The global energy transition is driving unprecedented demand for critical and strategic raw materials such as copper, lithium, nickel, cobalt, graphite and rare earth elements. In Europe, the Critical Raw Materials Act (CRMA) sets ambitious targets for domestic extraction and processing, and requires Member States to establish national exploration programmes, including geoscientific surveys, mineral mapping and geochemical campaigns. These programmes are generating a new wave of regional and national-scale geophysical datasets and renewing interest in the reinterpretation of legacy data. With most near-surface deposits in well-explored terranes already discovered, future discoveries will increasingly depend on our ability to image deeper, covered and geologically complex targets.
This session invites contributions on all aspects of geophysics applied to mineral exploration, from national to deposit scale. We welcome studies using potential field (gravity, magnetics), electromagnetic, magnetotelluric, induced polarisation, seismic, radiometric and borehole methods, as well as airborne, drone-based and passive-source approaches.
Contributions presenting results, strategies and lessons learned from national exploration programmes, as well as work on low-impact exploration, social acceptance, and secondary resources such as mine waste and tailings, are also welcome. The session aims to bring together academia, geological surveys and industry to discuss how geophysics can support a secure and sustainable supply of raw materials in Europe and worldwide.

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

Recent advances in geophysical observations, numerical simulations, remote sensing products, and high-performance computing are generating large and increasingly complex multidimensional datasets. Interpreting these datasets requires advanced processing and modelling together with effective methods for visualisation and integration.
This session focuses on the development and application of advanced visualisation methods for geophysical data. We particularly welcome approaches that improve the exploration and interpretation of multidimensional, multiscale, and time-dependent datasets, and that can improve geophysical interpretation and its practical application.
We welcome contributions addressing interactive visualisation, 3-D and 4-D representations, data fusion, and the integration of observational, experimental, and modelled data. Contributions combining geophysical data with artificial intelligence and machine learning, including AI-assisted visual analytics and interactive approaches for navigating large and heterogeneous datasets, are particularly encouraged.
The session will also consider practical applications of advanced visualisation across different operational and scientific scenarios. Examples include geophysical applications in engineering and infrastructure monitoring, environmental protection and assessment, natural hazard monitoring and early-warning systems, and the management and interpretation of large and heterogeneous datasets. We also welcome applications to planetary exploration, where observations acquired by spacecraft, landers, rovers, and orbital platforms need to be integrated with geological, geophysical, topographic, and numerical datasets to investigate planetary environments.
By bringing together developments across geophysics, engineering, environmental sciences, natural-hazard research, data science, and planetary exploration, the session will provide a forum to discuss how advanced visualisation can improve the interpretation of complex geophysical datasets and support more effective, transparent, and interactive approaches to understanding the Earth and planetary geophysics.

AI is rapidly transforming research in deep Earth geodynamics, shaping research strategy, driving new research pathways, and promoting new research directions. This session invites AI-driven contributions and Machine Learning applications from geodynamics and related disciplines focusing on the structure and evolution of the Earth's crust and upper mantle.

The equatorial and low-latitude ionosphere is a region of profound ionospheric activity and variability, presenting significant challenges for both scientific understanding and operational prediction. Its strong day-to-day (DTD) variability, and its direct impact on the occurrence of plasma irregularities and associated ionospheric scintillations, remains a critical and highly challenging subject in space weather research.

A key obstacle to progress has been the inherent difficulty in obtaining simultaneous, comprehensive observations of the coupled ionosphere-thermosphere system. To truly characterize the sources of DTD variability and determine their relative importance, an integrated, multi-disciplinary approach is essential.

This session invites contributions that explore new and integrated pathways to address these challenges. We are particularly interested in:

· New Observations: Novel ground-based and space-based observational techniques and networks that provide critical data on the equatorial and low-latitude ionosphere-thermosphere system.
· Advanced Modeling: Physics-based and first-principles modeling efforts that seek to simulate and understand the sources of DTD variability and their nonlinear interactions.
· Data-Driven Approaches: Innovative applications of machine learning, data assimilation, and other data-driven techniques to fuse disparate data sets, uncover complex patterns, and improve forecasting capabilities.

The ultimate goal of this session is to foster a dialogue that bridges observation, theory, and application, with a focus on improving the predictability of the equatorial and low-latitude ionosphere and its irregularities across a wide range of solar, geomagnetic, and lower atmospheric conditions.

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

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

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

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

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

Machine learning is reshaping the representation of complex physical processes in Earth system models, offering new avenues for parameterisation, emulation, and hybrid modelling. This session focuses on the use of machine learning to emulate computationally expensive or unresolved processes, accelerate physical simulations, enable data-driven discoveries, and improve representation across domains such as convection, turbulence, radiation, hydrology, sea ice, and other Earth system components. Topics include (but are not limited to):

- Subgrid-scale parameterisations via machine learning
- Emulators of physical processes, model components, or whole weather and climate models (including end-to-end learning and foundation models)
- Data-driven discoveries
- Hybrid ML-physics modelling frameworks
- Physics-informed neural networks, neural operators, and differentiable programming
- Reinforcement learning and other approaches for ensuring physical consistency, stability, and optimising model behaviour
- Calibration and parameter optimisation using ML
- Physical behaviour, encoding and analysis of ML models
- Verification and explainability (XAI) of data-driven models (including AI forecasting)
- Representation of extremes and downscaling
- Coupling of ML models with physical models
- Cross-domain applications (atmosphere, ocean, cryosphere, land)
- Impacts of architecture choices and design on physical behaviour
- Links between ML methods and fundamental physics or applied mathematics
- Novel AI methods and applications

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

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

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

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

Earth operates as a complex, self-regulating cybernetic system driven by both internal processes and external astronomical forcing. This session explores the intricate feedback loops between the Solar System’s gravitational dynamics and Earth’s interconnected geospheres—including the atmosphere, hydrosphere, lithosphere, biosphere, pedosphere, troposphere, magnetosphere, and ionosphere. We focus specifically on how these systemic interactions govern sub-system variations, with a core emphasis on water balance components and hydrological cycles.
To decipher these non-linear, multi-scale relationships, traditional deterministic models must be augmented with data-driven methodologies. We invite contributions that advance empirical research tasks, conceptual frameworks, and system-level models. We particularly welcome studies utilizing artificial intelligence (AI), statistical learning, machine learning, and advanced data-mining techniques to analyze observational and proxy datasets. By bridging planetary dynamics, cybernetic geoscience, and modern data science, this session aims to foster a holistic understanding of Earth’s systemic balance and improve our predictive capabilities in a changing cosmic and terrestrial environment.

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

Machine learning (ML) is increasingly advancing cryospheric science, offering new ways to extract information from the rapidly growing volume of satellite, airborne, and in situ observations and to accelerate numerical modeling. Deep learning has proven particularly effective for computer vision tasks such as calving front detection, glacier outline delineation, mapping of permafrost landforms, sea ice classification, and snow cover mapping. Beyond image analysis, ML-based emulators can reproduce the behavior of computationally expensive physical models. This enables large ensembles and the inversion of poorly constrained parameters. Hybrid and physics-informed approaches further promise to combine the flexibility of data-driven methods with physical consistency. More recently, foundation models pretrained on large Earth observation archives have raised expectations of strong transferability across regions and sensors, particularly for applications where labeled training data are scarce. However, their benefit for cryospheric applications has yet to be demonstrated, as they have not consistently outperformed task-specific architectures trained from scratch. Systematic evaluation on shared benchmark datasets is therefore essential to measure real progress. We invite contributions on all aspects of machine learning for the cryosphere, including but not limited to computer vision and remote sensing, model emulation and hybrid modeling, benchmark datasets, as well as trust and interpretability. We also welcome critical perspectives, such as barriers to adoption, computational costs, and cases where ML approaches did not outperform physics-based methods.

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

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

Early career and underrepresented scientists are particularly encouraged to participate.

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

Reliable hydrological information is essential for water-resource management, disaster risk reduction, ecosystem protection, and climate adaptation. However, hydrological prediction remains challenging in many regions due to sparse monitoring networks, limited observations, and rapidly changing hydroclimatic conditions. Advances in Earth observation, artificial intelligence (AI), digital technologies, and computational methods are transforming the field, enabling a shift beyond traditional Prediction in Ungauged Basins (PUB) toward a broader concept of hydrological intelligence that integrates observations, process understanding, modelling, and data-driven approaches.

This session invites interdisciplinary contributions that advance hydrological prediction and intelligence in data-scarce and poorly monitored catchments. We welcome methodological, observational, and applied studies that improve predictive capability, transferability, interpretability, uncertainty characterisation, and operational utility.

Topics include hydrological prediction and forecasting, early-warning systems, regionalisation and transfer learning, AI and machine learning, foundation models, explainable and trustworthy AI, hybrid and physics-informed modelling, digital twins, data assimilation, model calibration, uncertainty analysis, climate-change impacts and adaptation, groundwater-surface water interactions, ecohydrology, socio-hydrology, citizen science, and open hydrological science.

Particular emphasis is placed on integrating Earth observation products, environmental data streams, and emerging computational techniques to improve prediction, enhance process understanding, and support decision-making under uncertainty. Contributions spanning local to global scales are encouraged.

By bringing together researchers from hydrology, hydro informatics, Earth observation, and environmental data science, the session aims to foster collaboration and accelerate the development of next-generation hydrological intelligence systems for robust prediction, adaptive water management, and climate resilience in an increasingly uncertain and data-constrained world.

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

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

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

Remote sensing products have great potential to support the monitoring and modelling of water resources. Nevertheless, their uptake by water managers remains limited because of concerns related to data quality, spatial and temporal resolution, trust, accessibility, and technical expertise.

In this session, we look for contributions on new developments that support the use of remote sensing data for water-management applications, from local to global scales. We welcome research aimed at improving the quality and usability of remote sensing products, such as higher spatial and/or temporal resolution mapping of land use and agricultural practices; improved assessments of river discharge, lake and reservoir volumes, and groundwater resources; drought monitoring and modelling and its impacts on water-stressed vegetation; and the monitoring and modelling of irrigation volumes.

We are also interested in studies assessing the quality of remote sensing products through uncertainty analysis or validation against independent data sources. Contributions that combine different techniques —such as physically based models and artificial intelligence—or integrate multiple data sources from different sensors, including remote sensing and in situ observations, are particularly welcome. Studies using different types of imagery, including satellite, airborne, and drone data, are also encouraged.

Finally, we invite presentations on the development of user-friendly platforms that follow the FAIR principles and provide smooth access to remote sensing data for water-management applications. We are particularly interested in applications of remote sensing to characterise human–water interactions and assess the impacts of climate change across the entire water cycle, including the connections between inland and sea (coastal) systems.

Hydrologic data have traditionally been sparse in space and time, requiring process-based models to interpolate observations and predict fluxes under limited data availability. Rapid advances in observation technologies are now enabling near–real-time monitoring of complex hydrologic processes across a wide range of spatial and temporal scales, fundamentally transforming hydrology from data scarcity to data abundance. Fully exploiting these emerging observation systems requires parallel advances in digital tools for data processing, integration, modeling, and visualization. New digital solutions—including digital twins, real-time data platforms, and interactive visualization environments—play a critical role in translating novel observations and scientific insights into actionable information for water resources research, multi-sector management, and decision-making. Under accelerating environmental change, two-way data exchange between models, observations, and stakeholders is increasingly essential for responsive forecasting and intervention. This session invites contributions on next-generation hydrologic observation systems and multi-scale digital solutions that advance process understanding, modeling, and application. Topics of interest include, but are not limited to:
- Novel observation techniques enabling improved parametrization of hydrologic processes
- Integration and fusion of next-generation hydrologic observations into modeling frameworks
- Digital twins and other advanced digital representations of hydrologic systems
- Real-time forecasting, early-warning, and decision-support systems for water resources and hydrologic hazards
- Case studies demonstrating the operational use and societal impact of hydrologic digititalization
Hosted by the Digital Water Flagship (https://digitalwaters.fi)

The concept of Earth as the sole body in the Solar System with liquid water that can harbor microbial life has been overturned by the discovery of multiple 'ocean worlds'. The Solar System is home to several planetary bodies with subsurface oceans of liquid water, including icy satellites such as Europa, Ganymede, Callisto, Enceladus, Titan and Triton, as well as dwarf planets like Pluto, and chief among these ocean worlds, the Earth. Furthermore, new icy and ocean worlds are being continuously discovered in other planetary systems as well. Like Earth, the exploration of these oceans includes both aspects of planetary evolution and habitability. The geodynamic role of oceans in planetary evolution is thus a crucial aspect of understanding not only planet formation, but the onset of biological activity as well. In what ways can the oceans of Earth serve as analogs for other oceans of the Solar System? What instrumentation can be implemented on the Earth now to further our understanding of these ocean worlds, and what technological advances might we expect in future exploration of subsurface liquid water environments beyond Earth?

This session focuses on analog sites, laboratory simulation, modeling, instrumentation and mission proposals. Coordination between Earth, marine and planetary science communities is encouraged, as well as emphasis on upcoming (e.g. JUICE and Dragonfly) and proposed missions (e.g. Enceladus Orbilander). Analog sites might encompass either geological or biological themes in the broader frame of habitability. Interfaces of ice-water (e.g. underside of floating ice shelfs and subglacial lakes), clathrate-water (e.g. ocean floor sediments, veins/fractures/faults, layered horizons and atmosphere particulates), seafloor-ocean, and rock-ice (i.e. glaciers) are of particular curiosity. Instrumentation includes sensors, buoys, submersibles, drilling and coring, as well as satellite instrumentation (e.g. spectrometers, magnetometers and gravimeters).

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

The study of water-related ecosystems covers a wide range of applicative contexts, entailing many scientific challenges and several diversified technological solutions.
Nowadays, the sustainable management of water resources requires a holistic approach, which attains to the soil, vegetation and all the living things interacting with the water.
The transition from the mere monitoring of the processes related to water systems to the wider concept of “water habitats”, implies the study of such ecological interactions in various possible scenarios, which are often characterised by a strong relationship between natural and anthropogenic contexts.
In this challenging framework, research activities aimed at developing efficient monitoring technologies and management strategies are encouraged to embrace a highly multidisciplinary approach. Here, water management meets noticeable ecological, economic and social implications, and the public awareness of such implications is rapidly growing.
Accordingly, scientific/technological advancements have to go beyond the observation of water bodies and their related processes and infrastructures, by extending the scope to the water habitats and the many measurable indicators of their functions and health status, directly or indirectly related to water, such as water quality, biodiversity, plant ecophysiology, and resilience to environmental extremes.

This session welcomes contributions related to the monitoring of water systems and their characteristic habitats about:
• design of field measurement instrumentation
• development of new sensing techniques, innovative field experiments
• application of remote sensing products
• advancements in sensor networks
• Integration between sensor systems and computational tasks
• Investigations about data science aspects, e.g. geospatial analyses, big data and AI applications.

Contributions may regard (but are not limited to) rivers & lakes, wetlands, irrigated areas, forests and natural habitats, coastal zone, urban habitats and water infrastructures, including distribution networks. Both qualitative and quantitative assessments are appreciated.
Studies regarding groundwater monitoring and management and its interaction with surface processes are also relevant to this session and are very encouraged.

Effective and enhanced hydrological monitoring is essential for understanding water-related processes in a rapidly changing world. Image-based river monitoring, remote and proximal sensing, low-cost and opportunistic sensors, citizen science and artificial intelligence are reshaping the way hydrological processes are observed across scales, environments and conditions. Yet the value of these innovations depends on methodological rigour: new observational approaches need to be critically evaluated, benchmarked against established methods and integrated with existing datasets before their contribution to process understanding, modelling and operational hydrology can be established.

This session is co-sponsored by MOXXI (Measurements and Observations in the XXI century), the IAHS working group on novel observational methods, and provides a forum for research in which observation itself is the subject of investigation. We invite contributions on:

• Disruptive and Innovative sensors and technologies in hydrology (e.g., UAS, camera systems – RGB, thermal, multispectral and hyperspectral – low-cost and open-hardware sensors, distributed fibre-optic sensing).
• Advancing opportunistic sensing strategies in hydrology (e.g., commercial microwave links, GNSS reflectometry, smartphones, personal weather stations).
• Automated and semi-automated methods for extracting hydrological variables (e.g., water level, flow velocity, discharge, turbidity, plastic transport and river health parameters), including image processing, machine learning, data fusion, and edge-computing.
• Critical evaluation, benchmarking and intercomparison of observational approaches, datasets and products, including calibration/validation and uncertainty quantification.
• Integration of novel and conventional observations, and new approaches to long-term hydrological monitoring.
• Innovative citizen science and crowd-based methods for monitoring hydrological extremes.
• Novel strategies to enhance the detail and accuracy of observations in remote areas or data-scarce contexts.
• Demonstrations of how novel observations advance process understanding, model development and operational practice.•

The goal of this session is to bring together scientists advancing hydrological monitoring, to foster a critical discussion on the reliability and added value of emerging observational approaches, and to explore how these innovations can be scaled up to larger applications.

This session covers climate predictions from seasonal to multi-decadal timescales and their applications. Continuing to improve such predictions is of major importance to society. The session embraces advances in our understanding of the origins of seasonal to decadal predictability and of the limitations of such predictions. This includes advances in improving forecast skill and reliability and making the most of this information by developing and evaluating new applications and climate services, including windows of opportunity.
The session welcomes contributions from dynamical modeling, machine-learning or other statistical methods and hybrid approaches. It will investigate predictions of various climate phenomena, including extremes, from global to regional scales, and from seasonal to multi-decadal timescales (including seamless predictions). Physical processes and sources relevant to seasonal to (multi-)decadal predictability (e.g. ocean, cryosphere, or land) as well as predicting large-scale atmospheric circulation anomalies associated with teleconnections will be discussed. Analysis of predictions in a multi-model framework, and ensemble forecast initialization and generation will be another focus of the session. We are also interested in approaches addressing initialization shocks and drifts. The session welcomes work on innovative methods of quality assessment and verification of climate predictions. We also invite contributions on the use of seasonal-to-decadal predictions for risk assessment, adaptation and further applications.