Climate hazards are already causing disasters with huge environmental, humanitarian, and economic impacts across the globe. As climate change continues to worsen and weather extremes become more frequent and intense, the consequences of these disasters will continue to escalate and our ability to anticipate and recover from them will be limited, including irreversible impacts for physical and social systems.
Science has made significant progress in projecting climate and related natural hazard risks and developed useful approaches for risk management and adaptation. However, the policy response to climate risks and the implementation of such approaches have often been limited and fragmented across the continent. Whilst some policies and approaches have been taken to anticipate and minimise the harm of such hazards, action remains insufficient, and often reactive rather than preventive. This session will connect what we know about climate hazards and risks, what we are currently doing to prepare for them or manage them, and what can be done to ensure adapted and comprehensive policies can be implemented.
A panel of experts including scientists, policymakers and practitioners will outline Europe’s current adaptation approaches as well as other methods that are currently working well. They will then discuss the limitations of existing approaches, policies, and tools and some of the challenges that exist exist in various sectors and spatial scales. Among the themes to be tackled, this GeoPanel will discuss the challenge of preparing for and managing cascading and compounding risks, building cohesive, EU-wide legislation and tools, and address climate impacts such as chronic, slow-onset hazards.
Lastly, the panel will explore concrete solutions currently in development, including the Integrated Resilience Framework, tools such as the European Climate Viewer, and examples of how effective climate hazards management have been implemented on a local level by policymakers, practitioners, and industry leaders.

With global climate change affecting the frequency and severity of extreme meteorological and hydrological events, it is particularly necessary to develop models and methodologies for a better understanding and forecasting of present-day weather induced hazards. Future changes in the event characteristics as well as changes in vulnerability and exposure are among the further factors for determining risks for infrastructure and society, and for the development of suitable adaptation measures. This session considers extreme events that lead to disastrous hazards induced by severe weather and climate change. These can, e.g., be tropical or extratropical rain- and wind-storms, hail, tornadoes or lightning events, but also floods, long-lasting periods of drought, periods of extremely high or of extremely low temperatures, etc. Papers are sought which contribute to the understanding of their occurrence (conditions and meteorological development), to the augmentation of risks and impacts due to specific sequences of extremes, for example droughts, heavy rainfall and floods, to assessment of their risk (economic losses, infrastructural damages, human fatalities, pollution), and their future changes, to studies of recent extreme events, to the ability of models to reproduce them and methods to forecast them or produce early warnings (in line with the “Early Warnings for All” initiative, launched in March 2023 by the United Nations and the World Meteorological Organization), to proactive planning focusing on damage prevention and damage reduction. In order to understand fundamental processes, papers are also encouraged that look at complex extreme events produced by combinations or sequences of factors that are not extreme by themselves. The session serves as a forum for the interdisciplinary exchange of research approaches and results, involving meteorology, hydrology, environmental effects, hazard management and applications like insurance issues.

Hydroclimatic extremes are increasingly recognized as interconnected environmental and societal hazard than isolated episodes of water availability and scarcity. Their impacts can propagate through hydrological systems and ecosystems, alter water availability and quality, affect agricultural productivity and influence human health and well-being. This session will explore the mechanisms and cascading pathways through which drought and related hydroclimatic extremes generate risks across water, ecosystems, and society.
The session will bring together emerging approaches for understanding and characterizing different forms of drought, including long-term conventional droughts- meteorological, Agricultural, Hydrological, Ecological, and Socio-economic droughts as well as rapidly developing Flash Droughts and their potential transitions to flood conditions. Contributions addressing drought characteristics, drought-to-flood transitions, underlying drivers and mechanisms, detection and identification techniques, monitoring and prediction, and changes under future climate change are particularly encouraged.
This session welcomes studies investigating the impacts of drought and associated hydroclimatic extremes on water resources and water quality, ecosystems, agricultural productivity, and human health, as well as their interactions, compound effects and cascading risks. Studies focusing on drought dynamics and impacts in data-scarce regions are particularly encouraged. Interdisciplinary research employing remote sensing observations, modelling, artificial intelligence, and other emerging methodologies, including assessments of drought and drought-to-flood transitions under climate change and future projections.
By bringing together researchers from climate science, natural hazards, hydrology, environmental sciences, ecology, agriculture, and health, the session aims to advance an integrated understanding of drought dynamics, rapid hydroclimatic transitions, cascading impacts, and associated risks. It further seeks to foster advances in monitoring, prediction, early warning, adaptation, and resilience to evolving hydroclimatic extremes.

The frequency and intensity of extreme floods are increasing worldwide, with direct consequences such as loss of life and property. Cutting-edge monitoring and simulation technologies are instrumental in guiding flood risk management. A variety of physical and conceptual hydrological and hydrodynamic models, as well as data-driven approaches (such as artificial intelligence, including machine learning), are available to inform flood risk assessment and management, including prevention, preparedness and recovery. These techniques provide the scientific community with a platform to explore the drivers of flood risk and develop effective flood risk reduction strategies. However, they also come with associated uncertainties.
This session aims to bring together experts, researchers, and practitioners to present and discuss recent developments in the field of flood risk mapping, assessment and management. Topics such as 1D, 2D and 3D modelling for flood risk assessment, emergency action planning and the analysis of dam and levees breaching, as well as the design of structural, non-structural and nature-based measures, are welcome. Research on the associated uncertainties, sensitivity analysis, and flood impact modelling is also relevant to the session.

Rapid transitions between droughts and floods are an emerging frontier in compound hydrometeorological extremes under climate change. These dry-wet whiplash events can trigger cascading impacts on agriculture, ecosystems, water resources, energy, and critical infrastructure that exceed those of isolated drought or flood events. Warming-induced changes in precipitation variability, atmospheric circulation, moisture transport, land–atmosphere feedbacks, and human water management are reshaping their frequency, intensity, seasonality, and predictability, yet key mechanisms and risk implications remain poorly constrained.
This session invites cutting-edge abstracts on the identification, physical mechanisms, predictability, attribution, risk assessment, and governance of rapid drought–flood or flood–drought transitions across spatial and temporal scales. We welcome contributions on event definitions and indicators; flash drought-flash flood linkages; atmospheric rivers, monsoon variability, and moisture transport; land–atmosphere–human feedbacks; climate variability and anthropogenic forcing; nonstationarity, regime shifts, and tipping points; and cascading socio-economic and ecological impacts.
We particularly encourage studies using advanced approaches, including remote sensing, high-resolution Earth system modeling, and machine learning, with a specific emphasis on hybrid physics-AI approaches. Contributions that bridge process understanding with operational applications are also invited, such as subseasonal-to-seasonal prediction, impact-oriented extreme event forecasting, dynamic multi-hazard risk mapping, multi-hazard early warning, adaptive water management, and disaster risk reduction.
By linking physical science, data science, and decision-making, this session aims to advance actionable knowledge and strengthen societal resilience to increasingly complex hydroclimatic extremes. We welcome diverse regional perspectives, as well as interdisciplinary collaborations that bridge natural science, engineering, social science and management practices.

Droughts affect ecosystems, economies, and societies in diverse and often interconnected ways. Understanding drought risk requires not only characterizing drought hazards and water scarcity, but also examining who and what is exposed, how vulnerability shapes impacts, and how societies respond and adapt. These dimensions interact across sectors and scales, with drought impacts evolving over time and being influenced by water use, management and other human activities, as well as by changing conditions such as land use and climate change.

This session will have Kerstin Stahl (Albert-Ludwigs-Universität Freiburg) as a solicited speaker and it brings together interdisciplinary research across the different dimensions of drought risk, from drought impact monitoring and hydrological responses to impact assessments, exposure analysis, vulnerability studies and adaptation and risk management strategies. We welcome contributions focusing on individual dimensions as well as research exploring their interactions, feedbacks, and dynamics.

Topics of interest include, but are not limited to:
- Dynamics, feedbacks, and socio-natural interactions of drought risks
- Drought (impact) monitoring, indicators, forecasting, and early warning
- Human responses and adaptive behaviour to drought and water scarcity
- Exposure and vulnerability assessments, including their dynamics
- Interactions between impacts across sectors (agriculture, forestry, energy production, livelihoods, among others) and scales
- Drought early action, preparedness, and adaptation
- Water management and allocation under drought conditions
- Water governance and decision-making
- Novel conceptual, data, modeling, and AI-based approaches for understanding drought risk

We welcome contributions from hydrology, geography, climate science, socio-hydrology, disaster risk research, water management, environmental governance, behavioral economics, and humanities.

With climate change and an accelerating hydrological cycle, dry/hot hydrological extremes are no longer represented by classic, slow-evolving droughts. Over the past decade, heatwaves, dry spells and flash droughts have emerged as threats producing major impacts across multiple sectors. Furthermore, their rapid development hinders our ability to forecast and mitigate.

Beyond their rapid development, their timing, intensity, preceding conditions, and locally significant vulnerabilities can worsen their impacts, including high plant mortality and crop losses, depletion of freshwater resources, wildfires, degraded air quality and ecosystem health, and even fatalities. Due to the rapid intensification, rapid response is vital, which demands improving monitoring strategies beyond the existing standards developed for slow-evolving droughts. Challenging to identify, difficult to compare across different climates and ecosystems, and driven by a complex combination of various physical processes, rapidly emerging dry/hot-extreme events are not yet well understood nor clearly defined. Consequently, there are few practical solutions for risk management and stakeholders.

This session invites novel contributions on statistical, physically based, remote sensing-based, and qualitative methods for improving our physical understanding, monitoring, modelling, predicting, and assessment of environmental, social, and economic impacts of rapidly emerging dry/hot-extremes. Contributions that discuss and propose definitions for different types of rapidly emerging dry/hot-extremes, and investigate their changes, trends, and impacts are also invited. Contributions that investigate the atmospheric drivers and land-atmosphere feedback loops are highly encouraged. We encourage submissions from atmospheric sciences, hydrometeorology, hydrology, social sciences, and inter- and trans-disciplinary studies. Case studies, large-sample studies, statistical and time series analyses and machine-learning applications, socio-hydrology approaches, and citizen science experiences are welcome. Note that submissions that do not relate specifically to flash droughts, dry spells or other rapidly emerging droughts will be transferred into more relevant sessions. Submissions from early career researchers and diverse backgrounds are especially encouraged.

Heat extremes are one of the deadliest meteorological events and they are increasing in intensity and frequency due to climate change. Their impacts on society will increase dramatically in the future, with some studies suggesting that human habitability limits could be crossed locally.
This session invites new research that addresses the challenges posed by extreme heat and its impacts. We encourage submissions from a broad range of disciplines including weather and climate sciences, climate impact studies, global and occupational health and epidemiology.Studies focusing on the Global South as well as submissions focusing on recent heatwaves and their systemic impacts, extreme heat episodes associated with the 2026-2027 El Niño event and projected unprecedented events, are particularly welcome.
Suitable contributions may: (i) assess the definitions and indicators typically used to describe extreme heat (stress) and its impacts, (ii) quantify the drivers and underlying processes of extreme heat in observations and/or models; (iii) quantify historical climate trends and projections (iv) examine the challenges of monitoring and predicting extreme heat on all temporal scales; (v) assess impacts on human and occupational health and mortality; (vi) focus on societal response and adaptation to extreme heat in a warming climate, including heat-health early warning systems and anticipatory action, adaptation and management solutions; (vii) introduce transdisciplinary research frameworks for assessing impacts on human health, economic productivity, and the environment.

Hail is one of the most damaging weather hazards worldwide, with far-reaching impacts on infrastructure, agriculture, ecosystems, and society. Despite recent advances, significant challenges remain in understanding, observing, predicting, and projecting hail across weather and climate timescales.
This session welcomes contributions on all aspects of hail research, from fundamental process understanding to societal applications. Topics include, but are not limited to, observational studies using both historic and/or novel datasets, storm-scale and microphysical processes, extreme and compound events, numerical and statistical modelling, forecasting and nowcasting, machine learning and AI, climate variability and change, as well as risk assessment and insurance-related applications. Within these areas, studies may lean towards either process- or impact-oriented perspectives.
We also encourage contributions where hail is an important aspect of the broader research scope. This can be severe convective storm research, such as studies of supercells or mesoscale convective systems, but also natural hazard risk frameworks that include hail. Studies linking atmospheric science with hazard and risk assessment, impact modelling, insurance applications, impact-based forecasting and warning, resilience, and adaptation are especially encouraged.
This session aims to foster interdisciplinary exchange and strengthen links between atmospheric science, climate research, natural hazards, and the insurance and risk communities to advance our understanding of hailstorms across weather, climate, and society. We thus look forward to receiving interdisciplinary contributions connecting academia, operational weather services, industry, government, and decision makers.

Lightning is a powerful manifestation of atmospheric electrical breakdown, which results from micro- and macro-scale charge separation processes within thunderstorms. Lightning is associated with deep convective storms, severe weather, hail, torrential rains, and flash floods. It is also responsible for numerous wildfires, burned areas, and atmospheric fire emissions. Furthermore, lightning impacts Earth atmosphere from the troposphere to the mesosphere and serves as a strong indicator of regional and global convective processes linked to climate change. It produces nitrogen oxides, which are precursors to ozone production. Finally, thunderstorms and lightning are essential parts of the Global Electrical Circuit (GEC) and control the fair-weather electric field. They are also associated with energetic radiation up to tens of MeV, spanning from sub-millisecond Terrestrial Gamma-ray Flashes (TGFs) to tens of seconds of gamma-ray glows.

This session seeks contributions from research in atmospheric electricity with emphasis on:

Atmospheric electricity in fair weather and the global electrical circuit
Effects of dust and volcanic ash on atmospheric electricity
Thunderstorm electrification, dynamics and microphysics
Lightning initiation, propagation, and attachment
Middle-atmospheric Transient Luminous Events
Energetic radiation from thunderstorms and lightning
In natura and laboratory experimental investigations of lightning discharge physics processes
Remote sensing of lightning and related phenomena by space-based sensors
Thunderstorms, flash floods, tropical storms and severe weather
Lightning-ignited wildfires and ecological effects of lightning
Connections between lightning, climate and atmospheric chemistry
Modelling of thunderstorms and lightning
Nowcasting and forecasting of thunderstorms using machine learning and AI
Regional and global lightning detection networks
Lightning Safety and its societal effects
Planetary lightning in the solar system and beyond

The session includes studies presenting a critical analysis of the sources of uncertainty in volcanic hazard assessment and offering an integrated quantification of the multihazards associated with volcanic activity. The purpose of the session is to discuss the contributions of new methodological and technological advances and the results arising from the integration of well-established methodologies, which have permitted major advances in the assessment of volcanic hazard in specific sites and to highlight both positive and negative impact to develop educational programs on preparedness, response, and overall influence on vulnerability. We here invite contributions highlighting the i) most recent advances in volcanic hazard assessment, both on recently active volcanic systems and on volcanoes with long lasting quiescence periods and ii) exploring the influence of recent advances on educational strategies and, specifically, the role of Earth Science Museums and targeted research programmes including educational initiatives in modifying the adaptive response to, as well as the recovery of populations from volcanic disasters.

Volcanic islands are dynamic and hazard-prone environments where the interplay of complex geological processes poses unique scientific and logistical challenges for disaster risk reduction. Assessing these hazards is inherently difficult because phenomena operate across wide spatial and temporal scales, often complemented by limited observations and high uncertainty. These challenges are further amplified by the fact that islands geography severely restricts evacuation routes, logistics, and emergency response, as well as the study and the monitoring of large offshore sectors of the volcano. Moreover, high seasonal tourist mobility, land-use pressures, and offshore observation gaps further compound socio-economic vulnerabilities and monitoring limits. In this context, we request contributions of eruptive as well as non-eruptive hazards like flank instabilities, landslides, tsunamis, earthquakes, and gas emissions that can generate complex, cascading multi-hazard scenarios with particularly high impacts.
This session brings together researchers working on the assessment, monitoring, modelling, and mitigation of volcanic hazards in island settings. We welcome contributions addressing advances in hazard characterization, remote sensing, geophysical and geochemical monitoring, numerical modelling, and probabilistic hazard assessment. Particular attention will be given to approaches addressing uncertainty, variability, and multi-hazard interactions. The session explores cutting-edge methodologies in hazard modeling, innovative monitoring techniques, and interdisciplinary approaches to be translated into effective risk-reduction strategies and build resilient island communities.

Recent volcanic activity worldwide has provided exceptional opportunities to study magmatic systems in near-real time. These events show both the societal impact of volcanism, from threats to infrastructure and communities to the challenges of crisis response. They also offer important opportunities to investigate magma storage, transport, and eruption dynamics.

This session invites contributions encompassing geochemistry, petrology, and geophysics, as well as monitoring, modeling, and crisis management, to advance our understanding of eruptive processes and their consequences. Case studies from recent eruptions in Iceland, Hawai‘i, the Canary Islands, Italy, Mexico, Indonesia, the Philippines and other active volcanic regions are particularly welcome. We welcome studies based on field observations, laboratory experiments, geochemical and petrological analyses, numerical modeling and data-driven approaches, as well as contributions that consider the operational and societal dimensions of eruptive crises. By bringing together diverse perspectives, this session aims to promote dialogue across disciplines and between scientists and hazard managers. Topics of interest include magma plumbing system architecture, eruption triggers, timescales of magmatic processes, real-time geophysical and geochemical monitoring, and hazard assessment and mitigation strategies.

The session will provide a platform to synthesize lessons learned from recent eruptions, highlight methodological innovations, and promote integrated strategies for advancing volcanological research while strengthening responses to volcanic activity.

Artificial intelligence (AI) and machine learning (ML) are becoming increasingly important tools in volcano science, with applications spanning geophysical monitoring, remote sensing, numerical modelling, volcano-tectonics, igneous petrology, geochemical data analysis, and volcanic hazard assessment. These approaches offer new opportunities to automate tasks, enhance modelling strategies, analyse increasingly complex datasets, and accelerate knowledge discovery. At the same time, their integration into scientific research introduces important methodological and epistemological challenges, including understanding what AI models actually learn, assessing the physical and geological meaning of identified patterns, and ensuring transparency, reproducibility, robustness, and interpretability.
Beyond their predictive capabilities, AI and ML also raise broader questions about how data-driven methods contribute to scientific understanding. In volcano science, where observations are often incomplete and direct constraints on subsurface processes are limited, identifying correlations is not necessarily equivalent to understanding the mechanisms that generate them. Integrating domain knowledge, physical constraints, and causal reasoning may therefore help develop approaches that are both effective and scientifically meaningful. These issues become especially important in high-risk contexts such as volcanic hazard assessment, risk mitigation, crisis management, and risk communication.
This session welcomes contributions across volcano science, including geophysics, remote sensing, experimental and numerical modelling, volcano-tectonics, igneous petrology, geochemistry, monitoring, and hazard assessment. We particularly encourage work on data-driven discovery, physics-informed ML, automated data analysis, model interpretability and explainability, uncertainty assessment, reproducibility, and the integration of AI-derived results with geological and physical understanding. We also welcome emerging approaches such as digital twins and agentic AI, as well as epistemological perspectives on how AI may influence scientific inference, explanation, hypothesis generation, and scientific understanding. Finally, the session welcomes studies on the responsible use of AI in volcanic-hazard applications and its interaction with scientists, volcano observatories, stakeholders, and decision-makers.

Hazard situations involve multiple forms of knowledge and institutional action, including scientific assessment, scenario development, planning, risk communication and emergency management. These dimensions are often addressed separately, despite their interdependence across the different phases of risk governance. The session examines how scientific scenarios are translated into institutional decisions, how risks are communicated to exposed communities, and how knowledge and responsibilities evolve during and after emergencies. Contributions are invited to analyse the interaction between these dimensions across different hazards, institutional settings and geographical contexts, including approaches that focus on individual disciplines or phases.

Debris flows are among the most dangerous natural hazards that threaten people and infrastructure in both mountainous and volcanic areas. The study of the initiation and dynamics of debris flows, along with the characterization of the associated erosion/deposition processes, is of paramount importance for hazard assessment, land-use planning, design of mitigation measures and early-warning systems. In addition, climate change and economic development challenge risk management, and further research is needed to understand the consequences.
A growing number of scientists with diverse backgrounds are studying debris flows. The difficulties in measuring parameters related to their initiation and propagation have progressively prompted research into a wide variety of laboratory experiments and monitoring studies. However, there is a need to improve the quality of instrumental observations that would provide knowledge for more accurate modelling and hazard maps. Nowadays, the combination of distributed sensor networks and remote sensing techniques represents a unique opportunity to gather direct observations of debris flows to better constrain their physical properties. At the same time, computer-aided simulations of physical processes, hazard assessment, and mitigation design are undergoing a revolution due to the widespread adoption of AI and data-driven numerical models. Not only do these developments mark an exciting era for advancing our understanding of complex natural hazards, but they also require researchers from diverse disciplines to collaborate in order to unlock their full potential.
Scientists working in the field of debris flows are invited to present their recent advancements. In addition, contributions from practitioners and decision makers are also welcome. Topics of the session include field studies and documentation, mechanics of debris-flow initiation and propagation, laboratory experiments, modelling, monitoring, impacts of climate change on debris-flow activity, hazard and risk assessment and mapping, early warning, and alarm systems.

Landslides rarely occur as isolated events, nor can they be fully understood through a single methodological lens. As climate change and human activities alter environmental forcing, assessing landslide hazards requires capturing complex physical processes across diverse spatio-temporal scales, from micro-scale geomechanics to slope stability and catchment-wide hazard chains. At the same time, rapid advancements in data-driven methods, remote sensing, and machine learning / AI offer novel tools for spatial and temporal prediction, albeit often at the cost of physical explainability.

This session aims to bridge the gap between process-based physical understanding and data-driven prediction in landslide and (multi-)hazard assessments. We seek contributions that showcase how coupling empirical data, physics-informed statistical frameworks, and geomechanical models can yield robust, transparent hazard forecasting.

We welcome submissions addressing, but not limited to, the following topics:
- Bridging geomechanics-based slope stability with broad-scale susceptibility and hazard assessments.
- Integrating machine learning with physical process understanding, including explainable AI tools and physics-informed data-driven methods.
- Assessing process chains and cascading hazard interactions, covering landslide lifecycles with integrated methodologies, especially under non-stationary climate and environmental forcing.
- Mapping uncertainty propagation and information loss when scaling models from slope to catchment levels.

Debris flows and snow avalanches are among the most catastrophic hazards in mountainous settings. Surface erosion, slope failure or dam breaching are among the initiation mechanisms of debris flow, which can be magnified during the subsequent flowing process, with disastrous consequences for urbanized settings. Understanding whether an initial rainfall forcing evolves into a hazardous debris flow requires a modelling framework capable of representing the different stages of the process and their interactions. To this end, analytical approaches and numerical modelling provide fundamental insights into the interplay of mechanical equilibrium, soil saturation and flow propagation. These components can be combined into predictive applications for the design of early warning systems. We invite contributions addressing the modelling of debris flows at different levels of complexity and scales, from watershed to the interaction with hydraulic works, including rainfall-induced slope failure and surface erosion, transformation of landslides into debris flows, flow initiation and acceleration, entrainment of bed material, flow-topography interactions, rheological behaviour and deposition. Similar modelling challenges arise in snow avalanches, where the thermal forcing often causes the transition from snowpack instability to rapid flow development involving strongly nonlinear interactions between snow properties, terrain and flow dynamics. Contributions combining different modelling approaches or coupling different physical processes are particularly encouraged, as are studies investigating how information generated at one stage of the process can be transferred to the next. The session aims to foster discussion on how process-based numerical modelling can move from the simulation of individual phenomena towards integrated predictive frameworks, contributing to bridge the gap between fundamental process understanding and operational applications for hazard mitigation and early warning.

Mountain environments are highly sensitive to dynamic hydroclimatic forcing. Intense precipitation, rain-on-snow events, snowmelt and prolonged wet periods interact with topographic, geological, cryospheric, and ecological controls to govern the initiation of rapid alpine mass movements, including rockfalls, rock avalanches, shallow landslides, debris flows, and snow avalanches. Environmental changes such as rising temperatures, retreating glaciers, degrading permafrost, and shifting snow or precipitation regimes can fundamentally alter the thermal and hydrological dynamics of mountain slopes and subsequently the frequency, magnitude, timing, and spatial distribution of mass movements.

While the complex and nonlinear mechanisms of rapid alpine mass movements have traditionally been analyzed at the local slope scale, understanding and predicting them across regional, national, and transnational scales remains a major scientific frontier. Growing archives of Earth observation and environmental monitoring data, combined with continuous advances in modeling approaches and tools, propel the shift. These developments now enable the delineation of hazardous zones and the explicit inclusion of elements at risk across vast regions, facilitating a transition toward large-area, impact-based assessments. At the same time, the adoption of sophisticated sampling techniques is increasingly vital to address and mitigate data biases inherently present in large and heterogeneous hazard inventories.

We welcome contributions that leverage emerging data sources and technologies on a large scale, integrating physics-based simulations, empirical-statistical modeling, and machine learning to capture these complexities, improve spatio-temporal susceptibility mapping, hazard mapping, and regional early warning systems, and further integrate potential consequences, risks, and impacts. Contributions from across disciplines and areas of disaster risk management are highly encouraged. Particular interest lies in studies that bridge the gap between localized, process-based physical understanding and large-area predictive modelling, as well as research that translates scientific outputs into actionable strategies for climate adaptation and resilient mountain communities. We explicitly welcome submissions that address the limitations and bottlenecks of these approaches, enabling the scientific community to learn from shared challenges and avoid common pitfalls.

Landslides and geo-hydrological hazards affect every region of the world each year, and their impacts are being reshaped by climate change, urban expansion and land-use pressure. The rising toll of damaging events has pushed authorities, practitioners and scientists towards monitoring strategies that are reliable, operational and, where possible, low-cost. A well-designed monitoring system for landslides and ground deformation is today fundamental for both long- and short-term risk reduction.
The temporal evolution of a slope can be captured in many ways: conventional and novel in situ instrumentation (inclinometers, sensors arrays, distributed fibre-optic sensing, low-cost IoT networks), ground-based platforms (GB-InSAR, terrestrial laser scanner, robotic total stations), UAV surveys, and satellite data (both optical and radar), including the opportunities opened by recent and forthcoming SAR missions and constellations (Sentinel-1C/D, SAOCOM, LuTan-1, NISAR). All these approaches share one purpose: measuring displacement through time in order to anticipate future behaviour or, at least, to reconstruct the recent past. Real-time, near-real-time and deferred-time strategies can each be effective, depending on the process under study, the instrument selected and the level of risk considered acceptable.
The session is aligned with the aims of the International Consortium on Landslides, namely: i) promote landslide research for the benefit of society; ii) integrate geosciences and technology within cultural and social contexts in order to evaluate landslide risk; and iii) combine and coordinate international expertise.
We welcome contributions on: i) innovative in situ, ground-based, airborne and satellite monitoring applications; ii) multi-temporal, multi-sensor and multi-platform case studies supporting risk management; iii)integration and synergistic use of different techniques, including cross-validation between sensors; iv) displacement-based early warning, velocity thresholds and failure forecasting; v) new tools and processing chains, from big-data management to AI and machine-learning approaches; vi) lessons learned from operational monitoring systems, including their limitations and failures.
Case studies from data-scarce regions and low-cost, transferable solutions are particularly encouraged.

Landslide research benefits from a multidisciplinary approach that integrates observations of slope morphology, surface conditions, and their evolution over time. The growing availability of different sensor types and long-term datasets, along with recent advances in remote sensing techniques and data-driven methods, offers great opportunities to investigate landslides. This session brings together recent developments in landslide identification and characterization and particularly welcomes contributions including:
• Automatic and semi-automatic landslide detection and classification, including machine- and deep-learning approaches;
• Regional-scale landslide inventories, landslide susceptibility assessment, monitoring and early-warning applications;
• Integration of heterogeneous datasets from different techniques (optical, SAR, InSAR time series, DEM-derived, and geophysical information) for landslide identification and characterization;
• Application of geomorphological mapping, repeated topographic observations, long-term monitoring, and multi-temporal DEMs (e.g., bistatic SAR/TanDEM-X);
We welcome contributions addressing large landslides across different triggering mechanisms, scales, and environmental settings, from local case studies to regional-scale applications.

Landslides and slope instabilities are major hazards worldwide, yet the fundamental mechanisms governing their initiation, evolution, and failure dynamics remain active areas of research. These processes are controlled by complex, heterogeneous subsurface properties and dynamic processes operating across broad temporal (seconds to decades) and spatial scales (grain size to entire slopes). Improving the characterization and forecasting of slope instabilities requires innovative frameworks that explicitly account for these multidimensional variabilities.

This session invites contributions presenting novel methodologies, emerging trends, and case studies in the characterization, monitoring, forecasting, and early warning of slope failures. We particularly encourage submissions that demonstrate the integration of geophysical, geotechnical, geological, and remote sensing datasets to build robust landslide models capable of characterizing subsurface structure, identifying controlling processes, and tracking temporal evolution.

Specific topics of interest include:

Multi-method frameworks combining geophysical, geotechnical, and remote sensing techniques.
Applications of machine learning and AI to landslide hazard assessment, forecasting, data integration, and pattern recognition.
Time-lapse geophysical monitoring of subsurface deformation, hydrological processes, and other changes.
Joint or constrained inversion of geophysical and geomechanical parameters using geological data and geophysical imaging.
Integrated monitoring and early warning frameworks combining in situ, subsurface, and remote observations.

Recognizing the inherently interdisciplinary nature of slope stability research, we welcome contributions that bridge disciplinary boundaries and advance integrated approaches to understanding, monitoring, and anticipating slope failure addressing a broad spectrum of instability types, including natural landslides, engineered slopes, and climate-induced failures.

Large landslides in rock, debris, and ice in glacial masses, represent enormous risks. These complex systems are difficult to describe, investigate, monitor, and model. Hence a reliable model of these phenomena requires acquisition and analysis of all available data to support successive steps up to the management of early warning systems.
Large instabilities affect all materials (rock, weak rocks, debris, ice), from low to high altitudes, evolving as slow or fast complex landslides. This and the complex dependency on forcing factors result in different types and degrees of hazard and risk. Some aspects of these instabilities are still understudied and debated, because of difficult characterization and few cases thoroughly studied. Regional and temporal distribution, relationships with controlling and triggering factors are poorly understood resulting in poor predictions of behavior and evolution under present and future climates. How will it change their state of activity under future climatic changes? How this will impact on existing structures and infrastructures? How can we improve our predictions? Relationships among geological and hydrological boundary conditions and displacements are associated with the evolution in space and time of thermo-hydro-mechanical controls as well as the properties of the unstable mass. Even for well-studied and active phenomena warning thresholds are mostly qualitative, based on semi-empirical approaches. Hence a multidisciplinary approach and robust monitoring data are needed. Many modeling approaches can be applied to evaluate instability and failure, considering triggerings, and failure propagation, leading to rapid mass movements. Nevertheless, these approaches are still phenomenological and have difficulty explaining the observed behavior. The impacts of such instabilities on structures represent a relevant risk and an opportunity in terms of investigations and quantitative measurements of the effects on tunnels, dams, and roads. The design of these structures and knowledge of their expected performance are fundamental.
We invite to present case studies, share views and data, discuss monitoring and modeling approaches and tools, to introduce new approaches for threshold definition, including advanced numerical modeling, machine learning for streamline and offline data analyses, development of monitoring tools, and dating or investigation techniques.

Landslides can trigger catastrophic consequences, leading to loss of life and assets. In specific regions, landslides claim more lives than any other natural catastrophe. Anticipating these events proves to be a monumental challenge, encompassing scientific curiosity and vital societal implications, as it provides a means to safeguard lives and property.
This session revolves around methodologies and state-of-the-art approaches in landslide prediction, encompassing aspects like location, timing, magnitude, and the impact of single and multiple slope failures. It spans a range of landslide variations, from abrupt rockfalls to rapid debris flows, and slow-moving slides to sudden rock avalanches. The focus extends from local to global scales.

Contributions are encouraged in the following areas:

Exploring the theoretical facets of predicting natural hazards, with a specific emphasis on landslide prognosis. These submissions may delve into conceptual, mathematical, physical, statistical, numerical, and computational intricacies.
Presenting applied research, supported by real-world instances, that assesses the feasibility of predicting individual or multiple landslides and their defining characteristics, with specific reference to early warning systems and methods based on monitoring data and time series of physical quantities related to slope stability at different scales.
Evaluating the precision of landslide forecasts, comparing the effectiveness of diverse predictive models, demonstrating the integration of landslide predictions into operational systems, and probing the potential of emerging technologies.

Should the session yield fruitful results, noteworthy submissions may be consolidated into a special issue of an international journal.

Landslide early warning systems (LEWS) are cost effective non-structural mitigation measures for landslide risk reduction. For this reason, the design, application and management of LEWS are gaining consensus not only in the scientific literature but also among public administrations and private companies. LEWS can be applied at different spatial scales of analysis, reliable implementations and prototypal LEWS have been proposed and applied from slope to regional scales.
The structure of LEWS can be schematized as an interrelation of the following main components: monitoring, modelling, forecasting, warning, response. However, tools, instruments, methods employed can vary considerably with the scale of analysis, as well as the characteristics and the aim of the warnings/alerts issued. For instance, at local scale instrumental devices are mostly used to monitor deformations and hydrogeological variables with the aim of setting thresholds for evacuation or interruption of services. At regional scale hydro-meteorological thresholds are widely used to prepare a timely response of civil protection and first responders. Concerning modelling techniques, analyses on local slopes generally allow for the use of numerical models, while statistical, probabilistic and physical-based models are widely used for large areas.

This session focuses on LEWS at all scales and stages of maturity, from prototype to active and dismissed ones. Test cases describing operational application of consolidated approaches are welcome, as well as works dealing with promising recent innovations, even if still at an experimental stage.
Contributions addressing the following topics will be considered positively:
- real-time monitoring systems (IoT)
- prediction tools for warning purposes
- in-situ monitoring instruments and/or remote sensing devices
- analysis of hydro-meteorological drivers to enhance forecasting
- warning models for issuing warning
- operational applications and performance analyses
- machine learning techniques applied for early warning purposes

Landslides and slope instability phenomena – affecting diverse materials with a variety of mechanisms (e.g., earthslides, rockfalls, debris flows) – are recognised to be driven by weather patterns largely differing in terms of variables (rainfall, temperature, snow melting) and significant time span (from a few minutes up to several months). However, local modifications induced by human intervention, such as socio-economic-induced land use/cover changes, reduced soil management due to land abandonment, or the implementation and maintenance of Nature-Based Solutions, are recognised to play a key role in defining landslide hazard and risk. In turn, these local human-induced factors can be strongly influenced by weather dynamics and climate change. For instance, hydrological and thermal regimes regulate vegetation suitability, then land cover and, in turn, landslide hazard and risk.
A clear and robust evaluation of how ongoing and expected global warming and the resulting climate change can affect these factors and, hence, landslide risk represents a clear key need for practitioners, communities, and decision-makers.
This session aims to provide a discussion forum for studies concerning the analysis of the role of climate-related variables and slope-atmosphere interaction on landslide triggering, propagation, hazard, and risk, and/or on the effectiveness of protection measures across different geographic contexts and scales. Test cases and investigations (by exploiting monitoring and modelling approaches) to evaluate ongoing and future landslide activity are welcome. Furthermore, investigations focused on data-driven approaches (Machine Learning, AI), through which the variations induced by climate and environmental changes on triggering, dynamics, and hazard are analysed, are greatly welcome.

Over the last decade there has been increasing recognition that patterns of landsliding vary across temporal and spatial scales challenging previous assumptions regarding the invariance of processes. Short-term temporal effects such as landslide preconditioning following large earthquakes, deforestation or wildfire, and long-term temporal effects related to river incision, drainage caption and hillslope readjustments show that a few years of data may not fully capture longer timescales. At the same time, recent work on path-dependency and correlated landsliding highlights knowledge gaps in the understanding of how the spatial and temporal context shapes landslide patterns, with past damage shaping failure processes. These interrelated processes have profound implication for landslide susceptibility, risk mitigation and understanding the broader role of landslides in landscape evolution. This session invites contributions that explore these interrelated themes through field survey, laboratory analysis, remote sensing, data analysis, and diverse modelling approaches to understand the mechanisms and processes from small to large temporal and spatial scales.

Rockfalls, rockslides and rock avalanches as well as other alpine mass movements are among the primary drivers of landscape evolution in steep terrain and they are some of the most hazardous and least anticipated processes.

This session aims to bring together state-of-the-art methods for identifying, predicting, assessing, quantifying, and protecting against rock slope hazards and alpine mass movements. We seek innovative contributions from investigators dealing with all stages of rock slope and alpine hazards such as rock-ice avalanches, glacier-related hazards, debris flows or hazard cascades originating from the periglacial environment.

Innovative contributions dealing with alpine mass movement predisposition, triggering, transport, and deposition are welcome, including (1) insights from field observations and/or laboratory experiments; (2) applications of statistical methods and/or artificial intelligence to identify and map mass movements; (3) in-situ or remote-sensing based monitoring approaches; (4) mass movement modeling for the analysis and interpretation of the governing physical processes – from conceptual frameworks to theoretical and/or advanced numerical approaches; (5) the development of strategies applicable for hazard assessment, mitigation and protection; (6) the impact of weather and climate on alpine mass movements, climate change attribution strategies, as well as the role of science at the interface with society; as well as (7) preparedness and risk reduction, and studies that integrate social, structural, or natural protection measures.

Landslide Inventory Maps (LIMs) are the simplest tool to report the spatial distribution of landslides in a territory. They can be prepared using different techniques and base data (e.g. remote sensing images), each bringing intrinsic limitations and potential sources of mapping errors, hence affecting the overall accuracy and reliability.
LIMs are a precious source of information for any subsequent analyses in landslide research (e.g., land management and planning, model training and validation, susceptibility, hazard, and risk assessment, among others). A common operational assumption carried out when using such data is to consider them as “correct”, which results in transferring/propagating the mapping error(s) to the subsequent products.
Recent research works have defined the quality of LIMs as the result of three factors: geographic accuracy, thematic accuracy, and completeness/statistical representativeness. Geographic accuracy refers to the location, size, and shape of each landslide reported in the LIM. Thematic accuracy refers to the consistency of attributes assigned to each landslide in the LIM (e.g. classification, degree of activity, age/date of occurrence, among others). Completeness refers to the ratio of landslides reported in the LIM and the “ground truth”. Since the ground truth is hardly available, more recently the concept of statistical representativeness has been preferred, i.e. assuring that the statistical distribution of landslides reported in the LIMs is a statistically representative sample of the actual distribution of landslides in an area. Each of these aspects is currently under-explored in terms of evaluation/quantification/metrics, propagation, and handling/management in derivative maps.
Within this general framework, this session welcomes contributions specially focused on (but not necessarily limited to) the following topics:
• Definition of metrics (numeric, heuristic, morphometric, etc.) for the evaluation of mapping accuracy, errors, and uncertainty;
• Statistical modelling of mapping errors;
• LIMs quality assessment methods;
• Impact of error propagation in maps obtained from LIMs, including training of machine learning and/or AI-based detection algorithms, susceptibility models, hazard and risk assessment;
• Defining links between LIMs quality and use limitations.
In contributions, all methods for the preparation of landslide inventories are welcome, from manual to semi- and fully automated.

This session focuses on the hydrological and eco-hydrological processes that control landslide predisposing and triggering conditions, focusing on surface and subsurface water dynamics from local slopes to regional catchments. As rainfall regimes shift under climate change, understanding and quantifying these hydrological processes is critical for reliable landslide forecasting and early warning.
We welcome research on slope (eco-)hydrological processes and their upscaling to catchments, including evaporation, pore-water pressure buildup, preferential flow, and soil-bedrock interactions, as well as eco-hydrological mechanisms such as root-regulated drainage, suction dynamics, transpiration, and canopy interception. Disentangling these coupled water dynamics is essential for moving beyond the limitations of traditional empirical rainfall thresholds.
We invite contributions from field monitoring, novel measurement techniques, and physics-based, numerical, and data-driven modeling that advance our understanding of how these processes affect landslide occurrence.

Coastal cliff collapse across Europe poses an escalating threat to human lives and critical infrastructure due to accelerated erosion driven by climate change and sea-level rise. Coastal cliff response is governed by complex interactions between geological, geomorphological, oceanographic and climatic processes. Environmental forcings such as temperature fluctuations, moisture variations, rainfall, sea-level changes and wave impacts can progressively alter stress conditions within coastal cliffs. These processes can promote cumulative damage, progressive degradation and cliff retreat.

Understanding how these drivers influence the degradation, deformation and progressive failure of coastal rock masses is becoming increasingly important for reliable hazard assessment and long-term risk mitigation in highly vulnerable coastal environments. This session, promoted under the auspices of the AIGA’s (Italian Association of Engineering and Environmental Geology) Working Group on Rocky Coasts, brings together researchers investigating coastal landslides, rockfalls and cliff instability through monitoring, remote sensing, field and laboratory investigations, numerical modelling and data-driven approaches. While focused on coastal environments, the session also welcomes contributions investigating the response of similar slopes to environmental and climatic forcings in analogous settings, where progressive damage accumulation and climate-driven degradation processes play a key role.

We encourage submissions addressing multi-scale monitoring strategies, satellite and ground-based remote sensing, geophysical investigations, laboratory and in-situ testing, and the characterization of material properties controlling cliff response to environmental forcing. Contributions integrating monitoring observations with numerical models (e.g. FEM, DEM, FDEM, CFD and coupled thermo-hydro-mechanical approaches) are particularly encouraged. The session also seeks studies exploring future evolution scenarios through climate projections, digital twins, and applications of machine learning and artificial intelligence. Special emphasis will be given to predictive frameworks combining monitoring data, environmental forcing and climate scenarios to support hazard assessment and coastal adaptation.

Loess deposits cover large areas of the Earth and host dense populations and major infrastructure worldwide. Although widely known as a terrestrial paleoclimate archive, loess is above all a problematic geomaterial: its open metastable micro-fabric, high porosity and water sensitivity make it prone to liquefaction, subsidence, erosion, and catastrophic landsliding under intense rainfall, irrigation, water-table rise, human induced loading and vibrations, or seismic shaking. Climate change and rapid land use transformation are altering the drivers of these processes, making the hazard non-stationary. Developing hazard mitigation and sustainable engineered solutions requires a better understanding of loess behavior across multiple scales – from fabric dynamics at the micro-scale to to slope- and landscape-scale failure. This remains the central open problem, and it is one that no single discipline can solve. This session brings together geologists, geomorphologists, geotechnical engineers and disaster risk experts. It also fosters discussions with experts in machine learning and AI to better understand opportunities for connecting information across the scales. We welcome contributions on, but not limited to:
• Micro- to nano-scale characterisation: advanced imaging (CT, SEM, NMR), pore-network and fabric evolution, inter-particle bonding and cementation, physico-chemical controls, and their upscaling within unsaturated soil mechanics.
• Geomorphology and erosion dynamics: aeolian deposition and provenance, landscape evolution, gully initiation and headcut retreat, sediment connectivity, and land-use or vegetation-restoration effects.
• Upscaling and heterogeneity: developing tools to connect information and insights across the scales.
• Geotechnical behaviour and geohazards: wetting-induced collapse, static and seismic liquefaction, failure mechanisms and runout of loess landslides, monitoring, modelling and early warning, including InSAR, UAV and LiDAR approaches.
• Engineering solutions and risk mitigation: soil stabilisation and improvement, infrastructure design on collapsible ground, eco-engineering and nature-based solutions, and risk-informed prevention strategies.
We particularly encourage novel experimental techniques, multi-scale and coupled modelling, and case studies from loess regions worldwide, to build a genuinely comparative and global understanding of loess systems and sustainable methods to mitigate the impact of loess geohazards.

Changes in the natural and/or artificial electric, magnetic and electromagnetic fields have been observed during the past few decades in relation with the earthquakes and with the tectonic processes. For example, disturbances in the ground electric currents, in the geomagnetic field, in the VLF-LF-MF radio signals as well the appearance of electromagnetic emissions in the frequency range from ULF to VHF have been revealed. Usually, these effects take place before the occurrence of earthquakes, so that seismic precursors can be pointed out. In particular, a clear lithosphere-atmosphere-ionosphere coupling (LAIC) appears. This session will focus on: (1) electric/magnetic signals and electromagnetic emissions related to seismic-tectonic activity; (2) disturbances in the electromagnetic wave propagation in the lithosphere, atmosphere and ionosphere related to the previous activity; (3) underlying mechanisms of lithosphere-atmosphere-ionosphere coupling; (4) seismic electric/magnetic and electromagnetic precursors revealed by ground/satellite data; (5) laboratory experiments and theoretical models. Reviews of the past worldwide results as well presentations of future research plans are welcome. Likewise results on different precursors of earthquakes observed in ground and atmospheric parameters as well by mathematical-statistical analysis are welcome.

Mitigating earthquake disasters involves several key elements, from hazard assessment to impacts quantification and reduction. Core components are: a) The analysis of hazards– assessing ground shaking and cascading effects on natural/built environments; b) The assessment of vulnerability and exposure to hazards, for buildings, infrastructures and people (including social vulnerability); c) Risk management – from short-term emergency response and recovery, to long-term governance and preparedness actions.
Given the complexity of earthquake related hazards and their impact on different systems, diverse seismic hazard and risk models are needed at multiple spatial and temporal scales. These rely on multi-disciplinary data, and require testing and validation of all components to ensure effective mitigation.
We therefore invite contributions on seismic risk research and assessment, covering:
- Development of physical/statistical models, including AI and machine learning for hazard, exposure and vulnerability;
- Assessment of earthquake hazard and risk across scales, as well as their validation against observations;
- Time-dependent hazard and risk assessments, including the impact of aftershocks;
- Assessment of cascading hazards (e.g. earthquake induced landslides and tsunamis) and development of multi-risk scenarios;
- Development of systems providing post-event information, such as early warning and alerts for effective emergency management;
- Social vulnerability and mitigation challenges, from assessment to governance, along with advances in communication, citizen-science and risk awareness research.
This interdisciplinary session aims to foster knowledge exchange, share best practices, highlight current gaps, and outline future research directions.

Assessing the time-dependent probability of earthquakes and anticipating their cascading effects—including earthquake-generated tsunami waves and volcanic unrest—remain major challenges in Earth sciences. Recent advances in ground-based and spaceborne observations, laboratory experiments, numerical modelling, statistical analysis, computational methods and data-driven approaches offer unprecedented opportunities to investigate preparatory processes and evolving hazards by integrating heterogeneous observations across the different domains of the Earth system.
The session aims to foster interdisciplinary discussion on earthquake-related processes and to assess critically how multiparametric observations, physical knowledge and advanced data-analysis methods can improve our understanding, forecasting and time-dependent assessment of seismic and volcanic hazards and their impacts.
Particularly welcome are studies addressing:
- Ground-based and satellite observations of electric, magnetic, electromagnetic anomalies potentially associated with impending seismic and volcanic activity;
- Multi-parametric seismological, geodetic, geological, geochemical, hydrological, atmospheric and ionospheric observations;
- Lithosphere Atmosphere Ionosphere Coupling (LAIC) and related measurements;
- Transient deformation, fluid-related processes, seismicity patterns, earthquake-generated tsunami waves and other coupled phenomena;
- Possible relationships between seismic processes, Earth tides, lunar forcing, solar–Earth interactions and Space Weather;
- Theoretical models, numerical simulations and long-term data-analyses;
- Artificial intelligence, machine learning and deep-learning methods for anomaly detection, data integration, forecasting and uncertainty assessment.
Contributions presenting results from new instruments, innovative observational infrastructures and current or forthcoming satellite missions investigating atmospheric, ionospheric and electromagnetic disturbances potentially related to seismic and volcanic activity are especially encouraged.
Further contributions may focus on real-time monitoring, multimission and multiplatform data integration, open-data services and interoperable platforms supporting the development of integrated Earthquake Observing Systems.

Recent advances in physical and statistical modeling based on seismicity patterns provide new insights into the preparation of large earthquakes and the temporal, spatial, and magnitude evolution of seismicity.
Improvements in monitoring technologies now deliver seismic data of unprecedented quality and quantity. Earthquake catalogs are more complete and accurate than ever, and many are now publicly available, enabling analysis of understudied regions and expanding global knowledge. New-generation catalogs, sometimes compiled with machine learning, reveal seismicity structures in ways not previously possible.
Additionally, geodetic, geological, and geochemical data, fluid analyses, laboratory experiments, and earthquake simulators generating synthetic catalogs help refine models and test hypotheses. Integrating these multidisciplinary perspectives enhances our understanding of earthquake generation.
To exploit these datasets, statistical approaches and machine learning are essential. These tools uncover hidden relationships and clustering and address challenges of data inhomogeneity, paving the way for deeper understanding and more robust forecasting.
We invite contributions on developments in physical and statistical modeling and machine learning, including:

• Spatial, temporal, and magnitude properties of earthquake statistics
• Earthquake clustering analyses
• Earthquake forecasting, including related software and methods
• Effects of fluid diffusion and geodetic deformation on seismicity
• Applications in understudied regions (e.g., Africa, Southeast Asia)
• Quantitative testing, data requirements and validation of models
• Machine learning and pattern recognition applied to seismic data
• Model uncertainty quantification
• Reliability and completeness of catalogs
• Time-dependent hazard assessment

Tsunamis can be generated by a variety of mechanisms, including earthquakes, landslides, volcanic activity and atmospheric disturbances. They can cause widespread damage and fatalities in coastal areas including lakeshores, highlighting the need to advance tsunami science towards implementing effective disaster risk reduction measures and developing early warning systems. Since the 2004 Indian Ocean tsunami, the tsunami science has evolved significantly, expanding into new research areas and regions. However, recent events, such as the 2022 Hunga Tonga - Hunga Hapai and the 2025 Kamchatka tsunamis, have challenged the progress in tsunami science and warning, and raised further questions on modeling, hazard assessment and warning capabilities at different scales. They particularly underscored the importance of closer collaboration between different research fields and operational communities, especially in:
- Analytical and numerical modelling of tsunami generation, propagation and inundation from various triggering mechanisms, including single or multi-causative sources (from large subduction to more local earthquakes generated in tectonically complex environments, from subaerial/submarine landslides and glacier collapses to volcanic eruptions and atmospheric disturbances)
- Deterministic and probabilistic tsunami hazard, vulnerability, and risk assessments, including a multi-hazard perspective
- Forecasting tsunamis using emerging technologies, such as artificial intelligence and satellite altimetry (e.g. SWOT satellite)
- Early warning and monitoring, emphasizing innovative marine and seafloor observation methods, sensors and data processing techniques to improve the early characterization of tsunami sources and detection
- Societal and economic impacts of tsunami events on coastal communities
- Hazards perceptions, communication and engagement
- Present and future challenges related to global climate change (e.g. the impact of sea level rise)
The overall goal of this session is to enhance our understanding of the tsunami phenomenon and to strengthen our capacity to build safer and more resilient tsunami communities. The session welcomes both specialized and multidisciplinary contributions covering any of the topics mentioned above, including observation databases, real-time networks, numerical and experimental modeling, hazard-vulnerability-risk assessments, and operational tools and procedures for more effective warnings.

Remote sensing and Earth Observations (EO) are used increasingly in the different phases of the risk management and in development cooperation, due to the challenges posed by contemporary issues such as climate change, and increasingly complex social interactions. The advent of new, more powerful sensors and more finely tuned detection algorithms provides the opportunity to assess and quantify natural hazards, their consequences, and vulnerable regions, more comprehensively than ever before.
Several agencies have now inserted permanently into their program the applications of EO data to risk management. In fact, EO revealed fundamentals for hazard, vulnerability, and risk mapping from small to large regions around the globe, during the pre/post-hazards, the occurrence of disasters, the emergency response and recovery phases. In this framework, the Committee on Earth Observation Satellites (CEOS) has been working for several years on disaster management related to natural hazards (e.g., volcanic, seismic, landslide and flooding ones), including pilots, demonstrators, recovery observatory concepts, Geohazard Supersites, and Natural Laboratory (GSNL) initiatives and multi-hazard management projects. Many case studies can be taken into account for natural hazards processes such as landslides, floods, seismic and tectonic studies, infrastructure damages and so on.
The session is dedicated to multidisciplinary contributions focused on the demonstration of the benefit of the use of EO for natural hazards and risk management. The contributions might focus on:
- Addressed value of EO data in hazard/risk forecasting models;
- Innovative applications of EO data for rapid hazard, vulnerability and risk mapping, the post-disaster recovery phase, and in support of disaster risk reduction strategies;
- Development of tools for assessment and validation of hazard/risk models.
The use of different types of remote sensing data (e.g. thermal, visual, radar, laser, and/or the fusion of these) or platforms (e.g. space-borne, airborne, UAS, drone, etc.) is highly recommended, with an evaluation of their respective pros and cons focusing also on future opportunities (e.g. new sensors, new algorithms).
Early-stage researchers are strongly encouraged to present their research. Moreover, contributions from international cooperation, such as CEOS and GEO initiatives, are welcome.

Natural hazards and extreme events pose major challenges for monitoring and risk management across space and time, particularly due to interconnected exposure, vulnerability, and cascading impacts. During rapidly evolving crises, timely and reliable information on hazard evolution, exposure, damage, and impacts is essential. Before disasters occur, up-to-date information on exposure, vulnerability, critical infrastructure, and the built environment is equally important for preparedness, mitigation, and establishing reliable baseline conditions. Advances in Earth observation (EO), UAVs, ground sensors, IoT networks, and geospatial infrastructures are enabling near-real-time monitoring from local to global scales. In parallel, artificial intelligence (AI) and GeoAI are transforming the analysis of large and heterogeneous geospatial datasets, supporting hazard detection and forecasting, susceptibility and risk assessment, damage mapping, and decision support. They also provide new opportunities for continuously updating exposure and vulnerability information. Despite this progress, challenges remain in moving from experimental models to reliable operational systems, including model generalization, data scarcity and imbalance, multi-sensor fusion, uncertainty, explainability, scalability, reproducibility, and trustworthy AI. Interoperability with disaster-management standards and operational workflows is also essential for effective uptake by civil protection and emergency-management authorities. This session welcomes methodological, experimental, and operational studies combining remote sensing, geospatial information, and AI for floods, wildfires, landslides, earthquakes, volcanic hazards, droughts, coastal hazards, extreme weather, land deformation, subsidence, and multi-hazard or cascading events. Topics include rapid hazard mapping and forecasting; susceptibility, exposure, vulnerability, and risk assessment; damage analysis; explainable, trustworthy, and physics-informed AI; satellite-UAV-ground data fusion; transfer learning and foundation models; decision-support systems; and integration of EO/AI products into disaster-management practice.We particularly encourage studies that bridge research and operations, demonstrate transferability across regions or hazard types, provide open datasets or reproducible workflows, and connect scientific information products with established disaster-management practices.

Compound extremes and cascading hazards represent complex manifestations of coupled Earth-system processes in which multiple drivers interact simultaneously or sequentially across spatial and temporal scales. Heatwaves may develop in association with drought and atmospheric stagnation; precipitation extremes may trigger floods and landslides; drought and heat can precondition landscapes for wildfire; and cyclones can generate interacting wind, rainfall, flood and storm-surge hazards. Such events may involve nonlinear amplification, feedbacks, thresholds and transitions that are difficult to identify through conventional single-hazard analyses. Earth observation provides a unique observational basis for investigating these processes through thermal infrared, optical, microwave/SAR and atmospheric observations of land-surface temperature, soil moisture, evapotranspiration, vegetation stress, precipitation, surface water, fire activity, aerosols and atmospheric composition. We particularly welcome studies that integrate multisource and multiscale Earth-observation datasets with in situ observations, reanalysis, numerical models and high-resolution simulations.

The session also invites contributions demonstrating how artificial intelligence and machine learning, including deep learning, spatiotemporal learning, explainable AI, hybrid physics–ML approaches and data-driven process discovery, can improve the detection, characterisation and prediction of compound extremes and hazard cascades while addressing uncertainty and generalisation. Topics of interest include, but are not limited to, heat–drought, heat–humidity, heat–air-pollution, drought–wildfire, precipitation–flood, flood–landslide, cyclone–flood and other compound or sequential hazards. Contributions addressing land–atmosphere coupling, surface-energy and water-balance feedbacks, nonlinear dependence, threshold behaviour, regime transitions, hazard propagation, precursor identification, attribution, predictability and early warning are particularly encouraged.

The session aims to advance understanding of how compound extremes emerge, intensify, transition and cascade, and how Earth observation and AI/ML can reveal the underlying physical mechanisms across scales.

SAR remote sensing is an invaluable tool for monitoring and responding to natural and human-induced hazards. Especially with the unprecedented spatio-temporal resolution and the rapid increase of SAR data collections from various SAR missions (such as ERS, Envisat, ALOS, Radarsat, Sentinel-1a/b/c/d and NiSAR), we can exploit hazard-related signals from the SAR phase and amplitude imagery, characterize the associated spatio-temporal ground deformations and land alterations, and decipher the operating mechanism of the geosystems in geodetic timescales. Yet, optimally extracting surface displacements and disturbance from SAR imagery, synergizing cross-disciplinary big data, aggregating useful information by multimodal remote sensing fusion along with machine learning methods, and bridging the linking knowledge between observations and mechanisms of different hazardous events remain challenging. Therefore, in this session, we welcome contributions that focus on (1) new algorithms, including machine and deep learning approaches and multi-modal/platform integration, to retrieve critical products from SAR remote sensing big data in an accurate, automated, and efficient framework; (2) SAR applications for natural and human-induced hazards including such as flooding, landslides, earthquakes, volcanic eruptions, glacial movement, permafrost destroying, mining, oil/gas production, fluid injection/extraction, peatland damage, urban subsidence, sinkholes, oil spill, and land degradation; (3) multimodal remote sensing fusion to enhance information extraction related to hazards, agriculture, forestry, land management, and environmental monitoring; and (4) mathematical and physical modeling of the SAR products such as estimating displacement velocities and time series for a better understanding on the surface and subsurface processes.

European Ground Motion Service (EGMS) has significantly improved the ability to monitor and analyse geohazards using InSAR (satellite interferometry) data since its products became available for downloading in mid-2022. These interferometric products are provided by the Copernicus Land Monitoring Service (CLMS) under the responsibility of the European Environment Agency (EEA). EGMS overcomes the long-standing challenge of complex SAR (Synthetic Aperture Radar) images processing, making ground displacement monitoring accessible to a wider range of users. EGMS provides millimetre-accurate measurements, which are available for downloading via the EGMS platform. EGMS delivers full-resolution velocity and displacement time series for both ascending and descending satellite orbits (Level 2a), aligned with the GNSS (Global Navigation Satellite System) reference network within a common reference frame (Level 2b), and computed displacement vectors in the vertical and E-W directions (Level 3), with a spatial resolution of 100 x 100 m.
In this session, we welcome contributions that use EGMS data to monitor and investigate different kinds of geohazards. Topics of interest include subsidence, slow-moving landslides, sinkholes, groundwater and hydrocarbon exploitation, Underground Gas Storage (UGS) activities, mining impacts, volcanic activity, environmental analysis and many more. We also welcome studies transforming EGMS data into analysis ready products for several topics, e.g, coastal studies or climate change estimations. We also encourage studies that explore the impact of these geohazards on critical infrastructure and buildings, or that integrate EGMS data with other methods to improve geohazard assessment. We aim to highlight the versatility and value of EGMS data in understanding and mitigating the risks associated with natural and man-made induced geohazards. Contributions demonstrating innovative applications, cross-disciplinary approaches and case studies with practical implications are particularly welcome.

This session highlights recent advances in Earth observation, from high-resolution imaging to AI-driven analytics and near-real-time monitoring, and examines how these capabilities can move beyond research into disaster risk governance. It explores pathways for translating EO-derived insights into early warning, risk-informed planning, policy decisions, and institutional practice, emphasizing collaboration among scientists, practitioners, and decision-makers to build resilient, evidence-based disaster management systems. It would also cover aviation resilience to highlight the impacts of natural hazards on airports, air transport infrastructure and operations, including early warning, risk-informed planning and operational resilience.

Exposure, i.e. the situation of people, infrastructure, housing, production capacities, and other tangible human assets located in hazard-prone areas, is one of the main components for risk assessment. While exposure at the country scale is often well-defined, fine-grained exposure datasets are key to making risk assessments more detailed and reliable, both in terms of spatial and temporal resolutions, and in identifying which people and assets are most at risk.

Models, input, and output datasets range from geospatial representations of population or built-up area to complex datasets describing people’s characteristics (e.g., gender, age, education) and detailed asset information (e.g., building material, number of floors, road types). Models cover a wide range of scales and resolutions, with local implementations closely grounded in field observations and providing high-resolution detail, while others cover continents or even have a global reach. Other approaches are designed around specific sectors or economic activities, such as agriculture, tourism, or embodied carbon accounting. Various data sources range from census data, cadastral surveys, and grassroots activities, including OpenStreetMap-based exposure models, to big data generated through remote sensing and AI-driven methods, often developed by larger organisations (e.g., WorldPop, Open Buildings by Google, or Overture Maps). Recent efforts increasingly move beyond static representations of exposure to capture its dynamics, including the modelling of Shared Socioeconomic Pathways and human displacement. This broad and growing landscape of exposure science is reflected in the wide variety of stakeholders, ranging from the insurance industry to local and national governments, research organizations, the tourism sector, and NGOs.

In this session, we will welcome submissions addressing (1) geospatial methods and tools for the creation of exposure models, such as Volunteered Geographic Information (VGI) or Earth Observation (EO) and AI models; (2) assessment of the quality or completeness of the data sources of exposure models, such as remote sensing, crowd-sourced, or official registry datasets; (3) exposure models for single hazards, for multi-hazard or hazard-independent contexts; (4) comparison, validation, and analysis of exposure models; (5) Cat model, insurance, government, and financial exposure models and datasets; and (6) innovative applications of exposure models.

Wildfires pose a significant and growing threat to both human populations and the environment. Climate change exacerbates this risk by increasing their frequency, duration, and severity. Rising temperatures, prolonged droughts, and shifting weather patterns create increasingly favourable conditions for fire ignition and spread, expanding the range of vulnerable areas and making wildfires a complex global challenge.

The increasing availability of high-resolution, georeferenced data calls for advanced approaches to analyse and model wildfire dynamics. Remote sensing, geospatial analysis, artificial intelligence, and data-driven and numerical modelling offer new opportunities to transform large and heterogeneous datasets into actionable information for wildfire prediction, risk assessment, and management.

This session aims to bring together experts from geosciences, climatology, forestry, and territorial planning to improve our understanding of fire-related dynamics and explore innovative strategies for mitigation and resilience. By fostering interdisciplinary collaboration, we seek to advance wildfire prediction, prevention, and post-fire recovery, contributing to more effective responses to the growing threat posed by wildfires in a changing climate.

We welcome contributions on topics such as:
• Remote sensing, geospatial and computational methods for recognizing, modelling, and predicting wildfire spatio-temporal patterns.
• Pre- and post-fire assessments, including fire mapping, severity evaluation, and risk management.
• AI, machine learning, and data-driven approaches for wildfire analysis and prediction.
• Long-term analyses of wildfire trends in relation to climate and land-use changes.
• Fire spread modelling and fire-weather relationships.
• Post-fire vegetation recovery and phenology.

Join us in advancing wildfire research and developing innovative strategies to better understand and mitigate its impacts.

The destructive wildfire season of 2026 once again demonstrated that wildfire risk is intensifying and expanding across Europe and beyond. Severe fires affected traditional wildfire-prone regions, while significant events also occurred in areas with more limited experience of large fires, including the United Kingdom and western France. These developments highlight the need to understand wildfire risk as the outcome of interacting climatic, environmental, social, economic and institutional processes – and to strengthen resilience across landscapes, communities, infrastructure and the built environment.
This session welcomes research and practical initiatives addressing all dimensions of wildfire risk, vulnerability and resilience. Contributions may focus on risk assessment, exposure modelling, vulnerability indicators and functions, damage and loss assessment, the vulnerability and resilience of buildings and critical infrastructure, land and fuel management and nature-based solutions. We particularly encourage social and interdisciplinary studies addressing risk perception, preparedness, risk communication and public awareness, public participation and community-based risk reduction, stakeholder engagement, institutional capacity, governance, policy, responsibility and the social impacts of wildfire. Contributions may examine wildfires as an established hazard or as an emerging risk in regions with limited experience and preparedness. Studies can cover the entire risk management cycle from prevention activities to recovery and lessons learned.
By bringing together perspectives from the natural sciences, social sciences, engineering and practice, the session aims to promote exchange across disciplines and regions and support more integrated, equitable and effective approaches to wildfire risk reduction and resilience.

As wildfires intensify under changing climatic and socio-ecological conditions, Earth Observation (EO) has become indispensable for monitoring, modelling, and understanding wildfire dynamics across spatial and temporal scales. Advances in remote sensing have enabled the development of wildfire-related models. However, the reliability and transferability of such models depend fundamentally on the quality and accessibility of the datasets upon which they are trained. Over the past decades, rapidly expanding wildfire-related datasets have emerged from airborne and spaceborne observations. These datasets span diverse sensor modalities, spatial resolutions, temporal coverages, and geographic domains. Nevertheless, many remain highly application-specific and regionally constrained. Such fragmentation hinders cross-comparison, interoperability, benchmarking, and reproducibility, ultimately limiting the broader scientific and operational uptake of both datasets and downstream modelling approaches.
This session focuses on the challenges associated with the development, maintenance, validation, and reuse of remote sensing-derived wildfire datasets. We welcome contributions drawing upon airborne and spaceborne observations, including data acquired from polar-orbiting and geostationary platforms, and encompassing optical, thermal infrared, hyperspectral, SAR, and LiDAR sensors. Contributions addressing multi-sensor and multi-platform integration are particularly encouraged.
Topics of interest include, but are not limited to:
• Methodologies for the generation of wildfire-related datasets (data acquisition, annotation, semantic segmentation).
• Scalable data dissemination frameworks (FAIR-compliant data management, STAC-based catalogues, geospatial data cubes).
• Scientific reproducibility
• Dataset validation
This session seeks to advance best practices in the development, validation, stewardship, and dissemination of wildfire remote sensing datasets, while promoting interoperability, transparency, reproducibility, and long-term reusability. Contributions presenting openly accessible resources are especially encouraged. Early career scientists are particularly welcome to contribute.

Wildfires modify the physical, hydrological and mechanical properties of soils and rocks, influencing slope stability, sediment availability and catchment response. Loss of vegetation cover and root reinforcement, changes in soil water repellency and cohesion, and thermal damage to near-surface rock masses can initiate or condition geohydrological processes during and after fire. Understanding how these changes propagate across hillslopes and catchments, and evolve from the immediate aftermath to subsequent years, remains essential for assessing post-fire hazards and landscape evolution.
This session focuses on the links between fire-induced material alterations, process initiation and sediment transfer in burned landscapes. Relevant processes include rock spalling and rockfalls, dry ravel, shallow landslides, accelerated erosion, debris and mud flows, and sediment-laden floods. Particular attention is given to the controls exerted by burn severity, geological and geotechnical conditions, rainfall and sediment connectivity, and to their implications for cascading hazards. Contributions addressing individual processes and their interactions are equally welcome.
We invite new field evidence, laboratory characterization, inventories and monitoring studies using in-situ and remote sensing methods. Modelling advances may address post-fire slope instability, initiation thresholds, runout, sediment mobilisation and hillslope–catchment connectivity. We particularly encourage studies that relate measured changes in soil and rock properties to observed geomorphic responses, constrain spatial and temporal variability, and evaluate model performance and uncertainty. Comparative and multitemporal analyses can help establish the transferability of methods across geological settings, fire regimes and stages of post-fire recovery.
The session also welcomes rapid post-fire assessments, susceptibility and hazard mapping, exposure and risk analysis, early-warning approaches, and evaluations of mitigation and management strategies. Case studies from the Mediterranean region, California, Australia, higher-latitude environments and other fire-affected regions are invited, especially those providing practical lessons and transferable examples for future wildfire scenarios. By integrating process characterisation, inventories, monitoring and modelling, the session aims to advance the understanding and mitigation of post-fire geohydrological hazards.

From the uranium, thorium and potassium in rocks and soils to the cosmic rays reaching the ground, natural radioactivity shapes the environment we live in. Radon, a gas produced by the decay of uranium, is the main contributor to public radiation exposure and a major indoor health hazard. On top of this natural background, artificial radionuclides released by nuclear activities, accidents and legacy contamination leave a long-lasting footprint on environmental health.

Radioactivity surveys are needed to characterize environmental radiation, assess exposure and guide remediation. They drive the development of new detectors, portable instruments and analytical methods. Radionuclides are also used as tracers of groundwater flow, surface water–groundwater interactions, geological processes and ecosystem dynamics at different scales. Monitoring is becoming more autonomous and data-driven, with AI-based data analysis, robotic platforms for mapping complex sites and UAVs carrying sensors for fast, large-scale and 3D surveys.

The session covers all aspects of environmental radioactivity, including geological surveys, mineral exploration, atmospheric and groundwater contamination, radiological hazards in building materials, and radon hazard and risk assessment. We particularly welcome studies using natural and fallout radionuclides as environmental tracers, work on ecosystem dynamics and impacts on public health, and contributions on Naturally Occurring Radioactive Materials (NORM). Studies presenting methodological advances in measurement, modelling and mapping approaches and novel instrumentation for radioactivity monitoring are also encouraged.

The purpose of this session is to:
(1) showcase the current state-of-the-art in global and continental scale natural hazard risk science, assessment, application and uptake in decision-making;
(2) foster broader exchange of knowledge, datasets, methods, models, and good practice between scientists and practitioners working on different natural hazards and across disciplines globally; and
(3) collaboratively identify future research avenues.

Reducing natural hazard risk is high on the global political agenda. For example, it is at the heart of the Sendai Framework for Disaster Risk Reduction and the Paris Agreement. In response, the last decade has seen an explosion in the number of scientific datasets, methods, and models for assessing risk at the global and continental scale. Increasingly, these datasets, methods and models are being applied in collaboration with stakeholders during the decision-making process. Understanding how risk information is translated into decisions and actions is becoming increasingly important. This includes the co-production of knowledge with stakeholders, communication of uncertainty, governance of transboundary risks, and the role of risk assessments in supporting policy development, adaptation planning, and disaster risk reduction strategies

We invite contributions related to all aspects of natural hazard risk assessment at the continental to global scale, with particular emphasis on how risk information is generated, communicated, and used to support decision-making across sectors, scales, and borders. Topics include:
• single hazards, multi- or compound hazards;
• all facets of risk, including hazard, exposure and vulnerability;
• risk mitigation under current and future conditions (climate & socio-economic);
• the use of risk information in decision-making, policy development, adaptation planning, and disaster risk reduction;
• novel globally-applicable approaches for leveraging global datasets and models to inform local risk assessment and decision-making.

Natural hazards pose serious threats to human health, settlements and the environment. The nature of impacts can be monetizable or hard to measure through economic metrics. Impacts can occur immediately due to the effects of a physical forcing or might persist, evolve and aggravate or resolve in time.

This session aims at gathering researchers interested in the scientific advances related to the multiple facets of natural hazard impacts, i.e., direct, indirect, tangible and intangible losses.

The session welcomes novel approaches to address impact modelling, data analysis, uncertainty analysis, calibration/validation and theoretical frameworks across all natural hazard types, e.g., floods, droughts, heatwaves, earthquakes, wildfires, landslides, storms etc. The topics include but are not limited to:
- comprehensive assessment of the current and future economic impacts of natural hazards under a changing climate, emphasizing the importance of robust cost evaluations for informed decision-making in disaster risk reduction, hazard management, risk forecasting cost-effectiveness and efficiency of risk reduction strategies, and climate change adaptation planning.
- impact-based forecasting and scenario development, exploring how evolving hazard, exposure and vulnerability conditions translate into future impact expectations to support anticipatory decision-making.
- cascading impacts from direct losses to systemic indirect losses, e.g., business interruption, disruptions to critical services such as energy systems, or the influence of critical infrastructure interdependencies on hazard impact.
- indirect and intangible impacts of natural hazards, which are increasingly significant in today’s interconnected socio-technological world. These include loss of irreplaceable items or ecosystem services, and the impacts on physical and mental health. The session also welcomes research on socially vulnerable communities and the long-term health impacts of climatic stressors.
- challenges posed by the lack of empirical data and the diversity of methodologies currently applied to assess the costs associated with different natural hazards and impacted sectors, e.g., energy, agriculture, population, buildings etc.

Submissions are encouraged from those engaged in both theoretical and practical aspects of impact assessment, with a view to fostering interdisciplinary dialogue and advancing the field.

Urban environments are at the frontline of risk, shaped by rapid expansion, climate shocks, informality, and socio-economic pressures. This session welcomes contributions that cover the full spectrum of urban risk - from physical monitoring and modelling, to dynamic vulnerability assessments, to urban governance frameworks and resilience policy. We particularly welcome contributions that (a) address urban risk in the Global South and (b) address risk in small urban centres, where much of the projected urban growth will occur.

Potential themes include:
-Multi-hazard profiles of urban centres: compiling case studies and theoretical evidence for potential hazard interrelationships at the city scale
-Smart sensing and digital twins: exploring the use of AI, crowd-sourced data and big data to understand urban risk dynamics
-Urban expansion and adaptation: understanding how both formal and informal growth of cities interact with the urban hazardscape
-Policy, governance and management aspects of urban risk and resilience
-Early warning systems and anticipatory action: methods to combine local knowledge and predictive capabilities to issue effective early warnings in cities

We welcome interdisciplinary approaches, including:
-Case studies
-Modelling
-Empirical data collection and monitoring
-Inclusive methods such as stakeholder engagement, citizen science and participatory approaches
-Innovative methods in AI and machine learning
-Frameworks and tools for measuring risk and resilience

This session provides an interdisciplinary forum for researchers, policy-makers, and practitioners to share cutting-edge research and practical insights, highlighting diverse approaches to understanding and reducing urban risk.

The increase in global surface temperatures has changed the frequency and magnitude of socio-natural hazards and extreme weather events. Climate change is reshaping hydrometeorological patterns across the globe, with increasingly pronounced effects in sensitive regions, such as the Amazon. The Amazon Basin is a region of exceptional hydrological, climatic, ecological, and societal importance. Its diverse environments, extending from the Andes to the lowland rainforest and major river systems, are exposed to multiple hazards, including floods, droughts, extreme precipitation, landslides, riverbank erosion, and wildfires. Climate variability and change, land use transformations, deforestation, and infrastructure development can modify hazard processes, exposure, and vulnerability, creating complex and evolving risks.

Understanding these hazards requires moving beyond isolated events to investigate interactions across spatial and temporal scales. Extreme rainfall can trigger flooding, landslides, and sediment transport, while prolonged drought and heat can create conditions that favour vegetation fires and disrupt river navigation and water availability. Such processes may generate cascading impacts on ecosystems, infrastructure, livelihoods, and communities, including indigenous peoples and traditional riverine populations. Therefore, this session aims to bring together researchers and practitioners working on the drivers, dynamics, interactions, and consequences of socio-natural hazards throughout the Amazon Basin.

We welcome interdisciplinary studies combining observations, remote sensing, modelling, and local knowledge to advance hazard understanding, monitoring, forecasting, and risk reduction. Topics include, but are not limited to:
• Drivers of extreme weather events, such as heavy rainfall, prolonged drought, and heatwaves;
• Hydrological hazards, including floods and flash floods, sediment dynamics, river meandering process;
• Geological hazards: riverbank erosion, "terras caídas", gully erosion, landslides.
• Wildfire hazards and their interactions with drought, vegetation, and land use change;
• Compound and cascading hazards, and their spatial and temporal dependencies;
• Hazard monitoring, early warning, forecasting, and modelling using field observations, remote sensing, and emerging analytical methods;
• Exposure, vulnerability, risk assessment, and disaster risk reduction, including the integration of scientific and local knowledge.

Climate and environmental hazards increasingly interact with conflict, displacement, food insecurity and other social and structural vulnerabilities, creating compound and cascading crises that challenge conventional approaches to disaster preparedness and adaptation.

In these contexts, the effectiveness of adaptation and anticipatory action depends on a systemic understanding of risk: how it interacts across sectors and timescales, how to plan under uncertainty, how vulnerabilities evolve, how to protect vulnerable communities, cultural heritage and ecosystems, and how actions can be financed, implemented, and adapted to rapidly changing conditions.

This session explores the different stages of anticipatory action:, from understanding the dynamic interactions between natural and societal hazards in the evolution of crises and identifying early warning signs to designing, financing, implementing, and evaluating actions in complex and compound risk settings.

We welcome contributions across scales and disciplines: from single-site interventions and community-based action to global modelling, from social science to physical risk, from humanitarian responses to new insurance and financial instruments. The session's purpose is to encourage dialogue and sharing between researchers who might otherwise work separately.

Contributions may address, but are not limited to:
- Dynamics of risks, including spatio-temporal interactions between different hazards, conflict, food insecurity, displacement and migration
- Early warning monitoring and triggers for action
- Decision-making under uncertainty
- Anticipatory action in fragile, conflict-affected and violence-prone regions
- Impact- and trigger-based financing, and financial products for more vulnerable countries and communities
- Interventions to reduce economic and non-economic Loss and Damage, and approaches to staying within 'tolerable risk'
- Adaptation and action for the benefit of ecosystems and biodiversity
- Reviews and critiques of existing interventions and how we think about action

Risk reduction and adaptation measures remain fragmented, under-scaled, and unevenly distributed across population groups, struggling with the "implementation gap". The knowledge of citizens, communities, and practitioners most exposed to hazards is key to fill this gap, and yet it is treated as a data source or audience to be informed.

We argue that closing the implementation gap requires moving citizen and stakeholder participation upstream. First, moving from data provision and validation towards the co-design of research questions, methods, and assessment frameworks. Second, towards demonstrable uptake of the results in management and policy. Participatory research shows that meaningful engagement is context-dependent, adapting to local histories, capacities, and institutional settings. At the same time, this knowledge needs to resonate with the state of the art in hazard, risk, and climate adaptation science to become actionable. Equally, the value of participation is ultimately decided in policy and planning processes, where scientific evidence competes with political priorities, funding cycles, and institutional mandates.

Following successful editions at previous EGU meetings, this session invites scholars from a wide range of disciplines to contribute work on:
- Co-design of research, where citizens and stakeholders shape questions, priorities, and methods from the outset, like living and real-world labs or transdisciplinary frameworks.
- Integration of citizen and stakeholder knowledge into hazard and risk science: approaches that couple participatory evidence with modelling, remote sensing, impact chains, scenario analysis, or machine learning.
- Evaluation and uptake: methods and case studies that assess participatory processes or outcomes, including the enablers and barriers from co-produced knowledge to policy and planning practice.

We explicitly welcome contributions on understudied hazards (e.g., heat or drought events) and from the Global South and encourage submissions that critically reflect on what makes participation genuine rather than tokenistic from the standpoint of method and practice.

The purpose of this session is to:
- Showcase the state of the art in agent-based and other bottom-up modelling approaches of how natural hazards affect households and firms, from direct impacts to long-run recovery.
- Advance the use of such models to assess impact on multiple dimensions, distributional consequences, and policy effectiveness
- Stimulate exchange of models, data, calibration strategies, and identify priorities for making these models more empirically grounded, credible and useful for decision-making.

Risk assessment for natural hazards has traditionally focused on direct impacts to physical assets. But disaster losses and recovery trajectories vary widely across households and firms and depend on their characteristics such as household income and savings or firm size. Moreover, at least part of the loss is also indirect and unfolds over time, through lost income, forgone care, business interruptions, and disrupted supply chains. These consequences emerge from the decisions of many interacting, adaptive agents (e.g., households, firms) and from the feedbacks between them. Agent-based models (ABMs) can represent this heterogeneity explicitly, together with adaptive behaviour and feedback loops, making them well suited to study the distributional, direct and indirect consequences of hazards. The same applies to the policies trying to mitigate losses: they do not act on the average household or a representative firm alone, and a measure that is effective in aggregate can be regressive for some or be improved upon by targeting specific subpopulations. However, how much such models can inform real decision-making depends on how well they are grounded in data, account for uncertainty and are validated.

We invite contributions that develop, calibrate, validate or apply agent-based and other bottom-up models of disaster impacts on households and/or firms. Topics include:
- Representations of household and firm decision-making, adaptation and post-disaster recovery, including behavioural and economic mechanisms.
- Empirical grounding using household survey, census, administrative, firm-level, remote-sensing or post-disaster damage data.
- Coupling with hazard and climate models, including multi-hazard, compound and repeated events.
- Methodological advances: such as sensitivity analyses, comparison with other approaches and validation.
- Conceptual contributions on what these models can and cannot do for risk assessment and policy design.

This session will showcase innovative approaches to multi-(hazard) risk assessment and management, focusing on advancing the understanding of risk components (hazard, exposure, vulnerability, and capacity) in multi-hazard settings, as well as applications of multi-hazard thinking in disaster risk reduction (DRR) and climate change adaptation. Effective DRR and the translation of research results into practice require considering multiple hazards and their interactions as highlighted in international frameworks and reports, including the Sendai Framework, the IPCC’s AR6, and the EUCRA.
Multi-(hazard) risk assessment examines how interactions among hazards shape exposure and vulnerability through hazard impacts and adaptation measures, particularly in the context of climate change and slow-onset hazards (e.g., droughts). Yet, conventional frameworks still often overlook interrelated hazards and risks, opening the door to unintended consequences. The session will highlight a spectrum of approaches to multi-(hazard) risk, from analysing hazard interactions and vulnerability dynamics to characterising multi-hazard exposure. It will also discuss good practices and persistent challenges in managing multi-(hazard) risk across spatial and temporal scales. Finally, the session will engage with work on societal implications of multi-hazards, including societal impacts and resilience to multi-hazards. By addressing the full risk management chain, this session will identify research gaps, synergies, and opportunities for collaboration across disciplines and hazard types.
We welcome abstracts presenting original research, case studies, and critical reflections across the DRR cycle. Suggested topics include:
- Multi-(hazard) risk methodologies addressing interacting hazards, exposure, vulnerability, and impacts;
- Tools and frameworks for multi-(hazard) risk assessment and management;
- Cross-sectoral approaches that integrate physical, social, economic, environmental, and institutional dimensions;
- Implementation of DRR measures from a multi-hazard perspective, with attention to synergies and trade-offs between hazard-specific measures;
- Multi-hazard early warning systems;
- Societal implications of multi-hazard risks;
- Resilience in the context of interacting hazards.

This session focuses on innovations in methods, tools and technologies for climate and multi-hazard risk assessment. The increasing interconnections between socio-economic, technological, and natural systems have increased risk complexity, raising the likelihood and impact of multi-hazard events, which are further amplified by climate change. This highlights the urgent need to develop and apply these innovative technologies and methodologies (such as Artificial Intelligence (AI) and Machine Learning (ML), digital twins, remote sensing, decision-support tools, early warning systems, stress testing methodologies) to effectively assess these interconnected and evolving risks. Unlike single-risk assessments, multi-risk approaches offer a holistic understanding of risk interactions and compounding effects, providing the evidence base for better-informed adaptation and management decisions.
We particularly encourage submissions of research, case studies, and practical applications that showcase how these technologies provide valuable insights into the complexities of multi-risk dynamics under a changing climate, and optimise decision-making. We also welcome critical discussions of implementation challenges, barriers, and lessons learned from both successful and unsuccessful deployment experiences.
Potential research topics include, but are not limited to:

Examples of collaborative research efforts addressing implementation needs for real-world applications for climate and multi-hazard related risks
AI/ML and other advanced methods for projecting multi-hazard risk dynamics under climate change scenarios
Novel data collection technologies including LLMs, remote sensing for vulnerability and exposure mapping, and forensic assessment of past multi-risk events
Resilience stress-testing for multi-hazard and high-impact low-probability events
Innovative approaches in communications, knowledge-sharing, and capacity building across multi-hazard risk assessments and early warning systems
Best practices for transferring innovations across different contexts and hazards
Decision-support tools, open source software and novel risk assessment methods co-developed with stakeholders to enhance the preparedness of first responders and decision-makers to multi-risk.

Society faces immense challenges when natural hazards and disease outbreaks co-occur. For example, in the midst of the COVID-19 pandemic, the immediate response after natural hazard events was often complicated, because of travel restrictions and local lockdown measures. This arguably led to increased exposure to other hazards such as earthquakes or tropical cyclones (e.g. the 2020 Zagreb earthquake, tropical cyclones Eta and Iota in Honduras). Natural hazards can also trigger the outbreak of diseases, such as cholera and outbreaks following the devastating floods in Pakistan in August 2022. The co-occurrence of natural hazards and diseases creates cascading effects that worsen the overall impact. Consequently, natural hazards can be associated with public health emergencies. A limited understanding of these cascading impacts creates operational, ethical, and decision-making challenges for society, disaster management, and humanitarian organisations.

Building on the RiskKAN working group on disasters and diseases and the NHESS special issue "The convergence of disasters, diseases and health impacts", this session invites contributions on:
- the probability and dynamics of (climate-sensitive) disease outbreaks following natural hazards;
- health and health-system impacts as components of disaster risk;
- the compounding of hazard exposure and disease burden under climate change;
- integration of health metrics into multi-hazard risk frameworks;
- new measurement approaches, including remote sensing combined with public health and socio-demographic data;
- disease risk modeling for early warning systems and anticipatory action;
- and the synergies and trade-offs of disaster risk reduction measures in outbreak contexts.

This session focuses on hazard events and their interaction with disease dynamics and health systems, and the consequences for disaster risk management. We welcome quantitative, qualitative and mixed-methods work at any scale, and particularly encourage submissions from epidemiology, public health and humanitarian practice.

Natural hazards rarely occur independently. Earthquakes can trigger landslides, landslides can dam rivers and generate floods, volcanic eruptions can induce mass movements and tsunamis, wildfires can alter hydrological and sediment transport processes, and changes in glaciers and permafrost can initiate sequences of slope failure, debris flow, and flooding. In such cascades, an initial process modifies the physical state of the system and controls the initiation, magnitude, timing, or propagation of subsequent hazards. Risk evolves with these cascades, as exposure and vulnerability are reshaped by each successive event. Damaged infrastructure, depleted response capacity, displaced populations, and altered land use can amplify the consequences of later hazards, so that dynamic vulnerability becomes a first-order control on cascading risk.
Despite increasing attention to multi-hazard risk, the mechanisms governing these interactions remain poorly understood. Key questions concern how energy, mass, water, sediment, and stress are transferred between processes; how these processes interact; how antecedent conditions and event history influence subsequent hazards; and how feedbacks, nonlinearities, and differences in spatial and temporal scales control whether a disturbance attenuates or develops into a cascade. Answering these questions requires long-term observations to capture the slow preconditioning, relaxation, and recovery phases that link successive events, and allow rare but high-impact cascades to be placed in a meaningful statistical and process-based context.
This session brings together research on the physical processes governing cascading natural hazards. We welcome observational, experimental, theoretical, numerical, and data driven studies addressing topics including:
•Multiphysics modelling of triggering, coupling, amplification, and attenuation between hazard processes;
•Transfer of mass, momentum, energy, stress, water, and sediment between interacting processes;
•Reconstruction and analysis of well documented cascading events across different environments and spatial and temporal scales.
•Field observations, Earth observation, geodetic, seismological, hydrological monitoring, and other measurements used to resolve process interactions;
•Methods for identifying causal relationships and separating triggering from coincident or common forcing;
•Effects of environmental and climatic change on the physical conditions controlling hazard cascades;

Multi-risk events, intensified by climate change, environmental degradation and increasing human pressures, pose growing challenges to societies and ecosystems worldwide. The interaction between climate and social-environmental hazards, exposure and vulnerability can amplify impacts on food and water security, infrastructure, ecosystems, livelihoods and socio-economic development. While substantial advances have been made in understanding and modelling multi-risk events, applications remain relatively limited. Multi-risk approaches remain only limitedly integrated into disaster risk management, which continues to be largely organized around individual hazards, sectors and institutional mandates, limiting the ability to address interconnected and systemic risks.
This session aims to bridge science, policy and practice by exploring how multi-risk knowledge can be mainstreamed into disaster risk management policies, planning processes and decision-making. We invite studies that explore how multi-risk concepts, methods, tools and evidence can inform disaster risk management across the cycle, from mitigation and preparedness to response and recovery. We particularly welcome contributions that explore the usability and uptake of multi-risk research in practice, as well as challenges and enabling conditions that shape its implementation.
Contributions may address, but are not limited to:
● Real-world applications, evaluations and lessons learned from integrating multi-risk knowledge into disaster risk management;
● Development, evaluation and application of multi-risk early warning and early-action systems and preparedness strategies;
● Community-driven and participatory approaches to multi-risk disaster risk reduction and resilience building;
● Translation of multi-risk assessments, indicators, models and scenarios into actionable policies, plans and decisions;
● Risk awareness and communication approaches for making multi-risk information understandable and actionable beyond scientific communities;
● Integration of disaster risk reduction, adaptation, humanitarian action and development planning;
● Multi-risk-informed priority-setting, resource allocation and risk financing;
● Monitoring and evaluation approaches for assessing the effectiveness and outcomes of multi-risk approaches/systems in disaster risk management.

Hazard interactions are becoming more frequent and consequential globally. Climate warming is increasing the chances of compound and cascading climate hazards, including extreme rainfall, storms, heat, and droughts. Risks can become further intensified when they interact with inland/coastal flooding, landslides, earthquakes, tsunamis, and volcanic eruptions. When hazards interrelate, impacts on people and infrastructure can exceed extremes, deepen disruption and slow recovery.
While understanding of compounding and cascading disasters has advanced, these events continue to expose weaknesses in how risks are communicated, understood, and acted upon. Forecasts, probability statements, impact matrices and alerts are organised around single hazards - failing to represent interactions, overlapping impacts, evolving conditions, and spatiotemporal dependencies. Although many sectors have developed capabilities to assess multi-hazard impacts, impact-based operations and communications are not universally applied, requiring stronger coordination of user-centred practices.
The challenge, therefore, lies in producing and translating knowledge to practice in ways that can enable greater understanding and action. Communication, a key determinant of behavioural response during disasters, remains a bottleneck in effective risk reduction. The gap between hazard forecasts and loss-minimising action lies in the failure to account for how warnings are received, understood, and acted upon. Alerts especially are a critical link in the chain of rapid response and can fail in complex scenarios of multi-hazards and divided attention.
This session focuses on communication and alerting as central components of multi-hazard risk management, with the aim of exploring how interacting, compounding, and cascading hazards can be represented to make complex risk information more accessible, understandable, and practical for decision-making. We welcome contributions examining communication across the full chain - technological advances in alerting, risk assessment, trust and sense making to scientific underpinning of the local and community-level actionability. We encourage submissions that examine a) how uncertainty in the timing of cascading hazards can be communicated, b) how evolving impacts can be conveyed when the consequences of one event alter subsequent hazards, and c) how multi-hazard warnings and alerts can be made coherent and actionable while reflecting interconnected risks.

The increasing frequency, complexity and interconnectedness of disasters require a shift from hazard-specific approaches towards integrated multi-hazard disaster risk reduction and resilience. Interactions among hazards, exposure, vulnerability, capacity and changing climatic conditions can generate compound, cascading and systemic risks that challenge conventional risk assessment and disaster management approaches.
This session will explore how geospatial intelligence, Earth Observation, Remote Sensing, GIS, Geospatial Artificial Intelligence (Geo-AI), machine learning, digital twins, cloud computing and real-time geospatial analytics can strengthen the understanding, assessment and management of multi-hazard risks. Contributions are encouraged across the disaster risk management cycle, including hazard and risk assessment, prevention and mitigation, early warning and impact-based forecasting, preparedness, emergency response, recovery and resilience building.
The session welcomes research on multi-hazard and multi-risk assessment, hazard-exposure-vulnerability-capacity modelling, compound and cascading hazards, urban flood and drainage resilience; heatwave and climate-risk assessment, geophysical and geological hazard monitoring including landslides, permafrost and high-mountain hazards, geospatial approaches for environmental health risk assessment, multi-source Earth Observation and geospatial data fusion, multispectral, hyperspectral and InSAR-based monitoring, AI/ML and explainable AI, digital twins and decision-support systems, impact-based early warning, rapid disaster mapping and damage assessment and operational applications supporting disaster risk governance and decision-making.
The session aligns with the Sendai Framework for Disaster Risk Reduction 2015-2030, EU Disaster Resilience Goals and the UN 2030 Agenda for Sustainable Development, promoting science and technology-enabled approaches to understanding and reducing disaster risk, strengthening resilience, enhancing early warning and preparedness, and supporting risk-informed and sustainable development globally.
The session aims to connect researchers and practitioners from natural hazards, geoinformatics, Earth observation, AI/data science, climate science and disaster risk management, with emphasis on translating scientific advances into actionable, risk-informed decision support for resilient communities and infrastructure.

Urban areas increasingly face interconnected challenges from heat, flooding, biodiversity loss, and ecosystem degradation, intensified by climate change and rapid urbanization. Green infrastructure and nature-based solutions can address these challenges by regulating urban temperatures, managing stormwater, providing habitats to species, and supporting ecosystem services. However, their effectiveness depends on vegetation characteristics, spatial configuration, environmental conditions, and interactions among ecological, hydrological, and climatic processes.
This session invites contributions that use quantitative frameworks to assess, predict, and optimize the benefits and trade-offs of green adaptation.

Contributions may address:
1. Modelling interactions among urban heat, flooding, biodiversity, and ecosystem processes under different green adaptation scenarios.
2. Computational modelling, machine learning, remote sensing, field observations, and data-driven approaches for hazard prediction and resilience assessment.
3. Effects of vegetation characteristics and spatial configuration on microclimate regulation, runoff reduction, habitat quality, and ecosystem functioning.
4. Synergies, trade-offs, uncertainties, and unintended consequences associated with green adaptation.
5. Planning, optimization, and spatial prioritization of nature-based solutions under climatic, ecological, social, and economic constraints.
6. Comparative studies across cities, climate zones, ecological settings, and adaptation strategies.
7. Social, governance, and equity dimensions, including the distribution of adaptation benefits and residual risks among communities.
By bridging urban climate science, hydrology, ecology, and computational innovation, this session will advance understanding of how nature-based solutions can support multifunctional, equitable, and climate-resilient cities under global change.

Despite rapid advances in compound-extreme research, the global evidence base remains geographically uneven. Many studies rely on long-term gridded datasets and reanalysis products, while observationally constrained regions, including South and Southeast Asia, Africa, the Middle East and North Africa, Central Asia and other parts of the Global South remain comparatively underrepresented.
This session seeks to advance understanding of compound climate and hydrometeorological extremes in data-scarce and observation-limited environments. We welcome studies based on meteorological and hydrological observations, remote sensing, field campaigns, historical records, disaster databases, health and agricultural records, citizen science and other innovative sources of evidence.
Particular emphasis will be placed on the observed impacts and local manifestation of compound events, rather than solely their statistical characterization or future projection. Contributions addressing observational challenges, uncertainty, scale mismatch, data reconstruction, exposure and vulnerability, cascading impacts, risk assessment and locally relevant adaptation are encouraged.

Reducing tropical cyclone (TC) risk requires an integrated view that connects large-scale circulation and storm physics with nearshore processes, impacts, and the effectiveness of risk-reduction measures. This session invites studies that advance understanding across the full cascade: climate drivers and variability; TC genesis, track, and intensity-frequency change; atmosphere–ocean coupling (winds, waves, storm surge, rainfall); wind impacts, precipitation footprint, storm surge and wave dynamics; compound flooding, erosion, and other TC-related impacts; consequence modelling for people, health, ecosystems, the built environment, and critical infrastructure; and the appraisal and implementation of risk-reduction and adaptation options.
We particularly welcome contributions on modelling and downscaling (from global to local), ensembles and probabilistic methods, data assimilation and remote sensing, model evaluation, uncertainty quantification under climate change, and storylines or event attribution. The scope is global and includes unprecedented or record-breaking TCs, Medicanes or post-tropical transitions. Submissions addressing multi-hazard interactions, cascading and compounding effects, social vulnerability and exposure, as well as decision-support—such as early warning systems, risk communication, risk assessment, and appraisal of structural, nature-based measures and public policies—are encouraged.

Human-induced climate change has increased the frequency and intensity of many weather and climate hazards, including heatwaves and humid heat, droughts, floods and heavy precipitation, windstorms, tropical cyclones, and wildfires, often with severe societal impacts. Yet the confidence with which observed changes can be attributed to anthropogenic influence still varies between hazard types, as does climate models’ ability to reproduce observed extreme events. Both limit our ability to project future risks at different warming levels.

Understanding and projecting changes in these emerging hazards, including unprecedented, compound, consecutive, flash-change, and seasonally-shifting extremes, as well as their interaction with exposure, vulnerability, and adaptation, is a challenge that spans the physical sciences, statistics and machine learning, climate risk modelling, geography, and the socioeconomic sciences. The sheer number of recent extreme events underlines the urgency of improved understanding and modelling capacities for better adaptation, policymaking, and early warning systems.

This session highlights recent progress on understanding, modelling, and projecting weather and climate hazards across these disciplines and aims to identify open research questions and form new collaborations. Which hazards receive too little attention compared to, e.g., heat and drought, and which regions remain under-studied? Which sectors should work more closely with weather and climate scientists to make progress?

We invite contributions on changing weather and climate hazards and their impacts from all regions of the world. Studies on floods, storms, humid heat, and other hazards less represented in the literature are particularly welcome, as are contributions including, but not limited to:
- Understanding physical processes associated with weather extremes, including unprecedented, compound, consecutive, flash-change, and seasonality-related extremes
- Teleconnections and natural modes of variability as well as their links to hazards and impacts
- Advances in hazard and impact modelling, including bias-adjustments and data-driven approaches
- Detection and attribution of weather and climate hazards and their impacts
- Storyline and other approaches to low-likelihood, high-impact events
- Trends and projections of hazards on decadal to centennial timescales
- Changing exposure and vulnerability; interactions between hazards, local socioeconomics, & adaptation

Climate change and urbanisation increase climate risks and the negative consequences of extreme events. For instance, floods cost billions of euros of losses and result in thousands of fatalities every year. Accelerated adaptation by societies, including governments, insurance companies, households and others is urgently required. However, there are limits to the adaptability of societies. For instance, social vulnerability factors (e.g. income, education, etc.) may reduce households’ adaptation efforts. When societies stop implementing additional adaptation, risk may become ‘intolerable’, and adaptation limits may be reached. At such point, people may have to migrate to safer locations. However, very little is known about the extent to which societies can adapt, which factors drive these limits, and where and when limits to adaptation will be reached.
This session welcomes abstracts considering how to conceptualise, assess, project and avoid adaptation limits. The session aims to facilitate discussions and cooperation about interdisciplinary research on adaptation and its limits by bringing together experts in climate, natural hazards and the social sciences.
Relevant topics include, but are not limited to:
• Where were soft or hard limits of adaptation reached already?
• Which social and physical vulnerability constraints can lead to adaptation limits?
• How to conceptualise and measure intolerable risk
• Motivation for and hindrances of adaptation efforts
• Simulation of adaptation and migration decisions
• Quantification of adaptation effects

Recent decades have seen widespread increases in daily minimum temperatures across regions and seasons, with many projections showing that minimum temperatures will continue to increase at a faster rate than daily maxima in many regions. But low temperatures and cold extremes remain a significant source of environmental and societal risk. Cold spells, frost events, and cold-air outbreaks continue to affect human health, agriculture, transportation, energy systems, ecosystems, and infrastructure. Although the frequency and intensity of cold extremes are expected to decrease in a warming climate, such events may still produce substantial impacts, especially as natural and human systems have already started adapting to warmer conditions. Furthermore, important questions remain regarding the physical drivers, predictability, attribution, future evolution, and impacts of cold extremes and cold temperature-related phenomena across different temporal and spatial scales. This session aims to bring together researchers from various disciplines, including climatology, meteorology, hydrology, ecology, biometeorology and impact-focused disciplines, to advance our understanding of cold extremes and their role in the changing climate. We welcome contributions addressing observational, modelling, mechanism, and impact-based perspectives on cold temperatures and cold extremes.
We invite contributions related to:
- Past and future trends and variability in cold extremes at global, regional, and local scales
- Changes in frost occurrence, duration, timing, and seasonality
- Cold-air outbreaks, cold spells, and extreme cold weather events
- Physical drivers of cold extremes, including atmospheric circulation, Rossby wave breaking, blocking, jet-stream variability, stratosphere-troposphere coupling, and teleconnections
- Compound and cascading cold-related events (e.g., cold and snow, cold and wind, cold and energy-system stress)
- Attribution of cold extremes and assessment of underlying drivers
- Representation of cold extremes in numerical models
- Ecological and environmental impacts of cold temperatures, frost damage, and winter stress
- Impacts on agriculture, forestry, water resources, transportation, infrastructure, and energy systems
- Human health impacts and societal vulnerability to cold extremes
- Adaptation, resilience, and risk management in a changing climate
- Paleoclimate and future long-term perspectives on cold extremes and cold-related events

Climate change is reshaping hydro-climatic extremes by disturbing the long-term stability of hydrological systems, a pattern known as non-stationarity. This non-stationarity is far more pronounced wherever watersheds are physically disturbed. Wildfire is one of the most rapid forms of such disturbance. It strips vegetation, alters soil properties, and reduces the land's ability to absorb water. When heavy rainfall follows, floods expected once every 100 years can become up to three times larger in the first year after a fire. This risk is intensifying. Warming raises the likelihood of severe fire seasons followed within a year by extreme rainfall. Compound climate hazards are also becoming more frequent in fire-prone regions such as the Mediterranean. Most models, however, evaluate hazards one at a time. They rarely capture how hazards interact and cascade. This leaves the added complexity that a warming climate brings to post-fire hazard planning largely unaccounted for. This leaves the added complexity that a warming climate brings to post-fire hazard planning largely unaccounted for. This session aims to bring together researchers exploring how Earth observation, machine learning, and deep learning methods can model and monitor these compound and cascading hydro-climatic extremes under climate change. It welcomes hydrologists, remote sensing specialists, climatologists, and machine learning researchers to discuss practical approaches for understanding and predicting these evolving fire-to-flood extremes. By fostering interdisciplinary discussions, we aim to form new collaborations and identify outstanding research questions like: Which climatic regimes and conditions foster this fire-to-flood interaction? What role do geographical and anthropogenic influences play in this? What role can explainable AI play in detecting and providing early warnings for these non-stationary compound hydro-climatic extremes? How can data-scarce regions be better modeled using deep learning and Earth observation?

Why do soils that appear similar in properties respond so differently under cyclic loading? The answer lies in processes at the grain scale: changes in particle contacts, fabric, and pore structure that govern pore-pressure build-up, strain accumulation, and strength degradation. While engineering predictions are typically made at the scale of laboratory specimens, soil deposits, slopes, or geotechnical systems, bridging these scales remains a challenge, especially for soils containing fines or exhibiting complex gradation and fabric.
This session examines cyclic soil behaviour from various angles. We invite contributions involving laboratory and physical modelling, advanced imaging techniques, discrete element (DEM) and particle-based methods (SPH, MPM, etc.), constitutive and continuum modelling, and field observations and case histories. Special emphasis is placed on studies that use observations at one scale to better explain or improve predictions at other scales.
Relevant topics include stiffness degradation, pore-pressure buildup and dissipation, cyclic mobility, liquefaction, strain localisation, post-cyclic strength, settlement, lateral spreading, slope stability, and large deformations. Contributions may concentrate on a specific method or scale, as long as they enhance understanding of how soil behaviour evolves from grain-scale mechanisms to ground response and failure.

Agricultural systems are increasingly exposed to droughts, heatwaves, floods, and compound extremes, with impacts that can cascade across water resources, crops production, and rural livelihoods. Addressing such challenges requires better hazard monitoring or sensing technologies. A stronger link between observation, prediction, and the practical adaptation decisions is also needed that allow agricultural systems to anticipate, cope with, and recover from natural hazards.
This session brings together research that connects these different steps, from monitoring and prediction to adaptation and decision-making in agriculture under hydro-climatic extremes. Although hazard monitoring and precision agriculture technologies have advanced rapidly, turning these developments into tangible improvements in resilience remains challenging. This requires closer integration of hydrology, agriculture, remote sensing, climate science, and decision-support approaches.
We welcome contributions covering the full chain from observation to action, including Earth Observation and in situ monitoring of agricultural exposure and impacts; AI, machine learning, and hybrid modelling for impact prediction and early warning; agro-hydrological and land surface modelling under changing and compound climate conditions; and the use of monitoring and prediction to support adaptation, such as smart irrigation, precision water management, nature-based solutions, and climate-resilient cropping systems.
We particularly encourage studies that show how advances in observation and modelling can be translated into practical decision-support and adaptation. Contributions addressing compound and cascading hazards, especially those linking monitoring and modelling with agricultural impacts and management responses, are also welcome.

Climate change and environmental degradation constitute a growing threat to the stability of societal and economical systems. The observed and anticipated increase in the frequency and intensity of extreme weather events under future emission scenarios, combined with the projected long-term shifts in climate patterns are already leading to significant economic damages on a local to global scale with repercussions in insurance and finance sectors.

To mitigate systemic risks from extreme weather events an improved model representation of extreme weather and climate hazards at impact relevant scales and a better understanding of their often compounding ways they cause impacts is urgently needed. In recognition of this challenge climate risk assessments for the financial and insurance sectors have experienced amplified attention in both the academic and private spheres.

This session aims at providing a platform for actors from academia and the private sector to exchange information on strategies for assessing climate risk, and is organised under three main pillars:

-Physical Climate Risks: Trends, Processes and Modelling
-Identifying and Managing Climate Risks
-Quantifying Damages and Impacts from Climate Risks

We encourage submissions on a wide range of topics including innovative climate risk modeling and model evaluation, damage functions, integrated assessment modelling, bias adjustment and downscaling methods, climate emulators, climate hazard indicators and their projections for specific sectors (e.g. food, energy, insurance, real estate, supply chains), impact data collection and categorization.

Urban areas are increasingly vulnerable to severe weather events, including localized intense precipitation, extreme heat, and urban windstorms. Developing effective Early Warning Systems (EWS) that are fit for purpose requires bridging high-resolution physical science, cutting-edge AI methodologies, and actionable co-produced risk communications and policies. This requires understanding both physical forecast performance and how warnings are operationalized to drive effective anticipatory action, particularly in vulnerable urban environments.

This session, organized as part of the WMO Urban-PREDICT initiative, explores recent advances in high-resolution urban weather forecasting, multi-hazard prediction, and the interdisciplinary co-design of early warnings. We welcome contributions covering:
• High-Resolution Physical Modelling: Sub-kilometer and hectometric-scale Numerical Weather Prediction for urban environments and their application to early warning.
• AI & Machine Learning Applications: Standalone AI or physics-informed AI prediction systems, fast emulators, data-driven downscaling, and novel observational datasets (e.g., radar, crowdsourced sensors, urban IoT), including pathways for operational deployment and data accessibility.
• Impact & Hazard Modelling: Translating meteorological variables into actionable hazard metrics for urban infrastructure, energy systems, and public health, considering the consequences for community response.
• Interdisciplinary Co-Production: Frameworks and case studies linking physical hazard forecasts to societal response, policy, and community-level EWS resilience.

This session will feature contributions from ongoing research under the Urban-PREDICT framework, including international intercomparison initiatives (e.g., RUMI high-resolution modelling over Hong Kong, HEAT-X AI heat downscaling) and local case study approaches to early warning and data availability (e.g., Hong Kong, Lusaka, and Montevideo). We encourage submissions across physical meteorology, machine learning, and social sciences.

Considerable progress has been made in modelling multi-hazard and systemic climate and natural hazard risk, including the interactions, cascading effects, and feedbacks through which climate-related risks propagate across interconnected systems. However, stronger links are needed between these advances in risk assessment and the emerging discourses and approaches to systemic risk governance. In particular, approaches to governance need to reflect the distinctive characteristics of systemic risk while responding to diverse stakeholder perceptions, knowledge, capacities, and needs.
This interdisciplinary and transdisciplinary session aims to bridge systemic risk assessment, modelling, communication, and governance. We invite presentations and posters addressing three interconnected questions:
1. How can multi-hazard and systemic risks be assessed and communicated in ways that are scientifically robust, decision-relevant, and appropriate to the needs and perceptions of different stakeholders?
2. How do systemic risks propagate across interconnected infrastructure, economic, environmental, and social systems, and how can these dynamics, including cascading impacts, feedbacks, and potential tipping points, be simulated and assessed?
3. What science-based governance frameworks and discourses can translate systemic risk knowledge into effective climate change adaptation, disaster risk reduction, and progress towards the Sustainable Development Goals? In particular, how might systemic risk governance be organised around and combine instrumental, reflective, participatory, and epistemological governance discourses?
By bringing together perspectives from hazard risk science, modelling, governance, policy, and practice, the session seeks to advance a more integrated understanding of how knowledge about climate-related systemic risk can inform actionable and legitimate governance responses.
We will actively seek to enhance Equality, Diversity, and Inclusion (EDI) in the composition and conduct of the session.

Mountain regions are changing rapidly as climate warming, cryosphere loss, hydrological and ecosystem change, and socio-economic transformations interact. Looking toward 2050 provides a shared near-term horizon for asking how mountain social-ecological systems may evolve, which risks and opportunities may emerge, and how knowledge about possible futures can inform decisions today. The session addresses key and emerging themes in mountain research while remaining open to contributions from across the wider mountain research community.
A cross-cutting focus is on interactions between environmental and socio-economic change in mountain regions, including their implications for hazards, vulnerability, exposure and risk. We welcome conceptual and empirical research using observations, monitoring networks, remote sensing, models, scenarios, storylines and comparative studies to understand current trajectories and explore possible mountain futures. Contributions may address physical, ecological and societal dimensions of mountain change, from climate, cryosphere, water, ecosystems and hazards to land use, economies and governance, including mountain-lowland linkages.
We particularly encourage work that connects long-term observations with future scenarios and decision-making, investigates interactions and feedbacks between biophysical and human systems, or co-produces knowledge with communities, practitioners and decision-makers. Contributions that advance integrated approaches to monitoring, risk and adaptation, governance and the translation of knowledge into action are particularly welcome. By bringing these strands together, the session aims to clarify what different mountain futures could mean by 2050 and how choices made today may shape risks, resilience and sustainable development.
This session is endorsed and supported by the Mountain Research Initiative and the Institute for Interdisciplinary Mountain Research of the Austrian Academy of Sciences.

Measuring progress in climate adaptation is essential to track resilience-building, guide investments, and inform policy. Yet, adaptation measurement remains fragmented and contested: while some frameworks focus on process indicators (e.g., planning, governance, capacity), others emphasize outputs (e.g., implemented measures), or outcomes and impacts (e.g., reduced vulnerability, enhanced resilience). Each approach has strengths and limitations, and their combination is critical to capturing the complexity of adaptation and informing adaptation action.

This session invites contributions that advance understanding and practice in measuring and evaluating progress in climate adaptation across scales, hazards and sectors. We welcome research that develops or applies frameworks, methods, and tools for adaptation monitoring, evaluation, and learning (MEL), as well as critical reflections on their usability, comparability, and policy relevance.

Topics of interest include (but are not limited to):
- Development and application of process, output, outcome, and impact indicators for adaptation, with a focus on outcomes and impacts.
- Approaches to integrating multiple indicator types for holistic assessment.
- Use of novel data sources and methods (Earth observation, citizen science, AI, participatory surveys) for MEL.
- Cross-scale measurement: from local initiatives to national reporting and global stocktake.
- Addressing uncertainty, attribution, and time horizons in adaptation measurement within the MEL process.
- Considerations of equity, justice, and governance in defining and applying adaptation metrics.
- Case studies showcasing practical experiences of tracking adaptation progress across geographies and contexts.

By bringing together conceptual, methodological, and applied perspectives, this session seeks to identify pathways towards robust, inclusive, and actionable adaptation metrics that can guide decision-making and enhance accountability under the EU Mission on Adaptation, the Paris Agreement, and other global frameworks.

Climate change poses a significant threat to sustainability. It disproportionately affects different social groups, intensifying interconnected risks across socio-ecological systems and challenging conventional approaches to disaster risk reduction and adaptation. These challenges are particularly pronounced in climate-sensitive ecosystems, such as arid and semi-arid regions, where land degradation, water scarcity, biodiversity loss, and socio-economic vulnerability intersect.

Nature-based and community-led strategies offer effective, context-specific solutions that reduce climate risks, restore ecosystems, enhance biodiversity and ecosystem services, and support local livelihoods, enabling sustainable and equitable adaptation even in highly constrained environments such as drylands.

This session invites contributions that explore how nature-based and community-led approaches support disaster risk reduction, ecosystem restoration, and climate change adaptation across diverse ecological and socio-economic contexts, with a particular focus on research that:
- Assesses the effectiveness of these strategies in reducing risk and enhancing climate resilience
- Examines socio-ecological trade-offs and synergies by integrating ecological and social science perspectives within systems-based approaches
- Evaluates long-term resilience and restoration outcomes across varied ecological and socio-economic contexts, including arid and semi-arid landscapes
- Engages with Indigenous and local knowledge systems, emphasizing culturally grounded and community-driven solutions
- Investigates governance challenges, structural barriers, and enabling conditions, and explores inclusive frameworks that support equity, participation, and sustainability
- Investigates synergies and trade-offs between nature-based approaches and conventional measures
- Examines the effectiveness, resilience, and scalability of specific nature-based solution typologies (e.g., water harvesting, vegetation restoration, agroforestry)
- Examines innovative monitoring and assessment tools (e.g., citizen science, remote sensing, hydrological modelling, eDNA, AI) to evaluate, optimize, and scale nature-based and community-led strategies

This session is supported by the RISK-KAN Working Group on “Nature-Based and Community-Led Climate Risk Strategies.” Contributions from diverse regions are welcome, with a particular emphasis on early-career researchers and practitioners from underrepresented areas.

Over the past few decades, landslide research has expanded considerably, producing a wealth of scientific insights. Our understanding of slope failure processes has advanced significantly, yet it remains unclear how effectively engineering geologists and geotechnical engineers focused on slope stabilization and landslide risk reduction are translating this knowledge into practice.
This session aims to bring together researchers and practitioners from diverse backgrounds to:
1. Foster collaboration and networking across disciplinary boundaries
2. Encourage the exchange of theoretical insights and practical approaches to landslide investigation and mitigation
3. Promote more efficient use of limited resources for landslide risk reduction

We particularly welcome contributions on topics such as:
• Expanding the affordable use of innovative technologies for landslide detection and mapping (e.g., optical and radar satellite remote sensing)
• Advances in subsurface characterization using customized geophysical methods (e.g., electrical resistivity, seismic tomography)
• Integration of remote sensing and ground-based data for improved landslide monitoring
• Engineering geological models as integrative tools for site-specific landslide risk mitigation
• Data availability, quality issues, and handling geological uncertainty in slope stability modeling
• Approaches to slope stability analysis, from empirical methods to advanced numerical models
• Impacts of climate variability on landslide occurrence and engineered slope performance
• Low-cost, reconnaissance-level hazard assessments in data-scarce and disaster-affected regions
• Case histories of slope stabilization and landslide mitigation, including both successful and unsuccessful interventions
• Knowledge transfer between scientists and engineers, and effective communication of landslide risk to civil protection authorities, policymakers, media, and the general public

Session sponsored by the International Association of Engineering Geology and the Environment (IAEG – https://iaeg.info)

Cities worldwide are increasingly exposed to compound extreme weather and climate events—hazard combinations that occur concurrently, in close sequence, or across connected regions, such as heatwave–drought co-occurrence, storm surge coinciding with pluvial flooding, or extreme heat following tropical cyclones. Because their drivers interact and their impacts cascade through critical infrastructure, ecosystems, and vulnerable populations, compound events often inflict damages far exceeding the sum of individual hazards, challenging conventional single-hazard approaches to urban risk management.

This session aims to bring together researchers across climate science, hydrology, urban studies, engineering, and disaster risk reduction to advance the understanding, prediction, and governance of compound extremes in urban environments. We welcome contributions addressing, but not limited to:

1. Physical drivers and dynamics of compound events (concurrent, sequential, and spatially compounding) in a changing climate;

2. Detection, attribution, and projection of compound extremes using observations, reanalyses, climate model ensembles, and event storylines;

3. Novel analytical and modeling approaches, including statistical dependence modeling, machine learning, and AI-based methods;

4. Impacts of compound events on urban systems, including infrastructure networks, public health, energy and water security, and cascading or compounding risks;

5. Assessment and enhancement of urban resilience, including risk assessment frameworks, early warning systems, adaptation planning, nature-based solutions, and governance strategies;

6. Case studies from diverse urban contexts—including the Global South—and co-production approaches engaging stakeholders and practitioners.

We particularly encourage interdisciplinary submissions, contributions from early-career scientists, and studies that bridge the gap between hazard science and actionable resilience planning for cities.

The year 2026 has been marked by a sequence of destructive landslides and earthquake-triggered ground failures across different geodynamic and climatic settings, offering the opportunity to compare hazard mechanisms, early-warning performance, and civil protection responses on a global scale.
In January (16th and 25th), a destructive landslide hits the South of Italy, compromising roads, building stability, and essential services, and prompting evacuation of 1,500 people. Elsewhere, large earthquakes triggered severe secondary hazards: a Mw 7.8 quake offshore Mindanao, Philippines (8 June); a seismic doublet (Mw 7.2/7.5) in northern Venezuela (24 June) causing slope failures on steep coastal terrain; a Mw ~7.4 quake in Chocó, Colombia (10 August); and a Mw 7.7 quake off Flores, Indonesia (15 August), which blocked the Trans-Flores Highway. On 26 August, a glacier collapse in the Langtang range triggered a debris avalanche and flash flood along the Nepal–China border, destroying infrastructure and leaving over a thousand people missing.
This session aims to bring together scientists, practitioners, and civil protection agencies to discuss detection, characterization, monitoring, and response strategies applicable across these diverse contexts.
We invite contributions addressing, but not limited to:
• Remote sensing analysis (InSAR, optical/radar imagery, LiDAR, UAV) for detection, mapping, and rapid damage assessment);
• Monitoring systems and early warning, including geotechnical instrumentation and seismic detection of slope failures;
• Geomorphological characterization, including susceptibility mapping and controls on landslide reactivation;
• Civil protection and emergency response, including red-zone delimitation, evacuation planning, and cross-border coordination;
• Comparative and multi-hazard perspectives, linking seismically-induced, gravitationally-induced, and cryosphere-related slope failures, and exploring cascading hazard chains.
The session welcomes case studies, methodological advances, and policy-oriented contributions that can inform more effective cascading risks reduction strategies worldwide.

River deltas and coastal plains are highly vital environments, offering a wide range of ecosystem services that make them hotspots of biodiversity and human settlement, and key for economic production and food security. Human activities such as river damming, resource extraction and land-use land cover change have significantly altered coastal-deltaic morphodynamics, interact with and accelerate biophysical impacts from climate change. Consequently, these low-elevation environments face increased risks of land subsidence, erosion, relative sea-level rise, flooding and salinity intrusion, all of which impact the functioning of coastal social-ecological systems. These accelerating pressures are promoting key advances in understanding the driving processes, the threats generated and the ways humans are addressing these through management. Developments have come from earth observations using remote sensing, and from modelling, increasingly supported by artificial intelligence, in addition to conceptual advances in critically analyzing the human-coastal wetland relationship from both geoscience and management perspectives, preferably interlinked. Critical research gaps persist, however, regarding all aspects of processes and geomorphic responses to these threats, and these limit our capacity to predict and manage coastal lowland and delta change. A more integrated, multi- and interdisciplinary understanding of the interacting processes and impacts is needed to manage river deltas and coastal plains in a sustainable and inclusive manner. This requires taking a system’s perspective, connecting insights from coastal geomorphology and geology, hydrology and hydrogeology, natural hazards and disaster risk science, as well as human geography and social sciences. This session provides an interdisciplinary platform that welcomes contributions that advance an integrated understanding of the emerging changes, pressures and the interconnected processes and hazards they generate; assess the implications for coping capacity and resilience; explore developments in earth observation and modelling efforts to address them; point out persisting knowledge gaps and identify opportunities for effective management and sustainable development of these coastal lowlands.

The hazard isn't the event; the hazard is the sequence. Disaster risk is usually assessed event by event, yet many of the most damaging outcomes arise when hazards follow one another. Earthquakes alter drainage, slopes and sediment supply before the next flood or monsoon; floods weaken structures before the next earthquake; wildfires change how catchments respond to rainfall. Two examples show how one event changes the conditions under which the next unfolds. The 2015 Gorkha earthquake in Nepal was followed by increased landsliding in the monsoons that came after it. The 2023 Kahramanmaraş earthquakes in Türkiye were followed by flooding in the Asi (Orontes) catchment in 2026. Understanding such sequences means connecting physical processes to the people, buildings and institutions that experience them.

This session invites inter- and transdisciplinary contributions on sequential and cascading hazards and their consequences. Topics include field evidence and reconnaissance of hazard sequences; modelling of how earlier events change hazard, exposure and vulnerability, including damage accumulation; health, housing and indoor-environment outcomes in multi-hazard settings; governance, accountability and responsibility for multi-hazard risk; and methods for transferring lessons across hazards, regions and institutional contexts. We welcome geoscientists, engineers, public health researchers, social scientists and practitioners, and particularly encourage early-career researchers and colleagues from affected regions. The session complements multi-hazard assessment sessions in NH by bringing physical and societal perspectives on hazard sequences into one conversation.

Nature-based Solutions (NbS) are “actions to protect, conserve, restore, sustainably use and manage natural or modified ecosystems, that address socio-economic and environmental challenges, while simultaneously providing human well-being, resilience and biodiversity benefits”. Within the framework of a global ecosystem approach, NbS must encompass ecological, societal, political, economic and cultural issues at all levels, from the individual to the collective, from local to national, from the public or private sphere.

As underlined by the IPCC and IPBES, climate change and biodiversity loss are deeply interconnected and must be addressed jointly. This session therefore focuses on how NbS can serve as adaptation strategies to climate change, while simultaneously preserving or restoring biodiversity. Considering various ecosystems (marine and coastal, urban, cropland, mountainous, forest, rivers…), NbS as climate change adaptation solutions includes the adaptation to: sea level rise (flooding and erosion), changes of the water regime (floods, droughts, water quality and availability), rise in temperatures (heat waves, forest fires, drought, energy consumption), plant stress and increase of pests (variation of yields, forest dieback), to minimize their associated social and economic negative impacts.

Therefore, this session aims to promote discussion integrating multiple disciplines related to ecosystem restoration, preservation and management, to put forward the complexity that is often hidden by simplifying hypotheses and approaches (sector-based silo approach, homogeneity of environments...).

Specific topics of interest are the followings:
- Complexity: nature of ecosystems and risk of oversimplification, interconnection between NbS and complementary areas, consideration of uncertainties
- Scales: spatial scales with the integration of NbS in their environment, and temporal scales considering sustainability over time, variability of bio-physical processes and climate change effects
- Ecosystem services: bio-geophysical processes, spatial shift between the location of NbS and the beneficiaries one, modification under climate change (tipping point), co-benefits or negative effects
- Assessment and indicators: measurement and modelling protocols, capacity to measure the complexity, resilience and stability of NbS
- Co-development with stakeholders, engaging civil society, and integrating NbS into education, aligned with IAHS HELPING Decade objectives.

Extreme weather and climate events can disrupt many aspects of human societies, including physical and mental health, livelihoods, food and water service, infrastructure, and human mobility. Yet these impacts are still often assessed one hazard at a time, even though severe consequences frequently arise from interacting hazards. Compound events may involve concurrent, sequential, spatially connected, or preconditioned hazards, and their impacts can be nonlinear, cascading, and greater than the sum of impacts associated with individual hazards.

This session focuses on the impacts of compound weather and climate events on human societies. We welcome research on diverse types of compound events, including, but not limited to, heat and drought; drought followed by wildfire; extreme heat combined with wildfire; tropical cyclones accompanied or followed by extreme rainfall, flooding, and storm surge; and rapid transitions between opposing climate extremes. Contributions may examine mortality and morbidity, mental health, migration and displacement, food and water insecurity, livelihood losses and financial security, health-system burdens, infrastructure failure, conflict, and structural inequality. We particularly encourage work addressing unequal exposure, vulnerability, adaptive capacity, resilience, and recovery.

This session welcomes empirical, modelling, qualitative, participatory, and mixed-methods studies at any spatial scale. Relevant topics include definitions and metrics; detection and attribution; historical trends and future projections; evidence linking compound events to human outcomes; causal inference and uncertainty assessment; integration of climate data with administrative records, surveys, remote sensing, and community knowledge; and applications to early warning, anticipatory action, adaptation, preparedness, risk communication, and equitable risk reduction.

By bringing together climate and Earth-system science, epidemiology, public health, geography, economics, sociology, demography, engineering, disaster-risk research, and public policy and practice, the session aims to advance knowledge of how compound events are anticipated, measured, experienced, and managed.

Addressing today's environmental and climate-related challenges requires strong collaboration among researchers, public authorities, private companies, and local communities. This session will explore how the integration of field observations, scientific research, technological innovation, and stakeholder engagement can support more effective environmental protection and sustainable territorial development. Contributions are invited on multidisciplinary approaches that combine monitoring systems, Earth observation, environmental data, digital technologies, and decision-support tools with practical applications in risk management, climate change adaptation, ecosystem conservation, and sustainable land-use planning. Particular attention will be given to successful partnerships between academia, industry, public institutions, and civil society that facilitate knowledge transfer and the implementation of innovative solutions. The session aims to foster dialogue across disciplines and sectors, promoting the exchange of experiences, methodologies, and best practices to strengthen the resilience, sustainability, and long-term management of natural and human environments.

The Mediterranean is a climate change hotspot where warming and changes in the hydrological cycle are reshaping high-impact weather and climate extremes. Heatwaves, droughts, extreme precipitation and floods, wildfires, marine extremes, and compound and cascading events interact with exposure and vulnerability, generating complex environmental and societal risks.
Addressing these challenges requires connecting advances in observations, physical processes, modelling, prediction and climate projections with impact and risk assessment. Understanding exposure, vulnerability and adaptive capacity is equally essential for translating climate knowledge into effective early warning, decision-support and adaptation.
This session brings together physical and social sciences, technological developments and stakeholder perspectives to advance integrated understanding of Mediterranean extremes, their impacts and pathways to resilience.
We welcome contributions on:
• Physical processes, observations, modelling, attribution, prediction and projections of Mediterranean weather and climate extremes.
• Single, compound and cascading hazards and their interactions across spatial and temporal scales.
• Impacts, exposure, vulnerability and risk across health, water, agriculture, energy, ecosystems, infrastructure and urban areas.
• Methods integrating physical, environmental, social and economic dimensions of climate risk.
• Climate services, impact-based forecasting, early-warning systems and decision-support tools, including Earth observation, AI and digital twins.
• Adaptation and risk-reduction strategies, including nature-based solutions, and evidence of their effectiveness, limitations and trade-offs.
• Co-production of knowledge, stakeholder and citizen engagement, communication and capacity building.
We particularly encourage contributions bridging disciplines, sectors and Mediterranean regions, including approaches addressing inequalities in data, vulnerability and adaptive capacity.
Supported by the FutureMed COST Action (CA22162), this session welcomes contributions from within and beyond the network. It aims to strengthen transdisciplinary collaboration and connect climate science with societal needs, adaptation and resilience.

This session investigates the interaction between human-made interventions in nature and social systems, and how reliance on technology is intertwined with current and future climate hazards. While technology is meant to make the environment liveable, it can also obscure exposure to climate hazards and as such increase vulnerability. We aim to discuss risks and impacts in domains such as health, ecosystems, the built environment, financial and economic systems, as well as possible risk management and resilience practices.

Reliance on technologies to create amenable conditions and expand the human climate niche may amplify risk because these technologies can fail. In addition, the increasing engineeredness of our environment creates a general sense of control and security, which reduces awareness of and preparedness for unexpected events and circumstances.

We invite contributions that explore the effects of socio-technological interactions under changing climate hazards on risk perception, risk, and impacts. Topics of interest include but are not limited to: natural hazards; compounding and systemic risks; adaptation technologies; the Anthropocene. Methods of interest include, but are not limited to: modelling; case studies; event, impact, and damage attribution; climate and adaptation storylines; interviews and participatory research; and policy analysis. Presentations from all subject areas – physical sciences, geography, social sciences, and the humanities – are welcome.

Extreme events such as tropical cyclones, heatwaves, and earthquakes threaten populations worldwide. Climate change is increasing the frequency and intensity of many natural hazards, which can interact with community exposure, inequalities, and vulnerabilities to cause substantial harm. These harms include forced migration, health and wellbeing effects, and other societal impacts. A growing literature at the intersection of the natural and social sciences examines the impacts of extreme events on populations, as well as people’s behavioral, attitudinal, and emotional responses.

Understanding the diverse societal impacts of natural hazards requires approaches that connect knowledge across disciplines, scales, and sectors. Yet, the challenges of doing so remain underexplored. Challenges include methodological choices, scales of analysis (e.g., regional, national), and means of translating scientific outputs into actionable information for policy and practice. This session provides a platform for contributions that bridge disciplines and sectors to better understand societal impacts of, and responses to, extreme events and related compound hazards.

We invite contributions including, but not limited to, studies of:
- Challenges for interdisciplinary and cross-sectoral collaborations
- Migration, displacement, and immobility due to extreme events
- Health and wellbeing effects of climate change and extreme events
- Food production and security in relation to extreme weather
- The interplay between climate change, environment, and conflict
- Environmental attitudes and behaviors influenced by extreme events

Socio-environmental systems are increasingly exposed to interacting environmental, climatic and societal pressures, while capacities to anticipate, respond to and transform in the face of these pressures remain highly uneven across territories. Rethinking resilience therefore requires moving beyond isolated assessments of hazards and vulnerability towards approaches that consider the interconnected dimensions of exposure, vulnerability and resilience, and their implications for action and transformation.
This session brings together research from the Belmont Forum Resilience Collaborative Research Action (CRA), "Vulnerability and Resilience Management for Socio-Environmental Systems in Exposed Territories". The seven Resilience projects engage with diverse hazards, territories and socio-environmental contexts, including floods and droughts, wildfires, coastal hazards, climate-related risks, ecosystem degradation and changing conditions of habitability. Across these contexts, they combine natural and social sciences with participatory and transdisciplinary approaches to explore how resilience is understood, assessed and strengthened.
The session will focus on cross-cutting questions emerging across this research portfolio: How are vulnerability, exposure and resilience conceptualised and assessed across different contexts? How can social, environmental and institutional dimensions be better integrated? How do local knowledge, lived experience and scientific knowledge contribute to understanding and managing risk? Under what conditions can resilience approaches support equitable adaptation and transformative pathways?
Building on the inherently interdisciplinary and transdisciplinary character of the Belmont Forum, the session will foster dialogue across disciplines, territories and knowledge communities. We particularly welcome contributions that connect environmental and social sciences, integrate different knowledge systems, engage with societal partners, and address vulnerability, exposure, resilience, governance, decision-making, adaptation and transformation.
Co-organised with the leading funding agencies ANR (France) and NSTC (Chinese Taipei), we welcome contributions beyond the Belmont Forum Resilience CRA that engage with the session's cross-cutting themes.

Geology does not only shape the physical environment in which societies develop. Rocks, landforms, geological and hydrological processes, water bodies and other abiotic features also influence how people perceive, use, interpret and culturally appropriate their surroundings. Through these interactions, geodiversity becomes embedded in cultural landscapes, local identities, traditions, beliefs, myths and other forms of tangible and intangible heritage.
Cultural geology – also developed in some scholarly traditions under the term Culture Geology – provides an interdisciplinary framework for exploring relationships between geological environments and human societies, while geomythology examines how geological phenomena, extreme events and landscape features are interpreted, remembered and transmitted through myths, legends and oral traditions. Together, these approaches reveal dimensions of geodiversity and geoheritage that may remain difficult to capture through purely geoscientific assessment.
Building on the concept of mythogenic landscapes, we invite contributions examining how abiotic features and processes act as culture-shaping factors. Geological, geomorphological and hydrological characteristics can influence myths and imaginaries, settlement patterns, sacred and monumental landscapes, cultural practices, architecture, material culture, the use of geological materials, place names, local knowledge, and perceptions of environmental change and natural hazards. Conversely, cultural interpretations may influence how geological features are valued, protected and incorporated into heritage.
Particular attention will be given to intangible geoheritage, geocultural heritage, geomythology, geofolklore, hydromythology, Indigenous and local knowledge, cultural perceptions of geological materials, geological materials in architecture and archaeological heritage, collective memory of geological processes and extreme events, toponymy, geoconservation, geoeducation, geotourism and public engagement.
We welcome studies combining geosciences with archaeology, history, anthropology, folklore studies, ethnography, cultural geography and heritage studies. The session aims to bring together geoscientists and scholars from the humanities and social sciences to develop a holistic understanding of relationships between people and the abiotic environment and of how cultural meanings contribute to the recognition, interpretation and protection of geoheritage.

Climate hazards consistently expose and often intensify socioeconomic inequalities. Vulnerability to extreme events is not evenly distributed within or across societies; rather, it is shaped by existing social, economic, and political conditions. As such, inequality, defined as the uneven distribution of resources, opportunities, and power has been recognised by the United Nations and other global policy frameworks as a central factor influencing progress toward the Sustainable Development Goals (SDGs).
This session invites interdisciplinary contributions, bringing together geoscientists, social scientists, economists, and policy experts to examine the complex and often compounding interactions between social inequalities and climate hazards such as floods, heatwaves, droughts, storms, landslides, and wildfires across different scales, including within countries, between countries, and across continents.
Topics of interest include (but are not limited to):
-Case studies illustrating how environmental and social inequalities intersect.
-Types of inequality: social, gender-based, infrastructural, recovery time, education, income source, wealth distribution, climate justice, food security
-Impacts of climate hazards: displacement, fatalities, psychological and physical health, developmental setbacks.
-Long-term recovery challenges: absence of recovery, prolonged recovery periods, slower developmental trajectories.
-Historical and political-ecological perspectives on disasters and their long-term societal impacts.
-Innovations in data, metrics, or methods (e.g., AI, remote sensing, socio-environmental modelling) for assessing inequality and disaster risk across spatial and temporal scales.

Human health risks in a changing climate: observed impacts, adaptation and future risks
Climate change is increasingly affecting population health through heat, cold, floods, droughts, wildfires, storms and rapidly changing, or compound, hazards. These events contribute not only to premature mortality but also to cardiorespiratory and infectious diseases, injuries, adverse mental health outcomes and disruption of healthcare and other essential services. An estimated 489,000 heat-related deaths occur globally each year, and more than 181,000 heat-related deaths were recorded across Europe during the summers of 2022–2024. Occupational heat exposure also affects more than 2.4 billion workers globally and is associated with nearly 23 million injuries each year.
These impacts are unevenly distributed. Socioeconomic position, age, sex and gender, ethnicity, pre-existing health conditions, occupation and place intersect to shape exposure, susceptibility and adaptive capacity. Marginalised populations, including older adults, children, people with chronic conditions, outdoor and manual workers, and communities living in low-income or geographically exposed areas, often bear a disproportionate burden. An intersectional and climate-justice perspective is therefore essential for understanding health risks and developing effective and equitable responses.
This session, organised in collaboration with the Swedish Centre for Impacts of Climate Extremes (CLIMES), invites contributions examining the diverse and unequal effects of climate variability, climate change and extreme events on human health. We particularly welcome studies that:
1. quantify observed impacts and future health risks from single, compound and cascading climate hazards;
2. investigate the attribution of health impacts to anthropogenic climate change;
3. identify intersecting socioeconomic, demographic, occupational and spatial vulnerabilities;
4. develop or evaluate health early-warning systems, impact-based forecasting and other climate services;
5. assess the effectiveness, equity and potential unintended consequences of adaptation interventions; and
6. integrate climate, environmental, health and social data to strengthen public health preparedness, resilient health systems and equitable adaptation.
7. storylines methods and their capacity to inform decision makers.
We welcome contributions using epidemiological, modelling, qualitative, participatory and interdisciplinary approaches.

Agricultural production depends on climatic conditions such as precipitation and temperature, as well as their interactions with each other and with non-climatic factors. Disentangling these effects and understanding which climatic conditions affect agriculture, how strongly, and how future production could change under global warming is crucial for safeguarding future food production, supporting timely climate adaptation through management practices, and improving impact models and assessments. We invite submissions across a broad spectrum of approaches for assessment of climate change impacts on agriculture. Submissions may include, but are not limited to, the following themes:
1- Assessment of climatic conditions, including extremes (droughts, heatwaves) and averages, and their effect on agricultural production, variability and anomalies.
2- Assessment of adaptation practices, including but not limited to crop diversification, irrigation, and fertilization in crop production.
3- Projections of future agricultural production using multi-model climate simulations as well as single-model initial conditions and assessment of time of emergence.
4- Spatial and temporal variability and the role of teleconnections in climate risks to agriculture.
5- Supply chains, financial losses, and the role of insurance in alleviating financial losses in the agriculture sector.
6- Methodological advancements and intercomparison of process-based, statistical, and AI-based models for agricultural production and climate impact assessments.

Extreme heat is a leading weather-related threat to human health, with risk projected to intensify as the climate warms. Robust and transferable estimates of heat-related health risk require an understanding of how socioeconomic and demographic contexts influence who is most vulnerable in addition to the role confounding environmental stressors such as humidity and air pollution play in exacerbating dangerous conditions. This session brings together environmental epidemiologists, data scientists, climate scientists, and human physiologists to advance methods for predicting and forecasting heat-related morbidity and mortality, and to better understand the factors that might modify risk beyond temperature.

We seek machine-learning, statistical, physiological or hybrid approaches that provide new insights into heat-related health risks and/or translate these insights into improved predictive frameworks. Emphasis is placed on novel methodologies improving the validation and transferability of models across regions, climates, and populations. We are particularly interested in approaches that address limited or heterogeneous health data to enable prediction in data-sparse settings, where populations highly vulnerable to extreme heat may be poorly represented. These include physiologically based approaches that derive limits of human heat tolerance from thermoregulation rather than from local health records, and that resolve risk across distinct population cohorts. We also welcome contributions improving predictions under anthropogenic warming, extending model transferability into different climates. Advances in our understanding of compound risks, prediction methods and model transferability can support more effective and equitable Heat-Health Warning Systems.

Disturbances, such as extreme weather events, play a key role in shaping ecosystems. Under climate change, extreme weather hazards undergo changes in frequency, intensity and seasonality. While ecosystem-based adaptation and nature-based solutions are gaining traction, it is crucial to investigate the diverse interactions between extreme weather risk, ecosystems, and their services.

This session seeks to highlight research on the nexus of weather and climate related extreme events and ecosystems. We encourage submissions on: 1) investigations into the key attributes and patterns of extreme weather events which affect ecosystem composition, structure and functioning, 2) studies on how ecosystems respond to and recover from extreme weather events across past, present, and future climates, 3) Implications of extreme weather impacts on ecosystems for biodiversity and ecosystem service provision. We welcome a diverse array of contributions, including theoretical analyses, modelling approaches, field studies, experimental designs, and remote sensing analysis.

Key topics include:
- Identification of extreme weather risk hotspots, and subsequent ecosystem responses (terrestrial, coastal, or marine) in past, present and future climates

- Role of extreme weather in shaping ecosystem composition, biodiversity, structure and functioning, and its impact on ecosystems service provisions across temporal and spatial scales
Interactions of natural hazard, anthropogenic and biogenic disturbances with ecosystems (including compounding events)

- Ecosystem vulnerability, resilience and recovery dynamics under weather extremes, including regime shifts / tipping points in ecosystems
Impact and efficacy of nature-based solutions under extreme conditions, risk of maladaptation or disservices

As a result of a warming Arctic, there is an increased frequency and intensity of extreme weather events such as marine and terrestrial heatwaves, rain-on-snow events, and intense windstorms. Such extreme weather events can trigger cascading, multidisciplinary impacts across the Arctic system. This session bridges the gap between physical mechanisms and on-the-ground realities by investigating the physical drivers of extreme events within oceanic, atmospheric, and cryospheric systems, alongside their wide spectrum of impacts. These impacts range from rapid physical shifts, such as enhanced meltwater runoff, sudden sea-ice loss, and snowpack depletion, to societal and ecological disruptions, including infrastructure damage, traffic disruptions, and threats to reindeer herding and Indigenous livelihoods.
We welcome submissions based on modeling, observations, or interdisciplinary frameworks, such as those incorporating local and Indigenous knowledge systems, that investigate extreme weather phenomena, their physical drivers, and their direct and cascading impacts on the Arctic environment, ecosystems, and local communities.

Recent years have seen an increasing number of armed conflicts worldwide, both within and between states. Beyond their devastating humanitarian consequences, armed conflicts and associated military activities can have profound and long-lasting environmental impacts. These impacts include, but are not limited to, land-use and land-cover change, ecosystem degradation, biodiversity loss, soil and water contamination, disruptions to food production systems, increased climate vulnerability, and greenhouse gas emissions, with consequences that may extend from local to global scales.

This session welcomes empirical studies investigating the environmental dimensions of armed conflict across spatial and temporal scales. This means global as well as place-based analyses are suitable. We encourage contributions that employ a range of methods and data sources, including remote sensing, geospatial analysis, machine learning, artificial intelligence, field observations, environmental monitoring, and integrated modelling approaches.

We particularly welcome conflict-engaged research, in which armed conflict is treated not merely as contextual background but as an explicit analytical component. We seek studies that directly examine the relationships between conflict dynamics, military activities (also outside of direct war contexts), and observed environmental changes, advancing our understanding of the mechanisms, impacts, and consequences of armed conflict on the environment. Contributions addressing environmental monitoring, attribution, post-conflict recovery, environmental governance, or implications for sustainability and climate policy are particularly encouraged.

High-impact climate and weather events typically result from the interaction of multiple climate and weather drivers, as well as vulnerability and exposure, across various spatial and temporal scales. Such compound events often cause more severe socio-economic impacts than single-hazard events, rendering traditional univariate extreme event analyses and risk assessment techniques insufficient. It is, therefore, crucial to develop new methodologies that account for the possible interaction of multiple physical and societal drivers when analyzing high-impact events under present and future conditions. This session aims to address several challenges and topics.
These include: (1) identifying the compounding drivers, including physical drivers (e.g., modes of variability) and/or drivers of vulnerability and exposure, of the most impactful events; (2) Developing methods to better shape the definition and classification of compound events, i.e. legitimate the ‘cut-offs’ in the considered number of hazard types or variables to ultimately disentangle enough information for decision-making; (3) Understanding whether and how often novel compound events, including record-shattering events, will emerge in the future; (4) Explicitly addressing and communicating uncertainties in present-day and future assessments (e.g., via climate storylines/scenarios); (5) Disentangling the contribution of climate change in recently observed events and future projections (attribution); (6) Employing novel Single Model Initial-condition Large Ensemble simulations, which provide hundreds to thousands of years of plausible weather, to better study compound events. (7) Developing novel statistical methods (e.g., machine learning, artificial intelligence, and climate model emulators) for studying compound events; (8) Assessing the weather forecast skill for compound events at different temporal scales; (9) Evaluating the performance of novel statistical methods, climate and impact models, in representing compound events and developing novel methods for constraining/reducing uncertainties (e.g., multivariate bias correction and observational constraints); and (10) engaging with stakeholders to ensure the relevance of the aforementioned analyses.
We invite presentations on all aspects of compound events, including but not limited to the topics and research challenges described above.

The impacts of weather and climate extremes depend on interactions between physical hazards, exposure, and vulnerability. Beyond intensity, the duration, timing, spatial extent, and co-occurrence of extremes can strongly influence their consequences. Understanding these relationships is essential for assessing how climate change alters risks to societies and ecosystems.
This interdisciplinary session connects research on the physical drivers of weather and climate extremes with assessments of their societal and environmental impacts. We particularly welcome studies that investigate how the mechanisms and characteristics of extremes shape their consequences, and how these relationships change under different climatic and socioeconomic conditions.
Contributions are invited on:
- Physical drivers and impact pathways: Atmospheric dynamics, ocean–atmosphere interactions, land–atmosphere coupling, and thermodynamic processes shaping extremes and their consequences. Topics include the roles of persistence, spatial extent, antecedent conditions, and compound or sequential hazards in determining impacts.
- Attribution of extremes and their consequences: Assessments of how anthropogenic climate change and natural variability influence extremes and associated impacts, including approaches that distinguish climatic influences from changes in exposure and vulnerability.
- Prediction and assessment of climate risks: Integration of observations, climate simulations, statistical approaches, and impact models to understand and anticipate risks under present and future conditions. Contributions may address cross-sector and cascading impacts, impact-based forecasting, and the implications of improved physical understanding for preparedness and adaptation.
We welcome observational, modelling, theoretical, and empirical studies addressing heatwaves, droughts, heavy precipitation, floods, fire weather, and compound extremes. Contributions may examine individual links between physical drivers, hazards, and impacts; interdisciplinary studies connecting these elements are particularly encouraged. The session aims to strengthen dialogue between extreme-event science and impact research, advancing understanding of how physical climate processes translate into societal and environmental risks.

Nature-based coastal solutions (NBCS), such as saltmarsh and mangrove restoration, living shorelines, beach and dune rehabilitation, and reef structures (artificial and natural), are increasingly promoted as sustainable responses to the complex challenges of coastal zone management. These approaches aim to harness natural processes to stabilise coastlines, reduce flood and erosion risk, enhance biodiversity, and provide co-benefits, including carbon storage and improved water quality. While the physical and ecological advantages of NBCS are supported by growing evidence, significant uncertainties remain about their long-term effectiveness, particularly under changing climate and socioeconomic conditions. These knowledge gaps are especially critical for informing policy and investment decisions in coastal infrastructure and adaptation strategies. We invite contributions that integrate coastal geoscience, engineering, and broader system-level thinking to evaluate the long-term performance, risks, and trade-offs of NBCS across diverse environmental and governance contexts. We encourage submissions that include, but are not limited to:

(a) case studies on NBCS implementation across varied coastal geomorphologies and hydroclimatic regimes, with evidence of long-term outcomes or monitoring data;
(b) modelling studies that assess NBCS performance under future climate and sea-level rise scenarios;
(c) innovations in field methods, remote sensing, and data integration for evaluating NBCS impacts on geomorphic and ecological processes;
(d) systems-based approaches (e.g., coupled human-natural models, scenario planning) that address feedbacks between NBCS, coastal dynamics, and socioeconomic systems;
(e) studies that focus on advancing the engineering design of NBCS and may include recommendations for types and values of design parameters and criteria;
(f) perspectives on the institutional, regulatory, and financial frameworks influencing the deployment and long-term maintenance of NBCS.

We particularly welcome interdisciplinary studies that combine geoscientific analysis with insights from ecology, engineering, planning, and policy, and encourage dialogue on how transdisciplinary approaches can help guide the design, implementation, and long-term governance of effective NBCS.

The interconnections between climate, environment, and human health are becoming increasingly apparent, as climate change poses growing threats to public welfare worldwide. Rising temperatures, more frequent and intense extreme weather events (e.g., heatwaves, floods, droughts), and environmental stressors such as air pollution, degraded ecosystems, or shifting land use patterns have direct and indirect impacts on population health. Climate-related changes also affect the distribution of vector- and waterborne diseases, contribute to the severity of wildfires, and influence mental and physical health outcomes.
Addressing these multifaceted challenges requires close collaboration between disciplines, bringing together climate scientists, epidemiologists, environmental and public health researchers, as well as social scientists. This session provides a platform for presenting recent advances in understanding and quantifying environment- and climate-related health risks through the integration of diverse data sources, including remote sensing, environmental monitoring, climatological measurements, health records, and socio-demographic information.
We welcome contributions that explore methods to assess climate-sensitive exposures (e.g., heat, air pollution, allergens), model health impacts across different temporal and spatial scales, develop risk maps, and evaluate adaptation or mitigation strategies. Approaches using machine learning, statistical modelling, scenario analysis, and other innovative tools are encouraged. Both empirical studies and methodological advances, whether focused on local, regional, or global scales, are invited. The session aims to foster cross-sectoral exchange and support the development of data-driven strategies for climate-resilient and equitable public health.

Decision-makers are increasingly required to consider extreme weather events as part of evaluating, disclosing, and responding to complex climate risks. Interdisciplinary approaches are required to properly understand these complex risks and to inform the best responses, including mitigation and adaptation approaches. In this session we examine how interdisciplinary teams across academic, industry, and government sectors are working to understand extreme weather events and their changes in a warming climate. Event types can include but are not limited to temperature, precipitation, wind, and compound extremes, and their impacts on people, the built environment, or the natural world. A specific focus will be on collaborative efforts to find and solve shared challenges across disciplines.

Topics of interest include

- physical climate science required for decision making
- early warning systems and their use in real-world scenarios
- risk management in the financial and insurance sectors
- impact-based understanding of extreme weather events
- assessment of varying hazard, exposure, and/or vulnerability
- storyline approaches to risk understanding.

Climate risk and adaptation assessments generally estimate aggregate damages, avoided impacts, or cost-effectiveness. Yet aggregate metrics mask who is exposed, who can adapt, who benefits from adaptation investments, and who bears residual, transferred, or cascading risk. This session asks how conclusions about climate risk and adaptation change when distributional outcomes, adaptive capacity and adaptation limits become explicit components of assessment and decision-making.

We invite quantitative, data-driven and mixed-method contributions representing inequality throughout the assessment chain, from hazard, exposure and vulnerability to impacts, adaptation appraisal and decision-making. Of particular interest are studies moving beyond generic exposure and vulnerability to capture differentiated and dynamic adaptive capacity linked to income, wealth, access to services and infrastructure, housing tenure, mobility, informality and demography.

A central focus is the operationalisation of equity and justice as measurable outcomes. This includes inequality in baseline climate risk (e.g. via revised exposure and vulnerability functions), the distribution of adaptation costs and benefits, inequalities created or exacerbated by adaptation itself, and limits to adaptation. We welcome work that stratifies impacts and avoided losses across population groups, embeds equity metrics in cost-benefit and multi-criteria assessment, examines non-monetised losses, or tests whether conventional welfare and efficiency metrics underrepresent particular populations. Contributions on adaptation-induced inequalities, including displacement, green gentrification, unequal infrastructure protection, and the exclusion of underreported populations, are especially welcome.

The session spans hazards, sectors and scales, including urban systems, critical infrastructure, health, accessibility, macroeconomic impacts, and loss and damage. We particularly encourage assessments of direct, indirect and cascading risks, asking who inherits risk when adaptation protects particular places, infrastructures or groups while shifting impacts elsewhere. We also welcome methodological work on data challenges in distributional risk assessment, including high-resolution socioeconomic data, Earth observation, mobility and geospatial data, citizen science, and alternative sources complementing conventional statistics.

Mathematical models offer an imperfect and uncertain guide to future real-world climate impacts. These models (climate models, climate impact and hazard models, and catastrophe models) and their uncertainties influence how risks are understood, anticipated and managed, and therefore also play a direct role in shaping the human experience of climate risk. When informing real world decisions about climate mitigation and adaptation, the uncertainty of projections should be clearly communicated. However, open questions remain about the construction and use of these uncertainties. This includes explorations of how to navigate modelling approaches, the socio-scientific process of informing expectations of real-world outcomes and normative questions of how our uncertain expectations should inform decision making, now and in the future.

This session invites contributions from individuals and teams focused on exploring model uncertainty and decision making relating to future climate impacts, addressing topics such as:

1. Going beyond in-model uncertainty quantification (through e.g., ensemble methods, sensitivity analysis, Bayesian inference, expert judgement, scenario analysis, combining multiple lines of evidence, etc.)
2. Informing expectations of future climate impacts in the real world (e.g., escaping from model land, communicating uncertainty, social perspectives on uncertainty, storylines, etc.)
3. Informing decisions in the context of uncertainty (eg DMDU approaches, examples of co-production, ethical and responsible modelling research, integration of diverse knowledge and decision systems)

In particular, we welcome contributions which consider how modelling approaches situate themselves within a broader social context when informing decisions.

Climate policymaking and risk assessment sit at the confluence of deeply uncertain physical, social, and economic systems. Substantial uncertainty is introduced, propagated, and transformed at every stage of the climate-impact-decision chain: from projections and emulators, through sector-specific impact models, including hydrological, agricultural, and flood-loss models, to integrated assessment, energy-system, economic, financial, and insurance risk models. Yet these outputs increasingly inform decisions with long-term consequences. Understanding how uncertainty is introduced, propagated, transformed, and communicated across this chain is therefore critical for assessing the robustness of the information on which decisions are based.

This session invites contributions that advance the understanding and quantification of uncertainty across climate impacts, risk assessment, and decision-making. We welcome a range of approaches, including Monte Carlo methods, emulators, robust decision-making, sensitivity analysis, and Bayesian methods, as well as work on structural model uncertainty and inter-model comparison. We are particularly interested in studies examining how uncertainty within individual models is propagated and transformed across successive stages of an analysis, and how it can be communicated to policymakers, financial institutions, and the public in ways that support sound decision-making.

We particularly encourage submissions that bridge disciplinary boundaries among physical climate science, impact modeling, economics, decision theory, and science communication, as well as case studies examining how uncertainty is handled in real-world climate policymaking and in financial or insurance risk assessment. The session focuses primarily on analyses at regional to global scales. Sub-national case studies are also welcome when they provide insights into uncertainty relevant to broader-scale risk assessment or decision-making.

Rotational ground motions have emerged as an important complementary observable in
seismology, driven by advances in the development of rotational seismometers and independent
six-component measurement systems. The growing availability of these sensors is making it
increasingly possible to measure the complete six-component seismic wavefield generated not
only by earthquakes, but also by ambient and anthropogenic sources, providing new
opportunities to investigate wavefield properties and their interaction with buildings and critical
infrastructure. Sensitive to spatial gradients of the wavefield, rotational measurements carry
information that is fundamentally different from conventional translational measurements. They
therefore offer new opportunities to characterize seismic wavefields, resolve small-scale
subsurface heterogeneities, develop more complete descriptions of earthquake ground motion,
and improve our understanding of how seismic waves couple with infrastructure and contribute
to structural response and damage. Beyond traditional seismology, rotational observations are
increasingly being explored for seismic monitoring, rapid characterization of earthquake effects,
structural health monitoring, and earthquake engineering. Together, these developments open
new possibilities for integrating rotational observations across computational seismology,
geophysics, and engineering.
This interdisciplinary session brings together researchers working on the observation,
instrumentation, modelling, and application of rotational ground motions. We welcome
contributions focused on the development of rotation-measuring instruments and independent
six-component sensors; observations of earthquake- and ambient-noise-induced rotations;
theoretical and numerical modelling of rotational wavefields; full-waveform inversion;
computational seismology; seismic source and wave-propagation studies; near-field and strong
motion observations; seismic monitoring and event characterization; and applications to
structural health monitoring, earthquake engineering, and infrastructure resilience. We
particularly encourage contributions presenting new experiments and field deployments using
emerging rotational sensors, analyses of corresponding datasets, novel computational
approaches, and interdisciplinary applications of rotational ground-motion measurements.

Globally, landscapes are starting to dynamically respond to human-accelerated climate change hazards. Whilst in the past it was often possible to use geotechnical solutions to control natural landscape dynamics, such as using nature-based or engineered solutions to control river morphology, reduce coastal erosion and flood risks and reduce landslide hazards, these methods are increasingly unviable. As climate change impacts accelerate there will be growing economic, social or physical limits to continued use of ever more expensive geotechnical solutions. Managing many of our landscapes using geotechnical solutions will achieve hard adaptation limits. This session welcomes papers that explore the potentialities of hard physical adaptation limits and the impacts of these for our societies which are built assuming a relatively ‘fixed’ landscape. We welcome papers that can demonstrate how many nature-based solutions work against rather than with natural landscape responses to environmental factors such as sea level rise, increased river flows (leading to avulsions), increased rainfall triggering landslides more frequently and/or drought conditions driving more widespread wildfires reducing landscape stability. Papers exploring some of the wicked problems that arise from increasingly dynamic landscapes, including release of wastes, graves and buildings into the landscape are also sought.

Water management in Europe has often relied on emergency response, reacting to floods, droughts and scarcity once they occur. Three recent EU policy developments mark a shift in this framing: the Nature Restoration Law (2024), the EU Water Resilience Strategy (2025), and the Competitiveness Compass 2025). Together they treat water and landscapes not only as risks to manage, but as protected natural infrastructure that contributes to ecosystem health and a sustainable economy. This session invites contributions that move beyond reactive risk management toward proactive, systemic resilience: landscapes and catchments designed to absorb shocks, restore ecological function, and generate long-term value.
We welcome studies addressing how catchments, rivers, cities, and rural landscapes can be reimagined as resilient systems considering binding restoration targets. Relevant approaches include nature-based solutions, water reuse and circularity, digital twins for landscape and catchment planning, and governance models that align water security with economic strategy.
Of particular interest are creative, cross-disciplinary approaches connecting hydrology, ecological restoration, landscape design, engineering, and policy: how can we design landscapes that are simultaneously flood-resilient, drought-resilient, ecologically restored, and economically productive?
Contributions may range from local to continental scale, and include case studies, modelling work, policy analysis, or nature-based interventions, connecting risk science, legal restoration requirements, economic sustainability, and long-term resilience planning.

Climate projections, reanalyses and Earth observation products are increasingly available, but their spatial resolution, biases, uncertainties and limited connection to sector-specific needs often constrain their direct use in societal risk and impact assessments. At the same time, advances in artificial intelligence, statistical modelling and data integration offer new opportunities to translate climate information into actionable indicators of hazards, exposure, vulnerability and risk.
This session invites inter- and transdisciplinary contributions that connect high-resolution climate information with impacts on human health, agriculture and food systems, water resources and vulnerable populations. Relevant topics include statistical and machine-learning downscaling; integration of climate, environmental, health and socioeconomic data; satellite-derived exposure indicators; climate-sensitive health outcomes; crop and hydrological impact modelling; extreme, compound and cascading risks; vulnerability mapping; uncertainty propagation; early-warning systems; and the co-development of climate services and adaptation strategies.
We particularly welcome studies that evaluate whether climate information is fit for its intended societal application, connect methods and evidence across disciplines, compare alternative modelling or data-integration approaches, and address challenges faced by data-scarce and highly vulnerable regions. Contributions that address the needs of, collaborate with, or generate relevant information for public-health agencies, agricultural and water-management organizations, education systems, humanitarian actors, local communities and policy stakeholders are especially encouraged.

Low elevation coastal zones (LECZ, areas below 10 m above sea level) are among the most dynamic, densely populated, and intensively managed environments on Earth. They support major population centres, agricultural production, ecosystems and industries. At the same time, they are increasingly exposed to hazards due to accelerating global change, including sea-level rise, changing storm surge and precipitation regimes, heat waves, land subsidence and morphological changes of estuaries, deltas and open coasts.
These pressures create interconnected challenges for water management, infrastructure design and ecosystem stewardship, driving adaptation needs. LECZ are at the interface between terrestrial hydrology and marine dynamics, where flood protection, lowland drainage, freshwater availability, saltwater intrusion, nutrient loads, sediment transport and ecological functioning are governed by the interactions of catchment and coastal processes. However, scientific understanding, modelling approaches and management practices often treat landward and seaward processes separately. Taking a systems perspective is essential for assessing compound and multi-hazard risks, identifying adaptation options and supporting decision-making in LECZs.
This session welcomes contributions that improve system and process understanding, develop monitoring and modelling approaches and support decision making in LECZs across spatial and temporal scales, from local case studies to regional, continental, and global assessments. Contributions addressing data gaps and novel approaches for infrastructure design are encouraged.
We invite submissions on topics including, but not limited to:
i) Monitoring, understanding and characterising processes and hazards in LECZs
ii) Process-based, statistical, hybrid and data-driven modelling approaches for coastal catchment systems, water resources, compound and multi-hazard drivers, estuarine and deltaic dynamics and groundwater-surface-water-sea interactions.
iii) Tools and frameworks supporting integrated water management, infrastructure design and adaptation

Early warnings must be understandable, trusted and actionable to help protect lives and livelihoods from natural hazards such as floods, droughts, heatwaves, tropical cyclones, storms and tsunamis. Recent disasters, such as the 2021 floods in Western Europe, the 2020-2023 Horn of Africa drought, and the extreme heat affecting parts of Europe and the Mediterranean region in 2026 show that significant gaps in the early warning - early action chains persist. The Early Warnings for All initiative (led by WMO, UNDRR, ITU, and IFRC) recognizes that increased efforts are required to develop effective impact-based multi-hazard early warning systems.

The scientific community needs to move beyond natural hazard forecasting and towards impact- and action-based forecasting. This, in turn, requires commitment to the creation and dissemination of multi-hazard risk and multi-source impact data (including from social media) as well as the design, implementation and evaluation of impact-based forecasting services and linked early warning - early action (EW-EA) protocols.

However, significant knowledge gaps and operational challenges persist. This session aims to offer valuable insights and share best practices on impact-based early warning systems from the perspective of both the knowledge producers and users.

Topics of interest include, but are not limited to:
- Practical applications and operational use cases of impact-based forecasts, including the challenges faced in operational implementation
- Novel physics-based, Artificial Intelligence (AI) and hybrid approaches integrating forecasting, big data and earth observations for impact-based forecasting and decision support, including responsible, transparent and explainable approaches that support trusted, inclusive and people-centred systems
- Development of cost-efficient and evidence-based early action portfolios
- Linking preparedness and anticipatory action with response, recovery, reconstruction and prevention efforts
- Development of operational trigger systems (forecast-based financing) for humanitarian anticipatory action
- User-centred evaluation of impact-based forecasting and warning products including usability, trust, decision relevance and actionability
- Triangulation of indigenous and scientific knowledge for leveraging forecasts, multi-hazard risk information and climate services to last-mile communities
- Bridging the gaps in risk, impact and early action data to support impact-based forecasting.

Multi-hazard early warning systems increasingly rely on the technical core of modern disaster risk reduction. They utilise heterogeneous data streams, including earth observations, in-situ sensor networks, process-based models, AI and data-driven approaches, and increasingly citizen-reported and computer-vision-derived observations. These datasets need to be transformed into forecasts that are both scientifically robust and operationally actionable, ranging from minutes to hours for rapid-onset hazards, such as floods, landslides, and cyclones, to months and seasons for slow-onset hazards such as drought. Moreover, natural hazards increasingly occur concurrently, consecutively, or in combination, creating multi-hazards and cascading risks. Operational early warning systems, however, often focus on single hazards. In addition, warning thresholds and dissemination pathways frequently fail to translate warnings into effective community and institutional response, creating a persistent know-do gap that cannot be closed through additional sensing and technical sophistication alone.

This session welcomes contributions across the full arc of multi-hazard early warning, including: (i) AI/ML and process-based modelling approaches for multi-hazard forecasting and nowcasting (e.g., floods, droughts, landslides, and cascading hazards); (ii) data fusion architectures combining satellite EO, real-time sensor networks, and computer vision for hazard detection; (iii) participatory and citizen-science methodologies, local and traditional knowledge integration, and including living labs and serious games for co-creating warnings directly with at-risk communities; (iv) dashboard and decision-support system design that translate model outputs into actionable, trusted, inclusive, and often multilingual alerts for operational use; and (v) validation and lessons from deployed or piloted systems at the interface between technical performance and community uptake.

We particularly encourage submissions presenting multi-hazard forecasting approaches, warning thresholds, and deployed or piloted systems, including honest lessons on where technical sophistication and last-mile trust have, and have not, come together to support effective action.

Remote sensing is a critical tool for data collection and observation of floods and surface water dynamics, especially in regions where field surveys and gauging stations are limited, such as remote or conflict ridden areas and data-poor developing nations. The integration of dynamic variables derived from remote sensing (e.g. water extent, depth and width, river slope and cross sections, flow velocities, land cover and land use) into hydraulic models offers the potential to significantly enhance our understanding of hydrological processes and improve predictive capabilities. Case studies from the Global South are strongly encouraged, as are contributions from ungauged and transboundary basins where satellite observations are the primary record.

This session invites cutting-edge presentations on advancing surface water and flood monitoring and mapping through remotely-sensed data, focusing on:
-Flood and surface water mapping from satellite and airborne sensors, including commercial constellations and emerging observation concepts
-Machine learning, deep learning and foundation models for water body detection and flood segmentation
-Flood mapping in challenging environments such as urban, vegetated, arid, and cloud-persistent regions
-Multi-sensor and multi-frequency fusion for consistent flood extent retrieval
-Flood hazard, risk and impact mapping from Earth observation
-Assimilation of satellite-derived flood extent and inundation maps into hydrological and hydraulic models
-Calibration and validation of hydrological and hydraulic models using remotely sensed observations and field measurements, as well as the creation and use of benchmarks, uncertainty quantification methods, and standards for this purpose
-Monitoring surface water dynamics and change such as seasonal water body variation, wetland extent, river planform
-Operational rapid mapping and emergency response, and the path from research product to sustained service or tool
-Multi-satellite data products and services for improved monitoring of surface water dynamics including floods

Early career and underrepresented scientists are particularly encouraged to participate.

Extreme hydro-meteorological events drive many hydrologic and geomorphic hazards, such as floods, landslides and debris flows, which pose a significant threat to modern societies on a global scale. The continuous increase of population and urban settlements in hazard-prone areas in combination with evidence of changes in extreme weather events lead to a continuous increase in the risk associated with weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need to better understand the triggers of these hazards and the related aspects of vulnerability, risk, mitigation and societal response.
This session aims at gathering contributions dealing with various hydro-meteorological hazards that address the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies and communication strategies. Specifically, we aim to collect contributions from academia, industry (e.g., insurance) and government agencies (e.g., civil protection) that will help identify the latest developments and ways forward for increasing the resilience of communities at local, regional and national scales, and proposals for improving the interaction between different entities and sciences.
Contributions focusing on, but not limited to, novel developments and findings on the following topics are particularly encouraged:
- Physical and social vulnerability analysis and impact assessment of hydro-meteorological hazards
- Advances in the estimation of socioeconomic risk from hydro-meteorological hazards
- Characteristics of weather and precipitation patterns leading to high-impact events
- Relationship between weather and precipitation patterns and socio-economic impacts
- Socio-hydrological studies of the interplay between hydro-meteorological hazards and societies
- Hazard mitigation procedures
- Strategies for increasing public awareness, preparedness, and self-protective response
- Impact-based forecast, warning systems, and rapid damage assessment.
- Insurance and reinsurance applications

Understanding how urban environments interact with hydrometeorological extremes is becoming increasingly important as cities face growing risks from extreme precipitation, flooding, drought, and compound events. The characteristics of these extremes are shaped by interactions among local atmospheric and hydrological processes, land-surface properties, and urban form. Yet these interactions remain difficult to generalise across cities, climates, and spatial and temporal scales. This complexity reflects the high heterogeneity of urban environments and the multiple physical pathways through which urbanisation can influence hydrometeorological extremes. Addressing this challenge requires approaches that can disentangle nonlinear and scale-dependent relationships while retaining physical interpretability.
Machine learning provides opportunities to identify dominant drivers, characterise nonlinear relationships, and reveal spatially and climatically varying responses that are difficult to isolate based on conventional approaches alone. In particular, explainable machine learning (XAI), physics-informed machine learning, causal modelling and hybrid modelling can further support the transition from predictive performance to robust interpretation, hypothesis testing, and process understanding.
This session welcomes studies using machine learning and related data-driven approaches to advance understanding of hydrometeorological extremes in complex urban environments. We particularly welcome studies that use machine learning for physical interpretation, hypothesis testing, and process understanding. Key topics of discussion include:
• Identifying the drivers and nonlinear interactions shaping extreme precipitation, flooding, drought, and compound events.
• Investigating how urban form, land-surface properties, and infrastructure interact with atmospheric and hydrological processes across spatial and temporal scales.
• Applying explainable AI (XAI), physics-informed machine learning, causal modelling, and hybrid approaches to support physical interpretation and process understanding.
• Integrating remote sensing and other multi-source observations to characterise heterogeneous patterns and their scale dependence.

Hydroclimatic extremes such as floods, droughts, storms, or heatwaves often affect large regions and can cluster in time, therefore causing large socio-economic damages. Hazard and risk assessments, aiming at reducing the negative consequences of such extreme events, are often performed with a focus on one location despite their spatially compounding nature. Also, temporal clustering of extremes is often neglected, with potentially severe underestimation of hazard. While spatial-temporal extremes receive a lot of attention by the media, it remains scientifically and technically challenging to assess their risk by modelling approaches.
This session aims to explore advances in the study and modeling of hydroclimatic extremes, embracing a broad perspective that includes—but is not limited to—their spatial and temporal characteristics. Key challenges include the definition of multivariate and compound events; the quantification of uncertainties, of spatial and temporal dependence together with the introduction of flexible dependence structures; the identification and integration of physical drivers and processes across scales; the handling of high-dimensional data and the estimation of occurrence probabilities.
We welcome contributions that enhance our understanding of the mechanisms driving hydroclimatic extremes, propose innovative modeling frameworks, or offer new insights into the prediction, attribution, and risk assessment of these events across space and time. Studies addressing extremes from statistical, physical, or interdisciplinary perspectives are particularly encouraged.

Scientists are facing several challenges when applying climate models for hydrological variables, such as variety in ensembles, uncertainty or model decisions. Perspectives of climate and hydrological researchers might deviate and a gap exists between the information provided by climate scenarios and the data required for effective hydrological analyses and decision-making. Reducing this gap and improving the assessment of climate change impacts requires a deeper understanding of the interactions between climate drivers and hydrological processes across regional and local scales, as well as more accurate representations of these interactions within modelling frameworks. This is essential for developing reliable forecasts, attributing observed changes and assessing risk posed by extreme events. In this context, uncertainties, probabilistic approaches, and scenario-based predictions must be explicitly quantified and effectively communicated. Addressing these challenges calls for advanced modelling approaches and robust uncertainty quantification methods capable of accounting for both hydrological and anthropogenic influences on the water cycle under present and future conditions.

This session particularly welcomes, but is not limited to, contributions on:
- Advanced methods for the identification, simulation and prediction of hydrological processes and water resources, with emphasis on stochastic, data-driven and hybrid methods;
- Innovative techniques for simulating and forecasting hydroclimatic extremes, including compound and cascading events (e.g. heatwaves, floods and droughts);
- Holistic approaches for developing future water resources scenarios that explicitly incorporate climatic, environmental and anthropogenic drivers;
- Studies on hydroclimatic change attribution using probabilistic approaches and novel causality frameworks with uncertainty assessment;
- Evaluation of climate models performance, uncertainty and variability at regional and local scales using observational data;
- Downscaling of climate projections and related uncertainty propagation into hydrological variables;
- Advances in translating climate projections into actionable hydrological information for water-resource management, risk assessment, and adaptation planning;
- Socio-hydrological scenarios combining climate change, demographic dynamics, and land-use transformations within a coherent modelling framework.

How do soil-plant-atmosphere interactions shape hydrological and vegetation responses to climate extremes? How do antecedent soil-water and vegetation conditions influence the pathways through which droughts, heatwaves and floods affect agricultural and natural systems? Which approaches can explain contrasting responses and improve predictions?
This session explores soil-plant-atmosphere processes and hydrological pathways during the onset and development of climate extremes and subsequent recovery. We examine links between meteorological forcing, antecedent system states, plant water use, land-atmosphere exchanges and hydrological responses, from field to catchment scales. We welcome studies of contrasting responses under dry and wet conditions, drought-heat interactions and transitions between extremes.
Topics of interest:
1. Soil-plant-atmosphere responses and feedbacks. Root water uptake, plant water regulation, evapotranspiration, canopy energy balance and vegetation stress during droughts, heatwaves and excess water, including soil-water supply and atmospheric demand interactions.
2. Antecedent conditions and hydrological pathways. How soil moisture, root-zone water storage, vegetation condition and groundwater connectivity shape responses to extremes; how water storage and partitioning among evapotranspiration, drainage, recharge and runoff link plant water availability to broader hydrological behaviour.
3. Contrasting extremes, event sequences and recovery. Contrasting responses under different initial conditions; drought-to-flood transitions; compound hot-dry conditions; and hydrological and vegetation recovery: timing, thresholds and legacy effects.
4. Observation-informed process understanding and prediction. Combining observations and models to identify response pathways, test process explanations, link spatial scales and assess predictive performance across events and environmental conditions.
We welcome research using satellite and uncrewed aircraft system (UAS) remote sensing, field experiments, process-based models, data assimilation and interpretable or hybrid machine learning, especially integrating soil-water, vegetation, surface-flux and hydrological observations.
Studies may examine single extremes or processes or compare events and systems. We link soil-plant-atmosphere and hydrological extremes research to explain how initial conditions and interacting processes shape responses and recovery.

This session invites contributions advancing the understanding, modelling, and prediction of extreme events in weather, climate, and other geophysical systems. It brings together researchers from the geophysical sciences and those applying mathematical, statistical, and dynamical-systems approaches.

Topics of interest include, but are not limited to:
* Variability and projected changes in extremes under climate change
* Representation of extreme events in weather and climate models
* Attribution of extreme events
* Emergent constraints on extreme-event behaviour
* Predictability of extremes across meteorological and climate timescales
* Connections between extremes in dynamical systems and observed geophysical extremes
* Theoretical and applied studies of extremes in nonlinear and chaotic systems
* Downscaling methods for extreme events
* Links between the physical dynamics of extremes and their impacts on society and ecosystems

We particularly welcome interdisciplinary contributions, novel methodologies, and studies connecting theory with observed geophysical extremes. Submissions from early-career researchers are especially encouraged.

This session welcomes frontier research on foundational mathematical constructs underlying geophysically consistent system dynamic intelligence, with emphasis on rigorous, unifying formalisms that advance fundamental theoretical understanding across complex multiscale dynamics, including far-from-equilibrium behaviour, non-ergodicity, criticality, and emergence.

Mathematical contributions may encompass foundational paradigms (categorical, topological, algebraic), analytical frameworks (functional, geometric, stochastic), generalized operators (fractional, nonlocal, integro-differential, information theoretical), and unveil principled perspectives, theoretical advances and systems intelligence to shed light onto complex geophysical and multi-hazard problems.

Of interest is also how foundational mathematical structures inform and constrain modern paradigms in machine learning, explainable AI, physically informed and unified systems intelligence, enabling advances in interpretability, generalization, and robustness. Contributions are also encouraged where deep mathematical insight yields new understanding of scaling, regime behavior, extremes, and interacting hazards across the Earth system.

Collaborative dialogue is fostered among foundational mathematics, geophysical sciences and information technologies, co-evolving to shape new mathematical physics pathways in complexity science and systems intelligence.

Just as mathematical and systems intelligence advances can empower breakthroughs in the geophysical sciences, geophysical problems can inspire the development of new mathematical and systems intelligence methods and techniques that then vastly transcend the disciplinary scope that gave rise to it. Our session is therefore aimed not only at how mathematics can be developed and used to advance the geosciences, but also how new mathematics can fundamentally emerge from the challenges facing our planet.

Climate change is reshaping infectious-disease risks. Hydroclimatic extremes such as floods, droughts, and heatwaves can alter vector habitats, pathogen survival, water quality, sanitation, human mobility and patterns of human contact. Adaptation measures (including vector control, water management, healthcare preparedness, vaccination and behavioural responses) can reduce these risks or unintentionally create new pathways of transmission.

The session welcomes contributions on vector-borne, waterborne, foodborne, respiratory and zoonotic diseases, including research on compound hazards, unequal impacts and adaptation responses. We particularly invite diverse methodological approaches, including:

Dynamical systems approaches to modelling and understanding infectious diseases
Large-scale statistical, epidemiological and climate–health analyses;
Process-based and data-driven models;
Local, urban and neighbourhood-scale modelling;
Qualitative, participatory and mixed-methods approaches;
Surveys, behavioural studies and assessments of public perceptions;
Surveillance, early-warning and climate-informed forecasting systems;
Evaluations of adaptation, preparedness, resilience and risk-management strategies.
Veterinary medicine

The session aims to bring together researchers working across geosciences, climate science, epidemiology, microbiology, ecology, public health and disaster-risk reduction. By connecting these perspectives, we hope to improve understanding of when and where natural hazards become infectious-disease threats, and how these risks can be anticipated and managed.

As data-driven models increasingly rival or complement physics-based systems, a central question remains open: can AI reliably forecast the events that matter the most, the extremes that drive real-world impacts, and not just the average state of the atmosphere? This session brings together the latest advances in machine learning (ML) and artificial intelligence (AI) for forecasting weather, projecting climate, and simulating extreme events.

We invite contributions spanning the full range of timescales and methods, including but not limited to:
*data-driven and foundation weather models for short- and medium-range forecasting;
*generative and probabilistic approaches (e.g. diffusion models) for forecasting, downscaling, and uncertainty quantification;
*ML for sub-seasonal to seasonal (S2S) prediction and longer-term climate projections;
*hybrid AI-physics approaches that embed physical constraints into data-driven models or improve the representation of climate variables in numerical models and datasets;
*detection, attribution, and anticipation of extreme events such as hurricanes, floods, heatwaves, droughts, and compound extremes.

We particularly encourage submissions that go beyond forecast skill to address impacts on infrastructure, ecosystems, health, or energy systems, and that engage with questions of trust, explainability, and generalization to unseen or out-of-distribution extremes.

By bringing together experts from AI, data science, meteorology, climate science, and impact modelling, this session aims to foster interdisciplinary collaboration and push the boundaries of AI-driven understanding and prediction of extreme weather and climate events. We warmly welcome submissions from early-career scientists, established researchers, and industry professionals alike.

Extreme events are difficult to understand because observations are sparse in both space and time, especially for the less frequent but most impactful and severe events. Inference on environmental extremes is becoming increasingly challenging as their behavior can change over time, they can occur across large spatial regions, and they can interact with one another. Extreme Value Theory (EVT) provides a strong theoretical framework for studying these events, but challenges remain in linking methodological advances with environmental applications, prediction, and risk assessment.
We welcome contributions spanning theory, methodology, and applications, including but not limited to:
Advancing EVT methods
• Extreme quantile regression and estimation, conformal prediction, and non-stationary tail models
• EVT-constrained machine learning, uncertainty quantification, extrapolation, and inference under limited extreme event observations
• Methods to evaluate the appropriateness of EVT estimates, impacts of violations of EVT assumptions, and alternative extreme value analysis approaches
Applying EVT to environmental extremes
• Applications to hydrology, climate, weather, coastal hazards, earthquakes, landslides, wildfires, and infrastructure related risk
• Compound, connected, and cascading extremes, including drought-flood sequences, heatwave clusters, extreme precipitation, storm surges, and other interacting hazards
• Novel and under explored applications of EVT to environmental processes and hazards, including snowmelt, air turbulence, environmental epidemiology, and other emerging areas
EVT for prediction, risk assessment, and decision support
• Synthetic extreme event generation and scenario design for stress testing
• Tail focused calibration, validation, and verification, including extremal scoring rules, return level skill, and reliability in the tails
• Quantification and communication of uncertainty relevant to hazards, exposure, impacts, and risk
• Approaches that translate tail behavior and return level information into decision relevant metrics and services
We encourage contributions that bridge methodological EVT developments with real world environmental applications. Contributions may include new theoretical or methodological developments, open datasets and tools, model evaluation frameworks, and real world case studies.

Weather and climate extremes, such as recent events unprecedented in the observational record, have extensive impact globally. Some of these events would have been nearly impossible without human-made climate change, exhibiting conditions well beyond previous records due to complex, and at times unprecedented configurations of their underlying physical drivers. Furthermore, compounding hazards and cascading risks resulting from these high-impact extremes are becoming evident. Continued warming does not only increase the frequency and intensity of such extremes, it also potentially increases the risk of unseen non-linear behaviours or unprecedented impacts. To increase preparedness for high-impact climate events, developing novel methods, models and process-understanding that capture these hazards and their associated impacts is paramount.
This session aims to bring together the latest research quantifying and understanding high-impact climate events in past, present and future climates. We welcome studies across all spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios, with the ultimate goal to provide actionable climate information to increase societal preparedness to such extreme high-impact events.
We invite work addressing high-impact extreme events via, but not limited to, model experiments and intercomparisons, diverse storyline approaches such as event-based or dynamical storylines, climate projections including large ensembles and unseen events, insights from paleo archives, and attribution studies. We also especially welcome contributions focusing on physical understanding of high-impact events, on their ecological and socioeconomic impacts, as well as on approaches to potentially limit societal impacts.
The session is closely linked to the World Climate Research Programme lighthouse activities on Understanding High-Risk Events and Explaining and Predicting Earth System Change.

Past climate variability and extreme events cannot be understood from any single source of information. Climate-model simulations provide physically consistent representations of past climate states and the processes governing variability and extremes, but they cannot reproduce the unique sequence of events that actually occurred. Geological and biological proxies, historical documents, archaeological records and other event-based archives instead preserve evidence of the realised past, but with heterogeneous temporal resolution, spatial coverage, sensitivity, preservation and uncertainty. This session invites contributions that combine, compare or jointly interpret multiple sources of information to investigate past climate change, variability and extremes.

We particularly welcome studies integrating climate-model simulations with geological, palaeoenvironmental, documentary, historical or archaeological evidence; approaches addressing rare or high-impact events such as storms, floods, droughts and heat extremes; statistical or probabilistic methods designed to account explicitly for dating uncertainty, proxy sensitivity, observational thresholds, preservation biases and model uncertainty.

Of particular interest are approaches that move beyond simple model–data comparison towards a process-based understanding of what different sources can reveal about past climate states and events. Contributions exploring data assimilation, probabilistic inference, multi-proxy synthesis, event attribution, high-resolution palaeoclimate modelling, machine learning, or methods for identifying convergence and disagreement among heterogeneous evidence are encouraged.

The session aims to foster dialogue across traditionally separate communities and to explore how complementary information from models and archives can be combined to produce more physically grounded and uncertainty-aware reconstructions of past climate variability and extremes.

The dynamics of magmatic systems are driven by complex processes that span from deep mantle melt generation to volcanic eruptions at the surface. These processes include: melt generation in the upper mantle and lower crust, magma transport, differentiation and emplacement in the crust, complex melt-rock interactions, genesis of energy and mineral resources, and volcanic extrusions with related hazards. Such fluid-mechanical and thermo-chemical processes operate across sub-millimetre to kilometre scales and timescales ranging from seconds to millions of years , and involve multiple phases, such as liquid melt, solid crystals, volatile and metal-bearing fluids, and pyroclasts. Understanding these processes requires a multidisciplinary approach, combining observations, experiments, and computational methods including forward and inverse modelling and machine learning.

Despite the crucial role of computational methods in integrating and interpreting data from various sources, there has been limited development of a dedicated community across volcanic, petrology, and magmatic studies. For the fourth consecutive year, this session aims to address this gap by focusing on computational approaches to understanding volcanic and magmatic systems. We seek to bring together researchers working on forward and inverse modelling, machine learning, and other computational methods to foster a thriving and collaborative community that complements well-established observational and experimental lines of work.

We encourage contributions that explore the theory, development, application, and validation of computational approaches for integrating and interpreting experimental and observational data to improve our understanding of volcanic and magmatic processes. Topics of interest include, but are not limited to:

- Multiphase flow dynamics
- Thermodynamics and phase equilibria
- Magma transport and storage
- Chemical and rheological melt-rock interactions
- Crystallization, immiscibility, and degassing processes
- Energy and mineral resource genesis
- Magma-hydrothermal interactions
- Eruption dynamics and hazards

This session aims to provide a platform for in-depth technical discussions that are challenging to facilitate in broader multidisciplinary sessions, ultimately fostering a stronger computational community within volcanic and magmatic studies.

Active fault systems record deformation across a wide range of spatial, temporal, and depth scales, from fault exposures and surface landforms to crustal structures, earthquake processes, and regional tectonic frameworks. Connecting these different scales is central to understanding how faults develop, interact, and accommodate deformation, and to building robust tectonic and seismotectonic models of active regions. Now in its fifth edition, this session provides an inclusive forum for the active-tectonics and seismotectonics community to present new observations, methodological developments and interpretations that advance our understanding of fault-system geometry, kinematics, evolution and seismogenic behaviour.
We welcome contributions ranging from detailed studies of individual faults and earthquake sequences to meso- and regional-scale investigations, across different tectonic regimes and geological settings, including volcanic and submarine environments. Field observations, structural geology, neotectonics, paleoseismology, tectonic geomorphology, remote sensing, geodesy, geophysics and seismology all provide complementary constraints on fault geometry and segmentation, slip rates, stress and strain fields, crustal deformation, fault interaction and tectonic evolution. Single-method studies, comparisons among techniques and multidisciplinary investigations are equally encouraged. The session welcomes contributions from researchers at all career stages, with particular encouragement to early-career scientists.
Particular interest is placed on studies that strengthen links between surface observations and fault geometry at seismogenic depth, connect deformation across different timescales, or place local fault behaviour within broader tectonic and seismotectonic frameworks. Numerical, analytical and analogue modelling, together with innovative computational approaches such as artificial intelligence and machine learning, are also welcome where they support the analysis, integration or interpretation of active-tectonic and seismotectonic information.
By bringing together diverse observations, methods and scales, the session aims to stimulate exchange across disciplines and advance transferable, testable models of active fault systems, their evolution and their relationship with earthquake occurrence and seismic hazard.

Indigenous Peoples have lived with active landscapes and environmental change for millennia, developing sophisticated knowledge systems grounded in long-term observation, oral traditions, cultural practices, and enduring relationships with place. These knowledge systems provide important insights into Earth surface processes, including volcanic activity, earthquakes, landslides, floods, coastal hazards, environmental change, and the stewardship of culturally significant landscapes.

Addressing complex geohazard and geoheritage challenges increasingly requires approaches that move beyond knowledge sharing towards ethical, reciprocal, and sustained partnerships. This session explores how Indigenous communities, researchers, practitioners, and decision-makers can work together to co-produce knowledge and co-develop solutions that are meaningful, locally relevant, and respectful of Indigenous rights, values, governance, and data sovereignty.

We invite contributions that examine ethical engagement practices at the interface of Indigenous knowledge and geoscience, including collaborative research design, community-led monitoring, participatory hazard assessment, geoheritage stewardship, Indigenous governance, and approaches that foster mutual learning and benefit. We particularly welcome case studies that highlight how equitable partnerships and the respectful weaving of Indigenous and scientific knowledge systems can strengthen understanding of dynamic Earth processes, support disaster risk reduction, enhance geoheritage outcomes, and build trust across knowledge communities.

By focusing on ethical and reciprocal engagement, this session aims to advance more inclusive geoscience practices and showcase pathways for co-developed geohazard and geoheritage solutions that are scientifically robust, culturally appropriate, and socially just.

Climate change is increasingly interacting with rapid urbanization, socioeconomic transformation, environmental degradation, and uneven development across Asia. Floods, droughts, tropical cyclones, sea-level rise, salinity intrusion, landslides, etc., and compound extremes are affecting communities, ecosystems, infrastructure, livelihoods, and economic development. These challenges are particularly significant in emerging and developing regions, where high exposure often coincides with limited resources, institutional constraints, and social inequalities.
At the same time, communities, governments, researchers, and other actors across Asia are developing diverse ways to anticipate, absorb, adapt to, and transform in response to climate risks. Understanding which approaches are effective, under what conditions, and how they can support long-term climate-resilient development remains a major scientific and policy challenge.
This session brings together interdisciplinary research on the assessment, mechanisms, practices, and pathways of climate resilience in emerging and developing Asia. We welcome contributions that connect climate hazards and environmental change with social, institutional, technological, and policy responses, including:
 Measurement and assessment of climate resilience and climate-resilient development
 Innovative technologies, modelling approaches, and nature-based solutions for climate adaptation
 Empirical case studies, best practices, and lessons from successful and unsuccessful resilience initiatives
 Governance, institutional capacity, and policy mechanisms for climate resilience
 Community resilience, socio-cultural dimensions, equity, justice, and local knowledge
 Methodological advances in climate resilience research, including GIS, remote sensing, participatory methods, and data-driven approaches
 Adaptation pathways, transformative resilience, and opportunities for scaling up effective solutions
 Theoretical, conceptual, comparative, and review studies on climate resilience in emerging and developing Asia.

Mountains are home to over 1 billion people and one of the most hazardous landscapes on Earth. The combination of steep topography, active tectonics, shifting orographic rainfall patterns and changing glacier equilibrium lines mean hazards in mountain landscapes are highly dynamic.

In these high-elevation landscapes, processes such as landslides, debris flows and floods, often originate at high altitude and propagate downstream, amplifying the impacts and affecting downstream communities. These events may be widespread or highly localized, and are typically triggered by earthquakes, intense storms, or sequences of compounding factors such as rapid snowpack warming, rain on frozen ground, moraine-dam failures, avalanches, or landslides that initiate further mass mobilization. The cascading nature of these processes means that not only the triggering conditions, but also downstream channel morphology and characteristics (e.g., sediment availability and composition) play a role in shaping the magnitude and frequency of such hazards.

In this session, we are keen to include contributions that cover any or all aspects of the mountain, multi-hazard chain across various spatial and temporal scales, such as those that investigate:
· catastrophic sediment mobilization and cascading hazard chains
· processes and hazards linked to deposition and runout
· concepts of compounding and cascading dynamics
· connectivity between hillslopes and river networks
· controls on the downstream evolution of floods and flows.

We welcome presentations employing observational, conceptual, methodological, or modeling approaches, individually or in combination, across diverse mountain environments. Early-career scientists are particularly encouraged to contribute.

Recent years have seen extraordinarily large events and disasters originating from the high-mountain cryosphere in the Himalaya and beyond. Some of those events in the last five years include the 2021 Melamchi debris flow in Nepal, the 2021 rock-ice avalanche in Chamoli, Uttarakhand, India, or the 2023 South Lhonak GLOF, Sikkim, India.
The most devastating was the 26 August 2026 Rasuwa disaster in Nepal. A very large rock slope failure occurred on the northern slopes of Langtang Lirung (7240 m asl), together with the overlying glacier with an estimated failure volume of 110 to 130 million m3; it produced a rock-ice avalanche that rushed into the Lendhe valley and gradually transformed into a debris flood that devastated the Bhote-Koshi and Trishuli valleys on a length of more than 100 km, through Rasuwa, Nuwakot, Dhading, Gorkha, and Chitwan districts and across the border into Gyirong County, Tibet Autonomous Region. The path of destruction down the valleys is far beyond any historical monsoon-type flood. More than 1,500 people have confirmed dead, around 5,000 are missing, many have been injured, and large number of infrastructures have been damaged or lost. About 10% of Nepal’s hydropower production was wiped away. Total estimated loss as per preliminary estimate is USD 2.7 Billion (6.7% of Nepal’s GDP) and estimated cost for re-building is USD 4.8 Billion (11.8% of Nepal’s GDP).
In this context, understanding the processes behind increasing cases of cryosphere-originated disasters risks in the Himalayan region in South Asia, assessing hazards and residual risks, and identifying ways forward to minimize future risk is critical to support resilient development and planning of societies in the Himalayan region and beyond.
This session aims to bring together the various multi-disciplinary research that has been conducted in the weeks and months after the disaster. It includes but is not limited to aspects such as pre-disaster development in the source area, conditioning and potential preparatory or triggering factors of the initial rock slope failure, attribution to climate change, flow processes and dynamics of the avalanche and debris flood, landscape impact and longer term change, loss and damage estimates, implications for hazard and risk analysis in the region and related recommendations.
We also invite contributions on analysis of other recent large events in the region that can foster insights into the above outlined aspects.

Numerous cases of induced/triggered seismicity resulting either directly or indirectly from injection/extraction associated with anthropogenic activity related to geo-resources exploration have been reported in the last decades. Induced earthquakes felt by the general public can often negatively affect public perception of geo-energies and may lead to the cancellation of important projects. Furthermore, large earthquakes may jeopardize wellbore stability and damage surface infrastructure. Thus, monitoring and modeling processes leading to fault slip, either seismic or aseismic, are critical to developing effective and reliable forecasting methodologies during deep underground exploitation. The complex interaction between injected fluids, subsurface geology, stress interactions, and resulting fault slip requires an interdisciplinary approach to understand the triggering mechanisms, and may require taking coupled thermo-hydro-mechanical-chemical processes into account.
In this session, we invite contributions from research aimed at investigating the interaction of the above processes during exploitation of underground resources, including hydrocarbon extraction, wastewater disposal, geothermal energy exploitation, hydraulic fracturing, gas storage and production, mining, and reservoir impoundment for hydro-energy. We particularly encourage novel contributions based on laboratory and underground near-fault experiments, numerical modeling, the spatio-temporal relationship between seismic properties, injection/extraction parameters, and/or geology, and fieldwork. Contributions covering both theoretical and experimental aspects of induced and triggered seismicity at multiple spatial and temporal scales are welcome.

Glacial Lake Outburst Floods (GLOFs)—the sudden release of water from lakes dammed by moraines, ice, or bedrock—are among the most dynamic perturbations of Earth’s surface. Their geomorphic signatures can persist for hundreds to thousands of years, while recent events in mountainous regions have demonstrated their potential to cause damage and loss of life when floods reach downstream communities that may be unprepared. Major uncertainties remain in understanding where and when GLOFs occur, how they are triggered and propagate, how landscapes respond, and how these processes may change in the future.

This session explores links between glacial-lake evolution, outburst-flood processes, geomorphic legacies, and societal impacts, with the aim of advancing our understanding of landscape evolution and future hazard and risk. We particularly welcome contributions using sedimentary and geochronological evidence to reconstruct paleo-lakes and paleo-GLOFs, constrain their magnitude and frequency, quantify sediment fluxes, and investigate how outburst floods establish, reorganise, or abandon drainage systems. Contributions addressing the processes controlling GLOF initiation are also encouraged, including ice–lake interactions, dam failure, and triggering and preconditioning mechanisms. A particular focus is the changing abundance and size of glacial lakes, and potential feedbacks on GLOF frequency and magnitude, as well as downstream geomorphic and ecological impacts. We invite studies examining the role of anthropogenic climate warming in GLOF activity, as well as approaches for incorporating dynamically evolving lake populations into flood, hazard, and risk models. We therefore welcome new insights from field observations, remote sensing, and process-based modelling that can improve lake monitoring, early warning, risk management, and assessments of downstream vulnerability.

This session seeks to cover the full breadth of glacial-lake research, spanning paleo-lake and paleo-flood reconstruction, state-of-the-art glacial-lake mapping and remote sensing, field observations, process-based modelling, and assessments of the geomorphological, hydraulic, and ecological impacts of GLOFs. We therefore aim to synthesize studies connecting observations across timescales and spatial scales, from individual lake and flood processes to regional and global changes, in order to provide new perspectives on the future evolution of glacial lakes and GLOF hazards.

Assessing the impact of climate variability and changes on hydrological systems and water resources is crucial for society to better adapt to future changes in water resources, as well as extreme conditions (floods and droughts). However, important sources of uncertainty have often been neglected in projecting climate impacts on hydrological systems, especially uncertainties associated with internal/natural climate variability. From one model to another, or one model realisation to another, the impact of diverging trends and sequences of interannual and decadal variability of various internal/natural climate modes (e.g., ENSO, NAO, AMO) could substantially alter the impact of human-induced climate change on hydrological variability and extremes. Therefore, we need to improve both our understanding of how internal/natural climate patterns affect hydrological variability and extremes, and how we communicate these impacts. We also need to understand better how internal/natural variations interact with various catchment properties (e.g., vegetation cover, groundwater support) and land-use changes. Developing storylines of plausible worst cases, or multiple physically plausible cases, arising from internal climate variability can complement information from probabilistic impact scenarios.

We welcome abstracts capturing recent insights for understanding past, present, and future impacts of internal/natural climate variability on hydrological systems and extremes, as well as newly developed probabilistic and storyline impact scenarios. Results from model intercomparisons using large ensembles are encouraged.

River basins are increasingly affected by compound, sequential, and spatially clustered hydro-climatic extremes. At the same time, many basins have interconnected reservoirs, diversions, and regulated river reaches. These systems can change how extreme events move through a catchment. Reservoir storage and operational decisions can amplify, reduce, delay, or transfer these effects. Therefore, studying individual reservoirs or isolated extreme events may not fully capture the response of the entire basin.
This session aims to advance understanding of cascading hydrological extremes and basin-scale responses in regulated river systems. We welcome observational, theoretical, statistical, and modelling studies addressing the interactions between hydro-climatic extremes, catchment processes, reservoir storage and releases, and downstream hydrological responses.
Topics of interest include: compound and sequential floods and droughts; dependence structures and spatial propagation of hydrological extremes; hydrological connectivity in regulated basins; multi-reservoir and basin-scale hydrological modelling; reservoir storage, yield and reliability under climate variability and change; forecast-informed and adaptive reservoir operation; real-time hydrological forecasting and flood routing; and interactions between extreme precipitation, snowmelt, glacier melt, reservoir inflows, and downstream discharge.
We welcome contributions using remote sensing and Earth observation, hydroinformatics, telemetry, data assimilation, machine learning, and hybrid physics–data-driven approaches to improve monitoring, prediction, and understanding of cascading hydrological responses. Studies from a wide range of hydro-climatic and physiographic settings, including mountain, snow- and glacier-fed, monsoon, temperate, tropical, arid and semi-arid basins are encouraged, as are comparative studies across different hydro-climatic settings and regulated and unregulated river systems.
The session seeks to bring together researchers working across hydrological observations, process-based and statistical modelling, forecasting, Earth observation, and water-resources management to identify common mechanisms controlling cascading hydrological responses and to develop improved approaches for understanding and managing extremes in regulated river basins.

The field of socio-hydrology and hydro-social research emerged as an attempt to better understand the dynamic interactions and feedbacks within diverse coupled human-water systems and its implications for the assessment and management of water resources and associated risks.

An integrated perspective offers novel entry points for a more fertile engagement between hydrological and social sciences across different scales ranging from the plot level to entire watersheds, encompassing both surface water and groundwater. Its interdisciplinary nature involves (and integrates) various methodological approaches, epistemologies, and disciplines.

We welcome contributions from researchers from social and natural sciences who are keen to look beyond their research perspective and who like to discuss their research findings in a broader context of coupled human-water systems. Papers should

1) contribute to the understanding of complex human-water interactions and their management,
2) discuss the benefits and shortcomings of different inter- and disciplinary perspectives based on empirical, conceptual or model-based research; and
3) shed light on the added value of socio-hydrological modelling and hydro-social analysis for water resources management, risk management and adaptation design.

We specifically welcome contributions which reflect how the hydro-social and socio-hydrological research approach supports the new IAHS decade HELPING Science for Solutions aim.
This session is co-sponsored by the IAHS under the umbrella of the ICHWF - Commission on Human-Water-Feedbacks.
We are happy to have TBA as a solicited speaker.

This session welcomes abstracts that consider how to observe, analyse and model feedbacks between social, political and economic processes and hydrological and other environmental processes. The session is organised by the International Commission on Human-Water Feedbacks (ICHWF) of the IAHS, which provides a home for interdisciplinary research on the dynamics of human-water systems, particularly involving the social sciences.
Relevant topics include but are not limited to:
• Observations of human impacts on, and responses to, hydrological change
• Interactions of communities with local water resources
• Hydrological models that include anthropogenic effects
• Interdisciplinary qualitive and quantitative methods
• Theoretical models to isolate, conceptualize and/or simulate feedbacks in human-water systems
• Critical reflections on inter- and transdisciplinary projects (problem framings, roles, methods, exclusions, suggested interventions)
• Creation of databases describing the hydrology of human-impacted systems
• Data analyses and comparisons of human-water systems around the globe and especially in the global south
• Human interactions with hydrological extremes, i.e. floods, droughts and water scarcity
• The role of gender, age, and cultural background in the impacts of hydrological extremes, risk and risk perception, and during/after crises and emergencies

Urban areas are at risk from multiple hazards, including urban flooding, droughts and water shortages, sea level rise, disease spread and issues with food security. Consequently, many urban areas are adapting their approach to hazard management and are applying Green Infrastructure (GI) and Nature-based Solutions (NbS) as part of wider integrated schemes.

This session aims to provide researchers with a platform to present and discuss the application, knowledge gaps and future research directions of urban GI and how sustainable green solutions can contribute towards an integrated and sustainable urban hazard management approach. We welcome original research contributions across a series of disciplines with a hydrological, climatic, soil sciences, ecological and geomorphological focus, and encourage the submission of abstracts which demonstrate the use of GI at a wide range of scales and geographical distributions.

We invite contributions focusing on (but not restricted to):
· Monitored case studies which provide an evidence base for integration within a wider hazard management system;
· GIS and hazard mapping analyses to determine benefits, shortcomings and best management practices of urban implementation;
· Laboratory-, field- or GIS-based studies which examine the effectiveness or cost/benefit ratio of solutions in relation to their wider ecosystem potential;
· Methods for enhancing, optimising and maximising system potential;
· Innovative and integrated approaches or systems for issues including bioretention/stormwater management, pollution control, carbon capture and storage, slope stability, urban heat exchange and urban food supply;
· Catchment-based approaches or city-scale studies demonstrating opportunities at multiple spatial scales;
· Rethinking urban design and sustainable, resilient recovery following crisis onset;
· Engagement and science communication to enhance community resilience.

Natural hazards, such as floods, wildfires, droughts, and storms, are becoming increasingly frequent and severe due to the synergistic effects of climate change, environmental degradation, and the intensification of human activities. The growing complexity of these events demands long-term, high-resolution, and cross-domain environmental data to support monitoring, early warning, and comprehensive risk assessment. Environmental Research Infrastructures (RIs) provide an essential foundation for such an integrated approach by enabling standardized data collection, harmonized observation protocols, and shared analytical tools. Recent advances in open science and FAIR data practices, alongside emerging technologies such as artificial intelligence, cloud computing, and digital twins, have expanded the potential of RIs to enable real-time monitoring and forecasting. However, persistent challenges remain in linking these infrastructures across domains, ensuring interoperability, and translating scientific outputs into decision-ready knowledge for diverse stakeholders, including civil protection agencies, policy-makers, and the public.

This Session invites Research Topics which aims to strengthen the contribution of Environmental Research Infrastructures to understanding, forecasting, and managing natural hazards within multi-hazard frameworks. It seeks to explore how improved coordination, data integration, and innovative technologies can enhance the capacity of RIs to deliver timely and actionable intelligence. The goal is to highlight practical examples and theoretical developments that demonstrate the integration of heterogeneous environmental observations, the application of machine learning and digital twin technologies, and the coupling of scientific data with operational and policy mechanisms. In doing so, the Research Topic will support progress toward a resilient and risk-informed society through the more effective use of RI services in disaster risk reduction and climate adaptation.

The scope of this Session / Research Topic encompasses cross-domain environmental monitoring and the operational use of RIs for hazard-related applications. It encourages contributions from research, technical, and policy communities to foster integration and knowledge transfer across sectors.

This session explores how creative, interactive and participatory approaches are reshaping risk communication and engagement across the geosciences, disaster risk reduction and climate adaptation. Inspired by the approach being developed within the Horizon Europe AMPLIFY , which combines creative, participatory and scientific methods to support more inclusive and trusted risk communication, the session looks beyond one-way information provision towards dialogue, experimentation, co-creation and collective learning.
We invite contributions on serious games, simulations, role-play, scenario-based and counterfactual exercises, storytelling, arts-based methods, citizen science, co-design, participatory mapping and other innovative formats that support people in understanding, interpreting and acting on risk. We are particularly interested in how these approaches can engage diverse, vulnerable or underserved publics, integrate scientific and local knowledge, and strengthen preparedness, trust, risk awareness, behavioural change and collective action before, during and after disasters.
The session welcomes empirical studies, methodological papers, comparative analyses, case studies, conceptual contributions and lessons learned from research and innovation projects in disaster risk reduction, anticipatory action, climate resilience and adaptation. Contributions addressing climate-related risks, multi-hazard contexts, community resilience, transferability across regions and cultures, and the scalability of participatory approaches are especially encouraged.
A key focus will be on evaluation: how can the effectiveness of interactive and creative approaches be assessed, and how do they perform compared with, or in combination with, more conventional communication channels? We particularly welcome contributions proposing indicators, evaluation frameworks, mixed-method approaches, or evidence on short- and long-term impacts of creative and participatory methods on disaster preparedness and resilience.

The growing demands on marine space for energy security and renewable infrastructure, such as offshore wind farms, are complicated by the challenges of climate change-induced hazards and the need for environmentally sustainable development. Addressing these challenges requires a holistic and interdisciplinary approach that integrates ocean and marine sciences, marine and geotechnical engineering, geomorphology, natural hazards, and environmental science. Such an approach is essential for improving our understanding of the interactions between climate change, seabed processes, infrastructure performance, and ecosystem health across the entire life cycle of offshore assets.
This session invites contributions that advance our collective capacity to plan, design and manage offshore infrastructure that is both climate-resilient and environmentally sustainable. Key themes include, but are not limited to:
• Marine geomorphology and seabed processes: characterization of seabed morphology, sediment dynamics, erosion and deposition, and their evolution in offshore environments.
• Offshore geohazards identification: mapping and assessing geohazard potential, towards a holistic geological understanding of submarine mass movement initiation and evolution, particularly landslides.
• Geohazards and infrastructure modelling, monitoring and characterization: application of state-of-the-art data science, hydroacoustic, geophysical and in-situ monitoring approaches, and development of novel physical/numerical modelling to predict initiation, development and runout of sediments and performance of offshore infrastructure.
• Climate-resilient design: novel approaches and case studies coupling engineering optimisation with ecological and geomorphological considerations for building and retrofitting critical offshore infrastructure to withstand climate-induced hazards.
We particularly encourage contributions showcasing cross-disciplinary research, including those developed within intersectoral and international consortia. By bringing together diverse perspectives, this session aims to foster a new vision for a sustainable and resilient future for the coexistence of offshore infrastructures and the marine environment.

Meteorological events and climate extremes can generate a wide range of environmental processes, from intense rainfall, floods and sediment transport to landslides, snow avalanches, glacier dynamics and coastal and ocean responses. Many of these processes generate seismic signals that can be continuously recorded by seismic networks, dense nodal arrays and emerging technologies such as Distributed Acoustic Sensing (DAS). Environmental seismology therefore offers a complementary observational approach to conventional meteorological, hydrological, remote-sensing and geophysical techniques.
This session aims to bring together recent advances in the use of seismic observations to detect, characterize and quantify meteorological events and their environmental impacts. We welcome contributions addressing both the generation and interpretation of environmental seismic signals and their use for monitoring environmental processes.
Topics of interest include, but are not limited to, seismic signatures of rainfall, storms, floods and river discharge; sediment transport and debris flows; rainfall-triggered landslides and other mass movements; snow and avalanche dynamics; glacier and permafrost responses to meteorological forcing; ocean- and atmosphere-generated seismic signals; freeze–thaw processes; and cascading responses to extreme weather events. We particularly encourage contributions demonstrating how seismic observations can provide quantitative information on environmental conditions, process rates or event evolution.
The session also welcomes methodological advances, including ambient seismic noise analysis, seismic interferometry, dense seismic arrays, nodal instrumentation, Distributed Acoustic Sensing, machine learning and real-time seismic monitoring. Contributions combining seismic observations with meteorological, hydrological, remote-sensing or other environmental observations are especially encouraged.
By bringing together researchers from seismology, geomorphology, hydrology, cryospheric sciences and environmental monitoring, this session aims to highlight the potential of environmental seismology as an emerging tool for observing and understanding the response of the Earth system to meteorological forcing and extreme events.

Computational earth science uses modelling to understand complex physical systems which cannot be directly observed. Over the last years, numerical modeling of earthquakes has provided new approaches to apprehend the physics of earthquake rupture and the seismic cycle, seismic wave propagation, fault zone evolution, and seismic hazard assessment. Recent advances in numerical algorithms and increasing computational power enable unforeseen precision and incorporation of multi-physics components in physics-based simulations of earthquake rupture and seismic wave propagation but also pose challenges in terms of fully exploiting modern supercomputing infrastructure, realistic parameterization of simulation ingredients, and the analysis of large synthetic datasets. Meanwhile, advances in laboratory experiments link earthquake source processes to rock mechanics.

This session brings together modelers and data analysts interested in the physics and computational aspects of earthquake phenomena and earthquake engineering. We welcome contributions spanning all aspects of seismic hazard assessment and earthquake physics - from slow slip events, fault mechanics and rupture dynamics, to wave propagation and ground motion analysis, to the seismic cycle and interseismic deformation and links to long-term tectonics and geodynamics - as well as studies advancing the state-of-the art in the related computational and numerical aspects.

This special session will synthesize rapid scientific insights from recent large and/or disruptive earthquake sequences, highlighting what we have learned, what remains uncertain, and which methods prove most informative. We aim to feature concise, data-driven and integrative modeling spotlights on earthquake physics, rupture dynamics, ground motions, geological field analysis, geodesy, tsunami observations, early analyses, and community data/model sharing. Emphasis is on timely results emerging up to March 2026, with flexibility to include very recent events of scientific and societal relevance. The format is a moderated panel with invited talks and time for Q&A, designed to surface cross-disciplinary takeaways and priorities for follow-up studies. No abstracts are required.

Weather and climate extremes, such as intense precipitation, tropical and extratropical storms, drought, heatwaves, and compound events, pose substantial risks to society, infrastructure, and ecosystems across the globe. Yet directly and explicitly modelling such extremes and their future behaviour remains challenging, owing to the high computational cost of resolving relevant processes, incomplete understanding of many driving mechanisms, and the chaotic nature of atmospheric dynamics, which further limits predictability at longer lead times. Consequently, model-based projections often carry large uncertainties, especially in how complex and non-linear interactions among atmospheric variables shape these extremes in a future warmer world.

Observational constraints and statistical approaches offer a complementary pathway. Observations from ground-based networks, radars, and satellites, together with reanalysis simulations, can provide information on the characteristics and physical drivers that can be used to evaluate and constrain models. Statistical and data-driven models, when constrained by associated temperature, moisture, storm characteristics, and regional and global circulation teleconnections, can provide viable alternatives for problems that remain too computationally expensive, while incorporation of constraints in explicit physical simulations can help narrow uncertainties and reduce model biases.

This session aims to bring together research on the use of observational constraints, physical theory, and statistical approaches for improved understanding, prediction and projection of past and future extremes. We welcome contributions including, but not limited to, assessing the drivers of extremes, scaling relationships based on physical theory, storyline approaches, and statistical and hybrid approaches, as well as studies critically evaluating the physical basis and robustness of the proposed constraints. Contributions spanning various types of extremes, spatial and temporal scales, and observational and modelling frameworks are encouraged.

Weather forecasting and its application is one of the most important subject in meteorology. This session will focus on R&D on understanding the weather, forecasting techniques and its applications, both with AI and physics based. Contributions related to nowcasting, numerical weather prediction, ensemble techniques, NWP post-processing and forecast application are very welcome.

Topics may include:
- Nowcasting and warning methods and systems, use of observations and weather analysis
- Studies on high impact weather
- Data driven and physics based numerical modelling; explainable Artificial Intelligence (xAI) in weather models
- Development of AI for ensemble prediction techniques and products
- Weather forecasting application
- Seamless prediction and application
- Statistical and AI NWP Post-processing
- Use of machine learning, data mining and other advanced analytical techniques
- ML for weather warning