Measurements, monitoring and experiments are the basis of hydrological science. However, making these measurements is time-consuming, often financially expensive and not without risk. What do we need to measure, how do we need to measure it and where, to push the boundaries of our science? Where are observational gaps and how do we overcome them? This session aims to celebrate current pioneering work in field hydrology, new promising methods and courageous approaches, and to show the importance of field data for advancing hydrology.

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.

Water is our planet’s most vital resource, and the primary agent in some of the biggest hazards facing society and nature. Recent extreme heat and flood events underline the significance of water both as a threat and as an increasingly volatile resource.
The accurate and timely measurement of streamflow is therefore more critical than ever to enable the management of water for ecology, for people and industry, for flood risk management and for understanding changes to the hydrological regime. Despite this, effective monitoring networks remain scarce, under-resourced, and often under threat on a global scale. Even where they exist, in-situ observational networks are increasingly inadequate when faced with extreme conditions, and lack the precision and spatial coverage to fully represent crucial aspects of the hydrological cycle.

This session aims to tackle this problem by inviting presentations that demonstrate new and improved methods and approaches to streamflow monitoring, including:
1) Innovative methodologies for measuring/modelling/estimating river stream flows;
2) Real-time acquisition of hydrological variables;
3) UAS and satellite remote sensing techniques for hydrological & morphological monitoring;
4) Measurement in extreme conditions associated with the changing climate;
5) Measurement of sudden-onset extreme flows associated with catastrophic events;
6) Strategies to quantify and describe hydro-morphological evolution of rivers;
7) New methods to cope with data-scarce environments;
8) Inter-comparison of innovative & classical models and approaches;
9) Evolution and refinement of existing methods;
10) Guidelines and standards for hydro-morphological streamflow monitoring;
11) Quantification of uncertainties;
12) Development of expert networks to advance methods.

Contributions are welcome with an emphasis on innovation, efficiency, operator safety, and meeting the growing challenges associated with the changing climate, and with natural and anthropogenically driven disasters such as dam failures and flash floods.

Additionally, presentations will be welcomed which explore options for greater collaboration in advancing riverflow methods and which link innovative research to operational monitoring.

Effective and enhanced hydrological monitoring is essential for understanding water-related processes in our rapidly changing world. Image-based river monitoring has proven to be a powerful tool, significantly improving data collection, analysis, and accuracy, while supporting timely decision-making. The integration of remote and proximal sensing technologies with citizen science and artificial intelligence has the potential to revolutionize monitoring practices. To advance this field, it is vital to assess the quality of current research and ongoing initiatives, identifying future trajectories for continued innovation.
We invite submissions focused on hydrological monitoring utilizing advanced technologies, such as remote sensing, AI, machine learning, Unmanned Aerial Systems (UAS), and various camera systems, in combination with citizen science. Topics of interest include, but are not limited to:
• Disruptive and Innovative sensors and technologies in hydrology.
• Advancing opportunistic sensing strategies in hydrology.
• Automated and semi-automated methods.
• Extraction and processing of water quality and river health parameters (e.g., turbidity, plastic transport, water depth, flow velocity).
• New approaches to long-term river monitoring (e.g., citizen science, camera systems—RGB/multispectral/hyperspectral, sensors, image processing, machine learning, data fusion).
• 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 specific contexts.
The goal of this session is to bring together scientists working to advance hydrological monitoring, fostering a discussion on how to scale these innovations to larger applications.

Hydrology has developed through centuries of empirical observations, evolving paradigms, and a gradual accumulation of knowledge shaped by trial and error. This session argues that a critical synthesis of hydrology’s history is vital for both education and the continued evolution of the discipline. We highlight several key themes:
• Understanding conceptual change: Foundational ideas, such as catchment response to rainfall, often evolved slowly due to measurement limitations and entrenched perceptual models. Major conceptual shifts were frequently triggered by external innovations, such as environmental tracers in the 1970s and, later, remote sensing and spatio-temporal datasets.
• Reinterpreting past “errors”: Many now‐outdated ideas were products of their time, shaped by available technology, scientific paradigms, and societal context. Recognizing and moving beyond them required both new data and the willingness to challenge prevailing assumptions.
• Preserving historical data: Documenting past hydrological observations, catchment histories, and associated metadata—including uncertainties and fragmentary evidence—is crucial for reconstructing how knowledge and practices have changed over time.
From a teaching perspective, these insights call for curricula that go beyond presenting “accepted theory” to include historical case studies of how hydrological knowledge emerged, was contested, and transformed. Such approaches help students critically engage with uncertainty, appreciate the contingent nature of scientific progress, and better understand the origins and limitations of current models.
We welcome contributions that explore the future of education—and, by extension, the future of hydrology as a scientific discipline—while reflecting on lessons learned from the past. Submissions may address, but are not limited to, questions such as: What past trials and failures in hydrology have been under‐recognized? How can historical stories be made accessible and engaging in teaching hydrology? What role do archives, data preservation, biographies, and narratives play? And how might emerging tools (e.g. machine learning, digitization of old records) support both historical scholarship and improved hydrological education?

Predictions of physical, chemical, and biological processes in soils, aquifers, rivers and across compartments are strongly affected by uncertainties and errors in model structure, parameters and forcing data. Thus, finding new model formulations (data-driven, physics-based, knowledge-guided or hybrid), estimating parameter sets, quantifying uncertainty, correcting for errors and evaluating competing models are crucial for reliable predictions at any scale (lab/field/catchment). We invite contributions on improved concepts, approaches and computational algorithms in these areas, especially on:
- new methods for inference of model equations or their parameters, inverse modelling and data assimilation in spatially distributed systems,
- data worth and optimal experimental design strategies that maximize information from (computational) experiments and minimize uncertainty,
- novel theories and concepts for spatial and temporal analysis of model - data mismatches,
- formal and informal frameworks that diagnose, detect and resolve modelling errors and capture conceptual model uncertainty,
- approaches how to bring (and extract) sound scientific reasoning into machine learning, be it for inferring new models, model error correction or data analysis,
- constraint learning techniques and novel likelihood formulations as methods to incorporate expert knowledge and soft information into the model/parameter inference process,
- new measurement technologies (point-scale to remote sensing and “soft information”) that aid developing, learning or parameterizing models, or can help diagnose and detect structural model deficiencies, as well applications and benchmarking efforts in any of these fields.
- Bayesian approaches that address any of the topics above

The management of the environment and natural resources requires decisions that often rely on the results of numerical modelling. However, the use of hydro(geo)logical models to support these decisions can be confounded by the inherent uncertainty of model conceptualisation, simulation methods, and model parameters when predicting natural system behaviour. This uncertainty must be addressed within the modelling processes to ensure that model outputs can support robust and transparent decision making.

This session will provide an opportunity to present and openly discuss the practical challenges of predictive environmental modelling for decision support. We encourage practitioners to contribute case studies that demonstrate application of:
• Problem formulation/decomposition, including strategic model abstraction for prediction-focussed modelling design.
• Model Parameter estimation, history matching, and uncertainty analysis in predictive environmental modelling, including hydro(geo)logy and other disciplines.
• Management Optimization, including multi-objective optimization under uncertainty for finding optimal management strategies under risk.
• Decision support for communicating model results to decision makers through innovative tools, visualisations and metrics.
• Scripted workflows for repeatable, transparent predictions.

This session focuses on the occurrence, fate and transport of micro- and nanoplastic (MNP) particles in soil and groundwater environments. While MNPs in marine and surface waters have received considerable attention over the past decade, terrestrial and subsurface compartments remain comparatively understudied. MNP transport, retention processes and their interaction with other emerging contaminants are not yet well understood. Soil and groundwater systems exhibit complex hydrological and biogeochemical conditions that strongly influence MNP mobility, retention, and transformation. Furthermore, MNPs can act as transport vector for other emerging contaminants in subsurface environments, adding to their transport complexity and substantially impacts the health of subsurface environments.
We invite contributions that provide insights into MNP contamination in soil and groundwater across local to global scales. Topics may include field sampling, laboratory analysis and characterization techniques tailored to soil and groundwater environments. Moreover, we welcome contributions from experimental and modelling studies that improve our theoretical and practical understanding of MNP dynamics and their interactions with emerging environmental contaminants, heavy metals and biological constituents.
This session also aims to expand knowledge on how diverse subsurface hydrological and biogeochemical conditions—including soil characteristics (e.g., type, grain size), hydraulic properties (e.g., connectivity, flow velocity, recharge dynamics) and biochemical factors (e.g., organic matter, microbial activity, geochemistry)—shape the fate and transport of MNPs. Advancing this understanding will refine conceptual models of contaminant pathways and support robust assessments of exposure, hazards and long-term risks to soil and groundwater ecosystems.

Advances in hydrological science and technology are transforming water resource management and alleviating water and food insecurity challenges that are being intensified by climate change across Africa. Emerging methods in artificial intelligence, machine learning and digital innovations are being combined with process-based hydrological models. These approaches are improving provision of information on water availability, and enhancing early forecasts of floods, droughts, and water stress. This session aims to bring together communities working on different strands of African hydrology, climate risks, water and food security, and environmental risks. We invite contributions across three key areas: (1) Understanding and monitoring - hydrological process understanding and modelling, measurement and monitoring systems, remote sensing and AI-driven modelling; (2) Prediction and assessment - drought/flood forecasting, seasonal to decadal forecasting, climate change impact assessments including compound and multi-hazard risks; and (3) Management and solutions - water resources management and climate change adaptation strategies. Studies that utilize interdisciplinary approaches are particularly encouraged. By fostering collaboration among researchers, practitioners, and policymakers, this session aims to bring to the forefront the advances in understanding African hydrology and climate while promoting practical solutions. Science-for-solutions initiatives contributing to the IAHS HELPING decade are welcome.

Understanding hydrological processes in drylands, from hyperarid to semi-arid regions, is an urgent and evolving research frontier, particularly under the growing pressure of climate change, land use transformation, and increasing water scarcity. These regions, which span over 40% of the Earth’s land surface and support more than two billion people, face complex hydrological challenges due to their high climatic variability and limited water resources. This session welcomes contributions that advance our understanding of key hydrological processes in drylands (such as semi-arid zones in the Mediterranean or hyperarid ones in the Sahara and other desertic areas), including the spatial and temporal variability of rainfall, runoff generation mechanisms, soil moisture dynamics, and groundwater recharge. We particularly encourage studies that address the episodic nature of hydrological events, transmission losses, and the challenges of monitoring and modeling water fluxes in these environments. Contributions focusing on evapotranspiration partitioning, carbon assimilation, and the coupling of water and carbon cycles under changing climate and land cover conditions are also highly relevant. Given the scarcity of long-term, high-quality data in many dryland regions, notably in the Global South, we invite approaches that leverage remote sensing, citizen science, novel monitoring techniques, and integrative modeling frameworks. Finally, we seek studies that assess the impacts of climate change and support scenario-based planning for sustainable water resource management. By bringing together observational, experimental, and modeling perspectives, this session aims to foster interdisciplinary dialogue and identify pathways to improve hydrological understanding and resilience in dryland and semi-arid systems.

Water sustainability is becoming a key concern worldwide due to hydrological uncertainty, climate change, landuse landcover changes, and growing water pollution. These drivers greatly influence the catchment hydrology and thus their roles cannot be undermined while assessing both surface and groundwater resources. These aspects draw paramount significance in catchments with large heterogeneity and spatial complexities such as mountainous and urban catchments, data scare regions, and low-income countries where investment in hydrological monitoring network and installation of IoT sensors is very limited. It is therefore warranted to leverage geospatial, machine learning and decision science techniques to improve the understanding of catchment hydrology and the adverse consequences of climate change and anthropogenic drivers on surface and groundwater resources, which may play vital role in intensifying water, food and energy security. The worldwide readily available satellite remote sensing data and global data products of hydrometeorological and biophysical parameters enable us to leverage potential of geospatial and machine learning techniques in addressing challenges associated with climate change, landuse landcover changes, water scarcity, groundwater management, and ecosystem services.

This session aims to bring together professionals from multidisciplinary fields such as hydrology, hydrogeology, geosciences, agriculture, and environmental sciences & engineering to share their innovative ideas, research outcomes, and innovative insights obtained from case studies of different catchment settings by utilizing geospatial, artificial intelligence and machine learning techniques. We solicit novel contributions from the researchers to investigate and manifest revolutionary developments in the catchment hydrology by utilizing Remote Sensing with Satellite and Drone Platforms, GIS, Artificial Intelligence (AI), and Machine Learning (ML) techniques for addressing pressing challenges of water sustainability in mountainous and urban catchments and data scarce regions. The combined use of these technologies is revolutionizing and providing powerful tools in analysing and understanding complicated hydrological processes, which in turn will be very useful in evolving effective water resource management strategies to foster sustainable development and ecosystem-based adaptation to hydrological uncertainty, climate change and anthropogenic drivers.

Water in the snowpack and in glaciers represents an important component of the hydrological budget in many regions of the world and is crucial to sustaining life during dry seasons. Predicted impacts of climate change in catchments with snow and/or glacier cover (i.e., shifts from snowfall to rainfall, modified total precipitation amounts, earlier snowmelt, and decrease in peak snow accumulation) will reflect on water resources availability for environmental and anthropogenic uses at multiple scales. This may have implications for energy, drinking water and food production, as well as for environmentally targeted water management.

Runoff generation in catchments that are impacted by snow or ice profoundly differs from rainfed catchments. Yet, our knowledge of snow/ice accumulation and melt and their contribution to runoff remains highly uncertain, because of both limited availability and inherently high spatial variability of hydrological and weather data.

Contributions addressing the following topics (but not limited to) are welcome:
- Experimental research on snowmelt & ice-melt runoff processes and potential implementation in hydrological models;
- Development of novel strategies for snowmelt runoff modelling in various (or changing) climatic and land-cover conditions;
- Evaluation of remote-sensing or in-situ snow products and application for snowmelt runoff calibration, data assimilation, streamflow forecasting or snow and ice physical properties quantification;
- Observational and modelling studies that shed new light on hydrological processes in glacier-covered catchments, e.g. impacts of glacier retreat on water resources and water storage dynamics or the application of techniques for tracing water flow paths;
- Studies addressing the impact of climate change and/or extreme events (e.g., droughts) on the water cycle of snow and ice affected catchments.
- Studies on cryosphere-influenced mountain hydrology and water balance of snow/ice-dominated mountain regions;
- Use of modelling to propose snowpack, snowmelt, icepack, ice melt or runoff time series reconstruction or reanalysis over long periods to fill data gaps

Despite covering only about 25% of continental land, mountains are an essential part of the global ecosystem. They are also widely recognised as the source of much of the world's freshwater supply. An important portion of the global population relies on their water supply, with around 26% living in mountain communities and 40% living in the downstream plains. Owing to the heterogeneity of elevation-dependent hydro-meteorological conditions, mountain regions are particularly sensitive to climate variability and change. This makes them also unique areas for identifying and monitoring the effects of global change.

This session will bring together the scientific community developing hydrology research on mountain regions worldwide to share new findings and perspectives. We invite contributions that address past, present and future conditions, including changes resulting from climate and/or land use change, their impacts on local and downstream areas, and adaptation strategies to ensure the long-term sustainability of mountain ecosystem services, with a special focus on water cycle regulation and water resource generation.

Topics of interest include, but are not limited to
- sources of information for assessing past and present conditions (in either surface and/or groundwater systems);
- methods to disentangle climatic and anthropogenic drivers of hydrological change;
- modelling approaches for assessing and projecting hydrological change;
- evolution, forecasting and impacts of extreme events;
- case studies on adaptation to changes in water resources availability.

Mountain environments are among the most complex and dynamic landscapes on Earth, yet they remain some of the least observed. Harsh conditions, difficult access, and rapid environmental changes pose significant challenges to data collection and hydrological understanding.
We invite contributions that leverage low-cost sensor networks (e.g., LiDAR, motion-triggered cameras), stable isotopes, novel environmental tracers, citizen science, hydrological modelling, and indigenous knowledge systems to advance our understanding of mountain hydrology. Special emphasis will be placed on high-mountain alpine catchments in the Alps, Andes, and Himalayas—regions where data scarcity is acute, yet hydrological insights are critical for sustainable water resource management.
This session explores how recent technological and methodological innovations are helping to overcome these limitations.

Rapid glacier retreat, changes in snow and rain patterns and an intensifying water cycle are driving profound shifts in mountain hydrology, communities and ecology. Yet, substantial uncertainty remains about how these shifts affect water security in mountain and downstream regions and how they locally depend on exposure, vulnerability and adaptive capacity of social-ecological systems. In many mountain regions worldwide, hydrological changes have accelerated in past decades, raising the urgent questions: which threats to mountain water security can already be observed today, and what are early examples of successful adaptation? Can we learn from explicit cases of maladaptation leading to increasing risk of water insecurity to adapt in ways that strengthen resilience in both mountain ecosystems and communities?
This session brings together work on mountain water security and its potential future pathways. We ask: Are current environmental changes putting mountain water security increasingly at risk, and how can we adapt in ways that strengthen resilience in both ecosystem and communities? We welcome contributions from both natural and social sciences that assess past, present, and future trajectories of mountain water security and related impacts that help us better understand, plan, and test effective long-term adaptation strategies.
Possible topics include, but are not limited to:
1. Advances in understanding the impact of climatic, cryosphere and hydrological changes on mountain water security across diverse human–natural systems.
2. Methods (e.g. models, field data, remote sensing, surveys, expert insights) that quantify and capture the consequences of changing water security.
3. Reconstruction of long-term time series to evaluate hydro-ecological dynamics and assess potential tipping-points in water security shifts.
4. Quantifying the effect of peak water on water security.
5. Assessment of nature-based solutions and/or traditional mountain water management systems that can enhance resilience.
6. Responses of mountain communities and water governance regimes to increasing risks of water inesecurity

The Danube River Basin (DRB) is one of the most diverse transboundary river system in the world in terms of the number of countries that share over its territory with different natural, cultural, societal and economic backgrounds. The assessment of quantity and quality of water resources and their changes in such a diverse system requires transnational and interdisciplinary approaches and cooperation. This session aims to collect studies investigating the spatial and temporal patterns and changes of various aspects and components of water cycle in DRB and presenting approaches and methods strengthening transboundary cooperation in the field of catchment hydrology and water resources management.
We welcome contributions demonstrating:
(1) Modeling and inter-comparison of different models for simulating water balance components and water quality including climate change impact studies, sensitivity analyses, uncertainty evaluations.
(2) Evaluation of performance and uncertainty of transboundary datasets of climate and hydrological characteristics, including remote sensing products and climate projections.
(3) Applications supporting sustainable management of transboundary water including water abstractions, water-savings or water retention solutions in agriculture and industry.

The Critical Zone (CZ), encompassing the Earth's surface from the top of the vegetation canopy to the bottom of the circulating groundwater, is essential for sustaining life and maintaining environmental health. Understanding this region of complex intersections within the natural world and between the environment and society requires a collaborative, multidisciplinary approach that transcends disciplinary and national boundaries, bridging gaps between short-term and long-term environmental processes. This session will highlight CZ science, CZ methodologies, and the collaborative efforts of CZ research sites and networks from around the world. Topics of interest include, but are not limited to: Innovative techniques in CZ research and monitoring, such as integrated observation and modeling approaches or hybrid methods; Advances in understanding soils, hydrology, and biogeochemical cycling within the CZ; Intersections of society and the CZ; Policy or management implications of CZ research; Development of CZ science networks; And case studies of successful national and international CZ collaborations.

A multitude of processes contribute to the hydrologic functioning of catchments. Traditionally, catchment hydrology has been centered around surface runoff, which is readily observable. But at the same time, invisible below ground processes entailing the storage dynamics and flows of water are still underexplored. This includes subsurface runoff, as well as feedbacks of subsurface processes to the surface and the specific role of soil moisture in shaping these fluxes. This session aims to bring together contributions on the following topics and to address gaps in observations, models, and understanding of hydrologic systems:
- Identifying, tracing, and modeling subsurface runoff generation at the catchment scale.
- Factors and mechanisms controlling subsurface water storage and fluxes
- How soil moisture measurements at different scales can be used to improve process understanding, models, and hydrologic theory
- Interactions of surface and subsurface hydrologic processes

Stable and radioactive isotopes and other natural and artificial tracers are useful tools to (i) fingerprint the sources of water and solutes in catchments, (ii) trace flow pathways, and/or (iii) quantify exchanges of water, solutes and particulates between hydrological compartments both in the natural and simulated environments. We invite contributions that demonstrate novel applications and recent developments of isotope and other tracer techniques in hydrological field studies and data driven or physical modelling in the areas of surface water-groundwater interactions, unsaturated and saturated zone, rainfall-runoff processes, cold-region hydrology, nutrient or contaminant transport, ecohydrology or other catchment processes.

Water systems worldwide are under increasing pressure due to the combination of anthropogenic and natural drivers, resulting in the degradation of water resources in both quantity and quality. Among the key contributors, water pollution and climate change remain in the spotlight. In this framework, efficient and sustainable management of water resources is a pivotal challenge that humanity must face from now on, which demand integrated multidisciplinary and multiparametric approaches. Stable isotopes are a valuable tool with countless applications in water sciences. The inventory of stable isotopes implemented in water resources management studies, has continuously grown over the years, including traditional (e.g., oxygen, hydrogen, carbon, nitrogen, and sulfur) and unconventional isotopes (e.g., transition metals). Stable isotope analyses can be used to trace water origin and evolution as well as its circulation patterns and recharge processes in different geological contexts. Also, to unravel the origin of specific solutes (e.g., pollutants) in surface and groundwaters and its transformation processes, including the potential determination of these processes products. This session welcomes multidisciplinary contributions focused on applying stable isotopes through innovative approaches aiming to trace the origin and fate of pollutants as well as climate impacts in water systems aiming to advance towards sustainable water resources management.

Hydrological and water quality models are fundamental tools for exploring water, nutrient, and pollutant cycles. However, their applications are often limited by considerable uncertainties in model inputs, structures, and observations.

Tracers provide effective means to address these challenges by identifying the sources of water, nutrients, and pollutants, and by constraining their flow paths and residence times. Incorporating tracers into models can therefore reduce uncertainty, improve parameterization and calibration, and enhance the robustness of process-based understanding.

In this session, we welcome contributions on innovative modelling frameworks that integrate tracers into model development, calibration, and applications across spatial and temporal scales. This covers different tracers, including water stable isotopes, hydrochemical tracers, reactive tracers, biological tracers (e.g., eDNA), and physical tracers (e.g., temperature). Tracer studies using lumped, process-based, data-driven, and hybrid models/approaches that advance hydrological and biogeochemical process understanding are all encouraged.

Potential topics include (but are not limited to):
(1) Development or application of tracer-aided hydrological models (lumped or distributed)
(2) Development or application of tracer-aided water quality models (lumped or distributed)
(3) Development or application of data-driven or hybrid approaches incorporating tracers
(4) Introducing tracers to improve model parameterization, calibration, and evaluation
(5) Using tracers to improve understanding of sources, flow paths, and residence times
(6) Using tracers to test existing theories or explore new concepts in hydrology and biogeochemistry

Hydrological information is more available than ever, but substantial knowledge gaps remain which limit our ability to coherently explain and connect hydrological phenomena across space and time scales. Hydrological synthesis is the combination of components or elements of our knowledge to form a connected whole. These connections may be across geographic locations and scales, or across temporal windows and scales. This session will focus on the investigation of similarities and patterns among catchment hydrological responses and processes.
We welcome contributions that e.g. focus on:
- Using field observations across multiple sites or large sample hydrology datasets, to synthesize process-based explanations of hydrological phenomena
- Connections between residence time and catchment response time
- Theoretical explanations of hydrological phenomena across multiple places or scales, for example, what is the link of event recession timescales to seasonal streamflow patterns?
- Connections among hydrological signatures at different time scales, e.g. connection of the streamflow seasonality to the long term mean flow
- New approaches to use modelling tools to make process and pattern interpretations
- New methods to identify hydrological patterns in data

Understanding and representing hydrological processes is the basis for developing and improving hydrological and Earth system models. Relevant hydrological data are becoming increasingly available globally, opening new avenues for modelling (model parametrization, evaluation, and application) and process representation. As a result, a variety of models are developed and trained by new quantitative and qualitative data at various temporal and spatial scales.
In this session, we welcome contributions on novel frameworks for model development, evaluation and parametrization across spatio-temporal scales.

Potential contributions could (but are not limited to):
(1) advance seamless modeling of spatial patterns in hydrology and land models using new data products and earth observations;
(2) improve model structure by representing often neglected processes in hydrological models such as human impacts or vegetation dynamics;
(3) provide novel concepts for improving the characterization of internal and external model fluxes and their spatio-temporal dynamics;
(4) introduce new approaches for model calibration and evaluation, especially to improve process representation, and/or to improve model predictions under changing conditions;

Many papers have advised on careful consideration of the approaches and methods we choose for our hydrological modelling studies as they potentially affect our modelling results and conclusions. However, there is no common and consistently updated guidance on what good modelling practice is and how it has evolved in recent years. While many useful practices such as model benchmarking, controlled model comparisons, developing scripted workflows, carefully selecting calibration periods and methods, or testing the impact of subjective modelling decisions along the modelling chain exist, none of these can be considered common practice yet.

This session therefore intends to provide a platform for a visible and ongoing discussion on what ought to be the current standard(s) for an appropriate modelling protocol that considers uncertainty in all its facets and promotes transparency in the quest for robust and reliable results. We aim to bring together, highlight and foster work that develops, applies, or evaluates procedures for a trustworthy modelling workflow or that investigates good modelling practices for particular aspects of the modelling chain. We invite research that aims to improve the scientific basis of modelling and puts good modelling practice in focus again. This might include (but is not limited to) contributions addressing the following key questions:

1. The theoretical side of model application, centered around the question: “is my model any good?” (e.g., benchmarking, robust calibration/evaluation and controlled model comparison);
2. The practical side of model application, centered around the question: “how do I ensure my modeling work is efficient, reproducible and transparent?” (e.g., novel modelling protocols or workflows, examples of adopting the FAIR principles);
3. The social side of model application, centered around the question: “how do I communicate my model’s strengths and weaknesses?” (e.g., investigation of subjective choices along the modeling chain and communication of model outputs and uncertainties);
4. The future of model application, centered around our main question: “where are we today, and where do we want to be tomorrow?” (i.e., overviews of the current state of modeling, and visions for the future).

There are a plethora of models available for simulation of river flows, ranging from conceptual to physically based hydrological models, land surface models, and increasingly incorporating machine learning approaches. Given this diversity of models, it is vital that we implement systematic model intercomparisons, multi-model approaches and model benchmarking to learn about the relative merits of different model structures, guide model development, and quantify model structural uncertainties. This session aims to bring together researchers and practitioners involved in river flow modelling, with a focus on multi-model approaches and methods to evaluate and learn from model differences.

Real-time monitoring and modelling are essential for supporting decision making in water and environmental management. By combining observational data with numerical models, one can forecast how hydrological and environmental systems respond to different management actions or environmental changes. Numerical models can thus provide reliable guidance for decision-making.
Real-time modelling frameworks face several challenges, including the integration of diverse data streams and practical constraints on modelling, as well as the need to deliver timely and reliable outputs. Addressing these challenges is key to improve how water and environmental systems are managed under growing pressures from climate change and human activities. This session invites contributions on innovative approaches, tools, and case studies that demonstrate how observations and models can be combined to support operational and strategic decisions.
Topics of interest include, but are not limited to:
1) Integration of modelling with ground-based and satellite observations
2) Real-time data assimilation and forecasting for decision support
3) Conceptual, numerical, and data-driven approaches for decision-relevant modelling
4) Optimised monitoring, site characterisation, and data processing
5) Real-world case studies showing how modelling informs operational water and environmental management

The field of hydrology originally evolved in a data-scarce environment. Process-based models, grounded in physical theory, have been used to translate knowledge of hydrologic processes into interpolations and predictions of hydrologic fluxes when observations were too infrequent, distant, or limited. Currently, new observations from aerial, terrestrial, and aquatic drones; in-situ sensor networks logging continuous data; optical sensors, embedded nano, microbial, chemical, radiometric, and acoustic devices; and close-range remote sensing (incl. static and aerial platforms) and space-based platforms allow us to directly observe complex hydrologic processes at a variety of spatiotemporal scales, transforming the hydrologic data landscape from one of dearth to one of abundance. In translating insights from new observations into our process-based modelling frameworks, we develop a powerful interface for uncovering new knowledge about hydrologic systems from source to sea regions, i.e. from head-watersheds to coastal areas. At this session, we welcome abstracts that showcase breakthroughs into the dynamics of hydrological processes enabled by next-generation observation systems. Specific topics could include how cutting-edge technology has enabled reparameterization of processes such as overland flow; discharge and velocity estimation; wave propagation and timing; vadose zone and groundwater hydraulics; boundary layer dynamics ; convective processes; snow accumulation and melt; permafrost active layer dynamics; biogeochemical and nutrient cycling; extreme events; and localized or idiosyncratic hydrologic systems. Submissions from the Digital Waters Flagship and Pilot (https://digitalwaters.fi) are strongly encouraged.

The growing availability of large-sample and large-scale water quality datasets offers new opportunities to understand spatial and temporal dynamics of water quality across diverse natural and human influence settings. While challenges in homogenizing heterogeneous datasets from various sources, such datasets enable researchers to move beyond site-specific studies and perform comparative, multi-catchment analyses, revealing emergent patterns in water quality responses. They also help identify drivers of water quality patterns including climate, geology, soil, land-use and human activities, along with their interactions and temporal evolutions across hydrological settings. Large-sample or large-scale water quality analysis support robust statistical models development, facilitate machine learning applications, and improve transferability and generalization of findings across sites, aquatic ecosystem types, and regions, and thus increase the understanding of water quality controls and management options to support decision-making under changing environmental conditions.
This session invites scientists focused on the compilation and exploration of large water quality datasets, aiming to unravel natural and anthropogenic drivers shaping water quality patterns. We welcome studies on any solutes, such as major ions, nutrients, metals, and pollutants, in inland waters ecosystems (streams, lakes, groundwater) and soils.
Topics of interest include but are not limited to:
1.Development and improvement of large datasets:
•Dataset compilation, harmonization across regions and data platforms development
•QA&QC, addressing data gaps and uncertainty
•Emerging opportunities and challenges in large-scale research
2.Innovative approaches (e.g., machine learning) to handle big datasets:
•Application of diverse statistics and machine learning methods
•Empirical and mechanistic analyses of concentration–discharge (C–Q) relationships
3.Spatial patterns, temporal trends and controls:
•Regional contrasts and patterns, multi-scale temporal trend analysis
•Driver-response relationships including lagged responses to changes in management and pressures
•Influence of physical processes, land cover, climate variability, hydrological regimes, soil/geology and human activity
4.Hot spots and hot moments:
•Event-based or threshold-triggered changes during extremes (floods and droughts)
•Spatial clustering of high-impact areas or sources
•Seasonal timing of pollution loading or retention

Degradation of water quality due to natural and anthropogenic influences constitutes one of the most significant threats to water availability. Space-time modelling of water quality depends on the availability of long-term, reliable datasets, which, in reality, are often found to be incomplete, sparse, or unavailable. This results in incorrect or ineffective decision-making surrounding water safety, treatment, and public health. This session invites abstracts from researchers who explore problems related to surface and sub-surface water quality modelling by employing advanced statistical, numerical, analytical, or experimental approaches at various scales.
Surface water quality, though monitored more frequently, provides only a partial picture of the space-time variability of various contaminants. On the other hand, subsurface environments, which are highly heterogeneous, influence the flow and transport dynamics and also the surface-subsurface interaction mechanisms, making model calibrations quite challenging. In dynamic environments like wetlands, solute transport, sediment dynamics, and vegetation are also coupled through hydrodynamic and biogeochemical feedback, which require advanced modelling techniques for quantifying nutrient cycling, contaminant retention, and ecosystem functioning. Moreover, limited knowledge is available about the degradation/transformation pathways, sorption characteristics, and reaction mechanisms of the emerging contaminants, like micro- and nano-plastics, antibiotics, pathogens, etc., in surface and subsurface waters. Understanding all of these complex yet intertwined interactions provides a holistic picture of the pollutant mobilization mechanisms, paving the way for effective abatement strategies. Contributions related to the following topics and beyond are welcome, especially by researchers from the Global South:
*Innovative approaches for overcoming water quality data availability, accessibility, and integrity challenges
*Advanced statistical approaches to predict the impact of changing climate and anthropogenic forcings on both surface water quality and quantity
*Advanced mathematical/numerical approaches to model the transport dynamics of emerging contaminants in subsurface environments
*Microplastics in soil: Experimental insights and numerical modeling approaches
*Modeling solute-sediment-vegetation interactions in wetlands: Coupled hydrodynamic and biogeochemical perspectives

Water temperature is crucial for ecosystems and society, and is strongly driven by climate, environmental and human factors. Extreme water temperatures can severely impact these systems by altering biological functioning (e.g. increasing fish mortality) or limiting the usefulness to humans (e.g. cooling water use and power generation potentials). Water temperature is also used as a tracer for hydrological processes, enabling us to understand contributions of different hydrological components to runoff generation.

This session aims to bring together freshwater temperature researchers from both the surface (i.e. springs, rivers, streams, lakes and reservoirs) and subsurface waters (i.e. soil and groundwater) community. It aims to shed light on the natural and anthropogenic processes governing fresh water temperatures, and the resulting implications for ecosystem health, water resources management and sectoral uses. We are interested in spatio-temporal patterns and historic or future trends of water temperature and its extremes, their impact on aquatic biochemistry and ecosystems, as well as different types of water temperature modelling approaches (e.g. process-based, machine learning).

We welcome submissions that investigate water temperature dynamics across various temporal and spatial scales (local to global) in any inland water bodies using field and lab experiments, large-scale and -sample analyses, and modelling approaches.

The dynamics of solute and particulate concentrations, measured at a catchment’s outlet, reflect the multitude of processes that are occurring and potentially interacting at different temporal and spatial scales. These processes may include the hydrological mobilization and transport of solutes, but also biogeochemical transformation and retention. Data-driven statistical analyses of discharge and/or concentration time series are a powerful tool to illuminate these underlying processes. Long-term, high-frequency, or multi-solute data as well as measurements from different catchment compartments (e.g., soil- or groundwater) or synoptic sampling campaigns are becoming more available, allowing us to formulate and test hypotheses on dominant ecohydrological and geochemical processes moving “from pattern to process”. This session aims to bring together studies that use data-driven statistical analyses of stream concentration time series to draw conclusions about solute and particulate mobilization, retention, and export mechanisms. Presentations on the following topics are particularly welcome:
Interpretation of concentration-discharge relationships to understand the interplay between hydrological and biogeochemical processes in the terrestrial part of catchments and in the river network
Utilization of high-frequency or multi-parameter observations of water quality
Long-term trajectories in nutrient inputs, outputs, and nutrient stoichiometry
Integration of data from subsurface water or synoptic sampling campaigns
Role of climate change or extremes in altering nutrient export patterns.
Instream, network and wetland or lake effects on the dynamics of nutrient loads and concentrations
Relationship between water travel times and water quality dynamics
Perspectives from less frequently studied catchment settings, including boreal, arctic, mediterranean, (sub-)tropical regions
Perspectives from different land use/land cover settings and their changes (e.g., forest dieback, wetland restoration, urbanization, agricultural practices, etc.)

Land use and climate change as well as legal requirements (e.g. the EU Water Framework Directive) pose challenges for the assessment and sustainable management of surface water quality at the catchment scale. Sources and pathways of nutrients and other pollutants as well as nutrient interactions need to be characterized to understand and manage the impacts in river systems. Additionally, water quality assessment needs to cover the chemical and ecological status to link the hydrological view with aquatic ecology.
Models can help to optimize monitoring schemes and provide assessments of future changes and management options. However, insufficient temporal and/or spatial resolution, a short duration of observations and the widespread use of different analytical methods limit the potential for model application. Moreover, model-based water quality calculations are affected by errors in input data, model errors, inappropriate model complexity and insufficient process knowledge or implementation. In addition, models should be capable of representing changing land use and climate conditions to meet the needs of decision makers under uncertain future conditions. Given these challenges, there remains a strong need for advances in water quality modeling.

This session aims to bring together scientists working on both experimental and modelling studies to improve the prediction and management of water quality constituents (e.g. nutrients, organic matter, algae, sediment) at the catchment scale. Contributions addressing the following topics are welcome:

- Experimental and modelling studies on the identification of sources, hot spots, pathways and interactions of nutrients and other, related pollutants at the catchment scale
- New approaches to develop effective water quality monitoring schemes
- Innovative monitoring strategies that support both process investigation and improved model performance
- Advanced modelling tools for integrating catchments and/or simulating in-stream processes
- Observational and modelling studies at the catchment scale that relate and quantify water quality changes to changes in land use and climate
- Measurements and modelling of abiotic and biotic interaction and feedback involved in the transport and fate of nutrients and other pollutants at the catchment scale
- Catchment management: pollution reduction measures, stakeholder involvement, scenario analysis for catchment management

Quantifying and understanding how global change, such as climate change and extremes, land use change and socio-economic developments affects clean water availability across space and time is essential. This knowledge is key to ensuring sufficient water of suitable quality to meet both human and ecosystem needs at present day and in the future. Recent work has highlighted the importance of considering water quality as a key factor in limiting water supply for sectoral uses. Hence, there is an urgent need for tools such as models that span a gradient from purely statistical (e.g., machine learning) to process-based approaches, anticipating the combined impacts of climate and socio-economic changes on water quality and addressing the resulting environmental and societal consequences. Some of these tools, within both Bayesian and frequentist paradigms, enable consideration of prediction reliability, relating uncertainties to a decision makers’ attitudes and preferences towards risks, all while accounting for the uncertainty related to our system understanding, data and random processes. We seek contributions that apply modeling and data-analytic approaches to:
• investigate the combined impacts on water quality and quantity from climate change and/or extremes across local to global scales, including climate impact attribution studies;
• investigate the impacts of present and future socio-economic developments on surface and/or groundwater quality;
• investigate the implications of compound and cascading extreme climate events (e.g., wildfire and floods, drought and heatwaves) on water quality;
• quantify and couple supply and demand in support of water quality management including vulnerability assessment, scenario analysis, indicators, and the water footprint;
• project future water scarcity (combining water quality & quantity) supply and demand in the context of a changing climate;
• quantify the uncertainty of water quality models under drivers of global change;
• interpret and characterize uncertainties in machine-learning, AI and data-mining approaches that are trained on large data sets;
• address the problem of temporal and spatial scaling in water quality modelling;
• test transferability and generalizability of water quality predictions;
• involve stakeholders in water quality model development to inform risk analysis and decision support;
• application of remote sensing and/or citizen science in water quality estimates at multiple scales.

A large number of micropollutants, also known as trace contaminants or emerging contaminants, and their transformation products (veterinary and human pharmaceuticals, pesticides and biocides, personal care products, organic pollutants such as PFAS or chlorinated compounds) and heavy metals pose a risk for soil, groundwater and surface water. The large diversity of compounds and of their sources makes the quantification of their occurrence in the terrestrial and aquatic environment across space and time a challenging task. Regulatory monitoring programs cover a small selection out of the compound diversity and quantify these selected compounds only at coarse temporal and spatial resolution. Carefully designed monitoring, however, allows to detect and elucidate processes and to estimate parameters in the aquatic environment. Modelling is a complementary tool to generalize measured data and extrapolate in time and space, which is needed as a basis for scenario analysis and decision making. Mitigation measures can help reduce contamination of groundwater and surface water and impacts on water quality and aquatic ecosystems.
This session invites contributions that improve our quantitative understanding of the sources and pathways, mass fluxes, the fate and transport and the mitigation of micropollutants in the soil-groundwater-river continuum of catchments.

Topics cover:
- Novel sampling and monitoring concepts and devices
- New analytical methods such as new detection methods for micropollutants, non-target screening
- Experimental studies to improve process understanding and to quantify diffuse and point source inputs
- Biogeochemical interactions and impact on micropollutant behaviour
- Fate studies on parent compounds and transformation products
- Modelling approaches (including hydrology and sediment transport) to simulate pollutant transport and fate at several spatial and temporal scales
- Spatial and temporal monitoring to elucidate transport processes and to support modelling
- Modelling tools for decision support
- Setup of mitigation measures and evaluating their effectiveness.
- Methods to evaluate water quality modelling uncertainty, and/or combining data and modeling (data assimilation)

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.

This session is dedicated to the comprehensive investigation of small-scale transport processes governing the movement of plastic pollution (ranging from micro- to macroplastics) within the aquatic environment. While we aim to place special emphasis on laboratory experiments and modeling approaches, we also welcome presentations employing additional methodologies such as field work, and contributions focused on theoretical concepts.

The presentations will revolve around understanding and characterizing plastic movement, considering influential factors like particle size, shape, density, and environmental conditions such as temperature, salinity, flow velocities, water turbulence and suspended sediment concentrations. Additionally, relevant biological and chemical processes will be taken into account. Key processes to be addressed include sedimentation, resuspension, biofouling, aggregation and fragmentation, along with other interactions between plastics and the environment that may influence the transport and ultimate fate of plastic pollutants.

Beyond the presentation of research findings, this session will also focus on advancements in laboratory and numerical techniques, highlighting improvements in accuracy, complexity, and spatial-temporal resolution. Cutting-edge modeling approaches tailored to simulate the intricate transport dynamics of plastics in aquatic environments will be showcased.

Through engaging discussions, the session aims to enhance our comprehension and predictive capabilities, while also identifying unresolved questions and paving the way for future research endeavors in this vital area of study.

This session explores the forefront of hybrid modeling that integrates process-based hydrologic and water quality models with AI and machine learning (ML) techniques to improve predictions and management of water resources under climate change stresses. Hybrid modeling leverages the physical realism of process-based models alongside the adaptive learning and data-driven capabilities of ML – including frontier AI such as foundation models and Large Language Models (LLMs) to overcome limitations such as data scarcity, structural deficiencies in process-based models, and the challenges of simulating non-linear and complex hydrological processes.
Contributions are sought to advance the conceptual and practical understanding of hybrid models applied to hydrologic and water quality simulation, especially those focusing on:
• Improving streamflow and pollutant transport predictions in diverse hydro-climatic and data-scarce regions
• Hybrid approaches for simulating nonpoint source pollution and watershed-scale water quality dynamics
• Regional and catchment-scale applications demonstrating scalability, transferability, and robustness of hybrid frameworks
• Real-time forecasting and operational water management enabled by hybrid modeling
• Policy-relevant applications linking model outputs to climate adaptation, water allocation, and resilience strategies
• Methodological challenges and solutions regarding model interpretability, uncertainty quantification, computational efficiency, and equitable technology access
This session will foster interdisciplinary dialogue on designing, implementing, and applying hybrid modeling approaches that enhance hydrologic prediction and water quality assessment to support sustainable water resource management and climate resilience.

The efficiency of nature based solutions (NBS) for improved water quality and flood mitigation is often evaluated ex-ante by means of simulation models. Realistic estimates of this efficiency are important, because NBS compete with other land use for space and other resources. Informed societal decisions depend on reliable evaluation of mitigation strategies. The changes that make up the implementation of such strategies are represented in hydrological models by changing the values of existing parameters and/or the inclusion of new parameters and processes. The goal of this session is to compare approaches to numerical NBS representation in models, how parameter values are estimated, and how the latter can be validated.

Forests are primary regulators of water, energy, and carbon cycles. Maintaining forest functional integrity is fundamental to the sustainability of ecosystems, societies, and human development as described in the UN Sustainable Development Goals.
Global change and anthropogenic intervention are putting enormous pressure on forests, affecting the ecosystem services they provide through water quantity and quality, and biogeochemical cycles. The conventional wisdom that forest hydrology emphasizes the role of forests and forest management practices on runoff generation and water quality has expanded in light of rapid global change.
Improving our understanding of how forest-water interactions are shaped by physiographic, biogeochemical and hydrometeorological factors and how forested catchments respond to dynamic environmental conditions and disturbances, is critical for protecting and managing our forest ecosystems. Building this knowledge requires interdisciplinary approaches in combination with new monitoring methods and modeling efforts.
This session brings together studies that aim to improve our understanding of water-forest dynamics and stimulate discussion on the impact of global change on hydrological processes in forest ecosystems at different scales.
We invite field experimentalists and modelers working in forests from boreal to tropical regions to submit contributions that:
1) Improve our understanding of forest (eco)hydrological processes using an experimental or modeling approach or a combination of both;
2) Assess the hydrology-related impacts of land use/cover change and environmental disturbances on forested ecosystems;
3) Feature innovative methods and observational techniques, such as optical sensors, tracer-based experiments, monitoring networks, citizen science, and drones, that reveal new insights or data sources in forest hydrology;
4) Include interdisciplinary research that supports consideration of overlooked soil-plant-atmosphere components in hydrological studies.

Large-sample hydrology (LSH) datasets are crucial for understanding and predicting hydrological variability. These datasets have grown to encompass a range of hydrological conditions across time and space, facilitating research on a wide variety of topics. This includes testing hypotheses of hydrological theories, exploring uncertainties in data and models, and enabling predictions in ungauged basins. This session highlights recent advances in LSH, with a focus on the development of datasets, the organization and synthesis of hydrological processes, modeling approaches, and improved understanding of hydrological variability. We welcome abstracts that contribute to the field, particularly (but not exclusively) on the following topics:
1. Development and improvement of large-sample datasets:
How can we address current challenges, such as uneven geographical representation, uncertainty quantification, catchment heterogeneities and human interventions, for fair comparisons among datasets? How can we foster the harmonization of large-sample datasets? How can we expand existing datasets to include spatial and temporal higher-resolution data? How can we test the representativeness of the available samples? How can we (systematically) represent human influences in large-sample datasets?
2. Increase our process understanding:
How can we use large samples of catchments to transfer hydrological theories and understandings from well-monitored or experimental catchments to data-scarce catchments? Can we use large-sample datasets to draw improved perceptual models and better define hydrological similarity?
3. Advance catchment modeling:
How can we improve process-based and data-driven modeling by using large samples of catchments? How can functional information and knowledge from gauged catchments be learned and applied to ungauged or data-scarce regions? How can we develop new models and workflows to infer hydrological response under changing environmental conditions, particularly those influenced by human activities?
4. Hydrological synthesis:
How can we use catchment descriptors available in large-sample datasets to infer dominant controls for relevant hydrological processes? Do we need the definition of new catchment descriptors or the inclusion of new variables to further improve catchment characterization? How can we improve our classification of catchments, their connectivity and processes?

Research on changes in the pace of the water cycle dates back to the 1970s. In the last few decades, leveraging a number of new regional and global datasets, research has highlighted changes in the water cycle attributed to secular trends in relevant processes and/or to anthropogenic activities. For example, studies have investigated changes in the rate of exchange across terrestrial water storages and each of their components (soil moisture, snow, surface and groundwater, etc.) using different datasets, including remotely sensed, modeled, or data-assimilated, and reanalysis products. Past studies have also differed in their period of analysis, regional focus, and methods for change analysis and attribution.
This session aims to provide a platform to present and discuss studies that seek to understand (i) the current state of knowledge on accelerating or decelerating water cycle processes and (ii) uncertainties and open questions related to this critically important research topic. Studies focused on change analysis of individual or integrated water cycle components using one or more of the above-mentioned data types are encouraged, with either a regional or global focus. We are particularly interested in studies that highlight existing uncertainties in the state of knowledge regarding the magnitude or direction of these changes. Studies that lead to the development of new datasets to facilitate water cycle change analysis are also welcome, including studies focused specifically on the attribution of changes in the pace of the water cycle.

Flash floods remain one of the most destructive and least predictable natural hazards, driven by the complex interplay of natural (i.e. meteorological, hydrological, geomorphological, etc.) and human factors (i.e. land use, infrastructure, settlements, etc.). Despite recent advances in improved data availability, high-resolution modeling, and forecasting technologies, substantial uncertainties persist in understanding, simulating, and anticipating these rapidly evolving events.
This session invites contributions that focus on the hydrological and hydrodynamic aspects of flash flood modeling. Topics of interest include, but are not limited to:
- Rainfall inputs: advances in monitoring, nowcasting/forecasting, and ensemble generation
- Rainfall-runoff modeling in larger scales: spatial and temporal variability of parameters under extreme conditions
- Hydrodynamic modeling in smaller scales: representation of fine-scale topography and urban infrastructure
- Efficient preprocessing of the required data for modelling and approaches to address data scarcity
- Critical assessment of model assumptions and structural limitations
- Innovative calibration and validation strategies
- Integrated modeling frameworks combining rainfall-runoff and hydrodynamic modelling
- Uncertainty quantification and strategies for their reduction

By bringing together researchers with diverse expertise, this session aims to advance dialogue on both fundamental challenges and innovative, interdisciplinary solutions. We aim to highlight not only what we know, but also what we do not yet know, and how innovative approaches can help bridge the gap to more robust and reliable flash flood forecasting and management.

Floods and droughts have major impacts on society and ecosystems and are projected to increase in frequency and severity with climate change. These events, which lie at opposite ends of the hydrological spectrum, are governed by different processes that operate on different spatial and temporal scales, and they require different approaches and indices to characterize them. However, there are also many similarities and links between these two types of extremes, which are increasingly being studied.

This session on hydrological extremes seeks to unite the flood and drought research communities to learn from the similarities and differences in their work. The goals are to deepen understanding of the processes governing these hydrological extremes and their interplay, develop robust methods for modelling and analyzing floods and droughts and their transitions, assess the influence of global change on hydro-climatic extremes, and study the socio-economic and environmental impacts of both types of extremes.

We welcome submissions that present insightful flood and/or drought research, including case studies, large-sample studies, statistical hydrology, and analyses of flood or drought non-stationarity under the effects of climate-, land cover-, and other anthropogenic changes. Studies that investigate both extremes, or their interplay, are of particular interest. We especially encourage submissions from early-career researchers.

The space-time dynamics of floods are controlled by atmospheric, catchment, riverine and anthropogenic processes, and their interactions. The natural oscillation between flood-rich and flood-poor periods is superimposed on anthropogenic climate change and human interventions in rivers and catchments, such as the construction of reservoirs, alterations in river morphology, water retention capacity and land use. In addition, flood risk is further shaped by continuous changes in exposure and vulnerability. In this complex setting, it remains unclear what is the relative contribution of each factor to the space-time dynamics of flood risk. The scope of this session is to report when, where, how (detection) and why (attribution) changes in the space-time dynamics of floods occur. The session particularly welcomes presentations on attributing different drivers to observed changes in flood risk. Presentations on the impact of climate variability and change, land use transitions, morphologic changes in streams, and the role of pre-flood catchment conditions in shaping flood risk are also welcome. Furthermore, contributions on the impact of socio-economic factors, including adaptation and mitigation of past and future risk changes are invited. The session will further stimulate scientific discussion on the detection and attribution of flood risk change. Specifically, the following topics are of interest for this session:

- Long-term changes in rainfall patterns and flood occurrence;
- Process-informed extreme value statistics;
- Interactions between rainfall distribution and catchment conditions in shaping flood patterns;
- Detection and attribution of flood hazard changes, such as atmospheric drivers, land use controls, natural water retention measures, reservoir construction, and river training;
- Changes in flood exposure: economic and demographic growth, urbanisation of flood prone areas, implementation of multi-scale risk mitigation measures (particularly structural defences);
- Changes in flood vulnerability: changes of economic, societal and technological aspects driving flood vulnerability and private precautionary measures;
- Multi-factor decomposition of observed flood damages combining the hydrological and socio-economic drivers;
- Future flood risk scenarios and the role of adaptation and mitigation strategies.

Drought is a multiscale phenomenon unfolding across spatial and temporal dimensions, often combining meteorological, agricultural, and hydrological components. As climate change accelerates, rising temperatures and altered land-atmosphere interactions intensify drought persistence, clustering, and propagation. These shifts affect water availability, ecosystem resilience, and water-dependent sectors such as agriculture, industry, and domestic supply. We invite studies that explore how land use, land cover change, and evapotranspiration dynamics contribute to evolving drought patterns, as well as those linking these phenomena to risk assessment, adaptation strategies, and early warning systems. This session invites contributions on the spatio-temporal dynamics of compound droughts, including detection, attribution, monitoring, and modeling from local to global scales. We particularly welcome studies that use novel datasets, high-resolution observations, advanced indices, or integrated modeling frameworks to assess drought propagation, persistence, and clustering under climate variability and change. Contributions may also focus on linking these dynamics to risk assessment, adaptation, and early warning.
The session aims to foster interdisciplinary discussion and methodological advances supporting drought risk management, policy planning, and enhanced resilience to water stress in a changing climate.

Extreme hydrological events such as floods, droughts, and heatwaves have been changing considerably under a changing climate. Concurrent and compound events, where extremes occur simultaneously or in succession, have been exacerbating risks to infrastructure, society, economy, and ecosystems. Understanding the evolution of extremes and their concurrent/compound occurrences across spatial and temporal scales, climate drivers influencing these events, and the uncertainty in their characterization remains a critical challenge. Consequently, there is an urgent need for advancements in modeling, monitoring, prediction, and risk assessment to reinforce resilience against extremes and their concurrent/compound occurrences at global, regional, and urban scales. In this session, we welcome research contributions encompassing, but not limited to, the following areas: (1) analysis of changes in the severity, duration, intensity, frequency, and spatial extent of extremes including concurrent/compound events under changing climate conditions; (2) exploration of latest methodologies for characterizing and predicting extreme events, including the application of machine learning techniques; (3) understanding the climate drivers impacting extreme events and their spatial/temporal evolution; (4) characterizing risk, vulnerability, exposure, and adaptive capacity; and (5) quantifying uncertainties in extreme events.

Hydroclimatic modelling brings together surface water hydrology and climate science for understanding the interactions between precipitation, evaporation, runoff, land use dynamics and anthropogenic activities under increasing climate variability. This session invites contributions on model development and applications addressing floods, droughts, and water balance dynamics across scales and water resources. We welcome studies coupling climate and hydrological models, data assimilation, satellite–model integration, machine learning, and model intercomparison. By combining methodological advances with applied case studies, the session highlights innovations that enhance flood forecasting, drought risk management, and climate-resilient water resource planning in vulnerable regions worldwide.

In an era of climate uncertainty and evolving human influence on natural environments, understanding the dynamics of long-term climatic and hydrologic change has become critical. This session has a focus on real-world case studies and applications, through which we seek to explore the multifaceted implications of climate change on water availability, aquatic environments, and the dynamics of socio-ecological riverine systems.

We invite tangible examples of climate change impact assessments on hydrological and related systems, including resource management, policy and adaptation. We hope to showcase research across diverse geographical regions and varied contexts to facilitate sharing of methods, insights and lessons learned.

Submissions are encouraged across the full spectrum of available techniques, including so-called “bottom-up” approaches to decision making under deep uncertainty. Studies applying novel modelling paradigms, innovative risk assessment frameworks, or characterising multiple (compound) sources of risk are particularly encouraged. By showcasing diversity, we aim to foster a practical understanding of the implications of long-term change, leading to better decision-making for an uncertain future.

This session focusses on hydrological response to changes in climatic forcing at multi-annual to multi-decadal timescales. Catchments are complex systems responding to changes in climate, among external factors, on a variety of timescales and with many interacting processes. The poor performance of models in representing these responses suggests they potentially misrepresent (or omit) important processes, timescales, or interactions. To improve hydrological models, the multitude of responses, interactions and feedbacks in hydrological systems need to be disentangled and understood. Such insights can originate both from site-specific investigations or from studies that use large datasets and/or models to critique or improve hydrological simulations under changing conditions.
This session covers themes such as (but not limited to):
1. Better understanding of hydrological and/or biophysical processes that govern hydrological response to multi-annual or longer climate shifts. This could be based on insights from in-situ and/or remotely sensed data;
2. Studies of hydrological regularities (e.g. the Budyko hypothesis) for predictions under changing conditions;
3. Characterizations of catchment multi-annual “memory” and its representation in models;
4. Efforts to improve the realism and robustness of hydrological simulations under climatic variability, change and future scenarios.

Assessing the impact of climate variability and changes on hydrological systems and water resources is crucial for society to better adapt to future changes in water resources, as well as extreme conditions (floods and droughts). However, important sources of uncertainty have often been neglected in projecting climate impacts on hydrological systems, especially uncertainties associated with internal/natural climate variability. From one model to another, or one model realisation to another, the impact of diverging trends and sequences of interannual and decadal variability of various internal/natural climate modes (e.g., ENSO, NAO, AMO) could substantially alter the impact of human-induced climate change on hydrological variability and extremes. Therefore, we need to improve both our understanding of how internal/natural climate patterns affect hydrological variability and extremes, and how we communicate these impacts. We also need to understand better how internal/natural variations interact with various catchment properties (e.g., vegetation cover, groundwater support) and land-use changes. Developing storylines of plausible worst cases, or multiple physically plausible cases, arising from internal climate variability can complement information from probabilistic impact scenarios.

We welcome abstracts capturing recent insights for understanding past, present, and future impacts of internal/natural climate variability on hydrological systems and extremes, as well as newly developed probabilistic and storyline impact scenarios. Results from model intercomparisons using large ensembles are encouraged.

This session seeks to present recent scientific advances in the detection and attribution of hydrological change across diverse hydro-climatic regions. It welcomes contributions that integrate observational data, statistical techniques, physical modeling, and emerging hybrid (e.g., machine learning-enhanced) approaches to disentangle climate signals from human activities. Case studies demonstrating methodological innovation or policy relevance are especially encouraged.

Key Objectives:
• Promote interdisciplinary exchange across hydrology, climate science, and environmental engineering
• Showcase cutting-edge approaches for attributing hydrological change across spatial and temporal scales
• Critically evaluate current attribution techniques, including model-based decomposition, scenario analysis, and sensitivity frameworks
• Support the development of actionable tools for sustainable water governance, risk mitigation, and climate resilience

Topics of Interest:
1. Quantitative attribution frameworks for streamflow and runoff changes
2. Disimpassion of climatic and anthropogenic influences in hydrological records
3. Impacts of land use/land cover dynamics and hydraulic infrastructure on watershed hydrology
4. Role of climate change in altering precipitation, evapotranspiration, and storage dynamics
5. Scenario-based simulations using hydrological and Earth system models
6. Data-scarce region modeling: constraints, solutions, and innovations
7. Translating attribution science into policy-relevant insights and integrated water management strategies

Expected Outcomes:
Deepened scientific understanding of the causal factors behind hydrological change
Cross-regional exchange of methods, models, and data-driven approaches
Enhanced collaboration between researchers and decision-makers
Identification of research gaps and methodological needs in hydrological attribution

In the current context of global change, a better understanding of our large-scale hydrology is vital. For example, by increasing our knowledge of the climate system and water cycle, improve assessments of water resources in a changing environment, perform seasonal prediction, and evaluate the impact of transboundary water resource management.

We invite contributions from across hydrological, atmospheric, and earth surface processes communities. In particular, we welcome abstracts that address advances in:

(i) understanding and predicting the present and future state of water resources worldwide and within large-scale systems;

(ii) the use of global earth observations and in-situ datasets for large-scale hydrology and data assimilation techniques for large-scale hydrological models;

(iii) the representation and evaluation of various components of the terrestrial water cycle fluxes and storages (e.g., soil moisture, snow, groundwater, lakes, floodplains, evaporation, river discharge) and atmospheric modelling;

(iv) providing syntheses that combine knowledge gained at smaller scales (e.g. catchments or hillslope) to increase our knowledge on process understanding needed for further development of large-scale hydrological models and to identify large-scale patterns and trends;
(vi) evaluating the effects of climate change, land-use change, and water-use change on global groundwater and implications of large-scale groundwater understanding on monitoring design, integrated water management, and large-scale water policies.

Different approaches including global models, data-driven approaches, and machine learning are used to assess water balance components at the global, continental, and regional scales. By making use of in-situ as well as remotely sensed observations, they attempt to quantify water fluxes (e.g., evapotranspiration, streamflow, groundwater recharge) and water storage on the terrestrial part of the Earth as a whole (e.g., from GRACE/-FO and future missions such as MAGIC and NGGM) or in separate compartments (e.g., water bodies, snow, soil, groundwater). Increasing attention is given to uncertainties that stem from forcing datasets, model structure, parameters and combinations of these. Recent advances also include hybrid approaches that integrate machine learning with process-based models and assimilate Earth Observation data for improved water balance closure. Current research shows that flux and storage estimates differ considerably due to the methodology and datasets used, so a robust assessment of global, continental and regional water balance components remains challenging.

This session is seeking contributions, including:
1) past/future assessment of water balance components (fluxes and storages) such as precipitation, freshwater fluxes to the oceans (or inland sinks), evapotranspiration, groundwater recharge, water use, changes in terrestrial water storage or individual components at global, continental and regional scales,
2) application of innovative explorative approaches undertaking such assessments – through better use of advanced data-driven and statistical approaches, mechanistic models, machine learning and approaches to assimilate (or accommodate) in-situ and remote sensing datasets for improved estimation of terrestrial water storages/fluxes,
3) analysis and quantification of different sources of uncertainties in estimation of water balance components,
4) examination and attribution of systematic differences in storages/flux estimates between different methodologies, and/or
5) applications/consequences of those findings, such as sea level rise and water surplus or scarcity.

We encourage submissions based on different methodological approaches that estimate and analyze water balance components individually or in an integrative manner on global, continental, or regional scales. Assessments of uncertainty in past/future estimates of water balance components and their implications are highly welcome.

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.

Hydroinformatics has emerged over the last decades to become a recognised and established field of independent research within the hydrological sciences. It is concerned with the development and application of mathematical modelling, information technology, systems science and computational intelligence tools in hydrology. Hydroinformatics nowadays also deals with collecting, handling, analysing and visualising Big Data sourced from remote sensing, Internet of Things (IoT), earth and climate models, and defining tools and technologies for smart water management solutions.
This session aims to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent techniques and technologies in water-related contexts.
Topics addressed in the session include:
* Predictive and exploratory models based on the methods of statistics, computational intelligence, machine learning and data science: neural networks, fuzzy systems, genetic programming, cellular automata, chaos theory, etc.
* Methods for analysing Big Data and complex datasets (remote sensing, IoT, earth system models, climate models): principal and independent component analysis, time series analysis, clustering, information theory, etc.
* Optimisation methods associated with heuristic search procedures (various types of genetic and evolutionary algorithms, randomised and adaptive search, etc.) and their application to hydrology and water resources systems
* Multi-model approaches and hybrid modelling approaches that blend process-based (mechanistic) and data-driven (machine learning) models
* Data assimilation, model reduction in integrated modelling, and High-Performance Computing (HPC) in water modelling
* Novel methods for analysing and quantifying model uncertainty and sensitivity
* Smart water data models and software architectures for linking different types of models and data sources
* IoT and Smart Water Management solutions
* Digital Twins for hydrology and water resources
Applications could belong to any area of hydrology or water resources, such as rainfall-runoff modelling, hydrometeorological forecasting, sedimentation modelling, analysis of meteorological and hydrologic datasets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimisation of water resources, smart water management.

Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods that have proved to be very helpful include a) uncertainty analysis (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models).

This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs), which embraces all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.

Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty
2) Analyses of over-parameterised models enabled by AI/ML techniques
3) Single- versus multi-criteria SA/UA
4) Novel methods for spatial and temporal evaluation/analysis of models
5) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data etc.)
6) The role of SA in evaluating model consistency and reliability
7) Novel approaches and benchmarking efforts for parameter estimation
8) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, model identification and selection, model diagnostics, parallel computing, model pre-emption, model ensembles, etc.)
9) Methods for detecting and characterizing model inadequacy

The global climate is changing, and human pressures on land, water, and ecosystems have intensified, driving increased demand for resources and amplifying the frequency and severity of extreme events. These interconnected pressures exacerbate water insecurity, health risks, environmental degradation, social inequalities, and water-related conflicts across diverse regions.

This session highlights innovative and interdisciplinary approaches that strengthen resilience in water, health, and environmental systems. We emphasize the integration of hydroinformatics, numerical modelling, and emerging technologies, including artificial intelligence, remote sensing, Information and Communication Technologies (ICTs), and data assimilation—together with socio-economic and governance perspectives. Of particular interest are studies advancing attribution of hydrological and hydroclimatic extremes (floods, droughts, water quality degradation, and heatwaves) to climate change, and their cascading impacts on ecosystems and human health. Contributions are invited to demonstrate how new knowledge, innovative tools, and practices can enhance monitoring, forecasting, attribution, and decision-making to address these pressing challenges.
Key themes
• Advances in monitoring (low-cost sensors, Internet of Things), forecasting, and attribution of hydroclimatic extremes to climate change.
• Application of global datasets, field data and citizen science, and data assimilation methods for assessing climate-sensitive health risks.
• Integrated modeling frameworks to analyze compound impacts of climate variability, land use change, and ecosystem health.
• Cutting-edge hydroinformatics innovations, including physically-based model emulation, AI/ML-based decision support, and improved data assimilation for adaptive responses
• Socio-economic, policy, and governance innovations that complement technical solutions to enhance resilience in diverse contexts.

In recent years, the field of geostatistics has seen significant advancements. These methods are fundamental in understanding spatially and temporally variable hydrological and environmental processes, which are vital for risk assessment, input for other models, and management of extreme events like floods and droughts.
This session aims to provide a comprehensive platform for researchers to present and discuss innovative applications and methodologies of geostatistics and spatio-temporal analysis in hydrology and related fields. The focus will be on traditional approaches and the assessment of uncertainties, whereas Machine Learning approaches have their specific and other dedicated sessions.

We invite contributions that address the following topics (but not limited to):

1. Spatio-temporal Analysis of Hydrological and Environmental Anomalies:
- Methods for detecting and analyzing large-scale anomalies in hydrological and environmental data.
- Techniques to manage and predict extreme events based on spatio-temporal patterns.

2. Innovative Geostatistical Applications:
- Advances in spatial and spatio-temporal modeling.
- Applications in spatial reasoning and data mining.
- Reduced computational complexity methods suitable for large-scale problems.

3. Geostatistical Methods for Hydrological Extremes:
- Techniques for analyzing the dynamics of natural events, such as floods, droughts, and morphological changes.
- Utilization of copulas and other statistical tools to identify spatio-temporal relationships.

4. Optimization and Generalization of Spatial Models:
- Approaches to optimize monitoring networks and spatial models.
- Techniques for predicting regions with limited or unobserved data e.g., using physical-based model simulations or using secondary variables.

5. Uncertainty Assessment in Geostatistics:
- Methods for characterizing and managing uncertainties in spatial data.
- Applications of Bayesian Geostatistical Analysis and Generalized Extreme Value Distributions.

6. Spatial and Spatio-temporal Covariance Analysis:
- Exploring links between hydrological variables and extremes through covariance analysis.
- Applications of Gaussian and non-Gaussian models in spatial analysis and prediction.

Deep Learning has seen accelerated adoption across Hydrology and the broader Earth Sciences. This session highlights the continued integration of deep learning and its many variants into traditional and emerging hydrology-related workflows. We welcome abstracts related to novel theory development, new methodologies, or practical applications of deep learning in hydrological modeling and process understanding. This might include, but is not limited to, the following:

(1) Development of novel deep learning models or modeling workflows.
(2) Probing, exploring and improving our understanding of the (internal) states/representations of deep learning models to improve models and/or gain system insights.
(3) Understanding the reliability of deep learning, e.g., under non-stationarity and climate change.
(4) Modeling human behavior and impacts on the hydrological cycle.
(5) Deep Learning approaches for extreme event analysis, detection, and mitigation.
(6) Natural Language Processing in support of models and/or modeling workflows.
(7) Applications of Large Language Models and Large Multimodal Models (e.g. ChatGPT, Gemini, etc.) in the context of hydrology.
(8) Uncertainty estimation for and with Deep Learning.
(9) Advances towards foundational models in the context of hydrology and Earth Sciences more generally.
(10) Exploration of different training strategies, such as self-supervised learning, unsupervised learning, and reinforcement learning.

The complex interactions and interdependencies of hydrological and land surface processes within the Earth system pose major challenges for prediction and understanding. Machine learning has become a powerful tool for prediction across these domains, but leveraging its scientific potential goes beyond applying existing algorithms and data. It requires detailed understanding and problem-specific integration of domain knowledge with data-driven techniques to make models more interpretable and enable new process understanding. This session explores how machine learning techniques are currently used to integrate, explain, and complement physical knowledge in hydrology and land surface modeling, including studies of surface and subsurface water dynamics, soil-vegetation interactions, land-atmosphere exchanges, and eco-hydrological processes. Submissions are welcome on topics including, but not limited to:

- Explainability and transparency in data-driven hydrological and land surface modeling;
- Integration of process knowledge and machine learning;
- Domain-specific model development;
- Data assimilation and hybrid modeling approaches;
- Causal learning and inference in machine learning models;
- Data-driven equation discovery;
- Challenges and solutions for hybrid models and explainable AI.

Submissions that present methodological innovation, critically assess limitations, or demonstrate contributions to process understanding across scales are especially encouraged.

A known challenge in hydrological science is the robust uncertainty analysis and statistical representation of surface and underground processes across different scales and environments. Diverse last-generation datasets, encompassing Earth Observation data cubes, ground sensors, laboratory measurements, and physical model outputs, depict with unprecedented realism the spatio-temporal heterogeneity, showing high-order coherence and uncertainty. Stochastic processes and statistical analysis can be used to capture complex hydrological dynamics and inform decision making for water and landscape managing, natural hazard assessment, hydroclimate mitigation measures, and hydrological engineering.
This session welcomes, but is not limited to, contributions on stochastic spatio-temporal analysis, modelling, simulation, and prediction of hydrological-cycle and hydrodynamic processes (streamflow, precipitation, temperature, evapotranspiration, humidity, dew-point, soil moisture, groundwater, etc.), water-energy-food nexus processes (agricultural, financial and other related fields, solar radiation, wind speed, reservoir stage, etc.), laboratory measurements (i.e., small-scale models for large-scale applications), and computational outputs (e.g., concerning floods, droughts, climatic models, etc.).

Data imperfection is a persistent and multi-faceted challenge in hydrology and more broadly in geosciences. Researchers and practitioners regularly work with datasets that are incomplete, imprecise, erroneous, heterogeneous, or redundant—whether originating from in-situ measurements, remote sensing, modelling outputs, or participatory sources.
While traditional statistical methods have long been used to address these limitations, the growing complexity and diversity of hydrological and environmental data have created new demands—and opportunities—for innovation. Advances in artificial intelligence, data fusion, knowledge representation, and reasoning under uncertainty now allow for more robust integration and interpretation of heterogeneous information.
This session aims to gather contributions that explore how we can move from imperfect, fragmented data toward coherent and actionable hydrological and environmental knowledge. We welcome abstracts on:

• Applications and case studies in hydrology or other domains, addressing missing data imputation, model inversion, uncertainty propagation, or multi-source integration—using time series, spatial data, imagery, videos, etc. The case studies may focus on hydrological and natural hazards (floods, droughts, earthquakes, landslides, marine submersion, etc..) or resources management (water supply, treatment, etc…)
• Methodological developments in data fusion, completion, uncertainty quantification, and AI-based knowledge extraction from heterogeneous data.
• Cross-disciplinary approaches that connect geosciences, and specifically hydrological sciences, with AI, data mining, and knowledge systems, including citizen science, crowd-sourced data, or opportunistic sensing.
• Experimental contributions in hydrology and geosciences relying on AI, such as novel models and algorithms, explainable methods, and comparative studies on domain-specific datasets.
• Feedback from data integration initiatives into domain specific or cross disciplinary repositories.

We particularly encourage contributions that highlight novel practices or conceptual frameworks for dealing with imperfect and multi-source data in complex environmental systems.

Heavy precipitation events in small and medium size catchments can trigger flash floods, with very short lag times (usually a few hours) and high specific peak discharges. Often, they are also associated with geomorphic processes such as erosion, debris flows or shallow landslides mobilizing large amounts of unconsolidated material.
Early Warning Systems are seen as an effective measure to reduce the impacts of these events through effective emergency management; their improvement can be achieved by (i) increasing the forecast skill (with improved predictions of the physical variables or the hazard), or (ii) integrating additional information to support decision making.
The sources of uncertainty in the forecasting chain include the high variability of rainfall in space and time, limitations in observations and forecasts, the variability and nonlinearity of physical processes, and incomplete exposure and vulnerability data to translate hazard forecasts into impacts.
This session aims to illustrate current advances in monitoring, modeling, and short-range forecasting of rainfall-induced hazards, including their societal impacts with contributions on the following themes:
- Development of new measurement techniques adapted to flash floods and hydro-geomorphic hazards (including in-situ sensors and remote sensing data), and quantification of the associated uncertainties.
- Short-range rainfall forecasting adapted to heavy precipitation events, including seamless rainfall forecasting based on NWP models, nowcasts and/or ML, and representation of associated uncertainties through ensembles.
- 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 societal vulnerability and social responses to flash floods and/or hydro-geomorphic hazards.
- Development of impact-based approaches, including the integration of societal vulnerability.
- New approaches to validate hydrometeorological and impact forecasts, including the use of new direct and indirect observation techniques.
- Assessment of possible evolutions in frequency and hazard characteristics due to climate change.

Contributions related to recent events (e.g. those that affected Valencia, Spain, or the Guadalupe River, TX) 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 of water scarcity, particularly in regions that are already water-stressed, and where overexploitation of available water resources can exacerbate the consequences droughts have. This may lead to (long-term) environmental and socio-economic impacts. Drought Monitoring and Forecasting are recognised as one of three pillars of effective drought management, and it is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability, and to develop innovative indicators and methodologies that translate the data and information to underpin 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, groundwater levels, and the propagation of drought through the hydrological cycle. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and integrating these with the needs and knowledges of water managers, policymakers and other stakeholders, are further issues that are addressed and are invited to submit to this session. Contributions focusing on the interrelationship 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 in the fields of 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, as well as regions that are increasingly experiencing water shortages due to drought and where drought warning, supported by state-of-the-art monitoring and forecasting of water resources availability, is likely to become more important in the future.

This session focuses on advancing probabilistic hydro-meteorological forecasting from research to operations (and operations to research) across spatial scales and time horizons.

Building on the outcomes of the latest HEPEX workshop, this year’s session emphasizes the (co)creation of probabilistic hydrometeorological forecasting systems together with practitioners and stakeholders. In this session we will exchange and discuss how ensemble approaches are advanced to be scientifically robust while also being user-centric, impactful, and effectively communicating uncertainty to support decisions. Topics of interest include:

• Standardized evaluation frameworks for ensembles, including verification, benchmarking, and uncertainty quantification.
• Advanced computational science for scalable ensemble generation, data assimilation, and post-processing.
• Science communication to improve understanding of probabilistic forecasts and uncertainty.
• Impact analysis and inclusive forecasting, linking forecasts to diverse applications and sectors.
• Local implementation and policy science, embedding probabilistic forecasting in governance and practice.
• Behavioral science and crisis management, understanding how people act on forecasts, especially during extremes.
• Research-to-operations (R2O) and Operations-to-Research (O2R).

The session provides a platform for scientists, forecasters, and stakeholders to present advances, evaluate operational experiences, and explore pathways for delivering ensemble hydro-meteorological forecasts that create real value. Contributions from early-career professionals and interdisciplinary collaborations are especially encouraged.

Operational warning systems are the result innovations in the science of forecasting. New opportunities have risen in physically based modelling, AI/machine learning, hydro-meteorological forecasts, ensemble forecasting and impact-based forecasting, and real-time control. Often, the sharing of knowledge and experience about developments are limited to the particular field 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.
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. Case studies of system implementations - configured at local, regional, national, continental and global scales - will be presented. An operational warning system can include 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).
We particularly welcome contributions aligned with the objectives of the WMO World Weather Research Programme project InPHRA (Integration of Precipitation and Hydrology for Early Action). InPHRA aims to advance transdisciplinary knowledge and skills for the research and development of effective multi-hazard flood forecasting and early warning systems so that “no one is surprised by a flood.” This includes integrating meteorology, hydrology, and social science, together with local and Indigenous knowledge systems, to improve the value chain from forecasts to community action, with particular attention to vulnerable populations.

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 2024 Valencia floods, and the 2020-2023 Horn of Africa drought, show that significant gaps in the early warning - early action chains persist, despite major advances in forecasting capabilities over last decades. The Early Warnings for All initiative (led by WMO, UNDRR, ITU, and IFRC) recognizes that increased efforts are required to develop life-saving, 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 collaborative production of impact-based forecasting services and linked early action protocols.

However, much remains unknown and significant knowledge gaps 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. Such systems demand much knowledge about how hazards translate to impacts through exposure and vulnerability, novel impact-based forecasting technologies (including machine learning models), the costs and benefits of triggered actions, human decision-making and risk perception dynamics.

Topics of interest include, but are not limited to:

- Practical applications and operational use-cases of impact-based forecasts
- Novel physics-based, Artificial Intelligence (AI) and hybrid models for impact-based forecasting
- Innovative solutions to address challenges in impact-based forecasting effectively, including the application of AI, harnessing big data and earth observations
- Development of cost-efficient, evidence-based early action portfolios
- Impact and action-oriented forecast verification and post-processing techniques
- 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 and impact data to support impact-based forecasting
- Collecting and expanding datasets on interventions and adaptations to build an early action evidence base

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

• Advances in sub-seasonal, seasonal and decadal hydrological predictions;
• Process-based, data-driven, AI, machine learning, and hybrid methods;
• Seamless forecasting techniques and applications;
• Hydro-climate forecasts and scenario-based projections of water availability and hydrological extremes (floods, droughts, compound events);
• Methods for post-processing and refining the hydro-climate information (e.g., downscaling, bias correction, temporal disaggregation, spatial interpolation).
• From (near) real-time monitoring to predicting water availability;
• Propagation of uncertainty through the forecasting chain;
• Impact-based assessments of forecasts for decision-making, including approaches to communicate and visualize forecast information
• Co-development of forecasts between scientists and service providers;
• Operational hydro-meteorological forecasting systems and hydro-climate services;
• Forecast verification, sensitivity analysis and tools; and
• Perspectives on forecast value for end users.

The session will bring together research scientists and operational managers in hydrology, meteorology and climate, with the aim of sharing experiences and foster discussions on this momentous topic. We encourage presentations with implications for water resources management, drinking water supply, transport, energy production, agriculture, disaster risk reduction, forestry, health, insurance, tourism and infrastructure.

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, and expanding hydrometeorological networks 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 and emergency response, and efforts 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.

There has been a growing focus on non-structural approaches, particularly early flood forecasting and warning systems, as effective means to mitigate the adverse impacts of floods. As a result, Real-time flood forecasting (RTFF) systems have gained popularity in early flood warning.
Data for RTFF can be sourced from various outlets, though sometimes access to these sources can be limited or challenging. RTFF necessitates the modelling of complex distributed systems with high spatial and temporal intricacies. This demands substantial computing resources and may leave limited time for timely early warnings. Significant breakthroughs have occurred in recent decades to address major challenges in the key stages of RTFF, including data collection and preparation, model development, performance assessment, and practical applications.
The objective of this session is to address challenges and advancements in the field by leveraging state-of-the-art techniques, new frameworks, equipment, software tools, hardware facilities, and the integration of existing methods with contemporary algorithms. We will also explore digital innovations and their applications in new pilot studies. Specifically, this session will concentrate
on the following research areas related to RTFF, with a focus on but not limited to:
- Hydrological/hydraulic data collection, analysis, imputation, assimilation and fusion taken from various data sources including ground stations (IoT sensors), radar stations, remote sensing (Lidar, drones, and satellite)
- RTFF modelling including physically/processed-based, conceptually-based, experimentally based or data-driven modelling such as artificial Intelligence (AI), machine learning (ML), Data mining (DM), and deep learning (DL) or hybrid modelling including citizen-knowledge-informed modelling and physics-informed modelling
- Application RTFF for flood alleviation or engagement with the public and authorities, such as early warning and early action systems, citizen-knowledge based modelling, digital innovations such as digital twins (DT), or integrated with digital technologies such as augmented reality (AR), virtual reality (VR), and mobile apps.
- The broader implications of RTFF and early warning systems as soft engineering approaches, including their impact on flood risk management, insurance, capacity building, vulnerability assessment, and community resilience.

Climate Information Services (CISs) have significant potential to empower decision-makers in taking climate-smart actions and reducing the impacts of water and climate-related risks. These provide timely and relevant information, derived from sub-seasonal to seasonal forecasts to support early warning of droughts, floods, heatwaves, and water scarcity, as well as longer-term climate projections to support adaptation planning and management. Substantial advances have been made in recent decades in sub-seasonal to seasonal forecasting and climate modelling, and integrating these into global and regional CISs, ranging from natural hazard early warning systems (EWSs) to platforms, dashboards, and mobile applications that support sector-specific decisions in agriculture, water resources, energy, tourism, and transportation.
Despite these advances, challenges remain in crossing the "last mile"—ensuring the uptake and use of CISs by end-users. Barriers include limited understanding of user needs and decision-making processes, insufficient recognition of local and traditional knowledge, and gaps in co-creation with users. Research increasingly shows that more human-centred approaches and stronger engagement with local stakeholders can enhance the credibility, salience, and legitimacy of services, leading to greater preparedness and adaptation.
This session provides a platform to showcase grounded research and innovative developments in CISs that advance early warning and decision support across hazards and sectors. We welcome contributions on the co-creation of CISs, integration of local and scientific knowledge, development of multi-hazard EWSs, Decision Support Systems, dashboards, and sector-specific applications (e.g., farmer support apps). We particularly encourage abstracts presenting concrete cases where CISs are integrated into real-world decision-making processes across management or livelihood systems, demonstrating how information from (sub)-seasonal forecasts and climate predictions directly shapes choices, actions and outcomes. Contributions may include action-based, multi-disciplinary research showing tangible impacts through improved uptake of advance warning, enhanced decision-making, better preparedness for climate extremes, or clear links to policy and governance processes. The session aims to facilitate knowledge exchange among scientists, practitioners, and users to foster more effective, inclusive, and adaptive climate information services.

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 intensified 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 (e.g., meteorological forcings, catchment states, and responses) at multiple scales
• Advances in data acquisition capturing human activities (or proxies), including in-situ monitoring, remote sensing, and unconventional sources such as social media, with innovations in data integration and analytics
• 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

While water plays a critical role in sustaining human health, food security, energy production, and ecosystem services, factors such as population growth, climate, and land use change increasingly threaten water quality and quantity. The complexity of water resources systems requires methods integrating technical, economic, environmental, legal, and social issues within frameworks that help design and test efficient and sustainable water management strategies to meet the water challenges of the 21st century. System analyses adopt practical, problem-oriented approaches for addressing the most challenging water issues of our times. These include competing objectives for water, multi-stakeholder planning and negotiation processes, multisector linkages, and dynamic adaptation under uncertainty. The session will feature state-of-the-art contributions to water and multisector resource system management solutions under uncertainty.

The management and utilization of water storage systems, such as dams and reservoirs, have historically played a central role in ensuring a steady water supply during dry periods, supporting various sectors including domestic, industrial, and agricultural needs. However, increasing water demands due to population growth, coupled with ongoing climate extremes affecting drought and precipitation patterns, highlight the urgent need for efficient and sustainable management of water reservoirs. Projected global warming is expected to impact the operation and storage efficiency of water reservoirs (e.g., through intensified evaporative losses), posing serious risks to a wide range of stakeholders. Given the intensity and frequency of recent climate extremes (droughts, heatwaves, and heavy precipitations), it is more important than ever to develop sustainable and effective water management strategies that incorporate various environmental and socio-economic drivers and pressures affecting water storage.
This session invites theoretical, experimental, and applied studies that explore the management of both natural and human-made water storages under different local and global change scenarios, identifying the associated risks to sustainable water reservoir management. The goal is to unite diverse contributions including (but not limited to) remote sensing, in situ observations, AI-based methods, and hydrological modeling to enhance the sustainable management and implementation of freshwater storage systems.
Key topics include:
• Impacts of climate variability and change on reservoir performance and water availability
• Sedimentation, water quality, and ecological consequences of reservoir operation
• Role of digital tools, such as digital twins, in monitoring and optimizing reservoir management
• Governance, policy, and socio-economic dimensions of sustainable reservoir use
• Case studies highlighting innovative adaptation strategies and restoration initiatives

Water scarcity in arid and semi-arid regions such as the Middle East, Central Asia, and Southern Africa is increasingly worsened by climate change and unsustainable water management practices. Shared aquifers and river systems are under growing pressure from reduced rainfall, accelerated glacier melt, and upstream interventions, intensifying competition for already limited resources. Moreover, many inland terminal lakes, such as Aral Sea, Chad Lake and Lake Urmia, are drying up, further exacerbating the environmental and socio-economic impacts of water scarcity. In addition, gaps in data availability and technical capacity hinder effective water governance, making transboundary cooperation even more critical for regional stability and sustainability. To address these challenges, a holistic approach is required - one that integrates the Water-Energy-Food (WEF) Nexus to foster benefit-sharing between riparian states. By recognizing the interdependencies between water resources, energy production, and food security, WEF Nexus studies offer valuable strategies for optimizing resource allocation, reducing competition, and encouraging cooperative solutions across borders.
Furthermore, advancements in data-driven decision-making, including the use of remote sensing technologies and hydrological modeling, present opportunities to bridge data gaps and improve water management. As water demand grows across urban, industrial, and environmental sectors, innovative solutions are needed to enhance efficiency, minimize losses, and build climate resilience. Alongside technological solutions, human capacity building plays a central role in addressing these complex challenges. In this context, the session will promote cross-sector collaboration, improve data accessibility, and encourage science-based strategies for securing water resources in transboundary basins under the pressures of climate change.

Water scarcity and management under uncertain future conditions represent significant global challenges that necessitate adaptive, robust, and inclusive adaptation strategies. Climate change is causing increased frequency and severity of extreme weather events such as floods and droughts, making it difficult to predict and manage water resources since historical data is no longer a reliable guide for future conditions. Growing urban populations demand more water and can outpace water infrastructure development, leading to shortages and inequities in water distribution, often exacerbated by political, economic, and social factors that influence water governance. The pace of technological change in water treatment, distribution, and conservation can improve water systems, but it introduces uncertainty regarding their long-term viability and integration into existing systems.

Decision Making Under Deep Uncertainty (DMDU) represents a promising approach to help decision-makers confront such a wide range of unpredictable and variable future conditions. Unlike traditional frameworks that depend on accurate predictions and precise probabilities, DMDU accepts that the future is inherently unpredictable, especially in complex systems like human water systems, and emphasizes adaptive planning that evolves with new information on water supply, demand, and ecosystem health. This session aims to gather scientists to discuss and exchange knowledge of existing and emerging approaches for supporting the design and implementation of adaptive and robust water management strategies under deep uncertainty. We welcome contributions focused on recent methodological advances, including uncertainty and sensitivity analysis, scenario generation techniques, robust optimization, and experiences related to real-world applications.

Africa faces some of the world’s most pressing water challenges, with climate change amplifying existing stresses on water security, availability, and equitable access. Rapid urbanization, agricultural demands, ecosystem degradation, and population growth further complicate the landscape, making sustainable water management a central concern for policymakers, researchers, and communities alike. This session provides a platform to explore how action research, innovative strategies, collaborative partnerships, and evidence-based practices can transform water resource management in Africa under changing climatic conditions.
This session invites contributions that critically examine and advance strategies for water resource management in Africa under conditions of climate change. We particularly welcome empirical research, policy analysis, and case studies that highlight innovative approaches, as well as capacity-building initiatives and multi-stakeholder partnerships that demonstrate potential for scaling and replication. Submissions may address, but are not limited to, the following themes:
• Research and applied case studies on climate-resilient water management practices.
• Capacity development programs that strengthen institutional, community, or professional competencies in water governance and adaptation.
• Partnerships and learning alliances that foster cross-sectoral collaboration, knowledge sharing, and co-production of solutions.
• Innovative tools, technologies, and governance models that enable equitable and sustainable management of water resources.
Through presentations and dialogue, the session will examine how localized initiatives can inform broader regional and continental frameworks, and how lessons from Africa can contribute to global debates on water and climate resilience. By emphasizing the intersections of knowledge, practice, and policy, the session aims to generate actionable insights that can guide more adaptive and sustainable pathways for water management in Africa.
We invite prospective contributors to share original research, case-based evidence, or reflective practice that advances understanding of water management under climate stress. Submissions should demonstrate relevance to African contexts while offering broader implications for global water governance under climate change. We invite all session participants to attend this townhall meeting. More details of the timing and location of this splinter meeting will follow.

Sustainable water management is increasingly challenged by anthropogenic pressures that drive overexploitation, water quality degradation, and biodiversity loss. Traditional monosectoral approaches have proven insufficient for ensuring fair water allocation across competing users. The situation is further exacerbated by incomplete knowledge of system properties, data scarcity, and cultural, governance, political, and socio-economic constraints.
This session will discuss state-of-the-art advances and pathways towards equitable, multisectoral water management. We seek interdisciplinary and transdisciplinary contributions addressing innovative monitoring (in-situ, Earth observation, citizen science, text mining), advanced data-driven and process-based modelling (conjunctive use, hydro-economic, water quality), robust decision-support systems and digital twins, effective stakeholder engagement, and co-designed nature-based solutions. Special emphasis will be placed on bridging hydrological process understanding with governance and decision-making, while considering equity, allocation metrics, and policy instruments for climate adaptation and resilience.
The session provides a timely platform to showcase results, contrast approaches, and identify replicable pathways for scaling from the Mediterranean and comparable regions. Submissions spanning methodological developments, case studies, and comparative syntheses are strongly encouraged.
This session is co-organised by the PRIMA-funded OurMED Project and NextGen4MED, a youth-led initiative fostering collaboration among early-career researchers on sustainable water management in the Mediterranean.

The climate crisis, land use change and overconsumption are altering the water cycle beyond a safe planetary boundary. The so-called conventional water, i.e. snowfall, rainfall, river runoff and easily accessible groundwater, is often insufficient to meet growing demands for water and food. Sustainably increasing water availability, allocating water in an equitable way, and adapting to a changing climate are challenges that need to be urgently addressed. A promising option is to use Non-Conventional Waters (NCW), which include ancestral and modern techniques to generate, harvest, store, treat, distribute and use water. The effectiveness of such solutions has already been proven in many parts of the world, but their potential is under-recognized, limiting their widespread adoption, mainly due to socio-economic and governance barriers.
This session welcomes studies on non-conventional water resources spanning technical, socio-economic and political disciplines. More specifically, we accept contributions including, but not limited to:
● innovative methods to design, monitor and assess NCW;
● social, economic, environmental and cultural sustainability of NCW water use;
● good practice examples for NCW solutions implementation;
● global and local drivers and barriers to NCW implementation
● technical innovations in harvesting, storing, treating, and using NCW, and innovation valorization and transfer;
● Stakeholder engagement and multi-actor platforms for NCW planning and implementation
Co-organized by the IAHS Working Group on “Non-Conventional Water Use for Integrated Water Resources Management” of the International Association of Hydrological Sciences: https://iahs.info/Initiatives/Scientific-Decades/helping-working-groups/non-conventional-water-ncw-use-for-integrated-water-resources-management/

The field of socio-hydrology and hydro-social research emerged as an attempt to better understand the dynamic interactions and feedbacks within diverse coupled human-water systems and its implications for the assessment and management of water resources and associated risks.

An integrated perspective offers novel entry points for a more fertile engagement between hydrological and social sciences across different scales ranging from the plot level to entire watersheds. Its interdisciplinary nature encompasses (and integrates) various methodological approaches, epistemologies, and disciplines.

We welcome contributions from researchers from social and natural sciences who are keen to look beyond their research perspective and who like to discuss their research findings in a broader context of coupled human water systems. Papers should

1) contribute to the understanding of complex human-water interactions and their management,
2) discuss the benefits and shortcomings of different inter- and disciplinary perspectives based on empirical, conceptual or model-based research; and
3) shed light on the added value of socio-hydrological modelling and hydro-social analysis for water resources management, risk management and adaptation design.

We specifically welcome contributions which reflect how the hydro-social and socio-hydrological research approach supports the new IAHS decade HELPING Science for Solutions aim.

This session welcomes abstracts that consider how to observe, analyse and model feedbacks between social, political and economic processes and hydrological and other environmental processes. The session is organised by the International Commission on Human-Water Feedbacks (ICHWF) of the IAHS, which provides a home for interdisciplinary research on the dynamics of human-water systems, particularly involving the social sciences.
Relevant topics include but are not limited to:
• Observations of human impacts on, and responses to, hydrological change
• Interactions of communities with local water resources
• Hydrological models that include anthropogenic effects
• Interdisciplinary qualitive and quantitative methods
• Theoretical models to isolate, conceptualize and/or simulate feedbacks in human-water systems
• Critical reflections on inter- and transdisciplinary projects (problem framings, roles, methods, exclusions, suggested interventions)
• Creation of databases describing the hydrology of human-impacted systems
• Data analyses and comparisons of human-water systems around the globe and especially in the global south
• Human interactions with hydrological extremes, i.e. floods, droughts and water scarcity
• The role of gender, age, and cultural background in the impacts of hydrological extremes, risk and risk perception, and during/after crises and emergencies

In the Anthropocene, water resources are under unprecedented stress. It has become more important than ever to not only thoroughly understand the hydrological cycle but also its interactions with other complex physical systems and social dimensions to address water-related challenges and develop actionable, sustainable solutions. To do this effectively, we need to move beyond a “science-as-usual” approach and leverage transdisciplinary knowledge involving multiple actors, including scientists, policymakers, local communities and indigenous peoples, NGOs and local associations, media, and businesses. Each of these actors brings a unique perspective and expertise, and we must empower and value their contributions with practices such as co-creation, to arrive at integrated solutions for complex water management issues. This vision is within the framework of the IAHS HELPING (Hydrology Engaging Local People IN one Global world) decade aiming to empower bottom-up approaches that involve local stakeholders and more importantly local know-how. In this context, co-creation can be defined as an iterative and collaborative process centered on water-related management challenge(s) and/or hydrological question(s) that integrates different forms and origins of knowledge aiming to address complex societal issues. Such approaches are common in policy creation and public services development but up until now have been under-described, -formalized, and -utilized in the context of water resources management and hydrological sciences.
Therefore, this session welcomes studies on co-creation approaches in hydrology and water resources management. More specifically, we welcome studies including, but not limited to: experiences and case studies of participatory and co-creation approaches applied to hydrology and water resources management; co-modelling approaches and socio-hydrological studies involving participation of stakeholders; meta-analyses, review of other experiences, and literature reviews; critical geography, political ecology, new conceptual frameworks, and other critical approaches to co-creation and stakeholders involvement in water resources decision making.

Co-organized by the Working Group on Co-Creation of Water Knowledge of the International Association of Hydrological Sciences: https://iahs.info/Initiatives/Scientific-Decades/helping-working-groups/co-creating-water-knowledge/

Reducing water-related risk requires models that connect everyday water decisions—on farms, in households, and by utilities and agencies—with how rivers, aquifers, reservoirs, and coasts behave at catchment, basin and transboundary scales. This session focuses on risk (e.g., floods, droughts, tropical cyclones, coastal extremes), who and what are exposed, how vulnerable they are, and the resulting impacts and choices for management and policy. By “local to transboundary scale,” we mean risk-modelling frameworks that connect local water practices and infrastructure operations to basin-to-transboundary hazard, exposure, vulnerability and impact outcomes across spatial, temporal and institutional scales, using explicit up/downscaling and cross-scale validation to support decisions under uncertainty.

We explicitly welcome research advances in socio-hydrology and, in addition, socio-meteorology—the study of how society and atmospheric processes interact (e.g., weather forecast design and use, warning response, risk perception). Socio-meteorology is a natural partner to socio-hydrology where weather information and human action shape water risks, from impact-based forecasting and anticipatory actions. Studies that analyze the interconnected dynamics of hydrological and meteorological processes, water use, land management, hydraulic infrastructure, climate change, ecological/environmental flows, and socio-economic drivers are also encouraged.

Socio-hydrology & feedbacks: co-evolution of people, water and infrastructure; adaptation/maladaptation cycles.
Socio-meteorology & warnings: weather forecast usability, communication, protective action; links to water risk and operations.
Interconnected dynamics: hydrology, meteorology, water use, land management, infrastructure, climate change, ecological/environmental flows, socioeconomic drivers
Scale-bridging methods: up/downscaling micro decisions to basin/transboundary outcomes; cross-scale validation.
Decision support & governance: robust/adaptive planning under uncertainty; equity and transboundary cooperation in risk reduction.

Water sustains societies, economies, and ecosystem services locally and globally. Yet, competition over freshwater is intensifying worldwide, driven by rising demand and shifting availability under climate change and variability. Addressing these challenges requires integrative water management and policy approaches that balance trade-offs across social, economic, and environmental water uses. Equally, adaptive and flexible solutions are needed to allow policies to adapt to changing and uncertain climatic and socio-economic conditions, thereby strengthening the sustainability and resilience of water systems. This session provides a forum for showcasing novel and emerging research at the intersection of agricultural production, energy security, water supply, economic development, and environmental conservation. In particular, we encourage contributions that: (i) advance understanding of critical interconnections, feedbacks, and risks within water system components, (ii) introduce novel methods or tools for evaluating and monitoring trade-offs and performance in water allocation, management, and policy across sectors, (iii) evaluate technological, policy, and/or governance innovations to address the water-food-energy-environment nexus across scales (local, regional, and/or global), and (iv) advance methods to evaluate risks to water systems and identify solutions to enhance system and user resilience. We welcome real-world examples on the successful application of these methods to facilitate integrated planning and management of the water-food-energy-environment (WEFE) nexus.

Hydropower is a mature and cost-competitive renewable energy source, which helps stabilize fluctuations between energy demand and supply. The structural and operational differences between hydropower systems and renewable energy farms may require changes in the way hydropower facilities operate to provide balancing, reserves or energy storage. Yet, non-power constraints on hydropower systems, such as water supply, flood control, conservation, recreation, navigation may affect the ability of hydropower to adjust and support the integration of renewables. Holistic approaches that may span a range of spatial and temporal scales are needed to evaluate hydropower opportunities and support a successful integration maintaining a resilient and reliable power grid. In particular, there is a need to better understand and predict spatio-temporal dynamics between climate, hydrology, and power systems.

This session solicits academics and practitioners contributions that explore the use of hydropower and storage technologies to support the transition to low-carbon electricity systems. We specifically encourage interdisciplinary teams of hydrologists, meteorologists, ecologist, power system engineers, and economists to present on case studies and discuss collaboration with environmental and energy policymakers.

Questions of interest include:

- Prediction of water availability and storage capabilities for hydropower production

- Prediction and quantification of the space-time dependences and the positive/negative feedbacks between wind/solar energies, water cycle and hydropower

- Energy, land use and water supply interactions during transitions

- Policy requirements or climate strategies needed to manage and mitigate risks in the transition

- Energy production impacts on ecosystems such as hydropeaking effects on natural flow regimes.

This session has the support of the a) Cost Action : Pan-European Network for Sustainable Hydropower (PEN@Hydropower), and b) European Energy Research Alliance (EERA), that established the joint program “Hydropower” to facilitate research, promote hydropower and enable sustainable electricity production. Further information can be found here:
https://www.pen-hydropower.eu/
https://www.eera-set.eu/eera-joint-programmes-jps/list-of-jps/hydropower/

Hydroclimatic extremes, ranging from prolonged droughts to intense wet spells and flooding, are intensifying worldwide. These changes pose escalating risks to agriculture, water management, and, ultimately, global food security, livelihoods, and societies. Yet, projected changes and their cascading effects are often assessed in isolation, a practice that underestimates systemic risks. A more holistic perspective, that acknowledges uncertainties, interactions, and feedbacks of both droughts and floods, is essential to identify resilient and sustainable transition pathways for complex adaptive systems such as agriculture and other land-use systems.
Farmers are traditionally experts at navigating climate variability, but the increasing severity and frequency of hydroclimatic extremes demand new strategies to mitigate systemic risks. Agriculture plays a threefold role in this context: as a sector heavily affected by climate change, as a contributor to greenhouse gas emissions, and as a key source to implement solutions and adaptation strategies. This interplay both amplifies uncertainties and opens opportunities for transformative adaptation. Understanding how individual farmers, farming communities, and agricultural governance actors evaluate risks and adjust practices under dynamic and uncertain conditions is central to clarifying how decisions at different spatial and temporal scales may trigger, accelerate, or prevent broader regime shifts.
This session invites contributions from across the natural and social sciences to explore the multifaceted interactions between hydroclimatic extremes and agriculture. We particularly welcome studies that:
• Provide observational evidence, case studies and methodological advancements on the interplay between climate extremes, agriculture and other land use systems.
• Advance theoretical and conceptual perspectives on connected, compound, and cascading risks and uncertainties.
• Apply nexus approaches, integrating environmental, socio-economic, and governance dimensions.
• Share participatory, co-production, or transdisciplinary approaches engaging farmers, local communities, and other stakeholders
• Examine decision-making processes and responses at the farm, community, and governance levels, including adaptation strategies and barriers across spatial and scales.
• Explore agricultural adaptability, transformation pathways, and potential regime shifts under climate extremes.

Urban areas are at risk from multiple hazards, including urban flooding, droughts and water shortages, sea level rise, disease spread and issues with food security. Consequently, many urban areas are adapting their approach to hazard management and are applying Green Infrastructure (GI) and Nature-based Solutions (NbS) as part of wider integrated schemes.

This session aims to provide researchers with a platform to present and discuss the application, knowledge gaps and future research directions of urban GI and how sustainable green solutions can contribute towards an integrated and sustainable urban hazard management approach. We welcome original research contributions across a series of disciplines with a hydrological, climatic, soil sciences, ecological and geomorphological focus, and encourage the submission of abstracts which demonstrate the use of GI at a wide range of scales and geographical distributions.

We invite contributions focusing on (but not restricted to):
· Monitored case studies which provide an evidence base for integration within a wider hazard management system;
· GIS and hazard mapping analyses to determine benefits, shortcomings and best management practices of urban implementation;
· Laboratory-, field- or GIS-based studies which examine the effectiveness or cost/benefit ratio of solutions in relation to their wider ecosystem potential;
· Methods for enhancing, optimising and maximising system potential;
· Innovative and integrated approaches or systems for issues including bioretention/stormwater management, pollution control, carbon capture and storage, slope stability, urban heat exchange and urban food supply;
· Catchment-based approaches or city-scale studies demonstrating opportunities at multiple spatial scales;
· Rethinking urban design and sustainable, resilient recovery following crisis onset;
· Engagement and science communication to enhance community resilience.

Urban watersheds face unique challenges amidst the need of water to support society and nature. The compounding effects of rapid urbanization, increasing demand, degrading water quality, extreme heat and risk of flooding call for more sustainable and equitable urban water management and development, but require an improved understanding of urban watershed behavior.
This session invites research that focuses on all aspects of urban hydrological research, with a particular focus on urban hydrological processes and related water challenges, including:
- Urban catchment characterization and functioning.
- Runoff and pollution in urban watersheds.
- Flood management and drought risks in urban or urbanized regions.
- Urban stormwater management solutions, including green infrastructure and other nature-based solutions.
- Surface-groundwater interactions in urban areas.
- Exploring inter-linkages between hydrometeorology/hydroclimate, surface and subsurface water resources, urbanization and extreme weather in the context of urban water management.
- Integrated approaches for studying/monitoring urban watersheds
- Community engagement and climate adaptation and management strategies in urban areas.
We particularly encourage submissions focusing on urban water challenges in the Global South.

Water utilities and municipalities are embracing technological innovation at different paces to address the challenges and uncertainties posed by urbanization, climate and demographic changes. The progressive transformation of urban water infrastructure and the adoption of digital solutions are opening new opportunities for the design, planning, and management of urban water networks and human-water systems across scales, in pursuit of sustainability and resilience. The “digital water” revolution is enhancing the interconnection between urban water systems (drinking water, wastewater, urban drainage) and other critical infrastructure and ecosystems (e.g., energy grids, transportation networks). This growing interconnection calls for new approaches that take into account the complexity of these integrated systems.
This session aims to provide an active forum to discuss and exchange knowledge on state-of-the-art and emerging tools, frameworks, and methodologies for planning and management of modern urban water infrastructure, with a particular focus on digitalization and/or interconnections with other systems, looking at the bigger picture. Topics and applications may cover any area of urban water network analysis, modeling, and management, including intelligent sensors and advanced metering, digital twins, asset management, decision making, novel applications of IoT, and challenges to their implementation or risk of lock-in of rigid system designs. Methods and approaches may also include big-data analytics and information retrieval, data-driven behavioral analysis, graph theory, ontologies and artificial intelligence for water applications (including large language models and physics-informed machine learning), descriptive and predictive models of, e.g., water demand, sewer system flow/flood extent, experimental approaches to demand management, water demand and supply optimization, energy recovery from urban water networks, real-time control of urban drainage systems, anomaly identification in hydraulic and water quality sensor data (e.g., for leak detection, identification of contamination events). Investigations on interconnected systems could explore emerging areas such as cyber-physical security of urban water systems (i.e., communication infrastructure), combined reliability and assessment studies on urban metabolism, or minimization of flood impacts on urban networks and energy usage optimization.

Seasonal snow constitutes a freshwater resource for over a billion people worldwide. Climate warming poses a significant risk to snow water storage, potentially leading to a drastic reduction in water supply and causing adverse effects on the ecosystems. Therefore, understanding seasonal snow dynamics, possible changes, and their implications has become crucial for effective water resources management.

Remote sensing of seasonal snow is a key tool in this regard, as it provides a wide range of techniques and data across various spatial and temporal scales. This technology is essential for monitoring snow properties and their hydrological impacts, enabling a better understanding of the interaction between snow and its environment at a small scale, rapid snow changes, rain-on-snow events, and snow-vegetation interactions.

This session focuses on studies linking remote sensing of seasonal snow to hydrological applications to: (i) quantify snow characteristics (e.g., SWE, snow grain size, albedo, pollution load, snow cover area, snow depth and snow density), (ii) understand and model snow-related processes and dynamics (snowfall, melting, evaporation, wind redistribution and sublimation), (iii) assess the snow hydrological impacts and snow environmental effects.
We welcome contributions that integrate methods and data from diverse technologies, including time-lapse imagery, laser scanning, radar, optical photography, thermal and hyperspectral sensing, as well as emerging applications, across a range of spatial scales (from plot-level to global) and temporal scales (from instantaneous observations to multi-year time series).

We invite presentations concerning the past, present and future of soil moisture estimation, including remote sensing, field experiments, land surface modelling and data assimilation, machine learning and Cal/Val activities including the use and establishment of fiducial reference measurements (FRMs).
Over the past two decades, the technique of microwave remote sensing has made tremendous progress to provide robust estimates of surface and deeper-layer soil moisture at different scales. From local to landscape scales, several field or aircraft experiments have been organised to improve our understanding of active and passive microwave soil moisture sensing, including the effects of soil roughness, vegetation, spatial heterogeneities, and topography. At continental scales, a series of several passive and active microwave space sensors, including SMMR (1978-1987), AMSR (2002-), ERS/SCAT (1992-2000) provided information on surface soil moisture. Current investigations of L-band passive microwave observations with SMOS (2009-) and SMAP (2015-), and active C-band microwave observations with the Metop/ASCAT series (2006-) and Sentinel-1, enable an accurate quantification of the soil moisture at regional and global scales. Building on the legacy of these mission operational programmes like Copernicus but also novel developments will further enhance our capabilities to monitor soil moisture, and they will ensure continuity of multi-scale soil moisture measurements from agricultural to climate scales. At the same time, research has put a new focus on establishing rigorous guidelines for the installation, calibration, operation, maintenance, and use of in situ soil moisture measurements that are informed by metrological practices, as well as on the development of advanced quality control procedures for an ever growing suit of global in situ soil moisture measurement networks to obtain so-called fiducial reference measurements (FRMs) for soil moisture.
Over the last decade, efficient machine learning-based observation operators have been advocated to assimilate remotely sensed soil moisture observations, such as neural networks and gradient boosting trees.

This session focuses on methodological advancements in the use of remote sensing (RS) to improve the quantification of evapotranspiration (ET) across a range of climates and environments. We invite contributions that demonstrate how RS techniques can enhance ET assessment and prediction in diverse settings including agricultural landscapes, riparian zones, urban areas, and forest ecosystems. The session aims to provide an overview of recent developments in RS-based ET estimation, at different geographical scales-from regional to global. By applying innovative approaches, we seek to address the challenges of ET quantification and deepen our understanding of water dynamics and vegetation conditions across different landscapes.

With growing societal attention to climate change, drought and flood early warning systems, ecosystem monitoring, biodiversity conservation, and the pursuit of a sustainable future, the demand for accurate estimation, modelling, mapping, and forecasting of ET has expanded. We welcome contributions that leverage cutting-edge techniques such as artificial intelligence (AI), data fusion, sharpening algorithms, hybrid physical and process-based models, empirical and statistical methods, and machine learning (ML). The expanding variety of space and airborne sensors from current and upcoming missions opens new horizons for quantifying ET across different spatial scales and land cover types. In parallel, cloud computing platforms offer researchers powerful tools, data, and computational resources to estimate and analyse hydrological variables like ET while enabling scalable, efficient, and collaborative workflows. Remote sensing (RS) of ET supports evidence-based decision-making, contributes to sustainable water management practices, and better informs managers, end-users, and the scientific community working to improve the quantification of water cycle components.

In our session of RS of ET, we welcome your research findings, commentary pieces and debates on:
A. recent developments in RS of ET
B. application of AI, cloud computing, and technology advancement;
C. fusion of RS, modelling, and ground-based methods;
D. validation, calibration, and upscaling challenges and solutions;
E. future directions in RS of ET.

This session focuses on the hydrogeodetic measurement of water bodies such as rivers, lakes, floodplains and wetlands, groundwater and soil. The measurements relate to estimating water levels, extent, storage and discharge of water bodies through the combined use of remote sensing and in situ measurements and their assimilation in hydrodynamic models.

Monitoring these resources plays a key role in assessing water resources, understanding water dynamics, characterising and mitigating water-related risks and enabling integrated management of water resources and aquatic ecosystems. While in situ measurement networks play a central role in the monitoring effort, remote sensing techniques provide near real-time measurements and long homogeneous time series to study the impact of climate change from local to regional and global scales.

During the past three decades, a large number of satellites and sensors has been developed and launched, allowing to quantify and monitor the extent of open water bodies (passive and active microwave, optical), the water levels (radar and laser altimetry), the global water storage and its changes (variable gravity). River discharge, a key variable of hydrological dynamics, can be estimated by combining space/in situ observations and modelling, although still challenging with available spaceborne techniques. Interferometric Synthetic Aperture Radar (InSAR) is also commonly used to understand wetland connectivity, floodplain dynamics and surface water level changes, with more complex stacking processes to study the relationship between ground deformation and changes in groundwater, permafrost or soil moisture.

Traditional instruments contribute to long-term water level monitoring and provide baseline databases. Scientific applications of more complex technologies like Synthetic Aperture Radar (SAR) altimetry on CryoSat-2, Sentinel-3A/B and Sentinel-6MF missions are maturing, including the Fully-Focused SAR technique offering very-high along-track resolution. The SWOT mission, now opens up many new hydrology-related opportunities. We also welcome submissions of pre-launch studies for CRISTAL, Sentinel-3C/3D/3NG-Topography, Sentinel-6NG, MAGIC/NGGM and and other proposed missions such as Guanlan, HY-2 and SmallSat constellations such as the SMASH concept now called H2R, and covering forecasting.

frequent and impactful weather-related disasters. Conversely, declines in water availability make monitoring surface water dynamics, including seasonal water body variations, wetland extent, and river morphology changes crucial for environmental management, climate change assessment, and sustainable development. Remote sensing is a critical tool for data collection and observation, especially in regions where field surveys and gauging stations are limited, such as remote or conflict ridden areas and data-poor developing nations. The integration of remotely-sensed variables—like digital elevation models, river width, water extent, water level, flow velocities, and land cover—into hydraulic models offers the potential to significantly enhance our understanding of hydrological processes and improve predictive capabilities.
Research has so far focused on optimising the use of satellite observations, supported by both government and commercial initiatives, and numerous datasets from airborne sensors, including aircraft and drones. Recent advancements in Earth observation (EO) and machine learning have further enhanced the monitoring of floods and inland water dynamics, utilising multi-sensor EO data to detect surface water, even in densely vegetated regions. However, despite these advancements, the update frequency and timeliness of most remote sensing data products are still limited for capturing dynamic hydrological processes, which hinders their use in forecasting and data assimilation. This session invites cutting-edge presentations on advancing surface water and flood monitoring and mapping through remotely-sensed data, focusing on:
- Remote sensing for surface water and flood dynamics, flood hazard and risk mapping including commercial satellite missions and airborne sensors
- The use of remotely-sensed data for calibrating or validating hydrological or hydraulic models
- Data assimilation of remotely-sensed data into hydrological and hydraulic models
- Enhancements in river discretization and monitoring through Earth observations
- Surface water and river flow estimation using remote sensing
- Machine learning and deep learning-based water body mapping and flood predictions
- Ideas for developing multi-satellite data products and services to improve the monitoring of surface water dynamics including floods
Early career and underrepresented scientists are particularly encouraged to participate.

The Surface Water and Ocean Topography (SWOT) satellite mission, launched in December 2022, has marked a significant advancement in hydrological sciences. SWOT uses novel Ka-band radar interferometry to deliver, for the first time, simultaneous, high-resolution measurements of water surface elevation and inundation extent in rivers, lakes, reservoirs, and wetlands globally. SWOT is fundamentally transforming our ability to understand the movement of water across continental surfaces. The hydrology and remote sensing science communities have worked for over a decade to develop new methods and scientific understanding that will allow SWOT data to advance global hydrology. For this session, we solicit abstracts presenting recent advances using data from SWOT to unlock new frontiers in hydrology, inland cryosphere, and estuaries. We also welcome presentations of improved algorithms for extracting hydrologically relevant information from SWOT data, as well as new modeling and data assimilation techniques leveraging data from SWOT combined with other satellite data.

This session concentrates on extreme rainfall events, surface water dynamics, and flood events, exploring innovative remote sensing, AI, and digital twin technologies for real-time monitoring, risk assessment, and mitigation. It invites submissions on advanced data integration, modeling approaches, early warning systems, and decision-support tools to improve understanding, forecasting, and management of flooding and related surface water hazards.

The integration of AI with digital twin improves the analytical and operational capabilities of geospatial systems, which through the analysis of historical data and the integration of real-time information (IoT) are able to highlight even “hidden patterns” in the data, identifying new models capable of improving forecasts with greater control over the quantification of uncertainty and the variability of the phenomenon analysed.

This session aims to focus on flood hazard and risk assessment, monitoring, and management. This Topic invites the submission of articles focused on, but not limited to, the following areas:
• Monitoring of extreme rainfall events and flood hazards for risk assessment and communication.
• Digital twins (DTs)/prototypes of DTs in flood hazard forecasting, early warning, monitoring, and supporting tools for urban governance.
• DSSs to extract meaningful information in the artificial intelligence era, eventually serving to reduce risk and provide support tools to mitigate flood hazards.
• The role of AI and digital twins to assess the economic impacts of flood hazards and the cost-effectiveness of various mitigation strategies.
• Novel techniques to analyse big data coming from Earth observation platforms, drones, and other geospatial data in order to provide timely information related to the extend, exposure, and impacts of flood hazards.

Agriculture is the largest consumer of water worldwide and at the same time irrigation is a sector where huge differences between modern technology and traditional practices do exist. Furthermore, reliable and organized data about water withdrawals for agricultural purposes are generally lacking worldwide, thus making irrigation the missing variable to close the water budget over anthropized basins. As a result, building systems for improving water use efficiency in agriculture is not an easy task, even though it is an immediate requirement of human society for sustaining the global food security, rationally managing the resource and reducing causes of poverties, migrations and conflicts among states, which depend on trans-boundary river basins. Climate changes and increasing human pressure together with traditional wasteful irrigation practices are enhancing the conflictual problems in water use also in countries traditionally rich in water. Hence, saving irrigation water improving irrigation efficiency on large areas with modern techniques is an urgent action to do. In fact, it is well known that agriculture uses large volumes of water with low irrigation efficiency, accounting in Europe for around 24% of the total water use, with peak of 80% in the Southern Mediterranean part and may reach the same percentage in Mediterranean non-EU countries (EEA, 2009; Zucaro 2014). North Africa region has the lowest per-capita freshwater resource availability among all Regions of the world (FAO, 2018).
Several studies have recently explored the possibility of monitoring irrigation dynamics and by optimizing irrigation water management to achieve precision farming exploiting remote sensing information combined with ground data and/or water balance modelling.
In this session, we will focus on: the use of remote sensing data to estimate irrigation volumes and timing; management of irrigation using hydrological modeling combined with satellite data; improving irrigation water use efficiency based on remote sensing vegetation indices, hydrological modeling, satellite soil moisture or land surface temperature data; precision farming with high resolution satellite data or drones; farm and irrigation district irrigation management; improving the performance of irrigation schemes; estimates of irrigation water requirements from ground and satellite data; ICT tools for real-time irrigation management with remote sensing and ground data coupled with hydrological modelling.

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

Remote sensing products have a high potential to contribute to monitoring and modelling of water resources. Nevertheless, their use by water managers is still limited due to lack of quality, resolution, trust, accessibility, or experience.
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We welcome research aimed at improving the quality of remote sensing products, such as higher spatial and/or temporal resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources, drought monitoring/modelling and its impacts on water-stressed vegetation, as well as on irrigation volumes monitoring and modelling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (e.g., physically based models or artificial intelligence techniques) or an integration of multiple sources of data (remote sensing and in situ) across various imagery types (satellite, airborne, drone).
Finally, we wish to attract presentations on developments of user-friendly platforms (following FAIR principles), providing smooth access to remote sensing data for water applications. We are particularly interested in applications of remote sensing to determine the human-water interactions and the climate change impacts on the whole water cycle (including the inland and coastal links).

With the proliferation and wide accessibility of remotely sensed information, data from remotely piloted aircraft (RPA), piloted airplanes, and satellite missions such as Landsat, Sentinel, NISAR and VIIRS are being increasingly used to better our understanding of hydrological processes on the earth’s surface. This understanding is a crucial prerequisite to ameliorate resource management, optimise the development of infrastructure, and adjust land use practices to changing climate conditions and hazards such as floods and droughts. However, many analyses incorporate remote sensing data by default without a thorough critical examination of their applicability and limitations. In-situ data, though expensive to collect and therefore often less readily available, provide a valuable layer of information and a benchmark for methods relying on remotely sensed data.
This session invites contributions that highlight innovative approaches to synthesizing remotely sensed and in-situ data at local-to-regional scales. We welcome contributions that focus on combining multi-scale remote sensing and/or remote sensing with in-situ information and that critically engage this intersection to better understand:
* Processes such as evapotranspiration, infiltration, (Monsoon) inundations, water abstraction for agricultural use
* Hydrological extremes such as floods and droughts
* Improve monitoring and science in poorly gauged and ungauged basins
* Developing novel methods of gathering in-situ benchmark data to combine with remotely sensed approaches

The water cycle or hydrological cycle involves the continuous movement of water on, above, and below the surface of the Earth. In general, hydrological cycle components (e.g., precipitation, evaporation, water storage, and runoff) are characterized by large temporal and spatial variability. Accurate monitoring of various hydrological cycle components and developing hydrological models are important for improving our understanding of hydrological processes.

With significant development of sensor technology and sharply growing platforms in past decades, remote sensing offers enhanced capability to monitor various hydrological cycle components at different temporal and spatial scales to complement conventional in situ measurements. Considerable efforts have been made to explore the potentials of remotely sensed data from a vast range of different platforms (e.g., satellite, airborne, drone, ground-based radar) and sensors (e.g., optical, infrared, microwave) in advancing hydrology research, particularly in poorly gauged and ungauged regions. The application of remote sensing in hydrology is expected to increase with enhanced recognition of its potentials and continuous development of advanced sensors (e.g., new satellite missions) and retrieval methods (e.g., innovative machine learning and data assimilation techniques).

The session aims to present and discuss recent advances in the remote sensing of hydrological cycle components as well as the application of remote sensing in hydrological modeling. We encourage studies to investigate the performance of using remotely sensed data in driving hydrological models, multi-variable calibration and spatial evaluation of hydrological models. The added-value of spatially downscaling remotely sensed data in improving hydrological modelling is also particularly welcome.

The huge and continuous increase of remote sensing sources of data with direct or indirect relevance for hydrological sciences has opened new opportunities for the scientific community (e.g. high-resolution products) to new or enhanced applications in hydrology, especially in the framework of water fluxes modelling (e.g. estimations of evapotranspiration over large areas or soil moisture fields). Their integration in long-term series and large scale analysis still needs skilled assessment in many scientific and operational applications. For example, the scale effects of a coarse mapping of such variables on the modelling results can strongly affect the accuracy and skill of hydrological models, and pose a constraint for further embedment of remote sensing data in forecasting schemes or scenario assessments. The new context offers the possibility of increasing the skill of models to assess the spatial variability of descriptors and processes in heterogeneous areas, and quantify man-operated issues that greatly impact the hydrological processes on the local scale as irrigation practices, reservoir operation, or modification of the soil surface, and the water budget on the global scale.
The International Commission of Remote Sensing (ICRS) of the International Association of Hydrological Sciences (IAHS), in the context of the HELPING scientific decade in IAHS (Hydrology Engaging Local People IN one Global world), aims at advanced research to further embrace Observation of the Earth and hydrological knowledge, and pave pathways for societal impacts. ICRS offers a lively scenario of discusion within EGU’26 GA and invites contributions related, but not limited, to the following topics, all aimed at deepening on the current opportunities from remote sensing to better understanding and modelling hydrological processes, further discussing challenges associated to uncertainty and scaling issues in specific applications, and major needs from the scientific community towards operational tools:
Advanced remote sensing of the water cycle
Optimal integration of remote sensing and hydrological modelling
Tools tailored for end-users out of the remote sensing community
Promising developments towards integration of remote sensing in operational systems (flood alerts, drought risks, …)
Submissions from early career scientists, and examples of successful applications of operational tools and dissemination apps are especially welcome.

The Tibetan Plateau and surrounding mountain regions, known as the Third Pole, cover an area of > 5 million km2 and are considered to be the water tower of Asia. The Pan Third Pole expands on both the north-south and the east-west directions, going across the Tibetan Plateau, Pamir, Hindu Kush, Iran Plateau, Caucasian and Carpathian, and covering an area of about 20 million km2. Like the Arctic and Antarctica, the Pan Third Pole’s environment is extremely sensitive to global climate change. In recent years, scientists from around the globe have increased observational, remote sensing and numerical modeling research related to the Pan Third Pole in an effort to quantify and predict past, current and future scenarios. Co-sponsored by TPE (www.tpe.ac.cn), this session is dedicated to studies of Pan Third Pole atmosphere, cryosphere, hydrosphere, and biosphere and their interactions with global change. Related contributions are welcomed.

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.

Hydroclimatic conditions and availability of water resources in space and time constitute important factors for maintaining adequate food supply, the quality of the environment, and the welfare of citizens and inhabitants, in the context of a post-pandemic sustainable growth and economic development. 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.).

Scientists are facing several challenges when applying climate models for hydrological variables. Indeed, a gap exists between what is provided by climate scenarios and what is needed and useful for technical hydrological studies. In order to reduce this gap and enhance the assessment of climate change impacts, we need to improve our understanding, knowledge and model representations of the interactions between climate drivers and hydrological processes at regional and local scales. This is essential to outline forecasts and assess the risk associated with extreme events, where uncertainty, probabilistic approaches ad prediction scenarios should be properly defined.

This session particularly welcomes, but is not limited to, contributions on:
- Advanced techniques to simulate and predict hydrological processes and water resources, with emphasis on stochastic and hybrid methods.
- Advanced techniques to simulate and predict hydroclimatic extreme events including compound extreme events (e.g. heatwaves, floods and droughts).
- Holistic approaches to generate future water resources scenarios integrating also anthropogenic and environmental perspectives.
- Hydroclimatic change attribution studies using probabilistic approaches and novel causality frameworks with uncertainty assessment.
- Evaluation of climate models performance at regional and local scales using observational data

This session is supported by the International Association of Hydrological Sciences (IAHS), the World Meteorological Organization, the National Recovery Resilience Plan RETURN Foundation of Italy, and it is also related to the scientific decade 2023–2032 of IAHS, “HELPING”.

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 resulting from a high degree of imperviousness. Therefore, urban catchments are especially sensitive to space-time variability of precipitation at small scales. High-resolution precipitation measurements in cities are crucial to properly describe and analyses urban hydrological responses. 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 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 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.

Hydroclimatic extremes, along with their statistics, are crucial inputs of hydrological applications, which have increasing importance in the (re)insurance industry. Among the most common applications, catastrophe models are developed to manage risk accumulation; disaster response is used to prepare (re)insurers financially after major events; Real Disaster Scenarios are built to stress-test (re)insurance exposure; parametric (or index based) solutions are developed to cover the likelihood of a loss-causing event happening, like a flood event. Additionally, connections between hydrometeorological extremes and climatic oscillations, such as NAO or ENSO, and their evolution in a changing climate, provide insights for long-term risk management, as required for regulatory purposes.
The preparedness for, and intensification of these extreme events require a synergy with research in order to estimate them accurately. In this context, recent advancements have revealed the departure of hydrometeorological processes from classical statistical models, highlighting the scaling behaviour of extremes across space and time. For instance, the estimation of the design rainfall for flood estimation not only involves determining the absolute amount for a specific return period, but also requires understanding the intra-event rainfall distribution, spatial extension, and rainfall intensities at neighbouring stations. The integration of supporting information and the application of advanced AI approaches offer as well unprecedented opportunities to enhance these estimates.

This session invites submissions, among others, on the following topics:
- Applications carried out jointly by the (re)insurance industry and research institutions.
- Coupling stochastic approaches with deterministic hydrometeorological predictions to better represent predictive uncertainty.
- Developing robust statistics under non-stationary conditions for design purposes.
- Parsimonious models of hydrometeorological extremes across various spatial and temporal scales for risk analysis and hazard prediction.
- Improving the reliable estimation of extremes with high return periods, considering physical constraints.
- Linking underlying physics and hydroclimatic indices with the stochastics of hydrometeorological extremes.
- Exploring supporting data sets for additional stochastic information and utilizing novel AI and machine learning approaches.

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.

Human water management practices, including irrigation, groundwater pumping, dam and reservoir operations, and other water uses, lead to a massive redistribution of water across the land surface. These human interventions alter land-atmosphere interactions and influence a wide range of Earth system processes, including atmospheric boundary layer and large-scale circulation patterns.

This session aims to bring together research conducted at multiple spatial and temporal scales that investigates the influence of human water management on local atmospheric processes, regional and global climate dynamics, and associated hydrological and hydro-ecological impacts. Topics may include, but are not limited to, land-atmosphere interactions across heavily irrigated areas, hydroclimatological implications of groundwater pumping, dam and reservoir management, and the coupled hydroclimatological consequences of the large-scale overexploitation of water resources.

We invite submissions that address, but are not limited to:
(i) Recent advances in understanding water management-climate interactions.
(ii) New insights into land-atmosphere interactions in irrigated and heavily water-managed regions using innovative methods and conceptual approaches.
(iii) Current developments and persistent challenges in representing water management in climate and earth system models.
(iv) The interaction between human water management and the occurrence and intensity of local and remote hydroclimatological extremes, including droughts, floods, and compound events.

We welcome a variety of studies utilizing in-situ observations, field campaigns, remote sensing, model-based assessments, and future projections. We particularly encourage submissions that explore the broader implications of water management on atmospheric and land surface processes, aiming to enhance collaboration across disciplines and inform the development of sustainable water management strategies in heavily managed regions.

Fractured-porous media and karst systems remain one of the most challenging geological media to investigate due to their high heterogeneity and scale-dependent flow and transport properties. Given their abundance they are of importance for various research fields such as hydrogeology, geothermal energy, CO₂ and nuclear waste repository management, as well as petroleum and mining engineering. Decades of research have explored a broad method spectrum including field experiments, laboratory-scale approaches, analytical techniques and numerical modeling. However, the multiscale nature of these systems requires to bridge micro- and macro-scale processes in order to accurately represent flow and (reactive) transport processes. In this session we therefore welcome contributions that focus on flow and (reactive) transport in karst systems and fractured-porous media. Topics include but are not restricted to: (1) Advances in field methods and laboratory techniques to characterize flow and transport from pore to field scales; (2) Numerical and analytical modelling approaches models for multiscale process understanding; (3) studies targeting the management and efficient use of geothermal, mineral and petroleum resources; and (4) studies targeting water resources management and environmental risk assessment.

Dissolution, precipitation and chemical reactions between infiltrating fluid and the rock matrix alter the composition and structure of the rock, either creating or destroying flow paths. Strong, nonlinear couplings between the chemical reactions at mineral surfaces and fluid motion in the pores often lead to the formation of large-scale patterns: networks of caves and sinkholes in karst areas, wormholes induced by the acidization of petroleum wells, porous channels created as magma rises through peridotite rocks. Dissolution and precipitation processes are also relevant in many industrial applications: carbon storage or mineralization, oil and gas recovery, sustaining fluid circulation in geothermal systems, the long-term geochemical evolution of host rock in nuclear waste repositories or mitigating the spread of contaminants in groundwater.

With the advent of modern experimental techniques, these processes can now be studied at the microscale, with a direct visualization of the evolving pore geometry, allowing exploration of the coupling between the pore-scale processes and macroscopic patterns. On the other hand, increased computational power and algorithmic improvements now make it possible to simulate laboratory-scale flows while still resolving the flow and transport processes at the pore scale.

We invite contributions that seek a deeper understanding of reactive flow processes through interdisciplinary work combining experiments or field observations with theoretical or computational modeling. We seek submissions covering a wide range of spatial and temporal scales: from table-top experiments and pore-scale numerical models to the hydrological and geomorphological modelling at the field scale.

Global climate change is reshaping the hydrological cycle, leading many regions to face reduced natural water availability. Managed aquifer recharge (MAR) has emerged as a promising adaptation strategy to overcome these challenges and to reinforce sustainable groundwater management. MAR can support maintaining water quantity (water levels and volumes), improve water quality, support groundwater-dependent ecosystems, counteract subsidence and salinization, and create underground hydraulic barriers. For MAR to achieve long-term sustainability, it is essential to continuously improve techniques, adapt to local hydrogeological and socio-economic conditions, and apply robust monitoring and evaluation practices. This session aims to highlight the potential of MAR and share knowledge on MAR applications and the impact on local and regional scale, for both water management and dependent ecosystems.

We welcome contributions focusing on (but not limited to):
• Innovations in MAR methods.
• Case studies and lessons learned from MAR schemes describing and comparing different hydrogeological and climate settings.
• MAR and water quality issues, including online and in-situ analytical tools for infiltrated water, impacts on groundwater quality, treatment system effectiveness, and clogging challenges.
• Monitoring and operational management strategies for ensuring long-term sustainability of MAR schemes.
• Environmental impact assessment of MAR operations.
• Cost-effectiveness of MAR operations.

This session combines contributions on recent developments in subsurface hydrology; theoretical approaches and experimental work will be discussed to provide reliable insights for groundwater protection and site remediation techniques.

Much effort has been put into understanding transport processes in recent years because of their practical relevance in determining the fate of contaminants in surface and subsurface waters that may affect human health and the environment. Correct quantification of transport processes is challenging and reflects the complexity of flow paths and physical processes in aquifers, as well as the heterogeneity of . It strongly influences predicted contaminant dispersion and plume properties and is fundamental for assessing the effectiveness of remediation strategies. Further efforts are now needed to apply these new concepts in practice for contamination prevention, vulnerability assessment and risk management.
The aim of this session is to discuss the latest theoretical and practical developments in transport theories and how they can be applied to the problems of aquifer characterisation, transport dynamics and remediation techniques.

Our contributions will address the following questions
- What are the recent improvements in appropriate methods to characterise the relevant aquifer properties for comprehensive modelling of contamination?
- What are the recent improvements in transport measurement techniques?
- What are the most appropriate approaches for the practical application of theoretical advances in groundwater transport modelling?
- How can we assess the most appropriate remediation strategy and predict its effectiveness?

Case studies and multidisciplinary approaches are encouraged.

The session is co-sponsored by the Groundwater Commission of the IAHS.

Multiphase flows play a central role in a broad range of natural and engineered processes, such as nutrient cycles and contaminant remediation in soils, and geological storage of carbon dioxide and hydrogen in deep reservoirs. Understanding multiphase systems across scales is therefore fundamental for water resources management as well energy and climate concerns.

The presence of multiple fluid phases enhances heterogeneity at the level of flow, mixing, and reaction in structurally heterogeneous media. This impacts the transport of dissolved substances and fundamentally changes mixing patterns and effective reaction rates, posing major challenges for predictive modeling. Recent theoretical and experimental advances provide unprecedented insights into the pore-scale mechanisms governing these processes and open new opportunities to tackle these challenges.

This session aims to bring together researchers working on fundamental and applied aspects of flow, transport, mixing, and reaction in multi-phase systems across scales. In particular, we encourage submissions relating to experimental, numerical, and theoretical contributions pertaining to the following topics:

- Impact of medium heterogeneity on multiphase flow, from the pore to the continuum scale.
- Impact of multiphase flow patterns on mixing and reaction rates across scales in heterogeneous media.
- Biogeochemical processes in multiphase systems.
- Applications to vadose zone hydrology and geological storage.

This session invites papers on recent advancements related to terrestrial contamination by per- and polyfluoroalkyl substances (PFAS) as well as other emerging or traditional contaminants that:
• improve knowledge & understanding of fate and transport in the soil-groundwater system, including surface water interactions
• enhance, develop or create new models
• improve site characterization & remediation
• address new challenges in meeting regulatory goals and policy development

Per- and polyfluoroalkyl substances (PFAS) are a relatively new class of contaminants that pose a threat to drinking water production and the environment globally. PFAS combines aqueous mobility, extreme recalcitrance and adverse health effects at very low concentrations, which requires immediate actions to reduce their release and spreading, better understand their transport and associated risks, and remove them from the environment. The unique properties of PFAS also pose many additional challenges for groundwater management, risk assessment and remediation. Several PFAS-specific fate and transport processes need to be better understood and there is an urgent need for improved remediation and mitigation methods.

PFAS have produced additional challenges to our basic knowledge and models of solute fate and transport in the subsurface. To address these challenges and advance our fundamental knowledge, careful investigations over different scales as well as improved models to deal with both chemical and geologic site complexities are needed. This session will have particular focus on complex contaminant transport phenomena and their upscaling from the laboratory to real field sites as well as site characterization.

Climate change, land degradation, and biodiversity loss increasingly threaten aquifer recharge and groundwater quality. Ecosystem restoration offers multiple pathways to address these challenges by improving soil structure, reducing erosion and pollutant loads, and enhancing infiltration and groundwater replenishment. Restoration measures such as soil amendments, erosion control, river and floodplain rehabilitation, and wetland restoration can improve groundwater safety and resilience to climate extremes, including floods and droughts.
This session invites contributions on the hydrological and water quality impacts of restoration interventions, with emphasis on:
(i) groundwater recharge enhancement under current and future climates,
(ii) effects on chemical and microbiological groundwater quality and safety, including nutrients, trace contaminants, and pathogens,
(iii) biodiversity recovery, carbon sequestration, and other ecosystem co-benefits, and
(iv) monitoring and modelling approaches for assessing long-term sustainability and scalability.
We welcome field, laboratory, modelling, and socio-hydrological studies that bridge hydrology with soil science, ecology, water quality, and environmental health, and that explore the role of ecosystems and restoration interventions in supporting sustainable groundwater management under climate change.

Particles (inorganic particles, biocolloids, plastics) in environmental systems are of great concern because of their potential adverse effects on ecosystem functions, wildlife and human health. They may also alter the transport properties of dissolved contaminants and change the hydraulic properties of subsurface systems. On the other hand, engineered particles and biocolloids play an important role in site remediation and aquifer restoration. This interdisciplinary session fosters the exchange among scientists from hydrogeology, microbiology, ecotoxicology, engineering, and analytical chemistry in order to provide a general picture of the occurrence and fate of natural and engineered particles in aquatic and terrestrial systems.

We are expecting contributions in the following fields:
• occurrence, fate and transport of biocolloids, nanoparticles and other particles (microplastics, soot, ...) in aquatic and terrestrial systems
• methods to detect, characterize, and quantify particles in
aquatic and terrestrial systems
• advanced experimental methods to test the behaviour of particles in aquatic and terrestrial systems (mesocosms, non-invasive imaging, ...)
• interactions between biocolloids, particles and solid surfaces
• biocolloid biodegradation in the presence of solids
• toxicity of products generated from biological disruption of pollutants in the presence of biocolloids
• adverse effects of nanoparticles on microorganisms
• effects of climate change on biocolloid and nanoparticle migration
• public health risks associated with water and air polluted with biocolloids and nanoparticles.

Groundwater provides about 40% of all human water abstractions and is an essential water source for terrestrial ecosystems and freshwater biota in rivers, lakes, and wetlands, as well as a keystone ecosystem in itself. Aquifers may span political and natural boundaries, connecting people, ecosystems, and different parts of the hydrological cycle. However, our large-scale understanding of groundwater processes and the connection between ground and surface waters is still limited.

The development of global groundwater models and big-data assessments of groundwater wells have helped to push the boundaries of our large-scale understanding of groundwater processes. In particular, knowledge of the exchange between surface and subsurface waters is essential for determining the water balance at larger scales. Surface and subsurface water exchanges and inter-catchment groundwater flow affect water, pollutant, and nutrient fluxes, bio-organisms in streams, and the groundwater itself. Additionally, human activities (e.g., pumping/irrigation) increasingly affect groundwater flow processes and the exchange between surface and subsurface waters.

In this session, we want to highlight the increasing interest in the large-scale study of groundwater availability, quality, and processes (including groundwater recharge) and discuss current obstacles related to data availability and model design. Therefore, we seek contributions that address issues including:

* Continental to global groundwater-related datasets
* Regional to global big-data assessments with models and machine learning
* Transboundary and inter-catchment assessments of groundwater processes
* Identification of dominant controls on groundwater processes across large domains
* Recent methodological developments for the inclusion of small-scale hydrological processes into large-scale estimates
* Surface-subsurface water exchange and its effects on hydrological extremes (drought/flood), water availability, and solute and pollutant transport
* Effects of climate change, land use change, and water use change on global groundwater
* Implications of large-scale groundwater understanding on monitoring design, integrated water management, and global water policies
* Large-scale groundwater assessments related to the fulfillment of the UN sustainable development goals (SDGs)

The field of data-driven and hybrid groundwater modelling continues to gain significant momentum within the hydrological community, reflecting growing interest in machine learning, artificial intelligence, and approaches that integrate data-driven techniques with physical process understanding. These methods are increasingly essential for addressing complex challenges in groundwater quantity and quality forecasting, uncertainty quantification, and sustainable management under changing climatic and anthropogenic pressures. Data-driven approaches — including time-series models, statistical methods, machine and deep learning techniques, and emulators — are transforming how we study, manage and forecast groundwater systems. By learning directly from observations, remote sensing and other data sources, these methods can complement, accelerate or in some cases substitute detailed process-based models. Recent advances in physics-informed methods, spatio-temporal deep learning architectures, probabilistic machine learning and foundation-model approaches are rapidly expanding possibilities for groundwater science. We welcome novel methodological developments and practical applications addressing real-world groundwater management problems. Submissions may address (but are not limited to):
• Advanced data-driven techniques for predicting groundwater quantity and quality in space and/or time (ML/DL, statistical and time-series models).
• Hybrid approaches combining ML with physically based models, including Physics-Informed Neural Networks and surrogate modelling.
• ML-based emulation of numerical models to support efficient modelling and data assimilation.
• Uncertainty quantification and sensitivity analysis using probabilistic ML, quantile regression and deep ensembles.
• Explainable and interpretable ML to improve hydrogeological understanding.
• Methods for big and heterogeneous datasets, data fusion (satellite, models, in-situ), and solutions for data scarcity, non-stationarity and irregular time steps.
• Transferability and regionalization to ungauged sites, and foundation-model approaches for temporal and spatial extrapolation.
We especially encourage submissions that link methods to management-relevant outcomes, climate-change impact assessments, adaptation strategies, and practical case studies. Join us to share research at the intersection of data science and groundwater hydrology and to advance this dynamic field through knowledge exchange and discussion.

Information on groundwater residence times and flow paths can be used to understand the hydrological and biogeochemical functioning of aquifers, including impacts of subsurface heterogeneities, seasonal and long-term changing climatic conditions, groundwater–surface water interactions, and many other processes.
Tracer- and model-based estimates of residence times and flow paths are valuable tools to protect groundwater-dependent ecosystems, to estimate vulnerabilities and recovery times of aquifers impacted by pollution, to define drinking water protection areas, and for planning sustainable groundwater use. Multi-tracer approaches—combining stable isotopes, environmental radioisotopes, geochemical and biological indicators, and artificial tracers—can help delineate transit time distributions and reveal fracture connectivity, which are essential for understanding threats from hydrological extremes and contaminant transport.
The session wants to bring together experiences of applied resource management and advanced research using a wide range of different techniques, including new tracers and advances in modelling techniques in variable aquifers at various spatial scales. Especially welcome are presentations with new or not so frequently used tracers of long or short half-life, as well as those that combine tracers covering different residence times and investigate the behaviour of water flow and solutes in complex aquifer systems.

Coastal aquifers, pivotal freshwater reserves for millions worldwide, are increasingly vulnerable to seawater intrusion due to the joint impacts of climate change and intensive human activity. Rising sea levels, changing precipitation patterns, over-abstraction, land use change, and population growth intensify the stress on these valuable but fragile resources. As salinization threatens groundwater-dependant ecosystem health, agriculture, and urban water supplies, integrated approaches to monitoring, modeling, and managing coastal aquifers are urgently needed.
This session invites contributions that advance understanding, prediction, and management strategies for minimizing seawater intrusion and ensuring sustainable water resources in coastal regions. We aim to foster interdisciplinary discussion on innovative concepts, methods, and policies that support resilience under global change scenarios.

We encourage submissions related but not limited to:
• Field and modeling studies of seawater intrusion processes in coastal aquifers.
• Impacts of climate change (e.g., sea level rise, extreme weather, shifting recharge patterns) on groundwater salinization.
• Effects of groundwater pumping, land use, and urbanization on aquifer vulnerability.
• Advances in observation techniques: hydrochemical/ isotopic tracers, remote sensing, and geophysical methods.
• Integrated management strategies and decision-support tools for sustainable extraction and adaptation.
• Case studies of coastal aquifer restoration
• Nature-based solutions and Managed aquifer recharge (MAR) to enhance coastal aquifer resilience.
• Socioeconomic, legal, and policy dimensions addressing water quality, stakeholder involvement, and governance.
• Multi-scale approaches: from local to global perspectives.

Extreme hydroclimatic events are expected to increase in both frequency and intensity due to climate change. While their immediate socio-economic and environmental impacts are well recognized, their medium- to long-term implications remain insufficiently understood. Aquifer systems are particularly vulnerable to these effects over extended timescales. Consequences include reduced aquifer recharge and groundwater quality degradation, which can induce cascading processes such as land subsidence, threats to food security, freshwater scarcity, and saltwater intrusion. It is therefore essential to advance our understanding of the full spectrum of qualitative and quantitative impacts, and their computational representation through numerical modeling and machine learning approaches. Such knowledge can foster the implementation of effective mitigation and adaptation strategies to ultimately safeguard groundwater resources and ensure their sustainable use. These measures may range from improved forecasting and modeling of the impacts of extreme events on aquifer systems, to targeted engineered interventions and sustainable management practices.
This session invites contributions that provide insights into innovative methodologies, field case studies, or novel perspectives investigating the qualitative and quantitative impacts that extreme hydroclimatic events exert on groundwater systems. In particular, we welcome studies that address: (i) comparative analysis of methodologies and system responses across different spatial scales and climatic contexts; (ii) novel modeling frameworks including past, present, and projected variations of extreme hydroclimatic events; (iii) characterization and modeling of system memory, including how aquifers and subsurface catchments retain the effects of past events; (iv) analysis of aquifer systems recovery times; (v) evaluation of the consequences of combined climatic and anthropogenic stresses on groundwater systems; (vi) identification and assessment of potential mitigation and adaptation strategies.

This session invites contributions that advance the understanding of groundwater in cold-climate hydrosystems. Groundwater in high-latitudes is controlled by prolonged cold winters, warm summers with intense vegetation growth, and precipitation distributed year-round. In these regions, atmospheric water is stored over winter as snowpack, soil frost commonly occurs, and permafrost may be absent, discontinuous, or temporary. Temperature plays a dominant role in cold-climate hydrogeological dynamics, making these systems particularly sensitive to global warming. In addition, these regions have recently experienced climate extremes, including unusually high temperatures and prolonged dry spells imposing new stresses. Land use and land cover are also climate-dependent, further exacerbating global change impacts. Across these regions, common hydrogeological patterns emerge: strong groundwater–surface water connectivity, widespread presence of wetlands, seasonality in groundwater recharge, short aquifer memory, typically low groundwater mineralization, and pronounced freeze–thaw impacts on infiltration and storage. Rapidly changing climate conditions are altering these dynamics, with implications for water resources, ecosystems, carbon and nutrient fluxes, and the resilience of communities and infrastructure.
Despite the widespread anthropogenic and ecosystem reliance on groundwater in cold-climates, there remains a gap in understanding how groundwater systems will evolve under climate change. In response, this session invites topics on the characterization of hydrogeological processes and their seasonal variability, cryohydrogeological mechanisms, northern groundwater vulnerability, ecosystem and biogeochemical linkages, droughts and floods, post-wildfire hydrogeological responses, and the quantification of environmental and climate-change impacts on cold-climate groundwater resources. We also welcome studies addressing groundwater management, adaptation measures, and interactions with ecosystems and society. The session will provide a platform to exchange ideas on field methodologies (including geophysics), challenges of long-term and year-round monitoring (in situ and remote), advances in data-driven and modeling approaches (including coupled hydro-climate models), and strategies for mitigating the impacts of global change. All scales of investigation are of interest, ranging from laboratory and field scale processes to regional and sub-continental systems.

Most peatlands are groundwater-dependent ecosystems where interactions between aquifers, peat deposits, and surface waters determine their structure, functioning, and resilience. The role of groundwater and subsurface dynamics across the full peat column is fundamental for understanding ecosystem processes and services of the peatlands.
Groundwater levels and fluxes regulate the redox environment, nutrient and solute transport, and the vertical distribution of carbon cycling processes. They control peat accumulation and degradation, influence greenhouse gas emissions, and shape the capacity of peatlands to buffer floods and droughts. Bedrock-, aquifer–peatland interactions, subsurface heterogeneity, and vertical gradients within peat profiles critically affect how peatlands respond to climate variability, human influences, and restoration measures.
This session invites contributions that advance our understanding of groundwater processes in peatlands and their role in ecosystem functioning, across scales from pore structure to catchments to continental scale. We particularly welcome studies that look beyond the topmost active layer and consider the entire peat profile as well as aquifer–peatland interactions. Topics include, but are not limited to:
1. Novel field datasets on peatland/groundwater levels and dynamics
2. Process-based and data-driven modelling of groundwater dynamics and solute transport
3. Aquifer–peatland interactions and implications for biogeochemistry and carbon cycling
4. Geophysical and geotechnical investigations of peat structure and hydrogeology
5. Aquifer–peatland connectivity in terms of water and nutrient fluxes
6. Impacts of land use, groundwater abstraction and climate change on groundwater-driven peatland functioning
7. Approaches to integrate groundwater into peatland restoration and management strategies

Description
Submarine Groundwater Discharge is increasingly recognised as an important pathway of water and pollutants to the ocean However, estimating SGD remains challenging due to limited data - not only direct SGD measurements, but also the hydrological, geological and marine datasets required for model development and calibration (e.g. precipitation, evapotranspiration, runoff, soil and bedrock properties, river discharge, sea level). In some cases, these data sources may exist but are not known or easily accessible, and the challenge became how to identify and use them effectively.
At the same time, there is growing interest in applying existing hydrological and environmental models and freely available datasets or platforms to support SGD estimation. Combining these tools with innovative field techniques, tracer applications, geophysical methods, and remote sensing could help overcome the current data scarcity and improve our ability to model SGD in coastal catchments. This is particularly important for vulnerable regions such as the Baltic Sea, where SGD may represent a significant but unaccounted pathway of nutrients and contaminants to the marine environment.

This session aims to bring together researchers and practitioners working on SGD measurement, monitoring, data integration and modelling. We welcome case studies, methodological developments, and cross disciplinary approaches that shows how improved data access and integration can advance SGD research, coastal management, and sustainable water governance in coastal areas. This session will address following topics as well as related ones:

• New and established methods for direct and indirect measurement of SGD (e.g. tracers, seepage meters, geophysical methods, remote sensing).

• Monitoring strategies and networks to obtain hydrological, geological, and marine datasets relevant to SGD estimation.

• Experiences with existing hydrological/oceanographic models and platforms for SGD assessment.

• Use of freely available datasets and open platforms to support SGD modelling and calibration.

• Approaches to combine different data sources into groundwater–coastal models.

• Case studies from coastal regions.

• Opportunities for improved data availability and awareness for better quantification and management of SGD

• Management perspectives: experiences, challenges and solutions for addressing SGD related impacts on coastal water quality and resource sustainability.

Offshore Freshened Groundwater (OFG) represents a promising new frontier for hydrogeological investigations and water resource management strategies, that can help to address climate adaptation and water resilience of coastal regions. OFG refers to low-salinity groundwater stored within sediments beneath the seabed, and it has been acknowledged as an unconventional water resource by the UN-Water Task Force (2020). Despite being identified within the majority of passive continental margins worldwide, the investigation of this resource is challenging due to its limited accessibility and often limited data coverage.
The understanding of OFG potential as an unconventional water resource is of growing importance due to the critical conditions of many coastal areas worldwide in terms of water scarcity, quality, anthropogenic pressure and climate change. In order to properly understand and characterize OFG a multidisciplinary approach is needed, including geophysical surveys, drilling campaigns, stratigraphic and sedimentological analyses, geochemical analyses, numerical modeling of groundwater flow and solute transport, as well as the use of emerging technologies such as Artificial Intelligence and advanced remote sensing.
In this session, we invite contributions relating to different aspects of offshore freshened groundwater research, including (but not limited to) site specific case studies, geophysical surveys, characterization of the sedimentary architecture of the aquifer-bearing units, aquifer geometry and geological heterogeneity, as well as hydrogeological and stratigraphic numerical modeling. We also encourage presentations that address OFG detection, characterisation, and modelling, for both past emplacement, current status, future evolution and potential role in future water security. The goal is to foster an interdisciplinary discussion and advance the global understanding of offshore freshened groundwater systems.

Groundwater resources are under increasing pressure globally, threatened by overextraction, contamination, climate variability, and competing demands. Ensuring sustainable use requires a robust understanding of subsurface architecture, flow and transport dynamics, and geochemical processes at resource-relevant scales. Yet in many regions, especially in the Global South, groundwater systems remain poorly characterized due to sparse data and limited monitoring infrastructure. This gap hampers the development of data-driven strategies for managing groundwater quantity and quality.

Advances in hydrogeological methods are enabling a shift from fragmented, qualitative assessments to integrated, quantitative approaches that directly support groundwater management. This session invites contributions that advance such integrated measurement and modeling approaches to improve actionable groundwater resource assessments, including field-scale studies, numerical modeling, and applications in data-scarce regions.

We particularly welcome studies from, or relevant to, the Global South that:
- Integrate hydrogeophysical, hydrological, and geochemical observations
- Apply joint or coupled modeling frameworks, data assimilation, and uncertainty analysis.
- Demonstrate innovative strategies for groundwater monitoring and management in data-scarce regions (e.g., sensor networks).
- Showcase interdisciplinary collaborations linking observations and models to inform sustainable and climate-resilient groundwater use

This session deals with the use of geophysical methods for the characterization of subsurface properties, states, and processes in contexts such as hydrology, ecohydrology, contaminant transport, reactive media, etc. Geophysical methods potentially provide subsurface data with an unprecedented high spatial and temporal resolution in a non-invasive manner. However, the interpretation of these measurements is far from straightforward in many contexts and various challenges remain. Among these are the need for improved quantitative use of geophysical measurements in model conceptualization and parameterization, and the need to move quantitative hydrogeophysical investigations beyond the laboratory and field scale towards the catchment scale. Therefore, we welcome submissions addressing advances in the acquisition, processing, analysis and interpretation of data obtained from geophysical and other minimally invasive methods applied to a (contaminant) hydrological context. In particular, we encourage contributions on innovations in experimental and numerical methods in support of model-data fusion, including new concepts for coupled and joint inversion, and improving our petrophysical understanding on the link between hydrological and geophysical properties.

In a context of societal development and increasing demand for natural resources, human needs and environmental impacts must be considered together in order to sustainably manage these resources, especially with regard to groundwater resources. Therefore, a thorough investigation of groundwater availability that inspires sustainable water consumption and facilitates groundwater management is of high importance. This means considering not only the availability and quality of water resources, but also ensuring the preservation of related ecosystems. Moreover, the impacts on groundwater resources, ecosystems and societies due to ongoing climate change should also be considered.
The objective of this session is to gather case studies and scientific contributions connected to sustainable management of groundwater and its protection from degradation and deterioration, e.g., due to over-exploitation, competition for water resources, natural or anthropogenic contamination, and climate change. Contributions are invited on, but not limited to, the following subjects: (i) the use of environmental tracers (chemical species and isotopes) for investigating natural processes and human impacts on water resources, (ii) the assessment of hydrogeological budgets for the evaluation of water availability, and (iii) methods for preventing, managing and mitigating harmful environmental impacts related to groundwater, as well as (iv) identifying major existing challenges and critical issues. Contributions may span from local, to regional, to continental scale studies.
The Regional Groundwater Flow Commission (RGFC) of the International Association of Hydrogeologists (IAH) is sponsoring the session.

Sustainable groundwater use depends on reliable estimates of groundwater recharge—a critical but difficult-to-quantify flux. Before water reaches the aquifer, surface inputs, vegetation, and vadose zone processes alter both its quantity and timing. Climate and land use changes, along with extreme events such as droughts and intense rainfall, further complicate the spatial and temporal dynamics of the recharge. Now more than ever, refining our understanding of recharge is critical to informing decisions and managing groundwater sustainably.
This session offers a platform to exchange concepts, expertise, and methods related to groundwater recharge estimation across disciplines and application contexts.
We invite contributions focusing on the estimation of groundwater recharge across multiple temporal and spatial scales, including studies that compare or combine different methods. Estimation approaches may be based on:

• Field-based measurements from various compartments of the hydrological cycle:
- Land surface water balance components
- Vadose zone measurements (e.g. soil moisture)
- Groundwater heads (water table fluctuations)
- Discharge measurements (baseflow separation)
- Environmental tracers for recharge estimation and model calibration (e.g., stable isotopes, radioisotopes, dissolved gases)
- Including suggestions for improved monitoring concepts

• Model-based approaches (local to global scale), such as:
- Water balance models
- Land surface models
- Physically-based vadose zone or groundwater models
- Hybrid or machine learning-supported methods
- Groundwater time-series models

• Upscaling strategies from point-scale to landscape-scale assessments

• Varying temporal scales from short-term recharge quantification to long-term recharge trends (past or future scenarios)

We welcome studies addressing recharge estimation for various purposes, including (but not limited to) agricultural water management and irrigation, forest management and ecosystem transition, groundwater resource planning, and sustainable management.

Groundwater's strategic importance for ecosystems (biodiversity, and societies) is gaining prominence. Groundwaters play a critical role in natural cycles, redistributing water, energy and matters in the subsurface and sustaining surface water bodies and ensuring related biodiversity. Overall groundwater is key for continental areas, by providing essential ecosystem services hence ensuring water, energy, and food security.
Groundwater dynamics significantly impact ecosystems. Non stationarity of groundwater systems dynamics under global changes put these ecosystems at threat. Therefore, it is key to characterize these ecosystem-groundwater interrelationships by studying the quantitative and qualitative impacts of ecosystems on groundwater resources through a wide range of tools such as characterizing the transit and residence time of water and elements in groundwater systems, the vegetation-atmosphere-unsaturated zone interactions with the aquifers enabling to quantify in aquifer recharge and stream-aquifer exchanges.
To do that hydrogeological models are pivotal tools to characterise and anticipate potential change of these relationships between ecosystems and groundwater. However, due to the extreme heterogeneity of environmental processes and parameters, and our inability to fully characterise that heterogeneity, all hydrogeological models need to be calibrated against relevant geological, geophysical, hydrogeochemical and hydrological data to improve the robustness of predictions and reduce model uncertainty. Observatories provide long-term, spatially detailed information on groundwater resources, enabling in-depth studies, that consider the interplay of territorial changes together with climate change. These observatories therefore provide the opportunity to identify key processes driving these changes, occurring at the local or regional scales. Installed in heterogeneous environments, observatories consider different aquifer types at different geographical areas and reflect the interplay between land uses, climate zones, and human pressures on the dynamics of groundwater resources in ecosystems changing.
This session seeks to highlight innovative approaches that integrate field data with advanced modelling techniques to deepen the understanding of complex hydrological, hydrogeological, and ecohydrological systems under the impact of global change.

Observing soil moisture at the ground is essential to assess plant available water, manage water resources and calibrate, validate satellite products and conduct climate impact studies. Unfortunately, the availability of in situ observations is very limited in space and time. Whereas the spatial distribution is biased towards the global North, the temporal availability of soil moisture time series is on average 10 years as can be seen from the largest archive of in situ soil moisture, the International Soil Moisture Network (ISMN). Apart of the data availability issues, a substantial amount of the in situ observations face data quality issues that might result from sensor deployment, sensor calibration, data processing or other error sources.
This session will address issues in the development and deployment of state-of-the-art soil moisture observation networks, the financing of their long-term operation, data quality assurance, data imputation, and data scaling as well as sensor deployment and assessments of differences between these deployments. We further encourage contributions presenting developments of novel measurement techniques including citizen science initiatives and studies utilizing (primarily) in situ soil moisture to understand and assess hydrological processes, water availability, land-atmosphere feedbacks and soil moisture dependent hazards.

The Critical Zone (CZ) regulates many natural processes. Within the CZ, physical, chemical, and biological processes act at different spatial and temporal scales and it is a highly complex system with strong non-linear interactions. Recent advances highlight the opportunities of integrating novel measurement techniques with sophisticated modelling approaches to enhance process understanding and representation of CZ dynamics. This inter-disciplinary session brings together scientists to explore how cutting-edge measurement techniques (remote sensing, isotopic tracing, and high-resolution sensor networks, …) can be combined with advances modelling approaches (machine learning, data assimilation, coupled process-based models, constraint-based modelling, …) with the aim to foster comprehensive understanding of CZ processes. Topics may include: advances in measurements and modelling, processes in land-atmosphere interactions, upscaling of soil processes, interactions between soil hydrology and biogeochemical cycles, processes in the soil-plant system, parameterisation of soil processes across scales, and model-data integration.

Emerging contaminants (e.g., PFAS, pharmaceuticals, microplastics) and climate change pose new challenges to our already fragile ecosystems. The vadose zone is a dynamically changing heterogeneous system, which plays a key role in regulating water and solute exchanges between atmosphere, vegetation, and groundwater and hosts a large portion of subsurface biochemical reactions. Understanding the interrelation between hydrological, physicochemical, and biological processes in the unsaturated zone is paramount to developing sustainable management strategies. This can solely be attained by translating novel experimental insights into well-validated modeling tools, which can benefit from recent advances in machine learning.
This session welcomes research that advances the current understanding of the vadose zone hydro-biogeochemical functioning across multiple scales, including experimental or modeling approaches, and field or simulation studies. In particular, we encourage researchers to participate with contributions on the following topics:
• Monitoring of water flow, solute transport, and biochemical reactions from the pore scale to the field scale
• Experimental investigation and numerical modeling of the reactive transport of emerging contaminants in variably-saturated porous media
• Influence of static and dynamically changing soil structures (e.g., heterogeneity) on water flow and reactive solute transport
• Transport of water and contaminants in/from the rhizosphere into the plant
• Development of novel modeling approaches to predict water and chemical transport in the vadose zone
• Novel techniques for model appraisal, including calibration, sensitivity analysis, uncertainty assessment, and surrogate-based modeling for hydro-biogeochemical vadose zone modeling.

Interactions between plants and their environment shape terrestrial fluxes, biochemical cycles, and agro-ecosystem productivity. However, we still lack detailed knowledge of how these interactions impact plant access to soil resources and, hence, plant growth, particularly under deficit conditions. The main challenge arises from the complexity inherent to biophysical and biochemical processes in soils and plants across multiple scales. To address these knowledge gaps, an improved understanding of soil-plant-related transfer processes is needed.
Experimental techniques such as non-invasive imaging and three-dimensional root system modeling tools have deepened our insights into the functioning of water and solute transport processes in the soil-plant system. Quantitative approaches that integrate across disciplines and scales constitute stepping-stones to foster our understanding of fundamental biophysical processes at the interface between soils and plants.
This session targets research investigating soil-plant-related resource transfer processes across different scales (from the rhizosphere to the global scale) and welcomes scientists from multiple disciplines encompassing soil and plant sciences across natural as well as agricultural systems. We are specifically inviting contributions on the following topics:
- Bridging the gap between biologically and physically oriented research in soil and plant sciences
- Measuring and modeling of soil-plant hydraulics, water and solute fluxes through the soil-plant-atmosphere continuum across scales.
- Identification of plant strategies to better access and use resources from the soil, including under abiotic stress(es)
- Novel experimental and modeling techniques assessing belowground processes such as root growth, root water, and nutrient uptake, root exudation, microbial interactions, and soil aggregation
- Mechanistic understanding of plant water use and gas exchange regulation under drought and their implementation in Earth system models

The continuum approach is a classical framework to describe and understand the soil—water dynamics and the soil effective—stress state in unsaturated soils. This approach is robustly rooted in the definition of the soil—water constitutive laws (soil—water retention curve, soil hydraulic conductivity, Kirchhoff potential, etc.). They link the real soil and its model. Advancements along their development and the comprehension of their role stand at the intersection of experimental measurements, mathematical representation and modelling, numerical solutions, theoretical understandings and practical applications.

This session aims at stimulating an interdisciplinary discussion about the state of the art and recent advances about soil—water constitutive laws and soil physical and hydrological properties, in the framework of a continuum approach and contributing to define its limits.

Experimental, theoretical and numerical contributions are encouraged about, but not limited to, (1) scaling of soil—water constitutive laws and their changes in time and space as a consequence of seasonality, climatic changes, anthropogenic changes and pedogenesis; (2) physics of water—repellent soils, and of swelling, dispersive and collapsible soils; (3) constitutive laws for extremely dry conditions and for nearly saturated soils; (4) nonequilibrium and hysteretic behaviours; (5) limits of the Darcian approach in the presence of macroporosity; (6) heat transfer and dispersion; (7) freezing and thawing processes in permafrost; (8) mechanisms of incipient erosion; (9) mathematical functions of constitutive laws and their physical implications; (10) pedotransfer functions and database analysis.

Advancements along those lines will have major implications in many fields, ranging from hydrology, to soil science and soil physics, agriculture and geotechnics.

The proper management of blue and green water is vital for sustainable livelihoods and agricultural practices around the world. This is especially true in drylands, where any productive activity is deeply related to the understanding of soil hydrological behaviour, and irrigation is both a pillar of agroecosystems and a defence against desertification, but also in temperate or humid lands which can experience variations in the hydrological cycle and be prone to water scarcity due to climate change.
Improper practices, which are not able to cope with climate-induced variability and anomalies, may in fact contribute to soil degradation and depletion of the available water sources. For example, incorrect irrigation techniques may lead to soil salinization and groundwater depletion or salinization, with dramatic fallout on agricultural productivity. Irrigation efficiency improvements could paradoxically lead to increasing water consumption and water scarcity conditions through irrigation expansion and complex socio-hydrological dynamics. Finally, overgrazing may lead to exploitation of vegetation cover, soil compaction, and adverse effects on the soil capability of water buffering. It is thus clear that the role of irrigation goes beyond the technological aspects, as proved by traditional irrigation being a cultural heritage which is often structurally resilient, and which needs to be faced with an interdisciplinary approach involving humanities.

This session welcomes contributions with a specific focus on:
• The understanding of soil hydrological behaviour, of mass fluxes through the soil and of the related sociohydrological dynamics in drylands and environments under actual or projected stress conditions (e.g. water shortage, compaction, salinization)
• The interactions between irrigation, soil hydrology (including deep drainage) and socio-economic impacts.
• The analysis of the bio-geo-physical and social dynamics related to rainfed and irrigated agriculture in both arid and non-arid areas and oases, including the use of non-conventional waters (e.g. water harvesting), and managed aquifer recharge systems
• The management of rangeland areas, including their restoration

This session is co—sponsored by the International Commission on Irrigation and Drainage (ICID, to be confirmed) and the International Center for Agriculture Research in the Dry Areas (ICARDA, to be confirmed).

The main goal of this session is to bring together scientists, scholars, and engineers focused on researching, teaching, and applying innovative measurement techniques and monitoring approaches essential for analyzing sedimentary and hydro-morphological processes in diverse open water environments such as rivers, lakes, reservoirs, estuaries, and coastal areas.
This session emphasizes evaluating and quantifying sediment transport phenomena - such as bed load and suspended load, bedform migration, channel migration, bed armoring, and colmation - alongside examining sediment transport modes, flocculation, settling, and re-suspension dynamics.
We invite contributions with a focus on single and combined measurement techniques, post-processing methods, and innovative monitoring approaches for both field and laboratory settings. Additionally, recent insights on sediment budgets and sedimentary and morphodynamic processes, evaluated across temporal and spatial scales in open water environments, are highly encouraged.
Contributions may include, but are not limited to:
Suspended sediment transport measurements in open water environments, using optical, acoustic, traditional sampling, or other methods.
Bed load transport measurements via bed load samplers, sediment traps, tracers, or acoustic and optical techniques.
Sediment characterization through mechanical samplers or freeze-core techniques.
Innovative measurement approaches for validating and calibrating numerical models.
Critical bed shear stress measurements of cohesive sediments, using devices like benthic flumes.
Monitoring of morphological changes such as lake and reservoir sedimentation, bank erosion, bed armoring, meandering, and river bend evolution.
Measurement networks and multi-point datasets.
Large and small-scale monitoring concepts, including case studies.
In-situ or laboratory calibration of measurement data through traditional methods or novel approaches such as machine learning.

Hydromorphological processes, including sediment erosion, transport, and deposition, are crucial in shaping open water environments such as rivers, estuaries, lakes, and reservoirs. Accurate predictions of these processes are essential for both research and practical applications. Over the past decades, numerical models have become vital tools in hydraulic engineering and geosciences for simulating these complex interactions. With advances in algorithms and computational power, high-resolution simulations of water, sediment, and air interactions are now possible. Additionally, the growing availability of high-quality validation data from lab experiments and field studies has enhanced these models, leading to new insights into processes like dune development, riverbed armoring, and density-driven transport. As a result, next-generation numerical modeling techniques are enabling the exploration of intriguing questions in hydromorphology. Furthermore, Artificial Intelligence (AI) is emerging as a reliable alternative in these studies. This session aims to unite scientists and engineers who develop, improve, or apply numerical models of multiphase flows for sediment transport in open water environments. We welcome contributions that cover a range of spatiotemporal scales, from small-scale particle entrainment to large-scale morphological development, in various settings including rivers, lakes, reservoirs, estuaries, and coastal areas. Contributions may include, but are not limited to:
- Sediment entrainment processes, ranging from cohesive sediments to armored riverbeds
- Bed load and suspended sediment transport, including flocculation - Simulation of sediment management for the planning, operation, and maintenance of hydropower plants
- Design and assessment of river restoration measures
- Navigation-related issues, such as sediment replenishment, dredging, and erosion caused by ship-generated waves
- Flood-related impacts, including the long-term effects of morphological bed changes on flood security
- Eco-hydraulics, focusing on flow, sediment, and vegetation interactions
- Density-driven transport mechanisms.
Please note that the session “Hydro-morphological Processes in Open Water Environments – Measurement and Monitoring Techniques” shares a similar focus. If your contribution is more centered on measurement and monitoring, we encourage you to submit your abstract to that session instead.

Soil erosion leading to land degradation is a significant geo-environmental challenge that may adversely impact agricultural productivity and hence threaten food security, underscoring the need for implementation of sustainable management policies. Rapid population growth and intensified rainfall patterns driven by climate change have accelerated soil erosion, exposing the fertile topsoil layer to transport. The eroded sediments are then deposited downstream, contributing to sedimentation in reservoirs. Addressing such challenges requires systematic investment in monitoring and modeling of soil erosion and sediment transport.
This session will emphasize recent advancements in process-based modeling, the application of remote sensing, and AI/ML techniques for sustainable soil and water management across different temporal and spatial scales. Satellite platforms such as Landsat, Sentinel, MODIS, and other high-resolution sensors offer valuable opportunities to monitor LULC changes, map reservoir surface areas, and assess land degradation. To quantify soil erosion and sediment delivery, both process-based and empirical models -- including RUSLE, SWAT, and InVEST -- remain essential tools for evaluating hydrological and geomorphological processes. In recent years, the integration of machine learning techniques has further enhanced these approaches by improving predictive accuracy, supporting robust classification, and enabling comprehensive uncertainty assessments of model outcomes.
We would like to invite abstract submissions in the following sub-domains:
• Field observations related to soil erosion and sediment transport.
• Remote sensing applications for soil and water management.
• Advances in modelling of soil erosion and sediment transport.
• Advances in cloud computing, including platforms such as Google Earth Engine (GEE), high-performance computing, and big-data platforms for large-scale analysis.
• AI/ML and geospatial techniques for soil erosion and sediment transport modeling.
• Policy-level interventions for soil and water management.
This session aims to bring together researchers, engineers, scientists, and policymakers to share innovative methodologies and interdisciplinary perspectives through regional to global case studies. The primary goal is to foster an integrated approach to sustainable soil and water management by combining geospatial technologies, advanced modeling frameworks, and machine learning techniques.

Channel control structures and soil conservation techniques have always been used to control catchment dynamics and regulate water resources and forest and agricultural activities. Although structures and techniques are fundamental in certain contexts, there are still several open scientific questions concerning their interaction with sediment dynamics and the processes occurring. Topics such as the following should be explored in greater depth: i) effectiveness and suitable planning of interventions; ii) interaction between sediment connectivity and interventions; iii) quantification of the effectiveness of actions as a function of their desired purposes; iv) suitable design of restoration actions.
Remote sensing techniques (RS) can support the lack of long-term monitoring studies to monitor the evolution of catchment morphology and analyse past and current phenomena by exploiting multi-temporal surveys at different scales and open-source big data. This session offers a platform for collaboration and discussion among soil scientists, hydrologists, geomorphologists, foresters, and stakeholders, facilitating a dialogue on critical issues about planning, design, and management of torrent control structures and soil conservation techniques at the catchment scale. Research about the following topics is welcome: i) innovative protocols and guidelines for planning and design; ii) emerging techniques for multi-temporal or real-time monitoring of sediment dynamics effects exploiting RS; iii) standards for comprehensive analysis of impacts of the interventions on sediment connectivity in catchments; iv) identification of new challenges (i.e., soil-bioengineering techniques and integration of living vegetation in check dam systems).
Early-career scientists are encouraged to contribute to the session with original and advanced studies.

The concept of ‘Man as a geological agent’ in the Anthropocene, leading to profound changes (e.g. biodiversity, climate, land and water degradation) in the Earth's surface processes, is now widely accepted beyond the Earth Science community.
Environmental pollution and degradation are expected to intensify in the coming decades as pressure on terrestrial ecosystems and hydrosystems increases in the context of climate change.
The retro-observation of environmental responses to human disturbance can help to promote more responsible and sustainable land and water resources management policies in the future. The collection and multidisciplinary analysis of recently deposited sediments in lakes, dam reservoirs, and fluvial systems provide a unique and powerful tool for learning from the past to retrospectively reconstruct the inertia, trajectories, and resilience of terrestrial and aquatic ecosystems to anthropogenic forcing.

In this session, we invite studies that examine sediment dynamics perturbed by human activities over the past few centuries to recent decades. We encourage contributions showing the consequences of human-induced land use changes (e.g. deforestation, agricultural expansion), pollutant releases (e.g. metals, organic substances, microplastics), changes in biodiversity, etc., in particular those developing innovative methods and proxies applied to recent sediment records to understand better the impacts of these activities on land/water resources at different spatial and temporal scales.

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 sediment management strategies. It is also crucial to quantify the residence times of material moving along the sediment cascade 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;
• 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.

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 issues:
- Sediment and contaminant transfer along the Land–River–Lake Continuum
- Delivery rates of sediments and contaminants from various sources (i.e. agriculture, urban areas, mining, industry or natural areas);
- Transport, retention and remobilization of sediments and contaminants in catchments and river reaches, including the influence of human activities like hydropower and flood management.;
- Modelling of sediment and contaminant transport on various temporal and spatial scales;
- Biogeochemical controls on contaminant transport and transformation;
- Studies on sedimentary processes and morphodynamics, particularly sediment budgets;
- Linkages between catchment systems and lakes, including reservoirs;
- Analysis of sediment archives to appraise landscape scale variations in sediment and contaminant yield over medium to long time-scales;
- Impacts of sediments and contaminants on floodplain, riparian, hyporheic and other in-stream ecosystems;
- Response of sediment and contaminant dynamics in catchments, lakes and rivers to changing boundary conditions and human actions;
- Assessing human impact on landforms and geomorphological processes in sediment and contaminant transport.
- Novel, low-cost, and open-source methods for increasing the coverage and accessibility of measuring and modelling sediment and pollutant fluxes

Ecohydrology, i.e., the study of the interactions between water and ecosystems, is expanding rapidly as a field of research, beyond traditional discipline boundaries in terms of questions and approaches. This session aims to draw examples from this wide field, portraying the current diversity and common features of research frontiers in ecohydrological studies, as well as the range of methods employed. We thus encourage contributions showing novel results or methods when tackling questions related to the coupling of ecological, biogeochemical and hydrological processes, at scales ranging from the single organ or organisms to whole ecosystem/catchment. Contributions relative to all terrestrial and aquatic systems are welcome, including those relative to managed ecosystems, showing how human intervention alters the interactions between water and ecosystems.

Forest ecosystems interact very strongly with hydrological processes, at various spatial and temporal scales. They have co-evolved with soils and topography over a long period of time, and their potentially deep root systems enable cross-cutting exchange between the ground water, soil water, plants and the atmosphere. Our ability to detect these sometimes hidden interactions is limited, but new techniques, such as geochemical and isotopic tracers, various geophysical and remote sensing techniques provide ever new and often surprising perspectives into the complex interactions between forest ecoystems and the water cycle.

This session solicitates any contributions that share new insights about forest ecohydrological processes, or demonstrate new ways of observing and modelling water fluxes in forest ecoystems, forest water stress, drought resistance and resilience, or the links between forest hydrological processes and the water, carbon and nutrient cycles.

Peatlands form under specific hydrological settings and are sensitive to changes in hydrological conditions and climate. Peat hydrological properties and peatland greenhouse gas balance can change drastically after disturbances such as drainage, permafrost thaw, or mechanical compaction. Hydrological conditions are also a key control for ecosystem services offered or regulated by peatlands including biodiversity, carbon storage, and nutrient retention. In addition, the role of pristine and disturbed peatlands in flood retention, support of low flows and regional climate remains debated. As hydrological and biotic processes in peatlands are strongly coupled, predicting the eco-hydrological effects of climate change, degradation, and restoration on peatland ecosystem responses—including greenhouse gas emissions—is a demanding task for the peatland community.

This session addresses peatland hydrology and its interaction with ecosystem processes across all latitudes. We especially encourage papers from understudied regions where field studies are scarce and inclusion into Earth system models is largely pending. We invite submissions on: (1) hydrological processes operating in all types of peatlands (pristine, disturbed, degraded, drained, managed, rehabilitated or re-wetted) in boreal, temperate, and tropical latitudes; and (2) the first-order control of peatland hydrology on all kinds of peatland functions.

We aim to advance the transfer of knowledge and methods, and welcome laboratory, field, remote sensing, and modeling studies on hydrological, hydrochemical, biogeochemical, ecohydrological or geophysical topics, as well as ecosystem service assessments within peat-dominated landscapes.

Ecohydrology encompasses the interactions of the hydrological, biogeochemical, and ecosystem processes. Understanding and representing these processes accurately in ecohydrological models is essential in the context of rapidly changing climate and environmental systems. With the advancement of computing technology, AI/ML tools, and hydrological data, models are being improved and developed for various applications. This session aims to collate the ideas for novel frameworks for model development, improvement of existing models, improvement in parameterization methods, and uncertainty quantification. Potential contributions include (but not limited to):
1. Introduce experimental knowledge into watershed-scale models
2. Improvement in model structure and coupling between modelling systems
3. Improving parameterization schemes for ecohydrological models
4. Model evaluation strategies and criterion
5. Improvement in modelling in data-scarce regions.

Droughts, characterized by precipitation deficits and high evaporative demand, are becoming increasingly frequent, prolonged, and intense under global environmental change. Climatic drivers (such as altered precipitation regimes and rising temperatures) and land surface modifications (including vegetation greening, deforestation, land-use transitions, and wildfires) interact in complex ways to shape ecohydrological responses to droughts across spatial and temporal scales.
This session invites contributions that explore how ecosystems and hydrological processes respond to droughts (hereafter referred to as drought responses), aiming to uncover both underlying mechanisms and broader consequences. We welcome studies based on observational, modeling, and conceptual approaches. Topics of interest include, but are not limited to:
1. New insights into drought responses based on emerging in-situ and satellite observations of soil moisture, evapotranspiration, and vegetation dynamics.
2. Process-based understanding of ecohydrological responses to droughts of varying severity under changing climate and land surface conditions.
3. Long-term trends and resilience of ecohydrological systems under recurrent droughts, with a focus on resistance, recovery, and key environmental drivers.
4. Advances in modeling frameworks (process-based or AI-based) and observation-constrained approaches for improving the representation of drought responses.
5. Social and ecological impacts of evolving droughts, including implications for ecosystems, agriculture, water resources, and human well-being.
By integrating hydrology, ecology, and remote sensing, this session seeks to advance our understanding of ecohydrological drought responses and to inform sustainable adaptation strategies in a changing environment.

Trees can regulate the water cycle through transpiration and evaporation. They also influence rainfall interception, infiltration and runoff. These processes have direct impacts on society’s well-being, and therefore are of great importance in landscape and urban water management.

This session focuses on studies that link the role of trees with landscape and urban hydrology, highlighting research that addresses tree–water interactions across multiple scales (neighbourhood, city, watershed) and the potential for trees to mitigate stormwater, improve water quality, and enhance climate resilience.

Approaches that may be discussed include field observations, modelling, remote sensing, and studies on the integration of trees as green infrastructure and stormwater management. The session aims to bring together diverse perspectives to synthesize findings, identify knowledge gaps, and highlight pathways for leveraging trees as critical components of a sustainable urban water system.

Stable isotopes are powerful tools for tracing water fluxes and associated nutrients in the soil-plant-atmosphere continuum. Because subsurface water and nutrient fluxes, plant water uptake, and atmospheric processes are tightly interconnected, advances in field methods and laboratory techniques are critical for capturing these dynamics and their drivers with greater temporal and spatial resolution, precision, and accuracy. At the same time, ecohydrological models are expanding our ability to integrate these observations and assess fluxes in the soil-plant-atmosphere continuum. This session welcomes experimental and modeling contributions that apply isotope tracers to advance process-based understanding of water and nutrient fluxes across the subsurface, vegetation, and atmosphere, spanning scales from individual plants and forest stands to catchments. In our session, we aim to discuss i) innovative process-based interpretations of stable isotope data; ii) bridge ecophysiological and hydrological perspectives through field-based approaches; iii) development of novel modeling applications and frameworks or data analysis techniques; and iv) current methodological developments. We aim to foster interdisciplinary exchange among researchers investigating ecohydrological processes with natural tracers, spanning groundwater and vadose zone hydrology, plant physiology, and ecology. By linking those fields through natural tracers, the session will stimulate discussion to deepen our understanding of water and nutrient dynamics across the soil–plant–atmosphere continuum.

Dynamic bidirectional exchanges between groundwater and surface water systems give rise to critical transition zones - including the hyporheic zone, the benthic layer, riparian corridors, wetlands, and lake sediments - where unique hydrological, biogeochemical, and ecological processes converge. These transition zones regulate the transport and transformation of nutrients, microplastics, and pollutants across hydrological copartments and associated aquatic ecosystems. They also control the availability of heat, oxygen, and organic matter within sediments, shaping habitat conditions for microorganisms and macroinvertebrates. Despite intese research, further investigation is needed to establish a comprehensive understanding of the physical, biogeochemical, and ecological processes occurring at groundwater-surface water interfaces, and their implications for fluvial ecology and limnology. Furthermore, it is essential to consider how exchange fluxes respond to environmental and climate factors associated with different spatial and temporal scales, spanning the sediment layer, river channel, alluvial aquifer, and regional groundwater scales. Upscaling and downscaling of a general conceptual framework, as well as enhancing process comprehension, are identified as the most significant challenges in this field of research. We invite contributions that focus on the development and application of novel experimental methods for studying physical, biogeochemical, and ecological conditions at the groundwater-surface water interfaces in rivers, lakes, riparian zones, and wetlands. One of our main interests lies in investigating the role of hyporheic processes in the retention and natural attenuation of nutrients and pollutants, and their influence on surface and groundwater quality. Additionally, we encourage research involving hydrological, biogeochemical, and ecological modeling approaches (e.g. transient storage models, coupled groundwater-surface water models, etc.). Finally, we welcome presentations that investigate the impact of groundwater-surface water interactions on management and risk assessment in view of the European Water Framework Directive.

Identifying and characterizing groundwater-dependent ecosystems (GDEs) requires an understanding of the feedback processes linking groundwater with aquatic, terrestrial, and subterranean ecosystems. In turn, protecting GDEs and their myriad of ecosystem services requires understanding how these interacting systems have evolved, and will continue to evolve, under global change. This session invites contributions that advance knowledge of the role of groundwater in sustaining ecosystems and their services to capture ecological and hydrogeological feedback, resilience, and socioecological dimensions.
Groundwater is often a crucial source of water that is connected to entire habitats and ecological communities, ranging from wetlands, riparian zones, caves, aquifers, springs, forests, grasslands, and coastal environments. Conversely, ecosystems can significantly influence groundwater dynamics. These interactions involve a diversity of ecological and hydrological processes such as groundwater uptake by vegetation, biogeochemical reactions in aquifers, bioturbation and nutrient recycling by groundwater resident fauna, and soil–plant–atmosphere interactions. As these feedbacks are tightly connected to the land surface, GDEs can be highly sensitive and vulnerable to anthropogenic pressures. Conversely, GDEs can provide important buffering capacities to environmental variability and extremes, and present important forms of Nature-based solutions. Further, GDEs often support human activities, from supporting pastoral livelihoods to underpinning cultural values. Thus, understanding groundwater-ecosystem interactions is not only important for the field of ecohydrogeology, but moreover to inform management policies and strategies that holistically integrate ecological, hydrological, and social outcomes.
The session seeks to include work on GDEs across a wide range of physiographic and social contexts, including surface, unsaturated, and saturated zones; urban, rural, and coastal environments; and a wide range of socioeconomic and sociocultural contexts. Submissions that advance data acquisition methods; local-scale in-situ studies; mapping of GDEs; empirical data analysis; ecohydrogeological model simulations; scaling of processes and their detection from the plant to regional scales; the resilience of GDEs and their connected systems; socioeconomic, cultural interactions, and meanings of GDEs; and analytical developments are equally welcome.

With the recent declaration of the Hydrology Renaissance era, the perspectives on watersheds and river networks in hydrological sciences have remarkably broadened, moving from channels merely draining water fluxes to complex ecological corridors transporting energy and materials and connecting terrestrial and aquatic ecosystems. This new viewpoint requires a comprehensive understanding of the coupled hydrological, biogeochemical and ecological processes occurring in river basins and within their freshwater bodies. Hydrological drivers dictate the spatial structure and connectivity of riverine ecosystems (e.g., transport of nutrient and organic resources, organism dispersal). On the other hand, ecological communities affect the regional hydrology (e.g., through transpiration, ecosystem engineering) while regulating the biological and chemical cycles of nutrients in water (e.g., organic and inorganic matter, metals, pollutants).

This session aims at fostering the exchange of novel findings from interdisciplinary research on the interplay of hydrological, biogeochemical and ecological processes in watersheds and riverine systems. We welcome studies on ecohydrological dynamics, riverine metacommunities and food webs, stream metabolism, eutrophication, carbon and/or nutrient cycling and exchange, as well as works assessing how anthropogenic interventions may affect the interactions between these processes. We are particularly interested in contributions where tools and methods from one discipline are used to generate insights in another. We seek for contributions that employ a range of theoretical methods, monitoring techniques (e.g., in-situ, remote sensing) and/or modelling approaches (e.g., statistical, process-based, machine/deep-learning-based) at diverse spatial scales ranging from single watersheds and streams to entire river networks.

Inland waters (i.e., lakes, reservoirs and rivers) are crucial fresh water sources for both the environment and human society. Drastic variations have already been observed in these water bodies due to climatic change, presenting both urgent challenges and new opportunities. Our session will cover the most advanced studies on the inland waters’ responses to climatic change. It will promote communications among researchers using diverse tools (e.g., remote sensing, modelling and observations) and across various disciplines such as climatology, limnology, hydrology and social sciences. We welcome research exploring shifts in thermal processes, phenology, hydrodynamics, and how these physical changes drive variations in aquatic environments and their ability to provide ecosystem services. This session offers the opportunity to share inland water research from local to global scales and by bridging disciplines and methods, aiming to foster new connections and syntheses in our understanding of inland water change.