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

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

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

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

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

Climate change will lead to periods of intense rainfall, along with increased drought and heatwaves. This stresses landscapes in ways not experienced in our living memory. The critical zone, from tree canopy to unfractured bedrock, is likely compartmentalized, and the interactions between each compartment will evolve under climate extremes. The subsurface will control how landscapes respond, erode, and recover from extreme events (for example, how soils erode, gullies form, landslides initiate, and how rivers incise and transport sediment). In turn, these geomorphic changes further modify infiltration, groundwater recharge, subsurface flow paths, rock fracturing and weathering. Human activities and infrastructure such as dams, irrigation, deforestation, and urbanization further reshape these interactions by altering water and sediment connectivity. We propose a session that will explore the connectivity and feedbacks among the geosphere, hydrosphere, biosphere and technosphere, and how their interactions shape landscape response to change. We invite contributions that seek to model, measure, and observe the functioning of the surface and subsurface as well as interactions between the surface and subsurface, from soils to groundwater and from hillslopes and intermittent streams to rivers. 

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

Water is our planet’s most vital resource, and the primary agent in some of the biggest hazards facing society and nature. Recent extreme heat and flood events underline the significance of water both as a threat and as an increasingly volatile resource.
The accurate and timely measurement of streamflow is therefore more critical than ever to enable the management of water for ecology, for people and industry, for flood risk management and for understanding changes to the hydrological regime. Despite this, effective monitoring networks remain scarce, under-resourced, and often under threat on a global scale. Even where they exist, in-situ observational networks are increasingly inadequate when faced with extreme conditions, and lack the precision and spatial coverage to fully represent crucial aspects of the hydrological cycle.

This session aims to tackle this problem by inviting presentations that demonstrate new and improved methods and approaches to streamflow monitoring, including:
1) Innovative methodologies for measuring/modelling/estimating river stream flows;
2) Real-time acquisition of hydrological variables;
3) UAS and satellite remote sensing techniques for hydrological & morphological monitoring;
4) Measurement in extreme conditions associated with the changing climate;
5) Measurement of sudden-onset extreme flows associated with catastrophic events;
6) Strategies to quantify and describe hydro-morphological evolution of rivers;
7) New methods to cope with data-scarce environments;
8) Inter-comparison of innovative & classical models and approaches;
9) Evolution and refinement of existing methods;
10) Guidelines and standards for hydro-morphological streamflow monitoring;
11) Quantification of uncertainties;
12) Development of expert networks to advance methods.

Contributions are welcome with an emphasis on innovation, efficiency, operator safety, and meeting the growing challenges associated with the changing climate, and with natural and anthropogenically driven disasters such as dam failures and flash floods.

Additionally, presentations will be welcomed which explore options for greater collaboration in advancing riverflow methods and which link innovative research to operational monitoring.

Worldwide, disturbances modify the interactions and feedbacks among hydrologic, geomorphic, biogeochemical, and ecological systems, thereby reshaping the hydrological functioning of landscapes. Wildfires, deforestation, volcanic eruptions, earthquakes, and other disturbances alter water partitioning, flow paths, the transport of solutes and sediments. Although their immediate impacts may be spatially or temporally constrained, their hydrological consequences can persist and propagate across landscapes. This session explores how disturbances reshape hydrological processes across spatial and temporal scales, with particular interest in the mechanisms controlling the system response and recovery. We welcome observational, experimental, and modeling studies addressing individual, sequential, or interacting disturbances. We especially encourage contributions examining multiple or overlapping disturbances and their implications for predicting hydrological change and informing watershed and land management.

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

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

Understanding hydrological processes in drylands, from hyperarid to semi-arid regions, is a major research challenge, particularly under increasing pressure from climate change, land-use change, and water scarcity. These regions, which span over 40% of the Earth’s land surface and support more than two billion people, face complex hydrological challenges due to their high climatic variability. Their limited water resources are characterized by strong climatic variability, intermittent flows, and complex interactions between rainfall, soils, vegetation, surface runoff, and groundwater.

This session welcomes contributions that improve our understanding of key hydrological processes in drylands (such as semi-arid zones in the Mediterranean or hyperarid ones in the Sahara and other desertic areas), including rainfall variability, infiltration and runoff generation mechanisms, antecedent soil moisture controls, hydrological connectivity, transmission losses, evapotranspiration, and groundwater recharge. We particularly encourage studies addressing the episodic and threshold-driven nature of hydrological responses, surface–groundwater interactions, and the challenges of monitoring and modeling water fluxes across scales.

Given the scarcity of long-term observations in many dryland regions, notably in the Global South, we also welcome approaches combining field observations, remote sensing, novel monitoring techniques, citizen science, and modeling. Studies examining how climate and land-use changes may alter hydrological processes and water availability are especially encouraged.

By bringing together observational, experimental, and modeling perspectives, this session aims to improve process-based understanding of dryland hydrology and identify pathways towards more resilient water systems in dryland regions.

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Scientific machine learning (SciML) and artificial intelligence (AI) are increasingly used to interpret heterogeneous subsurface data, accelerate multiphysics simulation, infer uncertain parameters, and support decisions in geotechnical, environmental, and geoenergy applications. Their practical value, however, depends on physical consistency, generalisation beyond training conditions, uncertainty characterisation, and validation against independent evidence.
This session invites contributions on recent methodological advances and innovative applications of data-driven, physics-informed, and hybrid physics-AI techniques for geotechnical, geothermal, and subsurface systems. Relevant applications include, but not limited to, site and subsurface characterisation; geothermal exploration, reservoir development, monitoring, and operation; groundwater flow, transport in porous media, multicomponent reactive transport, and geochemical processes; coupled thermo-hydro-mechanical-chemical processes; underground storage of heat, hydrogen, or carbon dioxide; and geotechnical infrastructure.
We welcome work on supervised and unsupervised learning, deep learning, physics-informed neural networks, neural operators, surrogate and reduced-order models, inverse modelling, data assimilation, Bayesian inference, digital twins, and other relevant studies. Contributions addressing heterogeneous or sparse data, multiscale modelling, uncertainty quantification, sensitivity analysis, model discrepancy, transferability, interpretability, and computational efficiency are particularly encouraged.
A central objective is to connect methodological development with physical and observational evidence. We therefore welcome studies integrating laboratory experiments, imaging and sensing methods, field monitoring, operational data, and numerical simulations for training, calibration, verification, and validation. Comparative studies that establish when machine learning or physics-informed methods improve upon conventional numerical, statistical, or inverse approaches and where they do not are especially valuable.

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

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

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

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

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

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

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

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

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

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

We invite presentations on ocean surface waves, and wind-generated waves in particular, their dynamics, modelling and applications. This is a large topic of the physical oceanography in its own right, but it is also becoming clear that many large-scale geophysical processes are essentially coupled with the surface waves, and those include climate, weather, tropical cyclones, Marginal Ice Zone and other phenomena in the atmosphere and many issues of the upper-ocean mixing below the interface. This is a rapidly developing area of research and geophysical applications, and contributions on wave-coupled effects in the lower atmosphere and upper ocean are strongly encouraged.

In recent years, technologies based on Artificial Intelligence (AI), such as image processing, smart sensors, and intelligent inversion, have garnered significant attention from researchers in the geosciences community. These technologies offer the promise of transitioning geosciences from qualitative to quantitative analysis, unlocking new insights and capabilities previously thought unattainable.
One of the key reasons for the growing popularity of AI in geosciences is its unparalleled ability to efficiently analyze vast datasets within remarkably short timeframes. This capability empowers scientists and researchers to tackle some of the most intricate and challenging issues in fields like Geophysics, Seismology, Hydrology, Planetary Science, Remote Sensing, and Disaster Risk Reduction.
As we stand on the cusp of a new era in geosciences, the integration of artificial intelligence promises to deliver more accurate estimations, efficient predictions, and innovative solutions. By leveraging algorithms and machine learning, AI empowers geoscientists to uncover intricate patterns and relationships within complex data sources, ultimately advancing our understanding of the Earth's dynamic systems. In essence, artificial intelligence has become an indispensable tool in the pursuit of quantitative precision and deeper insights in the fascinating world of geosciences.
For this reason, aim of this session is to explore new advances and approaches of AI in Geosciences.

This session invites contributions on the latest developments and results in lidar remote sensing of the atmosphere, covering • new lidar techniques as well as applications of lidar data for model verification and assimilation, • ground-based, airborne, and space-borne lidar systems, • unique research systems as well as networks of instruments, • lidar observations of aerosols and clouds, thermodynamic parameters and wind, and trace-gases. Atmospheric lidar technologies have shown significant progress in recent years. While, some years ago, there were only a few research systems, mostly quite complex and difficult to operate on a longer-term basis because a team of experts was continuously required for their operation, advancements in laser transmitter and receiver technologies have resulted in much more rugged systems nowadays, many of which are already operated routinely in networks and several even being fully automated and commercially available. Consequently, also more and more data sets with very high resolution in range and time are becoming available for atmospheric science, which makes it attractive to consider lidar data not only for case studies but also for extended model comparison statistics and data assimilation. Here, ceilometers provide not only information on the cloud bottom height but also profiles of aerosol and cloud backscatter signals. Scanning Doppler lidars extend the data to horizontal and vertical wind profiles. Raman lidars and high-spectral resolution lidars provide more details than ceilometers and measure particle extinction and backscatter coefficients at multiple wavelengths. Other Raman lidars measure water vapor mixing ratio and temperature profiles. Differential absorption lidars give profiles of absolute humidity or other trace gases (like ozone, NOx, SO2, CO2, methane etc.). Depolarization lidars provide information on the shapes of aerosol and cloud particles. In addition to instruments on the ground, lidars are operated from airborne platforms in different altitudes. Even the first space-borne missions are now in orbit while more are currently in preparation. All these aspects of lidar remote sensing in the atmosphere will be part of this session.

This session aims at presenting current and forthcoming novel uses of spaceborne hyperspectral imagery acquired over several scales, especially the EnMAP mission, for geosciences and environmental applications. EnMAP provides high quality spectral data at 30 m spatial resolution covering the visible, near- and shortwave infrared regions with nearly global coverage and some regional time-series achieved after 5 years in orbit. Abstracts are solicited toward the characterization and quantification of geo- and bio-physical surface properties related to but not limited to soil and soil health, soil pollution, plastics, critical metals and minerals detection, carbon content in soils, hazards, volcanology, snow and ice properties, as well as vegetation, marine and atmospheric studies.

Time series are a common type of data generated by observational and modelling efforts across Earth, environmental and space sciences. Long-term observations are particularly important for understanding gradual changes and assessing risks, yet are often difficult to sustain and fund. Their characteristics can vary substantially, from short to long records, linear to nonlinear dynamics, univariate to multivariate data, and single- to multi-scale variability. These differences call for both tailored methodologies and general approaches.

A key challenge is distinguishing random fluctuations from long-term changes in order to better understand processes within and across Earth system components. This requires knowledge of temporal variability and, often, sufficiently long observations. For example, reliable sea-level trends may require several decades of continuous measurements because of decadal variability. Likewise, the stochastic variability of geophysical time series can exhibit power-law scaling, requiring long records for robust statistical assessment.

Time series analysis encompasses a broad range of tasks, including:
- characterizing nonlinear variability in the time and/or frequency domain;
- quantifying complexity, predictability and scaling properties;
- identifying statistical interdependencies within and between time series;
- distinguishing co-variability from causal relationships;
- reducing dimensionality and identifying meaningful modes of variability; and
- developing stochastic and deterministic statistical or dynamical models.

This session invites contributions on the development and application of modern methods for analysing observational and model time series across the EGU community, including geophysical, geodynamic, oceanographic, geodetic and climate observations from terrestrial observatories and remote sensing. Contributions addressing advances in sensors, instrumentation, monitoring, analysis and interpretation, as well as comparisons of different approaches, are welcome. Studies using novel methods, including AI, for the analysis of long time series are particularly encouraged. We aim to foster interdisciplinary exchange and cross-fertilization between different EGU divisions.

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

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

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

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

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

Early career and underrepresented scientists are particularly encouraged to participate.

Increasing climate variability and water scarcity are placing unprecedented pressure on agricultural systems worldwide. To address these challenges, the agricultural sector is rapidly adopting precision technologies that combine remote sensing capabilities with artificial intelligence to optimize crop water management. This session focuses on cutting-edge applications of remote sensing technologies and AI-driven analytics in transforming agricultural water management practices. We welcome research that demonstrates innovative approaches to monitoring, predicting, and managing agricultural water resources by integrating datasets from Earth observation satellites, unmanned aerial systems, Internet of Things (IoT) sensors and using advanced computational methods.
Key topics include:
Multi-platform remote sensing applications (satellite, UAV, hyperspectral, SAR, thermal infrared) for estimation of soil moisture and crop water requirement.
Deep learning and AI-driven detection of crop water stress and yield prediction.
Precision irrigation systems and automated water management technologies.
Multi-sensor data fusion combining space-based, airborne, and ground observations.
Real-time monitoring systems and subseasonal-to-seasonal water demand forecasting models.
Digital agriculture platforms and decision support tools for sustainable water management.
LLM-augmented modeling and digital twin of the agri-climatic or agri-weather landscape.
This session aims to showcase practical solutions that bridge the gap between technological innovation and real-world agricultural applications, emphasizing scalable approaches that support both productivity and environmental sustainability.

Coastal and submarine environments are among the most dynamic landscapes on Earth, continuously reshaped by the interplay of hydrodynamic processes, sediment transport, tectonics, land subsidence, sea-level change, climate variability, and anthropogenic activities. Understanding the evolution of these interconnected systems is increasingly important in the face of accelerating climate change, growing coastal populations, and expanding offshore development. Recent advances in remote sensing, marine geophysics, bathymetric mapping, LiDAR, InSAR, unmanned aerial systems (UAS), numerical modelling, and field observations have substantially enhanced our ability to investigate geomorphic processes and landscape evolution across the land–sea continuum.
This session welcomes contributions investigating the evolution, morphodynamics, and sedimentary processes of coastal and submarine environments across a wide range of spatial and temporal scales. We encourage studies focusing on beaches, deltas, estuaries, tidal flats, coastal dunes, rocky coasts, cliffs, barrier systems, continental shelves, submarine canyons, submarine landslides, and other coastal and marine landforms. Contributions addressing shoreline evolution, land subsidence, relative sea-level rise, sediment budgets, coastal erosion and accretion, sediment connectivity, and interactions between terrestrial and marine geomorphic processes are particularly encouraged.
We especially welcome multidisciplinary studies integrating field observations with remote sensing, satellite data, LiDAR, InSAR, bathymetric surveys, seismic reflection, magnetic and other geophysical datasets, GIS analyses, machine learning, and process-based numerical modelling to improve our understanding of geomorphic evolution and sediment dynamics. Contributions exploring climate change impacts, coastal hazards, ecosystem resilience, nature-based solutions, and sustainable coastal management are also encouraged.
The session aims to provide a platform for coastal geomorphologists, marine geologists, geophysicists, remote sensing specialists, natural hazard scientists, and numerical modellers to exchange ideas, foster interdisciplinary collaborations, and advance our understanding of the processes shaping coastal and submarine landscapes across the land–sea continuum.

We are looking herein for new methodological, new techniques, new processing and new applications using UAS / drones to study any Earth Science processes.
We would like first to share our various knowledge, techniques, processing, methodologies, and experience in the use of UAS. Then we would like to illustrate the so many applications existing nowadays in Earth Sciences.
Moreover, we would like to insist on the importance to get a whole high resolution - pluri centimetric Ground Resolution – multitemporal dataset of aerial photographs coverage of a studied area at a so low price. An UAS acquisition provides after processing orthophotos as well as a high resolution Digital Surface Model from which we may process a high resolution Digital Terrain Model with many applications.
Consequently, we would expect various examples and applications from very different field in Earth Sciences such as geological and geomorphological mapping, geomorphometry, geodesy, natural hazards such as volcanic and seismic hazards, or landslide monitoring, floods, or other applications such as neotectonics and active tectonics, structural geology, tectonics, sedimentology and mapping of inaccessible outcrops, monitoring of fluvial or marine terrasses in order to get their evolutions, climate changes, and so on...
Finally we hope by this session and with time to create an « UAS community of the different colleagues who use UAS with applications in Earth Sciences » in order to optimize our practise, our results and our knowledge.

Imaging the Earth’s surface and reconstructing its topography to study the landscape and (sub-)surface processes have advanced rapidly over the past two decades, sometimes separately within different geoscientific disciplines. Developments in satellite, airborne, uncrewed aerial vehicle (UAV), and ground-based sensing, alongside advances in photogrammetry, Structure-from-Motion (SfM), LiDAR, computer vision, and automated processing, have transformed our ability to reconstruct and monitor the Earth’s surface in 2D, 3D, and 4D (time series).
The spatial, temporal, and spectral resolutions of current measurements offer the opportunity to study the surface evolution from local to regional scale at unprecedented detail. Equipped with optimized workflows, either digitizing analogue data, such as historical aerial photographs, or processing near-continuous records of topographic change, geoscientists now have a variety of tools to better understand our rapidly changing environment and disentangle anthropogenic from natural drivers.
Yet, challenges still exist at both methodological and application levels. How can reliable data be acquired in harsh, remote, or non-ideal environments? How can observations from different time periods, scales, and platforms be reconstructed, co-registered, and compared? How should measurement precision, systematic errors, and uncertainty be quantified and propagated into estimates of surface change? And how can computer-vision, machine-learning, and automation extract meaningful information from large and complex 3D and 4D datasets?
In this session, we invite contributions from across the geosciences (e.g., geomorphology, glaciology, volcanology, hydrology, soil sciences) on the opportunities, limitations, and challenges of surface imaging, topographic reconstruction, and change analysis across diverse environments.
We especially welcome contributions addressing (1) novel data acquisition in complex, remote, or rapidly changing environments; (2) image and point-cloud processing, including image matching, reconstruction, co-registration, and correction of sensor distortions; (3) precision and uncertainty assessment, including systematic errors; (4) construction and analysis of multitemporal and 4D datasets, from historical records to high-frequency and near-continuous observations; (5) automated processing, analysis, and information extraction from large and complex datasets; and (6) innovative applications in geosciences.

Over the past several decades, topographic measurement techniques have undergone significant advancement, evolving toward increasingly sophisticated instruments capable of higher precision and spatial resolution. This technological progression has enabled diverse applications across Earth sciences, particularly in territorial analysis for monitoring and quantifying geomorphological processes and their associated changes at varying temporal and spatial scales.

The intersection between geomatics and geomorphology represents a fundamental pathway for quantifying landscape evolution, assessing geohazards, improving cartographic representation, and informing land management decisions. From debris flow channel activation to glacial retreat monitoring, and from geomorphic signature of active tectonics to landslide hazard mapping, integrated geomatics approaches provide critical insights into surface processes.

This session invites researchers to present work employing integrated methodologies across diverse environmental settings. We encourage submissions utilizing topographic surveying, GPS/GNSS data analysis, photogrammetry (including UAV-based approaches), remote sensing imagery, geomorphometric analyses, InSAR data analysis, and machine learning applications. Contributions demonstrating multi-scale and multi-temporal approaches are particularly welcomed.

The radioactive materials are known as polluting materials that are hazardous for human society, but are also ideal markers in understanding dynamics and physical/chemical/biological reactions chains in the environment. Therefore, man-made radioactive contamination involves regional and global transport and local reactions of radioactive materials through atmosphere, soil and water system, ocean, and organic ecosystem, and its relations with human and non-human biota. The topic also involves hazard prediction, risk assessment, nowcast, and countermeasures, which is now urgent important for the nuclear power plants in Ukraine, the Middle East, etc.

By combining long monitoring data (> halftime of Cesium 137 after the Chornobyl Accident in 1986, 16 years after the Fukushima Accident in 2011, and other events), we can improve our knowledgebase on the environmental behavior of radioactive materials and its environmental/biological impact. This should lead to improved monitoring systems in the future including emergency response systems, acute sampling/measurement methodology, and remediation schemes for any future nuclear accidents. Furthermore, the discharge of ALPS-treated water into the ocean, carried out as part of the decommissioning of the Fukushima Daiichi Nuclear Power Station, has attracted international attention and demonstrated that decommissioning a nuclear power plant that has suffered an accident requires a fundamentally different approach from that of a conventional decommissioning. Studies on past nuclear contamination events and other environmental radioactivity datasets are also welcome.

The following specific topics have traditionally been discussed:
(a) Atmospheric Science (emissions, transport, deposition, pollution);
(b) Hydrology (transport in surface and ground water system, soil-water interactions);
(c) Oceanology (transport, bio-system interaction);
(d) Soil System (transport, chemical interaction, transfer to organic system);
(e) Forestry;
(f) Natural Hazards (warning systems, health risk assessments, geophysical variability);
(g) Measurement Techniques (instrumentation, multipoint data measurements);
(h) Ecosystems (migration/decay of radionuclides).

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

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

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

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

The growth of urbanisation in areas exposed to multiple geological hazards, coupled with the rapid increase in extreme climate-related events, makes novel approaches to the geophysical monitoring of urban areas necessary. This scenario will present unprecedented challenges to the infrastructure and lifeline systems that are already overstressed and support urban centres. Programmes that promote the sustainability and resilience of cities and lifeline infrastructures require the development of methodologies for non-destructive or minimally invasive geophysical exploration and monitoring of surface and the subsurface. This session will present and discuss recent technological and methodological advances in geophysics, including multi-sensor, multi-resolution, and multi-scale approaches to the geophysical investigation of urban subsurface and strategic infrastructures. The focus will be on novel and effective geophysical methods, innovative sensors (e.g. fibre optics and MEMS) for dense and distributed network arrays, and AI-based algorithms and machine learning methods for processing and analysing geophysical data. Furthermore, we welcome and encourage presentations on case studies concerning the monitoring of urban areas and infrastructure, the developing of innovative systems for sharing and visualising digital data, and the use of digital twins of the urban subsurface. The session will also provide an opportunity for applied geophysicists, geologists, and engineers to share their expertise and discuss issues. Finally, the session will promote the activities of early career scientists in addressing open challenges in applied geophysics in programmes for the sustainability and resilience of tomorrow's cities.

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

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

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

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

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

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

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

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

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

Running Earth system models, downscaling outputs and driving process-based impact models are computationally expensive tasks that hinder the exploration of climate change impacts. New generations of statistical, physical, and hybrid climate change emulators reduce these bottlenecks by mimicking global, regional and extreme-event responses to generate projections of climate change and its associated impacts more efficiently. This capability transforms the assessment of climate change impacts and risks by enabling rapid policy-relevant analyses for sectors such as natural systems, agriculture, water resources, economics, energy systems and human health.

This session invites contributions on the developments and applications in climate change emulation, highlighting opportunities and challenges for robust and computationally efficient climate risk assessments. This includes simple climate models, probabilistic emulation techniques and machine learning approaches, including traditional pattern scaling, as well as benchmarking efforts that aid in interpretability and consistency between emulation methods. To foster interdisciplinary exchange, we also encourage submissions regarding approaches for climate impact projections.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Thermal remote sensing is an increasingly established technique employing passive sensors to deriveEarth’s surface properties from the radiation emitted in the Thermal Infrared (TIR) domain. Its main focus is the thermal state of an object or surface, together with the associated surface temperature and emissivity. These properties are relevant across geological, environmental, climatic, agricultural, biological, and engineering applications.

Recent technological advances have driven the development of TIR remote sensing: satellite sensors and data infrastructure systems can now acquire and manage large volumes of high-fidelity TIR data at a wide range of spatial and temporal resolutions. Besides airborne and ground-based systems, Unmanned Aerial Systems (UAS) are increasingly used as versatile platforms that combine high spatial resolution with flexible temporal revisit. Together with a growing catalogue of current and upcoming missions, this makes it a timely moment to take stock of where the field stands.

This session addresses established and emerging research directions in TIR remote sensing and discusses the community's upcoming challenges. We welcome contributions on new frontiers, case studies, and data-integration analysis related to:

• Geosciences: volcanoes, hydrothermal systems, geothermal potential, mineral exploration, rare earths, cryosphere.

• Climate, Urban Systems, and Ecosystems: urban heat islands, global warming impacts, ecosystem stress, forest health, fire risk assessment, water management.

• Agriculture and Precision Farming: crop stress monitoring, irrigation management, soil analysis and pest/disease monitoring.

• Technological and Methodological Innovations: new sensors for satellite, airborne, UAS and in-situ platforms, multi-platform and/or multi-sensor data integration, Cal/Val activities.

• Data Processing and Infrastructure: approaches for managing and processing large TIR datasets, data fusion techniques, advanced algorithms for atmospheric correction and temperature and emissivity separation.

Multi-disciplinary studies and contributions from Early Career Scientists are especially welcome.

Invited Speaker: Sabine Chabrillat, Helmholtz Centre for Geosciences (GFZ).

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

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

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

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

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

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

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

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

Landscape evolution models (LEMs) have matured into powerful and widely used tools in the geomorphologist’s toolbox. Increasingly robust representations of weathering, erosion, sediment transport and hillslope processes allow LEMs to function as digital laboratories in which hypotheses about landscape dynamics can be explored across spatial and especially temporal scales that are impossible to directly observe.
LEMs have been used to investigate a wide range of questions, including the response of landscapes to climate and tectonic forcing; river incision and sediment transport; hillslope evolution and landslide dynamics; glacial and periglacial processes; soil formation and weathering; drainage-network development; interactions between geomorphic processes and ecosystems; and the propagation of disturbances through coupled Earth-surface systems. Their ability to represent interacting processes also offers opportunities to explore cascading natural hazards, from landslide–river interactions to chains of sediment, flooding and slope instability, making LEMs increasingly relevant to a key societal concern.
At the same time, the field is entering an exciting period of methodological tension. As process-based models, LEMs seek to encode physical mechanisms and study their emerging interactions explicitly, while AI and data-driven approaches offer powerful alternatives for identifying patterns and emulating processes. Rather than treating these approaches as competing paradigms, this tension raises fundamental questions about what we expect models to represent, how we evaluate them, and how process understanding can be combined with emerging computational methods.
This session welcomes contributions from researchers who use, modify, or develop landscape evolution models, as well as those whose research questions could benefit from the application of LEMs.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

The Intergovernmental Panel on Climate Change (IPCC) describes adaptation as the process of adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities. With over 1 degree Celsius of global warming already experienced, adapting to current and future climate changes is now of far greater focus for all levels of policymaking than ever before. Yet, the IPCC and the UNEP-WASP Adaptation Gap Reports (2023) both conclude that despite progress, adaptation gaps exist and will continue to grow at current rates of implementation.

More recently, the UNFCCC had agreed a set of 59 Global Goal on Adaptation (GGA) Indicators, of which, monitoring, evaluation and learning is one of the four core targets. The text states that by 2030, all Parties have designed, established and operationalized a system for monitoring, evaluation and learning for their national adaptation efforts and have built the required institutional capacity to fully implement the system.
Satellite Earth Observation (EO) revolutionized systemic observations and has played a pivotal role in understanding climate changes to date, yet its potential to support adaptation is only beginning to be explored. EO has great potential for supporting climate action across all stages of the adaptation process, greater than what is already being achieved today.

This session will highlight ongoing research projects that use EO to plan, monitor and/or evaluate climate change adaptation across a multitude of scales and sectors.
References
IPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., doi:10.1017/9781009325844.
Connors, S., Schneider, R., Nalau, J. et al. Earth observations for climate adaptation: tracking progress towards the Global Goal on Adaptation through satellite-derived indicators. npj Clim Atmos Sci 8, 359 (2025). https://doi.org/10.1038/s41612-025-01251-1
United Nations Environment Programme (2023). Adaptation Gap Report 2023: Underfinanced. Underprepared. Inadequate investment and planning on climate adaptation leaves world exposed. Nairobi. https://doi.org/10.59117/20.500.11822/43796
The UNFCCC Global Goal on Adaptation Indicators: https://unfccc.int/topics/adaptation-and-resilience/workstreams/gga

The climate system is changing rapidly, with some regions experiencing increases in extreme events beyond what is expected from climate model simulations. To improve the accuracy of climate predictions and projections, it is necessary to (1) identify and explain what factors and processes drive observed and predicted climate changes, (2) critically assess how key processes are represented in climate models, (3) understand and explain the predicted signals, which often result from the interaction of multiple drivers, and (4) use this knowledge to calibrate and further develop predictions to provide more reliable and thus useful information to society. In combination, these research activities contribute to building the capability for an integrated attribution and prediction of climate change - a key goal of the WCRP Lighthouse Activity on Explaining and Predicting Earth System Change (EPESC) and the Horizon-Europe project EXPECT.

Progress in integrated attribution and prediction will benefit from combining diverse data sources, such as Earth Observations, and various climate model experiments, including those at very high resolutions. This session invites contributions on advancing integrated attribution and prediction, with a particular focus on annual to decadal timescales, which involves explaining, predicting and constraining climate changes from regional to global scales. Relevant topics include, for example, studies attributing the drivers of specific climate phenomena and extremes such as the atmospheric circulation during the boreal summer and related surface extremes, evaluating climate responses to different forcings and internal variability, correcting biased climate responses e.g. using process-based constraints, providing calibrated prediction and projections of future climate based on these constraints, and methods that exploit a variety of data in combination with novel analysis techniques including Artificial Intelligence.

Climate change is reshaping the interactions among land use, ecosystems, human activities, and spatial development, creating interconnected risks that extend across administrative and geographic boundaries. At the same time, land systems provide a critical arena for climate mitigation, adaptation, ecosystem restoration, and sustainable development. Strengthening climate resilience therefore requires moving beyond sectoral and place-based interventions towards integrated approaches that connect land-system dynamics with spatial planning and governance across scales.
This session explores how land systems can be understood, managed, and transformed to strengthen climate resilience and sustainability. We particularly welcome research examining the interactions between land-use and land-cover change, climate risks, ecosystem services, human well-being, and spatial development, as well as studies investigating how planning and governance can mediate trade-offs, enhance synergies, and support resilient development pathways.
Contributions may address urban, peri-urban, rural, regional, and coupled human–environment systems and employ empirical, modelling, remote-sensing, spatial analytical, participatory, or comparative approaches. Topics of interest include, but are not limited to:
• land-system responses and vulnerability to climate change and extreme events;
• land-use and spatial planning for climate mitigation, adaptation, and resilience;
• interactions and trade-offs among climate resilience, ecosystem services, biodiversity, and socio-economic development;
• nature-based solutions, ecosystem restoration, and multifunctional land management;
• cross-boundary, multi-level, and regional governance of climate and land-system risks;
• spatial inequalities, environmental justice, and differentiated adaptive capacities;
• Earth observation, spatial modelling, GeoAI, and decision-support tools for climate-resilient land governance;
• pathways for translating land-system science into planning, policy, and implementation.
By bringing together geoscientists, land-system scientists, planners, social scientists, practitioners, and policymakers, the session aims to strengthen dialogue across disciplines and scales and advance place-sensitive, evidence-based approaches to climate-resilient spatial governance.

Cold cloud thinning covers proposed climate interventions that looks to seed ice clouds with efficient ice nucleating particles, so they trap less outgoing longwave radiation. With cirrus cloud thinning, seeding aims to shift ice formation away from homogenous to heterogenous freezing nucleation, producing fewer but larger ice crystals that fall out faster, giving thinner, shorter-lived clouds. More recently, mixed-phase cloud thinning has sought to target supercooled clouds over the polar oceans in the winter, where cloud glaciation could reduce longwave radiation from reaching the surface and so help slow sea ice loss. Model estimates of the resulting cooling varies from net negative to net positive when clouds are overseeded, and much of this spread comes from processes that are difficult to constrain, such as the competition between nucleation modes, pre-existing ice, small-scale updrafts, and gravity waves.

This session focusses on work that helps improve our understanding of the ice cloud processes that decide whether cold cloud thinning could work, and what its side effects might be. We welcome laboratory studies of ice nucleation and possible seeding particles, field experiments including targeted glaciogenic seeding, in situ and satellite observations of cirrus and mixed-phase clouds (including early results from EarthCARE), and modelling from process- to global-scale climate models. Natural analogues are of interest, since mineral dust, volcanic eruptions and aviation all perturb ice clouds in ways that let us test seeding hypotheses in the atmosphere. Contributions on detectability, attribution, risks, and wider impacts are also welcome, and we encourage early career researchers to submit.