The MacGyver session focuses on novel sensors made, or data sources unlocked, by scientists. All geoscientists are invited to present:
- new sensor systems, using technologies in novel or unintended ways,
- new data storage or transmission solutions sending data from the field with LoRa, WIFI, GSM, or any other nifty approach,
- started initiatives (e.g., Open-Sensing.org) that facilitate the creation and sharing of novel sensors, data acquisition and transmission systems.

Connected a sensor to an Arduino or Raspberri Pi? Used the new Lidar in the new iPhone to measure something relevant for hydrology? 3D printed an automated water quality sampler? Or build a Cloud Storage system from Open Source Components? Show it!

New methods in hydrology, plant physiology, seismology, remote sensing, ecology, etc. are all welcome. Bring prototypes and demonstrations to make this the most exciting Poster Only (!) session of the General Assembly.

This session is co-sponsered by MOXXI, the working group on novel observational methods of the IAHS.

This session aims to explore how hydrology is taught and learned at university and in professional education. It will bring together experiences and perspectives on teaching methods, curriculum design, textbooks and educational resources, field and laboratory activities, digital and active-learning approaches, and the responsible use of artificial intelligence in education. Contributions will discuss successful practices as well as the challenges encountered in equipping future hydrologists with the skills required by a changing discipline.

The session consists of solicited presentations only, followed by a panel discussion, with the aim of encouraging exchange among educators and stimulating new ideas for the future of hydrology education.

Human and environmental health are threatened globally by the individual or joint impacts of legacy contaminants such as trace metals and emerging pollutants including pharmaceuticals, fluorinated compounds or micro- and nanoplastics, as well as pathogens including viruses and bacteria. Hydrometeorological extremes can further exacerbate human and environmental health impacts by altering contaminant and pathogen dynamics in the water cycle. While there is increasing understanding of the toxicological and ecotoxicological implications of exposures to these individual or combined stressors, mechanistic understanding of the hydrological processes controlling the (de-)activation of pathogens or contaminants as well as the environmental conditions that affect their toxicity are poorly understood.

Moreover, in our climate adaptation strategies to hydroclimatic extremes, the implications for ecosystem and human health are often overlooked or ignored. Hydrological science can contribute to better prepare society for One Health Challenges by understanding critical water systems and improving and designing appropriate solutions.

We therefore solicit contributions that specifically investigate hydrological controls on the fate, transport and exposure to pathogens and contaminants (from geogenic and agricultural sources, as well as legacy and emerging pollutants) across surface water and groundwater systems. We also invite presentations on the hydrological impacts on physical water quality parameters of health relevance such as water temperature and oxygen content.

This session integrates interdisciplinary innovations covering field and lab experimental as well as model-based approaches in order to improve understanding of the drivers of human and environmental health risks as well as technical and governance approaches for One Health solutions that cross traditional disciplinary boundaries.

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.

Hydrological predictions - simulations or forecasts - are fundamentally uncertain. This has been recognized more than a century ago (see Krzysztofowicz, 2001, and references therein), and it is still true today. For a complete picture, hydrological predictions should therefore not only provide point estimates, but probabilistic statements. Such probabilistic predictions are not only an honest account of what we know (and what we do not know), they also provide practical advantages for end users and decisionmakers (Buizza, 2008). Nevertheless, and despite considerable progress, to date the majority of hydrological models still provide single-valued output. With this session, we want to establish a platform to promote the paradigm-shift towards making probabilistic predictions in hydrology the standard rather than the exception.
We welcome contributions from the following fields (but not limited to these):
- Theory and methodology for identifying and quantifying sources and pathways of uncertainty from data through models to predictions, including approaches based on probability theory and information theory
- Development of model architectures and efficient training procedures enabling fast, accurate and reliable probabilistic predictions, including physics-based, data-driven, machine-learning and hybrid approaches, stochastic parameterisations, ensemble prediction systems, and post-processing
- Probabilistic benchmarks and evaluation frameworks, including benchmark models and datasets, verification methods, scoring rules, calibration, and large-scale initiatives for assessing probabilistic hydrological predictions
- Operational implementations and real-world applications of probabilistic hydrological modelling, including flood forecasting, climate change impact assessment, and water resources management
- Development of strategies for effectively communicating probabilistic predictions to end users

References
Buizza, R. (2008), The value of probabilistic prediction. Atmosph. Sci. Lett., 9: 36-42. https://doi.org/10.1002/asl.170
Krzysztofowicz, R.: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9, https://doi.org/10.1016/S0022-1694(01)00420-6, 2001.

Predictions of physical processes in aquifers, rivers and across compartments are strongly affected by uncertainties and errors in model structure, parameters and forcing data. Thus, reliable predictions at any scale (lab/field/catchment) require a rigorous and transparent treatment of these uncertainties, from parameter estimation to uncertainty quantification and model selection. Acknowledging uncertainties and equifinality as a fundamental part of modelling and understanding model-parameter interactions during calibration opens up otherwise-missed opportunities for scientific insight and decision support. This session is a platform for discussion of methodological advances and workflows addressing inverse problems in surface and subsurface hydrology, i.e., using available observed data to gain knowledge/ constrain uncertainty about related but unobserved quantities of interest. We invite contributions on improved concepts, approaches & computational algorithms (be they Bayesian, frequentist, optimization- or ML-based) as well as demonstrations of best practices, challenges & pitfalls, especially (but not exclusively) related to:

- parameter inference, model selection/ averaging, sensitivity and uncertainty analysis;
- representation of uncertain data and boundary conditions;
- integration of heterogeneous/multi-source data;
- identification and treatment of model-structural errors;
- distilling new model formulations (data-driven, physics-based, knowledge-guided or hybrid);
- data worth and optimal experimental design strategies toward maximum information/minimum uncertainty;
- constraint learning/ novel likelihood formulations to incorporate expert knowledge in inversion;
- other regularization strategies that help solve ill-posed problems;
- computational efficiency of solving inverse problems, including surrogate and ML-based techniques;
- Benchmarking and intercomparison efforts on synthetic or real-world, local or large-sample data-sets;
- transparent and reproducible workflows for robust predictions and visualization/communication of inference results to stakeholders;
- real-time inversion for operational forecasting;
- variations of all the above specific to low-dimensional, high-dimensional, dynamic, spatially distributed, geostatistical, linear, or non-linear inverse-problem settings.

In a world of decreasing global water availability and increasing demand, our ability to quantify current water resources, understand the global water cycle, and predict their future trajectories is of paramount importance for water, food, and energy. Mounting pressures in a changing climate and increasing extremes are exposing a widening gap between freshwater science and the information required for effective and equitable decision-making. Now more than ever, a comprehensive, internationally coordinated, and policy-relevant synthesis of the freshwater system across scales is needed to manage water and ensure sustainable resources for human and natural systems.

This session invites contributions that focus on challenges and opportunities in global freshwater science. We highlight emerging scientific and technological advances towards a unified global synthesis of the freshwater cycle—spanning precipitation, evapotranspiration, surface water, groundwater, the cryosphere, ecosystems, human use, and water quality—to reveal patterns and test hypotheses from catchment to global scales. We encourage innovative submissions that highlight integrative approaches—including both traditional and under-utilised observations (e.g., citizen science), reanalyses, process-based models, and emerging AI-enabled inference—to advance towards an improved understanding of global freshwater at unprecedented spatial and temporal resolution, and to enable a credible, routinely updated global assessment supporting discovery and actionable solutions.

The terrestrial water cycle is usually studied one compartment at a time: precipitation by meteorologists, runoff and recharge by hydrologists, aquifers by hydrogeologists, and evapotranspiration by land-surface scientists. Yet climate change, land-use change and human water use act mainly on the links between these compartments. They change how rainfall is partitioned at the surface, how much reaches aquifers and how long it is stored there, and how much returns to the atmosphere to fall again as rain.

This session invites studies that cross at least one of these boundaries. Relevant topics include precipitation extremes, monsoons and atmospheric rivers and their imprint on runoff and recharge; soil moisture, infiltration, and snow and glacier melt; groundwater recharge, surface water–groundwater exchange, and storage change from GRACE and in-situ networks; evapotranspiration, moisture recycling and irrigation feedbacks; and the carbon, solutes and pollutants that water carries along its flow paths, including greenhouse-gas emissions from reservoirs. We also welcome interventions that deliberately reconnect the loop, such as managed aquifer recharge and nature-based solutions. Methods that link compartments are equally welcome: isotopes and tracers, remote sensing, coupled and hybrid models, and explainable machine learning.

We particularly encourage work on water-balance closure across scales, studies from data-scarce and monsoon-dominated regions, and research that turns whole-cycle understanding into water-security decisions. Early-career scientists are strongly encouraged to submit.

Low elevation coastal zones (LECZ, areas below 10 m above sea level) are among the most dynamic, densely populated, and intensively managed environments on Earth. They support major population centres, agricultural production, ecosystems and industries. At the same time, they are increasingly exposed to hazards due to accelerating global change, including sea-level rise, changing storm surge and precipitation regimes, heat waves, land subsidence and morphological changes of estuaries, deltas and open coasts.
These pressures create interconnected challenges for water management, infrastructure design and ecosystem stewardship, driving adaptation needs. LECZ are at the interface between terrestrial hydrology and marine dynamics, where flood protection, lowland drainage, freshwater availability, saltwater intrusion, nutrient loads, sediment transport and ecological functioning are governed by the interactions of catchment and coastal processes. However, scientific understanding, modelling approaches and management practices often treat landward and seaward processes separately. Taking a systems perspective is essential for assessing compound and multi-hazard risks, identifying adaptation options and supporting decision-making in LECZs.
This session welcomes contributions that improve system and process understanding, develop monitoring and modelling approaches and support decision making in LECZs across spatial and temporal scales, from local case studies to regional, continental, and global assessments. Contributions addressing data gaps and novel approaches for infrastructure design are encouraged.
We invite submissions on topics including, but not limited to:
i) Monitoring, understanding and characterising processes and hazards in LECZs
ii) Process-based, statistical, hybrid and data-driven modelling approaches for coastal catchment systems, water resources, compound and multi-hazard drivers, estuarine and deltaic dynamics and groundwater-surface-water-sea interactions.
iii) Tools and frameworks supporting integrated water management, infrastructure design and adaptation

Nature-based Solutions (NbS) are increasingly promoted as a key strategy for climate adaptation, aiming to improve sponge functioning by intercepting, slowing, storing, and slowly releasing water. By reducing flood peaks, enhancing drought resilience, and delivering ecosystem services such as improved water quality and biodiversity, NbS are central to climate-resilient land and water management. However, implementation at scale remains limited due to insufficient evidence on the (eco-)hydrological impacts of NbS across spatial and temporal scales, and limited translation of this knowledge into decision-making.
Key questions remain regarding how effectiveness evolves over time, how maintenance influences long-term outcomes, how impacts propagate across scales, under which conditions local interventions contribute to landscape-scale resilience, and how scientific evidence can support upscaling.
This session explores the role of quantitative assessment in understanding Nature-based Solutions (NbS) and supporting their upscaling. It examines how evidence from local monitoring and modelling to catchment-scale assessments can guide implementation, inform policy, and strengthen confidence in climate adaptation investments, advancing climate-resilient sponge landscapes. We welcome contributions that:
•Quantify the effectiveness of NbS for multiple objectives, including flood mitigation, drought resilience, and associated co-benefits, trade-offs, and uncertainties to inform design, implementation, prioritization, and upscaling.
•Investigate temporal and spatial scaling, including long-term performance, delayed benefits, degradation processes, maintenance requirements, and cumulative impacts from plot to catchment scales.
•Integrate monitoring, experimental, and modelling approaches to strengthen attribution, process understanding, and impact assessment of NbS, using observed evidence to validate models and evaluate the scaling of flood, drought, and eco-hydrological benefits.
•Highlight the qualitative and quantitative evidence required by local actors, implementers, policy-makers, and investors to support decision-making.
•Develop decision-support tools, indicators, and assessment frameworks that strengthen links between quantification, implementation, governance, financing, and policy.
•Demonstrate how evidence from monitoring and modelling has informed planning, policy development, implementation strategies, investment priorities, and the mainstreaming of NbS.

Land surface processes play a crucial role in shaping Earth's climate system, mediating land-atmosphere interactions, and driving terrestrial water-carbon-energy feedbacks. Land Surface Models, as core components of Earth System Models (ESMs), influence climate projections in benchmarks such as the CMIP7. However, land hydrology and its interactions with other components of the Earth system (e.g. biosphere, biogeochemical cycles) remain poorly represented in most ESMs, potentially inducing erroneous responses to anthropogenic climate forcings at global to local scales and leading to misrepresentations of droughts and floods. For instance, ESMs do not represent the observed decline of groundwater levels in water-limited regions that threatens groundwater-dependent ecosystems and exacerbates drought persistence, thereby increasing the risk of ecosystem shifts and progressive desertification. This crosscutting session provides an open, interdisciplinary platform to bridge the gap between hydrologists, hydrogeologists, ecohydrologists, and climate modelers.

We invite observational, theoretical, and numerical modeling contributions that advance the integrated representation of hydrological, hydrogeological, biophysical, and ecosystem processes within land surface models across spatial and temporal scales. Key areas of focus include the representation of the soil-plant-atmosphere continuum, plant hydraulics, vegetation stress dynamics, and biosphere-mediated moisture recycling, alongside subsurface hydrogeology such as explicit groundwater-table dynamics, lateral flow, and deep aquifer linkages. Contributions addressing human-water-ecosystem interlinkages (e.g., groundwater abstraction, irrigation, land-use change), high-resolution ESM configurations, advanced observational networks, and emerging AI/machine learning techniques are also strongly encouraged.

The overarching aim of this session is to overcome historical disciplinary silos and establish a shared agenda across modeling communities. By aligning interdisciplinary priorities, addressing cross-scale parameterization challenges, and improving the evaluation of land-based mitigation and adaptation strategies, this session seeks to define future needs and collaborative opportunities for the next ESM generation.

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.

Much has been written on how hydrological modelling can be improved: better data, better models, better objective functions, better benchmarking, better uncertainty quantification, better communication, and so forth. Yet, it remains rare to find definitions of what “better” means in each context, and even more to see such recommendations implemented comprehensively.

This session provides a platform to discuss which gaps remain between current and recommended model application, and how such gaps may be bridged. We welcome contributions under the following themes:

1. The theoretical underpinnings of models, centered around the question:
“How good are our models and the approaches we use to decide if they are, and how far can we stretch their applicability?”

2. The technical side of model application, centered around the question:
“How do we efficiently configure, parametrize and run our models?”

3. The social side of model application, centered around the question:
“Why do we use the tools we do, and how do we communicate their strengths and weaknesses?”

4. The future of model application, centered around our main question:
“Where are we today, and where do we want to be tomorrow?”

Contributions may include comparison of existing models and techniques, and development of more robust alternatives; as well as examples of studies that implement existing recommended practices throughout their modelling chain; and lessons from post-audits comparing past predictions with observed outcomes. We particularly invite abstracts that investigate whether model assumptions and representations are supported by evidence and whether models are fit for their intended purpose, and those that take a step back and assess the current state of hydrological modelling in comparison to what may, by now, be theoretically and practically possible.

Plastic pollution in freshwater systems is a widely recognized global problem with potential environmental risks to water quality, biota and livelihoods. Furthermore, freshwater plastic pollution is also considered the dominant source of plastic input to the oceans. Despite this, research on plastic pollution has only recently expanded from the marine environment to freshwater systems. Therefore data and knowledge from field studies are still limited in regard to freshwater environments. Sources, quantities, distribution across environmental matrices and ecosystem compartments, and transport mechanisms remain mostly unknown at catchment scale. These knowledge gaps must be addressed to understand the dispersal and eventual fate of plastics in the environment, enabling a better assessment of potential risks as well as development of effective mitigation measures.

This session welcomes contributions from field, laboratory and modelling studies that aim to advance our understanding of river network and catchment-scale plastic transport and accumulation processes. We are soliciting studies dedicated to all plastic sizes (macro, micro, nano) and across all geographic settings. We are especially encouraging studies that can link plastic accumulation and transport to catchment-wide hydrological, ecological or geomorphological processes that we can better understand where, when and why plastics accumulation takes place in aquatic-terrestrial environments.
In this session, we explore the current state of knowledge and activities on macro-, micro- and nanoplastics in freshwater systems, focusing on aspects such as:
• Transport processes of plastics at catchment scale;
• Source to sink investigations, considering quantities and distribution across environmental matrices (water and sediment) and compartments (water surface layer, water column, ice, riverbed, and riverbanks);
• Plastic in rivers, lakes, urban water systems, floodplains, estuaries, freshwater biota;
• Effects of hydrological extremes, e.g. accumulation of plastics during droughts, and short-term export during floods in the catchment;
• Modelling approaches for global river output estimations;
• Legislative/regulatory efforts, such as monitoring programs and measures against plastic pollution in freshwater systems.

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.

Hydrological systems are undergoing profound changes in response to climate variability, to rising greenhouse gas concentrations, and to direct human interventions. Over recent decades, shifts in precipitation, evapotranspiration, streamflow, and water storage have been accompanied by increasing frequency and intensity of hydrological extremes. How these changes and their interactions will impact the terrestrial water cycle and other Earth system dynamics remains poorly understood and is the origin of much uncertainty, which limits our ability to build societal and ecosystem resilience – contributing to policy challenges for adaptation to water scarcity and other hydro-climatic risks.
At the same time, the research community now has unprecedented opportunities. Expanding in-situ networks, advances in remote sensing, and more complex and higher resolution Earth system and land surface models provide powerful tools to explore hydrological processes across scales. Yet, significant uncertainties remain and there have been concerns that advances in modelling and observational systems are not accompanied by advances in theory. Observational studies and global model simulations often yield divergent conclusions, revealing persistent knowledge gaps in how climate change, rising atmospheric CO2, and anthropogenic activities interact to reshape hydrological systems.
We invite submissions that address, but are not limited to, the following themes where we would like to hear about recent advances as well as current knowledge gaps:
1. Advanced ground- and space-based techniques and data-model fusion approaches for estimating hydrological variables (precipitation, evapotranspiration, streamflow, and water storage) and extremes (floods and droughts) from catchment to global scales.
2. Responses and feedbacks of hydrological processes and extremes to climate change and human activities.
3. Impacts of land use, land cover change, irrigation, and water withdrawals on streamflow regimes and hydrological extremes.
4. Projections of regional and global hydrological changes and extremes under near- and long-term climate scenarios.
5. Benchmarking hydrological and Earth system models against current observations, with particular attention to CO2 effects and human water use.
6. Hydrological processes and extremes in hotspot regions such as the Tibetan Plateau, the Arctic, the Amazon, and intensively irrigated areas.

Heavy precipitation events in small and medium-sized catchments can cause flash floods with very short lag times (a few hours) and high specific peak discharges. The rainfall and resulting rapid runoff can initiate geomorphic processes such as erosion, debris flows or shallow landslides, which mobilize large amounts of unconsolidated material and increase their destructive force.
Robust hazard assessments combined with early warning systems are essential to reduce the impacts of these events by enabling effective emergency management. This can be achieved by (i) increasing the forecast skill (improved predictions of physical variables or the hazard), or (ii) integrating additional information to support decision making.
Uncertainty sources in the forecasting chain include the high space-time variability of rainfall, limitations in observations and forecasts, the variability and nonlinearity of physical processes and their representation in models under extreme conditions, and incomplete exposure and vulnerability data to translate hazard forecasts into impacts.
This session presents recent advances in the monitoring, modeling and short-range forecasting of rainfall-induced hazards, the identification of hazard-prone areas, and the assessment of societal impacts with contributions on these themes:

- Development of new measurement techniques adapted to flash floods and hydro-geomorphic hazards (in-situ sensors and remote sensing data), and quantification of associated uncertainties.
- Short-range rainfall forecasting for heavy precipitation events, including seamless rainfall forecasting based on NWP models, nowcasts and/or ML, and ensemble-based uncertainty representation.
- Understanding and modeling of flash floods, hydro-geomorphic processes and their cascading effects, at appropriate space-time scales.
- Integrated hydrometeorological forecasting chains and new modeling approaches for flash floods and geomorphic hazards in gauged and ungauged basins.
- Observation, understanding and prediction of hazard, societal vulnerability and social responses to flash floods and/or hydro-geomorphic hazards.
- Development of impact-based approaches, integrating societal vulnerability.
- New approaches to validate hydrometeorological and impact forecasts, including new direct and indirect observation techniques.
- Assessment of possible evolutions in frequency and hazard characteristics due to climate change.
Contributions related to recent events are encouraged.

Drought and water scarcity affect many regions of the Earth, including areas generally considered water rich. The projected increase in the severity and frequency of droughts may lead to an increase in water scarcity, particularly in regions that are already water-stressed, and where overexploitation of available water resources can exacerbate the consequences of drought. This may lead to long-term environmental and socio-economic impacts.

Drought monitoring, forecasting and early warning constitute one of the three pillars of integrated drought management. It is therefore necessary to improve monitoring and sub-seasonal to seasonal forecasting of drought and water availability, and to develop innovative indicators and methodologies that translate data into actionable information to support effective drought early warning and risk management.

This session addresses statistical, remote sensing, physically-based techniques, as well as artificial intelligence and machine learning techniques aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and water scarcity. These include, but are not limited to, precipitation, extreme temperatures, snow cover, soil moisture, streamflow and groundwater levels, together with the interactions and propagation pathways linking different drought types across the hydrological cycle. The development and implementation of drought indicators meaningful to decision-making processes, and approaches for presenting and integrating these with the needs and knowledges of water managers, policymakers and other stakeholders, are also addressed in this session. Contributions focusing on the interrelationships and feedbacks between drought, low flows and water scarcity, and the impacts these have on socio-economic sectors including agriculture, energy and ecosystems, are welcomed.

The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as water resources and drought risk management. Particularly welcome are applications and real-world case studies, both from regions that have long been exposed to significant water stress and from regions that are increasingly experiencing water shortages due to drought, where drought early warning, supported by state-of-the-art monitoring and forecasting of water availability, is likely to become more important in the future.

This interactive session aims to bridge the gap between research and practice in operational forecasting, with a focus on impact-based approaches for flood, water scarcity and multiple hazards.
Operational (early) warning systems are the result of progress and innovations in the science of forecasting. New opportunities have risen in physically based modelling, AI/machine learning, coupling meteorological and hydrological forecasts, ensemble forecasting, impact-based forecasting, and real-time control. Often, the sharing of knowledge and experience about developments are limited to the particular field (e.g. flood forecasting or landslide warnings) for which the operational system is used. Increasingly, humanitarian, disaster risk management and climate adaptation practitioners are using forecasts and warning information to enable anticipatory early action that saves lives and livelihoods. It is important to understand their needs, their decision-making process and facilitate their involvement in forecasting and warning design and implementation (co-generation).

The focus of this session will be on bringing the expertise from different fields together as well as exploring differences, similarities, problems and solutions between forecasting systems for varying hazards including climate emergency. Real-world case studies of system implementations - configured at local, regional, national, continental and global scales - will be presented. An operational warning system can include, for example, monitoring of data, analysing data, making and visualizing forecasts, impact-based solutions, giving warning signals and suggesting early action and response measures.

Contributions are welcome from both scientists and practitioners who are involved in developing and using operational forecasting and/or management systems for climate and water-related hazards, such as flood, drought, tsunami, landslide, hurricane, hydropower etc. We also welcome contributions from early career practitioners and scientists, and those working in multi-disciplinary projects (e.g., EU Horizon Disaster Resilience Societies).

Early warnings must be understandable, trusted and actionable to help protect lives and livelihoods from natural hazards such as floods, droughts, heatwaves, tropical cyclones, storms and tsunamis. Recent disasters, such as the 2021 floods in Western Europe, the 2020-2023 Horn of Africa drought, and the extreme heat affecting parts of Europe and the Mediterranean region in 2026 show that significant gaps in the early warning - early action chains persist. The Early Warnings for All initiative (led by WMO, UNDRR, ITU, and IFRC) recognizes that increased efforts are required to develop effective impact-based multi-hazard early warning systems.

The scientific community needs to move beyond natural hazard forecasting and towards impact- and action-based forecasting. This, in turn, requires commitment to the creation and dissemination of multi-hazard risk and multi-source impact data (including from social media) as well as the design, implementation and evaluation of impact-based forecasting services and linked early warning - early action (EW-EA) protocols.

However, significant knowledge gaps and operational challenges persist. This session aims to offer valuable insights and share best practices on impact-based early warning systems from the perspective of both the knowledge producers and users.

Topics of interest include, but are not limited to:
- Practical applications and operational use cases of impact-based forecasts, including the challenges faced in operational implementation
- Novel physics-based, Artificial Intelligence (AI) and hybrid approaches integrating forecasting, big data and earth observations for impact-based forecasting and decision support, including responsible, transparent and explainable approaches that support trusted, inclusive and people-centred systems
- Development of cost-efficient and evidence-based early action portfolios
- Linking preparedness and anticipatory action with response, recovery, reconstruction and prevention efforts
- Development of operational trigger systems (forecast-based financing) for humanitarian anticipatory action
- User-centred evaluation of impact-based forecasting and warning products including usability, trust, decision relevance and actionability
- Triangulation of indigenous and scientific knowledge for leveraging forecasts, multi-hazard risk information and climate services to last-mile communities
- Bridging the gaps in risk, impact and early action data to support impact-based forecasting.

Real-time flood forecasting and early warning are rapidly evolving through advances in Earth observation, sensor networks, data assimilation, artificial intelligence (AI), machine learning (ML), high-performance computing, and digital technologies. These developments offer new opportunities to improve forecast accuracy, lead time, spatial resolution, uncertainty estimation, and the translation of forecasts into actionable warnings.
This session invites contributions covering the full chain from real-time data to forecasting, impact assessment, warning, and early action. We particularly welcome studies combining process-based knowledge with emerging data-driven and hybrid approaches, as well as innovative operational applications.
Topics include, but are not limited to:
• Real-time data: ground observations, radar, satellite and remote sensing, IoT, crowdsourcing, data quality control, imputation, assimilation, and multi-source data fusion.
• Flood forecasting: process-based, conceptual, hybrid, AI/ML and physics-informed approaches; nowcasting; rapid forecasting; and forecasting in data-scarce regions.
• Uncertainty and reliability: ensemble and probabilistic forecasting, uncertainty quantification, explainable AI, model transferability, robustness, and extreme-event forecasting.
• Forecast-to-action: impact-based forecasting, inundation and damage prediction, early warning and anticipatory action, decision-support systems, and communication of forecast uncertainty.
• Digital innovations: digital twins, cloud and edge computing, high-performance computing, open-source platforms, and immersive visualisation.
• Operational and community applications: real-world forecasting systems, stakeholder engagement, citizen science, emergency response, and community-centred early warning.
• Emerging directions: foundation models, generative AI, multimodal data fusion, autonomous forecasting, and next-generation Earth-system and hydrological digital twins.

Flood forecasting and inundation modelling are critical components of disaster risk reduction, especially under the increasing pressures of climate variability, rapid urbanization, and land-use change. Recent advances in high-resolution satellite observations, ensemble Numerical Weather Prediction (NWP) products, expanding hydro meteorological networks, and high-performance hydrodynamic models—enabled by localized data, growing computing power, and fast surrogate emulators provide new opportunities to enhance predictive capability and reliability. This session seeks contributions that highlight methodological innovations and practical applications in flood forecasting and floodplain inundation modelling. The session welcomes studies that integrate diverse data sources, explore multi-scale modelling strategies, and advance process-based, statistical, and hybrid machine learning/AI frameworks. Emphasis is placed on the role of data assimilation in improving forecast accuracy, reducing uncertainty, and supporting real-time decision making. Case studies demonstrating the transition from research to operations, applications in reservoir management, adaptive pump and gate operations informed by distributed sensor networks, and emergency response efforts, as well as strategies to communicate probabilistic forecasts to end users, are of strong interest. We also invite discussions of uncertainty quantification and the challenges of applying models across diverse hydrological and climatic settings. The session aims to bring together hydrologists, meteorologists, remote sensing specialists, and data scientists to foster cross-disciplinary dialogue and promote innovative approaches that strengthen flood risk management worldwide.

Multi-hazard early warning systems increasingly rely on the technical core of modern disaster risk reduction. They utilise heterogeneous data streams, including earth observations, in-situ sensor networks, process-based models, AI and data-driven approaches, and increasingly citizen-reported and computer-vision-derived observations. These datasets need to be transformed into forecasts that are both scientifically robust and operationally actionable, ranging from minutes to hours for rapid-onset hazards, such as floods, landslides, and cyclones, to months and seasons for slow-onset hazards such as drought. Moreover, natural hazards increasingly occur concurrently, consecutively, or in combination, creating multi-hazards and cascading risks. Operational early warning systems, however, often focus on single hazards. In addition, warning thresholds and dissemination pathways frequently fail to translate warnings into effective community and institutional response, creating a persistent know-do gap that cannot be closed through additional sensing and technical sophistication alone.

This session welcomes contributions across the full arc of multi-hazard early warning, including: (i) AI/ML and process-based modelling approaches for multi-hazard forecasting and nowcasting (e.g., floods, droughts, landslides, and cascading hazards); (ii) data fusion architectures combining satellite EO, real-time sensor networks, and computer vision for hazard detection; (iii) participatory and citizen-science methodologies, local and traditional knowledge integration, and including living labs and serious games for co-creating warnings directly with at-risk communities; (iv) dashboard and decision-support system design that translate model outputs into actionable, trusted, inclusive, and often multilingual alerts for operational use; and (v) validation and lessons from deployed or piloted systems at the interface between technical performance and community uptake.

We particularly encourage submissions presenting multi-hazard forecasting approaches, warning thresholds, and deployed or piloted systems, including honest lessons on where technical sophistication and last-mile trust have, and have not, come together to support effective action.

In recent years, there has been a strong increase in the use of machine learning techniques to enhance hydrological simulation and forecasting. These methods are receiving growing attention due to their ability to handle large datasets, combine different sources of predictability, increase forecasting skill and minimize the effect of biases, as well as enhance computational efficiency. Furthermore, the range of implementations is broad, from purely data-driven forecasting systems to hybrid setups, combining both physically-based models and machine learning techniques, from large to local scales as well as different time horizons. These all allow forecasters to address and cover various aspects and processes of the hydrological cycle, including extreme conditions (floods and droughts), which are important for water resources and emergency management.

This session aims to highlight and bring together recent efforts in hydrological forecasting, using machine learning based techniques and/or hybrid approaches. Contributions are welcome showcasing examples of model developments (ranging from implementations to operational setups), studies ranging from local to global scales and across different time horizons (short-, medium- and long-term), as well as studies showcasing the efforts data-driven/hybrid approaches to tackle challenges in hydrological forecasting. We particularly welcome talks that reach beyond the description of machine learning architectures to uncover physical and human-induced processes, account for uncertainties, generate novel insights about hydrological forecasting, or support efforts in reducing common forecasting difficulties.

Other topics related to the subdivision of Hydrological Forecasting and the corresponding sessions can be found here: https://www.egu.eu/hs/about/subdivisions/hydrological-forecasting/

Anthropogenic activities have profoundly altered the hydrological cycle, particularly in heavily modified systems. Human interventions such as reservoirs, dams, drainage networks, urban expansion, infrastructure development, deforestation/afforestation, water abstraction, and wastewater discharge have reshaped natural processes and management practices. Under climate change, these alterations further shift the frequency, magnitude, and seasonality of hydroclimatic extremes, potentially amplifying risks for societies and ecosystems.

Despite advances in hydrological science and technology, our understanding of human–water interactions across scales remains limited. Challenges stem from the complexity and uncertainty in quantifying human influences, the scarcity of long-term records, and the limitations of conventional models often designed for natural catchments under assumption of stationarity. Thus, the reliability of hydrological forecasting in human-influenced systems is compromised. Given the large populations exposed to water-driven hazards, there is an urgent need for research and innovation.

This session will highlight recent advances in understanding and forecasting hydroclimatic extremes in human-influenced catchments. We invite abstracts on (but not limited to):
• Development and application of statistical, process-based, machine learning, or hybrid models to forecast hydrological variables at multiple scales
• Advances in data acquisition capturing human activities (or proxies), including in-situ monitoring, remote sensing, and social media, with innovations in data integration and analytics
• Integration of data collected from public and through citizen science to improve the prediction of hydrological extremes
• Novel quantitative methods to assess diverse human impacts on hydrological processes and water cycle
• Coupled human-natural system modelling and scenario analysis to capture feedbacks between socio-economic drivers and hydrological processes
• Impact-based risk assessments of water-related hazards, spanning economic, health, social, and environmental dimensions
• Uncertainty quantification and risk analysis of singular and compound hydro-hazards under non-stationarity
• Enhanced visualization and communication for early warnings and short- to long-range predictions, including projections of unprecedented extremes
• Integration of nature-based solutions and adaptive management strategies into forecasting and risk reduction frameworks

This session focuses on recent advances in probabilistic modelling and ensemble forecasting of streamflow and other environmental variables using machine learning (ML) and deep learning (DL) approaches. Contributions are invited on uncertainty quantification, probabilistic prediction, ensemble generation and post-processing, the use of meteorological and climate data for both model training and real-time forecasting, and decision-support frameworks applicable in contexts of limited information and uncertainty. Topics include hybrid process-based and ML/DL models, explainable AI, foundation models, ensemble learning, mixed-model approaches, and operational forecasting systems. Applications may address streamflow, floods, droughts, water quality, sediment transport, groundwater, ecohydrological variables, and other climate-sensitive environmental processes. Emphasis is placed on reducing, explaining, and communicating predictive uncertainty, and on the use of ensemble and probabilistic models in risk-informed operational decision-making. Participations that clarify the relationship between ensemble spread and predictive uncertainty, or that benchmark probabilistic methods against each other, are particularly welcome.

This session addresses advances in climate and hydro-meteorological forecasts and projections, and their role in predicting water availability and servicing water sectors. It welcomes, without being restricted to, presentations on:

Developing the climate and hydro-meteorological forecast services:

• Seamless prediction and projection systems spanning real-time, sub-seasonal, multi-annual, decadal and long-term timescales for water availability and hydrological extremes (floods, droughts, compound events);
• Process-based, data-driven, AI-enabled, machine learning, and hybrid forecasting methods;
• Methods for post-processing and refining hydro-climate information (e.g., downscaling, bias correction, spatiotemporal disaggregation, blending and interpolation);
• Uncertainty propagation, forecast verification, sensitivity analysis and tools;
• Co-development of forecasts between scientists and service providers.

Applying the climate and hydro-meteorological forecast services:
• Assessment of forecast value, skill and usability for decision-making, including communication, visualization and user engagement;
• Operational hydro-meteorological forecasting systems and hydro-climate services;
• Case studies and water management applications based on these services (e.g., river/reservoir operation, agricultural, industrial and public water use).

The session will bring together researchers, service developers and operational managers in hydrology, meteorology and climate, to share advances, experiences and lessons learned in translating hydroclimate information into practical decision support. Contributions are encouraged from across sectors including water resources management, drinking water supply, transport, energy production, agriculture, disaster risk reduction, forestry, health, insurance, tourism and infrastructure.

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.

Rainfall is a “collective” phenomenon emerging from numerous drops. It reaches the ground surface with varying intensity, drop size and velocity distribution. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and for practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale space-time precipitation variability, which is a key driving force of the hydrological response, especially in highly heterogeneous areas (mountains, cities). This hydrological response at the catchment scale is the result of the interplay between the space-time variability of precipitation, the catchment geomorphological / pedological / ecological characteristics and antecedent hydrological conditions. Similarly to the small scales, accurate measurement and prediction of the spate-time distribution of precipitation at hydrologically relevant scales still remains an open challenge.

This session brings together scientists and practitioners who aim to measure and understand precipitation variability from drop scale to catchment scale as well as its hydrological consequences. Contributions addressing one or several of the following topics are encouraged:
- Novel techniques for measuring liquid and solid precipitation variability at hydrologically relevant space and time scales (from drop to catchment scale), from in-situ measurements to remote sensing techniques, and from ground-based devices to spaceborne platforms. Innovative comparison metrics are welcomed;
- Drop (or particle) size distributions, small scale variability of precipitation, and their consequences for precipitation rate retrieval algorithms for radars, commercial microwave links and other remote sensors;
- Novel modelling or characterization tools of precipitation variability from drop scale to catchment scale from various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Novel approaches to better identify, understand and simulate the dominant microphysical processes at work in liquid and solid precipitation.
- Applications of measured and/or modelled precipitation fields in catchment hydrological models for the purpose of process understanding or predicting hydrological response.
- Rainfall simulators developed to investigate the accuracy of disdrometer measurements in assessing drop size and fall velocity.

The statistical characterization and modelling of precipitation are crucial in a variety of applications, such as flood forecasting, water resource assessments, evaluation of climate change impacts, infrastructure design, and hydrological modelling. This session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation, including its variability at different scales and its sources of uncertainty.

Contributions focusing on one or more of the following issues are particularly welcome:
- Process conceptualization and approaches to modelling precipitation at different spatial and temporal scales, including model parameter identification, calibration and regionalisation, and sensitivity analyses to parameterization and scales of process representation.
- Novel studies aimed at the assessment and representation of different sources of uncertainty of precipitation, including natural climate variability and changes caused by global warming.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source ground-based, remotely sensed, and model-derived precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Modelling, forecasting and nowcasting approaches based on ensemble simulations for synthetic representation of precipitation variability and uncertainty.
- Machine-learning approaches for precipitation modelling, forecasting, and downscaling: Machine-learning and hybrid (physics-informed) methods for precipitation simulation, uncertainty quantification, bias correction, and spatio-temporal downscaling, including baseline comparisons, cross-climate transfer tests, and evaluations of explainability and robustness.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Dynamical and statistical downscaling approaches to generate precipitation at fine spatial and temporal scales from coarse-scale information from meteorological and climate models.

This session is designed to explore the impacts of hydroclimatic variability, climate change, and temporal and spatial availability of water resources on different factors, such as food production, population health, environment quality, and local ecosystem welfare.
We particularly welcome submissions on the following topics:
• Complex inter-linkages between hydroclimatic conditions, food production, and population health, including: extreme weather events, surface and subsurface water resources, surface temperatures, and their impacts on food security, livelihoods, and water- and food-borne illnesses in urban and rural environments.
• Quantitative assessment of surface-water and groundwater resources, and their contribution to agricultural system and ecosystem statuses.
• Spatiotemporal modeling of the availability of water resources, flooding, droughts, and climate change, in the context of water quality and usage for food production, agricultural irrigation, and health impacts over a wide range of spatiotemporal scales.
• Smart infrastructure for water usage, reduction of water losses, irrigation, environmental and ecological health monitoring, such as development of advanced sensors, remote sensing, data collection, and associated modeling approaches.
• Modelling tools for organizing integrated solutions for water supply, precision agriculture, ecosystem health monitoring, and characterization of environmental conditions.
• Water re-allocation and treatment for agricultural, environmental, and health related purposes.
• Impact assessment of water-related natural disasters, and anthropogenic forcing (e.g. inappropriate agricultural practices, and land usage) on the natural environment (e.g. health impacts from water and air, fragmentation of habitats, etc.).
• Assessment of climate mitigation and water impacts on food security and nutrition, especially effects on crop productivity, water availability for irrigation needs, and resilience of food production systems in a variable hydro-climatic environment.
• Nature-based solutions focusing on the connections between water, climate, food, and health, including how to improve water and soil quality, agricultural water productivity, ecosystem services, food systems resilience, and human welfare.
We especially encourage interdisciplinary studies that extend beyond regular hydroclimatic assessments, investigating/exploring their importance to food security and human health.

Scientists are facing several challenges when applying climate models for hydrological variables, such as variety in ensembles, uncertainty or model decisions. Perspectives of climate and hydrological researchers might deviate and a gap exists between the information provided by climate scenarios and the data required for effective hydrological analyses and decision-making. Reducing this gap and improving the assessment of climate change impacts requires a deeper understanding of the interactions between climate drivers and hydrological processes across regional and local scales, as well as more accurate representations of these interactions within modelling frameworks. This is essential for developing reliable forecasts, attributing observed changes and assessing risk posed by extreme events. In this context, uncertainties, probabilistic approaches, and scenario-based predictions must be explicitly quantified and effectively communicated. Addressing these challenges calls for advanced modelling approaches and robust uncertainty quantification methods capable of accounting for both hydrological and anthropogenic influences on the water cycle under present and future conditions.

This session particularly welcomes, but is not limited to, contributions on:
- Advanced methods for the identification, simulation and prediction of hydrological processes and water resources, with emphasis on stochastic, data-driven and hybrid methods;
- Innovative techniques for simulating and forecasting hydroclimatic extremes, including compound and cascading events (e.g. heatwaves, floods and droughts);
- Holistic approaches for developing future water resources scenarios that explicitly incorporate climatic, environmental and anthropogenic drivers;
- Studies on hydroclimatic change attribution using probabilistic approaches and novel causality frameworks with uncertainty assessment;
- Evaluation of climate models performance, uncertainty and variability at regional and local scales using observational data;
- Downscaling of climate projections and related uncertainty propagation into hydrological variables;
- Advances in translating climate projections into actionable hydrological information for water-resource management, risk assessment, and adaptation planning;
- Socio-hydrological scenarios combining climate change, demographic dynamics, and land-use transformations within a coherent modelling framework.

Extreme hydro-meteorological events drive many hydrologic and geomorphic hazards, such as floods, landslides and debris flows, which pose a significant threat to modern societies on a global scale. The continuous increase of population and urban settlements in hazard-prone areas in combination with evidence of changes in extreme weather events lead to a continuous increase in the risk associated with weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need to better understand the triggers of these hazards and the related aspects of vulnerability, risk, mitigation and societal response.
This session aims at gathering contributions dealing with various hydro-meteorological hazards that address the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies and communication strategies. Specifically, we aim to collect contributions from academia, industry (e.g., insurance) and government agencies (e.g., civil protection) that will help identify the latest developments and ways forward for increasing the resilience of communities at local, regional and national scales, and proposals for improving the interaction between different entities and sciences.
Contributions focusing on, but not limited to, novel developments and findings on the following topics are particularly encouraged:
- Physical and social vulnerability analysis and impact assessment of hydro-meteorological hazards
- Advances in the estimation of socioeconomic risk from hydro-meteorological hazards
- Characteristics of weather and precipitation patterns leading to high-impact events
- Relationship between weather and precipitation patterns and socio-economic impacts
- Socio-hydrological studies of the interplay between hydro-meteorological hazards and societies
- Hazard mitigation procedures
- Strategies for increasing public awareness, preparedness, and self-protective response
- Impact-based forecast, warning systems, and rapid damage assessment.
- Insurance and reinsurance applications

Urban hydrological processes are characterized by high spatial variability and short response times due to the high degree of imperviousness. As a result, urban catchments are especially sensitive to the spatial-temporal variability of precipitation at small scales. High-resolution precipitation measurements in cities are crucial to properly describe and analyze urban hydrological response. At the same time, urban landscapes pose specific challenges to obtaining representative precipitation and hydrological observations.

This session focuses on high-resolution precipitation and hydrological measurements in cities and on approaches to improve modeling of urban hydrological response, including:
- Novel techniques for high-resolution precipitation measurement in cities and for multi-sensor data merging to improve the representation of urban precipitation fields.
- Novel approaches to hydrological field measurements in cities, including data obtained from citizen observatories.
- Precipitation modeling for urban applications, including, amongst others, convective permitting models and stochastic rainfall generators.
- Novel approaches to modeling urban catchment properties and hydrological response, from physics-based, conceptual and data-driven models to stochastic and statistical conceptualization.
- Applications of measured precipitation fields to urban hydrological models to improve hydrological representation and prediction at different time horizons to ultimately enable improved management of urban drainage systems (including catchment strategy development, flood forecasting and management, real-time control, and proactive protection strategies aimed at preventing flooding and pollution).
- Strategies to deal with upcoming challenges, including climate change and rapid urbanization.

Rainfall for engineering design is usually estimated from records that are short and sparse, variable in space and time, and increasingly affected by changing hydroclimatic regimes that challenge the stationarity assumptions behind traditional Intensity-Duration-Frequency (IDF) and Depth-Duration-Frequency (DDF) estimation. A structural mismatch compounds this: design rainfall must be resolved for the durations, return periods, and spatial scales applications demand, whereas short records, sparse networks, and ungauged sites rarely support these targets directly. Reliable estimation, therefore, needs models that represent rainfall variability across scales while accounting for the storm types and processes shaping extremes.

This session collects recent advances in modelling rainfall for engineering design: new theory, physically based frameworks, statistical and stochastic models, high-resolution observations (e.g., radar, satellite), physics-based simulations (e.g., convection-permitting models), and hybrid methods. Contributions span scales relevant to hydrological and hydraulic applications, from sub-hourly to multi-day durations and from point to areal estimates. Topics include but are not limited to:

– Methodological advances across spatiotemporal scales, including IDF or DDF derivation and use of physical information and covariates;
– Stochastic models, disaggregation, and continuous simulation for design-relevant estimates across scales;
– Areal estimation, areal reduction factors, and point-to-catchment-scale relationships;
– Multivariate analysis of extremes, including tail dependence, process heterogeneity, and cross-duration dependence, translated into joint design quantities and IDF or DDF surfaces;
– Estimation under changing climatic regimes, including covariate-dependent and trend-informed frequency models, and translation of climate-model outputs into design quantities (e.g., bias correction, change-factor and direct methods, ensembles);
– Process- and regime-conditioned estimation, including atmospheric rivers, monsoonal regimes, seasonality, and storm-type classification;
– Regional frequency analysis, pooling, and estimation at ungauged, data-poor sites;
– Emerging data and methods, including radar, satellite, reanalysis, and convection-permitting products, downscaling, machine learning, and hybrid frameworks;
– Design hyetographs, probable maximum precipitation (PMP), and quantification, propagation, and communication of uncertainty.

Hydroclimatic extremes such as floods, droughts, storms, or heatwaves often affect large regions and can cluster in time, therefore causing large socio-economic damages. Hazard and risk assessments, aiming at reducing the negative consequences of such extreme events, are often performed with a focus on one location despite their spatially compounding nature. Also, temporal clustering of extremes is often neglected, with potentially severe underestimation of hazard. While spatial-temporal extremes receive a lot of attention by the media, it remains scientifically and technically challenging to assess their risk by modelling approaches.
This session aims to explore advances in the study and modeling of hydroclimatic extremes, embracing a broad perspective that includes—but is not limited to—their spatial and temporal characteristics. Key challenges include the definition of multivariate and compound events; the quantification of uncertainties, of spatial and temporal dependence together with the introduction of flexible dependence structures; the identification and integration of physical drivers and processes across scales; the handling of high-dimensional data and the estimation of occurrence probabilities.
We welcome contributions that enhance our understanding of the mechanisms driving hydroclimatic extremes, propose innovative modeling frameworks, or offer new insights into the prediction, attribution, and risk assessment of these events across space and time. Studies addressing extremes from statistical, physical, or interdisciplinary perspectives are particularly encouraged.

Traditionally, hydrologists focus on the partitioning of precipitation water on the land surface into evaporation and runoff, while ignoring factors that influence precipitation. However, more than half of the evaporation globally returns as precipitation on land. Given this important feedback of the water cycle, changes in land-use and water-use, as well as climate variability and change, impact not only the partitioning of precipitation water but also the atmospheric input of water as precipitation, at both remote and local scales.

This session aims to:
i. investigate the remote and local atmospheric feedbacks from human interventions such as greenhouse gasses, irrigation, deforestation, and reservoirs on the water cycle, precipitation and climate, based on observations and coupled modelling approaches,
ii. investigate the use of hydroclimatic frameworks such as the Budyko framework to understand the human and climate effects on both atmospheric water input and partitioning,
iii. explore the implications of atmospheric feedbacks on the hydrological cycle for land and water management.

Applied studies in this session may adopt fundamental characteristics of the atmospheric branch of the hydrological cycle on different scales. These fundamentals include, but are not limited to, atmospheric circulation, humidity, hydroclimate frameworks, residence times, recycling ratios, sources and sinks of atmospheric moisture, energy balance and climatic extremes. Studies may also evaluate different data sources for atmospheric hydrology and implications for inter-comparison and meta-analysis. Examples of data sources and methodological approaches include observation networks, isotopic studies, conceptual models, Budyko-based hydroclimatological assessments, back-trajectories, reanalysis and fully coupled Earth system model simulations.

Understanding how urban environments interact with hydrometeorological extremes is becoming increasingly important as cities face growing risks from extreme precipitation, flooding, drought, and compound events. The characteristics of these extremes are shaped by interactions among local atmospheric and hydrological processes, land-surface properties, and urban form. Yet these interactions remain difficult to generalise across cities, climates, and spatial and temporal scales. This complexity reflects the high heterogeneity of urban environments and the multiple physical pathways through which urbanisation can influence hydrometeorological extremes. Addressing this challenge requires approaches that can disentangle nonlinear and scale-dependent relationships while retaining physical interpretability.
Machine learning provides opportunities to identify dominant drivers, characterise nonlinear relationships, and reveal spatially and climatically varying responses that are difficult to isolate based on conventional approaches alone. In particular, explainable machine learning (XAI), physics-informed machine learning, causal modelling and hybrid modelling can further support the transition from predictive performance to robust interpretation, hypothesis testing, and process understanding.
This session welcomes studies using machine learning and related data-driven approaches to advance understanding of hydrometeorological extremes in complex urban environments. We particularly welcome studies that use machine learning for physical interpretation, hypothesis testing, and process understanding. Key topics of discussion include:
• Identifying the drivers and nonlinear interactions shaping extreme precipitation, flooding, drought, and compound events.
• Investigating how urban form, land-surface properties, and infrastructure interact with atmospheric and hydrological processes across spatial and temporal scales.
• Applying explainable AI (XAI), physics-informed machine learning, causal modelling, and hybrid approaches to support physical interpretation and process understanding.
• Integrating remote sensing and other multi-source observations to characterise heterogeneous patterns and their scale dependence.

The growing availability of hydrological data products derived from Earth observations, reanalysis systems, numerical models, in situ monitoring networks, and data assimilations has transformed our ability to monitor and understand the terrestrial water cycle.
These products have become indispensable for supporting applications ranging from flood and drought management to water resources planning, agriculture, ecosystem monitoring, and climate adaptation. Yet product selection still often relies on general and often partial performance rankings that may not reflect the requirements of a specific process, scale, hydrological regime, model, hazard, service, or decision. They also may not reflect large differences in the spatial distribution and historical depth of the supporting data.
This session invites contributions that explore how hydrological data products should be assessed, compared, and used to maximize their scientific and societal value, based on the intended application. We encourage discussions on methodologies that go beyond traditional validation and consider aspects such as uncertainty, robustness, consistency across scales, interoperability, usability, and fitness for purpose.
Particular emphasis is placed on understanding how different user communities define product quality and how assessment frameworks can better reflect their needs.
Topics include, but are not limited to:
● Assessment, benchmarking, , and uncertainty assessment of satellite-derived, reanalysis, model-based, and integrated hydrological datasets.
● Fitness-for-purpose assessments of gridded products for scientific analyses, models, operational services, climate applications, and decision-support systems.
● Scale dependence, representativeness, and cross-product consistency.
● Experiences from operational services and real-world applications that reveal strengths and limitations of existing products.
● Case studies demonstrating how dataset choice influences hydrological analyses, forecasts, or management decisions.
● Emerging approaches for evaluating products in the context of digital twins, Earth system modelling, and climate services.
● Analyses or discussions addressing the influence of changes in the constelation(s) of Earth orbiting satellite.
The session welcomes contributions from data producers, hydrologists, remote sensing scientists, model developers, operational agencies, policy experts, and end users.

Why do past climatic changes produce different societal outcomes across regions and through time? This session focuses on the pathways and feedbacks linking climate change, water availability, ecosystem productivity, resource landscapes, human agency, and societal changes during the Holocene and beyond. We invite empirical, theoretical, and modelling studies that identify these processes across past and present human-environment systems. Of particular interest are studies that integrate palaeoclimate and environmental records with archaeological, historical, or societal evidence; examine thresholds, feedbacks, and nonlinear responses; or use process-based, agent-based, network, complex-systems, and data-driven approaches to connect environmental forcing with human decision-making and societal change. By bridging together climate, environmental, and social perspectives, this session aims to advance a process-based understanding of why climatic changes produce diverse societal trajectories under different environmental and social context.

The type of flow (homogeneous versus preferential flow) is expected to have a major impact on pollutant transfer. We expect homogeneous flow to favor pollutant access to reactive particles and, thus, enhance pollutant retention. Preferential flow also called macro-pore flow, non-equilibrium, unstable flow or funnel and fingered flow, happens when water and solutes move through a porous medium with a limited number of fast pathways instead of having a uniform distribution. These pathways can be induced by biotic (e.g. earthworm and roots) and abiotic factors and processes (e.g. wet-dry and freeze-thaw cycles, lithology and structure) and they vary in space and time. They can carry substantial movement of water and solutes in both vertical and horizontal directions under saturated or unsaturated conditions and can dominate the flow and transport processes across a wide range of scales. As a result, preferential flow may regulate the mobility, distribution or removal of solutes, nutrients or contaminants. Similarly, preferential flow may affect the efficiency of salt precipitation or dissolution processes. Understanding preferential flow processes cannot be overstated, considering its relevance to the fate and transport of solutes, nutrients and contaminants in agricultural land, landscapes, catchments, mine waste covers and tailings storage facilities.
This session welcomes studies on experimental and theoretical challenges to identify, quantify, and model the effect of flow type on solute, nutrient and contaminant transport in porous media (e.g. soil, mine wastes, waste rocks and rocky and gravelly materials) across scales (from pore scale to catchment scale). The session accepts studies on but not limited to the following topics:
• Effects of flow type within the soil-plant-atmosphere continuum and their consequence for solute, nutrient or contaminant transport in the saturated and unsaturated zone;
• Coupling the physical processes of preferential flows and geochemical processes for improving the understanding of solute sorption and desorption, mineral precipitation and dissolution;
• Modelling of different types of flow and their effects on mass transport across scales, from pore to pedon scale and entire catchments and landscapes.
• Transport of particles and colloids, including nanoparticles, and the influence of flow type, in particular preferential and non-uniform flow, on their mobility, retention and remobilization.

Subsurface temperature, particularly groundwater temperature, is an emerging indicator of environmental change. Climate change and urban heat are altering subsurface temperature regimes, including their long-term trends, spatial patterns, and seasonality. Yet subsurface temperatures remain largely “out of sight, out of mind”: observations are fragmented, monitoring approaches vary widely, and we still lack a comprehensive understanding of how subsurface temperatures are changing and what these changes mean.
This session brings together research on subsurface temperature monitoring, from soil and borehole measurements to groundwater temperature observations, as well as approaches for estimation, modelling, and prediction in both urban and rural environments. We also welcome studies investigating changes in subsurface temperature and seasonality and their consequences. Contributions may span spatial and temporal scales, from individual monitoring sites and urban environments to regional and global assessments. We particularly encourage work exploring both the challenges and opportunities associated with changing subsurface temperature regimes.

Cosmic rays carry information about space and solar activity, and, once near the Earth, they produce isotopes, influence genetic information, and are extraordinarily sensitive to water. Given the vast spectrum of interactions of cosmic rays with matter in different parts of the Earth and other planets, cosmic-ray research ranges from studies of the solar system to the history of the Earth, and from health and security issues to hydrology, agriculture, and climate change. Although research on cosmic-ray particles is connected to a variety of disciplines and applications, they all share similar questions and challenges regarding the physics of detection, modelling, and the influence of environmental factors.
The session brings together scientists from all fields related to monitoring and modelling cosmogenic radiation. It will allow the sharing of expertise amongst international researchers as well as showcase recent advancements in their field. The session aims to stimulate discussions about how individual disciplines can share their knowledge and benefit from each other.
We solicit contributions related but not limited to:
- Health, security, and radiation protection: cosmic-ray dosimetry on Earth and its dependence on environmental and atmospheric factors
- Planetary space science: satellite and ground-based neutron and gamma-ray sensors to detect water and soil constituents
- Neutron and Muon monitors: detection of high-energy cosmic-ray variations and its dependence on local, atmospheric, and magnetospheric factors
- Hydrology and climate change: low-energy neutron sensing to measure water in reservoirs at and near the land surface, such as soil, snowpack, and vegetation
- Cosmogenic nuclides: as tracers of atmospheric circulation and mixing; as a tool in archaeology or glaciology for dating of ice and measuring ablation rates; and as a tool for surface exposure dating and measuring rates of surficial geological processes
- Detector design: technological advancements in the detection of cosmic rays and cosmogenic particles
- Cosmic-ray modelling: advances in modelling of the cosmic-ray propagation through the magnetosphere and atmosphere, and their response to the Earth's surface
- Impact modelling: How can cosmic-ray monitoring support environmental models, weather and climate forecasting, agricultural and irrigation management, and the assessment of natural hazards

Soil structure and its stability are important aspects of soil health as they regulate key soil physical, chemical, and biological functions. This includes water retention, hydraulic conductivity and gaseous transport, nutrient cycling, redox dynamics, erosion resistance, and root penetration. The arrangement and connectivity of pores, minerals, and organic matter govern the exchange of water, gases, and solutes within soils and provide habitat for soil biota, which in turn actively modify and engineer the pore network. These interactions create dynamic feedbacks between soil structure and ecosystem functioning.

Soil structure evolves continuously across spatial and temporal scales through the action of roots, soil organisms, land management, and abiotic drivers such as wetting and drying cycles. These processes alter pore architecture and aggregate organization, thereby modifying soil properties and functions over time. Recent advances in imaging, geophysical methods, experimental approaches, and process-based modelling have substantially improved our ability to observe and quantify these dynamics. Nevertheless, many of the mechanisms linking soil structure development, degradation, resilience, and soil functioning remain poorly understood.

Understanding the processes and feedbacks governing soil structure dynamics is essential for developing climate-smart and resilient soil management strategies. In this session, we invite contributions on the formation, stabilization, degradation, and evolution of soil structure and its associated functions across all spatial and temporal scales. We particularly encourage contributions integrating complementary measurement techniques (e.g., geophysics, digital image correlation, X-ray CT), novel modelling concepts, or approaches that bridge scales.

Special focus lies on:
• feedbacks between soil structure dynamics and soil biology,
• effects of land use and management on soil structural development and associated soil functions,
• biological, hydrological, and mechanical processes shaping pore architecture and structural resilience,
• integration of complementary measurement techniques and modelling approaches across scales.

The transfer of sediments and associated contaminants plays an important role in catchment ecosystems as they directly influence water quality, habitat conditions, and biogeochemical cycles. Contaminants may include heavy metals, pesticides, nutrients, radionuclides, and various organic, as well as organometallic compounds. The environmental risk posed by sediment-bound contaminants is largely determined by the sources and rate at which sediments are delivered to surface water bodies, the residence time in catchments, lakes, and river systems, as well as biogeochemical transformation processes. However, the dynamics of sediment and contaminant redistribution is highly variable in space and time due to the complex non-linear processes involved. This session focuses on sources, transport pathways, storage, re-mobilization, and travel times of sediments and contaminants across temporal and spatial scales, as well as their impact on freshwater ecosystems.

This session particularly addresses the following key themes:
1) Sources and Pathways of Sediment and Contaminant Transfer
Understanding how sediments and contaminants originate from natural and human sources (e.g., agriculture, urban areas, mining, industry) and move through the land, river, lake, and reservoir continuum.
2) Transport Dynamics and Environmental Controls
Investigating the transport, retention, remobilization, and transformation of sediments and contaminants, including the influence of biogeochemical processes, human activities (such as hydropower and flood management), and changing environmental conditions.
3) Innovative and Cost-effective Monitoring and Modelling Methods
Developing and applying novel, low-cost, and open-source methods to quantify sediment and pollutant fluxes across different spatial and temporal scales.
4) Impacts on Ecosystems and Landscapes
Assessing how sediment and contaminant dynamics affect river systems, floodplains, riparian zones, in-stream ecosystems, landforms, and geomorphological processes.
5) Long-term Change and Human Influence:
Using sediment archives, sediment budgets, and catchment-scale analyses to evaluate historical trends and understand the effects of human activities and environmental change on sediment and contaminant dynamics over time.

Soil erosion is a key driver of land degradation, leading to a cascade of impacts from on-site soil loss and reduced agricultural yields to off-site consequences such as flooding and sediment and contaminant pollution of aquatic environments. The environmental, economic, and societal impacts of soil erosion require a comprehensive scientific understanding of the physical processes controlling soil detachment, transport, storage, and redistribution from hillslopes to catchments, lakes, and estuaries.
This cross-disciplinary session covers the latest scientific developments in soil erosion and sediment delivery. By integrating hydrology, geomorphology, soil sciences, and biogeosciences, we aim to unify all driving forces of land degradation and catchment particle-bound transport.
Our goal is to bridge the gap between plot-scale soil loss and catchment sediment routing, linking short-term monitoring with long-term observations. Advancing this process knowledge connects methodological developments to conceptual frameworks, promoting sustainable management.

The following topics will form the core areas of presentation and discussion:
• Measurements and Tracing: Field and lab experiments developing process understanding (e.g., interrill to gully erosion) and catchment sediment tracing techniques.
• Monitoring: Short- to long-term assessments tracking landscape changes via local field assessments, UAS, and broad-scale remote sensing.
• Modelling Approaches: Innovative simulation techniques, from empirical and process-based to data-driven, addressing runoff, gully erosion, sediment transport, and catchment sediment budgets from plot to global scales under current and future climate scenarios.
• Sediment and Contaminant Dynamics: Quantification of sediment transit or residence times, storage, remobilisation, and the biogeochemical controls of associated contaminant transport impacting riverine and lacustrine ecosystems.
• Mitigation, Restoration and Impacts: Evaluation of conservation strategies, including successes and failures in addressing the on-site and off-site impacts of land use change and disturbances (e.g., agriculture, forestry, mining, urbanisation, wildfires).
Ultimately, we explore conservation strategies that support stakeholders and global initiatives, including the EU Soil Monitoring Law, Land Degradation Neutrality by 2030, and the UN Decade on Ecosystem Restoration (2021-2030).

Quantitative information on the spatial patterns of soil redistribution during storms and on the sources supplying sediment to rivers is essential for advancing our understanding of the processes that control sediment transfer and for designing effective management strategies. It is also crucial to quantify sediment residence times and to reconstruct changes in sediment sources across a range of temporal scales. These needs are becoming increasingly urgent in light of intensified climate- and land use-driven impacts on erosion, sediment delivery, and sediment-related pollution affecting freshwater and marine environments. Over recent decades, sediment tracing (or fingerprinting) techniques, used alone or in combination with other approaches (including soil erosion modelling and sediment budgeting), have provided valuable insights to understand sediment source dynamics. Yet, their widespread application remains constrained by several methodological and conceptual challenges that the research community should address. We welcome contributions that address any of the following aspects:
• Developments of innovative field measurement and sediment sampling techniques;
• Advances in the accuracy and robustness of soil and sediment tracing techniques for quantifying soil erosion and redistribution;
• Sediment source tracing studies using conventional (e.g. elemental/isotopic geochemistry, fallout radionuclides, organic matter) or alternative (e.g. colour, infrared, hyperspectral, particle morphometry, eDNA) properties including methodological developments;
• Investigation of particle-bound contaminant transfers in catchments and river systems using sediment tracing techniques;
• Investigations of the current limitations in sediment tracing studies (e.g. tracer selection, tracer conservativeness, uncertainty analysis, particle size and organic matter corrections);
• Applications of radioisotope tracers to quantify sediment transit times over a broad range of timescales (from the flood to the century);
• Association of conventional techniques with remote sensing and emerging technologies (e.g. LiDAR, satellite);
• Cross-regional and multi-scale applications of tracing techniques to establish generic characterisations of source contributions;
• Integrated approaches to developing catchment sediment budgets: combining different measurement techniques, monitoring, and/or models to improve our understanding of sediment delivery processes.

Human activities have profoundly altered Earth-surface processes and ecosystems during the Anthropocene, modifying erosion and sediment dynamics, biogeochemical cycles, biodiversity, and ecosystem functioning. At the same time, these anthropogenic pressures interact with hydroclimatic variability and extreme events, potentially amplifying their impacts and resulting in compound effects.

Recent sedimentary records from lakes, wetlands, reservoirs and fluvial systems provide high-resolution archives for reconstructing human–climate–environment interactions over timescales ranging from decades to centuries. By integrating environmental responses across spatial and temporal scales, these archives can document the timing, magnitude and persistence of disturbances, identify their sources and pathways, and reveal long-term trajectories, legacy effects, thresholds, and patterns of ecosystem resilience and recovery.

In this session, we invite contributions using recent sedimentary archives to reconstruct environmental changes and trajectories during the Anthropocene. We particularly welcome studies addressing land-use and land-cover changes and their impacts on erosion, sediment sources and connectivity; contaminant inputs and their fate (e.g. metals, organic contaminants, radionuclides and microplastics); changes in biodiversity and ecosystem functioning; and interactions between anthropogenic pressures, hydroclimatic variability and extreme events. Studies investigating pollution legacies, ecosystem recovery, and sedimentary responses to restoration and catchment-management practices are also encouraged. Methodological contributions using or developing innovative proxies (e.g. sedDNA, CSSI and isotopic approaches), dating methods, sediment-source tracing and multidisciplinary analyses of recent sediment records are also encouraged.

Rapid environmental change and increasing pressures on land and water systems highlight the need to better understand sediment dynamics across river basins. This session brings together research spanning the source-to-sink continuum, from soil erosion and sediment generation on hillslopes and uplands to sediment transport, connectivity, deposition, river processes, and reservoir sedimentation.
We welcome contributions that advance understanding through experiments, observations, monitoring, remote sensing, modelling, and data-driven approaches, including sediment tracing and fingerprinting. We particularly encourage studies addressing sediment dynamics under climate and land-use change, including extreme sediment-mobilizing events such as sediment-laden floods, debris flows, landslide-generated sediment pulses, and their downstream consequences.
Contributions addressing river and catchment management, reservoir sedimentation, restoration, nature-based solutions, and sustainable sediment management are also encouraged. The session aims to connect fundamental process understanding with practical applications and foster interaction among researchers working across hydrology, geomorphology, sediment transport, environmental engineering, and related disciplines. We particularly welcome contributions from early-career researchers alongside established scientists and practitioners.

Land–atmosphere interactions often play a decisive role in shaping climate extremes. As climate change continues to exacerbate the occurrence of extreme events, a key challenge is to unravel how land states regulate the occurrence of droughts, heatwaves, intense precipitation and other extreme events. This session focuses on how natural and managed land surface conditions (e.g., soil moisture, soil temperature, vegetation state, surface albedo, snow or frozen soil) interact with other components of the climate system – via water, heat and carbon exchanges – and how these interactions affect the state and evolution of the atmospheric boundary layer. Moreover, emphasis is placed on the role of these interactions in alleviating or aggravating the occurrence and impacts of extreme events. We welcome studies using field measurements, remote sensing observations, theory and modelling to analyse this interplay under past, present and/or future climates and at scales ranging from local to global but with emphasis on larger scales.