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

Recent advances in advanced machine learning models, agentic systems, and generative AI are opening up possibilities for addressing complex geoscientific challenges. These approaches enable novel ways to analyse data, support decision-making, and enhance scientific workflows, while raising important questions about alignment with human expertise, values, and responsibility. Generative AI enables the creation of new content across modalities, agentic AI allows autonomous systems of agents to act with minimal supervision and leverage tools, and hybrid intelligence integrates human contextual, causal, and ethical reasoning with AI’s computational power. In practice, causal reasoning is often where stakeholders' experience and domain insight are most naturally expressed, beyond what can be captured by model architectures, metrics, or post-hoc explainability alone.

This session explores both practical applications and conceptual frameworks of generative, agentic, and hybrid AI (inclusive of advanced ML/DL models) in the geosciences, with a strong emphasis on human-centred design. Topics include AI-assisted data analysis and modelling, knowledge discovery and curation, decision support, science communication, and AI-enhanced workflows guided by domain expertise. We particularly encourage contributions that demonstrate how human insight and causal reasoning complement AI.

The session also addresses ethical and societal aspects of AI deployment in geosciences, including transparency, bias mitigation, accountability, sustainability, and trustworthiness. The overarching goal is to highlight AI as a tool to empower and extend human expertise—keeping scientists and domain experts at the centre of innovation. Contributions from research, industry, and policy communities are all welcome.

Machine learning (ML) is being used throughout the geophysical sciences with a wide variety of applications. Advances in big data, deep learning, and other areas of artificial intelligence (AI) have opened up a number of new approaches to traditional problems.

Many fields (climate, ocean, numerical weather prediction, space weather etc.) make use of large numerical models and are now seeking to enhance these by combining them with scientific ML/AI techniques. Examples include ML emulation of computationally intensive processes, data-driven parameterisations for sub-grid processes, ML assisted calibration, and uncertainty quantification of parameters, amongst other applications.

Doing this brings a number of unique challenges, however, including but not limited to:

- enforcing physical compatibility, consistency, and conservation laws
- ensuring numerical stability,
- coupling of numerical models to ML frameworks and language interoperation,
- development and usage of differentiable models and model components,
- handling computer architectures and data transfer,
- adaptation/generalisation to different models, resolutions, or climates,
- explaining, understanding, and evaluating model performance and biases.
- quantifying uncertainties and their sources
- tuning of physical or ML parameters after coupling to numerical models (derivative-free optimisation, Bayesian optimisation, ensemble Kalman methods, etc.)

Addressing these requires knowledge of several areas and builds on advances already made in domain science, numerical simulation, machine learning, high-performance computing, data assimilation etc.

Following success over the past two years at EGU, we again solicit talks that address any topics relating to the above. Anyone working to combine machine learning techniques with numerical modelling is encouraged to participate in this session.

Machine learning (ML) is currently transforming data analysis and modelling of the Earth system. While statistical and data-driven models have been used for a long time, recent advances in machine learning now allow for encoding non-linear, spatio-temporal relationships robustly without sacrificing interpretability. This has the potential to accelerate climate science, by providing new physics-based modelling approaches; improving our understanding of the underlying processes; reducing and better quantifying climate signals, variability, and uncertainty; and even making predictions directly from observations across different spatio-temporal scales. The limitations of machine learning methods need to also be considered, such as requiring, in general, rather large training datasets, data leakage, and/or poor generalisation abilities, so that methods are applied where they are fit for purpose and add value.

This session aims to provide a venue to present the latest progress in the use of ML applied to all aspects of climate science and we welcome abstracts focussed on, but not limited to:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process, equations, and feature representations in observations or across models and observations
- Hybrid models (physically informed ML, emulation, data-model integration)
- Novel detection and attribution approaches, including for extreme events
- Probabilistic modelling and uncertainty quantification
- ML-based super-resolution and bias-correction for climate downscaling
- Explainable AI applications to climate data science and climate modelling
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science

Machine learning (ML) methods have emerged as powerful tools to tackle various challenges in ocean science, encompassing physical oceanography, biogeochemistry, and sea ice research.
This session aims to explore the application of ML methods in ocean science, with a focus on advancing our understanding and addressing key challenges in the field. Our objective is to foster discussions, share recent advancements, and explore future directions in the field of ML methods for ocean science.
A wide range of machine learning techniques can be considered including supervised learning, unsupervised learning, interpretable techniques, and physics-informed and generative models. The applications to be addressed span both observational and modeling approaches.

Observational approaches include for example:
- Identifying patterns and features in oceanic fields
- Filling observational gaps of in-situ or satellite observations
- Inferring unobserved variables or unobserved scales
- Automating quality control of data

- Modeling approaches can address (but are not restricted to):
- Designing new parameterization schemes in ocean models
- Emulating partially or completely ocean models
- Parameter tuning and model uncertainty

The session also welcomes submissions at the interface between modeling and observations, such as data assimilation, data-model fusion, or bias correction.

Researchers and practitioners working in the domain of ocean science, as well as those interested in the application of ML methods, are encouraged to attend and participate in this session.

Carbon monitoring is becoming ever more critical as climate change accelerates and society turns to carbon management strategies, ranging from carbon credits to the preservation and restoration of natural carbon sinks. Yet the success of these approaches depends on robust science: measurements must be accurate, verification must be rigorous, and promises must be grounded in evidence. Machine learning (ML) is rapidly transforming carbon cycle research, offering new opportunities to integrate diverse data streams, harness remote sensing, and connect multiple lines of evidence across scales. This session will highlight recent advances in ML applications for investigating, monitoring, and managing the carbon cycle, spanning satellite-based greenhouse gas estimation, biomass and forest monitoring, soil and peatland carbon dynamics, wetland and ecosystem restoration, and the mapping of terrestrial and oceanic carbon storage. We particularly encourage contributions that address hybrid modeling, uncertainty quantification, ecological mapping, knowledge-guided and trustworthy ML in carbon markets and policy contexts. By bringing together advances from Earth observation, process modeling, and policy-relevant applications, this session aims to explore both the promises and challenges of ML in delivering actionable insights for carbon management and climate mitigation.

The development of digital twins in Earth systems, such as Destination Earth, is revolutionizing our approach to understand and manage our planet’s complex dynamics under a changing climate. These advanced simulations enable us to integrate diverse types and sources of data, providing a comprehensive view of Earth-climate dynamics and human-environment interactions. In detail, digital twins allow to replicate a system behaviour, provide an up-to-date status of ongoing physical processes, support informed decision-making. They enable predictive Earth observation, exploring "what if" scenarios or simulating hazard cascades, and testing various adaptation strategies.

This session will explore the role of digital twins for bridging observations and simulations to applications in impact sectors. There will be a special focus on uncertainty quantification, data assimilation, multi-source data streams, hybrid modelling, and decision support. We are particularly interested in studies that highlight the synergies between digital twin technology and other AI-driven tools, such as predictive analytics and machine learning, in improving operational outcomes. This session aims to foster cross-disciplinary dialogue on how these converging technologies can accelerate resilience to climate-related risks and natural hazards, including in a variety of impact sectors (energy, food, carbon storage, etc…) and will extend to economic, social components and policy considerations. It will act as a forum for researchers and practitioners to share their insights and recent developments in this rapidly evolving field.

Downscaling aims to process and refine global climate model output to provide information at spatial and temporal scales suitable for impact studies. In response to the current challenges posed by climate change and variability, downscaling techniques continue to play an important role in the development of new services and products. While the refinement of downscaling techniques proceeds at an unprecedented pace, users of climate information are facing the novel challenge of how to select amongst the choice of available datasets or how to assess their credibility with respect to a particular application. In this context, model evaluation and verification is growing in relevance and advances in the field will likely require close collaboration between various disciplines.

Recent developments, including the integration of AI and machine learning applications, the emergence of kilometre-scale simulations, and the widespread availability of open-source downscaling products, add new dimensions to this challenge. These advances raise important questions about the ‘added value’ of downscaling, especially in light of the cascade of uncertainty and the need for robust evaluation frameworks.

In our session, we aim to bring together scientists from the various geoscientific disciplines interrelated through downscaling: atmospheric modeling, climate change impact modeling, machine learning and verification research. We also invite philosophers of climate science to stimulate our discussion about the novel challenges that arise from evaluating complex models and modelling chains in the face of the increasingly heterogeneous needs of the growing user communities.

Contributions to this session may address, but are not limited to:
- newly available downscaling products,
- applications relying on downscaled data and impact assessments,
- downscaling method development and machine learning,
- bias correction and statistical postprocessing,
- challenges in the data management of kilometer-scale simulations,
- verification, uncertainty quantification and the added value of downscaling,
- downscaling approaches in light of computational epistemology.

Understanding where people live, how populations and visitors are distributed across space, and how these patterns shift over time is central to planning in an era of climate change, natural hazards, and mounting pressures on natural environments. This session focuses on data-driven approaches that connect advances in gridded population and socio-demographic datasets with the management of nature-based tourism and outdoor recreation across rural communities, destinations, and protected landscapes. Emphasis is placed on methodological progress in building, validating, and integrating spatial population and human-activity data—along with assessing spatial accuracy, uncertainty, and data fusion methods for future projections under alternative scenarios. The session also focuses on real-world applications that translate these data products into actionable planning and governance, including climate change adaptation, disaster risk management, sustainable land-use planning, and destination resilience. Key thematic areas include geoscience methods for tourism and recreation; the role of biodiversity, geodiversity, and ecosystem services; natural hazards and risk communication; strategic decision-making and stakeholder trust in data; participatory and citizen approaches; and the use of local knowledge to support sustainable development and mitigation. Overall, the session highlights how robust spatial evidence can support transparent, impactful decisions for communities and environments under uncertainty.

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

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

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

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

Environmental challenges such as climate change, biodiversity loss, water scarcity, and ocean degradation demand new ways of observing, monitoring, and understanding the Earth system. Research Infrastructures (RIs) in the ENVRI community—spanning atmospheric, marine, terrestrial, and solid earth sciences—provide the backbone of European environmental observation and long-term data stewardship. Yet, the growing complexity of environmental change requires innovative technologies and services to enhance monitoring, strengthen interoperability, and accelerate the translation of knowledge into actionable insights.

This session brings together researchers, technologists, and stakeholders to showcase advances illustrating (1) the role of emerging technologies and (2) service-oriented approaches in shaping the future of environmental monitoring.

Emerging technologies include advanced instrumentation, miniaturized and autonomous sensors for atmospheric, hydrological, soil, and marine processes, as well as unmanned aerial systems, drones, satellite constellations, and IoT networks that link in-situ with remote sensing. Artificial intelligence (AI) is transforming how environmental data are processed, harmonized, and applied in predictive modelling.

The ocean, a key climate regulator, remains critically under-observed for carbon fluxes, particularly beyond shipping routes. Addressing this gap, the GEORGE project—a collaboration between EMSO ERIC, EURO-ARGO ERIC, ICOS ERIC, research institutions, universities, and industry—develops novel tools and methods to measure carbonate chemistry (e.g., pH, alkalinity, dissolved inorganic carbon, pCO₂) across diverse marine environments.

Services are equally vital. Trans-National Access (TNA) schemes offered by ENVRIs provide opportunities for researchers to use state-of-the-art facilities, advanced instrumentation, and high-quality data services beyond national systems. These services foster collaboration, accelerate innovation, and support co-created solutions to pressing challenges. The convergence of cloud-based infrastructures, FAIR data principles, interoperability frameworks, and user-centered service design ensures that resources are not only technically robust but also widely accessible and impactful for science, policy, and society.

Climate services are instrumental in translating local knowledge and scientific insights into practical applications, empowering communities at multiple scales to efficiently tackle climate change challenges.

The paradigm of Essential Variables (EVs) - ECVs, EOVs, EBVs - provides a data-driven foundation for global environmental monitoring (GCOS, GEO, UN SDGs). Yet, their full potential is hampered by interoperability gaps, fragmented governance, and siloed infrastructures, limiting integrated use and translation into local action.

Conversely, local demand for actionable information is growing. Earth Observation data, often as Analysis-Ready Data (ARD), must be transformed into locally relevant, co-created Action-Ready Information (ARI) for climate solutions. This requires integrating global EVs with local data and knowledge.

This session bridges these fronts. We explore all aspects of climate service development from the co-creation of climate services that emphasize inclusive and novel methodologies and the integration of multiple knowledge systems, through to the development of usable, equitable and impactful solutions for multiple stakeholder groups a focus on the use of technical, infrastructural, and socio-technical advancements to evolve EVs into a truly interoperable, global common language and ensure their effective translation for local decision-making. We welcome contributions on:

- Interoperability Foundations: Semantic frameworks (iADOPT, SOSA/SSN), FAIR principles, and lessons from research infrastructures (ENVRI, CRDCs) aligning EVs across domains and global programmes.
- From ARD to ARI: Case studies on transforming EV-based products into local insights via co-creation, integrating satellite data with in-situ, citizen science, and indigenous knowledge.
- Cross-Scale Infrastructure: Architectures and platforms (e.g., digital twins) enabling seamless data flow from global systems to local applications.
- Policy and Capacity: How interoperable EVs strengthen global policy (IPCC, SDGs) and how local insights inform action, including funding, capacity building, and governance models.

We invite scientists, data engineers, social scientists, and policymakers to connect the "essential" with the "actionable", forging a coherent path from global observation to local solution.

The advancement of Open Science and the democratization of computing services allow for the discovery and processing of large amounts of information, blurring traditional discipline boundaries. Being data heterogeneous in format and provenance, the ability to combine them and extract new knowledge to address complex challenges relies on standardisation, integration and interoperability.
Thanks to decades of work in this field, Research infrastructures (RI) worldwide, such as EPOS, Europe's RI for solid Earth science, are key enablers of this paradigm. By providing access to quality-vetted, curated open data, they enable scientists to combine data from different disciplines and data sources into innovative research and apply novel approaches such as Large Language Models (LLM) and AI/ML tools to obtain new insights and solve complex scientific and societal questions.
However, while data-driven science creates enormous opportunities to generate groundbreaking inter- and transdisciplinary results, many challenges and barriers remain.
This session aims to foster cross-fertilization by showcasing real-life scientific studies and research experiences in geosphere studies, especially from Early Career Scientists (ECS) worldwide. We also welcome contributions on challenges and user needs when establishing multi-disciplinary studies, including, e.g., need for reliable and trustworthy AI and the availability of training datasets. The session will not only focus on results, but also on challenges and solutions in connection to data availability, collection, processing, and inter-disciplinary methods.
A non-exhaustive list of topics includes:
- multi-disciplinary studies, involving data from different disciplines (e.g. combining seismology, geodesy, and petrology to understand subduction zone dynamics);
- inter-disciplinary research integrating two or more disciplines into new approaches (e.g. merging geophysics and geochemistry to probe mantle plumes);
- activities that advance interdisciplinarity and open science (e.g. enhancing FAIRness of data and services, enriching data provision, enabling cross-domain AI applications, software and workflows, transnational access and capacity building for ECS);
- experiences that cross disciplinary boundaries, integrate paradigms and engage diverse stakeholders (e.g. bringing together geologists, social scientists, civil engineers and urban planners to define risk maps and prevention measures in urban planning).