Machine learning (ML) is 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 ML and deep learning now allow for encoding non-linear, spatio-temporal relationships robustly without sacrificing interpretability. This has the potential to accelerate climate science through new approaches for modelling and understanding the climate system. For example, ML is now used in the detection and attribution of climate signals, to merge theory and Earth observations in innovative ways, and to directly learn predictive models from observations. The limitations of machine learning methods also need to 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:

More accurate, robust and accountable ML models:
- Hybrid models (physically informed ML, parameterizations, emulation, data-model integration)
- Novel detection and attribution approaches
- Probabilistic modelling and uncertainty quantification
- Uncertainty quantification and propagation
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science
- Green AI

Improved understanding through data-driven approaches:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process and feature representations in observations or across models and observations
- Explainable AI applications
- Discover governing equations from climate data with symbolic regression approaches

Enhanced interaction:
- The human in the loop - active learning & reinforcement learning for improved emulation and simulations
- Large language models and AI agents - exploration and decision making, modeling regional decision-making
- Human interaction within digital twins

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

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.

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.
Examples include ML emulation of computationally intensive processes, training on high resolution models or data-driven parameterisations for sub-grid processes, and Bayesian optimisation of model parameters and ensembles amongst several others.

Doing this brings a number of unique challenges, however, including but not limited to:
- enforcing physical compatibility and conservation laws, and incorporating physical intuition into ML models,
- 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.

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.

Recent advances in the field of Artificial Intelligence, Machine Learning and Data Assimilation have been massively applied to model, to anticipate, and to predict natural catastrophes, such as earthquakes, floods, landslides, volcanic eruptions, tsunamis, wildfires, glacier instabilities, in addition to multi-hazard and cascading effects, triggered by climate change. However, the adopted data-driven methods require a solid inductive bias, provided by the physics of the phenomenon at stake (or at least the understanding of it). Furthermore, due too often over-simplified assumptions, analytical models of natural catastrophes might encounter predictive limits. Therefore, several hybrid strategies, utilizing the ever increasing computational resources available, are currently being developed, to achieve more flexibility and full synergy between numerical physics-based simulations, machine learning and data-driven approaches.
The hybrid modelling of natural hazards benefits from the interpretability of numerical simulations and from the extrapolation and generalization capabilities of advanced Machine Learning methods. This synergy leads to multi-fidelity predictive tools that leverage all the available knowledge on the phenomenon at stake. Moreover, to tackle lack of data and representation, observational databases can be integrated with the synthetic results for re-analysis and for training machine learning algorithms on never-before-seen disaster scenarios.
This multidisciplinary session invites contributions addressing hybrid solutions to predict and to mitigate natural catastrophes (earthquakes, tsunamis, floods, wildfire, drought, hurricanes, hale etc.), blending high-performance computing, advanced numerical methods, reduced-order models, AI and data-driven statistical approaches, geospatial data analysis. The session welcomes both presentations on hybrid tools for hazard and vulnerability assessment (including environment-structure interaction).

Cities are complex multi-scale systems, composed of multiple sub-components (e.g. for population, energy, transport, climate) that interact with each other on various time scales (e.g. hourly, seasonal, annual). Urban models and digital twins for urban planning applications and policies aimed at shaping healthier and more sustainable urban environments should account for such complex interactions as they regulate the growth and functioning of cities, often resulting in emergent large-scale phenomena. Yet our ability to quantitatively describe city behaviour is still limited due to the variety of processes, scales, and feedbacks involved.
In this session we welcome modelling and monitoring studies that focus on multi-sector dynamics and city-biosphere interactions. These include – but are not limited to – demography, urban transport networks, energy consumption, anthropogenic emissions, urban climate, pollution, urban hydrology and ecology.
The aim is to elucidate complex urban dynamics, identify strategies and methods for the development of models and digital twins of cities, and understand how the form and function of urban environments can improve liveability and well-being of their citizens.

Climate change and widespread biodiversity loss are urgent challenges facing humanity, whose effects threaten human wellbeing, economies and planetary stability. There is increasing evidence that these two crises are strongly interconnected and might even be mutually reinforcing. However, climate- and biodiversity change are typically investigated through siloed approaches. This limits our ability to assess the feedbacks between these two major trends and to ultimately/eventually design policy solutions that fully take into account the trade-offs and synergies between climate change mitigation, adaptation, and biodiversity conservation.

In this session, we invite scientists from all disciplines working at the interface of these fields, and in particular on the linked relationships and processes between climate (change, variability, extremes) and biodiversity (taxonomic, functional, structural). We are especially interested in studies that investigate feedbacks mechanisms between biodiversity and the climate system at different spatial and temporal scales, from experimental, observational, data-science, and/or modelling perspectives, as well as on how human activities, such as land cover conversion or nature conservation, might influence these interactions.

Hydrological modeling plays a crucial role in understanding and predicting the behavior of water systems, which is important for water resource management, flood forecasting, and environmental planning. However, the accuracy of these models heavily relies on accurate input data, which can be challenging to obtain, especially in regions with limited ground-based observations. This is where remote sensing technology comes into play. By harnessing data from remote sensing platforms, researchers can provide spatially and temporally comprehensive information on precipitation and soil moisture, filling critical gaps in traditional observation networks. Data fusion in hydrological modeling involves combining remote sensing-derived data with ground-based measurements to create a more complete picture of the hydrological cycle. This integration is achieved through a synergy of advanced techniques such as data assimilation, machine learning algorithms and statistical methods.

This session welcomes, but is not limited to, contributions on:
• Novel data assimilation methods that effectively incorporate remote sensing precipitation and soil moisture estimates into hydrological models
• Applications of machine learning algorithms for fusing remote sensing data with ground-based observations to improve hydrological predictions
• Methodologies for quantifying and propagating uncertainties associated with remote sensing precipitation and soil moisture estimates through hydrological models
• Methodologies for downscaling remote sensing data to finer spatial and temporal resolutions, making them compatible with hydrological models that require higher detail for accurate predictions
• Techniques for validating and verifying hydrological models that incorporate remote sensing data
• Emerging trends in data fusion in hydrological modeling

Significant efforts are underway to simulate the interlinked evolution of Earth Systems on geological timescales in the context of plate tectonics, geodynamics, paleogeography, climate, and biological evolution. In tandem with these new modelling frameworks, geological constraints to validate the predictions also remain a key focus for the community. The results of whole-Earth modelling, ground-truthed by robust and independent data, can provide insights into basic science questions such as planetary habitability, the rise and demise of living organisms, and the interaction of processes shaping Earth’s surface. In addition, this new constellation of modeling tools also has the potential to aid industry in mineral exploration for a net zero future.

The interplay of deep Earth processes with evolving atmospheric and hydrospheric conditions has shaped our planetary surface over billions of years. Recent decades have largely seen a focus on paleogeographic models incorporating plate tectonic reconstructions and mantle convection models. In recent years, the improvement in computational resources and development of Earth system tools have cleared the way towards exciting deep-time Earth models with increasing complexity (such as biosphere feedbacks and carbon cycling) and spatio-temporal resolution. In addition, more attention has been given to sedimentological, paleobiological, and other geological and proxy data to constrain models of paleogeography, paleo-climate and surface processes.

We invite submissions from all disciplines that aim to model or constrain one or more Earth Systems over geological timeframes. We welcome submissions that are analytical or lab-focused, field-based, or involve numerical modeling. The session will also celebrate the contributions of early career researchers, open/community philosophy of research, and innovations that have adopted inter-disciplinary approaches.

This session delves into the potential of emerging data types in advancing our understanding of natural hazards and hydrology. With the increasing availability of vast amounts of text, digital trace, social sensing, social media, mobile phone, opportunistic sensing, audio-visual, and crowdsourced data, researchers have an unprecedented opportunity for natural hazards and hydrology research. By leveraging these new data types, this session will explore methods and interdisciplinary approaches that enable comprehensive analysis of the consequences of disasters (droughts, floods, landslides, storms, etc.), real-time event monitoring, behavior analysis, and public perception tracking. Key methods to be discussed include natural language processing, social media analysis, historical data rescue, deep learning, and machine learning.

The Coupled Model Intercomparison Project (CMIP) is instrumental in advancing our understanding of the Earth’s climate system and its future projections. However, Earth system models (ESM) exhibit disparities in critical aspects, particularly in their responses to anthropogenic forcings and the intricate coupling of physical and biogeochemical systems. Given that the Earth system science community, and notably the IPCC, relies on CMIP outputs to inform policy and mitigation strategies, it becomes imperative to address these inherent uncertainties through a multidisciplinary approach that unites atmospheric, oceanic, and terrestrial modeling analyses. In this session, we invite studies that investigate uncertainties and model disagreements across all facets associated with CMIP projections. These may include contributions that relate to:

1. Identification of processes and key entities with significant disparities across CMIP models: Quantifying sources of uncertainty across CMIP models, which may include i) internal variability, ii) process representations/model parameterization, iii) ESM architecture, and iv) external forcing.

2. Critical scientific priorities for future CMIP/Earth system model development: Recognizing and comprehending uncertainties and their underlying mechanisms are essential for guiding future model development and refining climate projections. We encourage contributions that address pivotal questions crucial for enhancing model performance and reducing uncertainties across disciplines in upcoming CMIP iterations.

3. Opportunities, challenges, and constraints in using CMIP output for impact research: Uncertainties are amplified at regional scales; nevertheless, CMIP model projections are extensively utilized for impact studies by researchers unfamiliar with these sources of uncertainty and structural limitations of CMIP projections. We invite contributions that focus on innovative approaches employing CMIP output to tackle these challenges in impact studies.

In summary, this session aims to cultivate a collaborative environment where climate scientists and modelers across disciplines can engage in constructive dialogues and collaboratively chart a course towards tackling CMIP output to effectively meet the pressing challenges posed by climate change.

Scientific disciplines strive to explain the causes of observed phenomena. In Earth sciences, in general, and in climate research, in particular, the notion of causality is discussed and understood from several different points of view. Hannart et al. (BAMS, 2016), following Judea Pearl, state that “Causal counterfactual theory provides clear semantics and sound logic for causal reasoning and may help foster research on, and clarify dissemination of, weather and climate-related event attribution.” Another approach does not attempt to explain single events but studies phenomena evolving in time, represented by time series, and quantifies causality in terms of improved predictability, as proposed by Norbert Wiener and formulated for time series by C.W.J. Granger. Granger causality evaluates predictability in bivariate autoregressive models. This concept has been generalized to nonlinear systems using methods rooted in information theory. Extensions from bivariate time series to multivariate ones can also distinguish between direct and indirect causations. Methods for turning multivariate data into causal graphs based on Bayesian reasoning and machine learning are also intensively applied in the Earth sciences. In the setting of deterministic, rather than stochastic, rules, maps between attractors of dynamical systems have ben constructed to infer causality or to distinguish driving from driven (sub)systems. Based on dynamical equations and first principles, X. S. Liang and R. Kleeman have derived formulas for information flows. Transferring causality questions from statistical to computer sciences, the Wiener-Granger idea of improved predictability has been recently translated into changed compressibility of the effect data due to knowledge of the cause data. The information-theoretic formulation of Granger causality has recently been shifted from Claude Shannon’s entropy framework into A. Rényi’s and C. Tsallis’s formulations in order to infer causality in systems with heavy-tailed probability distributions and extreme events.
We will consider contributions discussing any one of these approaches to causality analysis with applications in the Earth sciences and solicit, in particular, contributions that compare different approaches and frameworks.
Confirmed solicited speakers: Michael Ghil, X. San Liang

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 user-driven climate information and new climate services and products. In fact, the "user's dilemma" is no longer that there is a lack of downscaled data, but rather how to select amongst the available datasets and to assess their credibility. In this context, model evaluation and verification is growing in relevance and advances in the field will likely require close collaboration between various disciplines.

Furthermore, epistemologists have started to revisit current practices of climate model validation. This new thread of discussion encourages to clarify the issue of added value of downscaling, i.e. the value gained through adding another level of complexity to the uncertainty cascade. For example, the ‘adequacy-for-purpose view’ may offer a more holistic approach to the evaluation of downscaling models (and atmospheric models, in general) as it considers, for example, user perspectives next to a model’s representational accuracy.

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 enrich our discussion about novel challenges faced by the evaluation of increasingly complex simulation models.

Contributions to this session may address, but are not limited to:

- newly available downscaling products,
- applications relying on downscaled data,
- downscaling method development, including the potential for 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.

Geomorphology has entered a transformative phase, driven by the rapid development and integration of digital tools and technologies notably Remote Sensing (Google Earth Engine (GEE), Synthetic Aperture Radar (SAR),) and Artificial Intelligence (Machine and Deep Learning). As a result, improved Geographic Information System (GIS) and modelling approaches now provide novel opportunities to investigate the complex interactions between humans and the environment. This session, therefore, seeks to explore the evolving landscape of geomorphological research in the context of the digital era, focusing on the integration of various new and old methodologies. We encourage submissions that demonstrate the power of these methodologies in unravelling the intricate dynamics of geomorphological processes, landscape evolution, and the impacts of human activities. We welcome papers that delve into the exploration of past and present human-environmental interactions to inform potential future trends. As global climate warming becomes an increasingly pressing concern, understanding how geomorphological processes respond to human-induced changes is of paramount importance. We welcome papers that explore subjects such as land use transformation, soil erosion and land degradation, urban growth, deforestation, alterations to river channels, and their impacts on geomorphic systems.
We invite authors to provide perspectives on cross-disciplinary methodologies that connect conventional geomorphology with environmental science, humanities, remote sensing, and data analytics, fostering a holistic understanding of these phenomena. By fostering cross-disciplinary collaboration, this session aims to facilitate a deeper understanding of the complex and dynamic Earth surface processes, equipping researchers and policymakers with valuable insights for addressing contemporary environmental challenges.

Modelling and exploring forest ecosystems under future climate and management has never been more critical in the face of accelerated climate change and human-induced disturbances. Consequently, understanding the dynamics of forest ecosystems, which not only act as essential carbon sinks but also support biodiversity and a wide range of ecosystem services, and predicting their responses to changing environmental conditions and future management actions has become vital. To this end, this session aims to shed light on the innovations and advancements in forest modelling and assessment of ecosystem services, within the following focus areas:
1. Next-Generation Forest Models and Climate Dynamics: Overview of models designed to dynamically intertwine climate drivers with forest growth patterns, offering a thorough representation of carbon, nitrogen, and phosphorus cycles to predict forest responses to climate impact.
2. Model-Data Fusion in Forest Modelling: Discussion on how to integrate data from different sources (e.g., remote sensing, forest inventories, eddy-covariance) into forest modelling frameworks. Overview of computational techniques applied for model calibration, evaluation, and averaging and for data assimilation.
3. Large-Scale Forest Modelling for Feedback Mechanisms: Exploration of tools that assess the complex feedback loops among forest adaptive/mitigative strategies, localised climate changes, natural disturbances, and the permanence of forest carbon stocks.
4. Future Climate and Management Driven Forest Structural Modelling: In-depth look at how alternative management practices and climate drivers influence forest architecture, yielding structural indicators pivotal for the evaluation of biodiversity and ecosystem services.
5. Framework for Linking Forest Structural Indicators to Biodiversity and Ecosystem Services: Discussion on innovative methodologies establishing connections between forest structural variables from cutting-edge models and biodiversity and ecosystem service indicators, prioritizing the assessment and economic evaluation of potential synergies and trade-offs in forest management decisions.
This session invites contributions from researchers, practitioners, and policymakers. It seeks to become a vibrant forum for exchanging knowledge, insights, and best practices, furthering our collective goal of ensuring sustainable and resilient forest ecosystems in a rapidly changing world.

Space-based measurements of the Earth System, including its atmosphere, oceans, land surface, cryosphere, biosphere, and interior components, require extensive prelaunch and post-launch calibration and validation activities to evaluate scientific accuracy, characterise uncertainties and ensure the fitness for purpose of the geophysical information provided throughout lifetime of satellite missions. This stems from the need to demonstrate unambiguously that space-based measurements, which are typically based on engineering measurements by the detectors (e.g. photons), are sensitive to and can be used to reliably retrieve the geophysical and/or biogeochemical parameters of interest across the Earth.

Most geophysical parameters vary in time and space, and the retrieval algorithms used must be accurate and tested under the representative range of conditions encountered. Satellite missions also benefit from the availability of precursor data made available from other satellite missions, field campaigns, and/or surface-based measurement networks that are used in the definition of geophysical products and for the development and testing of the retrieval algorithms prior to launch during the satellite and ground segment development. Post-launch calibration and validation over the lifetime of missions assure that any long-term variation in observation can be unambiguously tied to the evolution of the Earth system. Such activities are also critical in ensuring that measurements from different satellites can be inter-compared and used seamlessly to create long-term multi-instrument/multi-platform data sets, which serve as the basis for large-scale international science investigations into topics with high societal or environmental importance.

This session seeks presentations on the use of surface-based, airborne, and/or space-based observations to develop precursor data sets and support both pre- and post- launch calibration/validation and retrieval algorithm development for space-based satellite missions measuring our Earth system. A particular but not exclusive focus will be on collaborative activities carried out jointly by NASA and ESA as part of their Joint Program Planning Group Subgroup on provision of precursor data sets for future ESA, NASA, and related partner missions, and the full range of pre- and post-launch calibration and validation and field activities for these satellite projects.

The field of geophysical inversion is critical to risk analysis and decision-making in a wide variety of geoscientific contexts, and has a long history: from the first theoretical studies focused on questions of existence, stability, and uniqueness, through nonlinear optimisation, large parameter estimation problems, to quantification of uncertainty in both parameter values and model choice. Systems studied have become larger, data set sizes have exploded, and many algorithms have been borrowed, adapted and devised anew. This session aims to explore cutting-edge methods, tools, and approaches that push the boundaries of geophysical inference and uncertainty analysis. We ask the question `Where to next?’

We invite researchers, practitioners, and experts to join us in a session focused on the future directions of geophysical uncertainty assessment and inference.

Example topics of Interest:

1. Advanced methods for Bayesian Inference: Explore the latest developments in Bayesian sampling and model choice. How do these techniques transform our ability to make robust inferences and predictions in the face of complex geophysical systems?

2. Machine Learning and related methods: Investigate the integration of machine learning and artificial intelligence in geophysical inference and uncertainty analysis. Share insights into how these technologies are influencing the future of geophysical inference via surrogate modelling, parameter estimation and uncertainty assessment.

3. Applications: Real-world examples of geophysical uncertainty assessment and inference in action, highlighting successes, challenges, and lessons learned.

In complex environmental systems, uncertain information (whether in measurements, maps or models) is the norm, and this impinges on most knowledge that earth scientists generate. Accounting for this uncertainty is particularly important when results are used in a decision-making process where the end user needs to be able to properly evaluate the risks involved.

The transdisciplinary challenge of quantifying and communicating uncertainty requires continuous improvement of tools. This concerns the quantification of uncertainty associated with measurement data and expert information, its propagation through any modelling procedure (machine learning, geostatistics, process models, …), as well as the visualization and communication of uncertainty to the end users including scientists, engineers, policymakers, and the general public.

In this session, we will examine the state of the art, and discuss fascinating advances of both uncertainty quantification, and communication in earth and environmental sciences. We welcome submissions on three components of the research field: 1) new methods and applications of uncertainty quantification; 2) use of uncertain information in decision-making, and for risk assessment; and 3) efficient and effective communication and visualization of uncertainty to end-users. It takes expertise from several different disciplines (including earth and environmental sciences, statistics, economics, and psychology) to successfully include, manage, and communicate uncertainty.

Learning causal relationships from Earth system data is of paramount importance for understanding its complex dynamics, predicting future changes, and informing effective mitigation and adaptation strategies. Causal inference provides a powerful framework for unraveling cause-effect relationships of different processes within Earth system sciences. This session welcomes contributions that highlight innovative approaches, methodologies, and case studies employing causal inference techniques across Earth sciences.

The session aims to foster interdisciplinary discussions, encourage collaborations, and promote the development of robust causal analysis frameworks tailored to the unique characteristics of the Earth system. We welcome presentations from researchers across different disciplines, highlighting theoretical advancements and practical applications of causal inference to improve our understanding of Earth system processes.

The topics of interest for this session include, but are not limited to:
- Causal discovery methods: algorithms and methodologies for uncovering causal networks among Earth system processes;
- Causal effect estimation: statistical techniques to estimate causal effects of interventions or natural forcings in the Earth system;
- Applications of causal inference: case studies investigating causal pathways and mechanisms driving natural and anthropogenic perturbations such as climate change, land-ocean interactions, extreme events, etc;
- Causal modeling and network analysis: development and application of causal models, network analysis, and graphical models to capture the intricate interconnections and feedbacks within dynamical systems;
- Causal model evaluation: application of causal dependencies to assess climate models performance;
- Challenges and limitations: associated with the application of causal inference, including issues related to violations of assumptions, or uncertainty quantification.

In the era of rapid environmental changes and increasing vulnerability to natural hazards and climate-related risks, ecosystem services emerged as a powerful perspective for disentangling and harnessing the benefits of nature in risk reduction. In this context, tools for modeling, mapping ecosystem services, and supporting the transfer of knowledge between multiple disciplines are key in the implementation of appropriate strategies for sustainable land management and risk reduction. However, current modeling approaches entail various challenges in their application, including limited access, findability, interoperability, reusability of data and models, as well as the need of integrating fragmented knowledge from multiple domains.
To optimize resource, knowledge and time, the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles permits that data and models are easily findable, accessible, interoperable between different systems and reusable at different spatial and temporal scales, from both scientists and machines.
This session seeks to convene experts, researchers and practitioners from diverse fields to explore and discuss the importance of adhering to FAIR principles in ecosystem services modeling. By sharing insights, case studies and best practices, we aim to emphasize how the application of FAIR principles can enhance the accessibility, usability and finally the impact of ecosystem services data and models in risk reduction strategies and sustainable land management
We invite speakers to present theoretical and methodological approaches, as well as case studies on:
-FAIR principles in ecosystem services modeling: an in-depth exploration of each FAIR principle and its relevance in the context of ecosystem services modeling, with a focus on making data and models more accessible and reusable;
-Data integration and interoperability: discussion on how adherence to FAIR principles enables seamless integration of data from various sources and knowledge domains, facilitating the development of comprehensive ecosystem services models for risk reduction;
-Case studies and applications: real-world examples of how FAIR-compliant ecosystem services modeling has contributed to effective risk reduction and sustainable land management strategies.
-Challenges and solutions: identification of challenges and barriers in implementing FAIR principles in ecosystem services modeling and potential solutions to overcome them.

Citizen Observatories, crowdsourcing, and innovative sensing techniques are increasingly relevant in hydrological monitoring, as water hazards intensify under climate change and new methods to simulate the hazards must be developed. Citizen scientists can provide timely and critical information during disasters, by providing key ground information for process identification and validation of remotely sensed data. For monitoring, modelling, and management of water resources, optimally utilising innovative sensing techniques especially in interdisciplinary settings, is crucial to further hydrological process understanding and solve the hitherto unsolved research questions.
This session is thus dedicated to multidisciplinary contributions, especially those focused on the demonstration of the benefit of the use of Citizen Science and innovative sensing techniques (Internet of Things, reflected GPS signals, new satellites and sensors) for water resource management.
We thus invite innovative applications of these novel data sources for (i) water resources monitoring, mapping, and modelling; (ii) hazard, exposure, vulnerability, and risk mapping; (iii) development of disaster management and risk reduction strategies. The session aims to serve a diverse community of research scientists, practitioners, end-users, and decision-makers, by showcasing the current state-of-the-art in hydrological monitoring; (2) foster a broader exchange of knowledge, datasets, methods, and good practice in dealing with unconventional data sources between scientists and practitioners; and finally (3) identify future research avenues.
Submissions that investigate issues related to the benefits and impacts of innovative sensing on studies of climate change, anthropogenic pressure, as well as ecological and social interactions related to water resources management and water hazards are very welcome. Early career researchers are strongly encouraged to present their research. Contributors to this session will be invited to develop full papers for a focused special issue in HESS.

The advancement of Open Science and the affordability of computing services
allow for the discovery and processing of large amounts of information, boosting the integration of data from different scientific domains and blurring the traditional boundaries between them. However these data come from diverse sources, and are often heterogeneous in format and provenance. Thus, the capacity to combine them and extract new knowledge to address scientific and societal problems relies on data standardisation, integration and interoperability.

Key enablers of the OS paradigm are Research infrastructures (RI), of which EPOS, the pan-European RI for solid Earth science (www.epos-eu.org), is an example. By making available data and research products thanks to decades of work on data standardisation, integration and interoperability, they enable scientists to combine heterogeneous data from different disciplines and data sources into innovative research by using novel approaches to solve scientific and societal questions.

However, while data-driven science is ripe with opportunity to ground-breaking inter- and transdisciplinary results, many challenges and barriers still exist that can hamper disclosing the full potential of these opportunities.

In this session we want to explore real-life scientific studies and research experiences from scientists and young researchers in solid Earth science. We will be focussing not only on results, but also on discussing the way forward to overcome the challenges experienced by these researchers in connection to data availability, collection, processing, and interpretation, and application of inter-disciplinary methods.

A non-exhaustive list of examples of topics for contributions includes:
- multi-disciplinary studies, involving data from different disciplines, e.g. combining seismology, geodesy, and petrology to understand subduction zone dynamics;
- inter-disciplinary works, integrating two or more disciplines seeking to create fresh approaches to new challenges, e.g. merging geophysics and geochemistry to probe mantle plumes;
- trans-disciplinary experiences that surpass disciplinary boundaries entirely, integrating paradigms and engaging stakeholders from diverse backgrounds, e.g. bringing together geologists, social scientists, civil engineers and urban planners to define risk maps and risk prevention measures in urban planning, or studies combining volcanology, atmospheric, health and climate sciences.

At scales of millions of years and longer, geoprocesses typically display nonlinear variability: strong (non Gaussian) extremes, strong (long range) correlations, nontrivial fractal patterns and other scaling behaviours.  Over these “mega” time scales, tectonic, climatic, ecological, and evolutionary processes interact with important consequences for the climate, macroevolution, and other biogeological processes. 
Advances in nonlinear data analysis, scale by scale decomposition of patterns, and nonlinear modeling, combined with the increasing availability of quantitative paleo data are enabling new and exciting discoveries in understanding the regional and planetary scale phenomena of physical and biological evolution. This session brings together paleontologists, climatologists, and other nonlinear geoscientists, for a common task of uncovering the multiscale variability and hierarchical interactions between physical and biological dominions of the Earth system.

Contributions relevant to processes at these long time scales are encouraged, in particular:
1) Data analyses: spectra, wavelets, structure functions, probability distributions (extremes), recurrence plots, trace moments and other nonlinear analysis techniques as well as numerical modeling studies.
 2) Modelling: Stochastic, scaling (fractal, multifractal), fractional equations, deterministic chaos, numerical models.
 3) Phenomena: macroevolution including dynamics of diversity, extinction, origination of species, climate including paleotemperature and other paleoseries, and sea-level changes.

Modelling in soil sciences is fundamental for assessing various soil processes and interactions at different scales and resolution. Knowledge from soil system modelling forms an integral part of expanded modelling approaches including biogeochemical and terrestrial ecosystem dynamics and soil-vegetation-climate interactions. As a result, soil modelling coupled with interrelated soil processes/mechanisms is undertaken across many disciplines, but often lacks sufficient knowledge exchange among different expertise.

Crossing interdisciplinary borders and integrating knowledge/techniques from various fields, such as soil science, ecology, hydrology, and atmospheric science, is essential in developing more accurate and comprehensive models to better capture the complexity of soil processes/mechanisms in natural and cultivated systems, address knowledge-gaps, and tackle the challenges related to data-integration, heterogeneity and uncertainty of modelling predictions across disciplines.

An interdisciplinary approach is also needed in light of recent technological advances, such as computational approaches, model-coupling, geomatics, remote sensing (RS)/earth observation (EO), machine learning (ML), sensors/sensor platforms for data collection and surveying, real-time data-streams, all of which provide opportunities for promoting new modelling generations integrating soil science across disciplines.

This session aims to promote and enhance communication and exchange of knowledge among scientists within modelling community, linking different disciplines, data integration and modelling approaches, and is open to contributions in a wide range of related topics, ranging from modelling soil systems to ecosystem and landscape modelling.

Contributions can include, but are not limited to, modelling in the areas of soil-vegetation-climate interaction, the effects of LU/management in terrestrial ecosystems, across different temporal/spatial scales, simulation of various processes such as soil C sequestration, nutrient cycling, GHG fluxes, primary productivity, species distribution, and many more. Contributions can range from studies implementing simple computational modelling approaches to various larger dynamic/mechanistic/process-based simulation models, coupled/hybrid models, including integration of ML, geospatial and RS/EO data, advanced and real-time data streams, and linking various measurements and data-evaluation approaches.

Life on earth evolved under the influence of multiple stressors within the ever-changing climate system. Ranging from large-timescale oscillations associated with orbital cycles and the glacial-interglacial transitions, to regionalized extreme events including massive volcanic eruptions, catastrophic floods and forest fires, a wide range of climatic fluctuations in the past may have contributed to shaping the distribution and evolution of various life forms in the terrestrial environment.

This session aims at bringing together multidisciplinary research addressing how climate has impacted and will impact different terrestrial life forms. We welcome all kind of research contributions in this context and the topics of interests include,

- Past climate change and mass extinctions
- Habitat loss and fragmentation
- Adaptations (species migrations, physiological and morphological modifications, and behavioral responses)
- Vegetation dynamics (shift in vegetation patterns, plant phenological responses, and seed dispersal)
- Genetic diversification and speciation
- Global biodiversity patterns
- Trophic-level interactions and community structure modulations.
- Vulnerability and extinction risk, under anthropogenic warming and land use change.

The history of life on Earth is closely intertwined with past climate fluctuations and by studying how organisms have adapted and evolved in response to changing climates over time, we gain a deeper understanding of the processes of evolution and natural selection and the potential impacts of future climate change. Recent advancements across the research domains have seen for example (1) improvement to taxonomic profiling of microorganisms and ancient DNA extraction from archaeological sites, (2) geochemical mapping and dietary reconstructions across food webs (3) combination of instrumental observations, climate and biome model enabling us to assess the influence of extreme climatic events on vegetation composition at fine spatio-temporal scale. Despite the significant progress within each respective scientific realm, few occasions have allowed for a general bridge across these fields. Integrating the scientific approaches available in these communities will provide an idealistic opportunity to unravel the past, present and future lives of macro and micro-organisms that coexist with people and their potential regulation by climatic forcing.

In this session, we invite work concerned with organism-related studies (DNA sequencing, physiological processes, ecological strategies), chemical analyses on the geological materials (teeth, bone collagen, guano/feces, middens, sediment cores), climate and biome model simulations, and especially welcome studies applying an interdisciplinary approach to these topics. We aim to bring together interdisciplinary research and hope that through this session, individuals can discover new methodologies, applications and collaborations within their own work that would help push such science forward.

High-impact climate and weather events typically result from the interaction of multiple climate and weather drivers, as well as vulnerability and exposure, across various spatial and temporal scales. Such compound events often cause more severe socio-economic impacts than single-hazard events, rendering traditional univariate extreme event analyses and risk assessment techniques insufficient. It is, therefore, crucial to develop new methodologies that account for the possible interaction of multiple physical and societal drivers when analysing high-impact events under present and future conditions. Despite the considerable attention from the scientific community and stakeholders in recent years, several challenges and topics must still be addressed comprehensively.


These include: (1) identifying the compounding drivers, including physical drivers (e.g., modes of variability) and/or drivers of vulnerability and exposure, of the most impactful events; (2) Developing methods for defining compound event boundaries, i.e. legitimate the ‘cut-offs’ in the considered number of hazard types to ultimately disentangle enough information for decision-making; (3) Understanding whether and how often novel compound events, including record-shattering events, will emerge in the future; (4) Explicitly addressing and communicating uncertainties in present-day and future assessments (e.g., via climate storylines/scenarios); (5) Disentangling the contribution of climate change in recently observed events and future projections; (6) Employing novel Single Model Initial-condition Large Ensemble simulations from climate models, which provide hundreds to thousands of years of weather, to better study compound events. (7) Developing novel statistical methods (e.g., machine learning, artificial intelligence, and climate model emulators) for compound events; (8) Assessing the weather forecast skill for compound events at different temporal scales; (9) Evaluating the performance of novel statistical methods, climate and impact models, in representing compound events and developing novel methods for reducing uncertainties (e.g., multivariate bias correction and emergent constraints); and (10) engaging with stakeholders to ensure the relevance of the aforementioned analyses.


We invite presentations considering all aspects of compound events, including but not limited to the topics and research challenges described above.

As highlighted by the UN development goals, climate change is a reality to which we need to adapt. However, the many disciplines required to effectively plan and adapt to climate change often work in isolation. For example, physical climate modelling, hydrology, and hazard impact and risk assessment are largely separate disciplines with difficulties interacting due to different terminologies and backgrounds. Moreover, until recently, climate modellers did not have the capability to generate long-term projections at a spatial and temporal resolution useful for impact studies.

With the advent of kilometre-scale atmospheric models, called convection-permitting models CPMs, high resolution remote sensed data sets, and global sub-daily rainfall observations, we are now in a position to bridge the gap between disciplines, sharing knowledge and understanding. With all these tools at our disposal we have substantially improved the representation of sub-daily precipitation characteristics and have model output at a spatial resolution closer to what many impacts modellers, for example hydrologists, need. Now is the time to exploit these high-resolution, consistent datasets as input for impact studies and adaptation strategies; to foster interdisciplinary collaboration to build a common language and understand limitations and needs of the different fields; to learn together how to provide policymakers with information that can be used to design effective measures at to adapt to climate change as well as to inform mitigation decisions.

This interdisciplinary session invites contributions that address the linkages between high-resolution climate scientists, impact modellers, and end users with a special focus on:
- Recent advances in climate modelling for impact studies, particularly using high resolution convection- permitting models.
- Bias correction techniques to overcome bias in climate models affecting impact models.
- Analysis of the uncertainty propagation from climate into impact models.
- Improved understanding of processes that will alter hazards resulting from climate change.
- Novel use of new and existing observational data sets in characterising and quantifying climate change hazards.
- Examples of good practice, storylines and communication to both stakeholders and policymakers.

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 a 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. Further research has examined the responses of individuals and households to these threats, including in the areas of migration, 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.

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
- Variances in vulnerability and adaptability to environmental stress
- The interplay between climate change, environment, and conflict
-Methodological challenges to interdisciplinary collaborations

Climate change is the result of perturbations to atmospheric composition or land use affecting the surface albedo, amongst other external natural of anthropogenic forcings. These “climate forcing” agents cause an energy imbalance at the top of the atmosphere, driving a warming Earth. This session invites research contributions assessing the climate responses to forcing and uncertainties in the evolution of different forcing agents. We are especially interested in time-dependent physical and biogeochemical responses to climate forcing, based on the coupled model intercomparison project phase six (CMIP6) or previous CMIP phases. Contributions on all aspects of climate-forcing research are welcome. These may include, but are not limited to, the development of historical and future forcing, studies that use idealized, single- or multi-Earth System Model approaches, observational methods to evaluate climate responses, as well as works accounting for multiple climate system realms, i.e., the ocean, atmosphere, cryosphere, land surface/subsurface, and biosphere, their linkages, and feedbacks in the system. This session is convened by the WCRP CMIP Forcing Task Team which is working to prepare next-generation climate-forcing datasets for CMIP6Plus and CMIP7.

Ocean-atmosphere fluxes of biogeochemically active constituents have significant impacts on global biogeochemistry and climate, inducing potentially important chemistry-climate feedbacks. The atmospheric deposition of nutrients (e.g., nitrogen, phosphorus, iron) to the ocean influences marine productivity, in turn affecting oceanic CO2 uptake and the emission of climate active species (e.g., nitrous-oxide, dimethyl-sulfide, marine biogenic organics and halogenated species) to the atmosphere. Atmospheric inputs of persistent organic pollutants and elements such as lead, mercury, cadmium, and copper, into the ocean may affect marine ecosystem health. Air-sea exchange is now also known to be an important but uncertain part of the marine microplastic cycle. The emission reductions for air pollution abatement, from both terrestrial and recently maritime transport sources, have repercussions on cloud and aerosol chemical composition, affecting atmospheric acidity, associated chemical processing and impacts via atmospheric deposition on ocean biogeochemistry.
Despite research advances over the past two decades, many of the physical and biogeochemical processes linking the ocean and the atmosphere through the atmospheric fluxes of chemicals, pollutants, nutrient availability, trace-gas sources and sinks, climate relevant species and marine biological productivity, are still not comprehensively understood.
This long-running EGU session on air-sea biogeochemical fluxes will focus this year also on the legacy and activities of the 20-year Surface Ocean - Lower Atmosphere Study (SOLAS). We welcome studies from all areas of the interdisciplinary SOLAS research, including atmospheric deposition of nutrients and pollutants to the ocean and impacts on ocean biogeochemistry, ocean-atmosphere fluxes of climate active species and potential feedbacks to climate, and from a range of analytical approaches (laboratory, in-situ and remote sensing, numerical models).
This session is jointly sponsored by the Surface Ocean-Lower Atmosphere Study (SOLAS ) and GESAMP Working Group 38 on ‘The Atmospheric Input of Chemicals to the Ocean’.

The polar climate system is strongly affected by interactions between the atmosphere and the cryosphere. Processes that exchange heat, moisture and momentum between land ice, sea ice and the atmosphere, such as katabatic winds, blowing snow, ice melt, polynya formation and sea ice transport, play an important role in local-to-global processes. Atmosphere-ice interactions are also triggered by synoptic weather phenomena such as cold air outbreaks, polar lows, atmospheric rivers, Foehn winds and heatwaves. However, our understanding of these processes is still incomplete. Despite being a crucial milestone for reaching accurate projections of future climate change in Polar Regions, deciphering the interplay between the atmosphere, land ice and sea ice on different spatial and temporal scales, remains a major challenge.
This session aims at showcasing recent research progress and augmenting existing knowledge in polar meteorology and climate and the atmosphere-land ice-sea ice coupling in both the Northern and Southern Hemispheres. It will provide a setting to foster discussion and help identify gaps, tools, and studies that can be designed to address these open questions. It is also the opportunity to convey newly acquired knowledge to the community.
We invite contributions on all observational and numerical modelling aspects of Arctic and Antarctic meteorology and climatology, that address atmospheric interactions with the cryosphere. This may include but is not limited to studies on past, present and future of:
- Atmospheric processes that influence sea-ice (snow on sea ice, sea ice melt, polynya formation and sea ice production and transport) and associated feedbacks,
- The variability of the polar large-scale atmospheric circulation (such as polar jets, the circumpolar trough and storm tracks) and impact on the cryosphere (sea ice and land ice),
- Atmosphere-ice interactions triggered by synoptic and mesoscale weather phenomena such as cold air outbreaks, katabatic winds, extratropical cyclones, polar cyclones, atmospheric rivers, Foehn winds and heatwaves,
- Role of clouds in polar climate and impact on the land ice and sea ice through interactions with radiation,
- Teleconnections and climate indices and their role in land ice/sea ice variability.

Climate change may regionally intensify the threat posed by future floods to societies. From a global change perspective, Holocene and historical floods and their spatial and temporal patterns are of particular interest because they can be linked to former climate patterns, a proxy for future climate predictions. Millennial and centennial time series include the very rare extreme events, which are often considered by society as 'unprecedented'. By understanding their timing, magnitude and frequency in conjunction with prevailing climate regime and human activities, we can overcome our lack of information and disentangle the so-called “unknown unknowns”.
The reconstruction and modeling of temporal and spatial flood patterns, related atmospheric variability and flood propagation in river basins under different environmental settings are the foci of this session supported by the PAGES Floods Working Group.
Because flood prone areas, particularly floodplains and wetlands, are in many regions hotspots of economic, social and cultural development (as evidenced, for example, by the location of cultural heritage sites), the historical role of human action in altering flood frequencies, hydro-sedimentary and environmental processes (e.g. contamination) is a priority topic. The key questions are where, when, and how floodplains have been heavily modified by land use, land reclamation, water management, industrialization, mining, etc., suggesting the onset of the Anthropocene?
We welcome interdisciplinary contributions using natural and documentary archives and instrumental data, which provide
i) knowledge from short-term to long-term development of cultural river-landscapes and human-environmental interaction,
ii) reconstruct and model temporal and spatial flood patterns related to atmospheric variability,
iii) develop (supra-) regional historical maps of extreme floods (MEF),
iv) highlight historical risk mitigation strategies of local (e.g. traditional) communities and assess the flood risk of cultural heritage sites, and
vi) collect evidences of the Anthropocene in floodplains.
These different foci and the interdisciplinary integration of information are critical for the provision of robust data sets and baseline information for future flood scenarios, impacts, disaster risk reduction and integrated river management.

Citizen science is a powerful tool for promoting the understanding of urban climate and increase awareness on potential climate change hazards. By engaging citizens in scientific research and data collection, we can harness the collective power of communities to gather valuable information and contribute to our knowledge of urban climate dynamics but also to define appropriate adaptation strategies. Citizens can be trained to collect different data, using simple, low-cost monitoring tools and to define requirement for definition of local adaptation measures. Another unconventional source of data is opportunistic observations from crowdsourcing and the cellular infrastructure which is abundant around cities. All these data sources can be aggregated and analysed to identify trends, patterns, and potential climate change hazards specific to urban areas. Engaging citizens in this process fosters a sense of ownership and responsibility for their environment while also increasing support to the adaptation mission. By actively participating in data collection and training activities, citizens gain first-hand experience of how climate affect their daily lives and communities. One way to promote this understanding is by organizing citizen science projects focused on monitoring and documenting climate variables within urban environments but also to create digital environment and tools improving their knowledge on climate science (for example by reducing disinformation). The H2020 projects I-CHANGE (Individual Change of HAbits Needed for Green European transition) and AGORA (A Gathering place to cO-design and co-cReate Adaptation) have exactly this purpose.
This session encourages submissions covering citizen science, crowdsourcing, and urban-climate informatics but also the sharing of best practices aiming to include citizens in the European adaptation mission.

Coastal areas are of particular concern due to their high susceptibility to environmental shifts and to the expected human pressure over the next few decades. Sea level rise, storm-surges, flooding, extreme precipitation events, coastal erosion are only some of the challenges the coastal communities copy worldwide. Moreover, other climate-related hazards such as strong winds, droughts, heatwaves and cold spells can also be significant in coastal areas. Studies addressing coastal impacts in the future pose new questions on the spatiotemporal of such impacts and on the effectiveness of adaptation activities. To enhance the understanding of the complex dynamics of climate change impacts in coastal areas is of critical importance for hazard assessment and for the development of sustainable mitigation and adaptation solutions for vulnerable urban areas.
This interdisciplinary session focuses on climate-change-related hazards in coastal areas and on tools and approaches to address physical and socioeconomic consequences of extreme events in coastal areas. We especially encourage studies related, but not limited to: i) the historical characterization and future prediction and assessment of coastal hazards and risks, ii) climate and marine services, data and models to develop urban-scale hazard modeling and scenarios, iii) smart technologies for real-scenario interventions (e.g., digital twin, low-cost sensors, Nature-Based Solutions), iv) participative approaches (e.g., living labs, citizen science) and strategies (e.g., coastal zone management plans) to make informed decisions that foster climate change resilience and adaptation in coastal areas.

The close relationship between climate, environment, and health is evident, as climate change presents substantial threats to human welfare. The increase in global temperatures, the occurrence of extreme weather events, and shifts in precipitation patterns all have direct and indirect repercussions on public health and also act through environmental exposures (e.g. air pollution). Climate and land use change can affect the spread of diseases transmitted by vectors, such as malaria, and heighten the risk of waterborne illnesses. Climate change may lead to severe wildfires and episodes of air pollution. To confront these intricate challenges, it is imperative to foster interdisciplinary collaboration among climate researchers, epidemiologists and public health researchers and social scientists, which is the primary goal of this session.

This multidisciplinary session seeks to create a platform for presenting the latest innovations in using remote sensing and other large datasets for characterizing exposures relevant for human health particularly in data limited regions. The session is anticipated to encompass a wide array of topics, including satellite data for applications in human health, planetary epidemiology, risk mapping of infectious diseases, exposure mapping of heat, air pollution to quantify their impacts on human health and the use of machine learning and AI for climate and health applications.

The Planetary Boundaries framework defines a safe operating space for humanity by identifying precautionary limits to nine critical global environmental change processes that together regulate the state of the Earth system and maintain resilience of the world’s ‘life support systems’. The framework has been widely discussed in global change science contexts, and it is increasingly being referred to in policy and business contexts, where its quantified boundaries are seen as a basis for guiding the trajectories of collective human activities. For this reason, solid scientific understanding of the interacting atmospheric, oceanic and terrestrial processes underlying Planetary Boundaries dynamics is essential.
This session invites contributions from the geophysical, biogeochemical and Earth system science communities to delve into the latest conceptual advancements, modelling techniques and research insights regarding the nine Planetary Boundaries.
We aim to bring together experts to present and discuss state-of-the-art research on the definition, quantification and analysis of the Planetary Boundaries. Oral and poster contributions are invited on studies assessing the status of single or multiple Planetary Boundaries, their spatio-temporal patterns, and their interactions. We welcome inputs on regional and global changes in major Earth system processes involved in such status changes, feedbacks that determine interactions between Planetary Boundaries, uncertainty analysis, and impacts of boundary transgressions. We also welcome conceptual proposals on how to improve assessment and application of the framework through modelling and integration of observation data, as well as cross-disciplinary discussions of the Planetary Boundaries framework.

This session aims to prove that geosciences and humanities have more common interfaces than boundaries separating them. Therefore, we call for papers with truly wide-reaching transdisciplinary themes. Many current “anthroposcenic" challenges require cooperation between the geosciences and humanities both in the combination of data and the integration of results for communicating research to a wider audience. We try to stretch this as far as possible to find the projects which use both the geosciences and humanities to answer new, fresh, and exciting questions and exploring the different ways in which geosciences and humanities interact. In this session we want to shine a spotlight on research topics that typically would not have space in a geoscience conference however where the link to the geosciences is clear. As well as this, we hope for the session to open discussion and collaboration between the disciplines and facilitate communication between the geosciences and the humanities as well as non-academic stakeholders. This can be in the form of environmental/climate history, performance sciences, history of science, political geology, science communication, museology, etc. Furthermore, we seek contributions that can shed light on how humanities can contribute (and has already contributed) to the field of geosciences.

The United Nations (UN) 2030 Agenda for Sustainable Development set a milestone in the evolution of society's efforts towards sustainable development which must combine social inclusion, economic growth, and environmental sustainability. The definition of the Sustainable Development Goals (SDGs) and the associated Global Indicator Framework represent a data-driven framework helping countries in evidence-based decision-making and development policies.

Earth observation (EO) data, including satellite and in-situ networks, and EO data analytics and machine learning plays a key role in assessing progress toward meeting the SDGs, since it can make the 2030 Agenda monitoring and reporting viable, technically and financially and be beneficial in making SDG indicators' monitoring and reporting comparable across countries.

This session invites contributions on how to make use of Earth Observations data to address SDG monitoring and reporting, in particular welcomes presentations about EO-driven scientific approaches, EO-based tools, and EO scientific initiative and projects to build, assess and monitor UN SDGs indicators.

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 past, present, and future environmental change, their associated impacts on 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 scenarios studies of mountain climate, cryosphere, ecology, hazards, and hydrology, which also incorporate studies on intersecting socio-economic dimensions and risks. Mountains as complex terrain can be difficult to adequately parameterize in (climate) models and many areas of the world lack high-elevation monitoring infrastructure that can record data at the relevant locations, densities, scales, frequencies, and resolutions needed. Likewise, there is a need to capture and account for socioeconomic 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. We particularly welcome contributions that describe how steps are being taken to address such knowledge gaps, including high-elevation integrated monitoring efforts, observations along elevational gradients, climate downscaling strategies and remote sensing innovations, and integration methods that include societal data and information to characterise and represent a more comprehensive systems approach to global change.

This session is endorsed and supported by the Mountain Research Initiative and the Institute for Interdisciplinary Mountain Research of the Austrian Academy of Sciences.

This session aims to (re)introduce biodiversity, an essential component of many aspects of life on Earth, as a notion that offers a wide array of multidisciplinary work from numerous fields of research, including but not limited to the geosciences and ecology. While biological diversity is vital for natural ecosystems such as forests and wetlands, and crucial for maintaining healthy freshwater ecosystems, soil systems, and oceans, it is also a factor that affects an ecosystems response to disturbances, affecting notions such as (ecosystem) integrity, health and resilience. Biodiversity is also intrinsically linked with the Earth’s processes, geomorphology, formation, and development. United Nation’s definition of biodiversity, or biological diversity, is: the variety of life on Earth and the natural patterns it forms. A wide range of studies on biological diversity also encompass ecological diversity, and ecosystem diversity, since the diversity of ecosystems also affects the diversity of organisms that inhabit them. Earth Science recognizes the role of biotic factors in governing geophysical processes across a wide range of spatial and temporal scales. Studies show that the control of biota might be part of a longer-term cycle, in which the dominance of biotic and abiotic processes not only switch, but depend on each other. Biota and abiotic processes may have co-evolved over both longer and shorter timescales. Scientific evidence from the geoscience community is therefore valuable in many political decisions for restoration, or rewilding, including the recent EU Nature Restoration Law. Also, research in these fields may contribute to policy on preparation for and/or prevention from natural hazards, including those that may be triggered by climate change. However, to be able to contribute to these processes, we need to be able to recognize the range of areas where our expertise is relevant and useful.

This session aims to recognize the wide range of geoscience research projects that focus on or highlight aspects of biodiversity, while welcoming those that favor inter- and/or transdisciplinary approaches. Through these presentations, we hope to demonstrate the broad spectrum of biodiversity-related areas in which the geosciences contribute and where more geoscience research is needed.

Traditional primary activities developed in an extensive low-input basis have been the core of economics in mountain areas for centuries, especially in the Mediterranean region. They have developed heterogeneous landscapes, which have been highly adapted to the environmental conditions, including climate variations or forest fires. However, most of these landscapes have been abandoned in the last century, resulting in rapid environmental alterations, and societies with poor adaptation and low resilience to climate/global change.
Changes in land use have direct implications on regional sustainability and they are one of the few climate change adaptation measures that can be adopted and adjusted over large areas in mountainous regions. A more heterogeneous landscape may improve the multi-sectorial resistance and resilience to the projected increase in aridity and drought severity or a decrease in water resources. In this context, a successful land use transformation must reduce not only the climate change-associated risks, but it must improve the social and economic sustainability as well.
Therefore, this session aims to promote a broad multi-sectorial biophysical and socioeconomic perspective to assess the efficiency of land management strategies and measures to improve landscape resilience and reduce climate change associated risks in mountain areas.
Specific topics of interest may address the followings:
- Land management strategies to improve landscape resilience in mountain areas.
- Recovering and maintaining traditional activities, such as extensive farming, as key tools for recovering mosaic landscapes.
- Key ecosystem services in cultural mosaic landscapes: i) soil fertility, ii) biomass provision iii) biodiversity, iv) maintenance of pastoral communities, v) reduction of forest fires, iv) blue water provision.
- Assessment of climate change adaptation measures from a multi- and cross-sectorial perspective.
- Modelling of environmental and socioeconomic impact of the landscape resilience measures in a spatially broader context.

In line with previous EGU sessions and major debates on urban geosciences, this transdisciplinary session welcomes studies that deal with complexity in urban geosciences on the basis of data and/or theories, either at the methodological level or at the level of original applications.
While complexity is too often confused with the complicated or the difficult, -it has fairly precise definitions such as "a system comprised of a great number of heterogeneous entities, among which local interactions create multiple levels of collective structure and organization....".
As analyses and simulations become accessible, as soon as appropriate tools are developed and used to analyse complexity in urban geosciences, this session welcomes concepts, methodologies and disruptive models to overcome current scientific bottlenecks, to better deal with non-linearities, multi-component systems, stochastic synchronisation, tipping points and elements, and extremes over a wide range of scales in geophysical and urban systems, as well as their interactions in human-environment system.

In recent years, armed conflicts inside and between countries are globally on the rise, causing drastic human and environmental harm at various scales. These have continued to compound lives and livelihoods that are already on the edge in the face of a changing climate. Yet, collecting field data in conflict zones is often challenging, if not impossible, hindering robust characterization for humanitarian response and eventual post-conflict recovery, reconstruction, and accountability. The increasing wealth of remotely sensed data, earth system modeling, and other empirical methods provide a unique opportunity to monitor the impact of armed conflicts on livelihoods and a wide range of environmental aspects including agriculture, water, and biodiversity in near-real time.

The session welcomes contributions that use innovative techniques and perspectives characterizing the environmental and geophysical dimensions of armed conflicts from around the world. Research outcomes based on empirical methods, remote sensing data products, or Machine Learning and Artificial Intelligence tools are appreciated. We aim to bring together scientists, nonprofits, and government representatives to discuss the developments and challenges in wartime response tools, as well as the long-term rehabilitation of affected regions.

Growth of Offshore Renewable Energy (ORE) developments, especially offshore wind farms, poses a huge interdisciplinary challenge when it comes to seabed characterisation, subsurface geology, sediment mobility, and assessing environmental impacts. The diversity of the challenge is even bigger when all necessary subsea cables are considered. Growing size and water depths of offshore wind farms, many of which are planned on formerly glaciated continental shelves, require a robust understanding of depositional processes responsible for forming the seabed and shallow subsurface which allows to understand and predict ground conditions better ultimately leading to project cost and risk reduction.
This session aims to bring together researchers and practitioners interested in geosciences applied to offshore renewable energy developments including wind, tidal, and wave as well as subsea cables or overburden characterisation for gas storage and CCS projects. We welcome submissions including, but not limited to, geophysical and geological subsurface, empirical, experimental, and modeling studies of sediment mobility, 2D and 3D UHRS seismic data interpretation, the use of seismic attributes and seismic geomorphology to better characterise the subsurface, environmental impact assessments of ORE developments, geohazards in the context of ORE, cable routing, and burial risk assessment, integration of geological and geotechnical information, approaches to nearshore and landfall characterisation as well as optimization of offshore and nearshore geophysical and geotechnical surveys.

Development and economic growth increase energy demand worldwide. In the quest of renewable energy sources a resurgence of large hydropower projects has been documented. Hydropower and multipurpose reservoirs are proposed with the aim of improving water, food and energy security, reducing volatilities in resource access and thus, in theory, alleviating existing inequalities in access to water, energy, and food. Yet, large infrastructure projects come with their own politics, and are underpinned by specific natural, social and moral orders. The costs and benefits of reservoirs are unevenly socio-spatially distributed, typically advantaging affluent urban populations and powerful industries at the cost of vulnerable groups in the affected river basins. Communities displaced by dam projects face radical livelihood changes, while the land surrounding reservoirs becomes subject to price speculation. Downstream fisheries are impacted by dam’s alteration of the flow regime, and flood plain subsistence farming is impacted by the disruption of fine sediment and nutrient replenishment.

In this session, we invite contributions from the various disciplines of knowledge, particularly if combined in the form of interdisciplinary and transdisciplinary research. We aim to explore the myriad of systemic changes induced by hydropower dams, taking into consideration the various physical processes interacting in space and time with the social system as well as the impacts imposed by these changes, such as perpetuating or amplifying existing local inequalities in access to fundamental natural resources.

Environmental issues are not only ecological but also social and cultural. To address them effectively, we need to understand how human societies interact with the environment. This session highlights the importance of social science in environmental research and vice versa, and invites contributions that explore how interdisciplinary collaboration can lead to innovative and sustainable solutions. We welcome researchers from various disciplines, such as environmental science, social science, data analysis, data providers and metadata specialists, to share their insights, case studies, and challenges. We aim to foster meaningful discussions and exchange of ideas across different perspectives and domains. By integrating the expertise of social scientists with environmental research and vice versa, we can develop a more comprehensive and holistic understanding of environmental problems and their solutions. Let's work together to contribute to a more sustainable relationship between humanity and the environment.
Topics may include, but are not limited to, the following:

– Air quality and climate indicator’s effects on urban citizens’s attitudes
– Climate action plans and solutions for green and sustainable cities
– Cultural heritage and environmental sustainability
– Environmental policy and governance
– Sustainable agriculture and land use
– Biodiversity conservation and ecosystem services
– Climate adaptation and resilience
– Citizen science and public engagement
– Project reports or infrastructure requirements related to multiiciplinary usecases

Citizen science (the involvement of the public in scientific processes) is gaining momentum across multiple disciplines, increasing multi-scale data production on Earth Sciences that is extending the frontiers of knowledge. Successful citizen science initiatives can potentially be scaled-up to contribute to larger policy strategies and actions (e.g. the European Earth Observation monitoring systems and the Sustainable Development Goals), for example to be integrated in GEOSS and Copernicus. Making credible contributions to science can empower citizens to actively participate as citizen stewards in decision making, helping to bridge scientific disciplines and promote vibrant, liveable, and sustainable environments for inhabitants across rural and urban localities. However, citizen science also poses challenges for researchers to facilitate effective participatory science, yet it is of critical importance to modern research and decision-makers.

We want to ask and find answers to the following questions:
Which citizen science approaches and tools can be used in Earth and planetary observation?
What are the biggest challenges in bridging scientific disciplines and how to overcome them?
What kind of participatory citizen scientist involvement (e.g. how are citizen scientists involved in research, which kind of groups are involved) strategies exist?
How to ensure transparency in project results and analyses?
What kind of critical perspectives on the limitations, challenges, and ethical considerations exist?
How can citizen science approaches and initiatives be supported on different levels (e.g. institutional, organizational, national)?

Citizen science, where people from outside academia contribute to data collection and/or analysis, comes in many forms, from the small-scale to very large-scale projects. In the context of hydrology and natural hazards, the value of such data lies in the high temporal and spatial resolution that can be obtained from such projects, as well as the improved relationships between communities and academia that arise from their participation and that can be used to improve both science and community preparedness. In this context, this session aims to bring together scientists and practionners working on citizen science tools in the fields of hydrology and natural hazards for use with concerned communities to share insights, challenges, and solutions.
General themes include :
• How can citizen science be used to both increase monitoring of natural hazards, as well as to increase community involvement and awareness?
• What data management approaches can be used to increase communities’ sovereignty and control over their data, as well as long-term project sustainability?
• What kind of participatory approaches exist to facilitate community involvement in different types of citizen science projects?
• How can academia’s often ingrained bias against data collected by non-academics be overcome?
• How can legitimate concerns about potential data biases, inaccuracies and long-term sustainability of citizen science projects be effectively addressed?
• How can distributed database technologies be used to both share and collect data in citizen science projects, and what major advantages and challenges does this bring?

Biogeophysicsis is an emerging discipline that applies near-surface geophysical techniques to detect subsurface (bio)geochemical reactions in an effort to develop non-invasive high-resolution monitoring approaches for environmental and engineering applications. Over the past two decades, there has been extensive work on using geophysical methods to address engineering and environmental problems, such as the role and fate of contaminants, the development of natural cements by microbially induced carbonate precipitation (MICP), the characterization of recycled materials such as biochars, and the monitoring of degradation and restoration of major natural carbon sinks such as peatlands and grasslands. Moreover, monitoring has been successfully applied at both the lab- and field-scales to gain detailed information on biogeochemical processes such as natural attenuation processes at contaminated sites, in-situ (bio)remediation and microbial activity, the production of greenhouse gases in landfills, and the quantification or root-activity and soil-root interactions. In this session, we invite abstracts presenting advances in the application of geophysical methods to monitor and/or better understand biologically-mediated and abiotic geochemical processes and properties in subsurface and anthropogenic systems that promote the Circular Economy, Net Zero, and Contaminants of Concern. Submission of laboratory or field experiments of monitoring as well as novel modeling approaches to better (quantitatively) describe bio- and hydro-geophysical signatures are encouraged.

Water scarcity, food security, energy transition and environmental protection issues represent challenges of paramount importance. Climatic and demographic change stressors determine further uncertainties. Governors are called to take important decisions to support fair allocation of resources, mitigate conflicts and sustain social cohesion while managing socio-economic pressures and foster climate change adaptation across diverse scales. Science studies validated methods and data for investigating and quantifying the interlinkages of the Water-Energy-Food-Ecosystem (WEFE) Nexus components. Nevertheless, WEFE Nexus knowledge and technology transfer is still falling behind.

Stakeholders engagement, ethics and gender dimension represent key topics while mainstreaming WEFE Nexus approaches. Citizens and stakeholders are not adequately informed and involved perceiving to receive Nexus-driven technological and policy advancements as a top-down enforcement, like a burden, rather than understanding their multiple benefits towards a safer and healthier water, energy, food production.

Science-driven WEFE Nexus models, data and scenarios are also approaching a mature stage, but the operationalization of available research for optimal resource and risk management plans following Nexus principles is being impacted by several problems. The knowledge and technological transfer of WEFE Nexus science is facing severe technical and non-technical barriers. Several WEFE Nexus scientific and innovation programs showed that technological innovation shall work in synergy with a behavioral and mindset change while considering social, cultural and historical dimension.

This session promotes contributions working on WEFE Nexus approaches with particular focus on research, innovation and case studies working across multiple scales. Transdisciplinary scientific efforts presenting outcomes and challenges are invited to share WEFE Nexus driven scientific models, geospatial solutions, stakeholder engagement, gender dimension, policy and guidelines innovations among further models and methods aiming to foster Nexus thinking for addressing WEFE security.

Land uses and land covers are among the key environmental factors that influence water cycles. Land use management and dynamics over the last decades have had dramatic impacts on water resources through: urbanization leading to the artificialization of water cycles in urban areas and the spatial concentration of water demand for drinking water, development of intensive agriculture leading to the pollution of surface and groundwater and the explosion of water demand for irrigation, abandonment of agriculture and animal farming and natural deforestation and mountainous areas leading to the disturbance of water systems, hazard risks arising from changes in runoff dynamics. In order to adapt territories to on-going global change, land use strategies leading to a sustainable management of water resources need to be designed. Supporting decision makers in the design of these strategies requires the use of interdisciplinary approaches for the mapping and monitoring of land use dynamics, the understanding of relationships between land use dynamics and water resources and the analysis of economic and social impact on the society. This session proposes to present scientific contributions on different disciplinary approaches of these questions, including through the use of state of the art and new technologies such as citizen science, as well as multidisciplinary and interdisciplinary contributions bridging over multiple disciplines to support the design of sustainable land management strategies.

Although urban areas cover a small fraction of the world’s surface area, over 50% of the world’s population lives in urban areas and is predicted to double by 2050. Urban soils play a key role in urban sustainability as they are responsible in regulating a plethora of ecosystem services and supporting multiple soil functions however, they are often overlooked and not fully considered during projects’ planning stages, during construction, or post-development and are not well integrated into policies at all scales. Moreover, more and more countries are implementing strategy plans on circular economy and green finance, inevitably bringing urban soils resilience and management to the forefront. Urban soils are characterised by high heterogeneity and are subject to multiple anthropogenic disturbances, such as sealing, compaction, degradation and erosion, mixing, pollution, landfilling, as well as premium land take and climate change. All of these anthropogenic influences have severe impacts on the multifunctionality of urban soils, their role in delivering ecosystem services and their resilience.
In this session, we aim to advance our understanding of urban soil multifunctionality and our capacity to integrate urban soil multifunctionality into policy, planning and development by connecting recent research and innovations across disciplines and sectors . We welcome interdisciplinary submissions focusing on field research, modelling, remote sensing or social sciences, as well as case studies from the industry and policy arenas. We seek to help bridge the gap between research and practice, bringing forward knowledge acquired from different disciplines and sectors helping to deepen our understanding and capacity to manage urban soil multifunctionality and resilience.

Irrigation is the activity of making sufficient soil water available to meet transpiration requirements, dictated by local climate and depending on plant cover and stage of growth. It also largely modifies the hydrological cycles by increasing evapotranspiration, storing water in reservoirs, extracting water from water bodies and releasing to others, etc.
While technical devices largely evolved in the last decades, the strategic components at the farm level remain unchanged for centuries and related to how much water is delivered and when. Today, water use in agriculture faces physical and social challenges in relation to its efficiency: first spatial bounding and temporal interval are crucial for performance evaluation, second the meaning of losses depends on one’s perspective and their assessment have many economic and political implications.
Information on agricultural water management at various spatial scales (plot, farm, district and region) is getting more and more accessible with the development of Information and Communication Technologies. ICT enable accurate monitoring, automate irrigation water application and facilitate the continuous exchange of information across the water supply chain. Still, the use of water for irrigation is in many regions poorly quantified and controlled. As a result, building regional or global recommendations to improve local irrigation performances is out of reach.
This session aims to provide a forum for discussion between methodologies that contribute to quantify agricultural water uses, whether direct water use or indirect modification of hydrological cycles. More specifically, the objective is to identify the diversity of methods on how to characterize water volumes involved in agriculture, drawing up on information-based technologies that could support the solving of socially relevant problems related to irrigation performance. Contributions about the use of massive information (notably those from new earth observation sensors, low-cost sensors, IOT devices) aiming at providing assessments at various scales are encouraged. This may include in particular information from decision support tools for farmers, using meteorological data (measured or forecat), soil water status, plant sensors, flow sensors in networks etc. and innovative methods to process those data. Reflexive contributions about linkage and spillover effects between site-based innovations and regional development processes are also welcomed.

Soil health is a multifaced concept considering soil capacity and functionality towards a wide range of ecosystem services. The soil health concept directly relates to soil degradation that can viewed as a reduction of soil health involving, e.g., soil loss through erosion, decline in soil organic matter, water and nutrient retention ability, damage to the soil structure, contamination, and soil fertility.

Several measures to support soil health and tackle soil degradation have been proposed in the scientific literature, along with several indicators to monitor expected benefits. The need for standardized data covering the broad concept of soil health and degradation is arising, along with the lack of information on relationships between soil quality and agriculture, forest and grassland resilience, and the socio-economic and environmental impacts of these measures.

The scattered data availability and their complex integration for agronomic or environmental management and policy decisions may partly be covered by many European and other international or national initiatives in the frameworks of the H2020, Horizon Europe, PRIMA, FAO programs, and other programs.

This session aims to collect contributions that embrace Soil Research and Monitoring, Sustainable Farming Practices, Policy and Regulations. In particular, we aim to collect the contribution of national and international initiatives addressing soil health and soil degradation and their relationship with agriculture, forestry, and pasture management, and the economic aspects of these systems at various scales (from the field to the landscape, to the region to the global scale).

We aim to establish a forum to foster collaboration among projects to support soil health, the resilience of agriculture and other land uses, and biodiversity in rural areas while taking into account social, economic and environmental sustainability.

The biogeochemistry of dissolved organic matter (DOM) involves a wide range of temporal and spatial scales. In this context, biosynthetic production of molecules is a fast process - as is the instant turnover of freshly produced DOM by microorganisms. Likewise, photodegradation happens on time-scale of milliseconds and other physico-chemical processes such as absorption to particles or coagulation are also spontaneous reactions. All these fast processes act on small molecular and microbial scales, while the largest fraction of DOM resides in the deep ocean for millennia, crossing ocean basins. Fast and slow processes on small and global scales are inherently connected and it is crucial to understand the mechanisms regulating these processes and their interlinkage to predict future changes in the bulk of this significant global carbon reservoir.
This session will bring together organic biogeochemists, microbial ecologists, oceanographers, limnologists, modellers and all other scientists who are interested in advancing the progress in DOM biogeochemistry. We welcome contributions from laboratory, field and modelling studies, and particularly encourage submissions of combined approaches. The session aims to cover the different temporal and spatial scales of DOM biogeochemistry, ranging from studies that focus on specific individual processes and environments to studies that integrate multiple processes and scale-up to global cycles.

Drone-based approaches are a mushrooming area of research within the geosciences, such that most Earth and environmental science departments will now include drone equipment, along with the people who know how to use it. Whilst there has been a particularly strong emphasis on developing reproducible workflows for drone data within the geosciences, there is less work that considers the role/s of drone technology in addressing cross-cutting themes. With drone workflows now relatively mature, it is time to think more critically about where drone data fits within the geospatial ‘ecosystem’ and to consider the benefits of fine resolution data offered by drones in a broader context than has been done up to now.

The UN SDGs provide a useful interdisciplinary framework for grappling with major social and environmental challenges. The SDGs offer a “shared blueprint for peace and prosperity” through 17 goals (https://sdgs.un.org/goals) that call for action. Core to the SDGs is the plan to end poverty hand-in-hand with other strategies, particularly those that improve health and education, reduce inequality, and spur economic growth, while also addressing climate change and global conservation priorities.

In this session we invite papers from across the diverse disciplines of the EGU describing work positioning drone-based approaches (e.g. drone applications, technical developments, social innovations, and community approaches) against the UN SDGs. We follow Chabot et al.’s (2022) definition of a drone to include all types of robotic vehicles including aerial, ground, water-surface, underwater and space drones. Some examples (not exhaustive) might include:

- Monitoring geohazards (e.g. near urban zones, the coast, volcanoes, forest fires); SDGs 1, 9, 11, 13
- Exploring sustainable energy futures, in smart mining for example; SDGs 7, 8, 9, 11, 12, 13
- Environmental remediation of contaminated lands and water bodies, legacy sites and tailings; SDGs 3, 6, 10, 14, 15, 16
- Oceanography, sea floor mapping; SDGs 13, 14
- Vegetation monitoring and carbon/biodiversity accounting; SDGs 11, 15
- Partnerships to embed drones in community projects; SDGs 4, 5, 8, 9, 16, 17
- Education through the drone, empowering marginalised groups; SDGs 4, 5, 10, 11, 13
- Exploring volumetric space, underground, oceans, understory; SDGs 7, 11, 14
- Agricultural drone approaches, food security; SDG 2

Reference: Chabot et al. 2022. Drone Systems and Applications, 10(1), pp.399-405.

Air pollution is responsible for close to 7 million premature deaths annually, majority of it in the Global South countries. Knowing the source of pollution and making the polluters accountable for their polluting activities is essential to control the menace of air pollution. Air pollution transcends boundaries, making it crucial to understand the interconnectedness between different regions. Developing economies in the Global South primarily contribute to pollution through rapid industrialization and urbanization. In contrast, the Global North, characterized by stringent environmental regulations, faces challenges in managing the transportation of pollutants. Internationally, legally binding agreements aimed at addressing air pollution is weakened by the difficulty in comprehending the intricate dynamics of pollutant transport. At regional and local levels, the complexity in assigning responsibility for pollution, especially in cases involving multiple polluters or unidentified sources, underscores the need for air pollution forensics.
Forensics refers to the use of science and technology to the matters of civil and criminal law. Air pollution forensics is the use of scientific tools and procedures for fixing liability in air pollution incidents. The tools and procedures used should stand the stringent scrutiny of the court, and hence, it is important for the practitioners of air pollution forensics to know the legal procedures and requirements in this regard.
In this session, we invite contributions pertaining to scientific methods and procedures adoptable/ adaptable in air pollution forensics. The contributions can focus on any of the following areas
• Adaption of existing techniques for pollution source identification to forensic practice
• Guidelines/protocols/procedures/modelling techniques for carrying out forensic investigations in air pollution cases
• Air pollution forensic case studies
• Local, regional and trans-boundary transport and transformation of air pollutants from the forensic perspective
• Any other topic directly relatable to air pollution forensics (e.g. Emission Inventories, design of monitoring network, development of sensors for forensic applications)
Air pollution forensics is an emerging area and this session is expected to bring together researchers and practitioners of the area to share their knowledge, best practices and to discuss about the strategies to tackle the challenges they face.

The Water-Energy-Food (WEF) Nexus has long been recognised as a conceptual model outlining the complex interrelationships between water, energy, and food systems. Though promising in its ability to capture these complexities, its practical application has been a challenging endeavour. This session proposes using digital innovation technologies, or a combination of these technologies, such as Digital Twins, as a groundbreaking method to translate the WEF Nexus from an abstract framework into actionable, real-world solutions.

Utilising advanced technologies like IoT, Machine Learning, and Blockchain, Digital Twins can model the intricate interdependencies within the WEF Nexus, providing a deeper understanding of system dynamics, simulating scenarios, and offering insights for optimisation. The capabilities of these digital innovation technologies can lead to more effective resource allocation, improved sustainability practices, and conflict resolution between competing demands.

Implementing the WEF Nexus through Digital Twins correlates directly with achieving multiple UN SDGs, including SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities). This session will also address the challenges of using digital technologies for SDGs, such as data privacy and security.

In realising the ambitious objectives of this session, the involvement of all stakeholders—ranging from policy-makers and industry experts to researchers and end-users—is paramount. A collaborative approach is crucial for fully understanding the WEF Nexus and leveraging Digital Twins as a practical solution. In this context, technology is viewed as a facilitative tool rather than the ultimate aim. By fostering a cooperative ecosystem where technology aids decision-making and resource optimisation, we can move closer to achieving the immediate goals of the WEF Nexus and broader United Nations SDGs.

This session continues the IEEE WIE UKI special session on the "Role of Digital Technology to Support UN SDGs," held at the 4th IEEE International Conference on Intelligent Engineering and Management (ICIEM 2023). It is technically sponsored by the Water-Energy-Food Nexus and Industrial IoT research groups at the University of West London, the Digital Twins for Sustainable Development Goals Lab at Queen Mary University, and the IEEE WIE UK & Affinity Group.

Plastic pollution is ubiquitous in terrestrial, freshwater, and marine ecosystems. Reliable data on plastic abundance and fluxes are crucial to study its sources, sinks, transport dynamics, and impact. Furthermore, long-term and large-scale monitoring is required to design, implement, and assess plastic pollution prevention and reduction measures.
In this session we invite contributions that present recent advances in plastic pollution monitoring across the entire Geosphere (land surface, soil, rivers, estuaries, oceans and beyond). Presentations may focus on:

• Novel monitoring methods, including advanced techniques (e.g. remote sensing, multi/hyperspectral cameras, acoustic sensors, artificial intelligence);
• Monitoring strategies, including large-scale and long-term efforts, and citizen science approaches;
• All plastic size ranges, from nano to macro;
• Baseline studies to assess current plastic pollution levels;
• Long-term trends or recent discoveries based on plastic monitoring data.

With this session we aim to bring together scientists that aim to contribute to novel approaches to provide reliable data on environmental plastic pollution.

Ecological engineering is an established discipline that focuses on the design that exploits ecological elements and ecosystems for the benefit of both humans and nature. The existential threat of climate change and the unsustainable use of resources indicate nowadays that we urgently need new approaches to deal with these challenges. As we are seeking for truly sustainable solutions, we look back to the inherent value of nature to get inspired and develop a new design paradigm. Ecological engineering is today redefined and emerges as a pivotal approach in addressing contemporary environmental challenges and promoting sustainable development. It integrates principles from ecology, engineering, and design to create harmonious interactions between human activities and natural systems forming a new, holistic approach for problem-solving. The critical step of this new approach is the adoption of systems thinking and of circularity in problem-solving methodology towards re-establishing material cycles to deal with resource scarcity and expanding the nature-based toolbox using ecosystem services and renewable resources. The goal of this session is to discuss and analyze these key concepts, benefits, and applications of modern ecological engineering.
Ecological engineering can provide integrated solutions and sustainable alternatives to conventional engineering practices that are based on ecological processes and elements. The emphasis on working with nature, rather than against it, represents a paradigm shift that encourages innovative problem-solving. These approaches yield co-benefits, such as increased biodiversity, enhanced ecosystem services, and improved social cohesion.
Nature-based solutions and nature-based inspiration are integral components of the modern approach to environmental management and sustainable development. These approaches hold promise for addressing complex challenges while promoting the conservation and restoration of natural systems and cycles. As the global community strives to find holistic solutions to pressing ecological and societal issues, the principles of ecological engineering provide valuable pathways to re-balance the relationship between human activities and the environment.