetta, S.: Experimental ERT Monitoring for Evaluating Seepage Vulnerability in Earthen Levees: A Case Study of the Tatarena Stream., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7500, https://doi.org/10.5194/egusphere-egu26-7500, 2026.

Climate change has led to increasingly complex alterations in flood characteristics across South Korea, with changes in flood frequency, magnitude, and duration occurring in different and sometimes opposing directions. This evolution has resulted in the expansion of multidimensional flood risk, which cannot be adequately captured by conventional flood assessments focusing solely on peak discharge. In particular, the increasing occurrence of extreme rainfall events and localized torrential storms highlights the need for a new assessment framework that integrates multiple flood characteristics to better anticipate future flood risks.

In this study, an Integrated Flood Risk Index (IFRI) was developed using IPCC AR6-based future climate scenarios and nationwide runoff simulations to comprehensively assess future flood risk across South Korea. Daily runoff was simulated from 1981 to 2100 using the Soil and Water Assessment Tool (SWAT), with 774 sub-basins across the five major river basins adopted as spatial analysis units. Among 20 climate change scenarios provided by the Korea Meteorological Administration, seven representative RCM–SSP combinations were selected based on a climate variability and extremity screening method proposed by Kim et al. (2025). Analyses were conducted for a historical reference period (1991–2020) and two future periods: mid-century (2031–2060) and late-century (2061–2090).

The IFRI was constructed by integrating information on both flood frequency and intensity derived from runoff simulations. As a core component, the Standardized Flood Index (SFI) was calculated by standardizing short-term accumulated runoff using a log-normal distribution, with flood events defined when SFI values exceeded +1.0 or corresponding high-percentile thresholds. Based on the IFRI, future flood regime changes were quantitatively classified into four distinct types (Type 1–4), representing different patterns of flood risk evolution.

The results reveal pronounced spatial variability in future flood risk across South Korea, with a marked intensification of flood hazards in many regions during the mid-century period, followed by partial moderation in the late century while maintaining elevated risk levels. The shortening of flood return periods indicates an increased likelihood of more frequent and severe flood events. These findings provide a robust scientific basis for national-scale flood risk assessment and emphasize the need to strengthen climate-adaptive flood management and planning strategies.

How to cite: Kim, S., Kim, J., and Kang, H.: Assessing Multidimensional Future Flood Risk across South Korea Using an Integrated Flood Risk Index Based on IPCC AR6 Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8910, https://doi.org/10.5194/egusphere-egu26-8910, 2026.

Recent increases in intense rainfall have exacerbated urban flooding, driven by impervious surfaces, drainage limitations, and topography. For predicting urban flood susceptibility, models have to consider the spatial configuration of urban hydrological infrastructure, such as urban water detention facilities (UWDF). Conventional physics-based hydrological and hydraulic models are constrained by extensive data requirements and long setup times. In contrast, machine learning (ML) models have been increasingly applied to large-scale flood prediction due to their ability to capture complex relationships among multiple factors. This study aims to assess flood susceptibility in Busan, Republic of Korea using ML models, categorize the characteristics of high-susceptibility areas, and propose optimal locations for UWDF. In this study, the dependent variable for binary classification was constructed by extracting flooded (1) and non-flooded (0) points at a 1:1 ratio, based on flood inventory maps from 2019 to 2023. The explanatory variables consisted of topographic, meteorological, land-use, and drainage infrastructure factors related to flooding (a total of 16 variables). All input datasets were prepared in raster format and resampled to a spatial resolution of 50 m, consistent with the Digital Elevation Model. The constructed dataset was randomly divided into training and testing sets at an 8:2 ratio and applied to Random Forest, Extreme Gradient Boosting, and Support Vector Machine models. Hyperparameter optimization was conducted for each model via Random Search. Then, model performance was evaluated using Accuracy and ROC-AUC metrics. For the best-performing model, Variable Importance and Partial Dependence Plot analyses were performed to interpret the relationships between key explanatory variables and flood susceptibility. Subsequently, the calculated flood susceptibilities were classified into five levels. K-means clustering was applied to high-susceptibility areas to categorize flooding event types based on shared topographic and environmental characteristics. Based on these results, area types with high potential effectiveness for UWDF were identified, and optimal installation sites were derived. The ML–based flood susceptibility analysis would quantitatively reveal and visualize the complex drivers of flooding in high-susceptibility areas.

How to cite: Koo, J., Kim, G., Yim, J., Lee, S., Kim, T., Lee, Y., and Lee, S.: A Machine Learning-based Assessment of Urban Flood Susceptibility and Priority Location of Urban Water Detention Facilities: A Case Study of Busan, South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8967, https://doi.org/10.5194/egusphere-egu26-8967, 2026.

Dike breaches along navigation canals can lead to rapidly evolving flood dynamics, posing significant risks to populations and critical infrastructures. This contribution presents a two-level modeling framework for assessing the hydrodynamic consequences of dike failures. It builds upon previous developments in real-time flood mapping and breach modeling by extending them to both detailed scenario analysis and long-term risk assessment under changing hydroclimatic conditions.

The first component of the methodology relies on a computationally efficient chain of simplified models combining: (i) a 1D shallow-water hydraulic model of the waterways network, (ii) a lumped, semi-empirical breach-growth model accounting for the multi-scale processes governing dike failure, and (iii) a simplified floodplain representation based on pre-computed inundation maps. This framework is applied both for short-term forecasting and for long-term assessments under future climate conditions. The latter uses ensembles of climate projections (seven climate models under three emission scenarios) to evaluate the evolution of breach likelihood and flood hazard up to 2100.

The second component of the methodology consists of detailed “what-if” simulations based on a GPU-accelerated 2D hydrodynamic model (WolfGPU) solving the full shallow-water equations and coupled with the same breach-growth model as in the simplified approach. This technique provides high-resolution predictions of inundation depth, arrival time, and flow velocity fields for selected breach scenarios, enabling a refined characterization of local impacts.

The overall framework is demonstrated through applications to dike-breach scenarios along the Albert Canal in Belgium. Results from both approaches are compared in terms of predicted flood extent, maximum water depth, and warning lead times. The complementarity between fast simplified modeling for real-time support and high-resolution 2D simulations for scenario exploration is highlighted. The study further demonstrates the operational relevance of the framework for waterway managers, particularly in evaluating preventive measures such as anticipatory drawdown of the waterways.

Overall, this work delivers an integrated multi-scale modeling strategy for dike-breach hazard assessment under both present and future hydrological conditions, combining efficiency, physical consistency, and operational applicability.

This research is co-funded by the European Union’s Horizon Europe Innovation Actions under grant agreement No. 101069941 (PLOTO project: https://ploto-project.eu/)

How to cite: Hardy, J., Archambeau, P., Mastricci, D., Schmitz, V., Champailler, S., Melitsiotis, A., Erpicum, S., Pirotton, M., and Dewals, B.: Two-level modeling of dike breach scenarios: from GPU-accelerated 2D hydrodynamics to a simplified real-time modeling chain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9741, https://doi.org/10.5194/egusphere-egu26-9741, 2026.

Global flood hazard assessments increasingly rely on large-scale modelling frameworks, yet practical use is often constrained by trade-offs between spatial resolution, computational cost, and interactivity. Due to higher computational costs, often global flood products are commonly available only as static maps, which are often limited to predetermined return periods. Such high-resolution global flood maps are not interactive and often restrict event-specific analysis and rapid response mapping. We introduce a new dynamic global flood model, FastFlood Global, which combines higher spatial resolution, interactivity, and computational efficiency to provide flood information for any point in the world with no additional input.  

 FastFlood Global integrates a computationally efficient flood simulation core with automated global parameterization derived from openly available Earth observation datasets, including topography, land cover, and soil parameters. It supports simulations of fluvial, flash flood, and dam break events and enables the generation of interactive flood maps with custom mitigation options integrated. It also supports an efficient global early warning system for pluvial and flash floods, which is based on the forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF).  

We will present this new model with a series of showcase applications demonstrating the model’s performance across diverse geographic and hydrological contexts, highlighting its potential for global early warning and rapid impact assessment. 

How to cite: Kolaparambil, F. J., van den Bout, B., van Roon, K., and Meijvogel, D.: FastFlood Global: Enabling Rapid High-Resolution Flood Modelling at Global Scale , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11931, https://doi.org/10.5194/egusphere-egu26-11931, 2026.

Flash floods pose significant hazards for settlements and infrastructure in small rural catchments, especially when steep terrain, erodible sediments, and intensive land use come together. Wagenhausen, a village in Lower Franconia (Germany), is located within a 3 km² catchment, dominated by mixed forest and agricultural land. It lies along a small creek, a tributary of the Main River. In response to repeated flooding during intense rainfall events in the past, a series of cascading retention basins was constructed upstream of the settlement in the summer of 2024. In addition, a land-use change is being implemented by reforesting an agricultural field, a potential sediment source, upstream.

This study presents a work-in-progress, comparative assessment of the effects of these hydrological and sediment-related mitigation measures. Event-based simulations are conducted using the SIMWE hydrological model (r.sim.water). We analyse changes in runoff generation and flood intensity under different scenarios, including retention basins, reforestation, and their combined implementation. The modelling approach focuses on relative differences between scenarios rather than absolute discharge values, on account of the data-scarce nature of the catchment and the absence of gauge measurements.

Field investigations include soil analyses, infiltration measurements, and UAV-based surveys, conducted before and after the implementation of the retention basins. This provides information on soil properties, topography, and potential sediment source areas.

The study aims to improve process understanding of flash flood generation and sediment mobilisation in small catchments and to evaluate the role of land-use-based and structural nature-based solutions. The work is part of the EFRE-funded MainPro project, which investigates future hazards and ecosystem-based adaptation strategies for geo-ecosystems, settlements, infrastructure, and agriculture.

How to cite: Schmieg, S., Dittmaier, F., Wolf, T., Krech, M., Kraus, D., and Terhorst, B.: A comparative assessment of nature-based flood mitigation measures in a small catchment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12270, https://doi.org/10.5194/egusphere-egu26-12270, 2026.

Floods are one of the costliest types of natural disasters. Flood simulation models play a critical role in flood risk prediction and damage prevention by delineating areas at risk of flooding and aiding the design of flood protection infrastructure. 2D hydrodynamic models can be used to simulate floods with high accuracy in complex topography or when detailed hydraulic outputs are required. These models are typically composed of terrain data, a computational mesh, arcs and polygons that form the mesh structure and affect cell size and orientation, boundary conditions, and a set of numerical equations representing flow dynamics. 2D hydrodynamic models can be time-consuming to configure, specific model generation steps can be subjective and based on modeller judgement, and they can be challenging to reproduce. This research focuses on increasing the accessibility of critical flood risk information by creating an automated workflow to generate hydrodynamic models in 2D HEC-RAS.

Models will be generated in the Assiniboine River in Manitoba, Canada. This river is bordered by urban areas and flat prairie topography, which present unique hydraulic modelling challenges. Models will be developed in test sites with varying topography to ensure the generalizability of this work. The 2D hydrodynamic software 2D HEC-RAS will be used, as it is publicly available and widely used across North America.

The workflow includes managing GIS data, generating the computational mesh, and adjusting computational parameters based on desired runtimes, model purpose, desired accuracy, and characteristics of the computational mesh. Automatic mesh generation is already an active area of research; however, the selection of mesh cell size is seldom well-justified, and only semi-automated approaches have been implemented in 2D HEC-RAS. The mesh is designed based on terrain characteristics, flow characteristics, numerical stability, and model accuracy and efficiency. Models will be developed at each test site using the automated workflow to generate an unstructured mesh, a manually generated unstructured mesh, and a manually generated structured orthogonal mesh with a consistent cell size. Each model will be compared in terms of accuracy and computational effort, and qualitatively in terms of mesh configuration. Given that the comparison between the manually and automatically generated unstructured meshes will depend on the modeller, a workflow will be created for the manually generated unstructured grid to increase transparency and reproducibility and to highlight the subjective steps involved in manually developing a model mesh. Both automated and manual workflows will be designed to ensure models and data are findable, accessible, interoperable, and reusable (FAIR principles).

This research will decrease the time required to develop 2D hydrodynamic models for applications such as flood mapping, producing training and validation data for machine learning models, water quality and sediment transport analyses, and stream crossing, control structure and flood protection infrastructure design. With reduced model development time, more time can be spent on model analysis. In addition, this work will increase model reproducibility, enabling more efficient uncertainty and sensitivity analyses, greater transparency in scientific experiments, and the repetition or expansion of experiments in other geographic locations or under different flood conditions. 

How to cite: Riddell, P., Asadzadeh, M., Stadnyk, T., and Razavi, S.: Flood modelling in the Assiniboine River Basin with automated mesh generation using 2D HEC-RAS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12797, https://doi.org/10.5194/egusphere-egu26-12797, 2026.

Coastal regions are projected to experience a continuous increase in storm surge heights due to climate change–induced mean sea level rise and the intensification of typhoons. These changes substantially exacerbate the risk of coastal inundation in low-lying areas, necessitating a reassessment of existing design standards and disaster mitigation frameworks. To proactively respond to the evolving coastal inundation environment, it is essential to move beyond deterministic design approaches based solely on historical maxima and instead adopt probabilistic analyses of Extreme Sea Levels (ESLs). This study was conducted as part of a project in Korea aimed at developing storm surge–induced coastal inundation prediction maps. A total of approximately 4,100 ESL datasets for each return period, derived through computation and analysis for return periods of 50, 100, 150, and 200 years over a 13-year period (2011–2024), were used to construct spatial distribution maps of ESL heights along the entire Korean coast. To minimize inconsistencies arising from temporal and regional differences in reference sea levels, all ESLs were standardized to a common datum based on the Approximate Highest High Water (AHHW) referenced to the mean sea level at Incheon. For coastal areas where inundation prediction maps were not available, ESLs were estimated using frequency analysis based on the Gumbel distribution. To evaluate the reliability of the constructed ESL distribution maps, the estimated ESLs were compared with ESLs derived from observed tide gauge records, as well as results from extreme value analyses based on the Empirical Simulation Technique (EST) and the Annual Maximum Series (AMS) approach. The comparisons showed similar magnitudes and spatial distribution patterns across regions and return periods, indicating overall consistency in the estimated ESL characteristics. The nationwide coastal ESL distribution maps developed in this study are expected to serve as fundamental baseline data for coastal municipalities in the era of climate crisis, supporting the establishment of comprehensive natural disaster mitigation plans, coastal inundation risk assessments, designation of coastal inundation hazard zones, and the design of coastal and harbor structures.

How to cite: Lee, H.-Y., Cho, W.-H., Park, J.-J., Gu, B.-H., Jeong, K.-Y., Kim, H., and Seo, G.-H.: Development of Return-Period-Based Extreme Sea Level Maps along the Korean Coast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13005, https://doi.org/10.5194/egusphere-egu26-13005, 2026.

The "Prediction in Ungauged Basins" (PUB) problem remains a central challenge in global hydrology, as the accuracy of rainfall-runoff models is fundamentally constrained by the availability of local streamflow observations for training and calibration. While recent advancements in data-driven modeling have improved our ability to generalize across catchments, the global distribution of streamflow gauges is characterized by severe geographical and socio-economic biases. Most available data are concentrated in the Global North, leaving vast regions, particularly in the Global South, functionally "ungauged" or under-represented in the training sets of global models.

In this study, we shift the focus from simply counting the fraction of ungauged watersheds to estimating the quantitative effect of this geographical bias on global flood forecasting skill. Using a large-sample machine learning framework based on Google’s flood forecasting model, we quantify the relationship between gauge network density (specifically upstream and downstream coverage fractions) and predictive performance. We utilize cross-validation experiments to isolate the information loss associated with geographical distance and hydrological connectivity from gauged locations.

Our analysis indicates that hydrological factors are the main driver of predictive performance, with basin aridity being a larger factor in model skill than whether a basin is gauged or ungauged. However, if streamflow gauges were hypothetically installed in all of the world’s watersheds, we could expect a 20% increase in the Nash-Sutcliffe Efficiency (NSE) skill score for state-of-the-art global models, including causing almost half of the basins globally currently scoring below NSE = 0.50 to rise above that threshold, with an average skill improvement of about ΔNSE = 0.1. Critically, this potential for improvement is not uniform, with Africa being the continent where our model predicts that largest overall skill improvement with higher density gauging networks. 

These findings emphasize that the path toward equitable global flood safety requires not just better algorithms, but a concerted effort to address the structural biases in the global hydrological data ecosystem.

How to cite: Nearing, G., Gauch, M., and Rothenberg, J.: The Effect of Geographical Bias in Streamflow Gauge Distribution for Global Flood Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13530, https://doi.org/10.5194/egusphere-egu26-13530, 2026.

Reservoirs are critical infrastructures for regulating natural flow regimes and reducing flood discharges, yet their effectiveness during extreme events strongly depends on operational strategies, particularly the initial storage level at the onset of a flood. Here we investigate the non-linear relationship between initial reservoir conditions and flood-attenuation efficiency for about 250 large dams across Italy, adopting a comprehensive, data-driven modelling framework. Flood hydrographs are generated using a simplified hydrological model and subsequently routed through each reservoir, using a “no gates management” approach and full hydraulic routing. We investigate different scenarios of input hydrograph and initial reservoir storage, derived from historical time series of stored volumes from approximately 70 reservoirs across the country and informed by regional flood seasonality.

The results indicate that the reduction in peak discharge is neither spatially homogeneous nor uniform with increasing flood return periods when initial storage levels are reduced. This relationship is strongly non-linear; for instance, as reservoirs reach their capacity limits, doubling the incoming flood peak leads to abrupt reductions in attenuation efficiency. Based on the initially available storage capacity for flood control and the actual reservoir geometries, dams were classified according to the flood severity level needed to cause a significant reduction in their attenuation capacity. This classification allows us to distinguish between dams that experience a gradual decline in performance with increasing flood return periods and those that undergo a threshold effect, which is often not accounted for in conventional regional dam-safety assessments. Notably, the commonly used assumption of a fully filled reservoir at the onset of a flood proves to be overly conservative: under this scenario, about 20% of dams reach their maximum allowable water level for events with return periods of 100 years or less.

By providing a national-scale assessment, the findings of this study can offer helpful insights for dam managers on the effectiveness of maintaining unfilled storage capacity for flood mitigation: by quantifying the impact of initial reservoir storage on flood attenuation, this research provides a data-driven basis for optimizing reservoir operations, particularly in balancing water storage needs with flood risk management. For instance, notable differences can be recognized between reservoirs in the Alpine region, primarily used for hydropower generation, and those in southern Italy, which serve mainly for irrigation and drinking water supply.

How to cite: Evangelista, G., Bertola, M., Blöschl, G., and Claps, P.: Nonlinear flood peak mitigation driven by initial reservoir conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14531, https://doi.org/10.5194/egusphere-egu26-14531, 2026.

Flooding is among the most recurrent natural hazards globally, with its impacts intensifying in urban areas due to rapid urban expansion, land use transformation, and the increasing occurrence of high intensity rainfall events. Flood modeling in ungauged urban catchments remains particularly challenging because of limited hydrological observations and the dominant role of impervious surfaces on runoff generation. This study presents a coupled hydrological- hydraulic and data-driven modeling framework to simulate flood inundation in an ungauged urban region of Hyderabad, India with a specific focus on Zone 5 of the Greater Hyderabad Municipal Corporation. Two sets of modelling scenarios were employed. In the first scenario, flood inundation mapping was simulated coupling HEC-HMS and HEC-RAS. For the hydrograph generation, SCS Curve number method was used with rainfall of finer temporal resolution and ward wise land use land cover data. These hydrographs were used as boundary conditions in the HEC-RAS model. In the second scenario, an Artificial Neural Network (ANN) model was developed using rainfall intensity and other meteorological variables along with lagged simulated discharges from the HEC-HMS model. The ANN-predicted discharges were then coupled with HEC-RAS to generate inundation depths. For validation with ground truth data, both the scenarios were validated using geotagged crowd sourced flood images. The second modelling scenario integrating data driven and hydraulic-hydrologic modelling, performed better than the conventional HEC-HMS HEC-RAS approach, as it showed closer agreement  with inundated flood depth. Overall, the findings demonstrate that coupling data driven techniques with hydrologic and hydraulic models significantly enhances urban flood simulation capabilities in data scarce environments.

How to cite: Bora, S., Saipriya, S., and Regonda, S. K.: A coupled hydrological- hydraulic and data-driven modeling framework for flood modelling in ungauged urban catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16513, https://doi.org/10.5194/egusphere-egu26-16513, 2026.

The MOVIDA (Modello per la Valutazione Integrata del Danno Alluvionale) project, launched in 2020, aims to develop GIS-based procedures for the quantitative and qualitative assessment of exposure and damage to assets (e.g., residential buildings, transportation networks, agricultural areas) located within the Po River Basin District Authority (AdBPo) and potentially affected by flood hazard. These procedures were implemented in the QGIS Graphical Modeler as automated geoprocessing workflows integrating multiparametric models for damage and exposure evaluation.

This development phase was supported by the construction of a district-scale geodatabase of exposed elements, obtained through the harmonization and integration of national and institutional datasets, as well as additional open-source datasets (e.g., OpenStreetMap).

In the second phase of the project, a modular and service-oriented WebGIS platform was designed and implemented by leveraging open-source geospatial software technologies to enable the dissemination and operational use of the developed methodology. The platform provides end-to-end support for the ingestion, processing, and analysis of both institutional flood hazard maps derived from the Flood Risk Management Plans (FRMPs) and user-defined flood scenarios. All input data are persisted within a spatial database, i.e., PostGIS and programmatically coupled with the processing and computation modules. The execution of the processing workflows is asynchronous, and users are automatically notified upon completion via email-based alerts.

The system provides both interactive visualization of the outputs and access to the generated datasets.

The MOVIDA platform is based on a containerized architecture composed of three Docker services: (i) a Redis service for caching, (ii) a PostgreSQL/PostGIS service for data storage, and (iii) an application service integrating Django and QGIS. Django manages the entire web application layer, including the web interface, user interactions, and notification services, while QGIS performs the geoprocessing tasks. The results are then returned to the web application and made available through the user interface.

How to cite: Zazzeri, M. and the MOVIDA Team: The MOVIDA platform: a WebGIS tool for the assessment of flood risk–related impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17715, https://doi.org/10.5194/egusphere-egu26-17715, 2026.

Estimates of hydrological hazards such as floods and landslides are highly sensitive to the spatial representativeness of precipitation inputs in data-scarce mountainous basins, where precipitation heterogeneity is strong and observational coverage is limited. This study assesses how precipitation dataset choice and station-network configuration affect SWAT streamflow simulations in the Barandouz catchment, a mountainous sub-basin of Lake Urmia in northwest Iran. The 1158 km² basin is characterized by elevations ranging from 1298 to 3483 m a.s.l. and predominantly pasture land cover.

Daily gauge precipitation from four stations was combined with ERA5-Land reanalysis data to construct three forcing scenarios: (S1) observed in-catchment gauges; (S2) gauges augmented with two upstream virtual stations forced by ERA5-Land; and (S3) the same configuration with bias-adjusted ERA5-Land precipitation. Comparison of daily gauge and ERA5-Land precipitation shows moderate agreement (R² ≈ 0.33–0.44), improving after bias adjustment (R² ≈ 0.43–0.47).

Daily streamflow simulations were evaluated at three main-stem gauges. Model calibration and uncertainty analysis are being conducted using SWAT-CUP (SUFI-2) with split-sample calibration (2006–2013) and validation (2014–2022). Preliminary simulations indicate systematic underestimation of observed discharge across all forcing scenarios, pointing to remaining inconsistencies in precipitation forcing and/or runoff generation. Ongoing calibration will quantify the extent to which these biases can be reduced and identify the precipitation forcing configuration that yields the most robust daily streamflow estimates, with direct implications for hydrometeorological hazard assessment in the Lake Urmia basin.

How to cite: Javanmard Ghoshouni, S., Montaseri, M., Hessari, B., Naderi, S., Prieto, C., and Fenicia, F.: Sensitivity of hydrological hazard estimates to precipitation representativeness in a data-scarce mountainous basin in Iran, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18380, https://doi.org/10.5194/egusphere-egu26-18380, 2026.

Assam, a flood-prone state, comprises multiple national parks and wildlife sanctuaries (Kaziranga, Orang, Manas National Parks, and Pabitro, Laokhawa, and Burachapori Wildlife Sanctuaries) located in the southern part of the Brahmaputra River. Floods significantly influence wildlife in these conserved areas, which are less discussed. The greater one-horned Rhinoceros (Rhinoceros unicornis) is listed as vulnerable on the International Union for Conservation of Nature (IUCN) Red List. While floods are ecologically integral to the Brahmaputra floodplain, extreme and frequent flood events increasingly threaten rhino habitat suitability, mobility, and survival, underscoring the need for spatially explicit risk assessment to support conservation planning.
The present study focuses on mapping the spatial risk index of the one-horned Rhino using Earth observation (EO) based Analytical Hierarchy Process (AHP) in the Google Earth Engine (GEE) platform. The Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) is used in conjunction with GEE to generate flood-influencing parameters, including elevation, slope, aspect, flow accumulation, distance to drainage, drainage network, and topographic wetness index. Flood depth, distance from roads, and distance from built-up areas have been used to develop layers for the Flood Risk Index (FRI) in rhino conservation. Rhino locations were collected from the Assam Biodiversity portal. 
The primary rhino habitat occupies approximately 23% of the study area, yet a substantial proportion of these habitats overlaps with zones of elevated flood risk. The Flood Risk Index (FRI) indicates that nearly 78% of the region falls within moderate to high flood-risk categories, with several rhino habitat  areas consistently exposed to high inundation susceptibility. Spatial overlay analysis highlights critical habitat patches where flood risk, anthropogenic proximity and low-lying terrain converge, and need priority zones for intervention during extreme flood events. The findings provide actionable insights for flood-responsive rhino management, including targeted evacuation planning, habitat restoration, and infrastructure placement, contributing to more resilient conservation strategies under intensifying hydro-climatic extremes.

How to cite: Ghosh, S., Sarkar, U., Kour, S., Bhattacharyya, S., and Nandy, S.: Greater One-Horned Rhino Habitat Risk Mapping Due to Flood Using Google Earth Engine for Informed Conservation Management in Assam, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19513, https://doi.org/10.5194/egusphere-egu26-19513, 2026.

Natural Flood Management (NFM) interventions and nature-based solutions are increasingly advocated as sustainable flood-mitigation strategies, yet empirical evidence of their hydrological impact at the catchment scale remains limited. This study uses an ensemble approach to characterise runoff-response distributions, drawing on long-term observed data (2011–2023) from the Eddleston catchment in Scotland. Unlike event-focused approaches, this method synthesises system behaviour across diverse hydrometeorological conditions to identify “typical” responses under pre- and post-intervention states.

Results from a small headwater catchment (2.3 km²) reveal statistically significant changes in runoff dynamics, including a delay in peak timing and a reduction in peak height after the installation of a series of leaky barriers. Comparable patterns in a larger catchment (34 km²), within which this smaller headwater catchment is nested, indicate that NFM effects extend beyond headwater sub-catchments. Ensemble-based summaries further highlight the dominant role of antecedent wetness in runoff generation, while also indicating increased infiltration and reduced runoff coefficients under high-flow conditions post-NFM installation.

By integrating ensemble hydrograph separation and impulse-response analysis, this framework provides a transferable tool for assessing NFM effectiveness across multiple scales. Findings strengthen the evidence base for NFM design optimisation and policy integration, supporting long-term strategies for flood resilience.

How to cite: Knapp, J. L. A., Jones, A., Reaney, S. M., Pattison, I., and Black, A.: Catchment-Scale Changes in Runoff Dynamics Following Natural Flood Management Interventions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19814, https://doi.org/10.5194/egusphere-egu26-19814, 2026.

Flash floods are among the most difficult natural hazards to deal with because they are usually associated with short, intense, and localised rainfall events in small, ungauged catchments. Their frequency and associated impacts have increased over the last few decades due to climate change impacts on extreme precipitation and land-use changes. The Northern Apennines River Basin District (Autorità di bacino distrettuale dell’Appennino Settentrionale - ADAS in Italian), which covers much of the river basins in Tuscany and Liguria, is particularly prone to such hazards due to its steep topography and proximity to the Tyrrhenian coast. Indeed, many flash flood events have occurred recently in these regions.

Because systematic observations of flash floods are scarce, especially in small river basins, regional-scale approaches are essential to support decision-making and to identify susceptible areas. In this context, the ADAS developed a rapid assessment procedure, known as the “Metodo Arno”, to map flash flood-prone areas at the management-district scale, which has been recognised as a best practice and adopted at the national level. Recently, a joint agreement between ADAS and the Department of Civil and Environmental Engineering of the University of Florence has been signed to further improve and harmonise the method.

In its recent version, the “Metodo Arno” is a multi-criteria flash flood susceptibility index derived from three indicators: the time of concentration and the average curve number of the river basin, and the return period associated with 50 mm in an hour. These three indicators are selected because they represent, respectively, the subbasin's response time, its propensity to generate runoff, and the frequency of extreme events. The indicators are normalised to a common scale and then combined using Simple Additive Weighting (SAW) to map the Flash Flood Potential index. All the river basins in the ADAS were delineated from a 10-meter-resolution Digital Elevation Model, with a minimum watershed size of 5 km². The time of concentration for each river basin was extracted using the Lekan software, which applies the more commonly used formulas in the literature. The return period associated with 50mm/h was estimated using local Intensity-Duration-Frequency curves. The curve number was estimated by combining information on the hydrological soil group and the Corine Land Cover. 

A flash flood database has been generated using an automated online news search with keywords to identify locations where past flash flood events occurred. The database is used to validate the procedure. The results show that the approach can map river basins prone to flash flooding. Future steps will focus on assessing the impact of climate and land-use change on the Flash Flood Potential Index.

How to cite: Lompi, M., Sadun, S., Checcucci, G., Spicchi, R., Franceschini, S., and Caporali, E.: Regional-scale mapping of flash flood susceptibility using a simple and operational approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20146, https://doi.org/10.5194/egusphere-egu26-20146, 2026.

Flood susceptibility mapping in the steep and geomorphically complex Himalayan terrain remains inherently challenging due to sparse observational data, strong process nonlinearity, and uncertainty embedded in judgment-based decision frameworks. Conventional Multi-Criteria Decision Making (MCDM) approaches, most notably the crisp Analytic Hierarchy Process (AHP), rely on deterministic pairwise judgments that inadequately represent the vagueness, subjectivity, and cognitive bias associated with hazard assessment in high-relief mountain environments.

This study addresses these limitations by systematically comparing classical AHP with a Fuzzy AHP (FAHP) framework for flood susceptibility mapping in the data-scarce Rudraprayag District of Uttarakhand, India (1984 km²), a region frequently impacted by extreme hydro-meteorological events. Thirteen geo-environmental conditioning factors were integrated within a GIS environment at 30 m spatial resolution, encompassing topographic attributes (elevation, slope, curvature, aspect), hydrological indices (HAND, TWI, drainage density, distance to river), and environmental controls (rainfall, geology, LULC, NDVI, distance to roads). To robustly handle the full 13×13 comparison matrix and avoid zero-weight artifacts commonly associated with fuzzy extent analysis, FAHP was implemented using Buckley’s geometric mean method with triangular fuzzy numbers, explicitly capturing uncertainty bounds in pairwise comparison judgments.

Results demonstrate that FAHP yields a smoother and more balanced weight distribution compared to crisp AHP. While slope remains the dominant control, its rigid dominance is reduced, allowing geomorphically subtle yet physically meaningful factors such as curvature and aspect to exert greater influence. Validation against an independent flood inventory derived from Google Earth Engine, evaluated using ROC–AUC analysis, confirms the superior predictive performance of FAHP (AUC = 0.837) relative to classical AHP (AUC = 0.806).

Overall, the findings highlight that incorporating fuzzy uncertainty into MCDM frameworks significantly enhances the robustness and defensibility of flood susceptibility assessments. FAHP thus provides a more uncertainty-aware and process-sensitive hazard baseline, particularly suited for data-scarce Himalayan regions where judgment-based weighting remains unavoidable in disaster risk reduction and spatial planning.

How to cite: Yadav, S. and Kumar, P.: Quantifying Uncertainty in Himalayan Flood Susceptibility Mapping: A Comparative Analysis of AHP and Fuzzy AHP in Rudraprayag District, Uttarakhand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20654, https://doi.org/10.5194/egusphere-egu26-20654, 2026.

Historical flood events provide critical insights into extreme flood dynamics that are often underrepresented in instrumental records. This contribution presents the application of hydrodynamic modelling to analyse major historical flood events in the Iberian Peninsula and to assess their relevance for flood risk assessment and mitigation. Several catastrophic floods affecting Portugal and Spain between the late 19th and the 20th centuries are investigated, including riverine and flash-flood events with severe societal impacts. A modelling framework is implemented using the physically based Iber+ hydrodynamic model. It integrates precipitation reconstructed through multiple methodologies, including interpolation of gridded and measured data, and topography compiled using different sources, ranging from historical maps to field-based measurements. This framework enables the estimation of peak river flows, one of the main unknowns in historical events, and reproduces flood propagation, inundation extent, water depths, and flow velocities. Model performance is evaluated against historical watermarks, documentary evidence, and witness testimonies, showing good agreement despite the scarcity of direct measurements and the associated uncertainties. The simulations enable a detailed analysis of the key drivers controlling flood severity, including the exploration of plausible scenarios, providing insights into the main causes of these events where uncertainties in flood development persist. The results highlight the role of hydraulic bottlenecks, infrastructure blockage, and local topographic constraints in amplifying flood impacts. Scenario-based simulations further demonstrate the potential of hydrodynamic modelling to explore mitigation strategies, such as optimized dam operation and improved infrastructure maintenance, and to assess extreme but plausible flood scenarios under current terrain and infrastructure conditions, supporting flood mitigation. Overall, the results emphasize the value of integrating historical flood information with modern hydrodynamic modelling. This approach contributes to improving flood hazard mapping, reduce uncertainties in flood risk estimation, and support more robust flood risk management strategies under current and future climatic conditions, in which flood events of comparable or even greater intensity than historical floods are expected.

How to cite: Fernandez-Novoa, D., Gonzalez-Cao, J., Garcia-Feal, O., Barreiro-Fonta, H., Trigo, R. M., and Gomez-Gesteira, M.: Hydrodynamic modelling of historical flood events in the Iberian Peninsula: implications for flood risk assessment and mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20999, https://doi.org/10.5194/egusphere-egu26-20999, 2026.

Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. To resolve fine-scale physical processes, allowing for local impact assessments, downscaling techniques are essential. Super-resolution is an innovative technique that upscales the resolution of an image and thus enables to reconstruct high-fidelity images from low-resolution data. This study performs super-resolution analysis for spatial downscaling of hydrodynamic data using various deep learning techniques to reconstruct high-resolution flow fields from low-resolution flow field data. It increases the spatial resolution of coarsened water depth and unit discharge norm from 4 m to 80 cm. The training data for these models was generated using a physically based hydrodynamic model. To evaluate their performance and accuracy, multiple tests were conducted using synthetic events. Our experiments indicate that these models successfully predicted water depths in the testing flood scenario for the dynamic case but could not preserve the steady states during the reconstruction. Furthermore, these models cannot satisfactorily generalize to flood scenario outside the training datasets with different boundary conditions. The results demonstrate that the proposed models are up to 30 times faster than the hydrodynamic model and promising in terms of accuracy. Therefore, it bridges the gap between detailed flood modelling and real-time applications.

How to cite: Ait-Ameur, K., Guinot, V., Marti, L., Rousseau, A., and Toulemonde, G.: Exploring super-resolution for the downscaling of urban floodsimulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21384, https://doi.org/10.5194/egusphere-egu26-21384, 2026.

Azerbaijan faces significant flood risks due to its diverse terrain, which includes low-lying areas near the Caspian Sea, situated 28 meters below sea level, and mountainous regions exceeding 4,000 meters. Increasing rainfall exacerbates this threat, while current monitoring systems are inadequate for timely flood warnings. This study introduces HydroAlert Azerbaijan, an artificial intelligence-driven system utilizing satellite imagery to detect floods and assess risk levels, capable of operating in adverse weather conditions. It employs a U-Net neural network to analyze Sentinel-1 SAR data, specifically leveraging VV and VH channels for efficient flood assessment.

The system processes 512×512 pixel tiles from the SAR data, overlapping by 64 pixels to ensure comprehensive coverage. Trained on the SEN12FLOOD dataset, consisting of 209 global flood examples, HydroAlert is designed to function effectively even in areas with limited flood event data. Initial evaluations of the Sentinel-1 SAR scenes and Azersky optical images confirm its efficacy, achieving an accuracy of approximately 85% in flood identification.
The Azersky optical data, characterized by a resolution of 1.5 meters, provides detailed insights into infrastructure vulnerability and validates the extent of floods derived from SAR data. The model generates precise vector shapes on maps, improving emergency response planning by visualizing flood extents.

This study shows that a platform facilitates user interaction with flood data, incorporating historical insights from the Dartmouth Flood Observatory and high-risk area alerts. The system supports data export in user-friendly formats to assist decision-making. The Hydroalert Project, which integrates SAR and optical data sources for comprehensive flood assessment, ensures reliable monitoring capabilities through its multi-sensor integration framework.

Additionally, the system incorporates a forecasting module using ConvLSTM architecture to predict flood risks over the following week, aiding proactive decision-making in disaster preparedness. Participation in the Azercosmos Earth Observation Competition 2025 has fostered collaboration with the Azerbaijani Space Agency, leading to systematic enhancements of the HydroAlert prototype using data from the competition.

Current efforts focus on refining the model for local conditions, utilizing satellite imagery to improve operational accuracy. This project demonstrates the potential of deep learning models for flood detection in developing regions lacking robust ground-level monitoring systems. By integrating global satellite images with advanced AI techniques, HydroAlert Azerbaijan offers a viable flood monitoring and management strategy for areas with limited existing resources and information.

Keywords

Flood mapping, SAR remote sensing, Optical imagery, Deep learning, Azerbaijan, Disaster management

How to cite: Elik, F., Rustamov, P. R., and Alaskarov, E.: AI-Powered Flood Monitoring for Azerbaijan Using Multi-Source Satellite Data: Operational Prototype Development and Initial Validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21567, https://doi.org/10.5194/egusphere-egu26-21567, 2026.

Understanding multi-hazard interactions in flood risk contexts has emerged as a critical area of environmental research. This study presents a comprehensive bibliometric analysis covering scientific publications indexed in Web of Science from 2010 to 2024, focusing specifically on compound and cascading flood events within multi-hazard frameworks. The dataset comprises 1,096 scientific documents published across 290 academic sources, authored by 4,166 scholars affiliated with 1,326 institutions from 94 countries. Annual scientific production increased significantly, rising from fewer than 10 publications per year prior to 2018 to a peak of 78 documents in 2023. Geographically, the literature is highly concentrated; China (27%) and the United States (24%) dominate publications, whereas contributions from African institutions remain minimal, reflecting critical geographic disparities in research engagement.  Using paper-level counting, 505 papers (46%) include at least one European affiliation, based on a European country set of Austria, Belgium, France, Germany, Greece, Italy, the Netherlands, Norway, Poland, Portugal, Romania, Serbia, Spain, Sweden, Switzerland, and the United Kingdom. European output is led by the United Kingdom (145 papers) and Italy (134), followed by the Netherlands (94) and Germany (83). A detailed keyword evolution analysis identified a pronounced thematic shift from vulnerability and traditional hazard management toward compound-flood risk, climate-driven extremes, and resilience-oriented approaches in recent years. Topic-cluster analysis further demonstrates fragmented research efforts across methodological domains, with limited interdisciplinary integration. Citation trajectory analysis reveals that key studies published between 2022 and 2023 received more than 45 citations within two years, indicating a rapid recognition and scholarly impact within the research community. However, uncertainty quantification remains notably underrepresented (only 17% of studies), and emerging approaches such as nature-based solutions constitute less than 5% of the total literature, underscoring key research gaps. These findings offer a robust, empirical foundation highlighting underexplored themes, geographic disparities, and methodological challenges, guiding future research priorities in multi-hazard flood risk management under changing climatic conditions. International collaboration is a defining feature of the European contribution. Almost half of the European papers also include non-European affiliations (245 of 505). The strongest links are with the United States (89 papers) and China (56 papers), while 16 papers include Europe, the United States, and China together. In full counting, European countries account for 39.88% of all countries’ occurrences in the dataset (737 of 1,848). These results position Europe as both a major producer and a collaboration hub in multi-hazard flood risk research.

How to cite: Gul, E., Geykli, A. N., and Hassanzade, E.: Bibliometric analysis of global multi-hazard flood risk research: trends, knowledge gaps, and emerging topics (2010–2024), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21974, https://doi.org/10.5194/egusphere-egu26-21974, 2026.

Intensifying humid heatwaves (HHWs) across the Indian subcontinent highlight the growing risk of elevated temperatures and increased humidity in urban agglomerates, characterized by dense populations and complex sustainability demand. While conventional Extreme Value Theory (EVT) approaches are commonly used to estimate design events of heat-humidity compound extremes, they are constrained by limited sample sizes and unstable upper tail estimates in data-sparse regions. Recent advances in Metastatistical Extreme Value (MEV) theory demonstrate significant skill improvements for hydroclimatic extremes such as rainfall and flash droughts, but its applicability to compound heat–humidity, i.e., HHWs, remains unexplored for Indian sub-continent, with diverse climate types, primarily dictated by monsoonal circulations. Here, we present the first observational assessment showing the skill of MEV probabilistic framework that can capture the year-to-year variability of the HHW distributions. Using in-situ observations from 54 urban and peri-urban sites across India for over four decades (1980–2025), we develop the MEV-based probabilistic estimates of extreme HHW magnitude, which we compare against the conventional EVT-based distribution. HHW events are identified based on daily observed maximum wet-bulb temperature exceeding the 90^th percentile daily variable threshold that persists for two consecutive days or more, while HHW magnitude is estimated as the positive anomaly of daily extreme wet-bulb temperature, exceeding the 90^th percentile variable threshold, normalized by its interquartile range. Our results show that MEV consistently outperforms conventional EVT in estimating design events for approximately, 68% of sites, indicating improved representation of moderate to rare HHW events.  The uncertainty bounds (indicated by the interquartile range) of the MEV versus EVT design events suggest that MEV offers lower uncertainty, represented by narrower interquartile ranges, compared to conventional EVT across approximately 70% of sites. For example, for a representative site across eastern coastal India, the quantification of the record HHW event during July 2020, with a 64-year return period, is illustrated. The MEV estimated quantile provides error estimates of ~1%, whereas the conventional model underestimates the design events by ~4%, suggesting the MEV model offers improved representation of compound heat and humidity design events, which have implication towards public health and ecosystem sustainability. Our study provides the first application of MEV models to understand the heat-humidity nexus across urban agglomerates of India, and demonstrates its potential to define impact-relevant metrics in a warming climate.

How to cite: Deb, S. and Ganguli, P.: Metastatistical Extreme Value Framework Reveals Robust Improvement in Characterizing Humid Heatwave across Indian Urban Agglomerates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1445, https://doi.org/10.5194/egusphere-egu26-1445, 2026.

Understanding the sources of uncertainty in future climate extremes is crucial for effective regional adaptation strategies. This study uses simulations from 26 global climate models to investigate projections of summer maximum temperature (TXx) across four southern South America regions: northern, central-eastern, and southern areas, and central Argentina. A storyline approach is applied to examine how different climate drivers interact to shape TXx changes by the late 21st century (2070–2099).

The storylines are based on changes in key physical drivers, including mid-tropospheric ridging, regional soil moisture, sea surface temperature in Niño 3.4 region and an OLR gradient index that reflects changes in atmospheric stability and the positioning of convective activity over the South Atlantic Ocean. A multi-linear regression framework shows that dominant drivers of projected TXx warming differ across regions. In northern areas, uncertainty is primarily controlled by remote influences, including tropical sea surface temperatures and OLR variations over the subtropical South Atlantic Ocean. Central-eastern areas and central Argentina show a combination of local and remote drivers, whereas southern regions are mostly governed by local factors, such as soil drying and atmospheric blocking. Together, these drivers account for up to 56% of the inter-model spread in regional TXx projections. Nonetheless, their capacity to capture the projected spread in percentile-based indices and regional heatwaves attributes is limited, suggesting that the drivers of heatwave responses vary with the metric.

How to cite: Suli, S., Barriopedro, D., García-Herrera, R., Collazo, S., Squintu, A., and Rusticucci, M.: Storylines of extreme summer temperatures in southern South America, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1753, https://doi.org/10.5194/egusphere-egu26-1753, 2026.

Humid heat has increased in intensity, frequency, and duration across the world, including in mid-latitude regions not acclimated to it. Wet bulb globe temperature (WBGT) is one of the most representative humid heat metrics in terms of impacts to human health. Here, an established WBGT estimation formula and the high-resolution (9-km) ERA5-Land reanalysis dataset are used to examine summer humid heat trends and drivers across Europe and the Southeast United States. In Europe, both daytime and nighttime WBGT are increasing significantly across nearly the entire domain. Daytime and nighttime trends are of similar magnitude in many areas, with daytime trends largest in western and northern Europe and nighttime trends greatest in eastern and southern Europe. In addition, there are statistically significant and large frequency and duration trends in extreme (90^th percentile) WBGT, especially at night near the Mediterranean, where there are approximately three additional extreme nights per decade and extreme event duration is more than one day longer each decade. Unlike in Europe, nighttime WBGT trends in the Southeast United States are larger and more widely statistically significant than daytime trends across most of the region. Like in Europe, there are large increases in the frequency and duration of extremes, particularly at night. For example, regions near the Gulf and in Florida are experiencing nearly three additional extreme summer nights per decade and extreme events are approximately one night longer per decade. A quantification of the importance of WBGT components (temperature, dewpoint, wind speed, solar radiation) shows that dewpoint increases exceed temperature increases and are the primary driver of WBGT trends across the Southeast United States, a region characterized by its hot humid climate and proximity to warm water. However, in the milder and drier climate of Europe, temperature increases are the dominant driver of WBGT changes, with a strong correlation to positive trends in solar radiation. Overall, results suggest that the drivers of humid heat trends depend on regional climate characteristics, which may have broad applications to climate modelling of future humid heat.

How to cite: Milrad, S., Ennis, K., Orletski, K., and Beaty, P.: Climate matters: Differences in trends and drivers of summer humid heat in Europe and the Southeast United States, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1886, https://doi.org/10.5194/egusphere-egu26-1886, 2026.

The vertical structure of heat waves has been an overlooked characteristic until recently. Here we present results of their analysis for Central Europe based on vertical cross-sections of temperature anomalies throughout the troposphere, including links to soil moisture conditions and atmospheric circulation. Heat waves were classified into four types based on the predominant location of positive temperature anomalies: near-surface, lower-tropospheric, upper-tropospheric, and omnipresent, using ERA5 data at multiple pressure levels since 1979. For each heat wave type, we summarize key characteristics, including vertical temperature structure, typical duration, seasonal occurrence within summer, links to atmospheric circulation, and soil moisture preconditioning. We also assess the ability of CORDEX regional climate models to reproduce the characteristics of the individual heat wave types in simulations for the recent climate. Finally, we outline ongoing follow-up studies on topics including sub-daily characteristics of heat waves, off-season events, and a global analysis on the 5° × 5° grid.

How to cite: Lhotka, O., Plavcová, E., Poppová, Z., and Kyselý, J.: Heat wave types in Central Europe: new insights based on vertical structure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3455, https://doi.org/10.5194/egusphere-egu26-3455, 2026.

The escalation of heat-related health risks due to climate change and population ageing is a growing global concern. However, their independent 
contributions to medically certified heatstroke mortality remain insufficiently quantified.

We analysed 28 years (1995–2022) of municipality-level records of medically certified heatstroke deaths (ICD-codes) across 1,831 municipalities and wards in Japan. We fitted hierarchical Bayesian spatiotemporal models incorporating summer temperature anomalies and the municipal share of residents aged ≥65 years.

Heatstroke mortality was higher in warmer summers and in municipalities with older population structures: 42% (RR=1.42, 95% CrI 1.36–1.49) per 0.65°C (1 SD) increase in summer temperature anomalies, and 24% (RR=1.24, 95% CrI 1.18–1.31)  per 10-percentage-point increase in the ≥65-year population share. Decomposition of national trends indicated that population ageing progressively increased baseline risk, while large positive temperature anomalies produced additional mortality peaks on this elevated baseline. A sustained upward shift in risk was observed from around 2007 onward. Residual spatial heterogeneity remained after adjustment, with clusters of elevated risk in both urban and rural municipalities.

These findings highlight the need for targeted, place-specific heat-health adaptation as climatic warming and population ageing continue to progress. As a super-aged nation, Japan offers valuable insights into mitigating future heat-related health burdens under ongoing climatic and demographic transitions.

How to cite: Kakinuma, K. and Inoue, N.: Heatstroke mortality under climate and demographic transitions: 28-year spatio-temporal analysis in Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3630, https://doi.org/10.5194/egusphere-egu26-3630, 2026.

Heatwaves are intensifying across the Middle East with distinct daytime and nighttime impacts. We quantify the climatology and trends of daytime, nighttime, and compound heatwaves using station observations for 2005 to 2025, and evaluate four AI-weather models for near-surface air temperature during a recent high-impact episode (Zittis et al., 2025).

Daytime heatwaves recur from the eastern Mediterranean through northern Iraq and Iran into Anatolia during warm seasons, whereas nighttime and compound events cluster along the southern Red Sea and Arabian Gulf coasts. Trends show significant summer and autumn increases across all classes (daytime: +4.09 and +6.09; nighttime: +4.13 and +6.22; compound: +3.42 and +2.40 per year), a winter increase led by nighttime events, and a spring increase in both nighttime and compound events, consistent with asymmetric diurnal warming, strong land–atmosphere coupling over arid interiors, and coastal humidity that limits nocturnal cooling.

A case study of 10 to 27 June 2024 documents record-breaking daytime anomalies over the northern Middle East and persistent nocturnal warmth along maritime margins, with peak impacts around 17 June in the Mecca region. ERA-5 diagnostics indicate a quasi-stationary Rossby wavetrain, an anomalous ridge over southwest Asia, and an intensified Arabian Heat Low that weakened low-level ventilation and sustained humid coastal nights.

We assess GraphCast, PanguWeather, FourCastNetv2, and Aurora using six-hourly forecasts initialized at 00 UTC on 12 June 2024 and verified against ERA-5 from 12 to 22 June. All capture synoptic timing and multi-day persistence but underestimate daytime peaks and show lead-dependent cold biases. PanguWeather provides the strongest deterministic temperature guidance; GraphCast corroborates synoptic evolution. Operationally, a bias-corrected PanguWeather and GraphCast blend is recommended.

Reference:

Zittis, G., Alberti, T., Almazroui, M. et al. Analysis of the 2024 Hajj heat event and future temperature extremes in Mecca. npj Nat. Hazards 2, 107 (2025). https://doi.org/10.1038/s44304-025-00159-3

How to cite: Nelli, N., Francis, D., Fonseca, R., Hassanzadeh, P., Cherif, C., and Ghedira, H.: The June 2024 Middle East compound heatwave: Dynamical drivers and AI-weather forecast models’ evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3815, https://doi.org/10.5194/egusphere-egu26-3815, 2026.

Heat stress is an escalating global public health concern, especially as climate change intensifies the frequency, duration, and severity of extreme heat events. Accurate identification of hazardous heat exposure is essential for the development of effective heat-health warning systems (HHWS) and the timely issuance of public alerts.

Traditionally, air temperature (Tair) has been used as the primary metric for triggering heat alerts. However, a growing body of evidence highlights the critical role of humidity in intensifying heat stress and increasing health risks. As a result, integrated heat stress indicators (HSIs)—which incorporate multiple meteorological factors such as temperature, humidity, solar radiation, and wind speed—are gaining attention.

In 2021, Japan updated its national HHWS by replacing Tair with Wet Bulb Globe Temperature (WBGT), a more comprehensive index that accounts for multiple variables. However, the effectiveness of this change, and the broader applicability of various HSIs in predicting heat-related morbidity and mortality remain insufficiently explored.

In this presentation, we synthesize recent research evaluating the performance of multiple HSIs in modeling heat-related health outcomes across Japan and other regions. By comparing various health outcome datasets, we assess how well different HSIs capture population-level vulnerability to heat.

Furthermore, we demonstrate how data-driven techniques can move beyond one-size-fits-all indicators. By leveraging local health datasets, we show how it is possible to fine-tune HSIs to reflect regional population sensitivities, ultimately enhancing the accuracy and effectiveness of heat-health warnings at the local level.

How to cite: Guo, Q. and Kong, Q.: Optimizing Heat Stress Indicators for Protecting Human Health: From Generic Metrics to Localized Solutions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4205, https://doi.org/10.5194/egusphere-egu26-4205, 2026.

How to cite: Jha, R. and Byrne, M. P.: Role of dry‑air entrainment in intensifying extratropical moist heat extremes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5010, https://doi.org/10.5194/egusphere-egu26-5010, 2026.

Heat, combined with high levels of humidity, can lead to hyperthermia, i.e. an increase in human body temperature beyond dangerous thresholds. In the most severe cases, it can be deadly, even for young and healthy adults. According to the literature, extreme humid heat over continents is projected to increase markedly at the global scale by the end of the century, particularly over the tropical belt. Yet, the case of small tropical islands is ambiguous because the available studies are based on coarse gridded datasets such as those from global climate models that ignore or distort these islands, calling for dedicated downscaling efforts.
Here, statistically downscaled and bias-corrected model output from CMIP6 using weather station observations from islands across the tropics are used to assess present and future conditions of extreme humid heat. The latter are estimated with the Heat Index (HI) that combines surface temperature and relative humidity.
Similarly to what has been previously shown for continents, the intensity of extreme humid heat events is projected to reach particularly dangerous levels by the end of the century, with higher HI values for islands located closer to the equator. Longer, more common humid heatwaves are also notably projected to increase in frequency, with more pronounced increases equatorward, because of the lower seasonal variability of HI.
Such severe humid heat conditions threaten the lives of millions of small island inhabitants across the tropics, calling for dedicated local adaptation strategies.

How to cite: Bald, L., Belmadani, A., Leroux, M.-D., Pannekoucke, O., Gentric, A., and Qasmi, S.: Small tropical islands also exposed to extreme humid heat by the end of the century, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5401, https://doi.org/10.5194/egusphere-egu26-5401, 2026.

Recent decades have seen an increase in the frequency and intensity of extreme heat events. Such events have a profound impact on society, as they increase human exposure to heat stress and can negatively affect energy consumption and agriculture. These effects are further amplified in urban environments due to climate change. Belgium, among the most urbanised countries worldwide, has experienced an increasing number of heatwaves in recent years, with several years recording multiple events. However, the future extent and characteristics of heatwaves remain unclear, making it difficult to assess whether current suggested adaptation strategies and heat action plans for today's climate will remain effective under future warming. With this research, we aim to develop a storyline-based selection method to quantify single future heat waves based on the characteristics of past events and thereby enable the identification of relevant case studies for assessing future risks.

With the new Belgian climate projections from the CORDEX.be II project, different global warming levels are being explored (1.5°C, 2°C, 3°C and 4°C), to gain insights into different future extreme events. In this study, we analyse high-resolution data from the regional climate model COSMO-CLM, in which urban areas are explicitly modelled, forced by the global climate model EC-Earth3-Veg (SSP5-8.5), at two horizontal resolutions: 12.5 km (all warming levels) and 2.8 km (2°C and 3°C). By identifying historical extreme events and mapping events with similar characteristics in higher warming levels using different heat metrics, we quantify changes in event duration, temperature characteristics, and intensity relative to the recent past to obtain interesting future cases. 

How to cite: Serras, F., Vanderkelen, I., Lampaert, W., and van Lipzig, N.: The next hot thing: Belgian heatwaves in a warming world, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5580, https://doi.org/10.5194/egusphere-egu26-5580, 2026.

Background: Extreme temperatures are a key indicator of climate change, and the frequency and intensity of deadly heatwaves are projected to rise. Heat already poses a major public health threat, with over a billion people exposed globally, and yet Africa is warming faster than the global average. Tropical countries such as Uganda are expected to experience hotter conditions and growing cooling needs. However, extreme heat remains under-studied in tropical climates, especially in Africa. Although there is no single universal definition of heatwaves, current evidence generally identifies heatwaves as periods of unusually high temperatures relative to local climates.

Objective: This study aims to assess the spatio-temporal characteristics of heatwaves in Uganda and their trends.

Methods: We analyzed daily temperature data from 31 meteorological stations across Uganda over a 34-year period to assess national and regional trends. Heat extremes were characterized using three complementary heatwave metrics: the 90th percentile of maximum temperature (TX90), the Heat Index (HI), and the Excess Heat Factor (EHF). To describe temperature extremes and warming rates, the mean Tmax was calculated per year, month, and zone. The hottest and coolest years and months were identified by their respective Tmax values, while trends in monthly mean Tmax were estimated using linear regression. The slopes were expressed as °C per decade, and the months with the fastest warming and cooling rates were reported alongside their corresponding p-values. Stations were classified according to the ten climatological zones and zonal aggregation was performed to summarize station-level heatwave metrics into broader climatic regions, thereby capturing spatial patterns while reducing sensitivity to local variability.

Results: Uganda has warmed by an average of 1.7°C over the past three decades. February consistently emerges as the hottest month, while July and November are generally the coolest, with some regional variation. The year 2016 was the hottest on record, with the highest mean maximum temperature of 30.35°C recorded in northern Uganda. Across all stations, the proportion of days classified as heatwave days increased significantly, rising by an average of 0.2 percentage points per year between 1990 and 2023.

Heatwave characteristics exhibit strong spatial and temporal variability. Annual heatwave prevalence at individual stations ranges from years with no heatwaves to years exceeding 20–30%, with notable peaks in 1998, 2003, 2010, and 2023. The annual average number of heatwave events fluctuates widely, from 0 to more than 15 events per year at some stations. After 2015, more stations experienced frequent and intense episodes, indicating a clear intensification of heatwaves. Seasonally, heatwaves occur most often from June to August, with additional clusters in February –April and October–November. Sparse heatwave prevalence in the 1990s contrasts sharply with the dense occurrence of heatwave days in recent years.

Conclusion: Uganda is experiencing significant warming and a marked increase in both the frequency and intensity of heatwaves. This multi-metric characterization provides a robust foundation for understanding heat risk in a tropical African context, informs the design of heat-related policies and early warning systems, and offers a methodological framework applicable to other countries assessing evolving heatwave hazards.

How to cite: Amuron, I., Sseviiri, H., de Perez, E. C., Aalst, M. V., Kiswendsida, G., Orach, C. G., and Blandford, J.: Characterizing Heat Extremes in Uganda: A Multi-Metric analysis of Heatwave Trends, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5946, https://doi.org/10.5194/egusphere-egu26-5946, 2026.

The African Sahel—home to 180 million people—faces escalating risks from the convergence of poverty, food insecurity, political instability, and climate change. While the African Union’s Great Green Wall (GGW) initiative aims to restore 100 million hectares of degraded land to alleviate these challenges, we find that greening coincides with a rapid rise in hazardous humid-heat days (HHDs), threatening human health and livelihoods. Using high-resolution (5 km) datasets from 1983–2016, we map where greening is coinciding with increasing HHDs, and we project 2050 exposure by age cohorts. We find that areas with increased short vegetation experienced a 158% faster rise in HHDs compared to non-greening regions, driven primarily by higher atmospheric moisture rather than air temperature. By 2050, nearly all Sahel residents will experience at least 30 HHDs per year, with children born in the past decade facing the greatest future impacts. Our findings suggest that climate-driven greening may intensify heat-health risks, underscoring the need for GGW and other climate adaptation policies to factor in humid-heat exposure. 

How to cite: Tuholske, C., Ivanovich, C., Williams, E., Horton, R., Shukla, S., Funk, C., Andam, K., Kibler, C., Yamba, E., Zimmer, A., and Brooks, N.: Rapidly Increasing Hazardous Humid-Heat Exposure Across Africa’s Great Green Wall, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5992, https://doi.org/10.5194/egusphere-egu26-5992, 2026.

As temperatures rise with ongoing anthropogenic climate change, extreme heat events are becoming more frequent and are starting to occur outside of the expected heat season. Unusually early or late extreme heat events can create outsized impacts as individuals may be less acclimated to intense heat or unready to employ cooling strategies. Here we characterize the historical baseline seasonality of extreme heat around the globe and quantify how this seasonality is shifting over time. We define heat seasons as the three months with the highest historical fraction of extreme heat events during a baseline period in the 1980s. We find that in this baseline period, the extreme heat season is distinct from meteorological summer throughout much of the world, but captures a high fraction of the total annual observed extreme heat days. This is true even in low latitudes where the amplitude of temperature seasonality is low. Heat seasons are also expanding asymmetrically: some regions now experience a higher fraction of extreme heat events in the months following the heat season, while others experience a higher fraction preceding the heat season. At most locations around the globe, this observed asymmetrical lengthening of the extreme heat season is not explained by annual mean warming shifting up preexisting seasonal mean temperatures. Regional trends in seasonal mean temperature and humidity underscore that additional local dynamics are altering extreme heat drivers differentially during the two shoulder seasons. These results highlight the need for heat alert systems to focus on new times of year and prompt further study of compound events where extreme heat seasons are encroaching on the peak seasons for hazards such as wildfire or hurricanes.

How to cite: Ivanovich, C., Cook, B., and McDermid, S.: Global Extreme Heat Seasons Are Lengthening Asymmetrically, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7630, https://doi.org/10.5194/egusphere-egu26-7630, 2026.

Economic expenditure incurred by urban residents to alleviate heat-related discomfort constitutes an observable economic manifestation of their adaptive responses to heat risk. Focusing on the core area within Beijing’s Sixth Ring Road in a megacity context, this study integrates field-based questionnaire surveys with heat exposure data to systematically examine the response characteristics, population heterogeneity, and spatial patterns of residents’ subjective adaptive expenditure under heat conditions. The results indicate that residents’ subjective adaptive expenditure is more strongly associated with subjective heat risk perception than with objective heat exposure risk. A pronounced mismatch exists between objective heat exposure and subjective adaptive expenditure: for every 0.1-unit increase in heat exposure risk, the average monthly subjective adaptive expenditure decreases by 142.99 CNY, while a 1 °C increase in the self-reported temperature threshold triggering adaptive behavior corresponds to an average monthly reduction of 11.70 CNY. The structure of subjective adaptive expenditure shifts from short-term consumption-oriented spending toward maintenance-oriented spending as heat exposure risk increases. Expenditures on protective clothing and equipment, electricity, and adaptive food items are more sensitive to changes in heat exposure risk, whereas health-related expenditures such as disease treatment exhibit relatively high stability. Heat-response patterns among residents display clear clustering characteristics and can be classified into four typical groups: “high exposure–low impact–low expenditure,” “low exposure–high impact–high expenditure,” “medium exposure–medium impact–medium expenditure,” and “low exposure–low impact–low expenditure.” Age, household registration status, and gender emerge as key attributes distinguishing these groups. The total subjective adaptive expenditure of urban residents in Beijing is estimated at approximately 112 million CNY per month, exhibiting a spatial pattern characterized by higher values in central areas and lower values toward the periphery. This study reveals the economic expenditure response associated with adaptive behaviors of urban residents under heat conditions, providing quantitative evidence to support differentiated heat risk management and targeted adaptation policies.

How to cite: Peng, Y., Xie, M., Chen, Y., and Liu, Q.: From Enduring to Adapting Heat Risks: Characteristics of Responses, Group Differences, and Spatial Distribution of Urban Residents' Subjective Expenditures for Heat Adaptation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9978, https://doi.org/10.5194/egusphere-egu26-9978, 2026.

As the frequency, duration, and severity of global heatwaves intensify, increasing proportions of urban populations are being exposed to extreme heat risks. Although the health impacts of high temperatures have been extensively documented, the mechanisms linking heat risk perception, experienced impacts, and adaptive behaviors remain insufficiently understood, particularly across different gradients of exposure intensity. Moreover, most cross-sectional studies on heat risk perception overlook the spatial heterogeneity of heat exposure risk, which may lead to biased assessments of residents’ perception levels and adaptive capacity. In this study, spatially explicit heat exposure risk assessments are integrated with household survey data, and structural equation modeling (SEM) is applied to examine the associations among heat exposure risk, residents’ heat risk perception, heat risk impacts, and adaptive behaviors. The results show significant negative associations between heat exposure risk and perception, impact, and adaptation. Residents in high-risk areas tend to have lower levels of heat risk perception, which in turn suppresses adaptive behaviors. Further analyses reveal that socioeconomic factors play moderating roles across different exposure gradients. Age and education are key determinants of heat risk perception, but their effects weaken in high-risk groups. Education significantly affects psychological and physical health impacts and promotes adaptive behaviors, particularly transportation-related protective actions. Income is also positively associated with transportation protective behaviors. These findings highlight the importance of incorporating objective heat exposure risk into studies of heat risk perception and adaptive behaviors. They also underscore the need for differentiated, community-level adaptation strategies to address growing spatial and social inequalities under climate change.

Keywords: Heat exposure risk; Heat risk perception; Adaptive behavior

How to cite: Liu, Q. and Xie, M.: Suppressing the Adaptation Pathway: Negative Effects of Heat Exposure Risk on Perception, Impact, and Behavior, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10075, https://doi.org/10.5194/egusphere-egu26-10075, 2026.

Introduction: The 2003 European heatwave marked a turning point in the development of heat early warning systems (HEWS), yet little is known about how these systems have evolved once implemented or about the rationale of underlying operational choices. Methods: This study examines the evolution of the Dutch heat warning system from its introduction in 2007 through 2025, operated by the Royal Netherlands Meteorological Institute (KNMI), focusing on changes in warning criteria, operational procedures, and their role in triggering the national heat-health action plan (HHAP). We analysed 18 internal evaluations of major heat events, policy documents, and institutional reflections from system developers and operators to trace the climatic, operational, and institutional drivers of system change. To assess future relevance, we applied the KNMI’23 climate scenarios to evaluate how current warning criteria perform under projected conditions for 2050 and 2100. Results: Results show a transition from a single trigger for the national heat-health action plan, toward a tiered warning system that increasingly integrates expert judgment and societal impact considerations alongside meteorological thresholds, with a fully impact-based Code Red. A key revision in 2021 removed minimum temperature requirements, leaving maximum temperature as the primary trigger. In 2024, the first evaluation of the national HHAP by health authorities, including epidemiological evidence, provides a basis for further development of the warning criteria. Conclusion: The Dutch case highlights how HEWS function as adaptive systems that must continuously balance operational simplicity, impact relevance, and future climate pressures. We conclude by situating these findings within ongoing research and developments, including the development of heat warnings for the tropical island of Bonaire (also under KNMI’s mandate), and introduction of the wet-bulb globe temperature as a complement to traditional heat warnings. These insights are relevant for advancing heat early warning systems in Europe and beyond.

How to cite: Pereira Marghidan, C., Sluijter, R., Blanford, J., Homan, H., Siegmund, P., Hagens, W., and van Aalst, M.: Two decades of operating a heat early warning system: lessons from the Netherlands (2007–2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11090, https://doi.org/10.5194/egusphere-egu26-11090, 2026.

Warm extremes are intensifying in a warming world; however, most heat-related metrics focus on summer heatwaves. Therefore, linear trends often underestimate anomalously warm conditions occurring throughout the year. This limitation hampers our ability to robustly quantify changes in the frequency and severity of warm spells. Here, we assess global and regional changes in year-round warm spells using the Warm Spell Magnitude Index daily (WSMId), a dimensionless and additive metric that integrates warm-spell intensity, duration, and frequency consistently across seasons.

We calculate WSMId with the aid of ERA5 daily maximum temperature data for the period 1980–2024, aggregating warm-spell magnitudes at annual and seasonal time scales to assess long-term changes in the cumulative severity of warm spells. Trend analysis reveals widespread and statistically significant nonlinear intensification of warm spells since 1980, with more than 70 % of global land areas exhibiting a multi-fold increase in annually aggregated warm-spell magnitude.

Spatial patterns indicate especially large relative increases in warm-spell severity in tropical regions, while pronounced intensification is also evident across the mid-latitudes. Comparison with the Warm Spell Duration Index, an index that captures only event duration, confirms the robustness of the detected trends and underscores the enhanced sensitivity of WSMId, which aggregates changes in warm-spell duration and intensity.

Overall, these findings demonstrate that warm spells are not only intensifying but are increasingly compounding across seasons, emphasizing the value of magnitude-based frameworks for assessing escalating thermal stress under climate change.

How to cite: Liakakos, A., Hadjinicolaou, P., Tyrlis, E., and Zittis, G.: A new magnitude-based approach to detect year-round warm spells and their recent intensification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11101, https://doi.org/10.5194/egusphere-egu26-11101, 2026.

Under the accelerating pace of global warming, extreme heat events are becoming more frequent, intense, and prolonged, posing significant threats to public health and energy security. This study characterizes the evolution and physical mechanisms of "compound extreme heat", defined by the simultaneous occurrence of high ambient temperatures and high humidity, within the complex topographical and urbanized landscape of Taiwan.

By integrating high-resolution observational data with regional climate simulations, we identify the distinct spatiotemporal fingerprints of heatwaves across different geographical regions of the island. Our analysis reveals that the intensification of extreme heat is driven by a synergistic interaction between synoptic-scale circulation and local-scale surface processes. Specifically, the anomalous westward extension and intensification of the Western North Pacific Subtropical High (WNPSH) provide the necessary large-scale subsidence and clear-sky conditions. On a local scale, this is further exacerbated by the Urban Heat Island (UHI) effect and restricted sea-breeze penetration in basin terrains, leading to localized "hotspots" with significantly elevated wet-bulb temperatures.

Furthermore, this research assesses the changing risks associated with these compound events under various CMIP6 warming scenarios. We quantify the drivers of extreme heat through a budget analysis of the surface energy balance and atmospheric moisture, highlighting how land-atmosphere feedbacks amplify heat stress in rapidly growing metropolitan areas. The findings provide critical insights into the physical drivers of subtropical heat extremes and offer a scientific basis for developing region-specific adaptation strategies and early-warning systems for heat-related risks in a warming climate.

How to cite: Juang, J.-Y.: Characterizing the Drivers and Spatiotemporal Evolution of Compound Extreme Heat Events in Subtropical Island Environments: A Multi-Scale Analysis of Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11999, https://doi.org/10.5194/egusphere-egu26-11999, 2026.

Hot days on tropical land warm faster than the average day, and evidence is growing that this enhanced warming is due to hot days being dry. Mechanisms rooted in tropical atmospheric dynamics and thermodynamics explain how low near-surface air humidity tends to give rise to high surface temperatures. But what makes the air dry in the first place and how the underlying processes may be affected by a changing climate remain open questions, impeding reliable predictions of tropical heat extremes in a warming world.

Here we present a composite moisture budget analysis for hot days on tropical land based on 45 years of daily ERA5 reanalysis data. Using a statistical approach and sampling the hottest day per location and year, we investigate the contributions of horizontal and vertical moisture transports, evapotranspiration, and precipitation to the change in specific humidity during the run-up to hot days.

Preliminary findings based on the vertically-integrated moisture budget indicate that moisture anomalies develop over a characteristic time scale of three to four days prior to the hot day. However, counter to our initial expectations, these anomalies are not generally negative. Only in about half of all cases, the atmosphere undergoes net drying leading up to a hot day. We find that the sign of the moisture anomaly correlates with the background climate as measured by the aridity index: In moist regions, hot days tend to be dry while in arid regions, hot days tend to be moist. In both cases, the anomaly is explained by a regime shift of the horizontal moisture transport, from convergence to divergence in the case of dry anomalies, and from divergence to convergence in the case of moist anomalies.

We discuss the implications of these results for understanding humidity on tropical hot days in a warmer climate.

How to cite: Schmidt, L. and Byrne, M.: What controls humidity on hot days in the tropics?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12018, https://doi.org/10.5194/egusphere-egu26-12018, 2026.

With rising global temperatures, extreme heat is expected to become more frequent worldwide. Although less severe compared to other parts of Europe, heat extremes are increasingly affecting countries with relatively cooler climates, such as Norway. The heatwave in July 2025 highlighted Norway's limited preparedness for addressing this kind of risk. It is therefore of interest to investigate the extent to which heat extremes represent an emerging hazard in the country.

Using the recently updated national climate projections for Norway, we analyze both historical and future trends in heat extremes within regions defined by climatologically consistent temperature patterns. These climate projections are dynamically downscaled from global simulations, and bias-adjusted using observational data. To quantify the frequency and intensity of heat extremes at global warming levels, we apply a non-stationary Generalized Extreme Value (GEV) model. This approach offers the advantage of tying the global climate to the local changes in heat extremes. Our analysis focuses on the expected intensities of 100-year heat extremes at different levels of global warming and under different emission scenarios. Specifically, we evaluate projections of daily minimum and maximum temperatures and assess heat events of different durations (e.g., 1-, 3-, 5-, 7-, and 14-day events). This includes examining both daytime heat extremes (high maximum temperatures) and nighttime events (high minimum temperatures). The key research questions driving our study are: (1) Do heat extremes intensities differ at the same global warming levels under different emission scenarios, and (2) at what level of global warming do "severe" heat extremes begin to emerge?

Initial results show that, as expected, all observations and climate models show an increasing trend in the intensity of a 100-yr event with rising global temperatures. However, the rate of increase with global warming level differs markedly between climate models. Furthermore, there is a slight but not systematic difference between climate projections with RCP45 and SSP3-7.0 emission scenarios. Moreover, climate projections generally show a weaker trend compared to observations. The difference becomes more pronounced for longer duration events. Thus, future projections may underestimate the increase in the intensity of heat extremes in Norway.

How to cite: Skålevåg, A. and Ødemark, K.: Future heat extremes in Norway: An emerging hazard?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13604, https://doi.org/10.5194/egusphere-egu26-13604, 2026.

Extreme heat is among the deadliest meteorological hazards and poses an increasing threat to human health and socioeconomic systems, including labor productivity.

Here, we present global and regional projections of population exposure to extreme heat stress and associated labor productivity losses across a range of emissions scenarios from the Shared Socioeconomic Pathways (SSPs) and NGFS (Network for Greening the Financial System), which are widely employed in private and financial sector risk assessments.

Robust projections of heat stress impacts require climate data that accurately represent temperature and humidity conditions during the hottest hour of the day, when physiological strain typically peaks. To this end, our analysis builds on a newly derived global dataset of future changes in the Heat Index (HI), a widely used metric of human heat stress integrating the combined effects of temperature and humidity, with enhanced temporal accuracy. Sub-daily relative humidity during the hottest hour is reconstructed from ISIMIP3 daily near-surface specific humidity using a physically and statistically consistent correction framework, enabling a more realistic representation of peak heat stress than standard ISIMIP3 humidity output.

Our results reveal pronounced hotspots of intensifying heat stress and labor productivity losses in densely populated low-latitude regions, including South Asia and West Africa, that are strongly dependent on labor-intensive sectors. In these regions, most projected heat-related productivity losses could be avoided by limiting global warming to 1.5 °C.

How to cite: Langer, R., Schwind, N., Schleussner, C.-F., and Kornhuber, K.: Estimating global labor productivity losses from heat stress under a range of long-term climate scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14667, https://doi.org/10.5194/egusphere-egu26-14667, 2026.

The probability of record heat is increasing under climate warming; however, the factors controlling spatial and temporal variations in the probability of record breaking heat have not been systematically investigated. Here, we use large ensemble seasonal hindcast data for 1980-2024 to quantify how the probability of breaking records for the climatologically warmest month has evolved across different regions. We isolate the roles of the evolving record margin, background climate change, and climate variability in shaping the probability of breaking the current record. An observed record-breaking event reduces the likelihood of further records under a stationary climate, with larger record margins associated with larger decreases in record probability. Global warming has increased record probability; however, this effect varies significantly at regional scales with largest increases in Arabian Peninsula and smaller increases in Parts of Russia and Central Asia. El Nino events increase the probability of record warmth across the tropics by around 5% compared with during La Nina. The results demonstrate that the probability of record warm months is controlled by a combination of regional factors, including the evolution of existing records and externally forced warming, leading to a heterogenous distribution of current risk.

How to cite: Li, Z., Maycock, A., Birch, C., and Zhang, Y.: Likelihood of record breaking heat controlled by current record margin and spatial pattern of warming , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14834, https://doi.org/10.5194/egusphere-egu26-14834, 2026.

South Asia is a global hotspot of extreme heat and aerosol pollution. Observations and climate projections consistently show an intensification of heat extremes across the region, particularly during the pre-monsoon season when the most deadly heatwaves occur. At the same time, South Asia experiences persistently high aerosol loading due to rapid industrialisation and urbanisation, with sulphate representing the dominant anthropogenic scattering component of the regional aerosol burden. While reductions in aerosol emissions are crucial for improving air quality and public health, changes in aerosol concentrations can also modify extreme heat through direct radiative effects and indirect impacts on clouds and circulation.

Previous observational studies have explored links between aerosols and extreme heat in South Asia, but most rely on direct comparisons of their spatial patterns or long-term trends, providing limited evidence for a causal relationship. Only a few studies have directly examined correlations between aerosol optical depth (AOD) and temperature, often using absolute values that may introduce spurious relationships due to shared seasonality or long-term trends. Moreover, both reanalysis-based and modelling studies rarely distinguish between absorbing and scattering aerosol components, despite their fundamentally different radiative and thermodynamic effects. These limitations hinder a physically interpretable quantification of temperature sensitivity to aerosols.

Here, we combine reanalysis data with CMIP6 experiments to investigate how different aerosol components influence extreme heat in South Asia. Focusing on pre-monsoon daily maximum temperature (Tmax) as a proxy for extreme heat, we find that sulphate aerosols exert a robust cooling effect on extreme heat, particularly over major megacity regions along the Indo-Gangetic Plain. CMIP6 experiments indicate a typical sensitivity of -2 to -6 K per unit sulphate AOD in these densely populated regions, a signal that is broadly consistent in sign with reanalysis-based estimates but more spatially coherent in the model framework. This study provides the first component-specific assessment of aerosol impacts on extreme heat in South Asia.

How to cite: Meng, Y. and Matthews, T.: Aerosol components modulate pre-monsoon extreme heat in South Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15025, https://doi.org/10.5194/egusphere-egu26-15025, 2026.

The urban heat island effect, a direct consequence of urbanization, compounded by climate change impacts, poses challenges to population heat stress exposure. Coastal cities, which experience land-sea breezes, are facing additional complexities to address this exposure concern.

This study utilizes the high-resolution Weather Research and Forecasting model coupled with a single-layer Urban Canopy Model to assess the effects of two well-established heat mitigation strategies, i.e., cool roof and urban forestry, on temperature dynamics and population heat stress exposure. Two coastal cities in Western Canada, Vancouver and Victoria, are taken as a testbed. We analyze two climate change scenarios based on the Coupled Model Intercomparison Project Phase 6, covering near- and far-future projections under SSP2-4.5 and SSP5-8.5 pathways.

The results indicate that by the end of the century, the population's exposure to heat stress under SSP5-8.5 will be three times greater than under SSP2-4.5, the urban population growth being the main factor driving increased exposure. Increasing urban vegetation can help reduce urban heat islands and exposure, but planting more trees in already vegetated areas might not yield further cooling benefits and could worsen water management issues during droughts. Conversely, widespread use of reflective roofs or efficient solar panels provides stronger advantages by reducing temperatures both indoors and outdoors, but the extent of their impacts is limited.

This study shows that both heat mitigation strategies are insufficient to counter the projected impacts of climate change on daily temperature extremes, heat-stress days, and population exposure. This emphasizes the crucial necessity of reducing greenhouse gas emissions for lowering population exposure to heat stress rather than betting on climate adaptation strategies.

How to cite: Azargoshasbi, F. and Minet, L.: How effective are adaptation strategies in reducing climate change–induced urban heat stress exposure? A case study of cool roofs and urban forestry in Vancouver and Victoria., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15969, https://doi.org/10.5194/egusphere-egu26-15969, 2026.

High temperatures and heatwaves are among the most significant climate extremes, with well-documented effects on human health, including increased mortality rates. Heatwave impacts may arise from a single extreme variable, such as air temperature, or from compound conditions in which multiple variables jointly contribute to heat stress without all being individually extreme. In humid tropical regions, the co-occurrence of high temperature and humidity substantially amplifies physiological heat stress. India is a major global heatwave hotspot, particularly during the pre-monsoon season.

In this study, we identify and characterize compound heatwave events over Kerala, India, using a multi-variable framework that integrates air temperature and Wet-Bulb Globe Temperature (WBGT) a humidity-sensitive indicator of physiological heat stress. Heatwaves are classified into dry and humid categories based on percentile-based thresholds and the duration of event. Dry heatwaves are defined using the 90^th percentile and a minimum three-day duration of daily maximum air temperature from the India Meteorological Department, while humid heatwaves are identified using the 90^th percentile and three-day persistence of WBGT.

Our results indicate that compound heatwave events are increasingly frequent over Kerala, with heat stress intensifying as atmospheric humidity increases. These compound heatwaves impose a substantially higher heat stress burden than dry heatwaves, highlighting the limitations of temperature-only indices and highlighting the importance of incorporating humidity-sensitive metrics for improved heatwave monitoring, early warning, and risk assessment in humid tropical regions.

How to cite: Anoop, P. and Resmi, S.: Identification and Characterization of Compound Heatwaves in Kerala a Humid Tropical Region of India., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16426, https://doi.org/10.5194/egusphere-egu26-16426, 2026.

Heatwaves are driving rapidly increasing cooling demand, placing power systems under growing stress with implications for energy resilience in a changing climate. While recent advances in climate prediction have improved understanding of near-term climate variability, the seasonal-to-multiyear predictability of extreme cooling demand remains insufficiently explored. Here, we assess the prediction skill of heatwaves, cooling degree days (CDDs), and derived categories of cooling demand across Northern Hemisphere hotspots using initialized and uninitialized simulations from the Community Earth System Model version 2 Multiyear Prediction System (CESM2-MP) over the period 1981–2020. We evaluate predictability associated with externally forced signals and internal climate variability, with a focus on summer thermal extremes relevant to cooling demand. We find that externally forced signals provide robust seasonal-to-multiyear predictability of terrestrial heatwaves, enabling skillful forecasts of dry and humid CDDs and associated categories of elevated cooling demand at multi-year lead times. Predictive skill is strongest in regions including the US Southwest, Arabian Peninsula, Central America, and Southeast Asia, where forecasts reliably distinguish between below-normal, elevated, and extreme demand years. Internal climate variability, including ENSO-related signals, contributes additional but more limited predictability at approximately one-year lead time. Our results indicate emerging multi-year predictability of heatwave-driven cooling demand, highlighting the potential for climate-informed approaches to anticipate future demand extremes and support energy-system resilience and adaptation planning.

Key words: terrestrial heatwaves, cooling degree days, cooling demand, predictability, external forcing, CESM2-MP, climate extremes, climate risk, energy resilience, hotspots, urban applications, socio-economic and health impacts.

How to cite: Karwat, A., Lee, J.-Y., Kim, Y.-Y., Yun, J.-E., and Lee, S.-S.: Emerging Multi-year Predictability of Heatwave-Driven Cooling Demand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16623, https://doi.org/10.5194/egusphere-egu26-16623, 2026.

How to cite: Pfleiderer, P., Noyelle, R., and Sippel, S.: Increase in persistence and intensity of heat waves in hot summers due to the intensification of the seasonal cycle, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17599, https://doi.org/10.5194/egusphere-egu26-17599, 2026.

How to cite: Reddington, C., Birch, C., Kennedy-Asser, A., Doherty, R., Schurer, A., Sarran, C., Ireland, L., Jackson, L., Simpson, C., and Hajat, S.: Humid heatwaves in the UK: Meteorological drivers and health impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19454, https://doi.org/10.5194/egusphere-egu26-19454, 2026.

Extending on the complete radiation patterns of the bremsstrahlung process involving bremsstrahlung asymmetry and Doppler shift. The mathematical model is simplified, preserving the forward-backward peaking radiation properties and involved asymmetries to help model the tendency of rotation in the wavefront of the emitted wave. Results show that the curl of the gradient of the radiation intensity is non-zero, and the wavefront of the bremsstrahlung radiation follows a tapered spiral wavefront in 2D and a tapered helical wavefront in 3D. The radius of the backward rotational wavefront was found to decrease as the wave propagates. Spiral geometry has different magnitudes of radius as the wavefront rotates as a result of the involved bremsstrahlung and Doppler asymmetries. This is further supported diagrammatically by applying Huygen's principle on a relativistic radiation pattern. Outcomes describe why the lightning discharges display a partial temporal and spatial coherence, hence why lightning sferics are not known to produce structured wavefronts. Bremsstrahlung emissions start with a backward rotating and irregularly shrinking radius wavefront. Therefore, spatial coherency degrades as their tapered helical structure breaks down due to the irregular shrinkage of radius, leaving the bremsstrahlung radiation with partial temporal coherency. Rotation always starts from the shorter, bremsstrahlung symmetric lobe.

Momentum transfer from particle to rotating wavefront photon, quantized via conservation of momentum,  p_f - p_i = - ΔP_field , and ΔP_field = (n' - n) ℏ k = Δn ℏ k, hence p_f = p_i - Δn ℏ k  where p_i, and p_f are initial and final particle momentum. Hence, the relationship between bremsstrahlung asymmetry, R, as a function of the whole-number multiple of the quantum of action "n", R(n), is found. A whole multiple of the quantum of action "n" is tuned, until the correct scale of the graph, for the bremsstrahlung asymmetry quantity, R, matches the classical prediction describing the asymmetry in radiation lobes due to the particle's curved trajectory. This allowed predicting the whole number multiple of the quantum of action "n", which is n ≅ 6.3 × 10¹⁰, following the Bohr correspondence principle. Since tuning is performed with the parameter whole multiple of the quantum of action "n", which only comes with the photon orbital angular momentum, this gives the traits of a rotating wavefront.

Position vector, r, is a function of bremsstrahlung asymmetry, R, which does not include the Doppler shift in its formulation. The results demonstrate the discovery of the Doppler asymmetry within the equation that relates the bremsstrahlung asymmetry, R, to the multiples of the quantum of action, n. This indicates that the two asymmetries are related to each other, which is explained using the idea that once there is an asymmetry about one axis of symmetry of an object, automatically, there are asymmetries about the other remaining axes of symmetry of the same object. Unless the center of what is causing asymmetry does not lie exactly on the symmetry axes (Otherwise, everything would be symmetric again), which is not the case with Bremsstrahlung radiation patterns.  

How to cite: Yucemoz, M.: How and Why Do Lightning Sferics have Unstructured Wavefronts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-38, https://doi.org/10.5194/egusphere-egu26-38, 2026.

Potential gradient (PG) is measured continuously at Xanthi, NE Greece, since 2011, along meteorological variables and, in the last years, also particulate matter (PM). We present here a 15-yr climatology of PG at the measurement site. 1-min values up to +/- 34 kV/m were measured. 1-hr and mean daily maximum (minimum) values were 10 kV/m (-12 kV/m) and 6 kV/m (-2 kV/m), respectively. The highest mean values were encountered during the winter months. PG was influenced by the local meteorology, specific humidity having the largest impact on PG values. Additionally, PG was influenced by lightning activity within 50-km from the site, as well as aerosol levels. PG was exhibiting some anticorrelation with PMK2.5, especially during the cloud-free summer months. This probably means that one or more of the following apply for Xanthi: PM has low hygroscopicity, the size of PM is small, the presence of PM is correlated with high ion concentrations, as there is a relatively high radon flux at the site. An increase of 20 μg/m³ in PM_2.5 leads to a decrease of 100 V/m in PG. Regarding global events, it was observed that during two Sudden Stratospheric Warming (SSW) events, mean daily values of PG were consistently higher by what would be expected by the influence of local meteorology alone.

How to cite: Kourtidis, K., Karagioras, A., and Kosmadakis, I.: A 15-year climatology of Potential Gradient at a rural site in Southern Balkans, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1907, https://doi.org/10.5194/egusphere-egu26-1907, 2026.

Lightning activity is influenced by both aerosols and cloud microphysics, particularly ice formation and charge separation. While aerosols can greatly modify microphysical processes via cloud condensation nuclei (CCN), the global relationship between CCN loading and lightning remains unclear. In this study, we used global lightning stroke density, aerosol, and microphysics data to investigate how CCN can alter lightning through microphysical pathways across different regions. Our preliminary analysis reveals a robust CCN-lightning relationship, with lightning peaks at moderate CCN (400-600 cm^-3) and decreases at both lower and higher concentrations. A metric used to quantify the microphysical impact is called ‘glaciation ratio (GR)’, which is defined as the ratio between cloud-ice water path and the total water path. We identify distinct continental (high CCN, high GR) and marine (moderate CCN, moderate GR) regimes. Analysis of glaciation ratio shows synergistic effects: optimal lightning requires both appropriate CCN loading and efficient cloud glaciation. Our findings show that more aerosols do not always mean more lightning. However, the hypothesis proposed is that excess CCN diminishes convection through reduced droplet growth, while low CCN suppresses electrification due to the efficient warm-rain process. Our analysis shows that CCN impacts on the observed lightning activity are regime-dependent, with cloud glaciation playing a central role in determining whether CCN enhances or suppresses electrification.

How to cite: Waman, D., Nassar, A., and Hoose, C.: Regime-dependent Impacts of CCN and Cloud Glaciation on Global Lightning Activity , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3745, https://doi.org/10.5194/egusphere-egu26-3745, 2026.

Observations over recent decades show weak, and in some regions non-positive, indications of ozone recovery in the lower stratosphere, in contrast with the clearer recovery observed at higher altitudes. The processes contributing to this behaviour remain insufficiently constrained, particularly where variability is driven by episodic and spatially confined phenomena. Better constraining such processes is essential for a more complete understanding of the ongoing evolution of the ozone layer.

In this context, we investigate the potential influence of thunderstorm-related electrical discharges in the blue spectral range, also known as blue corona discharges, as a source of localized perturbations to lower-stratospheric ozone. These blue events with strong 337 nm emissions, detected by the Atmosphere Space Interactions Monitor (ASIM), are typically associated with vigorous convection and may generate reactive nitrogen and hydrogen species capable of modifying the local chemical environment.

We apply a co-location framework that combines ASIM detections with coincident limb-sounding ozone observations in the vicinity of convective systems exhibiting blue corona discharges. Initial case studies demonstrate the feasibility of this approach and reveal signatures consistent with localized ozone variability in the lower stratosphere.

Although the current number of events coincident with limb-sounding measurements does not yet permit statistically robust attribution, the results motivate the expansion of the event catalogue and the inclusion of additional observational constraints. Taken together, these findings highlight blue corona discharges as a potentially under-characterized process that may contribute to small-scale variability and regionally limited weaknesses in lower-stratospheric ozone recovery.

How to cite: Rose, K., Li, D., Chanrion, O., Neubert, T., Marisaldi, M., Gordillo-Vazquez, F. J., and Dekemper, E.: Investigating the Influence of Blue Corona Discharges on Lower-Stratospheric Ozone Variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3818, https://doi.org/10.5194/egusphere-egu26-3818, 2026.

How is lightning triggered on the microscale? Despite decades of research, this question remains unanswered [1]. In our experiment, we use optical tweezers to gain a better understanding of the microscale physics of electric charging and discharging by levitating individual SiO₂ particles in the micrometer size range and observing their charging and discharging dynamics over days-to-weeks time periods and with elementary-charge resolution. Our approach allows us to study these processes without losing information to ensemble averages or external interference from other particles or substrates [2]. Using two-photon absorption from the trapping laser [3] we can charge the trapped particle at different rates and to different values, observing every charging and discharging event along the way. This new approach lets us watch, in real time, how a micron-scale airborne particle gains and loses charge, observing its electric evolution all the way from the neutral state to the point where it undergoes electric discharge. By studying the charging behavior of the particle and the spontaneous discharges it experiences, we hope to contribute to a better understanding of the microphysical processes involved in lightning initiation and adjacent electrical phenomena in the atmosphere.

Figure 1: Time vs. electric charge curve of a single SiO₂ sphere (d = 0.69 µm) showing spontaneous discharges.

This project has received funding from the European Research Council (ERC) under the European Union’s Starting Grant (A. Stoellner, I.C.D. Lenton & S.R. Waitukaitis received funding from ERC No. 949120, C. Muller received funding from ERC No. 805041).

[1] Dwyer, J. R., & Uman, M. A. (2014), Physics Reports, 534(4), 147–241.
[2] Ricci, F. et al. (2019), Nano Letters 19, 6711.
[3] Stoellner, A. et al. (2025), Phys. Rev. Lett. 135(21), 218202.

How to cite: Stoellner, A., Lenton, I., Muller, C., and Waitukaitis, S.: Measuring spontaneous discharges of individual aerosol particles with optical tweezers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5136, https://doi.org/10.5194/egusphere-egu26-5136, 2026.

Detection of lightning discharges on Jupiter and the estimation of their energy have been the subject of numerous studies using data from variety of spacecraft and probe instruments, operating mostly in the optical range. Individual datasets, however, report markedly different numbers of detected events and characteristic energies, largely due to differences in sensitivity, accumulation time and spatial coverage of individual instruments.

To provide a more unified view of optical lightning observations made by Voyager 1&2, Galileo, Cassini, New Horizons and Juno SRU, we use lightning density evaluated on the visible surface as a common metric. By dividing the energy range into logarithmically spaced bins, we compute the lightning density within each interval. This approach enables a direct comparison between instruments with different sensitivities and reveals a consistent log-normal distribution of lightning energies across multiple datasets.

Detections of lightning-generated whistlers on Jupiter by the Juno mission are substantially more prevalent than all previous optical detections. Unlike optical observations, the sensitivity of radio measurements is not constant. It varies by several orders of magnitude depending on the spacecraft’s position and local plasma conditions, complicating detection statistics.

We introduce also a method to estimate the minimum detectable whistler energy in individual Juno Waves LFR-Lo snapshots. The method is based on modeling the background incoherent noise, including both instrumental and natural contributions. Artificial whistler waveforms with known properties are injected into the modeled noise to test detectability and to evaluate the performance of the Poynting vector measurement.

By this approach, we are able to compare lightning density as a function of energy for both optical and radio wavelengths.

How to cite: Rosicka, K., Santolík, O., Kolmašová, I., and Imai, M.: Surface density of lightning discharges on Jupiter as a function of their energy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5138, https://doi.org/10.5194/egusphere-egu26-5138, 2026.

Lightning plays a critical role in the Earth system by shaping biogeochemical cycles, while also posing significant natural hazards and serving as a key geophysical indicator for storm monitoring and wildfire early warning. However, existing publicly available global lightning datasets are often limited in either spatial or temporal resolution and do not distinguish between intra-cloud (IC) and cloud-to-ground (CG) lightning, restricting their applicability for many scientific studies. Here, we present a newly developed global gridded lightning dataset, the Flash Location Aggregation from Strokes into a High-resolution Multi-scale Analysis Product (FLASHMAP). FLASHMAP is derived from lightning observations provided by Vaisala’s Global Lightning Detection Network (GLD360) and currently covers the period from 2019 to 2024. A gridding framework is applied to convert point-based lightning stroke detections into multi-scale products at 0.1° hourly, 0.25° daily, and 0.5° monthly resolutions. FLASHMAP provides comprehensive lightning characteristics, including counts of IC and CG strokes and flashes, stroke location uncertainty and peak current, and flash multiplicity. FLASHMAP can report more total lightning strokes than existing global lightning products in most of the land regions. Comparisons with regional lightning detection networks in Alaska (USA), Spain, and New South Wales and the Australian Capital Territory (Australia) indicate that FLASHMAP reports comparable CG stroke counts while detecting fewer IC strokes. FLASHMAP is expected to advance interdisciplinary research on global and regional lightning climatology, lightning-ignited wildfires, thunderstorm identification, and ecosystem impacts.

How to cite: Qu, Y., Brambleby, E., Janssen, T., Moris, J., Hunt, H., Joshi, M., Mataveli, G., Pérez-Invernón, F., Said, R., Yebra, M., Zhao, L., Jones, M., and Veraverbeke, S.: FLASHMAP: A new global gridded lightning dataset with high spatial and temporal resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5328, https://doi.org/10.5194/egusphere-egu26-5328, 2026.

After more than three decades of research on transient luminous events (TLEs), typical electrical and dynamical properties of the thunderstorms responsible for their production are still not completely understood. The reliability of prediction when and where TLEs occur is very limited, as numerous case studies focus only on individual TLE-producing storms.

To contribute to these efforts, we analyze 34 TLE-producing storms observed between 2018 and 2020 in Central Europe, each generating at least ten TLEs, specifically sprites and halos. Using products from the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF), we follow the full life cycle of each storm, from initiation to dissipation, defining storm boundaries by the presence of very high opaque clouds. Lightning activity and its temporal evolution are derived from LINET lightning detections within the identified storm boundaries. Cloud-top temperature and cloud-top height products are used to relate TLE occurrences to the convective structure of the storm. Statistical distributions of these parameters are compiled at TLE locations.

We show that TLEs statistically appear after the peak of cloud-to-ground lightning activity and at preferred locations relative to storm evolution. Rather than being distributed uniformly over the storm, TLEs are spatially confined to relatively small regions, forming clusters with typical horizontal dimensions of approximately 0.5° × 0.5° in geographic lat–lon coordinates. These regions exhibit persistence in time, as repeated TLE occurrences are frequently observed within the same localized areas of the storm, separated by up to several tens of minutes. Such preferred regions are most commonly located near the convective core, within the stratiform region, and above areas of former convective activity. Additionally, we classify the analyzed storms by area and morphological characteristics, providing insight into the storm structures most favorable for TLE production.

How to cite: Barotová, K., Kolmašová, I., Pišoft, P., and Popek, M.: Meteorological and Lightning Characteristics of Thunderstorms Producing Transient Luminous Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6296, https://doi.org/10.5194/egusphere-egu26-6296, 2026.

Although strong electric fields have been observed in lightning clouds, these fields are well below the limit to spontaneously initiate a spark that could be the beginning of a lightning flash. Understanding the lightning initiation process is thus one of (if not The) main topics in lightning research.

In this work we present very high frequency (VHF) radio observations using the LOFAR radio telescope [1].  Because of the high resolution and high sensitivity of LOFAR we could observe the faint initiating event for multiple lightning flashes.  The new imaging procedure (called ATRI-D) was shown to be able to distinguish different emission sites of VHF pulses on an airplane flying at an altitude of 8 km [2].

The propagating tip of this apparent initiating event carries positive charge, as is generally expected. Our observations show that the propagation speeds of this positive initiating event (PIE) are very similar at about 5 x 10^6 m/s. Very surprisingly, both the e-folding rates in VHF-intensity and peak intensities differ significantly for the investigated flashes and show no correlation with altitude. Additionally, these structures are extremely narrow, with diameters under 0.8 meters, and maintain this confinement over propagation distances exceeding 100 meters. Even more surprising is that subsequent dart leaders do not follow the path of the PIE, implying that the PIE has not formed a well-conducting structure and does not transform into a positive leader.

Lightning initiation is shown to be a very subtle process, in spite of the vigor of a lightning flash, and the high resolution and sensitivity of LOFAR shows, for multiple lightning flashes, that the initiating event is a very weakly radiating, positively charged propagating structure.

1) Olaf Scholten, Steven A. Cummer, Joseph R Dwyer, et al.; A Comprehensive analysis of High Resolution VHF Observations with LOFAR of the Positive Initiating Event for Several Lightning Flashes. ESS Open Archive . December 12, 2025. https://doi.org/10.22541/essoar.176556304.42772793/v1

2) O. Scholten, M. Lourens, et al. (2025) ; Measuring location and properties of very high frequency sources emitted from an aircraft flying through high clouds. Nature Communications, 16 (1), 10572. https://doi.org/10.1038/s41467-025-65667-2

How to cite: Scholten, O., Cummer, S., Dwyer, J., Hare, B., Liu, N., Lourens, M., Nelles, A., Sterpka, C., Turekova, P., and Wu, B.: A Comprehensive analysis of lightning Initiation with LOFAR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6448, https://doi.org/10.5194/egusphere-egu26-6448, 2026.

The UHU experiment (uhu.epss.hu) was conducted on the International Space Station (ISS) from 26 June to 14 July, 2025 to observe lightning activity and transient luminous events (TLEs) from space using a commercial color video camera. Several months before the mission, a call was issued seeking contribution to this experiment in the form of ground-based optical observations. One aim of the supporting ground campaign was to increase the chance of capturing one or more TLE from space and from the ground simultaneously, and use the respective images to quantify the effect of different propagation through the atmosphere on the recorded color and brightness distribution of the events. Although simultaneous observation of TLEs was not achieved eventually during the campaign, the attempt showed the currently already significant potential of the community of observers in supporting scientific missions targeting optical observations on a global scope. The result that TLEs were observed by contributors above thunderstorms which were also marked for the astronauts on the ISS as possible targets, validates the concept of the open call. The campaign has also provided useful experience that can be utilized in similar calls in the future to further increase the effectiveness of such activities and the scientific value of the collected observations. In this contribution, the preliminary results of the UHU ground-based optical observation campaign are summarised and the gained experiences are presented.

How to cite: Bór, J. and Yair, Y.: The contribution of citizen observers to the UHU lightning and TLE observation campaign on the International Space Station in 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6579, https://doi.org/10.5194/egusphere-egu26-6579, 2026.

Schumann resonances (SR) correspond to the around-the-globe eigenmodes of the thin spherical shell bounded by the Earth’s surface and the lower ionosphere. This system forms a waveguide for extremely low frequency (ELF, 3 Hz - 5 kHz) electromagnetic waves. The SR modes are primarily excited by the quasi continuous lightning activity worldwide. The lowest SR modes are at ~7.8 Hz, ~14.1 Hz, ~20 Hz. The actual peak frequencies and amplitudes of the spectrum depend on both the distribution and intensity of the global thunderstorm activity. SR parameters also carry information on the electrical state of the boundary layers of the waveguide and so they are capable of indicating significant and extensive changes in the vertical profile of the atmospheric electric conductivity. ELF-band spectra of the horizontal magnetic and vertical electric field components are the most suitable for studying these dependencies, but only if the ambient noise does not mask the otherwise rather weak SR signal. In this contribution, a methodology is introduced to determine the signal to noise ratio (SNR) near the low end of the ELF-band that includes the most often detectable lowest SR modes. The concept is based on fitting a model SR spectrum to the measured one and so separating the SR signal from the other components considered further as noise. This approach is demonstrated on the time series recorded at the ELF-band monitoring sites of the HUN-REN Institute of Earth Science and Space Research in the Széchenyi István Geophysical Observatory near Nagycenk Hungary (NCK, 16.72 E, 47.63 N) and in the Jeli Arboretum near Kám, Hungary (JAR, 16.89 E, 47.08 N). The same analysis can be made on any similar record. Practical aspects of setting up an empirical threshold in the SNR to exclude or include data in SR-based studies are discussed in the light of the presented experiences.

How to cite: Reichardt, A. B., Atta, J., and Bór, J.: Towards quantifying the suitability of ELF-band radio observations for Schumann-resonance research, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6764, https://doi.org/10.5194/egusphere-egu26-6764, 2026.

Predicted by Wilson in the 1920s, thunderstorms act as natural particle accelerators. Charged particles, mainly electrons, can be energized by the strong electric fields inside thunderclouds, becoming runaway electrons that reach relativistic energies. During this acceleration, these relativistic electrons produce secondary electrons through atmospheric ionization, leading to a Relativistic Runaway Electron Avalanche (RREA) while emitting X-rays through bremsstrahlung. This mechanism underlies the high-energy atmospheric phenomena generated by thunderstorms, such as terrestrial gamma-ray flashes (TGFs), flickering gamma-ray flashes (FGFs), and gamma-ray glows (GRGs).

GRGs are long-lasting X-ray emissions produced by sustained RREAs, typically lasting from seconds to tens of minutes. They are usually observed close to their sources either by aircraft, high-altitude sites located on mountain, or from the western coast of Japan where thunderclouds frequently develop near sea level.

Since 2023, we are conducting a ground-based observational campaign by equipping several strategic sites to detect these high-energy events and study their occurrence and characteristics. Three sites were instrumented with scintillators: Chofu (Tokyo, Japan), Pic du Midi de Bigorre (French Pyrenees), and Normandy (France).

In this presentation, we introduce a new statistical method designed to detect GRGs and potentially TGFs and FGFs. The method combines Gaussian filtering, continuous wavelet transforms, and Bayesian inference. It enabled the detection of more than ten GRGs at Pic du Midi between April and November 2025, as well as two GRGs at sea level in Chofu and Normandy demonstrating the method’s efficiency and showing that GRGs are common and associated with all thunderstorms.

How to cite: Hazem, Y., Celestin, S., Trompier, F., Hobara, Y., and Defer, E.: Gamma-Ray Glows: A Common Signature of Thunderstorms ?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7355, https://doi.org/10.5194/egusphere-egu26-7355, 2026.

In recent years, our knowledge of turbulence statistics inside cumulus, stratiform, and stratocumulus clouds is more complete thanks to a number of measurement campaigns and subsequent detailed analyses of collected data. However, similar cannot be stated for cumulonimbus clouds. This is primarily because very few measurements have been performed, majorly owing to safety issues amidst harsh atmospheric conditions. At the same time, even though the extent of electrification is present in all kinds of clouds, cumulonimbus clouds are particularly significant because of the final result of electrification, in the form of lightning. Thus the necessity to understand the evolution of electric field in such conditions is highly crucial. In this study, we use data from the campaign, Severe Thunderstorm Electrification and Precipitation Study (STEPS), performed in the May of 2000 in Kansas, USA. The campaign consisted of aircraft penetrations into the mature thunderstorm cloud and several balloon soundings. This involved in-situ measurements of electric field, vertical velocity, liquid water content, etc. 

Charges inside the clouds are under constant motion, owing to convective motions such as updraft and downdraft. This causes them to be scattered around in various regions of the clouds and form clusters depending on how turbulent these regions are. In our study, we  compared the evolution of turbulence and electric field inside the clouds. Our results show negative correlation between the turbulent kinetic energy dissipation rates and modules of the electric field vector, which suggests the growth of electric field in regions of weak turbulence and vice versa. This could mean that larger charges exists in those regions where turbulence is on the verge of decay or it is in the process of development. Vice versa, the presence of strong turbulence destroys the charges clusters. We also investigate the intermittency, which is a notable indicator for turbulent fields.  Specifically, we calculated the probability density functions of electric field differences at two points. For small differences those functions are clearly non-Gaussian, with long stretched tails and conical tip, which is a very typical picture for intermittency. For larger lags, the distributions are closer to gaussian, thereby signifying a homogenous arrangement of charges. 

How to cite: Sarkar, J., Wacławczyk, M., and Malinowski, S.: Study of electric fields and turbulence in thunderstorm clouds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7491, https://doi.org/10.5194/egusphere-egu26-7491, 2026.

Lightning serves as a fundamental indicator of convective intensity and an important mediator of atmospheric dynamics. Accurately modeling the potential for lightning occurrence is essential for understanding storm electrification and improving short-range forecasting. The Lightning Potential Index (LPI) is a physically based diagnostic parameter that quantifies the potential for charge generation within convective clouds. It combines model-resolved updraft velocity and precipitating ice content, thereby directly representing the mechanisms responsible for non-inductive charging (Yair et al., 2010). In contrast, thermodynamic indices such as the K-Index (KI) and Convective Available Potential Energy (CAPE) reflect the environmental instability and likelihood of convection, but lack an explicit representation of microphysical electrification processes (Peppler, 1988). Additionally, accumulated precipitation serves as a proxy for the integrated intensity of the storm systems. In this study, we evaluate the skill of this suite of atmospheric predictors - meaning LPI, KI, CAPE, and precipitation – all computed from WRF ensemble simulations, in reproducing observed lightning activity over the Eastern Mediterranean. Five case studies were selected, representing different synoptic conditions in winter. A comprehensive processing pipeline was developed to co-register model outputs and ground-based lightning detections from the ENTLN network onto a uniform 4 × 4 km grid and 3-hour temporal intervals. Spatially, all parameters were averaged per grid cell. Temporally, precipitation was summed, while other variables (LPI, KI, CAPE) were averaged over each period. All datasets were smoothed with a Gaussian kernel to reduce spatial noise and enable direct comparison across domains. Preliminary analyses indicate that thermodynamic indices and accumulated precipitation exhibit broad spatial footprints, significantly overestimating the areal extent of lightning activity. While LPI also displays a tendency towards broader coverage than observed, it demonstrates the highest degree of spatial localization among the examined parameters. To further quantify predictive skill, we employ a machine learning approach based on Random Forest algorithm. The spatial model matrices are decomposed into discrete single-cell vectors, utilizing the full suite of parameters. These features are used to classify the binary occurrence of lightning (presence/absence), independent of flash multiplicity, establishing a robust data-driven mapping between storm microphysics and lightning probability.

References

• Y, B. Lynn, C. Price, V. Kotroni, K. Lagouvardos, E. Morin, A. Mugnai, and M. d. C. Llasat (2010), Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields, J. Geophys. Res., 115, D04205, doi:10.1029/2008JD010868.
• Peppler, R. A. (1988). A review of static stability indices and related thermodynamic parameters. ISWS Miscellaneous Publication MP-104.‏

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How to cite: Yair, Y., Pitlik, K., Price, C., Korzets, M., Lerman, C., Alisse, J., Lynn, B., and Galili, B.: Predicting Eastern Mediterranean lightning: evaluating microphysical and thermodynamic indices using a machine learning approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8984, https://doi.org/10.5194/egusphere-egu26-8984, 2026.

We introduce the Lightning Differential Space (LDS) framework for multiscale, data-driven characterization of cloud-to-ground (CG) lightning, in which consecutive stroke intervals are mapped into a two-dimensional space spanned by their spatial and temporal derivatives. Using Earth Networks Total Lightning Network (ENTLN) observations, we analyze CG strokes during peak lightning seasons (2020–2021) across three climatically distinct regions: the Amazon (tropics), the Eastern Mediterranean Sea (subtropics), and the northern U.S. Great Plains (mid-latitudes).

The LDS topography reveals a robust and regionally consistent “allowed” and “forbidden” zones, with dominant clusters separating intra-flash successive strokes from inter-flash intervals at thundercloud and cloud-system scales. While the overall structure is stable across regions, systematic shifts in cluster location and separability reflect contrasting convective environments, including differences in characteristic inter-event times and system-scale distances.

We further introduce a Current Ratio LDS, which projects the ratio of absolute peak currents between successive strokes onto the same stroke interval coordinates. This diagnostic acts as a statistical partitioning tool that sharply distinguishes intervals likely to contain flash-initiating strokes (where the succeeding stroke tends to be stronger) from intervals dominated by subsequent strokes within multi-stroke flashes. Across all regions, a distinct short time interval feature (< ~0.02 s) spans distances from sub-kilometer to hundreds of kilometers, suggesting rare near-simultaneous remote CG events and motivating renewed investigation of long-range thunderstorm coupling (teleconnection).

Overall, the LDS framework (combining number distribution and current ratio information) provides a scalable pathway for extracting coherent multiscale lightning behavior from large network datasets, with direct relevance for evaluating model representations of stroke and flash processes and for developing diagnostics supporting probabilistic monitoring and nowcasting.

How to cite: Altaratz, O., Ben Ami, Y., Yair, Y., and Koren, I.: Diagnosing stroke, flash, and storm scale lightning variability using Lightning Differential Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9083, https://doi.org/10.5194/egusphere-egu26-9083, 2026.

LOFAR has been used to image lightning initiation, leader stepping, dart leaders, needles, and more
with sub-meter resolution and high sensitivity. Over the last few years LOFAR has been almost
completely rebuilt from the ground-up into LOFAR2.0. Apart from the physical antennas, nearly all of
the analog and digital processing chains have been completely replaced and upgraded. In addition to
greater bit-depth and better amplifiers, a new automatic white-rabbit based time calibration will allow
for easier and faster data processing. Combined with a faster network that allows for less down-time,
more lightning flashes per thunderstorm can be observed and mapped with high precision. LOFAR2.0
will also have triple the number of processing pipelines, thus allowing for observing simultaneously
with both the low-band antennas (10-90 MHz) and the high-band antennas (110-240 MHz). The higher
frequencies will allow for significantly higher resolution, perhaps even allowing for the resolving of the
sub-meter widths of streamer bursts during lightning initiation. This poster will discuss some of the
new and still-planned upgrades to LOFAR system, as well as our various imaging techniques such as
our impulsive imager and near-field beamforming (TRI-D and ATRI-D).

How to cite: Hare, B., Cummer, S., Dwyer, J., Liu, N., Lourens, M., Scholten, O., Sterpka, C., Turekova, P., Wu, B., and Nl, A.: Using the new LOFAR2.0 upgrade for lightning imaging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9296, https://doi.org/10.5194/egusphere-egu26-9296, 2026.

The polarization of VHF radio emissions from lightning offers valuable insight into the complex physics of lightning propagation by revealing the orientation of streamer-driven VHF radiation. Measuring and interpreting this polarization, however, remains challenging. In this work, we use the LOFAR radio telescope in combination with the latest near-field beamforming technique (A-TRID) that coherently combines antenna voltages while incorporating the full antenna response. This approach enables three-dimensional reconstruction of both the location and polarization of VHF lightning sources. In this presentation, we assess the accuracy of these results by means of a Monte Carlo error analysis. We simulate antenna voltage signals produced by a point-like dipole and an extended source, a cluster of indentical dipole emitters. Subsequently, we reconstruct them using the imaging algorithm. By comparing the reconstructed source parameters with the known inputs, we obtain an estimate of the location and polarization uncertainties. For point sources, we observe a sub-meter reconstruction accuracy in three-dimensional location; and an average one-degree reconstruction accuracy in three-dimensional polarization. These values vary with the source location and with the angle between the polarization vector and the radial vector. For extended sources, we see the reconstructed location (the source size) is smaller than the input; by up to a factor of two. The polarization reconstruction accuracy is different along the two axes; a sub-degree reconstruction accuracy along the azimuthal direction and an average 7.5-degree reconstruction accuracy along the zenithal direction. This report offers a comprehensive evaluation of the results, alongside a breakdown of our technical approach and algorithmic framework.

How to cite: Turekova, P., Hare, B., Scholten, O., Lourens, M., Sterpka, C., Cummer, S., Dwyer, J., and Liu, N.: How Accurately Does LOFAR Reconstruct Lightning? Point and Extended Source Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9545, https://doi.org/10.5194/egusphere-egu26-9545, 2026.

Thunderclouds are the largest natural gamma-ray laboratories on Earth, producing a large variety of gamma-ray phenomena of different shape, duration, and intensity. In our previous parametric study of a 0.5D fluid model of relativistic runaway electrons (RRE) in a thundercloud high-field region [1] – based on relativistic feedback discharge (RFD) theory [2] – we systematically reproduced the entire zoo of thundercloud gamma-ray signals, including the flickering gamma-ray flashes (FGFs) as detected by ALOFT [3], indicating that RFD may potentially play a significant role in these phenomena.

Here we present a new relativistic fluid model based on the same principles but expanded to include the non-uniformity along the vertical axis, allowing us to explore the effects of more realistic space charge distributions as well as simulating and comparing hard radiation signals from high-field regions with both negative and positive polarities. In addition to solving continuity equations for RRE and ions (as the 0.5D model did), this model also includes equations for positrons as well as upward- and downward-propagating photons, making it possible to estimate the flux of positrons compared to electrons as well as mimicking gamma-ray light-curves directly from the simulated photon density. While the 0.5D model provides excellent qualitative results regarding hard-radiation produced with (or without) the help of RFD, we expect this new model to give better quantitative results, for instance a better idea regarding the minimum charge layer separation distance needed to reproduce ALOFT’s FGFs. With this model, we should also be capable of forward-modelling radio and optical signals, which will make it easier to distinguish (multi-pulse) terrestrial gamma-ray flashes (TGFs) from FGFs. That could ultimately also give us a better insight into whether TGFs could be produced solely by RFD.

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[1] Ø. H. Færder, N. Lehtinen, D. Sarria, M. Marisaldi, N. Østgaard, I. Bjørge-Engeland, and A. Mezentsev. Numerical parameter-space studies of various types of thundercloud gamma-ray emissions. ESS Open Archive eprints, 776:essoar.175578737, Aug. 2025. doi:10.22541/essoar.175578737.77602064/v2.

[2] Dwyer, J. R., “Relativistic breakdown in planetary atmospheres,” Physics of Plasmas, vol. 14, no. 4, p. 042901 (2007).

[3] Østgaard, N., Mezentsev, A., Marisaldi, M., Grove, J. E., Quick, M., Christian, H., Cummer, S., Pazos, M., Pu, Y., Stanley, M., et al., “Flickering gamma-ray flashes, the missing link between gamma glows and TGFs”, Nature (2023). 

How to cite: Færder, Ø. H., Lehtinen, N., Sarria, D., Marisaldi, M., and Østgaard, N.: A relativistic fluid model for reproducing thundercloud hard radiation including ALOFT’s flickering gamma-ray flashes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9916, https://doi.org/10.5194/egusphere-egu26-9916, 2026.

Gamma-ray glows are persistent (seconds to minutes) gamma-ray emissions from thunderclouds associated to intense large-scale electric fields. Results from the ALOFT flight campaign in 2023 over Florida and the Gulf of Mexico [1,2] have shown that tropical thunderclouds can glow in gamma-rays for hours and over thousands of square kilometres, pointing at particle acceleration as a fundamental and ubiquitous phenomenon in thundercloud electrodynamics, likewise cloud electrification and lightning discharge. Moreover, ALOFT measurements evidence a significant intrinsic time variability of gamma-ray glows, likely matching the dynamics of large scale thundercloud electric fields. Despite earlier attempts, there is no direct measurement of gamma-ray glow spatial extent. With the ENLIGHTEN project we have the ambition to measure directly the spatial extent of glows in gamma-rays and their spatio-temporal evolution. We will use diverse gamma-ray imaging systems hosted onboard a high-altitude aircraft from NASA flying over active thunderclouds. The ENLIGHTEN flight campaign is currently scheduled for July 2028. Here we present the preliminary design of the gamma-ray imagers and their expected performance based on Monte Carlo simulations informed by the ALOFT gamma-ray glow measurements.

[1] Lang, T. J., et al., 2025: Hunting for Gamma Rays above Thunderstorms: The ALOFT Campaign. Bull. Amer. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-24-0060.1.

[2] Marisaldi, M., et al., 2024: Highly dynamic gamma-ray emissions are common in tropical thunderclouds. Nature 634, https://doi.org:10.1038/s41586-024-07936-6

How to cite: Marisaldi, M., Sarria, D., Grove, E., Shy, D., Mezentsev, A., Lehtinen, N., Østgaard, N., and Lang, T.: Imaging gamma-ray glows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10184, https://doi.org/10.5194/egusphere-egu26-10184, 2026.

In this study we present, to the best of our knowledge, the first three-dimensional (3D) reconstruction of an upward lightning flash using combined very high frequency (VHF) interferometric and high-speed camera (HSC) observations. Based on this reconstruction, we estimate the 3D velocities of different pulse fronts along the channel branches. Comparable 3D reconstructions have previously been reported for downward lightning flashes [1].

The Mt. Säntis Lightning Research Facility [2], located in the Appenzell region of Eastern Switzerland, features an electric field and current measurement system, as well as a Phantom HSC situated at a distance of 5 km from the namesake mountaintop tower. Additionally, during the summer 2021 experimental campaign, a VHF interferometer (IFM) belonging to New Mexico Tech was installed at the base of Mt. Säntis, 2 km away from the tower. The HSC operated at 24,000 fps and the IFM at 200 MS/s, corresponding to respective time resolutions of 42 μs and 5 ns. The spatial resolutions of the HSC and IFM were 512 x 512 pixels and 0.1°, respectively, both corresponding to ~3 m at the location of the tower tip. These two instruments were used in combination to reconstruct in three dimensions the bottom ~600 m of an upward negative flash that initiated from the Säntis Tower on July 30, 2021, at 15:38:10 UTC. This particular flash featured numerous “mixed-mode” pulses superimposed on the initial continuous current (ICC), in addition to the standard dart leader–return stroke sequences, identified as such from their current and E-field waveforms. The ICC pulses propagated downward along 4+ different visible branches; altitude change rates averaged -5.6 ± 2.0 x 10⁶ m/s and were observed to decrease slightly as the pulse fronts approached the strike point. 3D speeds of ~2 x 10⁷  m/s were observed, punctuated by spikes (spaced on the order of 10 μs apart) at times exceeding 1e8 m/s, indicative of step-like behaviour. Such an analysis of ICC pulse velocities is heretofore absent in the literature and lends itself to an improved understanding of leader dynamics and charge transfer mechanisms in upward lightning.

References:

[1] Li, Y., Qiu, S., Shi, L., Huang, Z., Wang, T., Duan, Y., 2017. Three‐Dimensional Reconstruction of Cloud‐to‐Ground Lightning Using High‐Speed Video and VHF Broadband Interferometer. JGR Atmospheres 122. https://doi.org/10.1002/2017JD027214

[2] Rachidi, F., Rubinstein, M., 2022. Säntis lightning research facility: a summary of the first ten years and future outlook. Elektrotech. Inftech. 139, 379–394. https://doi.org/10.1007/s00502-022-01031-2

How to cite: Oregel-Chaumont, T., Kasparian, J., Stanley, M., Rison, W., Šunjerga, A., Rubinstein, M., and Rachidi, F.: Combining VHF and optical observations to reconstruct an upward flash, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10290, https://doi.org/10.5194/egusphere-egu26-10290, 2026.

The Pierre Auger Observatory has detected downward terrestrial gamma-ray flashes (TGFs) with its water-Cherenkov detectors. Understanding this high-energy radiation occurring during thunderstorms requires combining such measurements with observations of lightning processes in their earliest stages. To meet this challenge, the Broadband Observatory of Lightning and TGFs (BOLT) is currently under construction to image lightning propagation in three dimensions with high time resolution using radio interferometry, extending the unique multi-detector capabilities of the Pierre Auger Observatory. 

BOLT is based on eleven modified Auger Engineering Radio Array (AERA) stations operating in the 30–80 MHz bandwidth and deployed at strategic locations within the Auger array. While the AERA stations by themselves already provide the necessary spatial and timing resolution, a key modification for BOLT is the implementation of a long buffer readout. This capability enables the reconstruction of lightning development and the correlation of radio emissions with TGF-related signals observed by the Observatory’s water-Cherenkov detectors.

This contribution presents recent hardware developments, including the long buffer readout, progress toward selective triggering and precision timing, and first field data analyzed using insights from previous AERA measurements, illustrating the growing capability of BOLT for combined lightning and TGF studies. Together with the existing detector systems of the Pierre Auger Observatory, BOLT establishes a powerful experimental framework for advancing our understanding of lightning physics and associated high-energy atmospheric phenomena.

How to cite: Weitz, M. J. and the Pierre Auger Collaboration: BOLT: Imaging Lightning and Terrestrial Gamma-ray Flashes at the Pierre Auger Observatory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10336, https://doi.org/10.5194/egusphere-egu26-10336, 2026.

In this work, we use lightning observations obtained by the LOFAR radio telescope to investigate the propagation dynamics of High Altitude Negative Leaders (HANLs), which have altitudes above 7 km. Operating in the very high frequency (VHF) range, LOFAR can probe lightning processes
occurring deep within the cloud at high altitudes with sub-meter precision and 100 ns integration times [2].

HANLs exhibit step lengths exceeding 100 m, an order of magnitude larger than those of negative leaders observed at lower altitudes [1]. The plasma processes underlying these HANL steps remain unknown, and it is unclear whether HANLs propagate through the same mechanism as lower altitude negative leaders. To study these structures with enhanced precision and sensitivity, we apply ATRI-D, a near-field interferometric beamforming algorithm, to LOFAR data.

Our observations reveal that the dynamics of HANL steps is increasingly complex at smaller scales. At large scales (kilometers and tens of milliseconds), HANL propagation appears as a sequence of discrete corona flashes. In contrast, on smaller scales (tens of meters and milliseconds), these “corona flashes” resolve into several branched networks of filaments that initiate at different times and locations. In addition, we find that each branched network begins with an intense VHF pulse occurring within 10 m of a previously formed filament. We will discuss some of the potential physics implications of these results.

[1] O. Scholten et al.; Distinguishing features of high altitude negative leaders as observed with LOFAR. Atmospheric Research, 260:105688, October 2021. ISSN 0169-8095. doi: 10.1016/j.atmosres.2021.105688.
[2] O. Scholten, M. Lourens et al.; Measuring location and properties of very high frequency sources emitted from an aircraft flying through high clouds. Nature Communications, 16(1), November 2025. ISSN 2041-1723. doi: 10.1038/s41467-025-65667-2.

How to cite: Lourens, M., Hare, B., Scholten, O., Sterpka, C., Turekova, P., Wu, B., Cummer, S., Dwyer, J., and Liu, N.: Fine-Scale Structure of High-Altitude Negative Leader Steps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11132, https://doi.org/10.5194/egusphere-egu26-11132, 2026.

Lightning is an important disturbance process in forest ecosystems, affecting trees both directly—when a strike kills a tree—and indirectly by igniting wildfires. While lightning–fire interactions are widely studied, direct lightning-induced tree mortality is not represented in global Earth System Models, limiting our ability to assess the full impact of lightning on forests under a changing climate.

To address this gap, we implement lightning-induced tree mortality in the dynamic global vegetation model LPJ-GUESS, using field-derived relationships from a Panamanian forest where lightning mortality has been systematically quantified. The model successfully reproduces observed lightning-induced tree mortality at several sites but simulates lower mortality than estimated at other locations. Running the model globally, we quantify the number of trees and associated biomass directly lost to lightning and compare these losses to biomass losses from lightning-ignited wildfires, highlighting key uncertainties in both pathways.

To place these present-day impacts in a future context, we synthesize existing lightning parameterizations used in global chemistry-climate models and assess their skill and projected changes in lightning activity. Applying projections from several well-performing parameterizations, we explore how future changes in lightning may alter both direct and indirect lightning-induced tree mortality. Together, our results demonstrate that lightning is a multifaceted and potentially growing driver of forest change, and that accurately representing lightning mortality is essential for robust projections of future forest dynamics.

How to cite: Krause, A., Gregor, K., Meyer, B., and Rammig, A.: Lightning impacts on forests: direct and indirect (wildfire) tree mortality under present and future lightning activity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11696, https://doi.org/10.5194/egusphere-egu26-11696, 2026.

The JUpiter ICy moons Explorer (JUICE) is an ESA-led mission that will investigate Jupiter’s atmosphere and the potential habitability of the Galilean satellites in 2031-2035 (Grasset et al., 2013). One of the goals of the investigation of Jupiter’s atmosphere is to determine the spatial distribution, frequency and intensity of lightning, providing a global picture of convective phenomena in Jupiter (Fletcher et al. 2024).

JUICE is in a long cruise to Jupiter that includes three close flybys of the Earth. The first of these flybys occurred on August 20, 2024 with two more flybys planned for Sept. 2026 and January 2029. JANUS is a high-resolution camera that operates in the 340-1080 nm spectral range and will obtain the highest spatial resolution images of the mission (Palumbo et al. 2025). During the 2024 flyby, JANUS obtained a sequence of 20 nightside images over a narrow strip from Madagascar to Vietnam at a spatial resolution of 146-257 m, and from a distance of 9,807-17,476 km with typical exposure times of 25 to 36 ms. While these images did not result in detection of lightning, the images show distinct compact lights from city lights, intense and mild fires and lights from maritime traffic that demonstrate the potential for lightning investigations on Jupiter (Hueso et al., 2026).

Lightning in Jupiter is considered to be much more intense and powerful than on Earth, and has been imaged by every spacecraft that has approached the planet (e.g., Becker et al., 2020). JANUS will obtain images of Jupiter over 3.5 yrs including multiple surveys of lightning in the planet’s nightside at different spatial resolutions and with different time cadences. Jovian lightning originates at pressures higher than 3 atm and can be observed in regions where no apparent storms are visible in the upper clouds at around 500 mbar. The spatial distribution, energy released and overall lightning activity connects observations of the upper atmosphere, where clouds of ammonia ice make most of the observable clouds, with intense phenomena at the base of the weather layer at pressure levels of 4-7 bar, where water condenses and lightning most likely originates.

We show JANUS observations of Earth’s nightside and review similarities and differences between lightning on Earth and Jupiter. We summarize our planned investigation of lightning activity in Jupiter and show how these Earth observations help us determine the sensitivity of the instrument towards the characterization of lightning at Jupiter.

References

• Becker et al., Small lightning flashes from shallow clouds on Jupiter. Nature (2020).
• Fletcher et al. Jupiter Science Enabled by ESA’s Jupiter Icy Moons Explorer. Space Science Reviews (2023).
• Grasset et al. JUpiter ICy moons Explorer (JUICE): An ESA mission to orbit Ganymede and to characterise the Jupiter system. Planetary and Space Science (2013).
• Hueso et al., JANUS observations of Earth in preparation for its investigation of Jupiter’s atmosphere. Annales Geophysicae, in preparation (2026).
• Palumbo et al. The JANUS (Jovis Amorum ac Natorum Undique Scrutator) VIS-NIR Multi-Band Imager for the JUICE Mission, Space Science Reviews (2025).

How to cite: Hueso, R., Palumbo, P., Tubiana, C., Portyankina, G., Lara, L. M., Yair, Y., Haruyama, J., Sato, M., Yukihiro, T., Simon, A., Coustenis, A., Agostini, L., Luchetti, A., Penasa, L., Aboudan, A., Antuñano, A., Roatsch, T., Kersten, E., Matz, K.-D., and Patel, M. and the JANUS Earth flyby team:: Prospects for Lightning Detection on Jupiter using JANUS on the JUICE mission: Insights from JANUS Earth observations , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12136, https://doi.org/10.5194/egusphere-egu26-12136, 2026.

The summer season of 2023 brought to Lomnický štít (Slovakia) one of the strongest gamma-ray glows (GrGs) ever recorded. As Lomnický štít is a unique observation point for GrGs, we equipped the observatory with multiple detectors in the frame of the CRREAT project. In addition to the existing Neutron monitor, SEVAN detector, and Boltek electric field mill, we also installed an RT-56 large NaI(Tl) gamma-ray spectrometer, a small Geodos gamma-ray spectrometer, silicon mosaic detectors, Timepix detectors, PIN diode detectors, a camera, and additional Boltek electric field mills. On 14 June 2023, a thunderstorm cell formed in the vicinity of the observatory and exhibited a strong electric field. This field caused a strong GrG detected by all of the above-mentioned ionizing radiation detectors, standing out as our finest recorded event to date, enriched by the deployment of an unprecedented set of advanced instruments. The duration of the GrG was at least five minutes and was ended by a discharge very close to the count rate's peak of a typical Gaussian curve. The thunderstorm cell remained active and produced two more detected GrGs. One of them also ended with a discharge.

How to cite: Šlegl, J., Kákona, M., Langer, R., Strhárský, I., Chum, J., Lužová, M., Velyčková, H., Sommer, M., Ambrožová, I., and Ploc, O.: An exceptionally strong gamma-ray glow at Lomnický štít observed by an unprecedented number of ionizing radiation sensors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12611, https://doi.org/10.5194/egusphere-egu26-12611, 2026.

Continuing current (CC) is a slowly varying lightning current that can follow a return stroke (RS) in cloud‐to‐ground lightning flashes and typically lasts from a few milliseconds to several hundred milliseconds. The necessary conditions and generation mechanism of CC formation have been studied for decades, motivated by the increased risk of physical lightning damage due to the long‐lasting current and large charge transfers. Nowadays, there is a growing interest in the study of CC, mainly because of the important role it plays in the natural ignition of forest fires. The Krakow ELF group operates a pair of broadband magnetic antenna (sampling frequency: 3004.81 Hz, antenna bandwidth: 0.02 Hz – 1.1 kHz) in an electromagnetically very quiet environment in Hylaty, south-eastern Poland, which is very suitable for the recording of CCs. In this contribution, we introduce our semi-automated procedure for detecting and characterizing CCs in this measurement data and describe some characteristics of the long CCs (>40 ms) identified by our method on three selected days (3-5 July, 2025). Our algorithm first searches for peaks in the magnetic data that represent the ELF manifestation of RSs, then estimates the beginning and end of the waveform based on classic signal processing techniques. The time passed between the RS peak and the end of the waveform is considered to be the CC duration. In order to increase the reliability of the data system, we manually discard ambiguous cases and correct the estimated CC durations. Over the three selected days, a total of 7,052 RSs have been detected, of which 349 (~5%) were followed by a clear and long lasting CC signature. 90% of the CCs were observed in the daytime, 36.1% of them lasted longer than 100 ms, but only 6.6%/0.6% lasted longer than 200/300 ms. Part of the CCs can be well described as an exponential decay, but there are also a number of more complicated waveforms with M-component signatures and prolonged, slightly fluctuating parts. Interestingly, in approximately 5% of the cases, the RS is preceded by some initial activity (current flow) lasting longer than 10 ms. Next, we plan to use WWLLN/ENTLN and MTG data to automatically identify the source lightning discharge of the detected events, as well as to employ machine learning techniques to make the CC detection more effective. We expect that our method will enable us to study lightning CCs in a much larger data set than ever before.

How to cite: Bozoki, T., Mlynarczyk, J., and Horvath, A.: Characteristics of Long Continuing Currents Observed in Broadband ELF Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12870, https://doi.org/10.5194/egusphere-egu26-12870, 2026.

We present observations of small-scale discharges, or sparks, within thunderstorms before the initiation of a lightning leader. Using LOFAR A-TRID, which provides exceptional precision and accuracy through near-field beamforming with hundreds of antennas [1], we detected multiple spark-like discharges with varying characteristics. Some show a collective propagation direction, with some speeds as low as 1 x 10⁶ m/s (similar to ultra-slow propagation), and some as fast as 1 x 10⁷ m/s [2, 3]. As these sparks occur in a kilometer sized region adjacent to the initiation region, they could be used to map the extent of high-field regions within thunderstorms. These results suggest that failed initiation events may be infrequent and difficult to detect as they occur in sparse clusters on short spatiotemporal scales. This work will provide an overview of the physical properties of the spark discharges and implications for lightning initiation.

1: Olaf Scholten, Steven A. Cummer, Joseph R Dwyer, et al. A Comprehensive analysis of High Resolution VHF Observations with LOFAR of the Positive Initiating Event for Several Lightning Flashes. ESS Open Archive . December 12, 2025.

2: Sterpka, C., Dwyer, J., Liu, N., Demers, N., Hare, B. M., Scholten, O., & ter Veen, S. (2022). Ultra-slow discharges that precede lightning initiation. Geophysical Research Letters, 49, e2022GL101597. https://doi.org/10.1029/2022GL101597

3: terpka, C., Dwyer, J., Liu, N., Hare, B. M., Scholten, O., Buitink, S., et al. (2021). The spontaneous nature of lightning initiation revealed. Geophysical Research Letters, 48, e2021GL095511. https://doi.org/10.1029/2021GL095511

How to cite: Sterpka, C., Hare, B., Scholten, O., Turekova, P., Lourens, M., Wu, B., Dwyer, J., Liu, N., and Cummer, S.: Small-Scale Discharges in the Thunderstorm Prior to Lightning Initiation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13571, https://doi.org/10.5194/egusphere-egu26-13571, 2026.

Since the 1953 Urey–Miller experiments, which produced organic precursors through electrical discharges in a simulated primordial Earth, electrical activity has been recognized as crucial for atmospheric evolution. Understanding planetary lightning, therefore, becomes essential when searching for life indicators across planets. Lightning activity has been confirmed through optical observations on Jupiter and Saturn, inferred electromagnetically on Uranus and Neptune, and theoretically predicted for Venus, Mars, and Titan. However, direct lightning detection faces significant challenges. Lightning typically originates in deep convective clouds, often below visible cloud layers where photons are heavily absorbed. This obscuration complicates direct optical detection from space. An alternative approach is to infer lighting by detecting transient luminous events (TLEs; sprites, jets, and Elves) which manifest in the upper atmosphere and produce distinctive optical and chemical signatures potentially more accessible to remote observation. Theoretical considerations based on a simple 1D quasi-electrostatic model (Yair et al., 2009; https://doi.org/10.1029/2008JE003311) predicted the possible occurrence of sprites on Jupiter, presuming that lightning discharges behave as on Earth (Kolamšová et al., 2023; https://doi.org/10.1038/s41467-023-38351-6). Recently, Giles et al. (2020; https://doi.org/10.1029/2020JE006659) reported the detection of unusual optical emissions in Juno images of Jupiter. Eleven bright transient flashes were observed by the spacecraft's UV instrument, with an average duration of 1.4 ms. They were located 260 km above the 1-bar level of Jupiter's atmosphere and were dominated by H₂ emission. These observations are consistent with TLEs (possibly Elves). We present results from a three-dimensional quasi-electrostatic model of TLE generation developed by Haspel et al. (2022; https://doi.org/10.1016/j.jastp.2022.105853), which has been adapted to the Jovian atmospheric conditions for this study. The simulations investigate TLE inception volumes across different cloud configurations (parameters include the magnitude and spatial distribution of charge moments in deep H₂O clouds at 5 bars, and shallow NH₃ clouds at ~1 bar). Results demonstrate that sprites can form in Jupiter's mesosphere when lightning-induced quasi-electrostatic fields exceed the breakdown threshold appropriate for H₂-He mixtures at mesospheric pressures. The simulations reveal the altitude ranges and conditions where electric field-to-neutral density ratios reach critical values for electron avalanche inception and streamer development. Results from simulations of thunderstorms and TLE generation on Saturn will also be presented.

How to cite: Bar-Zeev, J., Yair, Y., Haspel, C., and Hochman, A.: Numerical simulations of TLE generation in the Jovian atmosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14016, https://doi.org/10.5194/egusphere-egu26-14016, 2026.

Previous studies have demonstrated that electrodynamic effects can sometimes be important in simulations of elves, sprite halos, and sprite initiation. To examine the extent to which such effects contribute to the evolution of regions of possible sprite inception, we extend our fully three-dimensional quasi-electrostatic (QES) model of the electric field above thunderstorms to include dynamic effects. The original QES model employed the method of images for every charge in the domain at every time step, eliminating the need for spatial finite differencing of the electric potential or electric field and yielding a numerically stable and accurate solution of the QES equations (see, e.g., Haspel and Yair, 2025; doi:10.1016/j.asr.2025.01.013). In the present implementation, we add the electric induction (“velocity”) term to the Coulomb term in the expression for the electric field produced by each charge in the domain. In addition, we replace instantaneous time with retarded time, such that the model is also fully causal; a change in charge density at point A does not manifest in a change in the electric field at point B until that “signal” has time to propagate from A to B. The resulting Coulomb and induction contributions are structurally equivalent to the corresponding terms in Jefimenko’s formulation. This approach lies between traditional QES models and full-wave electromagnetic models and may be described as quasi-electrodynamic rather than quasi-electrostatic. It allows induction and causality effects to be included throughout the entire domain without spatial finite differencing and without an explicit representation of the lightning channel as used in transmission-line or EMP models. We find that the inclusion of causality delays the formation of regions of possible sprite inception and, together with the induction term, produces regions that persist longer than in traditional QES simulations with otherwise identical simulation parameters. Initial results from this extended model will be presented and discussed.

How to cite: Haspel, C.: A quasi-electrodynamic model for examining the effects of induction and causality in simulating regions of possible sprite inception in the mesosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14177, https://doi.org/10.5194/egusphere-egu26-14177, 2026.

Mineral dust influences radiation, clouds, and air quality, and can acquire electric charge through particle interactions and turbulent mixing. Observations of dust electrification at aircraft altitudes remain limited. Aircraft measurements provide a valuable opportunity to investigate how atmospheric electric fields respond to airborne dust under varying thermodynamic and aerosol conditions.

We analyse observations from the FAAM aircraft during flights sampling Saharan dust layers, to investigate electric field mill records associated with airborne dust regions. The electric field mill employed senses the ambient vertical electric field indirectly, but does not directly sample particles. It responds to electric fields induced by charged aerosols, the aircraft, and the surrounding atmosphere. Electric field measurements are analysed alongside co-located thermodynamic, wind, aerosol, and cloud observations, including ascent and descent profiles through dust plumes of varying intensity. In an intense dust case, the electric field signal strengthens as the aircraft approaches and enters the main dust layer at mid-tropospheric altitudes, coincident with decreasing relative humidity and enhanced aerosol loading. These results indicate that the electric field measurements are sensitive to electrically active dust layers aloft, providing new constraints on how dust charging evolves with altitude, humidity, and particle loading.

How to cite: Alblooshi, S., Nicoll, K., Harrison, G., and Ryder, C.: Electric Field Responses to Airborne Dust in FAAM Aircraft Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14486, https://doi.org/10.5194/egusphere-egu26-14486, 2026.

Long-term measurements of the atmospheric electric field, measured as the potential gradient (PG), were obtained at Lerwick Observatory (Shetland Isles), UK, from 1925 to 1984, and provide a unique resource for studying links between atmospheric electricity, the global electric circuit (GEC), and climate variability. Most of these historical observations were originally made as handwritten or printed records, limiting their accessibility for modern analysis. In this project, we have undertaken a comprehensive digitization of the Lerwick PG dataset, at hourly resolution, by combining contributions from a citizen science platform and various AI tools.

The earliest handwritten records, made from 1927–1956, were digitized through the Zooniverse citizen science platform by engaging volunteers in transcribing data.  To digitize the later records, from 1957–1984, which are mainly printed and scanned tables, we utilized AI-based optical character recognition (OCR) tools from several software packages. An essential part of the transcription the use of multiple validation steps to assess and correct errors introduced by both the AI-based tools and the citizen science activity. By these techniques, we optimised the effectiveness of the digitisation to provide the most scientifically useful dataset.

This work presents a summary of the digitized historical dataset from Lerwick and provides insights into the reliability and limitations of AI-assisted digitization of scientific archives. The resulting new dataset generated will underpin modern investigations into long-term trends in atmospheric electricity and its connection to climate processes.

How to cite: Mkrtchyan, H., Nicoll, K., and Harrison, G.: Combining human and AI approaches for effective digitization of historical atmospheric electricity records, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14791, https://doi.org/10.5194/egusphere-egu26-14791, 2026.

Lightning is a fundamental part of the Earth's climate system, occurring worldwide at a rate of about 45 flashes per second on average. It has recently been recognized as an Essential Climate Variable and serves as an indicator of thermodynamic instability.

In the past years, lighting climatologies have been derived for various regions worldwide. However, this does not yet include vast parts of Southwest Asia, i.e., the broader region encompassing the Eastern Mediterranean, Black Sea, Caspian Sea, Red Sea, and Persian Gulf, as well as the Middle East (Anatolia, Levant, and Arabian Peninsula) and the Caucasus regions. Unlike the tropical lightning hotspots (e.g., the Congo Basin or Venezuela), this area is often overlooked in global lightning studies due to its lower overall flash density. Despite its low average flash rates, the region displays complex and rather unique interactions between distinct atmospheric circulation patterns and local thermodynamic processes in the atmosphere.

This study aims to unravel the complex factors that control lightning activity in a transitional zone where these different physical processes intersect. Specifically, a reliable lightning climatology for Southwest Asia and the neighboring regions is developed that combines data from available space missions and different ground-based detection networks for the period 2017-2023. The resulting spatial patterns of lightning flash density, along with their seasonal and inter-annual variability, contribute to a better understanding of the effects of orography, land-sea configuration, land cover, and prevailing regional weather patterns on lightning. In order to attribute the obtained activity patterns to specific thermodynamic conditions and aerosol-cloud interactions that sustain electrification even in areas with limited moisture availability, atmospheric reanalysis data are employed with a focus on cloud properties.

How to cite: Karapetyan, G., Donner, R. V., Mkrtchyan, H., and Aslanyan, D.: Filling the Gap: Lightning Climatology of Southwest Asia, Northeast Africa, and the Eastern Mediterranean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15056, https://doi.org/10.5194/egusphere-egu26-15056, 2026.

Lightning continues to challenge high-voltage transmission reliability, accounting for approximately 40-60% of recorded line trips. However, the lightning nowcasting and short-term warning products currently used in power grid operations often provide insufficient lead time for actionable transmission-line protection and dispatch.

Here we integrate a 10-year utility dataset of lightning-induced 500 kV transmission line trips in North China with cloud-to-ground (CG) lightning observations and ERA5 reanalysis to quantify the 12 h pre-event evolution of the atmospheric environment. We define three 12-h pre-event samples using different reference points: (i) Line trips (LT) cases centred on the trip location; (ii) Thunderstorms without line trips (WLT) cases centred on the tower closest to where a thunderstorm intersects the line corridor; and (iii) Non-thunderstorm (NT) controls centred on the same tripping location, sampled at the same local time within ±7 days of each LT event under lightning-free conditions in the preceding 12 h.

Compared with NT controls, both LT and WLT events occur in a more convectively favourable environment, with higher total column water vapour (TCWV), convective available potential energy (CAPE), and lower lifting condensation level (LCL). They also show stronger lifting—more negative 700 and 850 hPa vertical velocity and enhanced low-level convergence. Within thunderstorms, however, LT events tend to occur in an instability-dominated regime, with higher CAPE and steeper 700-500 hPa temperature lapse rates than WLT events. By contrast, WLT events are more “water-loaded,” showing higher TCWV and stronger integrated water vapour transport (IVT), together with stronger lifting—yet weaker CAPE and lapse rates.

These results suggest that instability-focused precursors can help discriminate tripping risk and motivate environment-based indicators to extend operational lead time for transmission line lightning protection.

How to cite: Li, M., Wang, J., Fan, Y., Huang, Y., Li, Q., and Li, Y.: Thermal Instability as a Critical Precursor to Transmission Line Lightning Trips: A 12-Hour Pre-Event Analysis in North China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15307, https://doi.org/10.5194/egusphere-egu26-15307, 2026.

Uranus is an Ice Giant planet, a class of large, cold planets characterized by thick atmospheres and the absence of a well-defined solid surface. Hence, atmospheric processes are fundamental to understanding the planet’s physical and chemical environment. Atmospheric ionisation on Earth is driven by solar radiation and energetic particles, radioactive gases and Galactic Cosmic Rays (GCRs) [1].  GCR-induced ionisation is believed to be dominant on Uranus due to its distance from the Sun. In this work, we model the GCR air showers using CORSIKA8 Monte Carlo simulations [2] and calculate the vertical ionisation rate. We capture the variation of ionisation rates with geomagnetic latitude in a novel global map and, for the first time, present a quantitative comparison with ionospheric ionisation rates derived from parameters adopted from the literature. The results show GCR-induced ionisation in the lower stratosphere (peaking at ~10⁴ Pa) to be around two orders of magnitude larger than ionospheric ionisation (<10^-1 Pa), highlighting the significance of GCRs in Uranus’s atmosphere and raising questions about potential seasonal variability associated with solar-driven ionospheric processes.

The conditions in the lower stratosphere were carefully constrained, and with appropriate assumptions regarding steady-state conditions and dominant recombination mechanisms, the ion balance equation was solved to estimate the positive ion and electron number densities. Ion and electron densities peak at approximately the same altitude as the peak of GCR-induced ionisation with an upper limit of ~2×10⁹ ions m^-3 in the absence of aerosols, while the inclusion of aerosols leads to a difference between positive ion (~10⁹ ions m^-3) and electron densities (~10⁸ electrons m^-3). The electrical characteristics as well as cloud microphysics assumptions allow investigation of the possibility and nature of lightning activity expected on Uranus.

[1] Hillas, A. M. (1972). Cosmic rays (1st ed.). New York: Oxford ; New York : Pergamon Press.

[2] Gottowik, M. (2025). Corsika 8: A modern and universal framework for particle cascade simulations. arXiv preprint arXiv:2508.08755.

How to cite: Al-Khuraybi, O., Aplin, K., and Gambaruto, A.: Characterising Uranus’ Ionisation and Conductivity Profile, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15467, https://doi.org/10.5194/egusphere-egu26-15467, 2026.

In recent years, it has been pointed out that there has been an increase in the number of localised heavy rain and hailstorms in urban areas, which are thought to be caused by climate change and extreme weather. It is said to be difficult to distinguish whether a thundercloud (cell) that causes localised hailstorms or heavy rain is a cell that will produce hailstorms or heavy rain, based on observations of the reflection intensity of the weather radar alone. In this study, we consider extreme weather events that cause hailstorms and heavy rain from the perspective of lightning discharges, distinguishing between cells that lead to hailstorms and cells that do not lead to hailstorms but only to heavy rain. We compared two cells in the same meteorological field in three cases that occurred in the Tokyo metropolitan area. We compared the cells that led to hailstorms with the control cells that only led to heavy rain. As a result, we found the following common characteristics.

1) The number of ±CG strokes in cells with heavy rain but no hail is larger than in cells with hail.

2) The volume of ice calculated from polarimetric radar in cells with hail is larger than in cells with heavy rain but no hail.

As a result, the possibility of discriminating between cells with and without hail has increased. This study is a re-evaluation of the results obtained by Fujiwara et al, (J. Atmos. Electriciy, 2021; 2023).

How to cite: Kamogawa, M., Fujiwara, H., and Suzuki, T.: Characteristics of atmospheric electricity of thunderclouds accompanied by severe hailfall, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16186, https://doi.org/10.5194/egusphere-egu26-16186, 2026.

How to cite: Fuglestad, A., Marisaldi, M., Mezentsev, A., Sarria, D., Østgaard, N., Neubert, T., and Gordillo-Vázquez, F.: Gamma-ray Stacking Analysis of Streamer Dominated Discharges detected by ASIM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16950, https://doi.org/10.5194/egusphere-egu26-16950, 2026.

During the ALOFT flight campaign in July 2023, we discovered a population of weak TGFs in the range of 10^12 to 10^15 source photons in brightness (at a reference altitude of 15 km), not detectable by space-based instruments (such as ASIM on the ISS or Fermi) [Bjørge-Engeland 2024; Fuglestad 2025]. While extensive air showers (EAS) were previously discarded as a seeding source for space-observed TGFs (i.e., with source brightness above 10^16 photons) [Dwyer 2008; Carlson 2008], in principle, this does not exclude the possibility that the same mechanism could generate TGFs that are orders of magnitude weaker, like ALOFT’s weak TGFs. This hypothesis consists of EAS generating a large number of seed particles in a very short time, which are then multiplied by the RREA process by a few orders of magnitude. It could also involve some level of relativistic feedback. Furthermore, ALOFT observations suggest that weak TGFs may be due to an abrupt increase in the seed population rather than an increase in the electric field, given the fast rise time (too fast for relativistic feedback) and the fact that the TGF precedes the radio signal.

EAS originate from highly energetic cosmic-ray protons and nuclei showering in the atmosphere. Because Geant4 cannot simulate initial proton energies above 100 TeV, and such energies may be required, we will also use the CORSIKA code (high-energy part) with the FLUKA model (low-energy part), both of which are well-established reference models. A key here to evaluate this hypothesis, is the trade-off between initial cosmic proton fluxes (e.g., per hour per square kilometer) and their energies, as higher energies generate more seed electrons but are less frequent.

In this presentation, we will show a comprehensive evaluation of the possibility of generating weak TGFs via EAS energetic electron seeding in a realistic large-scale thunderstorm electric field close to the RREA threshold.

References:

I. Bjørge-Engeland et al. Evidence of a New Population of Weak Terrestrial Gamma-Ray Flashes Observed From Aircraft Altitude. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL110395

A. Fuglestad et al. The source brightness distribution of Terrestrial Gamma-ray Flashes from the ALOFT flight campaign.

J. R. Dwyer. Source mechanisms of terrestrial gamma-ray flashes. https://doi.org/10.1029/2007JD009248

Carlson, B. E., N. Lehtinen et al. (2008). Runaway relativistic electron avalanche seeding in the Earth’s atmosphere. https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2008JA013210

How to cite: Sarria, D., Marisaldi, M., Østgaard, N., Mezentsev, A., Lehtinen, N., Færder, Ø., Bjørge-Engeland, I., and Fuglestad, A.: Investigating Cosmic-Ray Extensive Air Showers as a Source of Weak TGFs Using GEANT4 and CORSIKA, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17409, https://doi.org/10.5194/egusphere-egu26-17409, 2026.

Tropospheric lightnings are fundamental sources of natural ultra-wide band (UWB) electromagnetic waves, utilised in various fields of exploration of the upper atmosphere and the plasma environment. Lightnings are observed by numerous detection systems, deployed as ground networks or operated on satellites/cubesats, all exhibiting specific limitations in detection efficiency (DE), sensibility, spatial coverage, and overall performance.

We have compared the flash database of the LI optical experiment onboard the Meteosat Third Generation (MTG) geostationary satellite (0° longitude, operational since July 2024) with the ground-based WWLLN VLF stroke data set in the period of July 2024 – May 2025, within the FoV of the MTG sensors. For that purpose the necessary correction of the location and time coordinates was first performed in the MTG data set. This step sets the spacecraft observation time backwards with the propagation time (approx. 120-140 ms), and also decreases the latitude and longitude coordinates towards the sub-satellite line due to the finite altitude of the observed optical phenomenon at cloud top. 

The ratio of the detected events, binned in a 1° by 1° raster in the African continental region, varying somewhat geographically, falls in the remarkable range of several hundreds. This difference can be explained partly by the known poor DE of the WWLLN over Africa, and by the fact that MTG LI detects total lightning (cloud-to-ground and intracloud/cloud-to-cloud), while WWLLN primarily detects strong CGs. A one-by-one matching of MTG flashes with detected WWLLN strokes, applying temporal and spatial windowing (±330 ms, and <25 km, respectively) was also completed. This analysis exhibited clear asymmetry in the distribution of the time offsets between matching events (time stamps of MTG flashes seem to precede the corresponding WWLLN time values by tens of ms). The distribution of spatial separation of matching pairs has a maximum at 8 km. Due to the reasonably strict conditions used in matching pair selection, the overwhelming number of detected lightnings in the MTG LI data set is not seen in the one-by-one comparison: 97.3 % of WWLLN to MTG LI matchings are single event pairs, a multiplicity factor of 2 is represented only by 2.5 % of matched events.

How to cite: Steinbach, P., Bozóki, T., Németh, K., and Lichtenberger, J.: Comparison study on the MTG LI and WWLLN lightning data sets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17570, https://doi.org/10.5194/egusphere-egu26-17570, 2026.

In this presentation we describe a proof-of-concept study on the possibility of detecting high electric fields in the atmosphere  e.g. around thunderstorms, using remote sensing techniques.  Current methods to measure electric field profiles through the atmosphere rely on radiosondes or aircraft operating in conditions where the instrument is prone to damage or give unreliable results.   We are investigating the feasibility of an alternative approach, which exploits the sensitivity of certain molecules to their electrical environment through the so-called Stark effect, whereby certain spectral lines are shifted in response to an external electric field.   This has the potential for the measurement of high electric fields above thunderstorms, although it does present a number of challenges .   In our study, we have been using radiative transfer models to simulate the effect of electric fields (such as are typically found around thunderstorm clouds)  on atmospheric spectra, looking in particular at THz wavelengths and focusing on selected candidate spectral lines of HDO.    Here, we will present our latest results.

How to cite: Waterfall, A., Cox, C., and McCormack, E.: Towards the Remote Sensing of Electric Fields in the Atmosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18193, https://doi.org/10.5194/egusphere-egu26-18193, 2026.

Intense lightnings and hailfall are both hallmarks of severe convective storms, but they are rarely associated with long-term climate studies. The main reason is the lack of long-term observations. But recently, the term “extreme weather” is often cited in media as a possible dire consequence of worsening global warming in the foreseeable future, however, it is often ambiguous of what type of extreme weather they are referring to. Most recent future predictions are done by performing climate model simulations under certain global warming scenarios. However, the resolution of the current generation climate models is not good enough to resolve individual storm system let alone pinning down the physical mechanisms. This ambiguity in physical mechanism impedes the better understanding of the nature of these extreme weather/climate events. In this paper, we present a unique study to show that severe storms with intense lightnings and hailfall are indeed connected with long-term climate change.

 In this study, we utilize the meteorological series derived from the REACHES climate database compiled from Chinese historical documents (Wang et al., 2018; 2024, Nature: Scientific Data) and extract temperature, lightning and hailfall times series for the period of 1368-1911 (a 543-year period) and performed correlation analysis among them. Our results show that there exists strong negative correlation between either temperature-lightning or temperature-hailfall pair. This means that severe convective storms as manifested by intense lightning and heavy hailfall occurred in colder climate periods. The correlation coefficients for both pairs are close to -0.9 for the 30-year moving average series. Such a stable correlation over such a long period indicates that this cannot be a random coincidence but there must be persistent physical mechanisms involved. The temperature-lightning correlation is stronger, indicating that the climate physical state must be closely connected with atmospheric electricity.

We have made further analyses by looking into different seasons to understand the seasonal variations of the above negative correlation. We will also investigate the regional variations of the above relation. These results will shed more lights to the physical mechanisms responsible for this phenomenon. We will also utilize physics-based storm model simulation results to understand the possible dynamical processes involved.  

How to cite: Wang, P. K.: A long-term atmospheric electricity-climate connection study using a 543-year long historical data set, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18253, https://doi.org/10.5194/egusphere-egu26-18253, 2026.

How to cite: Mezentsev, A., Østgaard, N., Marisaldi, M., Sarria, D., Lehtinen, N., Færder, Ø., Bjørge-Engeland, I., Cummer, S., Pu, Y., Quick, M., Lang, T., Pazos, M., and Stanley, M.: New class of TGF discovered during ALOFT 2023 campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18708, https://doi.org/10.5194/egusphere-egu26-18708, 2026.

From the discoveries by the Airborne Lightning Observatory for FEGS and TGFs (ALOFT), we know that thunderclouds can emit gamma-rays for hours over very large distances. Marisaldi et al. (2024) reported observations of numerous glowing regions, each containing several individual glows, as the aircraft passed over active thunderclouds. Overall, ALOFT detected more than 500 glows, showing a wide variety of time profiles, including glows with a gradual decay and those with a very sharp decrease in gamma-ray flux after reaching the peak intensity. Making use of the combination of instruments onboard ALOFT, as well as ground-based sensors, we explore the termination of gamma-ray glows detected by ALOFT. In this study, we focus on glows with a very fast decrease in flux (reduction by >50% in <20 ms) and explore which types of electric discharges are associated with this fast termination.

References:

Marisaldi, M. et al. (2024), Highly dynamic gamma-ray emissions are common in tropical thunderclouds, Nature, 634, 57, doi.org/10.1038/s41586-024-07936-6

How to cite: Bjørge-Engeland, I., Marisaldi, M., Østgaard, N., Mezentsev, A., Fuglestad, A., Cummer, S., Pu, Y., Quick, M., and Christian, H.: Discharge processes associated with fast decaying gamma-ray glows observed during the ALOFT aircraft campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19365, https://doi.org/10.5194/egusphere-egu26-19365, 2026.

Mini-EUSO is a space-based ultraviolet telescope operating aboard the International Space Station since 2019 as part of the JEM-EUSO Programme. The instrument monitors the Earth’s night-time atmosphere in the 290–430 nm wavelength range with a temporal resolution of 2.5 μs and a ground spatial resolution of approximately 5–6 km. In addition to its high time resolution, Mini-EUSO combines imaging capabilities with high sensitivity, enabled by a 25 cm diameter optical system, allowing the detection of fast and faint UV emissions associated with atmospheric electrical activity.

In this contribution, we present the analysis of a population of fast, short-duration UV transients, referred to as Short Light Transients (SLTs), with typical timescales between 100 μs and 200 μs detected by Mini-EUSO. These events are stationary within the spatial resolution of the detector and are frequently followed, at the same geographical location, by additional atmospheric emissions occurring within 1–200 ms. The observed temporal correlations and spatial localization suggest a connection with rapid atmospheric electrical processes, potentially related to lightning activity or to early-stage or precursor phenomena associated with transient luminous events.

The combination of microsecond-scale temporal resolution, imaging capability, and high optical sensitivity makes Mini-EUSO particularly well suited for the investigation of fast, localized UV emissions that are challenging to observe with conventional lightning and atmospheric monitoring instruments.
In addition to the SLTs discussed here, Mini-EUSO has recorded a wide range of lightning-related and transient luminous phenomena, highlighting the potential of space-based UV observations for the study of fast electrical processes in the atmosphere.

How to cite: Battisti, M.: Short Light Transients and millisecond-scale follow-up emissions observed by Mini-EUSO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19685, https://doi.org/10.5194/egusphere-egu26-19685, 2026.

From sandstorms and volcanic plumes, electrical charging of small particles is of critical importance in many geophysical settings. How do the particles in these systems become charged in the first place? In this talk, I will discuss our experimental work on the transfer of electrical charge that occurs when two solid objects are contacted and separated. We focus on oxides (e.g., SiO₂) as they are the most abundant and relevant class of materials on the earth, which presents a number of challenges. First, they are extremely hard, which means their contact areas—and hence charge exchange—are extremely small. Second, direct handling introduces spurious charge that can overwhelm the signal we wish to measure. We overcome these challenges using acoustic levitation, which enables thousands of automated, hands-free contacts and charge measurements with few-hundred-electron resolution on macroscopic samples. Our experiments reveal that oxide contact electrification is not due to any bulk material property, but instead arises from surface adsorbates—specifically adventitious hydrocarbons—acquired by objects from the air that surrounds them. These findings, now in press at Nature, are the long sought source of particulate charging in settings ranging from desert sands to volcanoes and beyond.

How to cite: Waitukaitis, S. and Grosjean, G.: Atmospheric adsorbates break symmetry in oxide electrification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20179, https://doi.org/10.5194/egusphere-egu26-20179, 2026.

How to cite: McGurk, C., Peters, D., McCormack, E., Clark, D., Hills, M., Stone, C., and Mitchard, D.: Measuring NOx and O3 emissions from laboratory generated lightning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20216, https://doi.org/10.5194/egusphere-egu26-20216, 2026.

Mini-EUSO (Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory) is a compact ultraviolet telescope operating aboard the International Space Station since 2019, observing the Earth’s atmosphere in the 290-430 nm band from nadir. It is a part of the JEM-EUSO programme, aimed at developing technologies for the observation of ultra-high-energy cosmic rays (UHECRs) from space. The instrument comprises two 25 cm Fresnel lenses and a focal surface of 36 multi-anode photomultiplier tubes (2304 pixels), providing a 44° field of view and a time resolution of 2.5 μs. With an angular pixel size of ∼0.86°, Mini-EUSO has a spatial resolution of ∼6 km at ground level and ∼5 km at ionospheric altitudes, allowing for detailed imaging of fast transient luminous events.

Since the beginning of operations, Mini-EUSO has recorded approximately 50 ELVES. A large fraction of the observed events exhibit complex morphologies, most notably multi-ring structures. Understanding the diversity of ELVES morphologies requires quantitative measurements of their dynamics (ring radius and expansion speed), energetics, and internal ring morphology.

We present results from a dedicated analysis pipeline that reconstructs the spatio-temporal development of ELVES UV emission at microsecond time scales. Mini-EUSO’s fast imaging allows us to measure ring properties such as thickness and brightness variations along the ring, and to follow how these features evolve in time. These measurements help constrain ELVES production mechanisms and the relative role of different EMP propagation paths. In several cases, the reconstructed timing and ring morphology are compatible with, and suggest, a “ground reflection” contribution, where additional ELVES rings may be associated with an EMP component reflected from the Earth’s surface. These observations highlight the capability of compact space-based UV instruments to advance ELVES physics and to probe EMP-ionosphere coupling with unprecedented detail.

How to cite: Plebaniak, Z.: Probing lightning-ionosphere coupling with Mini-EUSO: timing and morphology of multi-ring ELVES, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20296, https://doi.org/10.5194/egusphere-egu26-20296, 2026.

Lightning is a key manifestation of severe convective weather and poses a significant natural hazard to power infrastructure, particularly overhead transmission lines and towers. However, lightning occurrence is governed by the combination of multiple atmospheric and cloud-scale processes. Existing studies largely rely on correlation-based analyses, which provide limited insight into the temporal roles of different precursors prior to lightning.

In this study, we develop an event-driven, multi-scale causal analysis framework based on a large set of real-world lightning events over the UK. Each lightning event is temporally aligned with its preceding atmospheric evolution, combining hourly ERA5 reanalysis variables, including temperature, moisture, and precipitation, with high-temporal-resolution satellite-derived cloud-top height observations. Causal discovery methods are applied to identify lagged relationships at the hourly scale, while robust lag analysis is used to characterise short-timescale cloud-top evolution. The analysis reveals that lightning events are commonly preceded by physically consistent, ordered triggering processes. As a case study, we discuss the implications for power infrastructure risks. The proposed framework provides a data-driven and physically interpretable basis for assessing lightning-related risks to transmission networks and other assets.

How to cite: Wang, X., He, X., Ali, A., Fullekrug, M., and Gu, C.: Multi-scale causal analysis of processes causing lightning and implications to Energy infrastructure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20775, https://doi.org/10.5194/egusphere-egu26-20775, 2026.

Recent data from the ALOFT flight campaign have confirmed the lightning activity and high energy particle acceleration interconnection. The results comprised the discovery of a large fraction of low-brightness Terrestrial Gamma-ray Flashes (TGFs), signifying that the bright flashes observed from space account for only a small fraction of these events. Many of these weak TGFs, undetectable from space, are associated with a prominent 337.1 nm optical pulse and differ from those detected from space by the lack of 777.4 nm dominated lightning discharges [Mezentsev et al. 2025]. Brightness down to 10¹² photons at a source reference altitude of 15 km have been observed and there is no theoretical reason that opposes the existence of dimmer TGFs [Bjørge-Engeland et al. 2024; Fuglestad et al. 2025].

Although detection of individual events would be prevented due to instrument sensitivity, this project aims to tackle this obstacle by stacking the gamma-ray signals in timeframes prior to the emergence of blue dominated lightning discharges. If a substantial population of dim TGFs below the sensitivity threshold for ALOFT exists, it should appear as an enhancement in the cumulated gamma-ray signal.

This presentation focus on the stacking analysis of the gamma-ray data detected by ALOFT in association with the blue dominated lightning discharges. We will present the methodology, the data selection strategy and the preliminary results.

References:

A. Mezentsev et al. (2025). New Class of Gamma-Ray Flashes Indicate Gamma Glow Rest through Fast Streamer Discharge. https://doi.org/10.5194/egusphere-egu25-15838

I. Bjørge-Engeland et al. (2024). Evidence of a New Population of Weak Terrestrial Gamma—Ray Flashes Observed from Aircraft Altitude. Geophysical Research Letters, 51, https://doi.org/10.1029/2024GL110395

A. Fuglestad et al. (2025). The source brightness distribution of Terrestrial Gamma-ray Flashes from the ALOFT flight campaign. Submitted to JGR: Atmospheres

How to cite: Khan, R., Marisaldi, M., Bjørge-Engeland, I., Østgaard, N., and Quick, M.: Gamma-ray production associated with blue dominated lightning discharges in glowing thunderclouds observed by ALOFT, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21097, https://doi.org/10.5194/egusphere-egu26-21097, 2026.

It is essential to clearly separate pulse noise from lightning emissions to detect lightning on planets. Therefore, LAC (lightning and airglow camera) onboard Akatsuki spacecraft sacrificed high spatial resolution by using 32 pixels, instead opting for a high photometric sampling frequency of 20 kHz. This design allows smooth capture of brightness fluctuations, even for short-duration phenomena like terrestrial lightning. Furthermore, based on discharge experiments using CO2, the primary component of Venus's atmosphere, a narrow-band filter for the most prominent oxygen atomic emission line (777 nm) was installed. Sensitivity was set, referencing results from satellite observations on Earth, to detect emissions on Venus even when it is in close approach, down to levels less than one-tenth of those seen in terrestrial lightning. Although the extended elliptical orbit of Akatsuki and its longer period reduced the LAC observation time—which activates only during Venus's shadow—to about one-twentieth of the original planned rate, observations commenced successfully in 2016. However, for the first four years after the start of observations, only cosmic ray pulses were recorded; not a single light curve resembling lightning was obtained. Finally, in March 2020, a single event was triggered and recorded. Its duration was approximately 200 milliseconds, far longer than the typical few milliseconds of Earth lightning. This duration cannot rule out the possibility of a meteor (fireball). However, calculating the probability of a meteor of that brightness being observed by LAC based on the observed luminosity yielded a probability between 0.1% and 8.3%. Furthermore, considering that 200 milliseconds is short for a meteor, the probability of it being a meteor becomes even smaller. On the other hand, some Earth lightning events observed in Earth orbit also have durations exceeding several hundred milliseconds, similar to this LAC event. Based on these facts, while we cannot completely rule out the possibility of it being a meteor or meteorite fall, we believe it is highly likely to be lightning discharge luminescence. Moving forward, we intend to explore the significance of the lightning information obtained on Venus by using the light curve obtained by Akatsuki as a clue to investigate the meteorological conditions under which similar terrestrial lightning occurs. Simultaneously, using the LAC waveform as a reference, we are developing ground-based telescope measurements of lightning emissions utilizing the latest high-speed imaging observation equipment.  

How to cite: Takahashi, Y., Ono, T., Lorenz, R., Sato, M., Imai, M., Yair, Y., and Fischer, G.: Lightning detection on planets using spacecraft and grond-based telescope, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21100, https://doi.org/10.5194/egusphere-egu26-21100, 2026.

How to cite: Telisman Prtenjak, M., Simunec, D., and Strelec Mahovic, N.: Comparison of lightning detection from satellite and ground-based measurements during selected cases of convection with hail , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21267, https://doi.org/10.5194/egusphere-egu26-21267, 2026.

A phenomenon known as a "recoil leader" has now been reliably established experimentally. Ricoil leaders manifest themselves as waves of luminosity that move toward the channel of a pre-existing bright leader [Mazur, 1989]. On the other hand, radio interferometers have detected the movement of radio emission sources within thunderclouds toward a possibly existing positive leader channel. This phenomenon is known as "needles" [Hare et al., 2019].

We propose a hypothesis that explains recoil leaders and "needles" based on the phenomena of return corona and return leaders, which were observed during experiments with long sparks (30-60 m) [Lupeiko et al., 1984; Baikov et al., 1988, Mrázek, 1996, 1998].

Qualitatively, the process can be explained as follows. As leaders propagate, a streamer corona in front of the leader tip injects charge into the volume around the leader channel (leader sheath) [Bazelyn & Raizer, 1998]. The leader channel is analogous to a high-voltage wire. As the potential on the "wire" increases, the streamer zone expands, and the charge in the sheath increases. This process continues until the electric field in the "wire" (the leader channel) is balanced by the electric field of the sheath charge. If the potential inside the leader channel drops, the sheath's electric field exceeds the channel's electric field. The electric field reverses, resulting in a return corona and/or return leaders.

This mechanism was confirmed experimentally in [Baikov et al., 1988]. The Marx generator generated a positive voltage of 3.4 MV (rise time 300 μs, pulse duration – 10 ms). The leader moved for 2.2 ms and reached a length of 45 meters. The discharge was incomplete, since the leader did not reach the grounded plane, and the leader plasma decay in the air. Despite the nearly constant voltage (after reaching 3.4 MV), each branching or rotation of the leader resulted in pulsations in the current and leader glow (the sheath exchanged charge with the leader channel). After the leader stopped, the current and glow in the gap ceased, and a dark period began, lasting at least 2 ms. The dark period ended with a series of flashes ("recoil leaders"), the glow zone of which coincided in size with the charge sheath. Each individual flash was accompanied by a current pulse of reverse polarity and a voltage surge across the Marx generator's divider capacitance. The charge neutralized in these flashes was approximately 50 μC.

Similar results at positive voltages of 3-4 MV were obtained on a Marx generator in Prague [Mrázek, 1996; 1998].

Baikov A.P. et al. (1988). Electricity, 9, 60 (in Russian)

Bazelyan, E. M., &   Raizer, Y. P. (1998). Spark discharge. Boca Raton, FL: CRC Press

Hare B.M. et al. (2019). Nature, 568, 360

Lupeiko A. V. et al. (1984) Proc. of the All-Union Conf. on Gas Discharge. Tartu: TSU, 1984, v. 2. (in Russian)

Mazur V. (1989) JGR-A, 94, 3326

Mrázek J. (1996). Acta Techn. CSAV, 41, 577

Mrázek J. (1998). Acta Techn. CSAV, 43, 571

How to cite: Kostinskiy, A. and Ploc, O.: One possible mechanism for the formation of recoil leaders and "needles", EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21302, https://doi.org/10.5194/egusphere-egu26-21302, 2026.

A bolt from the blue [1,2] was observed simultaneously by ground-based video observations, space-based video with the Lightning Imager on the Meteosat Third Generation geostationary satellite (MTG-LI)  [3,4], and a low-frequency lightning interferometer during field work at Sutherland, South Africa, January 28^th, 2025.

The bolt from the blue was initiated by an intra-cloud discharge that connects two charged layers at the edge of a thundercloud. The stepped leader subsequently propagates horizontally away from the cloud. During the development, the lightning leader channel splits into two parts, one which propagates further away horizontally and one which returns towards the cloud but then curves down to Earth where it splits again into two separate strike points on the ground.

The ground-based video observations are paralleled by simultaneous space-based video observations with the Lightning Imager on the Meteosat Third Generation geostationary satellite (MTG-LI) with a temporal resolution of 1 ms. The illuminations of individual pixels (events) are summarised into clusters (groups) which measure the spatial extent of the bolt from the blue after correction for the parallax error using cloud top height measurements inferred from the Flexible Combined Imager (FCI) payload on MTG.

The electromagnetic emissions of the bolt from the blue are recorded with a low-frequency interferometer on the ground that consists of three radio receivers which are deployed in a triangular array, ~15 km away from the thundercloud. The radio receivers use horizontal electric field sensors (horizontal dipoles) [5] to measure the electromagnetic emissions of the bolt from the blue with 1 us resolution. These waveforms show a sequence of pulses with different shapes which indicate the occurrence of various physical processes during the development of the bolt of the blue.

The video observations from the ground and from space are compared to the recordings with the lightning interferometer and the benefits arising from these joint analyses are discussed in detail.

References:

[1] Krehbiel, P., Riousset, J., Pasko, V. et al. Upward electrical discharges from thunderstorms. Nature Geoscience 1, doi:10.1038/ngeo162, 233–237, 2008.

[2] J. Harley, L. Zimmerman, H. Edens, H. Stenbaek-Nielsen, R. Haaland, R. Sonnenfeld, and M. McHarg. High-speed spectra of a bolt from the blue lightning stepped leader. Journal of Geophysical Research, 26(3), doi:10.1029/2020JD033884, 1-10, 2021.

[3] A.M. Holzer, et al.: EUMETSAT-ESSL Application Guide on the Use of MTG LI in Severe Convective Storms Nowcasting, ESSL Report 2025-01, https://www.essl.org/cms/essl-testbed, 2025.

[4] M. Füllekrug, E. Williams, C. Price, S. Goodman, R. Holzworth, S.-E. Enno, and B. Viticchie, Novel lightning flash densities from space [in “State of the Climate in 2024”, Bulletin of the American Meteorological Society, 106 (8), doi:10.1175/2025BAMSStateoftheClimate.1, S85–S86, 2025.

[5] M. Füllekrug, M. Kosch, G. Dingley, X. Bai, and L. Macotela. Six-component electromagnetic wave measurements of sprite-associated lightning. ESS Open Archive, doi:10.22541/essoar.176296584.41929367/v1, 2025.

Acknowledgments:

The authors wish to thank Sven-Erik Enno from EUMETSAT for assistance with the MTG-LI data retrieval. The MTG-LI data used for this study were kindly provided by EUMETSAT  from https://user.eumetsat.int/resources/user-guides/mtg-data-access-guide

How to cite: Fullekrug, M. and Kosch, M.:  Bolt from the blue caught on video, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21746, https://doi.org/10.5194/egusphere-egu26-21746, 2026.

Thunderstorms produce high-energy radiation events such as Terrestrial gamma ray flashes (TGFs) and Gamma ray Glows (GRGs) via bremsstrahlung during the acceleration of runaway electrons to relativistic energies. Although TGFs and GRGs are believed to originate from the same physical process (relativistic runaway electron avalanche (RREA)), TGFs are intense sub-millisecond bursts of X-rays, while GRGs are less intense, long-lasting X-rays emissions. 

Several balloon flight campaigns are being prepared to observe and better understand these energetic phenomena such as Strateole-2 and OREO funded by the French Space Agency (CNES). Strateole-2 is a stratospheric balloon campaign using superpressure balloons flying for several months between 18 and 20 km in the intertropical region (planned for the end of 2026). OREO is a lightweight balloon project aiming to launch several radiosondes directly into thunderstorms to probe in-situ high-energy emissions associated with the electrical activity.

In order to participate in these projects a dedicated instrument named XStorm has been developed [Pallu, et al., JGR, 128, e2023JD039180, 2023, https://doi.org/10.1029/2023JD039180] with a view to perform in-situ and remote measurements. XStorm is a lightweight gamma-ray spectrometer its conception allow us to detect gamma ray glows and TGFs near the sources.

In addition, XStorm contributes to a new ground-based measurement campaign that involves installing it at key positions to detect and analyze TGFs as well as GRGs. 

In this contribution, we present the XStorm detector, detailing its electronic architecture, operational principles, and performances, as well as the campaigns in which it will be used.

How to cite: Aziz, C.: XStorm: a Lightweight Gamma-ray Spectrometer Designed to Detect Terrestrial Gamma ray Flashes and Glows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21751, https://doi.org/10.5194/egusphere-egu26-21751, 2026.

How lightning initiates in thunderstorm fields well below the conventional breakdown electric field Ek, which is defined by the equality of the ionization and dissociative attachment coefficients in air [Raizer, 81 1991, p. 135], remains an outstanding question. We investigate a robust pathway for streamer ignition through the collision of charged hydrometeors. By extending a two-particle image-charge model [Cai et al., 2018, https://doi.org/10.1029/2018JD028407] to include an initial charge Q, we quantify how polarization, particle dimensions, and background fields control ignition thresholds. We identify a "diagonal valley" of optimal radius ratios where the required charge is minimized, and is significantly below the corona discharge limit of a single isolated hydrometeor. In ambient fields near 0.3Ek, where photoelectric feedback [Pasko et al., 2025, https://doi.org/10.1029/2025JD043897] can provide a sustained supply of seed electrons, this collision-mediated mechanism provides a pathway to overcome the charge-limiting constraints of isolated particles. These findings offer a consistent physical basis for the birth of lightning leaders in typical thundercloud environments.

How to cite: Jánský, J., Janalizadeh, R., and Pasko, V.: Threshold electric fields for streamer ignition from colliding charged hydrometeors in thunderstorms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22081, https://doi.org/10.5194/egusphere-egu26-22081, 2026.

Separation of charge via noninductive ice-ice collisions in clouds is widely accepted as the primary mechanism behind cloud electrification. However, not all clouds end up with the same charge distributions, as observed in various field campaigns and laboratory experiments over the last several decades. The distribution of charge in a given thunderstorm controls the polarity, frequency, and other characteristics of lightning produced by that storm, but charge distribution is very difficult to measure directly, especially at statistically significant scales. Of particular interest to the lightning community is the relationship between thunderstorm environments and lightning characteristics, where the charge distribution of storms bridges the gap between the two. This study will use intracloud (IC) lightning data from the Earth Networks Total Lightning Network (ENTLN) to investigate the statistical relationships between the proportion of negative IC flash frequency to environmental parameters such as charge reversal temperature altitudes, cloud base height, cloud depth, and warm vs cold cloud depth fraction derived from global reanalysis data for multiple regions around the world

How to cite: DiGangi, E., Ringhausen, J., Lapierre, J., and Zhu, Y.: Statistical Relationships between Negative Intracloud Flash Fraction and Environmental Parameters Controlling Cloud Electrification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22197, https://doi.org/10.5194/egusphere-egu26-22197, 2026.

This study utilizes the Earth Networks Total Lightning Network (ENTLN) combined with the Geostationary Lightning Mapper (GLM) to investigate lightning activity in hurricanes relative to hurricane structure and evolution for 5 years of hurricane seasons. This combination enables a more complete estimate of lightning activity than one detection method can provide alone, including the derivation of the cloud flash fraction (CFF) for each hurricane. Additionally, lightning characteristics for each individual hurricane as well as average trends and correlations to hurricane wind speed and brightness temperatures are explored. Overall, this research has the potential to provide further insight into tropical cyclone intensification and weakening.

How to cite: Ringhausen, J., Haliczer, D., Lapierre, J., DiGangi, E., and Zhu, Y.: Relationship Between Lightning Characteristics and Hurricane Intensity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22200, https://doi.org/10.5194/egusphere-egu26-22200, 2026.

It has been known for many decades that nitrogen oxide compounds (NO_x) are formed by lightning flashes due to the high temperatures in the lightning channel, which allows the otherwise tightly bounded N₂ and O₂ to react with each other. Lightning NO_x is then oxidized in cloud and rain drops to form nitric acid and deposited at the surface as nitrate (NO^-₃) in precipitation. This nitrate is a form of fixed nitrogen that can be taken up by ecosystems, especially where biological N fixation is limited.

Since 2011, researchers have repeatedly observed the so-called Great Atlantic Sargassum Belt, a gigantic carpet of seaweed that drifts from the equator towards the Caribbean when easterly winds prevail. Until now, the sources of nutrients fueling their rapid growth are unclear. It was hypothesized that nutrient runoff from overfertilization and rainforest deforestation might be responsible or upwelling of phosphorus-rich deep waters. However, these processes cannot completely explain the increase in Sargassum biomass observed during the past years. Nitrogen is a key element governing the dynamics and function of many ecosystems as many of them are limited in biologically available nitrogen supply. The lack of N is an important inhibitor on primary production in the tropics. Owing to this limitation, an increase in available N from lightning could increase the primary production and biomass accumulation.

Our analysis of the spatial distribution of lightning and Sargassum blooms over the tropical Atlantic show remarkable agreement during specific months of the year, as well as the annual cycle of the blooms that peak in the northern hemisphere summer.  While global lightning activity is expected to increase with rising global temperature, it is not clear that there has been a significant increase in lightning over the Atlantic in recent decades.  Nevertheless, lightning has not yet been considered as a possible source of nitrogen impacting the Sargassum blooms. 

How to cite: Price, C., Shay, A., and Golberg, A.: Is Lightning a driver of the Sargassum blooms in the Atlantic?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22205, https://doi.org/10.5194/egusphere-egu26-22205, 2026.

This paper explores how volcanic eruptions might be linked to changes in weather patterns. Gases released, like CO2 and SO2, play a key role in the lower atmosphere, influencing temperatures at the surface. Water vapor from eruptions can travel through all atmospheric layers, potentially creating more atmospheric rivers and disrupting the polar vortex. These changes can significantly affect weather, depending on the eruption's size, the types of gases released, the type of volcano, and its location. Furthermore, eruptions can cause abrupt shifts in atmospheric layers, leading to unexpected seasonal variations.  The study of volcanic eruptions and the weather consequences is paramount for understanding climate change better. Several factors from eruptions allow disturbances in the atmospheric layers mainly at the troposphere, stratosphere and mesosphere. Volcanic eruptions inject sulfur gases into the stratosphere, which convert to sulfate aerososls with e-folding residence time about one year.

How to cite: Hagen, M.: Influence of Volcanism Activity on Weather and Climate Changes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1944, https://doi.org/10.5194/egusphere-egu26-1944, 2026.

The 2021 Tajogaite eruption on La Palma was the most destructive volcanic event in recent Canary Islands history, lasting 85 days and producing extensive lava flows and tephra deposits. Beyond its immediate impacts on infrastructure and air quality, the eruption raised critical questions about how tephra properties influence environmental hazards and public health. Fine ash particles (<10 µm) pose respiratory risks, while soluble salts and trace elements leached from lapilli and ash-sized pyroclastic material can affect water and soil quality. Understanding these processes is essential for volcanic risk management and environmental protection.

To address these issues, we collected and analysed tephra samples throughout the eruption, applying dynamic image analysis (DIA), scanning electron microscopy (SEM), X-ray diffraction, and batch leaching tests. DIA proved highly effective compared to laser diffraction for detecting ultrafine particles relevant to health hazards. SEM revealed diverse morphologies, including fluidal shards and Pele’s hairs, and identified salts such as fluorides and sulfates on particle surfaces. Leaching experiments showed the rapid release of sulfates, chlorides, fluorides, and nitrates, with potential implications for groundwater and ecosystems. multivariate statistical analysis linked these soluble phases to magmatic volatiles and eruptive dynamics, which evolved through six stages marked by lava compositional changes and intermittent phreatomagmatic activity.

Our findings show that tephra from mafic eruptions, though less explosive than silicic events, can have a significant environmental impact. These results inform hazard assessments, guide civil protection strategies, and highlight the need for continuous monitoring of water quality and air pollution during and after eruptions. Ultimately, this research supports better preparedness for future volcanic crises and contributes to safeguarding public health and ecosystems.

This research was supported by the Canary Islands Smart Specialisation Strategy (RIS3 Extended 2021–2027) through the NEVA2 project (ProID2024010012), funded by the Canary Islands Agency for Research, Innovation and Information Society (ACIISI) and co-funded by the European Union under the Canary Islands ERDF Programme 2021–2027. Additional support was provided by the MESVOL Project (SD RD 1078/2021 LA PALMA) funded by the Spanish Ministry of Science and Innovation, the LAJIAL Project (PGC2018‑101027‑B‑I00; MCIN/AEI/10.13039/501100011033 and ERDF “A way of making Europe”), Project PID2022‑139990NB‑I00 (MCIU), and a pre‑doctoral fellowship (FPU20/05157). Institutional support was provided by the GEOVOL research group  (iUNAT, ULPGC) and Structure and Dynamics of the Earth (Generalitat de Catalunya, 2021 SGR 00413).

How to cite: Perez-Torrado, F. J., Fernandez-Turiel, J. L., Rodriguez-Gonzalez, A., Benavente, D., Cabrera, M. C., Estévez, E., García-Martínez, N., Lobo, A., and Paris, R.: Analysing how the properties of tephra from the 2021 Tajogaite eruption affected the environment in La Palma, Canary Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3467, https://doi.org/10.5194/egusphere-egu26-3467, 2026.

A reliable reconstruction of long-term eruptive activity is fundamental for understanding volcanic behaviour and for improving hazard assessment at volcanoes. Vulcano Island (Aeolian Islands, Italy) is characterized by recurrent Vulcanian activity and long repose periods, and can be reconstructed by multiple historical chronicles and by field geology analyses. Despite its importance, existing catalogues are fragmented, heterogeneous, and often lack systematic integration of geological and historical records. Here we present a revised and harmonized eruption catalogue for Vulcano Island, spanning from ~3500 BC to present, obtained through the critical revision of historical sources and their integration with dated volcanic deposits. Historical accounts were systematically analysed to distinguish eruptive activity from fumarolic unrest and were cross-correlated with stratigraphic, sedimentological, and geochronological data available in the literature. The resulting catalogue includes 60 eruptive events (54 of La Fossa and 6 of Vulcanello), classified by eruptive style following a standardized scheme and supported by explicit documentary and geological evidence. Cumulative curves and style-specific analyses reveal strong temporal variations, largely controlled by changes in settlement history and observational capability. Completeness analysis suggests that the catalogue is reliable for all eruptive styles only after ~1700 AD, while earlier periods are likely biased toward longer-lasting and higher-impact events. Using hierarchical cluster analysis, we show that Vulcano’s eruptive history is organized into macro-cycles, consisting of multi-phase sequences that include effusive, Strombolian, and Vulcanian activity, rather than isolated eruptions. These macro-cycles are interpreted as potential prolonged open-conduit phases and are recognizable in the last 1100 years of activity (from 900 AD onward). This result provides a robust framework for reinterpreting the concept of Vulcanian cycles commonly adopted for Vulcano, and for linking eruptive styles within coherent dynamic units.

How to cite: Li Castri, E., Branca, S., Ciuccarelli, C., Lucchi, F., Panelli, G., Costa, A., and Selva\, J.: Catalogue of eruptive events at Vulcano Island, Aeolian Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5059, https://doi.org/10.5194/egusphere-egu26-5059, 2026.

The dissemination of knowledge about natural hazards is one of the most powerful tools for reducing risk and strengthening the resilience of communities exposed to natural threats. Effective risk communication—accurate, accessible, and scientifically sound—is the foundation for awareness and sustainable land management. Building a true culture of risk requires long-term education that involves all social groups, starting with the younger generations.

Schools play a central role in this process, as they are the most effective channel to shape awareness and promote a balanced relationship between humans and the environment. As formal educational institutions, they not only provide scientific understanding but also foster confidence in research and in the value of culture. Alongside schools, scientific institutions that monitor and study hazardous natural phenomena have the responsibility to complement research with outreach and educational initiatives, ensuring that knowledge reaches all parts of society.

Recently, science museums and Science Centers became vital actors in this mission. Evolving from traditional exhibition spaces into interactive learning environments, they now engage a wide and diverse public through digital and multimedia tools. This transformation made scientific knowledge more accessible and appealing, supporting a deeper public understanding of natural processes and associated risks.

Within this framework, the Vesuvius Observatory of the Italian National Institute of Geophysics and Volcanology (INGV), stands as an exemplary case. Founded in 1841 by King Ferdinand II of Bourbon, it is the world’s oldest volcano observatory. Its primary mission is the monitoring of the active volcanoes of the Neapolitan area through an advanced surveillance network. Its historic building on the western slope of Vesuvius also houses a museum that preserves valuable scientific and artistic collections, including early instruments, minerals, rocks, paintings, photographs, and films documenting volcanic eruptions. Permanent exhibitions and multimedia installations lead visitors through the history of Vesuvius and the origins of volcano monitoring, making the museum both a scientific archive and a tool for public education.

The Observatory also contributed to other cultural initiatives, such as the geological section of the Archaeological Museum of Villa Arbusto (Ischia, Naples). This exhibition presents rocks and fossils collected by archaeologist Giorgio Buchner and illustrates the close relationship among archaeology, volcanology and environmental studies on the island. Through panels, multimedia displays, and reconstructions of archaeological excavations, visitors explore the geological evolution of Ischia and its long interaction with human settlements, learning how natural forces shaped history and culture.

Another major achievement is the creation of the Museum of the Vesuvius National Park in Boscoreale (Naples). Conceived as one of the main cultural and tourist centers of the area, the museum combines science, environment, and heritage. Its exhibits—panels, dioramas, videos, and interactive installations—guide visitors through the volcanic processes that formed the landscape, its ecosystems, and the ways in which human civilizations used local resources.

By integrating scientific, historical, and cultural perspectives, these initiatives transform museums into permanent centers for education and volcanic risk mitigation, fostering public awareness, promoting respect for the environment, and contributing to a more informed and resilient society.

How to cite: de Vita, S. and Di Vito, M. A.: Mitigating Volcanic Risk through Knowledge Dissemination and Awareness Raising: The Experience of the Museums Operated by the Vesuvius Observatory (INGV), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5308, https://doi.org/10.5194/egusphere-egu26-5308, 2026.

Carbon dioxide (CO₂) may constitute a hazard not only associated with active volcanic environments and ongoing eruptions, but also during periods of dormancy. CO₂ may be released in a silent and permanent way through the volcanic soils and, if accumulated in high concentrations, may act as an asphyxiant. Soil CO₂ surveys highlight the association between anomalous CO₂ zones and tectonic structures, as well as show the topographic and lithological control on gas emissions. Gas released on diffuse degassing areas is also highly affected by environmental parameters, such as the barometric pressure, air temperature, rainfall and wind speed, which can cause significant variations in the gas flux and result in seasonal trends on the CO₂ emissions.

Hazardous CO₂ concentrations have been detected in several areas of the world (e.g., DR Congo, France, Italy, Portugal, Spain, USA), and the gas may introduce in buildings and accumulate in such concentrations that reaccommodation of residents is requested. In the Azores archipelago, indoor CO₂ concentrations as high as 90 vol.% have been measured, even during quiescent periods of activity.

CO₂ susceptibility maps have been performed for several diffuse degassing areas in the Azores, and some villages have several buildings classified with high risk of CO₂ exposure. This study not only aims to discuss the criteria used for the definition of maps but also evaluates the adequacy of the defined strategy as well as the limitations of the proposed methodology, highlighting the relevance of performing surveys with high density of points. Other critical aspects are the existence of thermal anomalous zones and the need to account with the topography to characterize the area. The adequacy of the maps will be complemented with indoor and outdoor CO₂ measurements carried out in the study areas. The adequacy of the maps is supported by indoor and outdoor CO₂ measurements carried out in the study areas.

How to cite: Viveiros, F. and Silva, C.: Assessing CO2 emissions in diffuse degassing areas – a valuable tool for land-use planning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5999, https://doi.org/10.5194/egusphere-egu26-5999, 2026.