NH – Natural Hazards
Programme Group Chair: Heidi Kreibich
- NH1 – Hydro-Meteorological Hazards
- NH2 – Volcanic Hazards
- NH3 – Landslide and Snow Avalanche Hazards
- NH4 – Earthquake Hazards
- NH5 – Sea & Ocean Hazards
- NH6 – Remote Sensing, AI, data science & Hazards
- NH7 – Wildfire Hazards
- NH8 – Environmental, Biological & Natech Hazards
- NH9 – Natural Hazards & Society
- NH10 – Multi-Hazards
- NH11 – Climate Hazards
Proposals are marked in red.
GMPV11 | Volcano! - hazards, monitoring, human response, mitigation and risk
From legislation to implementation: How tools for hazard and risk reduction work in practice
Remote sensing measurements from ground, UAV, aircraft and satellite platforms have increasingly become established technologies to study and monitor Earth’s surface, to perform comprehensive analysis and modeling, with the final goal of supporting decision making. The spectral, spatial and temporal resolutions of remote sensors have been continuously improving, making environmental remote sensing more accurate and comprehensive than ever before. Such progress enables understanding of multiscale aspects of high-risk natural phenomena and development of multi-platform and inter-disciplinary surveillance monitoring tools. The session welcomes contributions focusing on present and future perspectives in environmental remote sensing, from multispectral/hyperspectral optical and thermal sensors. Applications are encouraged to cover, but not limited to, the monitoring and characterization of environmental changes and natural hazards from volcanic and seismic processes, landslides, and soil science. Specifically, we are looking for novel solutions and approaches including the topics as follows: ecosystem assessment and monitoring, land use/cover changes, coastal environments and climate change, techniques for data fusion (spectral, spatial and temporal), disaster monitoring, new sensors and platforms for environmental studies.
As data-driven models increasingly rival or complement physics-based systems, a central question remains open: can AI reliably forecast the events that matter the most, the extremes that drive real-world impacts, and not just the average state of the atmosphere? This session brings together the latest advances in machine learning (ML) and artificial intelligence (AI) for forecasting weather, projecting climate, and simulating extreme events.
We invite contributions spanning the full range of timescales and methods, including but not limited to:
*data-driven and foundation weather models for short- and medium-range forecasting;
*generative and probabilistic approaches (e.g. diffusion models) for forecasting, downscaling, and uncertainty quantification;
*ML for sub-seasonal to seasonal (S2S) prediction and longer-term climate projections;
*hybrid AI-physics approaches that embed physical constraints into data-driven models or improve the representation of climate variables in numerical models and datasets;
*detection, attribution, and anticipation of extreme events such as hurricanes, floods, heatwaves, droughts, and compound extremes.
We particularly encourage submissions that go beyond forecast skill to address impacts on infrastructure, ecosystems, health, or energy systems, and that engage with questions of trust, explainability, and generalization to unseen or out-of-distribution extremes.
By bringing together experts from AI, data science, meteorology, climate science, and impact modelling, this session aims to foster interdisciplinary collaboration and push the boundaries of AI-driven understanding and prediction of extreme weather and climate events. We warmly welcome submissions from early-career scientists, established researchers, and industry professionals alike.
This session invites contributions advancing the understanding, modelling, and prediction of extreme events in weather, climate, and other geophysical systems. It brings together researchers from the geophysical sciences and those applying mathematical, statistical, and dynamical-systems approaches.
Topics of interest include, but are not limited to:
* Variability and projected changes in extremes under climate change
* Representation of extreme events in weather and climate models
* Attribution of extreme events
* Emergent constraints on extreme-event behaviour
* Predictability of extremes across meteorological and climate timescales
* Connections between extremes in dynamical systems and observed geophysical extremes
* Theoretical and applied studies of extremes in nonlinear and chaotic systems
* Downscaling methods for extreme events
* Links between the physical dynamics of extremes and their impacts on society and ecosystems
We particularly welcome interdisciplinary contributions, novel methodologies, and studies connecting theory with observed geophysical extremes. Submissions from early-career researchers are especially encouraged.
The Earth system is a complex, multiphysics system with nonlinear interactions on multiple spatial and temporal scales. Understanding constituent processes (linear, nonlinear, stochastic, etc.) on the one hand, and the complexity of individual subsystems or the full integrated system on the other, is key to being able to better model the Earth System in a predictive fashion. The renaissance of machine and deep-learning in the past decade has led to rapid progress in the development of advanced approaches in, e.g., nonlinear time series analysis, dynamical and stochastic systems theory, critical slowing down theory, complex systems theory, and these approaches, in turn show promise in facilitating further advances in modeling the Earth system.
In this context, this session seeks contributions on all aspects of complexity, nonlinearity, tipping points and stochastic dynamics of the Earth system, including the atmosphere, the hydrosphere, the cryosphere, the solid earth, etc. Communications on theoretical, experimental and modeling studies are all welcome, where the latter modeling studies can span the range of model hierarchy from idealized models to complex Earth System Models (ESM). Studies based on emerging approaches such as data driven models, Artificial Intelligence approaches, complex network methods, critical slowing down analysis, dynamical and stochastic systems theory, etc., are particularly encouraged.
The Navier-Stokes equations, initially formulated in the early 19th century, have since become the cornerstone of fluid mechanics, subsequently extending their relevance to fluid geophysics. The existence and regularity of their solutions pose a significant challenge within a substantial domain of geophysics.
Over the years, a series of partial results have been obtained, particularly in the pursuit of proving one of the four statements proposed by Charles L. Fefferman for the Millennium Clay Prize. A definitive proof of the third statement regarding the breakdown of the Navier-Stokes equations was unveiled by OpenAI on September 8th, utilising extensive IA resources. This revelation has sparked a substantial debate, encompassing various aspects such as the physical significance of the blowing-up singularity, the utilisation of intensive AI resources in disruptive research, and the connections with concepts like intermittency, cascades, multifractals and enstrophy catastrophe. It may also inspire new approaches to resolve fundamental questions of geosciences.
This PICO session seeks to provide the geophysical community with an opportunity to contribute to this ongoing discourse.
Weather and climate extremes, such as recent events unprecedented in the observational record, have extensive impact globally. Some of these events would have been nearly impossible without human-made climate change, exhibiting conditions well beyond previous records due to complex, and at times unprecedented configurations of their underlying physical drivers. Furthermore, compounding hazards and cascading risks resulting from these high-impact extremes are becoming evident. Continued warming does not only increase the frequency and intensity of such extremes, it also potentially increases the risk of unseen non-linear behaviours or unprecedented impacts. To increase preparedness for high-impact climate events, developing novel methods, models and process-understanding that capture these hazards and their associated impacts is paramount.
This session aims to bring together the latest research quantifying and understanding high-impact climate events in past, present and future climates. We welcome studies across all spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios, with the ultimate goal to provide actionable climate information to increase societal preparedness to such extreme high-impact events.
We invite work addressing high-impact extreme events via, but not limited to, model experiments and intercomparisons, diverse storyline approaches such as event-based or dynamical storylines, climate projections including large ensembles and unseen events, insights from paleo archives, and attribution studies. We also especially welcome contributions focusing on physical understanding of high-impact events, on their ecological and socioeconomic impacts, as well as on approaches to potentially limit societal impacts.
The session is closely linked to the World Climate Research Programme lighthouse activities on Understanding High-Risk Events and Explaining and Predicting Earth System Change.
Extreme events are difficult to understand because observations are sparse in both space and time, especially for the less frequent but most impactful and severe events. Inference on environmental extremes is becoming increasingly challenging as their behavior can change over time, they can occur across large spatial regions, and they can interact with one another. Extreme Value Theory (EVT) provides a strong theoretical framework for studying these events, but challenges remain in linking methodological advances with environmental applications, prediction, and risk assessment.
We welcome contributions spanning theory, methodology, and applications, including but not limited to:
Advancing EVT methods
• Extreme quantile regression and estimation, conformal prediction, and non-stationary tail models
• EVT-constrained machine learning, uncertainty quantification, extrapolation, and inference under limited extreme event observations
• Methods to evaluate the appropriateness of EVT estimates, impacts of violations of EVT assumptions, and alternative extreme value analysis approaches
Applying EVT to environmental extremes
• Applications to hydrology, climate, weather, coastal hazards, earthquakes, landslides, wildfires, and infrastructure related risk
• Compound, connected, and cascading extremes, including drought-flood sequences, heatwave clusters, extreme precipitation, storm surges, and other interacting hazards
• Novel and under explored applications of EVT to environmental processes and hazards, including snowmelt, air turbulence, environmental epidemiology, and other emerging areas
EVT for prediction, risk assessment, and decision support
• Synthetic extreme event generation and scenario design for stress testing
• Tail focused calibration, validation, and verification, including extremal scoring rules, return level skill, and reliability in the tails
• Quantification and communication of uncertainty relevant to hazards, exposure, impacts, and risk
• Approaches that translate tail behavior and return level information into decision relevant metrics and services
We encourage contributions that bridge methodological EVT developments with real world environmental applications. Contributions may include new theoretical or methodological developments, open datasets and tools, model evaluation frameworks, and real world case studies.
Sitting under a tree, you feel the spark of an idea, and suddenly everything falls into place. The following days and tests confirm: you have made a magnificent discovery — so the classical story of scientific genius goes…
But science as a human activity is error-prone, and might be more adequately described as "trial and error". Handling mistakes and setbacks is therefore a key skill of scientists. Yet, we publish only those parts of our research that did work. That is also because a study may have better chances to be accepted for scientific publication if it confirms an accepted theory or reaches a positive result (publication bias). Conversely, the cases that fail in their test of a new method or idea often end up in a drawer (which is why publication bias is also sometimes called the "file drawer effect"). This is potentially a waste of time and resources within our community, as other scientists may set about testing the same idea or model setup without being aware of previous failed attempts.
Thus, we want to turn the story around, and ask you to share 1) those ideas that seemed magnificent but turned out not to be, and 2) the errors, bugs, and mistakes in your work that made the scientific road bumpy. In the spirit of open science and in an interdisciplinary setting, we want to bring the BUGS out of the drawers and into the spotlight. What ideas were torn down or did not work, and what concepts survived in the ashes or were robust despite errors?
We explicitly solicit Blunders, Unexpected Glitches, and Surprises (BUGS) from modeling and field or lab experiments and from all disciplines of the Geosciences.
In a friendly atmosphere, we will learn from each other’s mistakes, understand the impact of errors and abandoned paths on our work, give each other ideas for shared problems, and generate new insights for our science or scientific practice.
Here are some ideas for contributions that we would love to see:
- Ideas that sounded good at first, but turned out to not work.
- Results that presented themselves as great in the first place but turned out to be caused by a bug or measurement error.
- Errors and slip-ups that resulted in insights.
- Failed experiments and negative results.
- Obstacles and dead ends you found and would like to warn others about.
For inspiration, see the collection of BUGS - ranging from clay bricks to atmospheric temperature extremes - at https://meetingorganizer.copernicus.org/EGU25/session/52496
Indigenous Peoples have lived with active landscapes and environmental change for millennia, developing sophisticated knowledge systems grounded in long-term observation, oral traditions, cultural practices, and enduring relationships with place. These knowledge systems provide important insights into Earth surface processes, including volcanic activity, earthquakes, landslides, floods, coastal hazards, environmental change, and the stewardship of culturally significant landscapes.
Addressing complex geohazard and geoheritage challenges increasingly requires approaches that move beyond knowledge sharing towards ethical, reciprocal, and sustained partnerships. This session explores how Indigenous communities, researchers, practitioners, and decision-makers can work together to co-produce knowledge and co-develop solutions that are meaningful, locally relevant, and respectful of Indigenous rights, values, governance, and data sovereignty.
We invite contributions that examine ethical engagement practices at the interface of Indigenous knowledge and geoscience, including collaborative research design, community-led monitoring, participatory hazard assessment, geoheritage stewardship, Indigenous governance, and approaches that foster mutual learning and benefit. We particularly welcome case studies that highlight how equitable partnerships and the respectful weaving of Indigenous and scientific knowledge systems can strengthen understanding of dynamic Earth processes, support disaster risk reduction, enhance geoheritage outcomes, and build trust across knowledge communities.
By focusing on ethical and reciprocal engagement, this session aims to advance more inclusive geoscience practices and showcase pathways for co-developed geohazard and geoheritage solutions that are scientifically robust, culturally appropriate, and socially just.
NH1 – Hydro-Meteorological Hazards
Sub-Programme Group Scientific Officer: Cristina Prieto
Proposals are marked in red.
GM5 | Erosion, Sediments, Weathering, and Landscapes
SSS11 | Material and Methods in Soil Sciences
Advances in Flood Risk Modelling: Forecasting, Monitoring, Assessment, Mitigation and Recovery
AS1 | Meteorology
CL | Climate: Past, Present & Future
Flash Droughts, Heatwaves and Dry Spells: Drivers, Monitoring and Impacts of Rapidly Emerging Extremes
Drought-Flood Whiplash in a Changing Climate: Mechanisms, Predictability, Cascading Risks, and Resilience
AS1 | Meteorology
BG9 | Earth System Remote Sensing and Modelling
CL2 | Present Climate – Historical and Direct Observations
HS2.4 | Hydrologic variability and change at multiple scales
Hydroclimatic Extremes and Cascading Impacts: From Water Scarcity to Water Quality, Ecosystems and Human Health
CL0 | Inter- and Transdisciplinary Sessions
HS5 | Water and society
Drought in the Anthropocene: understanding risk, impacts, vulnerability and adaptation
HS5 | Water and society
Extreme meteorological and hydrological events induced by severe weather and climate change
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.
NH2 – Volcanic Hazards
Sub-Programme Group Scientific Officer: Andrea Di Muro
Proposals are marked in red.
Bridging the Gap: Translating Advances in Hazard Science into Educational Action for Safer Volcanic Communities
GMPV10 | Physical and chemical processes in volcanic systems
Opportunities, risks, and perspectives of AI applications in volcano science and hazard assessment
GMPV11 | Volcano! - hazards, monitoring, human response, mitigation and risk
GS6 | Geoscience, Risk & Decision-Making
Science, before and after: hazard, institutions and risk communication in parallel cases
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.
NH3 – Landslide and Snow Avalanche Hazards
Sub-Programme Group Scientific Officer: Neelima Satyam
Proposals are marked in red.
GM3 | Geomorphology, extreme events, and hazards
GM3 | Geomorphology, extreme events, and hazards
Integrating data-driven and physically-based approaches in landslide hazard modelling
GM3 | Geomorphology, extreme events, and hazards
GM3 | Geomorphology, extreme events, and hazards
CL0 | Inter- and Transdisciplinary Sessions
Integrating Geophysical, Geotechnical, and Remote Sensing for landslide monitoring and early warning systems.
GM5 | Erosion, Sediments, Weathering, and Landscapes
HS8.2 | Subsurface hydrology – Groundwater
SM6 | Seismic Imaging (from near-surface to global scale, incl. methodological developments)
SSS2 | Soil Erosion and Conservation
TS1 | Deformation Mechanisms, Rheology, and Rock-Fluid Interactions
Space-time variation in landsliding across short and long timescales – from antecedent conditions to landscape memory
GM3 | Geomorphology, extreme events, and hazards
NP3 | Scales, Scaling and Nonlinear Variability
CR2 | lce sheets, ice shelves and glaciers
GM3 | Geomorphology, extreme events, and hazards
Process-based numerical modelling of debris flows and snow avalanches: from triggering to deposition, hazard assessment and early warning applications
CR5 | Snow and ice: properties, processes, hazards
GM3 | Geomorphology, extreme events, and hazards
From landslide detection to monitoring: integrating multi-sensor and multi-temporal observations
ESSI1 | Next-Generation Analytics for Scientific Discovery: Data Science, Machine Learning, AI
G2 | Reference Frames and Geodetic Observing Systems
GM3 | Geomorphology, extreme events, and hazards
Modelling Rapid Alpine Mass Movements across Mountain Regions: Drivers, Hazards, and Risk
GM3 | Geomorphology, extreme events, and hazards
GS6 | Geoscience, Risk & Decision-Making
HS2.4 | Hydrologic variability and change at multiple scales
Coastal Cliffs in a Changing Climate: From Advanced Monitoring Strategies to Predictive Modelling of Future Scenarios
EMRP1 | Rock and Mineral Physics
GM | Geomorphology
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.
NH4 – Earthquake Hazards
Sub-Programme Group Scientific Officer: Ioanna Triantafyllou
Proposals are marked in red.
SM8 | Seismic Hazard (earthquake forecasting, engineering seismology, seismic and multi-hazard assessment)
SM8 | Seismic Hazard (earthquake forecasting, engineering seismology, seismic and multi-hazard assessment)
From Grain-Scale Mechanisms to Ground Response and Failure: Bridging Scales in Cyclic Soil Behaviour
Short-Term Earthquake Forecasting (StEF) and Time-Dependent Assessment of Seismic Hazard (t-DASH) through Earth Observations and Data-Driven Approaches
AS | Atmospheric Sciences
EMRP | Earth Magnetism & Rock Physics
ESSI | Earth & Space Science Informatics
GD | Geodynamics
GI | Geosciences Instrumentation & Data Systems
SM | Seismology
ST | Solar-Terrestrial Sciences
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.
NH5 – Sea & Ocean Hazards
Sub-Programme Group Scientific Officer: Naveen Ragu Ramalingam
Proposals are marked in red.
Tsunami science and warning: advances in modelling, disaster risk reduction, forecasting and hazard communication
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
NH6 – Remote Sensing, AI, data science & Hazards
Sub-Programme Group Scientific Officer: Kasra Rafiezadeh Shahi
Proposals are marked in red.
Earth Observation and Trustworthy GeoAI for Hazard Prediction, Risk Assessment and Disaster Monitoring
Application of remote sensing and Earth-observation data in natural hazard and risk studies
Earth Observation of Compound Extremes and Cascading Hazards: Land–Atmosphere Coupling, Nonlinear Dynamics and Transitions
AS2 | Boundary Layer Processes
BG9 | Earth System Remote Sensing and Modelling
ERE5 | Process coupling and monitoring
ESSI1 | Next-Generation Analytics for Scientific Discovery: Data Science, Machine Learning, AI
GI4 | Earth Observation Systems and Instrumentation
GM3 | Geomorphology, extreme events, and hazards
HS6 | Remote sensing and data assimilation
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.
NH7 – Wildfire Hazards
Sub-Programme Group Scientific Officer: Andrea Trucchia
Proposals are marked in red.
Wildfire-induced Geohydrological Hazards and Landscape Evolution: Processes, Modelling and Risk Management
GS8 | Science for Policy & Governance
BG1 | General Biogeosciences
SSS9 | Soil, Forestry and Agriculture
Remote Sensing-Derived Datasets for Wildfires: From Data Acquisition and Fusion to Dataset Curation, Validation, Maintenance, and Reuse
BG9 | Earth System Remote Sensing and Modelling
ESSI3 | Open Science Informatics for Earth and Space Sciences
NH8 – Environmental, Biological & Natech Hazards
Sub-Programme Group Scientific Officer: Jasmine Rita Petriglieri
Proposals are marked in red.
GI2 | Data networks and analysis
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.
NH9 – Natural Hazards & Society
Sub-Programme Group Scientific Officer: Dominik Paprotny
Proposals are marked in red.
GS6 | Geoscience, Risk & Decision-Making
Global and continental scale risk assessment for natural hazards: data, methods, and practice
GS6 | Geoscience, Risk & Decision-Making
HS2.5 | Global and (sub)continental hydrology
Socio-natural hazards in the Amazonas Basin: understanding extremes, cascading processes, and evolving risks
GS6 | Geoscience, Risk & Decision-Making
Beyond the Cost-Benefit: Adaptation and Anticipatory Action in Complex Risk Systems
ERE6 | Inter- and Transdisciplinary Sessions (ITS)
From participation to impact: co-produced disaster risk reduction between citizens, hazard science, and policy
From participation to impact: co-produced disaster risk reduction between citizens, hazard science, and policy
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.
NH10 – Multi-Hazards
Sub-Programme Group Scientific Officer: Silvia De Angeli
Proposals are marked in red.
CL3.2 | Future Climate – Climate and Society
GS6 | Geoscience, Risk & Decision-Making
Geospatial Intelligence for Multi-Hazard Disaster Risk Reduction and Resilience: From Risk Assessment to Early Warning, Preparedness and Response
GM3 | Geomorphology, extreme events, and hazards
NP1 | Mathematics of Planet Earth
Quantitative approaches to green adaptation for urban heat, flood, and ecological resilience
BG3 | Terrestrial Biogeosciences
Compound Extremes Where Data Are Scarce: Observations, Impacts and Evidence from the Global South
Multi-(hazard) risk assessment and management: innovative approaches for disaster risk reduction and climate change adaptation
CL0 | Inter- and Transdisciplinary Sessions
GS6 | Geoscience, Risk & Decision-Making
HS5 | Water and society
Multi-hazard risk communication: interactions, understanding, protective action, and challenges
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.
NH11 – Climate Hazards
Sub-Programme Group Scientific Officer: Steven Hardiman
Proposals are marked in red.
CL | Climate: Past, Present & Future
Cold extremes in the warming climate: changes and impacts, processes and attribution
Fire-to-Flood: Earth Observation and Machine Learning for Compound Hydro-Climatic Extremes Under Climate Change
Resilient Agricultural Systems under Hydro-Climatic Extremes: Advances in Monitoring, Prediction and Adaptation
CL | Climate: Past, Present & Future
ESSI | Earth & Space Science Informatics
HS | Hydrological Sciences
Integrated advances in tropical cyclones: linking physics, impacts, risk and adaptation
BG8 | Biogeosciences, Policy and Society
CL3.2 | Future Climate – Climate and Society
HS5 | Water and society
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.