Land degradation - driven by natural hazards such as floods, droughts, wildfires and other factors - is one of the great challenges of the Anthropocene. It can lead to reduced ecosystem functions and services, biodiversity loss, and a decline in agricultural productivity. The challenge lies not only in halting land degradation but, even more importantly, in restoring degraded lands and soils - also in the framework of the new European regulation on environmental restoration.
In this debate, we will address challenges related to land and soil degradation and restoration, focusing on the impacts of Natural Hazards and adopting a Critical Zone perspective. We will discuss how to monitor, understand, model and manage critical zone processes and related natural hazards, with the goal of supporting the health of soils and land within a “one health” perspective that recognizes the interdependence of human and environmental well-being.

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 (toxic) 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.

Weather- and climate-related extremes are intensifying in frequency, severity, and spatial extent, heightening the vulnerability of networked systems. Despite rapid scientific progress, a persistent science-to-operations gap limits the uptake of state-of-the-art hydrometeorological knowledge in planning and operations. This session highlights research that translates advances in computational hydrometeorology into decision-relevant tools that strengthen the resilience of networked systems, including transportation, energy, water, communications, and trade, against myriad of extreme events and their compounding, cascading effects. We invite contributions that (i) advance the fundamental understanding and prediction of extremes across the hydrologic cycle and (ii)operationalize these advances for resilient design, asset management, emergency response, and long-term adaptation across interdependent networks and supply chains.
Topics of interest include:
Improved understanding of physical drivers, interdependence, and nonstationarity of hydroclimatic extremes.
Impacts of extremes on interdependent infrastructure networks (transport, energy, water, communications) and trade networks.
Cascading failures, fragility and functional loss modeling, and recovery dynamics in networked systems.

Hydrologic extremes, including floods, droughts, and abrupt flood-drought alternations, are intensifying in frequency, severity, and complexity due to global warming. These events trigger cascading effects, such as landslides, infrastructure failures, ecosystem degradation, public health crises, and socioeconomic disruptions, posing significant challenges to disaster risk reduction and resilience-building. This session explores their spatiotemporal dynamics, inherent risks, cascading effects, and adaptive strategies. We invite abstracts advancing interdisciplinary approaches to forecasting, mitigation, and adaptation to bolster resilience in a changing climate.

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.

As our climate system climbs through its current warming path, temperature and precipitation are greatly affected also in their extremes. There is a general concern that climate change may affect also the magnitude and frequency of river floods and, as a consequence, that existing and planned hydraulic structures and flood defences may become inadequate to provide the required protection level in the future. While a wide body of literature on the detection of flood changes is available, the identification of their underlying causes (i.e. flood change attribution) is still debated.
In this session we invite contributions on works on how floods of different kind and their impacts on the landscape are related to climate extremes (of precipitation and temperature) and how these extremes are related to large scale predictors (e.g. climate oscillations, teleconnections). This session invite contributions on (but not limited to) the following questions:
- What are the large scale predictors of climate extremes that are relevant to river floods and their change?
- What is the role of spatio-temporal scales when mapping climate to flood extremes?
- How are climate extremes and river floods of different types related to each other?
Mapping climate to flood extremes is of interest from both theoretical and practical perspectives. From a theoretical point of view, a better understanding of the connection between climate extremes and floods will allow to better attribute flood changes to their underlying causes. From a practical point of view, the identification of climate indices relevant to flood extremes may allow to better incorporate climate projections in the assessment of flood hazard and risk, leading to a more informed selection of adaptation measures compared to what is now possible.

Climate change and socio-economic developments will further increase the risk of floods and droughts. To prepare for these challenges, societies need to step up their investments in adaptation. Cross-border cooperation on adaptation is of crucial importance, as shown by recent disasters such as the 2021 floods in Western Europe.

While hydrological systems (e.g. river basins) often cross administrative borders (federal state or national), cooperation between these different parts is often insufficient. For example, interventions or adaptation measures upstream often have negative consequences for the risk on countries and communities downstream. Moreover, early-warning systems require accurate (real-time) data from upstream areas, which can be sensitive to share (e.g. reservoir levels). Lastly, emergency response greatly benefits from international cooperation. A lack of understanding and the absence of cooperation across borders hampers the design of effective adaptation strategies and policies.

Therefore, this session aims to increase our understanding of flood and drought management in transboundary contexts, including (international) river basins, aquifers and reservoirs. We encourage research in all parts of the disaster risk reduction cycle and on different spatial scales (international, regional and local).

Topics of interest include, but are not limited to:

1) Risk analysis of floods and/or droughts in small and large (international) river basins, including upstream/downstream cost and benefit dynamics;
2) Flood and drought forecasting, early-warning, and early-action systems to improve disaster preparedness;
3) Socio-economic disaster impact studies, such as those derived from (post-) disaster surveys, to increase knowledge on people’s behavior, disaster damages, response and recovery;
4) Challenges and opportunities in governance and integrated water resources management for transboundary aquifers and river basins;
5) The implementation and effectiveness (including co-benefits) of Nature-Based Solutions.
6) Case studies of international cooperation in flood and drought management.

Urban areas are increasingly facing dual challenges of thermal discomfort and flooding, both aggravated by anthropogenic climate change. Addressing these hazards requires integrative computational modelling and machine learning frameworks that can assess, predict, and optimize the role of green adaptation strategies.
This session invites contributions that investigate how urban greening, from large-scale green infrastructure to fine-scale vegetation attributes such as species density and leaf morphology, can mitigate urban heat island intensity and thermal stress. We also welcome studies demonstrating how green infrastructure reduces flood impacts by attenuating flood peaks, enhancing infiltration, and protecting built environments.
We particularly encourage approaches that integrate urban climatology, hydrology, ecology, and data science, including:
Modelling of heat-flood interactions under varying green adaptation scenarios.
Machine learning and computational methods for hazard prediction and resilience assessment.
Modelling urban vegetation impacts on microclimate, runoff reduction, and biodiversity.
Urban heat island analyses and strategies for mitigation through green design.
Comparative studies across climate zones, cities, or adaptation typologies.
By bridging climate adaptation science with computational innovation, this session will highlight how nature-based solutions can build climate-resilient cities under global change.

Climate change unfolds into effects on different physical processes and environmental conditions, generating cascading effects that result in severe hydrometeorological events with significant impact on society and ecosystems. Among these severe events, dry hazards, including droughts, heatwaves, and fires, have caused devastating disasters across multiple regions in recent years, notably in the Amazon, the west coast of the United States, Mediterranean regions, and Australia.

When these events are compounded, overlapping each other in time and spatial coverage, or following one another, their aggregate impact is amplified due to synergies among environmental processes and variables. This amplification effect represents a critical knowledge gap, as the emerging field of compound dry hazard research is still taking its first steps, and a comprehensive research effort is needed to investigate in detail the physical processes and societal drivers behind such events as well as how these events emerge, interact, and impact the physical system, ecosystems, and human population.

Therefore, in this session, we invite contributions from researchers tackling compound dry hazards, encompassing droughts-heatwaves-fires or their pairwise combinations. We seek studies that describe the physical processes involved, their preconditions and enabling factors, emergent properties, or synergistic effects. Additionally, we welcome research addressing prediction, monitoring, and rapid response methodologies. Contributions can be related to case studies, time series analysis, experiments, spatial analyses, and modelling. The geographical scope is broad and inclusive: encompassing local case studies through regional and global-scale assessments.

Heat extremes are already one of the deadliest meteorological events and they are projected to increase in intensity and frequency due to climate change. The impacts of these extreme events on society will increase dramatically, with some studies suggesting that human habitability limits could be crossed locally. We invite researchers from a range of scientific disciplines to join the session and contribute to understanding of these burning issues.
This session welcomes new research addressing the challenge of extreme heat and its impacts, with studies focusing on the Global South particularly welcome. Suitable contributions may: (i) assess the definitions and indicators typically used to describe extreme heat stress conditions and human habitability limits, (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 vulnerability and exposure to extreme heat associated with diverse socio-economic impacts; (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.
We encourage submissions from a broad range of disciplines including environmental and climate sciences, climate impact studies, global and occupational health and epidemiology.

Groundwater is central to hydroclimatic extremes because it both buffers and amplifies them. Large subsurface storage sustains baseflows and supply during dry spells, yet slow, lagged responses can prolong impacts and delay recovery; when recharge arrives over shallow or low-storage systems, rising heads can tip conditions toward groundwater flooding or waterlogging.
With this in mind, this session concentrates on the understanding of physical dynamics of groundwater extremes. We invite contributions that quantify how aquifers evolve across dry–wet sequences, clarifying how factors such as antecedent wetness or aquifer properties influence the development of groundwater extremes (i.e. onsets, drought severity, recovery rates, etc.).
We are keen on studies and methods that measure and model the effects of adaptation options on groundwater states across climates and aquifer types, including when and by how much interventions may unintentionally worsen drought impacts or elevate wet-period hazards if poorly timed or placed. Methodological innovations are encouraged, such as new diagnostics of drought recovery, process-based and data-driven modelling, feedback-aware modelling, or hybrid approaches that benchmark system dynamic models that include socioeconomic aspects against process-based groundwater models.
We also encourage work that traces compounding effects and cascading impacts to ecosystems and sectors (for example, demand spikes or irrigation shifts) and back again to groundwater through altered recharge and abstraction patterns. We particularly welcome presentations on hypothetical storylines and stress-testing approaches to map the possibility space of future groundwater behavior.

Lightning is the energetic manifestation of electrical breakdown in the atmosphere, occurring as a result of charge separation processes operating on micro and macro-scales, leading to strong electric fields within thunderstorms. Lightning is associated with tropical storms and severe weather, torrential rains and flash floods. Lightning is also responsible for a vast number of wildfires, burned area, and fire emissions to the atmosphere. It has significant effects on various atmospheric layers and drives the fair-weather electric field. It is a strong indicator of convective processes on regional and global scales, potentially associated with climate change. Lightning produces nitrogen oxides, which are a precursor to ozone production. Thunderstorms and lightning are essential parts of the Global Electrical Circuit (GEC) and control the fair weather electric field. They are also associated with the production of energetic radiation up to tens of MeV on time scales from sub-millisecond (Terrestrial Gamma-ray Flashes) to tens of seconds (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 dynamics and microphysics
Middle atmospheric Transient Luminous Events
Energetic radiation from thunderstorms and lightning
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
Modeling of thunderstorms and lightning
Now-casting 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

Our planet faces the existential threat of a warming climate alongside a catastrophic loss of biodiversity and species. As the climate changes, so too does the frequency and intensity of extreme weather events, with costs associated with flood risk estimated to increase up to thirty-fold in the EU and UK by 2100. Simultaneously, human beings have transformed landscapes for food, energy, transport and shelter and have limited the natural capacity of landscapes to retain water, clean water, sequester carbon and enable habitats to thrive and connect to one another.

Nature-based Solutions (NbS) offer relief from these threats, with the potential to mitigate climate change, adapt to extreme events and provide restored and new habitats for plant and animal species. Planning and designing NbS can be complex and there are few established methods to enable greater uptake at the speed and scale required to combat climate change and biodiversity loss. This session targets the impacts that NbS can have on the water environment, how do we maximise their benefits, and how to scale up delivery across sectors? We would like to show evidence from multiple measure types, demonstrating how they can be optimised for multiple purposes, and emphasise the role of people in making NbS a reality through case studies.

This session will bring together scientists with the tools for the quantification of benefits with policy decisions and funding routes to ensure investment in resilience and mitigation strategies is accelerated. The session therefore invites contributions on (but not limited to) the following questions:

- What quantitative assessment and modelling tools can be used to develop business cases for supporting NbS?
- What policy drivers can be leveraged to increase uptake in NbS?
- What is the cost of ignoring NbS in planning for resilience and climate mitigation?
- How can we produce thriving landscape for all, removing the barriers to the NbS approach?

We here invite contributions highlighting the most recent advances in volcanic hazard assessment, both on recently active volcanic systems and on volcanoes with long lasting quiescence periods.
The purpose of the session is to discuss both 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.
The session will include 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.
Volcanic hazard studies of volcanic settings integrating several closely sitting or overlapping volcanoes or summarizing extended datasets acquired both on land and in submarine environments are highly welcome.

The coping capacity of human societies is strongly influenced by education, which modifies risk perception, the use of available scientific information and the ability of facing multi-hazard and multi-risk scenarios.
We here invite contributions which explore the influence of 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.
Case studies highlighting both positive and negative impact of educational programs on preparedness, response, and overall influence on vulnerability are welcome, as are analyses of the role of public and private investments in enhancing the response capacity through education.

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 of improving 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.

Large-scale mass movements 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 mass movements. 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 behaviour 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 modelling approaches can be applied to evaluate instability and failure, considering triggering, and failure propagation, leading to rapid mass movements. Nevertheless, these approaches are still phenomenological and have difficulty explaining the observed behaviour. 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, to discuss monitoring and modelling approaches and tools, to introduce new approaches for threshold definition and offline data analyses, to present advanced numerical modelling and machine learning techniques, and to show innovative dating and investigation methods.

Landslides and slope instabilities represent significant global hazards, causing substantial damage and loss of life annually. Despite this impact, the fundamental triggering mechanisms remain a key area of ongoing research. Landslide-prone areas and slope instabilities are characterized by complex, heterogeneous subsurface properties and dynamic processes operating across a wide range of timescales – from seconds to decades – and spatial scales – from grain size to slope dimensions. Effectively identifying and predicting instability processes and ultimately failure requires innovative approaches that account for these wide temporal and spatial variabilities.

This session seeks contributions presenting novel methods,emerging trends, and case studies in landslide and slope instability reconnaissance, monitoring, and early warning. We particularly encourage submissions showcasing the integration of geophysical, geotechnical, geological, and remote sensing data to build a landslide model able to characterize the landslide architecture and track its evolution.

We especially invite abstracts demonstrating:

• Multi-method approaches combining geophysical, geotechnical, and remote sensing techniques.
• Applications of machine learning to landslide hazard assessment and prediction.
• Time-lapse geophysical surveys for monitoring subsurface changes.
• Determination of geomechanical parameters through integrated geological (e.g., borehole data, geotechnical surveys) and geophysical studies.

Recognizing the cross-disciplinary nature of this challenge, we welcome contributions addressing a broad range of slope instability types, including avalanches, natural and engineered slopes, and climate-induced failures.

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 (precipitation, 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. 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, and activity and/or on the effectiveness of protection measures across different geographic contexts and scales. Test cases and investigations (by exploiting monitoring and modelling) 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.

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 processes.

This session aims to bring together state-of-the-art methods for 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 hazards as well as alpine mass movements 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 (i) insights from field observations and/or laboratory experiments; (ii) statistical methods and/or artificial intelligence to identify and map mass movements; (iii) in-situ or remote-sensing based monitoring approaches; (iv) mass movement modeling for the analysis and interpretation of the governing physical processes – from conceptual frameworks to theoretical and/or advanced numerical approaches; (v) the development of strategies applicable for hazard assessment, mitigation and protection; (vii) 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 (viii) preparedness and risk reduction, and studies that integrate social, structural, or natural protection measures.

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

Under the influence of global climate change, urban expansion and human activities, landslides (and geo-hydrological hazards in general) occur frequently every year around the world, posing a great threat to human life and property safety. The global increase in damaging events has attracted the attention of governments, practitioners and scientists to develop functional, reliable and (when possible) low-cost monitoring and management strategies. Numerous case studies have demonstrated how a well-planned monitoring system of landslides (and ground deformation in general) is of fundamental importance for long and short-term risk reduction.
Today, the temporal evolution of a landslide is addressed in several ways, encompassing classical and more complex in situ measurements or remotely sensed data acquired from aerial platforms and satellites, with particular focus to new platforms (SAOCOM, Sentinel-1C, LuTan). All these techniques are adopted for the same final scope: measure motion over time, trying to forecast future evolution or, at least, reconstruct its recent past. Real time, near-real time and deferred time strategies can be profitably used for landslide analysis, depending on the type of phenomenon, the selected monitoring tool and the acceptable level of risk.
This session follows the general objectives of the International Consortium on Landslides, namely: (i) promote landslide research for the benefit of society, (ii) integrate geosciences and technology within the cultural and social contexts to evaluate landslide risk, and (iii) combine and coordinate international expertise.
The session is expected to present various topics of innovative applications of remote sensing techniques, as well as case studies in which multi-temporal and multi-platform data are exploited for risk management. The integration and synergic use of different techniques is welcomed, as well as newly developed tools or data analysis approaches, including big data management strategies and Artificial Intelligence-based methods.

Landslides and slope instabilities pose significant global risks, driven by complex subsurface conditions and dynamic processes operating across multiple timescales. Effective hazard assessment and mitigation require robust characterization of landslide-prone areas and accurate monitoring of movement. This session focuses on the application of advanced geophysical techniques – specifically Spectral Induced Polarisation (SIP) and Transient Electromagnetic (TEM) methods – to investigate soil movement, characterize subsurface architecture, and improve our understanding of landslide dynamics. We aim to showcase cutting-edge research integrating these methods with complementary geotechnical and geological data for enhanced landslide characterization, monitoring, and prediction. This session welcomes contributions exploring the innovative application of SIP and TEM to address key challenges in landslide research. We encourage submissions demonstrating how these techniques can be used to:

• Characterize subsurface properties: Mapping lithology, groundwater conditions, clay content, and fracture networks influencing slope stability.
• Monitor temporal changes: Tracking soil moisture variations, pore-fluid movement, and deformation patterns associated with landslide activity.
• Improve landslide models: Integrating geophysical data with geotechnical and geological information to build robust, predictive models of slope instability.

We particularly invite contributions showcasing:

• Time-lapse SIP and TEM surveys: Demonstrating the effectiveness of repeated measurements for monitoring subsurface changes related to soil movement, including pre-, co-, and post-failure scenarios.
• Multi-method integration: Presenting successful workflows combining SIP and TEM data with geotechnical investigations (e.g., borehole logging, shear strength testing) and geological mapping for comprehensive site characterization.
• Advanced data processing & interpretation: Exploring novel techniques, including machine learning algorithms and inversion strategies, to enhance the resolution and interpretability of SIP and TEM data.
• Case studies: Presenting real-world examples illustrating the application of SIP and TEM to diverse landslide types (e.g., debris flows, slow-moving earthflows, rockslides) and geological settings.

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.

Landslides are major natural hazards that cause loss of life, infrastructure damage, and economic disruption worldwide. Their prediction remains challenging due to the complex interplay of geological, hydrological, and mechanical factors. Physical modelling and numerical simulations have become indispensable tools for elucidating landslide processes, advancing our understanding from initiation to runout. Recent progress in computational methods and experimental techniques has significantly improved predictive capabilities and informed risk assessment. By integrating these approaches, researchers can more effectively evaluate hazards and design mitigation strategies, thereby supporting safer communities.
This session highlights advances that combine physical experiments and numerical simulations to improve landslide hazard assessment. We welcome contributions addressing initiation, propagation, deposition, and impact processes; data–model integration; and scaling from laboratory to field. The emphasis is on approaches that link fundamental process understanding with practical applications, including early warning systems, scenario analysis, and risk-informed design, across diverse landslide types, materials, and environmental settings.
We invite presentations on landslide hazards that employ advanced physical experiments (laboratory or field) and numerical simulations (e.g., DEM, SPH, MPM, CFD). Relevant topics include, but are not limited to: innovative experimental methods at multiple scales; hybrid and multiphase modelling approaches; triggering mechanisms; material and rheological characterization; runout and entrainment modelling; model calibration and validation; multi-hazard interactions; and applied case studies.

Landslides and other types of ground failure (e.g., liquefaction and subsidence) are among the most damaging effects triggered by earthquake shaking. Observations from several recent earthquakes have shown that the death toll and destruction following strong earthquakes are not confined to the coseismic phase. Damaging mass movements are also observed in the post-seismic period due to disturbances caused by earthquakes. Overall, cascading earthquake hazards, and specifically landsliding in co- and post-seismic periods, are commonly treated separately, even though an integrated approach to the problem is clearly desirable. The purpose of this session is to provide a forum for discussion among researchers and professionals who study landslides and related hazards caused by seismic activity. It also aims to foster multidisciplinary research and collaboration among experts to better understand and mitigate earthquake-induced landslide hazards and risks in both co-seismic and post-seismic phases. Topics of interest include: (a) case histories of earthquake-triggered landslides analyzed at local or regional scales; (b) case histories of mass movements occurring in post-seismic periods; (c) assessments of landslide and other ground-failure hazards in relation to deterministic earthquake event scenarios or regional probabilistic evaluations; (d) application of numerical techniques to evaluate and portray seismic ground-failure hazards in co- and post-seismic periods; (e) studies regarding physical modeling of the influence of dynamic loading on slope stability and seismically induced landslide displacements; (f) site effects such as amplification and the influence of pre-existing landslide masses; (g) comparisons of regional differences in the factors associated with landslide occurrence; and (h) user requirements regarding hazard assessment and persisting challenges.

Landslides are among the most widespread and destructive natural hazards. Effective monitoring and forecasting are crucial for risk management, yet traditional approaches remain limited by coarse observations, high cost, complex deployment requirements and analysis of large and heterogeneous datasets.
Recent advances in the Internet of Things (IoT), low-power sensor networks, real-time communication systems, and machine learning (ML) methods are providing the means to effectively monitor and understand landslide processes. IoT-based monitoring offers scalable, low-cost, and flexible solutions for continuous data acquisition in challenging environments, while ML enables the detection of patterns, early warning signals, and predictive modeling based on heterogeneous datasets (e.g., displacement, rainfall, soil water content, suction, pore water pressure). The use of these technologies is enabling innovative applications in landslide hazard assessment, operational early warning systems, and risk management.
This session focuses on contributions dealing with design, implementation, and application of IoT monitoring systems and machine learning methods for landslide studies. Test cases describing operational applications are particularly welcome, as well as studies dealing with promising recent innovations, even if still at an experimental stage.
Topics of interest include, but are not limited to:
● design and deployment of IoT-based monitoring networks
● processing of geotechnical, hydrological, and meteorological data in IoT/ML frameworks
● methods for the analysis of complex datasets
● ML applications for landslide detection and forecasting
● real-time monitoring and analysis.

This session focuses on the role of hydrological processes on slopes for improving landslide hazard assessment and early warning. It addresses the analysis of hydrological processes at both local and large scales, combining field monitoring studies using novel measurement techniques with advanced and data-driven modeling approaches.
Water circulation within a catchment in both shallow and deep hydrological systems represents the most common factor controlling and triggering slope movements. Nevertheless, the integration of hydrological knowledge into landslide occurrence analysis, such as water storage, water-rock interactions, soil-bedrock exchange, preferential flows, and frost conditions, is still limited. Similarly, the incorporation of hydrological information into rainfall threshold development is still not fully developed or widely adopted. Researchers from all fields are warmly invited
to submit contributions ranging from field monitoring, modelling and novel data-driven approaches to advance the knowledge of processes leading to landslide occurrence.

Shallow landslides induced by rainfall or snowmelt are among the most common mass movements on slopes in steep terrain, which in turn causes sediment transfer, erosion, and deposition, significantly altering existing infrastructure, hydraulic works, and agroforestry production. While the cause of landslides is often linked to the buildup of pore-water pressure from precipitation, the underlying mechanism is a complex interplay of interconnected influencing factors, such as the slope's unique chemical composition, physical properties, geological structure, and hydrological characteristics. The prediction of locations that are most susceptible to collapse is of great importance for zonation purposes and for the design of early warning systems to prevent human casualties. Recent innovations in monitoring and modeling offer new avenues for investigating these multifaceted processes. This session aims to promote discussion among researchers studying how the coupled effects of these different conditions can affect the triggering of shallow landslides, with the goal of better understanding, modeling, and monitoring these processes across various spatial and temporal scales.
We encourage presentations related to:
· laboratory or innovative field techniques to assess the chemical, physical, geological, pedological, and hydrological conditions leading to the triggering of these phenomena;
· field hydrological monitoring for the assessment of main pore-pressure build-up areas and triggering conditions of shallow landslides;
· proximal and remote sensing methods for measurement and monitoring hillslopes prone to shallow landslides, to identify precursory evidence and to map new phenomena;
· development, application, and validation of existing or novel methodology for the prediction of shallow landslides, which may consider the coupling between thermal, hydrological, and mechanical processes;
· effects of climatic global changes and land use changes on the susceptibility and hazards towards shallow landslides;
· mitigation measures to reduce the proneness of a territory towards shallow landslides.

Landslides are a landscape modelling process inducing geomorphological changes on slopes in coastal, hilly, and mountainous areas. Their occurrence is generally controlled by predisposing (e.g., morphology, lithological and structural setting, vegetation cover, land use, climate, etc.) and triggering factors (e.g., heavy rainfall and snowfall events, wildfires, earthquakes, human activity, etc.). Therefore, paying attention to these factors in landslide analyses is essential to set an organic correlation between climate regime, geological, morphostructural and seismic setting, and slope instability phenomena. This type of analysis, together with the investigation and monitoring of existing landslides, is critical for mitigating their impact on human settlements and infrastructure. Field investigation, coupled with remote sensing technologies are essential tools in the analysis of landslides and predisposing factors, offering the ability to collect detailed and accurate data over large and inaccessible areas. This session aims to explore the use of these different types of techniques: field survey and remote sensing techniques, including LiDAR (Light Detection and Ranging), InSAR (Interferometric Synthetic Aperture Radar), and optical satellite and drone imagery, for the detection, mapping, and monitoring of landslides. These technologies provide valuable data that enable the analysis of terrain morphology, identification of landslide-prone areas, and monitoring of ground movements. The integration of remote sensing data with traditional geotechnical and geomorphological approaches can enhance the understanding of landslide dynamics and improve the development of predictive modelling and scenario reconstruction. This session gathers field survey and remote sensing studies, methodological and case studies, to highlight the advancements in innovative approaches and their vital role in landslide and geomorphological risk assessment, contributing to the development of effective mitigation strategies and early warning systems.

The assessment of the earthquake hazard and risk and the enhancement of the society’s resilience are greatly dependent on the knowledge of impact data sets of past earthquakes. For earthquakes that occurred in the historical period, such data sets could be based on various types of historical documentation and, in addition, on geological observations and possibly on archaeological evidence. After the establishment and gradual improvement of macroseismic scales the earthquake impact data sets are translated to macroseismic intensity with the use of several methods and techniques. In the modern period the collection of macroseismic observations and the assignment of intensities has been expanded to the so-called citizen seismology. These new achievements are of significance to advance the methods that may contribute to the assignment of macroseismic intensities to historical earthquakes.
This session is devoted to the advancement of methods and techniques that may contribute to the compilation, storage, and elaboration of impact data sets useful for the intensity characterization of historical earthquakes as well as for seismic hazard and risk assessment purposes. Also welcomed to this session are similar studies focusing on the collection and elaboration of impact data sets for other earthquake-related natural hazards, e.g., tsunamis and landslides, with the aim to help the assessment of hazards and risks.

From the real-time integration of multi-parametric observations is expected the major contribution to the development of operational t-DASH systems suitable for supporting decision makers with continuously updated seismic hazard scenarios. A very preliminary step in this direction is the identification of those parameters (seismological, chemical, physical, biological, etc.) whose space-time dynamics and/or anomalous variability can be, to some extent, associated with the complex process of preparation of major earthquakes.
This session wants then to encourage studies devoted to demonstrate the added value of the introduction of specific, observations and/or data analysis methods within the t-DASH and StEF perspectives. Therefore, studies based on long-term data analyses, including different conditions of seismic activity, are particularly encouraged. Similarly welcome will be the presentation of infrastructures devoted to maintain and further develop our present observational capabilities of earthquake related phenomena also contributing in this way to build a global multi-parametric Earthquakes Observing System (EQuOS) to complement the existing GEOSS initiative.
To this aim this session is not addressed just to seismology and natural hazards scientists but also to geologist, atmospheric sciences and electromagnetism researchers, whose collaboration is particular important for fully understand mechanisms of earthquake preparation and their possible relation with other measurable quantities. For this reason, all contributions devoted to the description of genetic models of earthquake’s precursory phenomena are equally welcome.

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 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.

The estimation of ground motion for future earthquakes is one of the main tasks of seismology. Among the processes affecting ground motion, local site conditions play a significant role. Earthquake site effects encompass several phenomena, such as amplified ground shaking due to local geological and topographical features, liquefaction events, ground failures, cavity collapses, and earthquake-triggered landslides. The estimation of these effects is a necessary step for seismic hazard and seismic risk mitigation as well as to build effective strategies for urban planning and emergency management.
This session aims to gather multidisciplinary contributions that bridge the fields of seismology, geology, geotechnics, and engineering and will focus on the following topics:
- Site characterization and seismic microzonation;
- Empirical assessments of stratigraphic and topographic amplification effects;
- Quantitative evaluation of seismic site response in 1D, 2D, and 3D configuration;
- Earthquake-induced ground effects, such as landslides, liquefaction and cavity collapse;
- Soil-structure interaction and characterization of building response to seismic events;
- Proposals for integration and/or revision of building codes;
- Analysis of historical and cultural heritage sites.
The session also aims to collect results based on different geophysical techniques (e.g., earthquake data, ambient noise analysis, HVSR, array measurements, active surface wave prospecting, ERT, GPR, seismic refraction tomography, etc.) and their integration. Contributions regarding innovative methodologies as Distributed Acoustic Sensing (DAS) systems and dense arrays are well accepted.

Earthquake-induced ground failures such as liquefaction and landslides are among the most damaging natural hazards, threatening urban areas, infrastructure, and vital facilities worldwide. Understanding and predicting these events is especially challenging because soil mixtures respond complexly under cyclic and dynamic loading, with fines, particle morphology, and fabric significantly influencing strength degradation and pore pressure buildup. Despite extensive research, the fundamental mechanisms behind cyclic mobility, static liquefaction, and post-failure deformations remain debated.
Recent advances in particle-based numerical methods, such as the Discrete Element Method (DEM), the Material Point Method (MPM), and Smoothed Particle Hydrodynamics (SPH), open new opportunities to study soil behaviour across multiple scales and to simulate dynamic processes from grain interactions to slope failures. Combined with laboratory and field observations, these methods are increasingly utilised to connect micromechanical insights with hazard-focused modelling.
This session invites contributions that address complex soil structures and their cyclic responses, earthquake-induced landslides, static liquefaction, and the application of particle-based and other advanced numerical methods in geohazard analysis. We particularly welcome interdisciplinary perspectives that connect geotechnical mechanics, seismology, numerical modelling, and hazard assessment to enhance the predictive understanding of earthquake-induced ground failure.

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 alert tools 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.

Understanding earthquakes and their impacts requires a comprehensive and interdisciplinary approach. This session invites contributions that span the full spectrum of earthquake science—from seismological, geological, and geodetic observations, to experimental and numerical studies of earthquake physics, and further to seismic hazard and risk assessment. We particularly welcome studies that integrate multiple components of this chain, bridging gaps between data acquisition, physical modelling, and hazard quantification.
The session aims to foster dialogue among earthquake geologists, geodesists, seismologists, engineers, and modellers, encouraging submissions that reflect innovative and integrative methodologies. It is inspired by the vision of the MSCA-DN TREAD project, which promotes a unified framework connecting small-scale laboratory experiments with large-scale observational and modelling efforts. While showcasing recent outcomes from the TREAD scientific community, we warmly invite contributions from researchers outside the consortium who share this interdisciplinary perspective.

Recent advances in physical and statistical modelling 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 catalogues are more complete and accurate than ever, and many are now publicly available, enabling analysing understudied regions and expanding global knowledge. New-generation catalogues, sometimes compiled with machine learning, reveal seismicity structures in ways not previously possible.
Additionaly, geodetic, geological, and geochemical data, fluid analyses, laboratory experiments, and earthquake simulators generating synthetic catalogues help refine models and test hypotheses. Integrating such 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 robust forecasting.
We invite contributions on developments in physical and statistical modelling and machine learning, including:
• Spatial, temporal, and magnitude properties of earthquake statistics
• Earthquake clustering analyses
• Effects of fluid diffusion and geodetic deformation on seismicity
• Physical and statistical models, including for understudied regions (e.g., Africa, Southeast Asia)
• Quantitative testing of models
• Data requirements and analyses for validation
• Machine learning applied to seismic data
• Uncertainty quantification in pattern recognition and machine learning
• Reliability and completeness of catalogues
• Time-dependent hazard assessment
• Software and methods for earthquake forecasting

Tsunamis can be generated by a variety of mechanisms, like earthquakes, landslides, volcanic activity and atmospheric disturbances. They can cause widespread damage and fatalities in coastal areas, highlighting the urgent need to advance tsunami science towards implementing effective disaster risk reduction measures and developing early warning systems (EWS). In the past 20 years, tsunami science has advanced significantly, branching into new areas. The effectiveness of these efforts was proven, for example, during the tsunami that followed the great Mw8.8 Kamchatka earthquake in July 2025, when timely alerts were issued and likely helped save lives. Nonetheless, other non-seismic events like the 2022 Hunga Tonga tsunami have highlighted persistent challenges in understanding and responding to tsunami hazards. These situations have raised important questions about risk assessment, modeling, and EWS, emphasizing the need for stronger collaboration between scientific and operational communities.
The range of topics currently addressed by the tsunami scientific community includes
-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 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 AI
-EWS, 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, engagement
-Present and future challenges related to global climate change (e.g. the impact of sea level rise)
The session aims to deepen understanding of tsunamis and improve the ability to build safer, more resilient communities. It welcomes contributions on observation data, real-time networks, modeling, risk assessments, and tools for effective warnings. Submissions on recent events, like the 2025 Kamchatka tsunami, are especially encouraged as they are expected to provide valuable insights for advancing research and improving preparedness strategies.

Offshore geohazards including earthquakes, mass gravity flows, volcanic eruptions, and tsunamis are capable of significant loss of human life and economic disruption. Recent advances in geophysical imaging, scientific ocean drilling, and seafloor instrumentation have increased the understanding of offshore geohazards. However significant knowledge gaps remain in understanding the timing and interplay of geological processes at the origin of geohazards. For example, high-latitude regions are experiencing dynamic changes in response to global warming that can lead to geohazards but are complicated to predict. Forecasting and risk assessments including probabilistic approaches are complex given the uncertainties involved and therefore geohazard quantification is poorly constrained. The sedimentary record of past offshore and coastal hazardous events is often well preserved in marine and lacustrine environments and can be investigated in detail with high-resolution geological and geophysical tools. We welcome contributions that highlight new results, methodologies, monitoring techniques, and lessons learned from case studies in areas of paleoseismology, submarine landslides and sediment flows, tsunami generation, and volcanic processes. We invite contributions from all margins and environments, including lakes. The aim of this session is to bring together the scientific community, marine industry, and governmental agencies involved in geohazard research and management to promote cooperation and better understanding of offshore geohazards.

Land subsidence is a critical concern in coastal areas worldwide, as it leads to land elevation loss from natural processes (tectonics, compaction of unconsolidated sediments, glacial and sediment isostatic adjustment, growth faults) and human-induced processes (aquifer over-exploitation, hydrocarbon production, peat oxidation, and urbanization-related loading of Earth’s surface). Human activities can accelerate subsidence, often surpassing the rate of climate-driven sea-level rise (SLR). Effectively addressing the impact of coastal subsidence requires considering the concept of relative SLR and assessing surface and subsurface processes at the correct spatial and temporal scales.
This session invites contributions across various disciplines to deepen our understanding of coastal land subsidence and elevation changes. We welcome studies on quantifying, monitoring and modeling coastal vertical land motion; disentangling the processes driving subsidence; projecting future subsidence; assessing impacts; and developing mitigation and adaptation strategies. Monitoring approaches include in-situ geomorphologic and geodetic techniques and remote sensing methods (e.g. RSET-MH, leveling, GNSS, tide gauge, InSAR, and satellite altimetry), as well as field-based and modeling methods, and we particularly encourage studies that integrate these perspectives. Contributions that bridge disciplinary gaps to enhance projections of coastal relative SLR are especially welcomed, aligning with the goals of the International Panel on Land Subsidence (IPLS; www.IPLSubsidence.org). The IPLS aims to consolidate knowledge and connect global research communities working on coastal vertical land motion, produce consistent assessments and projections of land subsidence, and ensure that coastal elevation change is properly integrated into international assessments such as the IPCC AR7.
By linking expertise from geodesy, remote sensing, hydrology, ocean sciences, geomorphology, and urban studies, this session seeks to foster a comprehensive view of the drivers, impacts, and solutions to land subsidence and relative SLR in natural and urban coastal environments.

Remote sensing and Earth Observations (EO) are used increasingly in the different phases of 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 identify vulnerable regions, more comprehensively than ever before.
Several agencies have now permanently inserted into their program the applications of EO data to risk management. In fact, EO have proven to be crucial 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, landslides and floods), 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 informing decision makers on natural hazards related to volcanic unrest/eruptions, earthquakes, landslides, floods and their potential for damage to infrastructure etc.
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 or algorithms).
Early-stage researchers are strongly encouraged to present their research. Moreover, contributions from international cooperation, such as CEOS and GEO initiatives, are welcome.

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 legacy SAR missions, we are allowed to 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, and bridging the linking knowledge between observations and mechanisms of different hazardous events are still 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.

Over the past decade, Interferometric Synthetic Aperture Radar (SAR, InSAR) technology has seen significant growth, propelled by the launch of satellite missions such as Sentinel-1, ALOS-2, TerraSAR-X, LuTan-1, SAOCOM-1, NISAR, and various commercial satellites. This rapidly expanding wealth of data offers tremendous opportunities to improve our understanding of hazard processes across diverse temporal and spatial scales, including earthquakes, volcanic eruptions, landslides, glacier dynamics, underground fluid changes, sea-level rise, tsunamis, coastal subsidence, and more.

This session aims to highlight innovative SAR/InSAR processing methodologies and provide new insights into the physics governing geohazards. We welcome contributions on a wide range of topics, including but not limited to: (1) Novel algorithms for mitigating SAR/InSAR errors, incorporating advanced techniques such as deep learning; (2) Advanced strategies for processing and analyzing SAR big data; (3) Applications of SAR/InSAR in geohazards, integrated with complementary geodetic and geophysical datasets, such as GNSS and seismic waveforms; (4) Hazard assessments and disaster risk reduction, focusing on vulnerability, capacity, and resilience.

Recent advances in AI and digital technologies are transforming how we assess and manage risks from climate extremes and natural hazards. LLMs, GenAI, and foundation models enable integration of diverse data sources, while XAI ensures transparency in high-stakes decision-making. Digital twins of the Earth system and human–environment interactions provide powerful platforms for simulating hazard cascades, testing adaptation options, and supporting anticipatory action. This session invites contributions on AI-driven knowledge extraction, hazard prediction, risk assessment, disaster response, and multi-hazard simulation. We particularly welcome work that explores synergies—for example, digital twins generating synthetic data for foundation models, or LLMs embedded as reasoning layers within simulation environments. By highlighting these intersections, the session aims to advance cross-disciplinary dialogue on how converging digital technologies can accelerate resilience to climate and natural hazard risks.

This session will provide a platform for showcasing state-of-the-art techniques in the use of remote sensing, AI and physics-based models to address hydro-climatic extremes. Participants will gain insights into how machine learning algorithms, data mining approaches, physical models and satellite data integration can significantly enhance our predictive capabilities for floods, droughts, heatwaves, storms, landslides, and other climate-induced hazards. The session will highlight innovative applications and real-world case studies that demonstrate how these technologies can be applied for disaster risk reduction, emergency response, and climate adaptation. Through discussions on the latest methodologies and practical applications, the session will facilitate cross-disciplinary collaboration between remote sensing experts, climate scientists, AI researchers, hydrologists, and policy makers.
Key Themes:
Remote Sensing and AI Synergies: The integration of satellite observations and machine learning models to enhance the detection, monitoring, and prediction of hydro-climatic extremes.
Data Mining Techniques for Climate Extremes: Harnessing the power of data mining to uncover hidden patterns in large-scale climate data, improving risk assessment and predictive capabilities.
Hybrid Modeling Approaches: Combining physical-based hydrological and climatological models with AI-driven simulations to offer more precise, real-time predictions.
AI for Early Warning Systems: How machine learning models are being employed to build more accurate early warning systems for various hydro-climatic hazards, including floods, droughts, heatwaves, and tropical storms.
Real-Time Risk Assessment: The use of AI to assess risks associated with hydro-climatic extremes, helping policymakers and disaster management agencies to make data-driven decisions quickly and effectively.
Predicting Long-Term Hydro-Climatic Impacts: AI applications in understanding the long-term effects of climate change on water resources, agriculture, and infrastructure, allowing for more sustainable planning and management.
Physics Modelling Based approaches: Physics based hydrological and hydrodynamics models in understanding the flood and drought complexities.
Hydro-climatic Extreme Events: Understanding the impacts of a range of hydro-climatic events, including: Flooding, Droughts, Heatwaves, Storms and Cyclones, Landslides, Wildfires.

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, 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.

Exposure, i.e. the description of people and assets at risk, 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 make risk assessments more detailed, both in terms of resolution and in identifying which people and assets are most at risk.

Models, input-, and output datasets range from raster-based descriptions of population distribution or built-up area, to complex datasets that describe people’s characteristics (e.g., gender, age and education) and detailed asset information (e.g., building material, number of floors, road types). Some models are local implementations, that are close to the ground truth and have a high-resolution, while others cover continents or even have a global reach. Some find their origins in grassroots activities, such as OpenStreetMap-based exposure models, while others rely on big data, through remote sensing and AI-driven methods, and are often created by larger agencies (e.g., the work of commercial parties like Google Open Buildings; WorldPop; or mixed organisations like Overture). The broad landscape of exposure is reflected in the wide variety of stakeholders, ranging from the insurance industry, to local and national governments, research institutes, 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 or earth observation 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.

Multi-hazards —simultaneous or successive natural and anthropogenic events—cause escalating human, economic, and environmental losses, threatening societal resilience. Within this context near-real-time monitoring via satellite Earth Observation (EO) is critical for effective crisis management. Currently the low repeatability/reoccurrence of EO satellites combined with latency due in data downlink, processing, analysis and delivery currently limits their explotability during crises
To address these challenges, the emerging trend of deploying EO satellite swarms to reduce revisit times highlights the need to address critical gaps and accordingly enhance various components by integrating advanced technological solutions
This session invites studies presenting innovative approaches to optimize Earth Observations (EO) using satellite swarms and near future microwave (SAR) geostationary satellites for rapid, dense, continuous data streams. Explainable AI accelerates human intervention and automates decision making for satellite tasking and acquisition, processing methodologies, product scalability and customization for proper target groups. Additionally, quantum-centric computing, though not yet practical, holds significant promise for impactful reducing processing latency in the near future.
Digital Twins of the Earth complement these advancements by enabling proactive risk assessment, scenario modeling, and optimized resource allocation, thereby improving early response strategies.
The session fosters collaboration among geoscientists, engineers and disaster managers. It aims to revolutionize multi-hazard resilience through integrated, AI-enhanced space with dynamic, high-fidelity virtual models of the planet targeted on early response.

In crisis situations, emergency responders and aid organizations require timely and accurate information on hazard extent, exposed assets, and resulting damage to coordinate effective response operations. Remote sensing has long played a crucial role in disaster management: the increasing availability of satellite, airborne, and UAV data, combined with enhanced spatial, spectral, and temporal resolution, now allows for near-real-time and global-scale monitoring of vulnerable regions. However, turning this wealth of data into actionable information remains a major challenge. Recent advances in artificial intelligence (AI) offer promising solutions by automating the extraction of critical information from remotely sensed data, paving the way to real-time, large-scale impact assessments.

This session invites contributions on innovative methods for automatic and scalable information retrieval from remote sensing data in crisis contexts, including: (i) AI models for damage detection and impact assessment, (ii) multi-sensor, multi-platform data integration for scalable disaster analysis, (iii) UAV-based data for inaccessible or rapidly changing disaster zones, (iv) real-time processing, alert systems, and rapid mapping and, (v) data standards, benchmark datasets, and best practices for algorithm training and evaluation.

Natural hazards—such as floods, landslides, land subsidence, fires, droughts, and soil erosion—continue to pose serious threats to lives, infrastructure, and economies around the world. On the other hand, technologies such as remote sensing, unmanned aerial vehicles (UAVs), ground-based sensors, and the Internet of Things (IoT) are playing an increasingly important role in monitoring, modeling, and managing these hazards. At the same time, in recent years, advanced computational tools, particularly artificial intelligence (AI) and machine learning (ML), have become indispensable in hazard research. However, a critical challenge remains: improving the interpretation of these models to enhance trust, transparency, and meaningful decision-making based on their results.
This session aims to examine how the integration of cutting-edge technologies and explainable Artificial Intelligence (XAI) is reshaping the way we assess natural hazards. We invite contributions that combine satellite and ground-based monitoring data with advanced big data analytics and modeling techniques, driven by AI and machine learning. Topics of interest include hazard detection, event forecasting, and the development of susceptibility, hazard and risk maps. Special emphasis is placed on the application of XAI methods to improve transparency in AI-based decision-making and to identify the key factors that influence hazard occurrence.

Artificial intelligence (AI) is transforming how natural and anthropogenic hazards are monitored, modelled and governed, from early warning to scenario-based planning. This session invites contributions that critically examine both opportunities and limitations of AI across the hazard sciences. Example topics include representative and open datasets (including small, sparse or biased data), uncertainty quantification and communication, explainability and accountability, multi-hazard and cascading applications, and operational deployment with practitioners. We particularly welcome work that: (i) demonstrates reproducible methods and rigorous evaluation; (ii) addresses sustainability concerns (compute, energy and water footprints) and ethical equity; and (iii) translates models into decisions under real-world constraints at different decision-making levels. Policy- and practice-oriented perspectives are also encouraged, including analyses of how EU initiatives and funding programmes (e.g. the Union Civil Protection Mechanism, “Preparedness Union”) and the evolving EU AI regulatory framework can support trustworthy, fit-for-purpose AI in risk management. Case studies, benchmarks, negative results and dataset papers are likewise welcome.

Large language models (LLMs) and agentic AI are emerging as transformative technologies across geosciences, natural hazards, and disaster management. Their potential ranges from advancing risk knowledge and hazard monitoring to enhancing early warning systems, situational awareness, and operational decision-making. Yet, important challenges remain: reliability of outputs, data interoperability, transparency, and trust among operational stakeholders. This session invites contributions that explore conceptual, methodological, and applied aspects of LLMs and agentic AI in geosciences. We welcome case studies demonstrating their use in fields such as hazard forecasting, risk communication, and emergency response; research on retrieval-augmented generation, agent frameworks, and multi-modal approaches; and critical perspectives on ethics, governance, and the operational adoption of these tools. By bringing together scientists, technologists, and practitioners, the session aims to advance the discussion on how LLMs and agentic AI can bridge the gap between complex risk/climate knowledge and actionable insights for disaster management.

Wildfires pose a significant and growing threat to both human populations and the environment. Climate change exacerbates this risk by increasing the frequency, duration, and severity of wildfires. Rising temperatures, prolonged droughts, and shifting weather patterns create conditions more conducive to wildfire spread, expanding the range of vulnerable areas and turning wildfires into a complex global challenge.
The availability of high-resolution, geo-referenced digital data underscores the need for advanced tools to model wildfire dynamics. A critical task is transforming these vast datasets into actionable insights for stakeholders. Recent advancements in computational science, particularly in the development of innovative algorithms, are essential for understanding and addressing wildfire behaviour and vulnerability.

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

We welcome contributions on topics such as:
• Methodologies for recognizing, modelling, and predicting wildfire spatio-temporal patterns.
• Pre- and post-fire assessments, including fire mapping, severity evaluations, and risk management.
• Long-term analysis of wildfire trends in relation to climate change and land use changes.
• Fire spread modelling and studies on fire-weather relationships.
• Post-fire vegetation recovery and phenology.

Join us in advancing the study of wildfires and developing strategies to mitigate their impact.

Following a series of destructive wildfire events in the summer of 2025, it has become clear that governments must prioritize and allocate greater resources to wildfire prevention. Prevention requires a thorough understanding of exposure, vulnerability and risk in the Wildland-Urban Interface (WUI) to manage the wildfire risk successfully. This session aims to showcase studies, projects and initiatives that focus on risk and vulnerability, prevention and community and built environment resilience in the context of disaster risk reduction in the WUI.

We welcome contributions across a broad range of topics, including methods for assessing vulnerability and risk, approaches to damage evaluation, research on local adaptation strategies and ways to reduce vulnerability, participatory methodologies, community and infrastructure resilience, initiatives to raise public awareness and enhance education, community-level preparedness, household self-assessments, stakeholder involvement, tools for disaster risk reduction, as well as emergency response, recovery processes, and lessons learnt from past events.

Climate change is affecting wildfire risk worldwide. Consequently, countries with limited experience with wildfires, such as those in Central and Northern Europe, may be also affected. We therefore welcome abstracts from all regions and contexts—whether wildfires are a regular hazard or a newly emerging risk. By sharing experiences, lessons learnt, methods and strategies, we aim to foster a dialogue on wildfire risk management and support the development of practical, effective solutions that can be applied internationally.

The severity of wildfire damage increases due to dry weather and climate change around the world. While climate change is a contributing factor to the increasing incidence of wildfires, the consequences of these fires extend far beyond their initial outbreak. Wildfires not only contaminate soil, pollute groundwater, and saturate the atmosphere with harmful substances, but they also devastate ecosystems and release greenhouse gases, further exacerbating the long-term effects of global warming. There remain numerous challenges that we need to understand, such as these complex relationships and the nature of wildfires. To improve understanding of wildfire behavior, various sources can be utilized such as remote sensing, numerical models, and chemical transport model. These days, artificial intelligence is actively used in environmental science, and not only it shows better performance than traditional techniques in monitoring or forecasting, but it is also widely used to understand essential information or complex relationships between disasters.
Therefore, this session invites contributions providing new insights into wildfire behavior through satellite data and artificial intelligence. It includes any extended application for air quality or climate extremes related to wildfires. This session also welcomes case studies of large fire events. The expected topics for this session are listed, but not limited to that.
- Wildfire monitoring and forecasting
- Smoke and air quality modeling
- Carbon emission estimation
- Wildfire risk assessment
- Ecosystem recovery and rehabilitation
- Wildfire behavior analysis (e.g. fire spread)
- Climate change and wildfire trends

High-impact wildfire events in 2025 in the US, France, Spain, Cyprus, South Korea, Japan, Syria, Canada resulted in extensive areas burned, mass evacuations, carbon emissions, smoke impact, and fatalities, further underlining the urgency for ramping up wildfire initiatives at local structural to landscape levels. This is the case for countries where fire is part of ecosystem dynamics such as Mediterranean countries as well as for emerging wildfire prone countries such as central and northern Europe.

This trend has been foreseen and predicted as an outcome of ongoing climatic and environmental changes and linked to socially constructed vulnerabilities. Factors include heatwaves, altered landscapes, losses of wildfire-knowledge, the abandonment of traditional land-use practices, and intersecting social crises where wildfire only forms one disruption that societies are coping with individually and collectively.

Moving forward, systemic solutions will be needed that attend multiple and competing values, interests and land uses, ultimately determining territorial development trajectories. We invite contributions that challenge current management approaches and
capture how the wildfire challenge is linked to large-scale land use changes and associated agricultural, nature conservation and climate mitigation policies.

This session invites inter- and transdisciplinary studies with insights on how to make academic findings policy- and action-relevant. We welcome perspectives and disciplines addressing the social and political dimensions of the wildfire challenge. This includes works that deploy different methodological approaches and data sources, ranging from theoretical studies to remote sensing, statistical analysis and/or qualitative methods.

Natural and artificial radioactivity both shape our environment. Natural sources include cosmic radiation and primordial radionuclides in rocks and soils, such as Uranium, Thorium and Potassium. Among these, Radon is the main contributor to public radiation exposure and a major (indoor) health hazard. Artificial radionuclides, released through nuclear practices, accidents and legacy contamination, represent an additional source of radioactivity and an often long-lasting burden to environmental health.
Monitoring both natural and artificial radioactivity is essential for mapping high-hazard areas and guiding decontamination strategies while minimizing direct personnel exposure. At the same time, it poses significant challenges, driving innovation in detection technologies, portable instrumentation, and advanced analytical methods. Beyond surveillance, natural radioactivity also serves as a powerful tracer for investigating ecosystems, groundwater flow systems, understanding geological processes, surface water-groundwater interactions and exploring environmental dynamics across multiple scales. Environmental radioactivity monitoring is evolving from manual approaches to proactive, autonomous and data-driven methodologies. Artificial Intelligence, robotics and UAVs are expanding the possibilities of data collection and analysis: robotic platforms enable detailed environmental mapping in complex settings, while UAVs equipped with advanced sensors provide rapid, large-scale and 3D observations.
This session embraces all the aspects and challenges of environmental radioactivity including geological surveys, mineral exploration, atmospheric, groundwater contamination, radon hazard and risk assessment. We particularly welcome studies exploring the use of natural and/or fallout radionuclides as environmental tracers, their applications to ecosystem dynamics, and their impact on public health, including challenges related to Naturally Occurring Radioactive Materials (NORM). Equally encouraged are contributions presenting innovative methodologies and instrumentation for radioactivity monitoring.

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, and application; (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.
We invite contributions related to all aspects of natural hazard risk assessment at the continental to global scale, focussing on:
• single hazards, multi- or compound hazards;
• all facets of risk, including hazard, exposure and vulnerability;
• risk mitigation under current and future conditions (climate & socio-economic);
• the appropriate use of continental to global risk assessment data in risk management practice;
• Novel globally-applicable approaches for leveraging global datasets and models to inform local risk assessment.

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, earthquakes, wind storms etc.. The topics include but are not limited to:
- comprehensive assessment of the economic impacts of natural hazards, emphasizing the importance of robust cost evaluations for informed decision-making in disaster risk reduction, hazard management, cost-effectiveness and efficiency of risk reduction strategies, and climate change adaptation planning.
- cascading impacts from direct losses to systemic indirect losses, e.g., business interruption, disruptions to critical services or the influence of critical infrastructure interdependencies.
- 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. Special attention will be given to the effects on specific population groups (e.g., vulnerable communities), and the long-term health impacts of climatic stressors. Given the complex nature of these impacts, the session will also focus on novel systemic approaches to assess the interplay of hazards with social vulnerability, particularly through the use of advanced data analysis techniques (e.g., ML and spatial disaggregation).
- 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., 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. Outstanding contributions will be highlighted as “solicited talks”.

“Severe”, “creeping”, “multisectoral”, “widespread”, “complex”: drought risks and impacts are diverse and disruptive at all latitudes, creating direct and cascading effects for people, sectors and ecosystems. Drought risks and impacts emerge from the interplay of multiple hazards (e.g. precipitation deficits, low flows, flash droughts, snow droughts, etc.), direct and indirect exposures, and diverse dimensions of vulnerability (e.g. social, environmental, infrastructural, etc.). This complexity is further amplified by the fact that drought risks and impacts propagate across temporal and spatial scales, driven by human actions and decisions (e.g., water use and demand) and by interconnected systems (e.g., food production and trade, energy production, navigation, etc.), ultimately contributing to globally networked risks.
As we enter a future of shifting patterns of water availability, growing water uses and demands, and evolving societal and environmental vulnerabilities, do we fully understand the extent of drought risks and impacts (including their drivers, root causes, trends and dynamics)? And to what extent does this understanding translate into prospective and systemic solutions? This session aims to advance our knowledge of how systemic drought risks emerge and manifest (especially for the most vulnerable), and to inform pathways for drought risk management and adaptation. We invite contributions that connect science, policy, and practice to:
i) deepen our understanding of the systemic nature of drought vulnerability, risks and impacts, including their root causes and social dimensions such as equity and justice;
ii) showcase new methodological approaches for the assessment and monitoring of drought risks (including Impact-based EWS or forecasting) and impacts (including impact data collection);
iii) explore innovative approaches for comprehensive and systemic drought risk management and adaptation, including governance systems that can anticipate, coordinate across scales and sectors, and adapt to systemic drought risks..
We welcome perspectives from socio-hydrology, hydrosocial studies, behavioral science, disaster risk management, social sciences, and adaptation, and we encourage case studies from all regions, especially Global South, less represented geographical contexts and differential vulnerabilities.

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.

Over the past decade, the risk of humanitarian crises has continued to rise, despite progress in disaster preparedness and response. These crises often emerge from the intersection of natural hazards with conflict, epidemics, political instability, and structural poverty, leading to food insecurity, displacement, and widespread disruption. This creates an urgent need for improved methods of risk assessment, forecasting, and anticipatory action in humanitarian contexts, where data are scarce, risk interactions are complex and dynamic, and decisions must be made under compressed timelines.

This session welcomes contributions that address these challenges, exploring innovative approaches to understand, anticipate, and respond to crises at the intersection of natural and societal hazards. Topics may include, but are not limited to:

• Analysis and prediction of compounding and cascading risks in fragile, conflict-affected, and violence-prone contexts;
• Influence of natural hazards on food insecurity, displacement, and conflict;
• Integration and estimation of dynamic vulnerabilities in multi-risk assessments;
• Innovative use of Earth Observations and AI/ML to fill data gaps and improve understanding and forecasting in humanitarian settings;
• Scenario-based and narrative approaches (e.g., storylines, dynamic adaptive pathways) for anticipating plausible future crises and uncertainty quantification.

The session aims to advance methodologies that enhance humanitarian emergency anticipation and response, from multi-hazard identification and monitoring to early warning, early action, response, and recovery in highly vulnerable contexts.

Disasters triggered by natural hazards increasingly cause profound and long-lasting disruptions to economic, social, and ecological systems. These challenges are intensifying under climate change, with compound and cascading events (e.g. floods, wildfires, heatwaves, droughts) emerging from interacting physical, social, and economic drivers. Conventional risk assessment frameworks—focused on single hazards—often fail to capture these systemic, interdependent dynamics.

Strengthening the systemic resilience of communities, cities, regions, and countries —i.e. their ability to resist, recover, adapt, and transform under rising uncertainty— is gaining urgency. Yet empirical evidence remains fragmented, definitions and metrics are inconsistent, and robust methods for understanding resilience dynamics are still emerging. Advancing disaster- and climate-resilient development therefore requires innovative frameworks, assessment methodologies, and actionable strategies that explicitly address multi-hazard and cascading risk contexts.

This session invites inter- and transdisciplinary contributions on systemic resilience to multi-hazards, including studies on single hazards that reveal broader mechanisms, drivers, or strategies. Topics include but are not limited to:
• Conceptual and analytical frameworks for assessing and modelling resilience (e.g. indicators, process- vs. outcome-based metrics, agent-based modelling, remote sensing).
• Mechanisms of resilience to compound and cascading hazards, linking infrastructures, ecosystems, and institutions.
• Strategies and interventions for building systemic resilience, including digital tools, AI, adaptive planning, nature-based solutions, early warning systems, built infrastructure, and the roles of social capital and adaptive capacity in enabling transformation.
• Justice and equity perspectives: integrating local knowledge, historical lessons, cultural legacies, and ethical considerations into climate-resilient development.
• Drivers, constraints, and enabling conditions across social, economic, ecological, technological, political, and psychological domains.
• Comparative or longitudinal studies identifying resilience mechanisms and context-specific interventions across scales.
• Stakeholder engagement through citizen science, participatory approaches, and co-production bridging research and practice.

Flooding, drought, tornado, hurricane, wildfire, landslide are some of the hazards triggered by climate change. Climate change affects both our cultural heritage and people, residents, tourists and experts alike. The level of impact can go up to disaster risk. To cope with disaster risk, we need to understand better the drivers of risk perception and their link to behavioural change in preparedness, ensuring that systemic risk reduction strategies are informed not only by hazard modelling but also by the social dynamics that determine real-world readiness and response. The session welcomes contributions at the intersection of social science and natural hazard research such as that on risk perception, behaviour and reaction as a result of awareness and mental health impact. A special focus will be on the contribution of nature based solutions in diminishing negative effects of climate change and improving mental health. Another focus of the session will be the mental map of heritage habitat (investigated through approaches of psychogeography) to be preserved in retrofit, emergency and rebuilding in the context of interventions.

Vulnerability is a central component of risk, shaping the extent to which individuals or other elements at risk are affected by climate hazards such as floods, storms, wildfires, droughts, and heatwaves. The research on physical, social and environmental aspects of vulnerability is advancing rapidly to meet the challenges of the Anthropocene. Vulnerability research progresses especially by widening the scope of definitions, addressing constant evolution (i.e., spatial and temporal dynamics), and assessing shifts across multiple interacting hazards and contexts. Recent efforts demonstrate potential by integrating local and community-scale drivers, taking advantage of data and methods interoperability, and filling in some of the substantive gaps in social, behavioural, engineering, and contextual information. However, the growing complexity of managing multiple domains, scales, and disciplines makes holistic perspectives difficult to achieve.
Given these challenges and the fertile research being developed in the field, it is timely to call for contributions on vulnerability in the Anthropocene. This session welcomes work focusing on:
- Concepts and frameworks capturing the multidimensional and dynamic nature of vulnerability.
- Interdisciplinary and mixed-method approaches fostering exchange across fields and that integrate academic and non-academic knowledge.
- Methods that integrate vulnerability to multiple social sectors (e.g., economy, health), different drivers (e.g., poverty, gender, ethnic segregation, among others) and interactions between spatial scales (e.g. local, regional).
- General interdisciplinary vulnerability conceptualisations, fostering exchange between academic disciplines and mixed methods studies.
- Reviews, knowledge synthesis, and systematic mapping of vulnerability and vulnerability drivers that generally support improved climatic vulnerability assessment.
- Data management and interoperability methods that expand the use of existing datasets or allow for filling the gaps in the characterisation of vulnerability drivers.
- Climate adaptation studies focusing on addressing climatic vulnerability drivers (e.g., poverty, gender, ethnic segregation, among others).
- Vulnerability dynamics assessments across the disaster cycle (preparedness, prevention, response, and recovery), including process-based recovery and resilience assessments that, for instance, account for state-dependent vulnerability of the built environment.

Coastal areas are prone to a multitude of pressures, including population increase, land degradation, sea level rise, and climate-related weather hazards. While responses such as protect, accommodation, and retreat are widely discussed as adaptation options, the relevance of advance (i.e., land reclamation), including its potentials and limitations, is less understood. Land reclamation is common practice in coastal cities and settlements worldwide, offering options to adapt to sea level rise (e.g., in small islands). However, it is a highly disruptive intervention in the coastal system, due to potential trade-offs with conservation, biodiversity impacts, and equity issues. The assessment of land reclamation as a climate adaptation option requires a better understanding of its (positive or negative) impacts on exposure, vulnerability, and well-being of coastal communities, its political and economic drivers, and the extent to which maladaptive outcomes of land reclamation can be prevented. In this session, we invite theoretical, methodological, and empirical studies to better understand the interaction between land reclamation and past, current, and future coastal climate risks. We explicitly welcome local and regional case studies, particularly on small islands and coastal cities, as well as global perspectives.

The increasing frequency and severity of climate hazards such as drought and extreme heat stress demand effective strategies for risk management and resilience building. Leveraging artificial intelligence (AI) offers significant advantages for detecting, attributing, and establishing the causality of extreme and compound events, enabling more precise and timely responses. This session will explore cutting-edge approaches for climate hazard management by using AI to enhance the accuracy and reliability of climate information, prediction, observations and visualisations within an interdisciplinary framework, focusing on innovative methodologies for addressing climate hazards.

The session will emphasise the contributions of AI to the study of climate hazards by refining indicators, improving the accuracy of climate information, and advancing visualisations and communications. Additionally, participants will discuss AI’s role in optimising strategies for climate financing and ensuring rigorous compliance and reporting practices. By convening experts and practitioners, the session aims to integrate cutting-edge AI technologies with practical ones for risk mitigation, hazard attribution, and adaptation, strengthening resilience against the backdrop of evolving climate realities.

We welcome contributions from researchers, practitioners, policymakers, and interdisciplinary teams at the intersection of climate science, environmental policy and AI. We encourage submissions that offer innovative solutions, theoretical advancements, and practical applications, as well as case studies that showcase the integration of AI in climate risk management and communication. We also invite papers addressing the challenges and limitations of using AI in this domain, discussing policy and practice implications, and proposing frameworks for ethical and equitable AI-driven climate strategies. Collaborative projects and cross-disciplinary insights that bring new perspectives to climate resilience are highly encouraged.

Land subsidence is one of the most critical geohazards threatening urban and industrial regions worldwide. This session highlights the integration of artificial intelligence (AI), geodesy, and remote sensing for the detection, monitoring, and modeling of surface deformation, with a particular focus on subsidence.

Subsidence results from both natural processes (e.g., tectonic activity, sediment compaction) and human activities (e.g., groundwater and hydrocarbon extraction, urban expansion). These complex drivers call for innovative approaches to hazard assessment, risk forecasting, and mitigation.

We invite contributions from data-rich and data-scarce regions alike, especially those employing AI and machine learning with GNSS and InSAR observations to improve time series interpretation, identify deformation patterns, and forecast subsidence trends. Topics of interest include automated classification, hazard mapping, deep learning, and multi-sensor data fusion. Interdisciplinary studies bridging tectonics, geodesy, engineering geology, remote sensing, and AI for enhanced risk assessment are strongly encouraged.

The imperative for disaster risk reduction is increasingly clear, especially due to the increase in frequency and intensity of hazards due to climate change. The so-called “implementation gap”, however, reveals a lack of effective measures and on the scale needed. It also demonstrates that a large proportion of human populations are still ill-advised, assisted, or lack inclusion in the decision-making processes pertaining to their adaptation and risk mitigation. The problem thus is that risk mitigation and management need to be more frequent, more intense, and adequately distributed across the different population groups.
Conversely, recent research demonstrates that most of the effective and transformative adaptation and risk mitigation happens at the local level, often through grassroots citizen-led movements. Such movements frequently stem from a deep connection with place and are motivated by the need to sustain livelihoods, preserve settlement conditions, or protect the environment. Community-led initiatives share important affinities with participatory and stakeholder-based approaches in disaster risk reduction and could contribute to addressing implementation gaps through more robust engagement with scientific assessments and evidence-based frameworks.
In this context, and following successful editions at previous EGU meetings, this session seeks to fill in the gap on accounting, analysing, and empowering citizen and stakeholder-centred risk management and disaster risk reduction approaches. We invite scholars from a wide range of disciplines to contribute their work on:
- Transdisciplinary approaches and integrative methods in disaster risk management, vulnerability, risk analysis, and disaster risk reduction that combine knowledge from both academic and non-academic stakeholders.
- Innovative methods and data sources that leverage citizen and stakeholder knowledge into risk frameworks, including mixed methods research with high transferability potential into other applications (e.g., integration with remote sensing and climate models).
- The interaction between societal dynamics and natural hazards, including the influence of urban development on the occurrence and impact of single and multiple natural hazards.
- Case studies and lessons learned that demonstrate the active involvement of citizens and other stakeholders in the design or implementation of risk assessment frameworks, risk mitigation strategies, and governance actions.

Resilience building requires effective communication, teaching and understanding of hazard and risk. Traditional outreach methods often struggle to engage diverse audiences; connect science and practice; or influence policy. Innovative approaches can address some of these challenges. For example, digital tools such as serious games, (massive) open online courses (MOOCs), simulations and immersive virtual/augmented reality can bring hazard scenarios to life. Equally, non-digital methods such as role-play, participatory mapping, classroom activities and tabletop demonstrations can foster engagement and deeper understanding of risk. This session welcomes abstracts that explore the development, application and evaluation of education and communication innovations across a spectrum: from primary through the postgraduate learning, and from public to expert engagement. We particularly welcome contributions of serious games, VR/AR simulations and digital platforms in addition to non-digital methods such as classroom demonstrations and participatory activities. Presentations that reflect on co-production with stakeholders, inclusivity and approaches for evaluating outcomes are strongly encouraged. In this session, we hope to bring together researchers, educators and practitioners to share best practice, showcase cutting-edge tools and teaching methods, and critically reflect on the role of innovation in hazard and risk education and communication. We plan on having a PICO session to ensure a lively combination of discussion and poster presentation.

This trans-disciplinary session goes beyond the disciplinary boundaries of Earth science research to address socially relevant issues by integrating disciplinary paradigms or conducting participatory research. External communication of geological risk is essential for approving effective risk prevention policies and implementing mitigation plans, as well as for implementing resilience pathways for the institutions and populations concerned and increasing trust in institutions. For this reason, studying how these phenomena have been addressed in the past can help plan future resilience actions. The session deals with the communication of different types of geological risks through time. Volcanic eruptions, earthquakes, floods and landslides have always been part of the Earth's dynamics, but actions aimed at ensuring the mitigation of this type of disaster by society, and therefore, institutional actions aimed at mitigating risk - as assessed and represented in the relevant scientific forums - have become an increasingly urgent objective with the worsening of climate change and the current increase in land use and land consumption. For these reasons, stakeholders, collaborators from academia and beyond, first and second responder and civil society, along with the third sector are invited to participate in the session with contributions related to case studies on how institutions have addressed social crises induced by one or more geological phenomena; how institutions incorporate risk into their actions and policy programs, preparing appropriate measures for its mitigation; and what have been the difficulties in communicating the various types of risk present or that have become present as a result of a catastrophic event and any mitigation measures that may have been taken; how scientists have sought in the past to communicate a risk situation in order to activate the chain of civil responsibility for the possibility of one or more events occurring.

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 requires 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, particularly in the context of climate change and slow-onset hazards (e.g., pandemics). Yet, conventional frameworks still often overlook interrelated hazards and risks, leading to unintended consequences. The session will highlight a spectrum of approaches to multi-(hazard) risk, from analysing hazard interactions and dynamics of vulnerability to characterising multi-hazard exposure. It will also discuss good practices and persistent challenges in managing multi-(hazard) risk across scales. By addressing the full risk management chain—analysis, evaluation, and implementation—this session will identify research gaps, synergies, and opportunities for collaboration across disciplines.
We welcome abstracts presenting original research, case studies, and critical reflections across the DRR cycle. Suggested topics include:
- Multi-(hazard) risk methodologies addressing exposure, vulnerability, and impacts.
- Tools and frameworks for multi-(hazard) risk assessment, management, and inclusive risk-informed decision-making.
- Methods for defining and managing multi-hazard scenarios for (near) real-time applications.
- Cross-sectoral approaches that integrate physical, social, economic, environmental, and institutional dimensions.
- Treatment of uncertainty in multi-(hazard) risk and compounded impact assessment.
- Evaluation of multi-(hazard) risk under climate change and slow-onset hazards, including pandemics.
- 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.
- Climate and impact attribution studies of complex extremes, addressing the role of climate change, exposure, and (dynamic) vulnerability in shaping impacts.

Early Warning Systems (EWS) are central to disaster risk reduction, climate change adaptation, and resilience building. The global Early Warnings for All initiative calls for universal access to life-saving multi-hazard early warning systems (MHEWS) by 2027. Yet, in practice, most systems labelled as “multi-hazard” still address multiple single hazards independently, rather than accounting for hazards that are interrelated—occurring consecutively, amplifying each other, or compounding in time and space. Evidence shows that such interrelated hazards often lead to greater impacts than isolated events, and that overlooking dynamic drivers of risk (exposure, vulnerability, inequality) can result in maladaptive outcomes.

This session seeks to advance both the methodological foundations and the practical implementation of truly multi-hazard EWS. We are interested in contributions spanning the four recognized components of MHEWS (as defined by WMO/UNDRR): (1) risk knowledge, (2) monitoring and forecasting, (3) dissemination and communication, and (4) response and preparedness. Cross-cutting aspects—such as governance and institutional arrangements, involvement of local communities, and the integration of gender, equity, and social considerations—are particularly welcome.

We invite abstracts that present:

- Methodological advances (e.g., conceptual frameworks and models for forecasting compound and cascading hazards; approaches to dynamic vulnerability and exposure; protocols for multi-hazard communication; methods to identify synergies and trade-offs in preparedness and response; advances in linking multi-hazard and multi-risk methodologies).

- Practical experiences (e.g., operationalization of anticipatory action, forecast-based financing, impact-based forecasting, and lessons learned from real-world MHEWS implementation).

- Research based on diverse methodologies, including statistical modelling, machine learning, systems analysis, scenario development, qualitative research, and participatory and survey-based approaches.

- Case studies across geographic contexts (Global South and Global North) and scales (local, national, regional, and global).

By bringing together methodological innovation and on-the-ground experiences, this session aims to critically assess progress toward multi-hazard EWS that genuinely reflect the complexity of risk, and to identify opportunities for accelerating their development and implementation worldwide.

Natural hazards, including multi-hazards, compound events and connected extremes, can put pressure on industrial facilities and critical infrastructure systems beyond their design specifications. Natural-hazard triggered technological (natech) accidents or disruptions can lead to disastrous consequences and may have far-reaching impacts beyond the directly affected area. Especially in the light of ongoing climate change, urbanization, industrialization, and an ever-increasing interconnected society, it is crucial to understand and incorporate such effects into planning and systemic risk assessments as well as to prepare for extreme events and worst-case scenarios.
This session aims to increase our understanding, modelling and management capabilities of the risk of natech accidents and infrastructure failures with potentially severe societal, economic, or environmental impacts. We invite contributions considering all aspects related to such risks, including but not limited to the topics described below:
* Methods for improving our understanding and monitoring capabilities, exposure and vulnerability of critical entities to (multiple) natural hazards.
* Data collection/database development and mapping of hazards, exposure and vulnerability of critical entities
* Collecting and analyzing empirical data of past events/disruptions to inform, validate and improve risk modelling.
* Methods for assessing and modelling natural hazard triggered accidents and disruptions
* Methods for assessing and modelling direct, indirect, tangible, intangible or systemic impacts of natural hazard triggered accidents or disruptions including complex, compounding and cascading effects
* Methods for improved impact forecasting capability and scenario building for enhanced stress testing of critical entities
* Methods for improved cross-discipline, cross-sector and cross-boundary disaster risk management and governance
* Methods to enhance understanding and knowledge and situational awareness of disaster-related risks by citizens

Society faces immense challenges when natural hazards and disease outbreaks co-occur. Disasters associated with natural hazards are often also public health emergencies. For example, in the midst of the COVID-19 pandemic, the immediate response phase 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 (e.g. the 2020 Zagreb earthquake). Natural hazards can also trigger the outbreak of diseases, such as cholera and diarrhoea 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. A limited understanding of these cascading impacts creates operational, ethical, and decision-making challenges for society, disaster management, and aid organisations.

Recent events underscore the critical need to enhance our scientific understanding of the complex interactions between natural hazards, society, public health and disease outbreaks. Equally pressing is the imperative to advance our modelling capabilities, enabling us to capture the nuances of risk stemming from multi-hazard scenarios and disease outbreaks. Additionally, we must deepen our grasp of the synergies and trade-offs inherent in disaster risk reduction measures when addressing this multifaceted challenge.

This session serves as a platform to bolster our understanding of the convergence of disasters, public health and disease outbreaks. We invite abstracts studying all aspects of this co-occurrence, such as cascading impacts, including health impacts that follow from natural hazards, difficulties that arise when natural hazards and diseases coincide, and challenges and lessons for adaptation management facing natural hazards and diseases. We are particularly keen to see new developments in measuring - for example through integration of remote sensing with public health and socio-demographic datasets - and modelling these interactions. Discussions on the compounding effect of climate change on health outcomes, and the spatial and temporal variability of exposures and vulnerabilities to these complex hazards are also strongly encouraged.

The frequency and magnitude of hazards have significantly over the years, and mountains are the most vulnerable for a series of hazards from avalanches, landslides to glacial lake outburst floods. However, there are significant differences in communicating the growing threats of these hazards in mountain regions.
This session focuses on how the vulnerabilities and risks of multi-hazards are communicated to the mountain communities to mitigate cascades including lives, livelihoods and economic losses, drawing particular attention to the most recent cascades in the Blatten valley in Switzerland, Dharali, Chamoli, Sikkim in India, floods in Pakistan and earthquake in Afghanistan.

The increasing interconnections between socio-economic, technological, and natural systems have amplified risk complexity, raising the likelihood and impact of multi-hazard events. This highlights the urgent need to understand complex risk dynamics and simultaneously develop innovative technologies and methodologies (such as Artificial Intelligence (AI) and Machine Learning (ML) digital twins, remote sensing, decision-support tools, early warning systems) to effectively assess and manage these interconnected risks. Unlike single-risk assessments, multi-risk approaches offer a holistic understanding of risk interactions and compounding effects for better adaptation planning.

This session provides a platform to demonstrate the latest technological advancements and innovations in multi-hazard risk assessment across various sectors and regions. It will feature presentations and discussions highlighting the implementation of cutting-edge technologies into useful applications to advance systemic disaster risk management and climate adaptation planning and ultimately contributing to Sustainable Development goals.

We particularly encourage submissions of research, case studies, and practical applications that showcase how these technologies can provide valuable insights into the complexities of multi-risk dynamics, optimise decision-making, and enhance resilience-building efforts.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 stakeholder needs for multi-hazard tools and approaches
- AI/ML applications and digital twins for multi-hazard, multi-sector risk management
- 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

True multi-hazard analysis captures cascading and compounding interactions—rather than stacking independent layers (e.g., earthquakes triggering landslides; floods amplifying post-wildfire erosion; climate and geohazard interactions). Current practice remains overlay-based, black-box, static, siloed, and point-predictive. This session spotlights AI that encodes physics-based interactions, is interpretable and uncertainty-aware, learns continually, future projections, overcomes data scarcity via transfer learning and simulation, enables federated multi-agent operations, and fuses multimodal foundation models for time–space integration.

We invite contributions that implement and validate:
1. Higher-order interaction modeling (hypergraphs, attention, Physics-Informed Neural Networks) with explicit cascade activation/propagation rules.
2. Interpretable architectures with process-consistent explanations.
3. Dynamic Bayesian networks with online learning for non-stationary hazards.
4. Forecasting integrating projected climate, land use, and topography changes.
5. Transfer/zero/few-shot learning and physics-constrained generative simulation for sparse cascades; calibrated adaptation.
6. Federated multi-agent learning with privacy-preserving aggregation for cross-agency model updates.
7. Uncertainty-aware decision support with probabilistic ensembles, cascade-aware uncertainty propagation, decision-centric intervals.
8. Multimodal foundation models unifying digital elevation models, InSAR, seismic, hydrometeorology, and other data sources.
9. Infrastructure cascade vulnerability analysis via dependency graphs and higher-order networks with intervention prioritization.

We welcome susceptibility; nowcasting/forecasting/early-warning systems; quantitative risk for applications spanning but not limited to earthquake-induced cascades, flood-induced cascades, climate-driven sequences, and earthquake-flood compounded landslides.

Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) processes represent one of the most intriguing frontiers in geoscience. Strong atmospheric events—such as severe storms, hurricanes, tsunamis, volcanic eruptions, cyclones, and earthquakes—can produce detectable perturbations in the ionosphere and upper atmosphere. These disturbances are observed through a variety of techniques, including ground-based GNSS networks, satellite missions, radio occultation, airglow imaging, ionosondes, and in-situ particle and field measurements.

This session welcomes contributions that advance our understanding of the physical mechanisms, modeling approaches, and observational evidence of LAIC. We particularly encourage interdisciplinary studies linking atmospheric dynamics, space weather, seismo-electromagnetic phenomena, and climate-related impacts. Case studies of recent extreme events, statistical analyses, and novel methodologies (including AI/ML applications for signal detection) are also of interest.

The session aims to foster collaboration between researchers in geophysics, geodesic monitoring, atmospheric science, and space physics to better characterize the signatures of strong atmospheric events in the ionosphere and upper atmosphere, and to assess their implications for both fundamental science and societal applications such as early warning systems and space-based communication/navigation resilience.

Understanding the processes controlling landslides, earthquakes, volcanic eruptions and tsunamis requires utilizing natural experiments and integrated models to identify and isolate controlling factors. Subduction zone hazards often occur as a

cascading series of events, requiring a system wide and integrative approach to understand. How do climate and tectonics interact to determine the susceptibility to landslides? What is the relative importance of magma supply and crustal faulting in controlling eruptive frequency? Are the size and location of earthquakes affected by structural boundaries? How do cascading sequences of events impact
subduction zone hazards? These and other geohazard questions can be addressed by studying behaviors across subduction zones. We invite contributions that use the power of comparison across multiple subduction zones to develop new insights. SZ4D is a community-driven initiative for a long-term, interdisciplinary research program to define the limits and possibilities of predicting geohazards. Observational, theoretical and laboratory studies comparing the SZ4D focus areas of Cascadia, Alaska and Chile are particularly welcome.

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.

Anthropogenic climate change has increased the frequency and magnitude of weather and climate hazards such as droughts, heatwaves, heavy precipitation, wildfires, and tropical cyclones, often with severe societal impacts. However, varying confidence in attributing observed trends to anthropogenic activity and biases in reproducing extreme event characteristics in climate models present challenges to projecting future risks from weather and climate hazards under various climate scenarios.

Understanding and accurately projecting changes in these hazards, their compounding nature, and their interactions with local socioeconomics and population changes is as complex as it is important to avoid future harm. This requires conversations across a broad range of disciplines: physical sciences, climate risk-modelling, statistics and machine learning, geography, and socioeconomic sciences. Recent record-breaking extreme weather events highlight the urgent need to improve our scientific understanding and modelling capacities for informed adaptation measures, policy decisions, and early warning systems.

This session showcases recent research progress in our understanding of weather and climate hazards under past, present, and future climate conditions, including advances in modelling and projections over decadal to centennial timescales. By fostering interdisciplinary discussions, we aim to identify outstanding research questions and form new collaborations; for instance, which hazards receive less attention from the community in specific geographical regions? Which hazard sectors should work more closely with weather and climate scientists to make progress?

We invite contributions on the changing risk and prediction from natural hazards, including but not limited to studies of:
- Detection and attribution of climate and weather hazards and impacts
- Drivers and trends in unprecedented and compound weather extremes
- Advances in climate and weather hazard and impact modelling
- Trends in hazards on decadal to centennial timescales
- Extreme weather early warning systems
- Global weather and climate teleconnections and their links to environmental hazards and impacts
-Interactions between climate and weather hazards with local socioeconomics and adaptation strategies

Recent advances in Large Language Models (LLMs) and Natural Language Processing (NLP) are rapidly changing geosciences research, offering new opportunities for knowledge discovery, data analysis, and real-time monitoring. At the same time, the increasing availability of digital text and image data—from scientific literature and newspaper articles to social media and historical archives—offers unprecedented opportunities to explore new data sources in geosciences research.

This session examines how geoscientists are using LLMs, NLP, and text-as-data approaches across various hydrology, natural hazards research, and the broader earth system sciences research fields. We invite contributions that showcase innovative uses of LLMs and NLP, discuss methodological challenges, or integrate text mining techniques into geoscientific workflows.

We particularly welcome submissions on topics including, but not limited to:
- Chatbots and AI assistants in geosciences
- Assessment of natural hazard impacts (e.g., floods, droughts, landslides, heatwaves, windstorms)
- Real-time disaster monitoring and early warning systems
- Evidence synthesis and literature mapping
- Public sentiment and perception analysis
- Policy tracking and narrative analysis
- Social media analyses
- Enhancement of metadata and data descriptions
- Automation of historical data rescue
- Integration of LLMs with remote sensing or image data
- Methodological challenges in using LLMs and NLP-based analyses, including bias, reproducibility, and interpretability

By sharing case studies, technical developments, and lessons learned, we aim to promote the effective use of these tools while also highlighting the challenges that newcomers may encounter, including issues with data coverage, quality control, and concerns about reproducibility. By sharing best practices, this session aims to inspire collaboration and innovation in harnessing LLMs, NLP, and text-as-data in geosciences.

Mountains are complex social-ecological systems and natural laboratories in which to tangibly explore and understand how drivers and processes of global change manifest, and the impacts (or effects these have for specific places and beyond. In this session, we invite inter- and transdisciplinary contributions that examine past, present, and future environmental change, their associated impacts for ecosystems and people in mountain environments, and measures taken to address these impacts. This session is open to conceptual as well as empirical research on observations, modelling or scenarios studies of mountain climate, cryosphere, ecology, hazards, and hydrology, and their interactions, which could also incorporate intersecting socio-economic dimensions and risks. Mountains as complex terrain can be difficult to adequately parameterize in (climate) models and many areas of the world lack high-elevation monitoring infrastructure that can record data at the relevant locations, densities, scales, frequencies, and resolutions needed. Likewise, there is a need to capture and account for socio-economic changes such as demographic and land-use change and their projections, thereby enhancing our understanding of how hazards, vulnerability, and exposure interact in terms of impacts and risks.
We particularly welcome contributions that describe how steps are being taken to address such knowledge gaps, including high-elevation integrated monitoring efforts, observations along elevational gradients, climate downscaling strategies and remote sensing innovations, and integration methods that include societal data and information to characterise and represent a more comprehensive systems approach to global change.
This session is endorsed and supported by the Mountain Research Initiative and the Institute for Interdisciplinary Mountain Research of the Austrian Academy of Sciences.

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.

Climate change intensifies hydro-geological hazards by altering precipitation and temperature patterns, affecting soil moisture, vegetation, groundwater, and surface runoff. These changes generate spatially and temporally variable triggers for floods, landslides, and droughts, challenging traditional methods based on historical records. Artificial intelligence (AI) offers a promising pathway by integrating multi-source observations with physics-informed learning to capture complex processes and incorporating future climate scenarios to enhance community resilience. This session explores AI integration for hydro-geological hazards under a climate-driven context, focusing on modeling, evaluation, and decision support. Key questions include: How can AI models account for complex physical processes and dynamically update triggering thresholds? How can multi-timescale climate variability and CMIP6 scenarios be embedded while preserving physical consistency? How can predictions remain robust under nonstationarity and inform early warning and climate-resilient planning?
We invite contributions addressing these challenges, with interest in AI, climate scenarios, and multi-scale process coupling. Topics include: 1) AI for hydro-geological hazards:
• Prediction and early warning of floods, landslides, and droughts using machine/deep learning for susceptibility mapping, monitoring, and real-time alerts.
• AI-driven coupled hazard modeling integrating rainfall, surface water, groundwater, and geological processes using multi-source data.
• Remote sensing and big data applications for hazard detection, evolution tracking, and mapping from satellite, UAV, or radar data.
• Assessing impacts of climate variability and extreme events on hazard occurrence.
• AI methods integrating CMIP6 scenarios with bias correction and downscaling for training and inference.
• Modeling physical processes, e.g., hydrological interactions among atmosphere, vegetation, and soil.
• Explainable AI and decision support systems for transparent hazard management, urban planning, and engineering measures.
2) Evaluation and decision support under climate change:
• AI-driven or GIS-based decision support platforms for adaptive management, policy-making, and disaster risk reduction.
• Assessing socio-economic vulnerability, resilience, and adaptation trade-offs under climate change.
• Evaluating nature-based and sustainable solutions as strategies for climate-resilient planning.

Climate change has been an inherent aspect of Earth's history, shaping ecosystems, landscapes, and human societies from ancient times to the present. Since the 19th century, urbanisation has increased climate risk in densely populated areas, but also created new possibilities for shaping resilience through technological improvements, environmental management, and changes in societal structures. Understanding how societies in the past responded to climate-related hazards and how social resilience formed provides valuable insights into today's challenges. These historical perspectives inform strategies for sustainable adaptation amid ongoing global environmental change. Drawing on insights from archaeology, climatology, anthropology, history, and geography, scholars can elucidate the complex interconnections between climate variability, human adaptation, and societal resilience across different temporal and spatial scales.
Data-driven methods—including spatial analysis and statistical modelling of spatiotemporal information—can detect patterns of change over time, informing how resources were allocated and adaptive strategies developed over time. Results from case studies can detect how social, built environment, and infrastructure systems (co-)evolved, contributing to a deeper understanding of systemic change in hazard-prone areas. At the same time, Indigenous perspectives, community-based approaches, and participatory methodologies can enhance the resilience of vulnerable populations and foster sustainable responses to climate change in the twenty-first century.
This session aims to explore the complex interplay between humans and their environment, examining how societies have responded to and coped with the impacts of climate-related hazards in the past. Key themes include, but are not limited to:
• Historical perspectives on climate variability and societal change
• Case studies of resilience in ancient and medieval societies
• The development of adaptive strategies in relation to urbanisation processes
• Indigenous knowledge systems and adaptive strategies
• Technological innovations and agricultural practices change over time
• Adaptive strategies for responding to historical climate-related hazards and their transformation
• Case studies on the (co-)evolution of social and environmental systems in hazard-prone areas
• Lessons learned from past experiences for contemporary climate resilience

Acknowledging the rise of flood hazards globally, the proposed session is intended to investigate Artificial Intelligence (AI) applications for scenario planning to enhance urban resilience. There will be a specific focus on exchanging best practices and experiences from diverse geographic contexts from within the European region and beyond, noting New Zealand and the South Pacific region. The proposed session will feature: Oral (discussion block) plus Poster Session from presenters. Proposed outcomes from this session include compilation of a summary reference paper.

Recent advances in computational science and data-intensive methods are significantly improving our ability to detect, model, and respond to natural hazards in real/near-real time. From earthquakes, tsunamis and floods to wildfires, volcanic eruptions, and extreme weather events, the integration of high-performance computing, predictive modeling, and intelligent systems is enabling more effective and timely emergency response and operational frameworks and services, as illustrated from the outcomes of several EU-funded projects (e.g. ChEESE, doi: 10.3030/101093038; DT-GEO, doi:10.3030/101058129; or the EU-India partnership GANANA, doi:10.3030/101196247).
This session focuses on the role of scalable, adaptive, and AI-enhanced computing approaches in supporting the entire natural hazard management cycle: from early detection and warning to modelling, impact forecasting and decision support. We invite contributions that explore but not limited to innovative methods and real-world applications across the areas such as:
(i) Early detection and rapid warning systems, leveraging sensor networks, remote sensing, and predictive analytics, (ii)Time-critical simulations and forecasting models, (iii) AI applications in natural hazard contexts, including real-time/near real-time earthquake signal analysis, landslide and wildfire risk mapping, flood extent detection, and uncertainty-aware forecasting using ML-based ensemble models, (iv) Operational platforms and decision-support tools, integrating real-time data streams with adaptive modeling and, (v) Case studies demonstrating the application of such methods etc.
We invite contributions that showcase novel approaches in computational science, AI / machine learning, modeling systems, or hybrid workflows that improve readiness and responsiveness during natural disasters. We particularly encourage interdisciplinary submissions that highlight collaborative work across geoscience, computer science, and emergency management. This session aims to bring together researchers, practitioners, and system developers working at the intersection of geoscience and urgent computing to advance the state of natural hazard mitigation and civil protection.

Early Warning Systems (EWS) represent a critical cornerstone of disaster risk reduction as they provide an essential foundation for protecting lives and livelihoods through the timely provision of actionable information. However, the efficacy of EWS is dependent not only on scientific robustness but also on seamless integration across disciplines, from disaster risk knowledge and hazard detection to communication strategies and community response. Subsequently, these systems require innovative advancements across the warning chain to meet the ambitious targets outlined in the Early Warnings for All (EW4ALL) initiative action plan and the Sendai Framework towards multi-hazard, all-vulnerability, and impact-based EWS. This session aims to foster a dialogue on the implementation and methodological innovations surrounding EWS, particularly between researchers working toward more effective, inclusive, and actionable EWS.

This interdisciplinary session invites contributions from a wide range of disciplines and sectors involved with the full spectrum of EWS development and implementation, including but not limited to natural hazards science, atmospheric and hydrologic research, social sciences, and disaster management practice. We encourage submissions addressing the following key themes and sharing of lessons from successes and failures:
● Early warning and anticipatory action: Frameworks and multi-stakeholder implementation in translating early warnings/EWS into effective disaster response and preparedness mechanisms;
● Impact-based approaches: methodologies and approaches for design and implementation of impact-based EWS;
● Technological innovations: advances in AI, machine learning, Earth observation, IoT and other cutting-edge technologies in components of EWS;
● Risk communication and community engagement: strategies that integrate behavioral and psychological insights, building trust, and ensure effective warning communication and dissemination, particularly at the community level;
● Data integration and system interoperability: approaches to integrate diverse data sources that address challenges in cross-agency data sharing and platform integration.

Climate change is one of the pressing issues of our contemporary society with great implications for sustainability and diverse social groups. Moreover, climate change is intensifying risks across interconnected ecological and social systems, creating cascading, yet differentiated impacts that challenge conventional approaches to disaster risk reduction and adaptation. Nature-based and community-led strategies are increasingly recognized as promising pathways, offering opportunities to mitigate hazards such as floods, droughts, heatwaves, and erosion while simultaneously enhancing biodiversity, ecosystem services, and local livelihoods. By grounding adaptation in socio-ecological systems and empowering community leadership, these strategies can deliver solutions that are equitable, context-specific, scalable, and sustainable.

This session invites contributions that critically examine the role of nature-based and community-led approaches in advancing disaster risk reduction and adaptation across diverse ecological and socio-economic settings. We particularly welcome research that:
- Assesses the effectiveness of nature-based and community-led strategies in reducing disaster risk and enhancing climate resilience
- Examines socio-ecological trade-offs and synergies, including impacts on biodiversity and livelihoods
- Evaluates long-term resilience outcomes across varied ecological and socio-economic contexts
- Investigates governance challenges, enabling conditions, and structural barriers to implementation and scaling
- Bridges ecological and social science perspectives to foster integrated, systems-based approaches
- Engages with Indigenous and local knowledge systems, emphasizing culturally grounded and community-driven solutions
- Explores inclusive governance frameworks that promote equity, participation, and sustainability in adaptation planning
- Investigates the synergies and trade-offs of nature-based approaches and non-nature-based/conventional measures
- Explores how AI can complement or optimise nature-based and community-led strategies

This session is supported by the RISK-KAN Working Group “Nature Based and Community Led Climate Risk Strategies“, which promotes interdisciplinary dialogue and exchange across contexts to advance socio-ecological resilience and bridge the science–practice gap in disaster risk reduction and climate change adaptation.

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

Climate change and environmental degradation constitute a growing threat to the stability of societal and economical systems. The observed and anticipated escalation in the frequency and intensity of extreme weather events under future emission scenarios, combined with the projected long-term shifts in climate patterns and consequential impacts on biodiversity, have the potential to significantly affect specific sectors such as insurance and finance leading to significant economic damages on a local to global scale.

To accurately understand climate risks, baseline historical understanding of hazard is required and what large-scale factors influence this for different geographic regions. Then as the climate continues to change, an understanding of changes to frequency, severity, exposure, and vulnerability are all required for a multitude of different perils. To avoid an underestimation of future physical climate risks. Further challenges include the accurate representation of extreme events, their compounding and cascading effects, and the integration of non-linearities associated with tipping points in the climate system.

In recognition of this challenge climate risk assessments have experienced amplified attention in both the academic and private spheres and a growth in climate risk services aiming at setting standards and frameworks as well as the provision of comprehensive climate impact information for the private sector and financial institutions.

Therefore, providing a platform to foster interactions between scientists, risk modellers and assessors, economists and financial experts is urgently needed. With the goal of facilitating such dialogue, this session aims at providing a platform for actors from academia and the private sector to exchange information on strategies for assessing climate risk.

The session 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.

Extreme weather events such as tropical cyclones, heatwaves and floods threaten populations around the world. Climate change is increasing the frequency and intensity of extreme weather events, which can combine with community exposure, inequalities and vulnerabilities to cause substantial harm, including forced migration, human displacement, and other societal impacts. There is a growing literature at the intersection of the natural and social sciences studying the impacts of extreme weather events on populations as well as peoples’ behavioral, attitudinal, and emotional responses. For instance, studies have investigated how extreme weather events influence food and water security, conflict and security risks, climate action, and health outcomes. Additionally, the field of environmental human mobility has witnessed remarkable progress in data collection, analytical methods, and modeling techniques, advancing scientific understanding of the impacts of extreme weather on mobility and displacement.

Yet only few studies are currently harnessing the full potential of interdisciplinary collaborations in this space and several challenges pertaining to the choice of methods and the scale of analysis (e.g., regional, national) remain underexplored. This session aims to provide a platform for interdisciplinary work on extreme weather events and invites contributions from natural and social scientists interested in interdisciplinary studies on the societal impacts of and responses to extreme weather events. Furthermore, we highlight the topic of human (im)mobility with a perspective on addressing recent advancements, methodological innovations, novel use of data, challenges, or future prospects in modeling human mobility in the past, present, and future.

We invite contributions including but not limited to studies of:

Migration and displacement due to extreme events
Environmental attitudes and behaviors influenced by extreme events
Health and wellbeing effects of climate change and extreme events
Food production and security in relation to extreme weather
The interplay between climate change, environment, and conflict
Methodological challenges to interdisciplinary collaborations

The Aegean Sea is a dynamic convergent-margin exhibiting shallow subduction, back-arc volcanism and a long history of coupled geo-marine extreme events, including earthquakes, volcanic activity, submarine landslides, and tsunamis. These extreme events often occur in a cascading manner, posing a significant hazard to densely populated coastal areas, tourism-focused economies and critical infrastructure. To understand, characterize and mitigate the compounding hazards requires a transdisciplinary approach, integrating marine earth sciences, geophysics, hazard modelling, social sciences, engineering and stakeholder engagement to foster participatory research in the Aegean Sea.
This session invites contributions (particularly from Early Career Scientists), that will broaden and deepen scientific and societal understanding of marine and coastal geohazards in the Aegean Sea, adjacent Mediterranean regions and similar environments worldwide.
Topics of interest include:
• Geohazard processes and cascading events: seismic, volcanic, and submarine mechanisms leading to multi-hazard cascades, such as tsunamis.
• Monitoring and early warning systems: advances in seafloor instrumentation, seismic and geodetic networks, satellite remote sensing, and real-time modeling.
• Scenario development and risk assessment: earthquake, landslides, tsunami and coupled simulations, probabilistic hazard assessments, and uncertainty quantifications.
• Societal integration and resilience: participatory approaches, co-designed risk strategies, innovative communication tools (e.g., Augmented and Virtual Realities), and their applications to tourism, public safety and cultural heritage protection.
• Comparative perspectives from other tectonically active coastal regions.
This session builds on the ongoing MULTI-MAREX consortium of the German Marine Research Alliance’s (DAM) third research mission, which is developing integrated 'living laboratories' in the Aegean Sea to study and communicate risks of cascading marine geohazards. We encourage contributions from other research initiatives and independent studies, providing a platform for transdisciplinary exchange and dialogue between geoscientists, engineers, social scientists, tourism researchers and stakeholders.

Losses from natural hazards continue to rise despite extensive efforts. While climate change plays a crucial role in increasing the frequency and magnitude of many hazards, other factors such as changes in exposure and vulnerability remain insufficiently understood. This session delves into the the complex dynamic interplay of these factors, explores key drivers of risk, and aims to uncover the underlying mechanisms shaping their evolution.

The dynamic nature of hazard triggers and cascading effects is often overlooked in current
mitigation and adaptation strategies. Many existing measures, including technical interventions and land-use planning, rely on static concepts, whereas the effects of hazards are inherently dynamic.

Exposure is a critical component of risk assessment, and it is expected to increase in the future as human settlements expand and industrial activities intensify. However, there is limited information on the spatio-temporal dynamics of exposure across different scales. To accurately assess the evolution of risk, these dynamics must be analysed alongside the effectiveness of existing technical mitigation measures. Such insights also inform discussions on the impacts of climate change on exposed communities, particularly in the context of shared socio-economic pathways (SSPs).

Understanding the vulnerability of elements at risk is another key objective in reducing future losses. Current models used to describe vulnerability require further validation through empirical data, laboratory experiments, and alternative assessment methods. Integrating observational methods other techniques and incorporating additional dimensions of vulnerability, particularly institutional vulnerability, is essential for a more comprehensive and nuanced understanding of risk.

We invite submissions that integrate these interconnected topics, including hazard and exposure analysis, vulnerability assessment, adaptation strategies, and disaster risk reduction tools. This session will focus on the interactions between landscape processes and human activities, promoting transferable and adaptive approaches to risk management. Contributions should tackle the key risk drivers behind natural hazard impacts through a holistic examination of risk components, ultimately contributing to more sustainable risk reduction strategies and solutions for managing climate risks.

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 or disaster-affected regions (e.g., co- and post-seismic landslide events)
• Case histories of slope stabilization and landslide mitigation - including both successful and unsuccessful interventions - to highlight the limitations of “one-size-fits-all” solutions
• 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)

As highlighted by the UN development goals, climate change is a reality to which we need to adapt. However, the many disciplines required to effectively plan and adapt to climate change often work in isolation. For example, physical climate modelling, hydrology, and hazard impact and risk assessment are largely separate disciplines with difficulties interacting due to different terminologies and backgrounds. Moreover, until recently, climate modellers did not have the capability to generate long-term projections at a spatial and temporal resolution useful for impact studies.

With the advent of kilometre-scale atmospheric models, called convection-permitting models CPMs, high resolution remote sensed data sets, and global sub-daily rainfall observations, we are now in a position to bridge the gap between disciplines, sharing knowledge and understanding. With all these tools at our disposal we have substantially improved the representation of sub-daily precipitation characteristics and have model output at a spatial resolution closer to what many impacts modellers, for example hydrologists, need. Now is the time to exploit these high-resolution, consistent datasets as input for impact studies and adaptation strategies; to foster interdisciplinary collaboration to build a common language and understand limitations and needs of the different fields; to learn together how to provide policymakers with information that can be used to design effective measures at to adapt to climate change as well as to inform mitigation decisions.

This interdisciplinary session invites contributions that address the linkages between high-resolution climate scientists, impact modellers, and end users with a special focus on:
- Recent advances in climate modelling for impact studies, particularly using high resolution convection- permitting models.
- Bias correction techniques to overcome bias in climate models affecting impact models.
- Analysis of the uncertainty propagation from climate into impact models.
- Improved understanding of processes that will alter hazards resulting from climate change.
- Novel use of new and existing observational data sets in characterising and quantifying climate change hazards.
- Examples of good practice, storylines and communication to both stakeholders and policymakers.

Artificial intelligence has become central to Earth system science, yet a core challenge remains: how can we move from models that learn correlations to those that capture and reason with structure, especially under hazards and compound extremes? Many current methods swing between flexible learners that overfit and complex explainers that rationalise black boxes. This limits both understanding and robustness as system complexity, data diversity, and societal stakes grow.

This session focuses on interactions between atmosphere and hydrosphere, highlighting applications to extremes and related water–ecosystem impacts.

We invite contributions that address the transition from learners to knowers, asking for example:
- How can AI models reflect the organising logic of nature, not just the statistical shape of data?
- What happens when predictive skill is high but reasoning is flawed?
- How can models generalise across regions, scales, and regimes while remaining interpretable and trustworthy?

We particularly welcome studies that:
- Embed physical, hydrological, or causal structure into AI models
- Diagnose why current methods fail and what this reveals about their assumptions
- Introduce inductive biases and constraints that promote generalisation under distribution shift
- Move beyond post-hoc explanation toward structurally grounded modelling
- Share FAIR datasets, benchmarks, or reusable tools and workflows
- Explore the role of causal ML, physics-informed networks, or foundation models in linking data and knowledge

Who should submit?
Earth and environmental scientists, hydrologists, hazards researchers, and AI specialists interested in structuring machine learning for process understanding. We welcome both theoretical and applied work; from those developing hybrid or interpretable models to those testing their limits in complex environmental systems. Case studies may span regions or scales but should highlight what makes a model explain rather than merely predict.

Our goal is to redefine how AI advances Earth system science by turning learners into knowers: models that reason with structure, are accountable, and generalise under change.

Water sustainability is becoming a key concern worldwide due to hydrological uncertainty, climate change, landuse landcover changes, and growing water pollution. These drivers greatly influence the catchment hydrology and thus their roles cannot be undermined while assessing both surface and groundwater resources. These aspects draw paramount significance in catchments with large heterogeneity and spatial complexities such as mountainous and urban catchments, data scare regions, and low-income countries where investment in hydrological monitoring network and installation of IoT sensors is very limited. It is therefore warranted to leverage geospatial, machine learning and decision science techniques to improve the understanding of catchment hydrology and the adverse consequences of climate change and anthropogenic drivers on surface and groundwater resources, which may play vital role in intensifying water, food and energy security. The worldwide readily available satellite remote sensing data and global data products of hydrometeorological and biophysical parameters enable us to leverage potential of geospatial and machine learning techniques in addressing challenges associated with climate change, landuse landcover changes, water scarcity, groundwater management, and ecosystem services.

This session aims to bring together professionals from multidisciplinary fields such as hydrology, hydrogeology, geosciences, agriculture, and environmental sciences & engineering to share their innovative ideas, research outcomes, and innovative insights obtained from case studies of different catchment settings by utilizing geospatial, artificial intelligence and machine learning techniques. We solicit novel contributions from the researchers to investigate and manifest revolutionary developments in the catchment hydrology by utilizing Remote Sensing with Satellite and Drone Platforms, GIS, Artificial Intelligence (AI), and Machine Learning (ML) techniques for addressing pressing challenges of water sustainability in mountainous and urban catchments and data scarce regions. The combined use of these technologies is revolutionizing and providing powerful tools in analysing and understanding complicated hydrological processes, which in turn will be very useful in evolving effective water resource management strategies to foster sustainable development and ecosystem-based adaptation to hydrological uncertainty, climate change and anthropogenic drivers.

Drought is a multiscale phenomenon unfolding across spatial and temporal dimensions, often combining meteorological, agricultural, and hydrological components. As climate change accelerates, rising temperatures and altered land-atmosphere interactions intensify drought persistence, clustering, and propagation. These shifts affect water availability, ecosystem resilience, and water-dependent sectors such as agriculture, industry, and domestic supply. We invite studies that explore how land use, land cover change, and evapotranspiration dynamics contribute to evolving drought patterns, as well as those linking these phenomena to risk assessment, adaptation strategies, and early warning systems. This session invites contributions on the spatio-temporal dynamics of compound droughts, including detection, attribution, monitoring, and modeling from local to global scales. We particularly welcome studies that use novel datasets, high-resolution observations, advanced indices, or integrated modeling frameworks to assess drought propagation, persistence, and clustering under climate variability and change. Contributions may also focus on linking these dynamics to risk assessment, adaptation, and early warning.
The session aims to foster interdisciplinary discussion and methodological advances supporting drought risk management, policy planning, and enhanced resilience to water stress in a changing climate.

Extreme hydrological events such as floods, droughts, and heatwaves have been changing considerably under a changing climate. Concurrent and compound events, where extremes occur simultaneously or in succession, have been exacerbating risks to infrastructure, society, economy, and ecosystems. Understanding the evolution of extremes and their concurrent/compound occurrences across spatial and temporal scales, climate drivers influencing these events, and the uncertainty in their characterization remains a critical challenge. Consequently, there is an urgent need for advancements in modeling, monitoring, prediction, and risk assessment to reinforce resilience against extremes and their concurrent/compound occurrences at global, regional, and urban scales. In this session, we welcome research contributions encompassing, but not limited to, the following areas: (1) analysis of changes in the severity, duration, intensity, frequency, and spatial extent of extremes including concurrent/compound events under changing climate conditions; (2) exploration of latest methodologies for characterizing and predicting extreme events, including the application of machine learning techniques; (3) understanding the climate drivers impacting extreme events and their spatial/temporal evolution; (4) characterizing risk, vulnerability, exposure, and adaptive capacity; and (5) quantifying uncertainties in extreme events.

Flash floods remain one of the most destructive and least predictable natural hazards, driven by the complex interplay of natural (i.e. meteorological, hydrological, geomorphological, etc.) and human factors (i.e. land use, infrastructure, settlements, etc.). Despite recent advances in improved data availability, high-resolution modeling, and forecasting technologies, substantial uncertainties persist in understanding, simulating, and anticipating these rapidly evolving events.
This session invites contributions that focus on the hydrological and hydrodynamic aspects of flash flood modeling. Topics of interest include, but are not limited to:
- Rainfall inputs: advances in monitoring, nowcasting/forecasting, and ensemble generation
- Rainfall-runoff modeling in larger scales: spatial and temporal variability of parameters under extreme conditions
- Hydrodynamic modeling in smaller scales: representation of fine-scale topography and urban infrastructure
- Efficient preprocessing of the required data for modelling and approaches to address data scarcity
- Critical assessment of model assumptions and structural limitations
- Innovative calibration and validation strategies
- Integrated modeling frameworks combining rainfall-runoff and hydrodynamic modelling
- Uncertainty quantification and strategies for their reduction

By bringing together researchers with diverse expertise, this session aims to advance dialogue on both fundamental challenges and innovative, interdisciplinary solutions. We aim to highlight not only what we know, but also what we do not yet know, and how innovative approaches can help bridge the gap to more robust and reliable flash flood forecasting and management.

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.

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 2024 Valencia floods, and the 2020-2023 Horn of Africa drought, show that significant gaps in the early warning - early action chains persist, despite major advances in forecasting capabilities over last decades. The Early Warnings for All initiative (led by WMO, UNDRR, ITU, and IFRC) recognizes that increased efforts are required to develop life-saving, 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 collaborative production of impact-based forecasting services and linked early action protocols.

However, much remains unknown and significant knowledge gaps 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. Such systems demand much knowledge about how hazards translate to impacts through exposure and vulnerability, novel impact-based forecasting technologies (including machine learning models), the costs and benefits of triggered actions, human decision-making and risk perception dynamics.

Topics of interest include, but are not limited to:

- Practical applications and operational use-cases of impact-based forecasts
- Novel physics-based, Artificial Intelligence (AI) and hybrid models for impact-based forecasting
- Innovative solutions to address challenges in impact-based forecasting effectively, including the application of AI, harnessing big data and earth observations
- Development of cost-efficient, evidence-based early action portfolios
- Impact and action-oriented forecast verification and post-processing techniques
- 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 and impact data to support impact-based forecasting
- Collecting and expanding datasets on interventions and adaptations to build an early action evidence base

Climate Information Services (CISs) have significant potential to empower decision-makers in taking climate-smart actions and reducing the impacts of water and climate-related risks. These provide timely and relevant information, derived from sub-seasonal to seasonal forecasts to support early warning of droughts, floods, heatwaves, and water scarcity, as well as longer-term climate projections to support adaptation planning and management. Substantial advances have been made in recent decades in sub-seasonal to seasonal forecasting and climate modelling, and integrating these into global and regional CISs, ranging from natural hazard early warning systems (EWSs) to platforms, dashboards, and mobile applications that support sector-specific decisions in agriculture, water resources, energy, tourism, and transportation.
Despite these advances, challenges remain in crossing the "last mile"—ensuring the uptake and use of CISs by end-users. Barriers include limited understanding of user needs and decision-making processes, insufficient recognition of local and traditional knowledge, and gaps in co-creation with users. Research increasingly shows that more human-centred approaches and stronger engagement with local stakeholders can enhance the credibility, salience, and legitimacy of services, leading to greater preparedness and adaptation.
This session provides a platform to showcase grounded research and innovative developments in CISs that advance early warning and decision support across hazards and sectors. We welcome contributions on the co-creation of CISs, integration of local and scientific knowledge, development of multi-hazard EWSs, Decision Support Systems, dashboards, and sector-specific applications (e.g., farmer support apps). We particularly encourage abstracts presenting concrete cases where CISs are integrated into real-world decision-making processes across management or livelihood systems, demonstrating how information from (sub)-seasonal forecasts and climate predictions directly shapes choices, actions and outcomes. Contributions may include action-based, multi-disciplinary research showing tangible impacts through improved uptake of advance warning, enhanced decision-making, better preparedness for climate extremes, or clear links to policy and governance processes. The session aims to facilitate knowledge exchange among scientists, practitioners, and users to foster more effective, inclusive, and adaptive climate information services.

Operational warning systems are the result innovations in the science of forecasting. New opportunities have risen in physically based modelling, AI/machine learning, hydro-meteorological forecasts, ensemble forecasting and impact-based forecasting, and real-time control. Often, the sharing of knowledge and experience about developments are limited to the particular field for which the operational system is used. Increasingly, humanitarian, disaster risk management and climate adaptation practitioners are using forecasts and warning information to enable anticipatory early action that saves lives and livelihoods. It is important to understand their needs, their decision-making process and facilitate their involvement in forecasting and warning design and implementation.
The focus of this session will be on bringing the expertise from different fields together as well as exploring differences, similarities, problems and solutions between forecasting systems for varying hazards including climate emergency. Case studies of system implementations - configured at local, regional, national, continental and global scales - will be presented. An operational warning system can include monitoring of data, analysing data, making and visualizing forecasts, impact-based solutions, giving warning signals and suggesting early action and response measures.
Contributions are welcome from both scientists and practitioners who are involved in developing and using operational forecasting and/or management systems for climate and water-related hazards, such as flood, drought, tsunami, landslide, hurricane, hydropower etc. We also welcome contributions from early career practitioners and scientists, and those working in multi-disciplinary projects (e.g. EU Horizon Disaster Resilience Societies).
We particularly welcome contributions aligned with the objectives of the WMO World Weather Research Programme project InPHRA (Integration of Precipitation and Hydrology for Early Action). InPHRA aims to advance transdisciplinary knowledge and skills for the research and development of effective multi-hazard flood forecasting and early warning systems so that “no one is surprised by a flood.” This includes integrating meteorology, hydrology, and social science, together with local and Indigenous knowledge systems, to improve the value chain from forecasts to community action, with particular attention to vulnerable populations.

Drought and water scarcity affect many regions of the Earth, including areas generally considered water rich. The projected increase in the severity and frequency of droughts may lead to an increase of water scarcity, particularly in regions that are already water-stressed, and where overexploitation of available water resources can exacerbate the consequences droughts have. This may lead to (long-term) environmental and socio-economic impacts. Drought Monitoring and Forecasting are recognised as one of three pillars of effective drought management, and it is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability, and to develop innovative indicators and methodologies that translate the data and information to underpin effective drought early warning and risk management.

This session addresses statistical, remote sensing, physically-based techniques, as well as artificial intelligence and machine learning techniques; aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and water scarcity. These include, but are not limited to: precipitation, extreme temperatures, snow cover, soil moisture, streamflow, groundwater levels, and the propagation of drought through the hydrological cycle. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and integrating these with the needs and knowledges of water managers, policymakers and other stakeholders, are further issues that are addressed and are invited to submit to this session. Contributions focusing on the interrelationship and feedbacks between drought, low flows, and water scarcity, ; and the impacts these have on socio-economic sectors including agriculture, energy and ecosystems, are welcomed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the fields of water resources and drought risk management. Particularly welcome are applications and real-world case studies, both from regions that have long been exposed to significant water stress, as well as regions that are increasingly experiencing water shortages due to drought and where drought warning, supported by state-of-the-art monitoring and forecasting of water resources availability, is likely to become more important in the future.

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

Reducing water-related risk requires models that connect everyday water decisions—on farms, in households, and by utilities and agencies—with how rivers, aquifers, reservoirs, and coasts behave at catchment, basin and transboundary scales. This session focuses on risk (e.g., floods, droughts, tropical cyclones, coastal extremes), who and what are exposed, how vulnerable they are, and the resulting impacts and choices for management and policy. By “local to transboundary scale,” we mean risk-modelling frameworks that connect local water practices and infrastructure operations to basin-to-transboundary hazard, exposure, vulnerability and impact outcomes across spatial, temporal and institutional scales, using explicit up/downscaling and cross-scale validation to support decisions under uncertainty.

We explicitly welcome research advances in socio-hydrology and, in addition, socio-meteorology—the study of how society and atmospheric processes interact (e.g., weather forecast design and use, warning response, risk perception). Socio-meteorology is a natural partner to socio-hydrology where weather information and human action shape water risks, from impact-based forecasting and anticipatory actions. Studies that analyze the interconnected dynamics of hydrological and meteorological processes, water use, land management, hydraulic infrastructure, climate change, ecological/environmental flows, and socio-economic drivers are also encouraged.

Socio-hydrology & feedbacks: co-evolution of people, water and infrastructure; adaptation/maladaptation cycles.
Socio-meteorology & warnings: weather forecast usability, communication, protective action; links to water risk and operations.
Interconnected dynamics: hydrology, meteorology, water use, land management, infrastructure, climate change, ecological/environmental flows, socioeconomic drivers
Scale-bridging methods: up/downscaling micro decisions to basin/transboundary outcomes; cross-scale validation.
Decision support & governance: robust/adaptive planning under uncertainty; equity and transboundary cooperation in risk reduction.

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.

This session concentrates on extreme rainfall events, surface water dynamics, and flood events, exploring innovative remote sensing, AI, and digital twin technologies for real-time monitoring, risk assessment, and mitigation. It invites submissions on advanced data integration, modeling approaches, early warning systems, and decision-support tools to improve understanding, forecasting, and management of flooding and related surface water hazards.

The integration of AI with digital twin improves the analytical and operational capabilities of geospatial systems, which through the analysis of historical data and the integration of real-time information (IoT) are able to highlight even “hidden patterns” in the data, identifying new models capable of improving forecasts with greater control over the quantification of uncertainty and the variability of the phenomenon analysed.

This session aims to focus on flood hazard and risk assessment, monitoring, and management. This Topic invites the submission of articles focused on, but not limited to, the following areas:
• Monitoring of extreme rainfall events and flood hazards for risk assessment and communication.
• Digital twins (DTs)/prototypes of DTs in flood hazard forecasting, early warning, monitoring, and supporting tools for urban governance.
• DSSs to extract meaningful information in the artificial intelligence era, eventually serving to reduce risk and provide support tools to mitigate flood hazards.
• The role of AI and digital twins to assess the economic impacts of flood hazards and the cost-effectiveness of various mitigation strategies.
• Novel techniques to analyse big data coming from Earth observation platforms, drones, and other geospatial data in order to provide timely information related to the extend, exposure, and impacts of flood hazards.

frequent and impactful weather-related disasters. Conversely, declines in water availability make monitoring surface water dynamics, including seasonal water body variations, wetland extent, and river morphology changes crucial for environmental management, climate change assessment, and sustainable development. Remote sensing is a critical tool for data collection and observation, 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 remotely-sensed variables—like digital elevation models, river width, water extent, water level, flow velocities, and land cover—into hydraulic models offers the potential to significantly enhance our understanding of hydrological processes and improve predictive capabilities.
Research has so far focused on optimising the use of satellite observations, supported by both government and commercial initiatives, and numerous datasets from airborne sensors, including aircraft and drones. Recent advancements in Earth observation (EO) and machine learning have further enhanced the monitoring of floods and inland water dynamics, utilising multi-sensor EO data to detect surface water, even in densely vegetated regions. However, despite these advancements, the update frequency and timeliness of most remote sensing data products are still limited for capturing dynamic hydrological processes, which hinders their use in forecasting and data assimilation. This session invites cutting-edge presentations on advancing surface water and flood monitoring and mapping through remotely-sensed data, focusing on:
- Remote sensing for surface water and flood dynamics, flood hazard and risk mapping including commercial satellite missions and airborne sensors
- The use of remotely-sensed data for calibrating or validating hydrological or hydraulic models
- Data assimilation of remotely-sensed data into hydrological and hydraulic models
- Enhancements in river discretization and monitoring through Earth observations
- Surface water and river flow estimation using remote sensing
- Machine learning and deep learning-based water body mapping and flood predictions
- Ideas for developing multi-satellite data products and services to improve the monitoring of surface water dynamics including floods
Early career and underrepresented scientists are particularly encouraged to participate.

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.

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

Environmental change, in particular changing climate, land-use and water management practices together with increased pressures due to locally and regional anthropogenic drivers such as population growth and mobility (displacement and migration) lead to inequalities in water security and access to safe water infrastructure. The resulting inequality bears the risk of increased prevalence, distribution and emergence of water-born pathogens. This will inevitably lead to increased human- but also wider ecosystem-exposure to existing and new types of pathogens with adverse health and economic consequences associated with it.
The spatial dispersal and migration mechanisms of many pathogens as well as the conditions that determine their replication and growth are largely controlled by hydrometeorological conditions. Spread of water-borne disease remains poorly understood and its impacts difficult to quantify. In particular, mechanisms of transport and vectors of transmission together with their spatiotemporally varying controls and the impact of hydrometeorological extremes on the efficiency and functioning of mitigating Water, Sanitation and Hygiene (WASH) infrastructure are poorly understood.
In this session, we solicit contributions that
1. Explore, quantify, analyse and predict the spread of water-borne pathogens in terrestrial-aquatic systems
2. Advance the understanding of the role of rivers and subsurface water as vectors of dispersal.
3. Analyze potential links between water-borne pathogen dispersal in various environments under changing climate, land-use and water management.
4. Develop approaches/methods for robust predictions of spatio-temporal pathogen distribution as a result of hydrological extremes across river basins, including floods, droughts or heat waves.

Mid-latitude cyclones and storms are key drivers of weather variability, extremes, and associated socio-economic impacts across densely populated regions of the globe. Understanding their observed and projected trends is crucial for improving climate diagnostics, risk assessments, and adaptation strategies in a warming climate. This topic therefore addresses both fundamental scientific challenges and urgent societal needs by linking physical processes, climate change signals, and potential impacts.

This session encourages contributions covering mid-latitude storm systems, including but not limited to the following topics:

• Fundamental dynamics of cyclones - in all different stages of their life cycle - and their mesoscale features (fronts, jets, precipitation structures, dry intrusions)
• Representation of mid-latitude storms in AI-based weather and climate models
• Diagnostics of observed and projected trends in cyclone frequency, intensity, and storm tracks; including potential insights from contributions from measurement campaigns
• Predictability and forecasting on synoptic to sub-seasonal time scales
• Innovative methods, including AI/ML approaches, for cyclone detection, classification, or impact assessment
• Storm-related impacts, vulnerabilities, and risk-transfer mechanisms under a changing climate

By bringing together communities working on dynamics, diagnostics, impacts, field campaigns, and new methodologies, this session aims to provide a comprehensive platform for advancing our understanding of mid-latitude cyclones and their role in the past, present, and future climate system.

Rossby wave dynamics stands at the intersection of several open research questions, ranging from our basic understanding of mid-latitude variability, to the short- and medium-range predictability of high-impact weather events, and to the circulation changes expected from anthropogenic global warming. Rossby waves exist and propagate along vorticity gradients such as the one related to the tropopause-level jet stream, whose complex meandering often "breaks" creating nonlinear circulation features, such as atmospheric blocking.

Recent extreme weather and climate episodes, like heavy rainfall events leading to flash floods, recurrent and concurrent summer heatwaves or unforeseen winter cold spells, highlight the need to improve our understanding of jet streams and of the associated linear and non-linear, planetary and synoptic-scale Rossby wave dynamics in the atmosphere to better constrain the impacts of Rossby waves on extreme weather and climate events.

Abstracts are invited on a wide range of topics, with a focus on, but not limited to, the following areas:

(1) Theoretical developments in the dry and moist dynamics of Rossby waves, wave breaking, atmospheric blocking, and of jet streams acting as atmospheric Rossby waveguides. This includes the role of local and remote drivers (e.g., the tropics, Arctic, or stratosphere) in affecting Rossby wave evolution.
(2) Linkages between extreme weather/climate events and the jet stream, as well as the associated linear and non-linear Rossby wave evolution during such events, including wave breaking, cut-off formation and re-absorption, and atmospheric blocking.
(3) Application of cutting-edge methods to study the multi-scale interaction of Rossby waves from the convective scale to the large-scale dynamics, and its representation in existing weather and climate models (e.g. hierarchical and/or high-resolution modelling, machine learning/AI-based approaches).
(4) Exploring the effect of Rossby wave packets on predictability at lead times from medium range (~2 weeks) to seasonal time-scales. This includes the potential role of blocking and of teleconnections involving Rossby wave propagation.
(5) Projected future changes in planetary or synoptic-scale Rossby waves, or in their future connection to weather and climate events.

Europe is warming faster than any other continent, with climate-related hazards such as heatwaves, droughts, floods, and wildfires becoming more frequent and intense. These events not only pose direct threats to human systems but also trigger cascading effects across ecosystems, biodiversity, and biogeochemical cycles. This panel discussion explores the complex interplay between climate change and compounding natural hazards—such as wildfires, landslides, and extreme weather—and their cascading impacts on ecological systems, biogeochemical processes, and carbon dynamics. It will examine how these interactions affect ecosystem services, resilience and adaptation, drawing on insights from ecological modelling, Earth observation, and multi-risk analysis.

To effectively address these complex and cascading risks, the session also draws on expertise in governance and science-policy communication, recognising that scientific insights must be translated into actionable strategies, informed decision-making, and inclusive policies that enhance societal and ecological resilience.

This session brings together experts in ecological modelling, Earth Observation, multi-risk assessment, governance, and science-policy communication, including members of the EGU Climate Hazards Task Force. Panellists will respond to questions from the chairs and the audience, addressing how scientific research can better inform policy, what tools are needed to anticipate complex hazard-ecosystem interactions, and how to foster resilience in the face of uncertainty. The session aims to bridge disciplinary boundaries and spark dialogue between scientists, policymakers, and civil society, encouraging a shift from reactive to proactive risk and ecosystem management.

Fire is the primary terrestrial ecosystem disturbance globally and a critical Earth system process. Its frequency and intensity are expected to increase across most regions in the future, posing significant challenges for ecosystems, the carbon cycle, and society. Fire research is rapidly expanding across disciplines, underscoring the need to advance our understanding of fire's interactions with climate, the biosphere, and human systems. This session invites contributions investigating the role of fire in the Earth system at any spatiotemporal scale, using statistical (including AI) or process-based models, remote sensing, field and laboratory observations, proxy records, and data-model fusion techniques. We strongly encourage abstracts on fire's interactions with: (1) weather, climate, atmospheric composition, chemistry, and circulation, (2) vegetation composition and structure and biogeochemical cycle, ocean ecosystems; (3) cryosphere elements and processes (such as permafrost, sea ice), and (4) human health, land management, conservation, and livelihoods. Moreover, we welcome submissions that address: (5) spatiotemporal changes in fire (especially extreme fires) in the past, present, and future, 6) fire products and models, and their validation, error/bias assessment and correction, as well as (7) analytical tools designed to enhance situational awareness for fire practitioners and to improve fire early warning systems.

Extreme fire events have become increasingly frequent all over the world, as seen in recent fire seasons in Turkey, Southern Europe, Brazil, Chile, California, South Korea, and Canada. These extremes and megafires have disproportionate impacts on society and all components of the Earth system, yet much remains to be understood about their characteristics, drivers, links to climate change, methods for quantifying their impacts, and effective mitigation and prevention strategies.

A key area is how extreme fires are represented in fire models. Their stochastic behaviour, uncertainties in observations, and the difficulty of capturing local processes within global frameworks make simulating extremes and their impacts a persistent challenge for coupled models. Emerging big data and machine learning approaches show promise in capturing such events but remain limited in their ability to represent feedback to vegetation, soils, and the broader Earth system.

This session also invites case studies of regional extreme wildfire events, their impacts, and experiences with prevention and mitigation strategies from around the world. We welcome contributions from a wide range of disciplines, including global, regional, and landscape-scale modelling; statistical and process-based modelling; observational and field studies; and social science research on all time scales. Our goal is to foster knowledge exchange across disciplines and between scientists, decision-makers, and practitioners, to advance our collective ability to understand, model, and respond to the challenges posed by present and future extreme wildfires.

Despite ample literature, research is not exhaustive about the wildfire impacts on the different components of the forest ecosystem (plants, water and soil) and the related post-fire issues. In particular, literature has not clearly identified the most suitable restoration strategy, due to the variability of the environmental conditions, and thus clear guidelines still lack. There is evidently the need to better understand the impacts of wildfire and post-fire management techniques at hillslope and channel scales on hydrological, geomorphological and ecological processes in forest ecosystems. This session aims at proposing the most recent researches evaluating the effectiveness of the several post-fire management techniques experienced worldwide. Bringing together contributions from several contexts, dealing with detailed field experiences, validated models and effectiveness assessment methods, can help to support the restoration actions of land managers in fire-affected areas, and, at the same time, identify scientific literature gaps and future research directions.

The integration of satellite remote sensing and ground-based in-situ observations provides a powerful foundation for understanding environmental variability and change. When coupled with hybrid modeling approaches combining Artificial Intelligence/Machine Learning (AI/ML) with physics-based models, these multi-source datasets enable deeper process-level understanding, improved prediction, and actionable insights. This session emphasizes advances at the intersection of observations and hybrid modeling, focusing on how they improve our ability to analyze, attribute, and predict variability and extremes in environmental time series ranging from precipitation and aerosols to carbon fluxes and land–atmosphere feedbacks.
Recent progress in various satellite-derived products (e.g., precipitation from GPM/IMERG, aerosol optical depth/AOD, black carbon concentrations, vegetation and carbon flux indicators) and ever-expanding ground-based networks has greatly enhanced our capability to detect and monitor environmental parameters and their variability. At the same time, hybrid approaches such as physics-informed ML, data assimilation with AI, explainable AI, and transfer learning are emerging as transformative tools to improve predictive skill for extremes, attribute their sources, and assess long-term trends. Together, these innovations are reshaping how we study historical variability and future projections, and how results are translated into actionable information for climate adaptation and resilience.

Unmanned Aerial Vehicles (UAVs) have become indispensable platforms for high-resolution monitoring of landslide processes, offering rapid deployment, flexible acquisition strategies, and integration of diverse sensors such as RGB, LiDAR, multispectral, thermal, and hyperspectral systems. Recent advances in data fusion and AI-driven analytics enable UAV-derived products to move beyond traditional photogrammetry toward comprehensive digital twins of landslide-prone environments. This session invites contributions exploring UAV applications for landslide detection, mapping, kinematic monitoring, hazard assessment, and post-event analysis. Topics include novel workflows for integrating multi-sensor datasets, automated feature extraction using computer vision and deep learning, and 3D point cloud fusion for terrain change detection that leverages UAV products. We welcome case studies from a range of geomorphological contexts as well as methodological innovations that address challenges such as vegetation cover, temporal repeatability, and scaling from local to regional assessments. The session aims to bring together researchers and practitioners to showcase cutting-edge UAV solutions that can enhance landslide science.

Due to climate change trends and trajectories related geohazards have started to shape the earth’s surface in unprecedented magnitude and frequency. Extreme events are also linked to tectonic activity and volcanism, which together contribute to a large part of the damage to infrastructure and disturbance severity in natural ecosystems. Among various types of geohazards, landslides represent one of the most widespread and damaging geological factors, often occurring in response to intense rainfall, seismic activity, or human-induced landscape changes. Their impacts are especially severe in mountainous and densely populated regions, where early detection and monitoring are critical. Recent advances in remote sensing technologies particularly Interferometric Synthetic Aperture Radar (InSAR) and Unmanned Aerial Vehicles (UAVs)- have revolutionised how we observe and analyse slope movements, and other types of geohazards, enabling high-resolution, real-time assessments of ground deformation and terrain instability.
This session explores the broader spectrum of geohazard trends and tendencies across diverse climate zones, temporal frameworks, and landscapes. By integrating satellite-based observations, UAV surveys, geospatial modeling, and historical data, we aim to uncover emerging patterns in geohazard behaviour, improve predictive models, and evaluate adaptive strategies for risk mitigation. Case studies from varied environments, ranging from alpine regions to tropical coasts and urban slopes, will illustrate how climate variability, land-use dynamics, and geological processes interact to shape hazard profiles. The session encourages interdisciplinary collaboration to enhance resilience, guide sustainable planning, and support decision-making in geohazard-prone areas.

Mountains are iconic landscapes, vital water sources, and home to millions of people. In steep, high-elevation environments such as the Alps, Himalaya, Andes, and Rockies, extreme floods, debris flows, and other catastrophic hazards often originate at altitude and propagate downstream, amplifying their impacts. 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.

Ongoing climate warming is shifting glacier equilibrium lines and freezing zones upslope, exposing vast areas of formerly ice-bound sedimentary material to potential mobilization by extreme floods or mass flows. Their high-altitude position, combined with gravitational potential energy on steep mountain slopes, makes them especially susceptible to cascading hazards in the future.

This session invites contributions that investigate, across spatial and temporal scales:
• 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
• feedbacks between stabilizing and destabilizing slope processes

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.

Flooding is one the deadliest and most costly natural hazards on the planet. Nearly one billion people are exposed to the risk of flooding in their lifetimes and about 300 million people are impacted by floods any year resulting in extreme yearly effects on both individuals and societies with estimated costs of 60 billion (US$) in annual losses.
There is clear convergence that climate change is hitherto causing increases in the frequency of extreme rainfall events. These trends are doomed to intensify in the coming decades generating a substantial rise in global flood hazard, with society’s exposure to this risk aggravated still further as a result of population growth and the spreading of people and infrastructure along river courses and onto floodplains.
Climate change is not the only component that can increase global flood hazard. Assessing its dependence on multiple other factors of environmental change such as morphodynamic effects, floodplain connectivity and variation in inundation frequency, and sea level variation is becoming of fundamental importance.
This session invites contributions that explore the response of rivers to hydrological, geomorphological, morphodynamic, and climatic changes that can pose at risk of flooding populations and floodplains as well as investigating how manmade interventions such as flood barriers, managed floodplains and hard engineering are contributing to the increase or reduction of such risk, advancing our understanding on the existing feedback between climate, hydrology, and river morphodynamics in driving changes in future flooding and floodplain alterations.
We encourage interdisciplinary researchers working across the experimental, numerical modelling, and field-based approaches who are advancing methods and providing new insights into: (i) morphodynamic functioning of fluvial systems in driving changes in recent past, present, and future trajectories of flood hazards; (ii) human-induced perturbations that can affect flood hazard and risk; (iii) climate related impacts of future trends in flood hazard; (iv) patterns, trends and drivers of flooding and morphological changes across present and historical records.

Southern Europe is experiencing an unprecedented wildfire season in 2025, driven by the combined effects of climate change, prolonged heatwaves and anthropogenic pressures. According to the Joint Research Centre (JRC), by mid-August 2025 more than 439,000 hectares had burned across the European Union since the beginning of the year, reflecting an exceptional and fast-evolving wildfire season. In particular, Spain reports its worst wildfire year in decades, with extreme fires during August burning hundreds of thousands of hectares.
Beyond immediate losses of vegetation and biodiversity, and the well-documented impacts on air and water quality, recent events have underscored the high probability that burned catchments and hillslopes will produce cascading geohydrological hazards in the weeks to years following wildfire. Post-fire responses such as rockfalls, debris flows and accelerated erosion can be triggered even by moderate rainfall, substantially increasing risk to people, structures and infrastructures.
Although significant progress has been made in this field, much remains to be done to understand the geohydrological hazards associated with extreme fires. Bridging this gap requires integrated approaches that combine rapid field assessments, characterization, inventories, modelling, and management strategies to enable timely mitigation and adaptation.
This session invites contributions that address these challenges by presenting new field evidence, monitoring techniques (including remote sensing and in-situ methods), modelling advances for post-fire slope instability and sediment connectivity, and case studies with lessons learned that can use as examples for future wildfires.

In the past years, the analysis of compound events has emerged as an essential step to enhance our knowledge of and response to multi-hazard high-impact events that occur simultaneously or sequentially, causing interconnected or aggravated impacts. Compound events involve two (or more) events happening together. These can be independent events (in which the outcome of one event has no effect on the probability of the other), or dependent events (when the outcome of one event affects the probability of another). Compound weather, climate or hydrological events refer to combinations of multiple drivers or hazards that may lead to large impacts and disasters. These events can be related to extreme conditions (e.g. storms, heatwaves, floods and droughts), or to combinations of events that are not themselves extremes but lead to an extreme event or significant impact when combined.
In this Short Course, we will introduce compound events, their types (preconditioned, multivariate, temporally compounding, and spatially compounding events), and the methods used to detect and characterize them. We will highlight the advantages and limitations of statistical methods (regression, multivariate statistics, and classification), empirical approaches based on large datasets, high-dimension approaches such as copulas, and complex network-based techniques that help to identify non-trivial spatio-temporal patterns of extreme events.
The Short Course will focus on sharing experience from a wide range of applications worldwide, state-of-the-art methodological approaches, open access code and datasets, and will allow participants to discuss their own challenges in detecting, characterizing and assessing the risk of compound events in diverse contexts (climate, atmospheric, hydrologic, ocean and natural hazards sciences).

With natural hazards increasing in frequency, extent, and complexity, the geosciences are central to understanding risks and supporting disaster preparedness, response, and recovery. This session provides a platform to exchange perspectives on how geoscientific knowledge and methods can be effectively integrated into education and training for Disaster Risk Management (DRM).

We invite contributions that highlight innovative approaches to teaching and capacity building in DRM across geosciences. Possible topics include field-based training, scenario-driven simulations, digital platforms, risk communication strategies, and case studies that connect scientific insights with real world challenges. Of particular interest is the exchange between scientists, practitioners, and stakeholders on how educational efforts can address evolving societal needs and improve decision-making under uncertainty.

This session aims to explore both foundational DRM concepts, and novel educational practices that translate geoscientific expertise into actionable knowledge. By showcasing diverse teaching methods, institutional programmes, and collaborative initiatives, we seek to support the development of an informed and resilient workforce capable of meeting the challenges of a changing risk landscape.

Noam Chomsky has said that humanity is approaching its most dangerous period. Earth and its main irresponsible invasive species have reached a state of unprecedented emergency.

This session aims to address the vital space between science and societal change—a space defined by the intertwined challenges of how we educate and communicate:
o about the increasingly dangerous human and planetary predicament that we face (individually and as a species),
o about devastating global heating (climate change) and ocean degradation, and
o about the accelerating destructive impact of humanity on the very resources that it needs to survive.

We believe that climate, ocean and geoethics literacy must become the focus of all education and training, in all subjects, at all levels, accompanied by vital skills, such as long-term critical thinking, science mindset and resisting denial. Also, strategic communication must mobilize public awareness, shape discourse around specific issues like sea-level rise and marine biodiversity, and create the conditions for clear policy formation and immediate political will. All good communication educates, and all good education involves clear communication.

We invite abstracts on a broad range of topics that bridge any of the above issues and that show promise in progressing positively towards viable, realistic, geoethical and science-based solutions. This includes:
• Novel and traditional pedagogical approaches for educating about climate change, ocean degradation, ecocide, policy, war and other topics.
• The integration of geoethics into climate and ocean curricula.
• Strategies for fostering dialogue, developing intercultural understanding and promoting peace.
• Geoscience pedagogical and curricular innovations and traditional methods.
• Geo-communication and public engagement, such as visualising ocean data, telling compelling stories about climate impacts and using digital outreach.
• Education, communication and strategies for: policy and stakeholder-governance dialogue, the lay public, policymakers, coastal communities and industry leaders.

This session invites you to share your research, practice, experience, action and vision for how our local and global communities can build a more conscious and engaged society ready to safeguard our planet's vital resources upon which humanity depends for survival.

The science policy interface is key to addressing current and future water resilience through translating scientific output into actionable evidence for decision making and policies. Interactions with policy makers are key to formulating academic research towards water resilience and addressing social challenges to support realistic and feasible local adaptation strategies. With the ever-increasing pressures on water availability (both in quantity and quality) and the profound social, economic, ecological, and political impacts, a deeper understanding is needed of the science-policy context of water security and resilience to hydrologic extremes. This can identify hydrological research priorities and improve knowledge transfer and translation to support adaptive local, national, and global policies that focus on water resilience in the face of climate extremes. The recently published European Water Resilience Strategy is a good example of one such initiative.

This session provides the opportunity to show how water research across the entire hydrologic cycle can inform dialogues for science-informed policies on the regional, national, and international level, with a particular focus on shared waters. Our session promotes dialogues focused on understanding the complex interplay between academic water research and policies through stakeholder dialogues and policy labs to promote sustainability. Additionally, we want to address the impact of adaptive policies and directives on promoting water resilience across all stores (i.e. oceans, lakes, rivers and groundwater), as well as across interdisciplinary avenues such as societal or economic uses both locally or globally. We also want to highlight the role of science in providing scientific evidence-based guidelines for fostering blue diplomacy in transboundary river basin cooperation initiatives, ocean governance, water use tradeoffs, and the need for interdisciplinary approaches to addressing water resilience in a changing and extreme climate.

Therefore, we welcome abstracts that contribute to interdisciplinary science-policy research on building water resilience, transboundary water issues, stakeholder dialogues, results from living labs, water diplomacy initiatives, and related topics.

Explosive volcanic eruptions are sudden and violent events in a volcano’s life cycle. Despite significant advancements over the past decades, they remain largely unpredictable regarding onset, duration and style. Amongst the diverse volcanic hazards, pyroclastic density currents (PDCs) stand out as the most fatal.

PDCs are high-energy mixtures of hot gas and particles that travel down a volcano’s flanks, driven by a combination of initial particle momentum and gravity, and influenced by local topography and vegetation. While their internal dynamics remain poorly constrained, deposit analyses suggest large variability in particle concentration, velocity and temperature. As sedimentary features vary quickly over close range, all those factors are likely highly dynamic in space and time. Although commonly associated with major explosive eruptions, PDCs can also be generated by the collapse of lava domes, flow fronts, or hot deposits during moderately explosive or even effusive activity. The impacts of such events can rival or exceed those of more classically explosive eruptions. Recent advances in numerical modelling have improved our ability to simulate key aspects of PDC behaviour, from generation to deposition, significantly contributing to our understanding of their dynamics.

This session aims to bring together researchers working on all facets of PDCs, from field studies and laboratory experiments to remote sensing and numerical modelling. By fostering interdisciplinary dialogue, we aim to advance understanding, improve hazard assessment and reduce risk in future eruptions.

Explosive eruptions can generate large volumes of juvenile and lithic material (tephra), which can be transported vast distances from the volcano. Depending upon the eruption style and/or the interaction with external factors (e.g., water), the processes involved in the generation and dispersion of the tephra can be varied, and this diversity can enhance, and/or preclude, its effective preservation in the geological record – a key input for hazard assessments. By better understanding the syn- and post-eruptive processes involved in tephra-generating eruptions, our ability to prepare for and mitigate against a wide range of hazards (e.g., impacts on health, infrastructure and the economy) vastly improves, in turn in turn reducing the impact of explosive eruptions on society.
Advancements in volcanology since the early 2000’s have seen a steady increase in our understanding of the way tephra is generated, transported and deposited, and has facilitated a much more comprehensive understanding of (1) how frequently explosive eruptions occur on a global scale, (2) how different volcanic systems behave, and (3) the timescales upon which different hazards may emerge across different regions. Coupled with advances in numerical/computational tephra dispersion modelling, we are becoming increasingly informed of past eruptions and their processes, as well as the tracking and forecasting of current and real-time explosive eruptions.
We invite contributions that continue to improve our understanding of explosive eruption dynamics through the study of tephra emission, dispersal, and preservation; encouraging submissions from a variety of research themes including (but not limited to) physical volcanology, tephrochronology, geochemistry/petrology, stratigraphy, computer modelling, environmental management, and hazard forecasting. This session runs in parallel with an open call for paper submissions to a Geological Society of London and AGU GeoHorizons book volume titled “Tephra: from reconstructing past volcanic eruptions to modelling and forecasting future hazards” edited by Hodgetts et al. Thus, we particularly encourage submissions that demonstrate interdisciplinary science to further expand our knowledge of tephra-generating eruptions and their processes.
This session is sponsored by the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI) Commission on Tephra Hazard Modelling (THM) and Commission on Tephrochronology (COT).

Hydrometeorological hazards – including droughts, floods, and their compound manifestations – are intensifying in a warming climate, posing unprecedented challenges for disaster risk management, adaptation, and resilience. Persistent heavy rainfall, flash droughts, and rapid drought-to-flood transitions are causing significant societal and economic damages, while human activities such as land-use change, flood control and drought relief add further complexity to their mechanisms and predictability.
This session invites contributions on cutting-edge advances in cooperative observation systems, high-resolution Earth System modeling, and Artificial Intelligence (AI) integration for improving sub-seasonal to seasonal prediction and early warning of compound hydrometeorological extreme events at regional to local scales.
By fostering interdisciplinary collaboration across observation, modeling, AI, and applications, this session aims to showcase novel methodologies and operational pathways toward building globally resilient societies against hydrometeorological hazards.

Cultural heritage - whether coastal, underwater, landscape, or urban - is increasingly exposed to the cascading effects of climate change and natural hazards. As the frequency and intensity of extreme events rise, so does the urgency to rethink how we assess, manage, and protect heritage in a changing world.
This session, co-organised by the Horizon Europe Green Cluster (RescueME, THETIDA, TRIQUETRA, STECCI), invites contributions that explore transdisciplinary approaches to heritage resilience, integrating insights from climate science, disaster risk management, social sciences, and heritage studies. We particularly welcome work that addresses the complex interplay between cultural landscapes, underwater heritage, and climate-related risks, and that advances co-creation with communities and stakeholders as a central strategy for sustainable adaptation.
We encourage submissions that showcase innovative digital tools - including decision support systems, AI applications, serious gaming, and immersive technologies (AR/VR) - as well as modelling techniques for risk analysis and scenario planning. The session also seeks to highlight governance frameworks, participatory methods, and living lab approaches that foster inclusive, evidence-based decision-making and long-term resilience. Depending on session interest and attendance, conveners may explore the option of proposing a related special issue in a peer-reviewed journal (Heritage Science, STOTEN, Climate Risk Management or similar).
Topics of interest include, but are not limited to:
• Integrated risk assessment models for heritage exposed to climatic, natural, and anthropogenic hazards
• Co-creation and participatory methods for stakeholder engagement, including serious gaming and tabletop exercises
• Digital innovations for heritage monitoring, management, and communication (e.g., AI, AR/VR, digital twins)
• Governance structures and policy tools for heritage resilience and sustainability
• Underwater and coastal heritage risk assessment and protection strategies
• Cultural landscapes as dynamic systems of climate adaptation and community identity
• Living labs and knowledge co-production for heritage risk and resilience
• Multi-hazard and compound risk modelling for heritage sites
• Decision support systems and early warning tools tailored to heritage contexts

Extreme weather and climate conditions, 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, and broke records by large margins. 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 crossing tipping points and triggering abrupt 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 sponsored and closely linked to the World Climate Research Programme lighthouse activitIES on 'Understanding High-Risk Events' and 'Explaining and Predicting Earth System Change'.

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 Fault2SHA session will focus on state-of-the-art progress in Earthquake Rupture Forecast (ERF) and its integration into probabilistic seismic hazard assessment (PSHA) and probabilistic fault displacement hazard analysis (PFDHA). Recent developments highlight the importance of combining physics-based simulators, inversion-based fault system solutions, and fault-based approaches with geologic and geodetic data to produce models that are modular, transparent, and more suitable for practical applications in hazard and risk mitigation.
Geological investigations continue to provide critical insights into fault behavior and earthquake recurrence. Paleoseismological trenching, high-resolution coring, structural geology, tectonic geomorphology, and geodesy extend the earthquake record from recent events to multi-millennial timescales, enabling the characterization of earthquake source parameters and long-term fault behavior. These multidisciplinary observations, when combined with physics-based and multi-cycle earthquake simulations, offer new opportunities to address epistemic uncertainties, capture complex rupture processes, and refine time-dependent hazard models.
The session aims to foster dialogue on how innovative approaches and diverse datasets can be integrated into seismic hazard frameworks, ultimately improving our ability to quantify uncertainties and support applications ranging from building codes and land-use planning to insurance and risk management.
Topics of interest include, but are not limited to:
• ERF approaches and their role in PSHA and FDHA
• Advances in physics-based earthquake cycle simulations
• Incorporation of paleoseismological and geological constraints into hazard models
• Structural geology, tectonic geomorphology, and geodesy applied to fault characterization
• Methods to quantify and reduce epistemic uncertainties in hazard assessments
• Case studies linking recent earthquakes, long-term fault behavior, and hazard analysis
We particularly encourage contributions that present innovative, integrative, and multidisciplinary approaches to studying active faults and their role in seismic hazard assessment.

Earthquakes are one of the most impactful natural phenomena responsible for many losses of life and resources. To minimize their effects, it is important to characterize the seismic hazard of the different areas, understanding the variables involved. To better estimate the seismic hazard, earthquake source(s) and seismicity need to be better understood. Moreover, local site conditions have to be characterized to produce a reliable model of the ground shaking in the sites of interest. The goal of this session is to understand what are the cutting-edge studies on the topics of seismic hazard, site effect, and microzonation.

In this session, studies related to the following topics, but not limited to, are welcome:
● Seismic hazard analysis
● Seismic source characterization
● Characterization of seismicity in seismic hazard analysis
● Ground motion prediction analysis
● Site effect and microzonation
● Earthquake-induced effects (e.g. liquefaction and landslide)
● 1D, 2D, and/or 3D numerical site effect modeling
● Soil-structure interaction and analysis
● New approaches in seismic hazard characterization
● Machine learning for seismic hazard, site effect, and microzonation

Fracture systems are fundamental structural features controlling the mechanical, hydraulic, and geochemical behaviour of rock masses. Their influence ranges from the stability of natural and engineered slopes to fluid migration processes.
This session aims to bring together researchers from different fields to explore and compare methodologies for investigating fractured rock masses, emphasising the value of integrated multi-scale (from grain-scale microcracks to meso-scale fracture networks, up to tectonic-scale systems) and multidisciplinary approaches.
We welcome contributions across a broad geological and process-based context, linking observations and methods from field-based surveys, outcrop characterisation, laboratory testing, microstructural analysis, numerical and analogue modelling, remote sensing, and geophysical imaging. Applications to natural hazards (e.g., rockfalls, landslides), energy and resource exploration, fluid transport and storage, structural geology and tectonics, are particularly encouraged. By bringing together structural geology, rock mechanics, and engineering geology, the session aims to foster a constructive and stimulating discussion on fractures across scales and disciplines, addressing both scientific and practical challenges.

Every year brings new observations about earthquakes with a level of detail never reached before. In parallel, observational and computational methods keep improving significantly in seismology, geodesy, and in paleoseismology-geomorphology. Hence, on one hand, the number of earthquakes with well-documented rupture processes and deformation patterns is increasing. On the other hand, the number of studies documenting long time series of past earthquakes, including quantification of past deformation, has also increased. In parallel, the modeling community working on rupture dynamics, including earthquake cycle, is also making significant progress. Thus, this session is the opportunity to bring together these different contributions to foster further collaboration between the different groups all focusing on the same objective of integrating earthquake processes into the earthquake cycle framework. In this session, we welcome contributions documenting earthquake ruptures and processes, both for ancient events or more recent ones, such as the 2023 Turkey sequence, the 2025 Myanmar earthquake, or the 2025 Kamchatka M 8.8 earthquake, from seismological, geodetic, or paleoseismological perspectives. Work combining different approaches is particularly welcome, as are contributions documenting deformation during pre-, post-, or interseismic periods, which are highly relevant to understanding earthquake cycles. Finally, we seek contributions looking at the earthquake cycle from the modeling perspective, both numerical or analogue, especially including approaches that mix data and modeling.

In a rapidly changing world, the relevance of tectonics and structural geology to societal challenges is more pressing than ever. This session invites abstracts that demonstrate how geological structures and tectonic processes can be harnessed to support the energy transition, mitigate natural hazards, and contribute to sustainable development.
We are particularly interested in studies that examine how tectonic and structural factors influence the safety, feasibility, and long-term sustainability of energy transition technologies. This includes the assessment of tectonic risks associated with geothermal energy development, subsurface energy storage, and carbon capture and storage, especially in seismically active or structurally complex regions. Contributions that explore the role of structural geology in managing critical raw materials, groundwater resources (heat storage), and infrastructure resilience are also welcome. We encourage submissions that integrate fieldwork, geophysical data, numerical modelling, and remote sensing, as well as those that present innovative case studies or cross-sector collaborations.

This session invites contributions focused on the understanding, modeling, and prediction of extreme events in weather, climate, and broader geophysical systems, from both theoretical and applied perspectives. We aim to bring together researchers from the traditional geophysical sciences with those working in mathematical, statistical, and dynamical systems approaches, fostering an interdisciplinary dialogue and discussions.
By highlighting the complementary nature of physical intuition and mathematical formalism, this session seeks to advance our understanding of the processes that give rise to extremes, improve predictive capabilities, and assess the extremes' societal and environmental impacts.
Topics of interest include, but are not limited to:
- Variability and projected changes in extremes under climate change
- Representation and performance of climate models in simulating extreme events
- Attribution of extreme events
- Emergent constraints on extreme behavior
- Predictability of extremes across meteorological to climate timescales
- Connections between extremes in dynamical systems and observed geophysical extremes
- Theoretical and applied studies of extremes in nonlinear and chaotic systems
- Downscaling techniques for extreme events
- Linking the physical dynamics of extreme events to their impacts on society and ecosystems.
We particularly encourage submissions that bridge disciplines, propose novel methodologies, or offer new insights into the mechanisms and consequences of extreme geophysical phenomena. We encourage submissions from the "Transdiscipinary Newtork to bridge Climate Science and Impacts on Society" (FutureMED) and the "Seasonal-to-decadal climate predictability in the Mediterranean: process understanding and services" (MEDUSSE) COST action communities.

Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are both very well established approaches for measuring tectonic and volcanic activity. Over recent years, improvements in data quality, the automation of processing pipelines, cheaper measurement hardware, and cheaper computation has put our research community on the brink of complete surveillance of surface motions – a situation in which, for some regions, we will be able to map every actively slipping fault and actively deforming volcanic zone, more easily identify areas of heightened stress accumulation, and automatically report when surface motions accelerate.

In this session we would like to foster an exchange of ideas and experiences about how to best continue towards this state of surveillance. In particular, we would like to explore how the GNSS, InSAR, and tectonic- and volcanic- modelling communities can work together in the coming years to extract the most value from our continued installation, processing, and interpretation efforts. How many stations will we need? Do we have enough data? What experiments and simulations should we prioritise? What are the economic (personnel, computation, satellites, natural disasters), sustainability, and strategy (e.g. centralisation vs. decentralisation) considerations?

We invite scientists and related stakeholders from our communities to come together to share their work on full value extraction of our GNSS and InSAR data. Whether you want to present some case study of a certain region, or you want to present some analysis at a larger spatial and computational scale, we welcome you to the discussion.

The dynamics of magmatic systems are driven by complex processes that span from deep mantle melt generation to surface eruptions. 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 emerge at sub-millimetre to kilometre scales and second to million-year times, and involve different phases, such as liquid melt, solid crystals, volatile 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 progress in establishing a dedicated community within volcanic, petrology, and magmatic studies. This session aims to address this gap by focusing on computational approaches applied to these areas. We seek to bring together researchers working on forward and inverse modelling, machine learning, and other computational methods to foster a thriving community which complements well established observational and experimental communities.

We encourage contributions that explore the theory, application, and validation of computational approaches in the context of experimental and observational data. 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 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.

Plate tectonics theory explains convergent and divergent plate boundary volcanism, readily accounting for ~90% of the Earth’s volcanism. However, our understanding of intraplate volcanism, within both continental and oceanic plates, and unusual on-boundary volcanism, is less advanced. Modern improvements in instrumentation, techniques, and data availability have greatly expanded our understanding of Earth dynamics and structure. Re-evaluation, refinement, and creation of new models for intraplate and unusual on-boundary magmatism have advanced our understanding of the complex interactions between the Earth’s interior and surface. This work is critical to understanding Earth’s surface dynamics, volcanism, and chemical evolution through time, including the initiation of plate tectonics, climate, and life. It is also key to understanding lithospheric deformation in the presence of underlying magma, past and present volcanic catastrophes, and the environmental impacts of magmatism through time. Earth is also our most accessible laboratory for understanding tectono-magmatism on other planetary bodies.
This session aims to bring together cross-disciplinary work on intraplate and unusual plate boundary magmatism to stimulate interactions between researchers with diverse ideas, observations, approaches, and backgrounds. We welcome contributions that apply any appropriate method including geochemistry, petrology, geophysics, volcanology, seismology, numerical and analogue modelling, isotope geochemistry, ocean drilling, plate kinematics, tectonics, sedimentology, field and structural geology, or thermo- and geo-chronology. Studies focusing on Large Igneous Province (LIP) magmatism, wide magmatic rifted margins (e.g., the Laxmi Basin), or magmatism associated with continental material far offshore (e.g., the Rio Grande Rise) are particularly encouraged. We also encourage cross-disciplinarity, innovative studies, the spanning of spatio-temporal scales, and thought-provoking ideas that challenge conventions.

Glaciers and volcanoes interact in several ways, including instances where volcanic/geothermal activity alters glacier dynamics or mass balance, via subglacial eruptions or the deposition of supraglacial tephra. Glaciers can also impact volcanism, for example by directly influencing mechanisms of individual eruptions resulting in the construction of distinct edifices. Glaciers may also influence patterns of eruptive activity when mass balance changes adjust the load on volcanic systems, the water resources and hydrothermal systems. However, because of the remoteness of many glacio-volcanic environments, these interactions remain poorly understood.

In these complex settings, hazards associated with glacier-volcano interaction can vary from lava flows to volcanic ash, lahars, landslides, pyroclastic flows or glacial outburst floods. These can happen consecutively or simultaneously and affect not only the earth, but also glaciers, rivers and the atmosphere. As accumulating, melting, ripping or drifting glaciers generate signals as well as degassing, inflating/deflating or erupting volcanoes, the challenge is to study, understand and ultimately discriminate these potentially coexisting signals. We wish to fully include geophysical observations of current and recent events with geological observations and interpretations of deposits of past events. Glaciovolcanoes also often preserve a unique record of the glacial or non-glacial eruptive environment that is capable of significantly advancing our knowledge of how Earth's climate system evolves.

We invite contributions that deal with the mitigation of the hazards associated with ice-covered volcanoes in the Arctic, Antarctic, globally and extraterrestrial, that improve the understanding of signals generated by ice-covered volcanoes, or studies focused on volcanic impacts on glaciers and vice versa. Research on recent activity is especially welcomed. This includes geological observations, e.g. of deposits in the field or remote-sensing data, together with experimental and modelling approaches. We also invite contributions from any part of the world and other planets on past activity, glaciovolcanic deposits and studies that address climate and environmental change through glaciovolcanic studies. We aim to bring together scientists from volcanology, glaciology, seismology, geodesy, hydrology, geomorphology and atmospheric science to enable a broad discussion and interaction.

Volcanic systems are dynamic entities, shaped by the interplay of magmatic, tectonic and geomorphological processes. This session will explore the mechanisms that drive their construction, deformation and evolution, from magma ascent and emplacement to the surface expression of volcanic landforms. Contributions examining the interaction between tectonic stress fields and volcanic activity in influencing edifice growth, deformation and the development of distinctive morphological features in various tectonic and climatic settings are particularly welcome. The geomorphological and sedimentary consequences of volcanism, such as the erosion, transport and redeposition of volcaniclastic materials, are also crucial as they reshape landscapes and affect terrestrial and submarine environments alike. We strongly encourage multidisciplinary approaches, including field studies, remote sensing, geophysical methods and laboratory analyses, to capture the complexities of volcanic systems throughout their lifecycle. Given the prevalence of coastal and submarine volcanic settings, investigations addressing submarine morphology and geophysical characteristics are of particular interest. Case studies from various tectonic environments, including arc, rift, hotspot and intraplate settings, will provide valuable comparative insights. By bringing together volcanology, structural geology, marine geology, geomorphology, and sedimentology, this session aims to promote discussion on how volcanotectonic processes influence volcanic landform evolution and its implications for hazard assessment and risk reduction.

The session deals with the documentation and modelling of the tectonic, deformation and geodetic features of any type of volcanic area, on Earth and in the Solar System. The focus is on advancing our understanding on any type of deformation of active and non-active volcanoes, on the associated behaviours, and the implications for hazards. We welcome contributions based on results from fieldwork, remote-sensing studies, geodetic and geophysical measurements, analytical, analogue and numerical simulations, and laboratory studies of volcanic rocks.
Studies may be focused at the regional scale, investigating the tectonic setting responsible for and controlling volcanic activity, both along divergent and convergent plate boundaries, as well in intraplate settings. At a more local scale, all types of surface deformation in volcanic areas are of interest, such as elastic inflation and deflation, or anelastic processes, including caldera and flank collapses. Deeper, sub-volcanic deformation studies, concerning the emplacement of intrusions, as sills, dikes and laccoliths, are most welcome. We also particularly welcome geophysical data aimed at understanding magmatic processes during volcano unrest. These include geodetic studies obtained mainly through GPS and InSAR, as well as at their modelling to imagine sources.

The session includes, but is not restricted to, the following topics:
• volcanism and regional tectonics;
• formation of magma chambers, laccoliths, and other intrusions;
• dyke and sill propagation, emplacement, and arrest;
• earthquakes and eruptions;
• caldera collapse, resurgence, and unrest;
• flank collapse;
• volcano deformation monitoring;
• volcano deformation and hazard mitigation;
• volcano unrest;
• mechanical properties of rocks in volcanic areas.

In sedimentary volcanism, underground sediments, water and gases ascend to the surface, both inland and offshore, within a compressive tectonic regime. The ejected material builds up edifices resembling volcanoes, hence the term Mud Volcanoes (MVs). Some of these structures exhibit paroxysmal activity, characterized by violent gas blasts or sudden expulsions, releasing huge volumes of mud that represent a severe geohazard. In general, MVs emit significant CH4 and minor CO2 and light hydrocarbons amounts affecting the life cycles of animals and plants.
MVs constitute natural laboratories for investigating several poorly understood processes, such as geochemical and physical dynamics during ongoing eruptions, the interaction between faulting and fluid reservoirs, the hydrological cycle or periodic inflation-deflation cycles at the crustal scale (e.g., those driven by Earth tides), as well as their buried structure.
MVs are often hosted within Nature Reserves that provide a safe environment for monitoring activities, whose main goal is to intercept potential precursors of paroxysmal events. Moreover, since these Reserves are visited by many people every year, monitoring is crucial not only for scientific purposes but also for ensuring the safety of visitors and nearby populations.
This session is addressed to investigations of:
- the reconstruction of the deep engine dynamics of MV activity and their stratigraphic structure;
- the processes that form mud volcanos and drive material migration to the surface;
- the hydrological regime and its influence on MV activity;
- outcomes from long-term monitoring and spot-survey;
- the interplay between the regional/local seismicity and MV activity, as manifestation of crustal dynamics;
- the remote sensing terrain and surface modeling, and geophysical imaging;
- the impact of MVs activity on ecosystems and climate.
Multidisciplinary approaches to the MVs study, aimed at identifying reliable indicators of their activity state, are welcome.

Monitoring volcanic hazards through the combination of field observations, satellite data and numerical models presents extremely complex challenges, from the identification and quantification of hazardous phenomena during pre-/syn-eruptive phases to the assessment of impact and risk to people and property.

This session welcomes contributions addressing open questions in the study and modelling of volcanic processes and associated hazards, including but not limited to field and satellite data analysis, physico-mathematical formulations of natural processes, probabilistic forecasting, data assimilation and data fusion, and the development and application of numerical methods. We particularly encourage interdisciplinary contributions that bridge traditional volcano monitoring with emerging innovations in computational science, statistical analysis, Machine Learning (ML), and Artificial Intelligence (AI).

The objectives of the session include: (i) expanding knowledge of complex volcanic processes and their spatio-temporal dynamics; (ii) advancing methods for monitoring, modelling, and forecasting of volcanic phenomena; (iii) assessing the robustness of models through validation against real case studies, analytical solutions, and laboratory experiments; (iv) quantifying uncertainty propagation through both forward (sensitivity analysis) and inverse (optimisation/calibration) modelling; and (v) exploring the potential of AI- and ML-driven techniques to integrate and process multidisciplinary datasets for improved volcanic hazard assessment, risk reduction, mitigation strategies, and decision-support applications.

Our understanding of volcanic hazards is evolving rapidly, driven by breakthroughs in satellite Earth observation, novel ground-based instruments, and artificial intelligence. The integration of artificial intelligence techniques, including machine learning, facilitates the rapid analysis of vast datasets, uncovering hidden patterns and improving the forecasting of volcanic hazards. In an era where volcanic activity poses increasing risks to populations and infrastructure globally, leveraging multidisciplinary approaches is essential to enhance our ability to forecast eruptions and to assess volcanic hazards. By incorporating data from diverse sources—ranging from satellite platforms to ground-based sensors—researchers can build comprehensive models that better capture the complexity of volcanic systems. The session aims to highlight advances that are redefining how we detect, interpret, and respond to volcanic activity. Emphasis is placed on cross-disciplinary methods that couple remote sensing with machine learning, probabilistic frameworks, and impact assessment tools. We particularly encourage submissions that demonstrate advancement of knowledge in volcanology, near-real-time applications, scenario-based forecasting, and integration of diverse datastreams from ground-based and orbital platforms. By fostering collaboration across geophysics, computer science, and risk management, we seek to build a next-generation framework for volcanic hazard anticipation, response, and long-term resilience in the face of increasingly complex global challenges.

Are you unsure about how to bring order in the extensive program of the General Assembly? Are you wondering how to tackle this week of science? Are you curious about what EGU and the General Assembly have to offer? Then this is the short course for you!

During this course, we will provide you with tips and tricks on how to handle this large conference and how to make the most out of your week at this year's General Assembly. We'll explain the EGU structure, the difference between EGU and the General Assembly, we will dive into the program groups and we will introduce some key persons that help the Union function.

This is a useful short course for first-time attendees, those who have previously only joined us online, and those who haven’t been to Vienna for a while!

This short course aims to provide Early Career Scientists with the knowledge and skills on the state-of-the-art methodology for analysing multi-hazard disasters: (Enhanced) Impact Chains. Master students, PhD students, and Postdoctoral researchers with backgrounds in Natural Hazards (NH), Climate (CL), Geodynamic systems (GD), Nonlinear Processes in Geosciences (NP), Geomorphology (GM), and Hydrological Sciences (HS) are welcome to join us to advance their disaster analysis skills.
The increasingly frequent and impactful hazard events that occur simultaneously or in cascade have created a new set of challenges for communities worldwide, requiring a leap forward in both research and science communication. Therefore, the need to develop conceptual and operational frameworks capable of untangling the complex interactions among multiple hazards, their (compounded) impacts, evolving vulnerabilities, exposed elements, and mitigation measures becomes more pressing. This session addresses these needs, providing ECS training in conventional and Enhanced Impact Chains.
Impact Chains are models that were initially developed by UNDRR (2022) to analyse climate-related risks and grew to be applied for multi-hazard, cross-sectoral analyses or flood risk management. Taking the capability of these models a step further, we developed Enhanced Impact Chains as the first tools capable of tracking vulnerability dynamics across time and space in multi-hazard settings.
Leveraging the organisational, visualisation, and analytical prowess of conventional and Enhanced Impact Chains is a game changer for disaster analysis. Such tools equip scientists and practitioners with a clear framework to cut through complexity by identifying key disaster elements (hazards, impacts, vulnerabilities, exposed elements, and adaptation options) and, most importantly, mapping the connections established among them. Combining short theoretical presentations with interactive exercises and discussions, this workshop will guide participants in unlocking the full analytical potential of these essential tools.

The Meteorological Archival and Retrieval System (MARS) is the world’s largest meteorological archive and ECMWF's main data repository. It stores operational weather analyses and forecasts, reanalyses, observations and research experiments that support a wide range of Earth system science applications.
This short course provides a practical introduction of MARS archive to the new users of the archive. Participants will learn how to explore the MARS data catalogue to identify datasets relevant to their research. The session will demonstrate how to construct and run MARS requests to download data efficiently.
Through step by step examples, attendees will gain a clear understanding of the archive’s structure and the main concepts behind exploring the data and retrieving the data they need for their research.

This short course will train you how to use robust Machine Learning methods to do statistical downscaling of coarse climate model scenarios. A sample dataset will be used: daily surface temperature from one Global Climate Model of the CMIP6 database (historical and future climate time periods), along with a high resolution reanalysis.
Introduction on climate statistical downscaling
Methodology: classical and Machine-Learning based
Steps to perform downscaling
Sample datasets
Results
All material will be made available online, and a sample Jupyter Notebook will be provided.

Why this short course
Earth and environmental sciences thrive on data diversity: from ocean temperatures to biodiversity records, from climate indicators to geological observations. Yet, this very diversity can also be a barrier: different datasets are described with different standards, stored in different formats, and are difficult to connect across research infrastructures. The ENVRI-Hub provides a set of tools to overcome these challenges. It offers researchers a unified framework to discover, access, and reuse complex and multidisciplinary data.

This short course will give researchers a practical introduction to how ENVRI-Hub workflows can directly support their own projects, to build more reproducible and impactful science.

What researchers will learn
By joining this short course, researchers will:
- Get a clear picture of why Essential Variables matter in Earth and environmental sciences and how variable harmonisation improves scientific collaboration;
- Explore datasets through different pathways, including LLM-based search;
- Draft a mini workflow using curated Jupyter notebooks to map and query essential variables and visualise results;
- Share ideas with peers on how ENVRI-Hub workflows could advance their own research projects.

Interactive format
This 1h45min researcher-focused applied training session will blend live demonstrations, guided practice with curated tools, and participation discussions.

The interactive outline will engage participants by offering them an opportunity to:
- Navigate the ENVRI-Hub services and datasets: knowing what’s available and what fits their needs;
- Understand how to integrate ENVRI-Hub analytical tools into their research workflows: from data discovery and annotation to analysis and sharing;
- Present research use cases by reflecting on common challenges and benefits across domains

Who should join
This short course is tailored for:
- Researchers in Earth and environmental sciences, project coordinators, and data scientists looking to improve their data workflows;
- Anyone interested in applying interoperable approaches to interdisciplinary research;
- Anyone with basic familiarity with Python/Jupyter.

In April 2023, EPOS, the European Plate Observing System launched the EPOS Data Portal (https://www.ics-c.epos-eu.org/), which provides access to multidisciplinary data, data products, services and software from solid Earth science domain. Currently, ten thematic communities provide input to the EPOS Data Portal through services (APIs): Anthropogenic Hazards, Geological Information and Modelling, Geomagnetic Observations, GNSS Data and Products, Multi-Scale Laboratories, Near Fault Observatories, Satellite Data, Seismology, Tsunami and Volcano Observations.
The EPOS Data Portal enables search and discovery of assets thanks to metadata and visualisation in map, table or graph views, including download of the assets, with the objective to enable multi-, inter- transdisciplinary research by following FAIR principles.
This short course will introduce the EPOS ecosystem and demonstration of integrated virtual research environment where users can stage their data and run Jupyter Notebooks, either from existing examples or their own. We see this interactive coding and development environment as a gate towards faster scientific progress and enabling open science.
It is expected that participants have scientific background in one or more scientific domains listed above. The training especially targets young researchers and all those who need to combine multi-, inter- and transdisciplinary data in their research. The use of the EPOS Platform will simplify data search for Early Career Scientists and potentially help them in accelerating their career development. Feedback from participants will be collected and used for further improvements of the EPOS system.