The advancement of Open Science and the affordability of computing services allow for the discovery and processing of large amounts of information, boosting data integration from diverse scientific domains and blurring traditional discipline boundaries. However, data are often heterogeneous in format and provenance, and the capacity to combine them and extract new knowledge to address scientific and societal problems relies on standardisation, integration and interoperability.
Key enablers of the OS paradigm are ESFRI Research infrastructures, of which ECCSEL (www.eccsel.org), EMSO (https://emso.eu/) and EPOS (www.epos-eu.org), are examples currently enhancing FAIRness and integration within the Geo-INQUIRE project. Thanks to decades of work in data standardisation, integration and interoperability, they enable scientists to combine data from different disciplines and data sources into innovative research to solve scientific and societal questions.
But while data-driven science is ripe with opportunity to groundbreaking inter- and transdisciplinary results, many challenges and barriers remain.

This session aims to foster scientific cross-fertilization exploring real-life scientific studies and research experiences from scientists and ECS in Environmental Sciences. We also welcome contributions about challenges in connection to data availability, collection, processing, interpretation, and the application of interdisciplinary methods.
A non-exhaustive list of of topics includes:
- multidisciplinary studies involving data from different disciplines, e.g. combining seismology, geodesy, oceanography and petrology to understand subduction zone dynamics;
- interdisciplinary works, integrating two or more disciplines to create fresh approaches, e.g. merging solid earth and ocean sciences data to study coastal/oceanic areas and earth dynamics;
- showcase activities enabling 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;
- transdisciplinary experiences that surpass disciplinary boundaries, integrate paradigms and engage stakeholders from diverse backgrounds, e.g. bringing together geologists, social scientists, civil engineers and urban planners to define risk maps and prevention measures in urban planning, or studies combining volcanology, atmospheric, health and climate sciences.

The increasing frequency and severity of natural hazards, including floods, landslides, earthquakes, volcanic eruptions, droughts, wildfires, and ground subsidence, pose significant risks to the environment, infrastructure, and human societies. This trend is expected to continue, influenced by climate change and extreme weather events, underscoring the urgent need for improved disaster preparedness, environmental management, and resilient urban planning.

This session focuses on the use of advanced Geoinformatics technologies—such as Geographical Information Systems (GIS), Remote Sensing, and Artificial Intelligence (AI)—to understand and mitigate the impact of natural hazards. Emphasis will be placed on the application of explainable AI techniques (e.g., Shapley Additive Explanations, Local Interpretable Model-agnostic Explanations, and Explainable Boosting Machines) to enhance decision-making in disaster management. We invite contributions that explore the integration of new and historical data, remote sensing technologies, and innovative analytical methodologies aimed at understanding the manifestation and evolution of catastrophic events. Special attention will be given to successful case studies from diverse environments and climate scenarios, leveraging cutting-edge technologies to foster safer, more resilient societies.

In addition, the session will delve into the dynamic relationship between natural hazards and human activities—such as migration, construction, urban planning, and resource management—which influence and are influenced by environmental risks. Understanding these complex, spatiotemporal relationships is crucial for improving disaster risk reduction and building sustainable resilience. We encourage interdisciplinary contributions that combine Earth observation data (e.g., optical, hyperspectral, RADAR, GNSS, LiDAR) with historical, social, and demographic datasets to investigate these interconnections. The session seeks to bring together a broad range of experts, including geodesists, natural and social scientists, historians, anthropologists, engineers, urban planners, policymakers, and community workers, to promote transdisciplinary discussions on the integration of Earth observation data for disaster risk reduction and sustainable development.

The current climate change has been shown to exacerbate extreme weather events, such as storms with exceptionally strong winds, temperature extremes, heavy rainfall, and flooding. Climate scenarios predict an intensification and increased frequency of these extremes that can have destructive impacts on human activities. Not surprisingly, the United Nations states that 'extreme weather events have come to dominate the disaster landscape of the 21st century' (McClean, 2020). In this context, multi-hazard and multi-risk modelling and data collection of climate extremes are key to enhancing disaster prevention, risk reduction, and adaptation to climate change impacts; thus, they have become imperative requirements for our society.

Modelling hazard and risk is a challenging problem because of the complexity of the environmental system and the different temporal and spatial scales under analysis (e.g. regional, local and building scales). In addition, multiple weather hazards need to be considered at the same time for an effective assessment of the risks (and the enhancement of resilience) of different assets. Among the most impacted consequences of climate extremes are the deterioration, loss of functionality, or even structural damage to buildings and infrastructures crucial to society. These infrastructures play a crucial role in socio-economic activities. Still, the progress in adaptation planning of such critical assets remains low. This can also be ascribed to the lack of actionable tools for multi-risk analysis.

Addressing these complex challenges necessarily requires interdisciplinary work between experts from climate and atmospheric science, materials and structural analysts, and social and economic science. The aim is to bring together and present in an integrated manner the latest research advances in the assessment, mitigation, and adaptation of hazards and risks associated with extreme events for assets. The session encompasses various topics including modelling and quantification of meteorological stresses with numerical or experimental techniques; risk assessment of extreme events; and assessment of social, cultural, and economic impacts on society. The session emphasizes methodologies for determining meteorological exposures of assets, forecasts of near future extreme events impacts, operational models for damage and structural stability of infrastructures, and analysis of direct and indirect socio-economic cascading damages.

Advances in forest system modelling and monitoring techniques are crucial for deepening our understanding of forest ecosystems and their dynamic responses to environmental stresses and disturbances. These advancements are instrumental in addressing global environmental challenges by improving predictions and adapting management strategies accordingly. This session aims to bring together scientists and researchers focused on the latest advancements in forest systems modelling, observational techniques, and analytical methodologies to enhance our understanding of forest structural dynamics, soil carbon (C) dynamics, and the impacts of natural disturbances such as wildfires, insect’s outbreaks, pathogens/disease, droughts, and windstorms. Specifically, this session covers the following topics:

• Advancements in Forest System Modelling: Presentations on new models or significant improvements in existing models, that help predict and analyse forest growth, structural dynamics, C sequestration in biomass and soils, and ecosystem resilience. This includes models that integrate hydrological, meteorological, and biological processes.

• Innovative Monitoring Techniques: Studies showcasing novel observational technologies or methodologies, including remote sensing, isotopic tracing, or ground-based monitoring systems that provide new insights into forest mortality, growth patterns, and C cycling.

• Impact of Natural Disturbances: Research on how wildfires, insect’s outbreaks, pathogens/disease, droughts, and severe wind events alter forest structure, soil C stocks, and overall ecosystem functions. Contributions may include forward-looking information, post-disturbance recovery processes, disturbance modelling, and strategies for disturbance mitigation and adaptation.

• Cross-Scale Integration: Contributions that demonstrate the integration of innovative integrations of data and models across different spatial and temporal scales to understand forest biomass and soil dynamics comprehensively.

• Implications for future Management Strategies: Insights into how advanced modelling and monitoring approaches can shape policy development, offer a range of adaptation strategies, and inform management practices to enhance forest resilience and C retention.

Data imperfection is a common feature in Geosciences. Scientists and managers alike are faced with uncertain, imprecise, heterogeneous, erroneous, missing or redundant multi-source data. Traditionally, statistical methods were used to address these shortcomings. With the advent of Big Data, Machine Learning methods, the development of new techniques in data mining, knowledge representation and extraction as well as artificial intelligence, new avenues are being offered to tackle the shortcomings of data imperfection.
This session aims to provide a venue to exchange on the latest progress in assessing, quantifying and representing data imperfection in all of its forms. We welcome abstracts focused on, but not limited to:
- Use cases and applications from all fields of Geosciences on missing value imputation, data fusion, imprecision management, model inversion. Examples may be built on any type of data: alpha-numerical time series, georeferenced field data, satellite, areal or ground imagery, geographical vector data, videos, etc...
- Theoretical developments for data fusion and completion; uncertainty assessment and quantification, knowledge extraction and representation from heterogeneous data, reasoning and decision making under uncertainty.
- Multi-disciplinary approaches including artificial intelligence and geosciences are encouraged. Contributions addressing data issues and solutions related to participatory sciences, crowd-sourced data and opportunistic measurements will be particularly appreciated.

As climate change causes impacts from weather extremes to increase around the world, decision makers in government and industry are increasingly required to address changes to climate hazards when considering, disclosing, and acting to mitigate risks. Given that risk is the nexus of hazard, vulnerability, and exposure, a complete understanding of risk requires an interdisciplinary approach with input from experts in changes to all three of these pillars. In this session we address specifically those risks related to extreme weather events, including temperature, precipitation, and wind extremes, with a focus on interdisciplinary approaches that bridge the gap between the physical sciences and decision makers. We invite contributions from interdisciplinary teams working to address these challenges, as well as from those working in single disciplines but seeking to make interdisciplinary connections. Topics of interest include storyline approaches in which societal challenges are considered alongside physical climate risks; addressing knowledge gaps in physical hazard understanding when providing information to decision makers; issues related to the financial and insurance sectors’ responses to extreme weather events; impact-based forecasting as a tool for risk understanding; and studies of early-warning systems and associated decision making.

Embedding climate resilient development principles (IPCC, 2022) in the regional and local context means ensuring that any sectoral (e.g. agriculture) or cross-sectoral (e.g. built environment) transformation contributes to achieve simultaneously carbon neutrality, adaptation and well-being for people and nature. It is a complex and systemic challenge that requires new integrative models of research and practice that can accelerate the pace of change with respect to conventional approaches.
Policymakers, practitioners and communities who aim to achieve a just climate transition must pursue systemic change across sectors by integrating different methods and co-creation practices to support science- and community-driven transformative approaches. This critical inter-disciplinary and multi-dimensional dialogue is aimed at integrating carbon neutrality and adaptation with a focus on context-specific climate change impacts (to expand local priorities for risk adaptation) and systems transformation (energy, mobility, land use, construction, agriculture, etc.) while creating value for local stakeholders and assessing the full range of social, economic and environmental co-benefits of local development processes across sectors.
The session will bring together representative from relevant Horizon Europe projects exploring inter-disciplinary methods and tools to support climate resiliet development at regional and local level.

Why ITS?
Achieving climate resilience in a timeframe compatible with major international agendas requires no “demonstrators” but a radical change in the “business as usual”, bringing equity and environmental justice hand in hand with measurable impacts on climate and environmental goals.
Inter-disciplinary approaches and methods presented in this session are aimed at overcoming both the limits of conventional scientific approaches (e.g. Siloed VS Collaborative; Complicated VS Complex; Patended VS Open), and those of conventional community-driven approaches (e.g. Isolated VS Widespread; Small scale VS Scalable and Replicable; Discussion VS Co-production).

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, NWP, 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 and conservation laws, and incorporating physical intuition,
- ensuring numerical stability,
- coupling of numerical models to ML frameworks and language interoperation,
- handling computer architectures and data transfer,
- adaptation/generalisation to different models/resolutions/climatologies,
- 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.

We 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.

Extreme weather events such as tropical cyclones, heatwaves and floods threaten populations around the world. Climate change is increasing the frequency and intensity of many kinds of extreme weather events, which can combine with community exposure, inequalities and vulnerabilities to cause substantial harm. There is growing literature at the intersection of the natural and social sciences studying the impacts of extreme weather events on populations as well as peoples’ behavioral, attitudinal, and emotional responses. For instance, studies have investigated how extreme weather and climatic changes influence food and water security, conflict and security risks, and health outcomes. Additionally, the field of environmental human mobility has witnessed remarkable progress in data collection, analytical methods, and modeling techniques. Further research has examined the responses of individuals and households to these threats, including climate-related emotions, environmental concerns, and climate policy support. These studies have been conducted in interdisciplinary settings, where social scientists closely collaborate with natural scientists to study populations that have been, or will be, impacted by extreme weather events.

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

We invite contributions including but not limited to studies of:

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

Mountains are complex social-ecological systems and natural laboratories in which to tangibly explore and understand how drivers and processes of global change manifest in specific places. In this session, we invite inter- and transdisciplinary contributions that examine environmental change, their associated impacts for ecosystems and people in mountain environments, and measures taken to address these impacts. This session is open to conceptual as well as empirical measurement and/or modelling or scenario studies of mountain climate, cryosphere, ecology, hazards, and hydrology, which also incorporate studies on intersecting socio-economic dimensions and risks.
Mountains as complex terrain are challenging to parameterize in models and also due to the lack of data in high-elevation. Climate change, including elevation-dependent warming, shifting precipitation patterns, retreating glaciers, degrading permafrost, are reshaping high-mountain landscapes. These changes have a direct impact on population of these regions. Likewise, there is a need to capture and account for socio-economic changes such as demographic and land-use change and their projections to improve our understanding of how hazards, vulnerability, and exposure interact in terms of impacts and risks.
This session seeks to explore problems to environmental change. Contributions focused on mountain system dynamics through remote sensing, numerical modelling, laboratory techniques, and field observations are welcome, as are efforts to reduce uncertainties surrounding future compounding hazards and risks. We welcome contributions that describe how steps are being taken to address such knowledge gaps, and integration methods that include societal data and information to characterise and represent a more comprehensive systems approach to global change.
As 2025 marks the UN-declared International Year for Glaciers' Preservation, and the kick-off to the UN Decade of Action for Cryosphere Sciences, this session especially invites contributions that addressing the challenges and opportunities posed by a changing cryosphere, with particular attention to the human dimensions associated with adaptation and resilience.
This session is endorsed and supported by the Mountain Research Initiative, the Institute for Interdisciplinary Mountain Research of the Austrian Academy of Sciences, and the Sediment Cascades and Climate Change initiative.

Terrestrial ecosystems emit and sequester carbon dioxide. Terrestrial carbon sources and sinks are crucial components of the global carbon cycle, especially now as the atmospheric CO2 concentration rises.
Terrestrial ecosystems include forests, rangelands, croplands, steppes, agroforestry systems, World residential areas and other lands. Interdisciplinary research has assessed land use transitions with unprecedented progress in recent times. Social and economic development, population growth, urbanization and globalization affect land conversion on all continents. The on-going climate change and the rise of green house gases in the atmosphere pose challenges to scientific research. Advances of remotes sensing intermingled with national statistics and citizen science assist in updating our perceptions on global changes in terrestrial ecosystems.
This session invites scientists from different disciplines to attend interdisciplinary and transdisciplinary dialogues on drivers affecting the sinks and sources of global terrestrial carbon. Global overviews based on different methodologies are invited as well as case studies at continental, national and regional levels. Presentations should address changes in global terrestrial ecosystems in the 20th and 21st century.