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. Contributions are particularly invited on works on how impacts on the landscape are related to weather and climate extremes and how these extremes are related to large-scale predictors (e.g. climate oscillations, teleconnections) on different spatio-temporal scales. 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. To understand fundamental processes, papers are also encouraged to 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.

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.

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.

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

We here invite contributions highlighting the i) most recent advances in volcanic hazard assessment, both on recently active volcanic systems and on volcanoes with long lasting quiescence periods and ii) exploring the influence of 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.
The purpose of the session is to discuss the contributions of new methodological and technological advances and the results arising from the integration of well-established methodologies, which have permitted major advances in the assessment of volcanic hazard in specific sites and to highlight both positive and negative impact of educational programs on preparedness, response, and overall influence on vulnerability.
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.

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.

Landslides and slope instabilities induced by rainfall or snowmelt 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. Furthermore, the prediction of such locations is of great importance for zonation purposes and for the design of early warning systems to prevent human casualties. Recent innovations in monitoring and modelling offer new avenues for investigating these multifaceted processes.

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.
• Effects of climatic global changes and land use on the susceptibility and hazards towards shallow landslides.
• Field hydrological monitoring for the assessment of main pore-pressure build-up areas and triggering conditions of shallow landslides.

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 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.

This combined session focuses on landslides and large mass movements in rock, debris, and ice, together with other types of ground failure such as liquefaction and subsidence, in settings where seismic activity plays a key role. Observations from recent earthquakes show that impacts are not confined to the coseismic phase, because damaging mass movements can also occur in the post-seismic period due to disturbances caused by earthquakes. These cascading hazards are often treated separately, even though an integrated approach is clearly desirable, and the session provides a forum for researchers and professionals to discuss processes, case histories, and hazard implications across both co-seismic and post-seismic phases.
Large-scale instabilities in rock, weak rocks, debris, and ice represent enormous risks and are complex systems that are difficult to describe, investigate, monitor, and model. Their evolution can range from slow to fast complex mass movements and depends on forcing factors, geological and hydrological boundary conditions, and the evolution in space and time of thermo-hydro-mechanical controls, as well as the properties of the unstable mass. Many aspects remain understudied and debated due to difficult characterization and the limited number of thoroughly studied cases, and regional and temporal distribution and relationships with controlling and triggering factors are often poorly understood, resulting in poor predictions of behaviour and evolution under present and future climates. The session welcomes contributions on case studies, monitoring and modelling approaches and tools, numerical and physical modelling of dynamic loading and instability, deterministic event scenarios and probabilistic evaluations, threshold definition and offline data analyses, advanced numerical modelling and machine learning techniques, innovative dating and investigation methods, site effects such as amplification and the influence of pre-existing landslide masses, and impacts on structures and infrastructures including tunnels, dams, and roads, with the goal of improving hazard assessment and supporting early warning systems.

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.

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.

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.

Mitigating earthquake disasters involves several key elements, from hazard assessment to impacts quantification and reduction. Core components are: a) the analysis of hazards, ground shaking and cascading effects on natural and built environments; b) the assessment of vulnerability and exposure to hazards for buildings, infrastructures and people; 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, relying on multi-disciplinary data and requiring testing and validation of all components to ensure effective mitigation.
From the real-time integration of multi-parametric observations is expected the major contribution to the development of operational time-Dependent Assessment of Seismic Hazard (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, etc.) whose space-time dynamics and/or anomalous variations can be, to some extent, associated with the complex process of preparation of major earthquakes.
This session includes studies on various aspects of seismic risk research and assessment, ground and satellite-based data analysis and methods, within the t-DASH and Short-term Earthquakes Forecast perspectives:
- Development of physical and statistical models (including AI and machine learning) for hazard, exposure and vulnerability;
- Studies on time-dependent seismic hazard and risk assessments
- Development of systems providing pre- and post-event information, together with early warning and alert tools for effective emergency management;
- Earthquake cascading hazards (e.g. landslides and tsunamis) and multi-risk scenarios development;
- Social vulnerability assessment, along with advances in citizen-science, communication, governance and risk awareness research.
- Studies devoted to the description of genetic models of earthquake’s precursory phenomena
- Infrastructures devoted to maintain and further develop our present observational capabilities of earthquake-related phenomena, also contributing to building a global multi-parametric EarthQuakes Observing System (EQuOS) to complement the existing GEOSS initiatives.

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.

Remote sensing and Earth Observation (EO) data are used increasingly in the different phases of risk management, 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.
EO data have proven to be crucial for hazard, vulnerability, and risk mapping from small to large regions around the globe, during the occurrence of disasters and the pre/post hazard 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. Moreover, European Ground Motion Service (EGMS) has significantly improved the ability to monitor and analyse geohazards using Interferometric Synthetic Apeture Radar data. Data are available since mid-2022 from the Copernicus Land Monitoring Service (CLMS) under the responsibility of the European Environment Agency (EEA).
The session is dedicated to multidisciplinary contributions focused on the demonstration of the benefit of the use of EO for assessment of natural hazards and risk management.
The contributions may include:
- Innovative applications of EO data for rapid hazard/risk assessment
- Development of tools for assessment and validation of hazard/risk models
- Use of EGMS data/products to monitor and investigate different kinds of geohazards and their impact on both environment and infrastructure
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. Contributions demonstrating innovative, cross-disciplinary approaches and case studies with practical implications are particularly welcome. In addition, we invite contributions from international collaborations, such as CEOS, GSNL and GEO.

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, geodetic and remote sensing techniques have experienced significant growth, driven by the expansion of GNSS-based networks and the launch of satellite missions such as Sentinel-1, ALOS-2, TerraSAR-X, LuTan-1, SAOCOM-1, NISAR, and various commercial satellites. This rapidly increasing volume of data enables the acquisition of continuous and spatially extensive datasets over large regions of Earth, offering unprecedented opportunities to improve our understanding of natural and human-induced geohazards across a wide range of temporal and spatial scales, including earthquakes, volcanic eruptions, landslides, glacier dynamics, underground fluid changes, sea-level rise, land (coastal) subsidence, and tsunamis.

This session invites contributions across various disciplines and techniques to quantify, monitor and model the above-mentioned natural and human-induced processes, with particular emphasis on coastal vertical land motion and subsidence-related hazards. Interdisciplinary studies bridging tectonics, geodesy, volcanology, engineering geology, remote sensing, hydrology, ocean sciences, geomorphology and AI for enhanced risk assessment are strongly encouraged. We welcome contributions on a wide range of topics, including but not limited to: 1) Novel algorithms for mitigating SAR/InSAR errors, including deep learning approaches; 2) Advanced strategies for processing and analyzing SAR big data; 3) Integration of AI and machine learning with GNSS and InSAR observations to improve time series interpretation, identify deformation patterns, and disentangle driving processes, 4) multi-sensor and in-situ monitoring using geomorphologic, geodetic, field-based, and modeling approaches; 5) 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 provides a platform for showcasing state-of-the-art methods and techniques to assess risks associated with hydro-climatic extremes like floods, storms, landslides, and on compound dry hazards such as droughts, heatwaves, and fires. When these events are compounded, overlapping each other in time and spatial coverage, or following one another, their compounded nature generates cascading impacts on water resources, ecosystems, infrastructure, and human systems that cannot be captured by single hazard analyses alone. We aim to exchange knowledge and insights into how machine learning algorithms, data mining techniques, physical models, and the integration of satellite data can significantly enhance predictive capabilities for analyzing the societal risks associated with hydro-climatic extremes and compound hazard events. The session highlights innovative applications and real-world case studies demonstrating 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, ecologists, climate scientists, AI researchers, hydrologists, and decision makers.

Key Themes:

Processes:
Physical processes involved in hydro-climatic extremes and compound hazards (e.g., droughts-heatwaves-fires), their precondition factors, enabling mechanisms, feedbacks, emergent properties, and synergistic effects. Interaction and impact of such events in the physical system, ecosystems, and human population.

Methods & techniques:
Integration of remote sensing, data mining, and machine learning approaches to enhance the detection, monitoring, and prediction of hydro-climatic extremes and compound events. Combination of physically-based hydrological and climatological models with AI-driven simulations, as well as applications across multiple spatial and temporal scales, from local case studies to regional and global assessments.

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.

In crisis situations, decision-makers rely on timely, accurate, and trustworthy information about hazard extent, exposed assets, and potential impacts to guide response actions and reduce risk. Recent advances in satellite, airborne, and UAV remote sensing—combined with ground-based sensors and IoT—now make near-real-time monitoring possible at regional to global scales, even in highly vulnerable areas. At the same time, AI and machine learning are accelerating the conversion of these data streams into actionable insights. However, key challenges remain, including scalability, robustness across diverse conditions, uncertainty quantification, and transparency in model behavior.

This session invites contributions that integrate multi-sensor observations with AI—particularly explainable and interpretable methods—to support hazard detection, damage and impact assessment, forecasting, and susceptibility/hazard/risk mapping. Relevant topics include rapid mapping and alert systems, multi-platform data fusion, UAV-enabled monitoring, benchmark datasets and standards, and best practices for training, evaluation, and trustworthy deployment in operational and crisis settings.

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.

High-impact wildfire events in 2025 across the United States, France, Spain, Cyprus, South Korea, Japan, Syria, and Canada resulted in extensive burned areas, mass evacuations, substantial carbon emissions, severe smoke impacts, and loss of life. These events further underline the urgency of strengthening wildfire prevention and risk reduction efforts, from local and structural scales to broader landscape levels. Effective wildfire prevention requires a robust understanding of exposure, vulnerability, and risk in the Wildland-Urban Interface (WUI).
This session aims to showcase studies, projects, and initiatives addressing wildfire risk and vulnerability assessment, damage analysis, prevention measures, and local adaptation strategies in the WUI. We particularly welcome contributions focusing on participatory approaches, community-based risk reduction, resilience of communities and the built environment, as well as public awareness and education, household and community preparedness, stakeholder engagement, recovery processes, and lessons learned within disaster risk reduction frameworks. We also encourage submissions that critically examine prevailing wildfire management approaches and explore wildfire risk in relation to large-scale land-use change and associated agricultural, nature conservation, and climate mitigation policies. Inter- and transdisciplinary research addressing the social and political dimensions of wildfire risk, and translating scientific knowledge into policy- and action-relevant insights, is especially encouraged.
By sharing experiences, methods, and lessons learned across diverse geographical and socio-environmental contexts, this session aims to foster dialogue on wildfire risk management and support the development of practical, transferable solutions for reducing wildfire impacts in the WUI worldwide.

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

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, continental and transboundary 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. As many natural hazard processes, particularly hydrological ones such as floods and droughts, cross administrative and national borders, risk assessment and management increasingly require consideration of transboundary systems, upstream–downstream interactions, and cross‑border cooperation.

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), including nature-based solutions;
- case studies showcasing appropriate use of continental to global risk assessment data in risk management practice, including in transboundary contexts where shared basins, aquifers, or other cross‑border systems require coordinated action;
- novel globally-applicable approaches for leveraging global datasets and models to inform local risk assessment.
- challenges and opportunities in governance and integrated water resources management for transboundary aquifers and river basins;

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.

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.

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.

Covener: L. Giani e M. Bostenaru Dan.
This trans-disciplinary session goes beyond the disciplinary boundaries of Earth and environmental sciences to address socially relevant issues associated to geological and climate-related hazards, by integrating disciplinary paradigms and participatory research approaches. Geological and climate related hazards have always been part of Earth’s dynamics, but climate change, together with increasing land use and consumption, is intensifying their frequency, impact and societal consequences, escalating into disaster risk situations.
Effective external communication of risk is essential for the approval and implementation of prevention policies, mitigation plans and resilience pathways for institutions, communities and territories, and for strengthening trust between society and institutions. Understanding how these risks have been communicated and managed in the past can provide insights for planning future resilience actions. In this context, attention is given to how institutions incorporate risk into policies and programmes, how scientific knowledge is transferred into decision-making, and what challenges arise in communicating complex and evolving risk scenarios before, during and after catastrophic events.
The session welcomes contributions that explore the intersection of natural hazard research and social sciences, including studies on risk perception, behavioural change in preparedness and response, awareness, and the mental health impacts associated with hazardous events. Disaster risk reduction strategies should be informed not only by hazard modelling, but also by the social dynamics that shape real-world readiness and response among residents, tourists, experts and other stakeholders, as well as the protection of cultural and natural heritage.
A special focus will be devoted to case studies illustrating how institutions and scientists have addressed social crises induced by one or more geological or climate-related phenomena, how communication has activated—or failed to activate—the chain of civil responsibility, and how nature-based solutions can contribute to reducing the negative effects of climate change while supporting well-being and mental health. An emphasis will be placed on the concept of the mental map of heritage habitats, investigated through psychogeographical approaches, as a key element to be preserved and considered in retrofit, emergency management and post-disaster rebuilding processes.

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.
- Cross-sectoral approaches that integrate physical, social, economic, environmental, and institutional dimensions.
- Treatment of uncertainty in multi-(hazard) risk and compounded impact assessment.
- 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.
- 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.

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

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 (MSES) and natural laboratories where the impacts of global environmental change become particularly visible. Rapid climate warming, cryosphere loss, shifting hydrological regimes, land-use change, and socio-economic transformation are jointly reshaping mountain environments. These changes affect MSES or specific parts such as ecosystems, water resources, natural hazards, livelihoods, and human well-being, with consequences that extend far beyond mountain regions. As the planet’s water towers, mountains regulate freshwater availability along the mountain-to-lowland continuum and provide essential ecosystem-services. At the same time, mountain communities are often highly exposed and vulnerable to climate-related hazards such as floods, landslides, droughts, and compound or cascading events. Understanding how hazards, exposure, and vulnerability interact in space and time is therefore essential for effective climate risk management and long-term adaptation.
This session invites inter- and transdisciplinary contributions that examine past, present, and future environmental change in MSES and contributing from different perspectives to the understanding of MSES. Mountain regions present specific scientific and societal challenges.
Complex terrain remains difficult to adequately parameterize in models, high-elevation monitoring infrastructure is limited in many parts of the world, and socio-economic dynamics are often insufficiently captured in environmental assessments. Addressing these knowledge gaps is critical for developing robust and equitable adaptation strategies.
We particularly encourage contributions that integrate physical and social processes, explore cross-scale feedbacks and compound risks, advance high-elevation monitoring and remote sensing, apply climate downscaling approaches, and combine process-based, data-driven, and participatory methods. Studies engaging stakeholders, co-producing knowledge, and linking science to decision-making and policy are especially welcome.
By fostering dialogue across disciplines and between science and practice, this session aims to advance a systems-based understanding of MSES and support transferable approaches to sustainable adaptation under global environmental 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.

Natural hazards have been an inherent aspect of Earth’s history, shaping ecosystems, landscapes, and human societies from ancient times to the present, with climate variability and fire playing particularly influential roles. 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 and fire hazards, and how social resilience emerged, 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, improve understanding of past fire stewardship, and foster sustainable responses to climate change in the twenty-first century.
This session explores how societies have historically responded to climate-related hazards and environmental challenges. Key themes include, but are not limited to:
• Historical perspectives on climate variability and societal change
• Cultural pyroscapes: landscapes shaped by the interactions between societies and fire
• Case studies of resilience in ancient and medieval societies
• The development of adaptive strategies in relation to urbanisation processes
• Indigenous knowledge and adaptive strategies
• Technological innovations and agricultural practices change over time
• Adaptive strategies for responding to historical hazards and their transformation
• Case studies on the (co-)evolution of social and environmental systems in hazard-prone areas

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 HPC, 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; 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, (v) Climate change impacts on hydro-geological hazards, with a focus on floods, landslides, and droughts, (vi) Physics-informed learning and the integration of climate scenarios, (vii) AI-driven coupled hazard modeling using multi-source data, (viii) Representation of hydrological interactions among atmosphere, vegetation, and soil, and, (ix) 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 poses a significant threat to sustainability. It disproportionately affects different social groups, intensifying interconnected risks across socio-ecological systems and challenging conventional approaches to disaster risk reduction and adaptation. These challenges are particularly pronounced in climate-sensitive ecosystems, such as arid and semi-arid regions, where land degradation, water scarcity, biodiversity loss, and socio-economic vulnerability intersect.

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

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

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

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 many 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 people’s behavioral, attitudinal, and emotional responses.

In some contexts, particularly fragile and humanitarian settings where exposure, vulnerability, and institutional capacity are constrained, extreme weather events may interact with societal stressors such as conflict or political instability, producing compound and cascading risks. These dynamics pose particular challenges for risk assessment, forecasting, and anticipatory action. Addressing them requires closer integration of natural and social sciences, combining advances in hazard assessment and forecasting with insights into societal exposure, vulnerability, behaviour, mobility, and decision-making.

Yet only few studies are currently harnessing the full potential of interdisciplinary collaborations in this space, and several challenges pertaining to the choice of methods and the scale of analysis (e.g., regional, national) remain underexplored. This session provides a platform for interdisciplinary contributions that bridge natural and social sciences to better understand societal impacts of, and responses to, extreme weather events and related compound hazards.

We invite contributions including, but not limited to, studies of:
- Migration and displacement due to extreme events
- Environmental attitudes and behaviors influenced by extreme events
- Health and wellbeing effects of climate change and extreme events
- Food production and security in relation to extreme weather
- The interplay between climate change, environment, and conflict
- Anticipatory action and risk-informed decision-making for humanitarian preparedness and response;
- Methodological challenges to interdisciplinary collaborations

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.

Climate- and weather-related losses continue to rise, even as scientific understanding and risk management efforts expand. While climate change intensifies the frequency and magnitude of many hazards, evolving exposure patterns and the multidimensional nature of vulnerability are equally decisive drivers of risk. This session examines the dynamic interplay of these factors across physical, social, environmental and institutional dimensions to understand how hazards, exposure, and vulnerability co-evolve in space and time, and how those dynamics shape risk outcomes in the Anthropocene.

We invite contributions that move beyond static assumptions and address nonstationarity, compounding events, and cascading failures. Hazard regimes are changing, and their interactions can amplify impacts in the built environment. Submissions that analyze triggers, propagation, and recovery processes are particularly welcome.

Exposure is growing as urbanization intensifies, economies expand, and infrastructure networks densify, yet its spatio-temporal dynamics remain under-characterized. We encourage work that maps and models exposure trajectories under shared socio-economic pathways, evaluates the effectiveness and unintended consequences of mitigation and land-use measures, and explores how mobility, land-use change, and supply-chain linkages redistribute risk.

Understanding the vulnerability of elements at risk is crucial, because it governs the severity of impacts from climate hazards and is key to reducing future losses. The challenges of the Anthropocene require widening definitions and assessing shifts across multiple interacting hazards and contexts to address the multidimensional, dynamic character of vulnerability. However, the growing complexity of managing multiple domains, scales, and disciplines can impede holistic perspectives. We welcome studies that integrate socio-ecological, behavioral, engineering, institutional, and contextual information. Interdisciplinary and mixed-method approaches that bridge datasets and improve data interoperability, validation of vulnerability functions, and synthesis of evidence are encouraged.

We aim to foster transferable, adaptive risk management that connects landscape processes with human activities and supports equitable climate adaptation. By integrating the dynamics of hazards, exposure and vulnerability, this session advances coherent pathways to manage climate risk in the Anthropocene.

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)

Water sustainability is becoming a key concern worldwide due to hydrological uncertainty, climate change, landuse landcover changes, and growing water pollution. Degradation of water quality due to natural and anthropogenic activities poses significant threat to freshwater availability. Space-time modelling of water quality depends on the availability of long-term reliable datasets, which are often found to be incomplete, sparse, or unavailable. Water quality, though monitored frequently, limited knowledge is available about emerging contaminants. Subsurface environments, which are highly heterogeneous, influence flow and transport dynamics and surface-subsurface interaction mechanisms, making model calibration quite challenging. These drivers greatly influence catchment hydrology, hydrodynamics, biogeochemical processes and ecosystem. In dynamic environments, solute transport, sediment dynamics, and vegetation are also coupled through hydrodynamic and biogeochemical feedback for improved understanding of processes, nutrient cycling and ecosystem functioning.
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 and water quality monitoring networks and installation of IoT sensors is very limited. It is therefore warranted to leverage geospatial, machine learning, decision science, statistical and modelling techniques to improve the understanding of catchment hydrology and consequences of climate change and anthropogenic drivers on surface and groundwater resources at various scales. The worldwide readily available satellite remote sensing data and global data products enable us to leverage these techniques in addressing water and environmental challenges.
We solicit novel contributions from researchers in catchment hydrology by utilizing Remote Sensing, GIS, Artificial Intelligence (AI), Machine Learning (ML), Decision Science, and advanced statistical techniques for addressing pressing challenges of water sustainability in mountainous and urban catchments and data scarce regions. The combined use of these technologies will revolutionize understanding of complicated hydrological, hydrodynamic and biogeochemical processes, and will be useful in evolving effective water resource management and ecosystem-based adaptation strategies to foster sustainable development.

Extreme hydrometeorological events, such as floods and droughts, are changing significantly under a warming climate and are amplifying risks to infrastructure, ecosystems, economies, and society. These extremes, including their compound occurrences, are driven by complex climate–land–atmosphere processes, making their characterization, prediction, and risk assessment challenging. Addressing these challenges requires advances in monitoring, modeling, detection, attribution, and uncertainty quantification to strengthen resilience at global, regional, and local scales. This session focuses on investigating changes in the frequency, intensity, duration, severity, spatial extent, and clustering of extreme events,like floods and droughts, under climate variability and change. We welcome studies on understanding the role of climate drivers, land–atmosphere feedbacks, land-use/land-cover change, and evapotranspiration dynamics in shaping the evolution and propagation of floods and droughts.. Submissions employing innovative datasets, high-resolution observations, advanced indices, machine learning approaches, and integrated modeling frameworks to improve detection, attribution, prediction, and early warning of extreme and compound events are welcome. Studies linking extremes to risk, vulnerability, exposure, adaptive capacity, and decision-making across water, agricultural, ecological, and socio-economic systems are also welcome.

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

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

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. Improved representation of spatial–temporal dependence, clustering, and uncertainty is also critical for robust hazard and risk assessments, with direct implications for infrastructure design, disaster preparedness, climate adaptation strategies, and risk management in the (re)insurance sector.
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

Extra-tropical 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. Most of our fundamental theories for the large-scale atmospheric circulation in the extra-tropics are based on “dry” atmospheric dynamics. Despite these recent efforts, large uncertainties in representing diabatic processes and their impact remain, leading to upscale error growth and enhanced ensemble spread, highlighting the continued need to further our understanding and to develop new and revise existing paradigms. This session therefore addresses both fundamental scientific challenges and urgent societal needs by linking physical processes, climate change signals, and potential impacts.

This session welcomes 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)
• Role of diabatic processes for the dynamics, predictability, and future changes of extratropical weather system
• 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.

Solicited authors:
Suzanne L. Gray, Julian F. Quinting

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 and of atmospheric blocking 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.