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.

To properly characterize uncertainty remains a major research and operational challenge in the Environmental Sciences. Uncertainty is inherent to many aspects of modelling as it impacts model structure development; parameter estimation; appropriate representation of data (for model input, calibration and evaluation); initial and boundary conditions; and hypothesis testing. Addressing these uncertainties is particularly important for predictive models used to support water management and decision making.

To address this challenge, methods that have proved to be very helpful include a) sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models) and b) the closely-related methods for uncertainty analyses (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model.

This session invites contributions that discuss advances, both in theory and/or application, in Bayesian or frequentist methods and 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 including robust quantification of predictive uncertainty for model surrogates and machine learning (ML) models
2) Approaches to define meaningful priors for ML techniques in hydro(geo)logy,
3) Novel methods for spatial and temporal evaluation/analysis of models
4) The role of information and error on SA/UA as well as in evaluating model consistency and reliability
5) Novel approaches and benchmarking efforts for parameter estimation
6) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)
7) Methods for detecting and characterizing model inadequacy
8) Problem formulation/decomposition and scripted workflows for prediction-focused modelling design
9) Cases studies on applied predictive modelling for decision support, management optimization under uncertainty and tools for communicating model results to stakeholders

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. Degradation of water quality due to natural and anthropogenic activities poses significant threat to freshwater availability. Space-time modelling of water quality depends on the availability of long-term reliable datasets, which are often found to be incomplete, sparse, or unavailable. Water quality, though monitored frequently, limited knowledge is available about emerging contaminants. Subsurface environments, which are highly heterogeneous, influence flow and transport dynamics and surface-subsurface interaction mechanisms, making model calibration quite challenging. These drivers greatly influence catchment hydrology, hydrodynamics, biogeochemical processes and ecosystem. In dynamic environments, solute transport, sediment dynamics, and vegetation are also coupled through hydrodynamic and biogeochemical feedback for improved understanding of processes, nutrient cycling and ecosystem functioning.
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 and water quality monitoring networks and installation of IoT sensors is very limited. It is therefore warranted to leverage geospatial, machine learning, decision science, statistical and modelling techniques to improve the understanding of catchment hydrology and consequences of climate change and anthropogenic drivers on surface and groundwater resources at various scales. The worldwide readily available satellite remote sensing data and global data products enable us to leverage these techniques in addressing water and environmental challenges.
We solicit novel contributions from researchers in catchment hydrology by utilizing Remote Sensing, GIS, Artificial Intelligence (AI), Machine Learning (ML), Decision Science, and advanced statistical techniques for addressing pressing challenges of water sustainability in mountainous and urban catchments and data scarce regions. The combined use of these technologies will revolutionize understanding of complicated hydrological, hydrodynamic and biogeochemical processes, and will be useful in evolving effective water resource management and ecosystem-based adaptation strategies to foster sustainable development.

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.

In addition, the session will explore recent technological and methodological innovations, such as leveraging the use of low-cost sensor networks (e.g., LiDAR, motion-triggered cameras), stable isotopes, citizen science, indigenous knowledge systems, and other emerging techniques that help overcome observational challenges.

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 experiments, modeling, 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, modeling, and experimental 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.

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
Sollicited speaker: Shaozhen Liu

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).

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

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

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

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 focuses on the transport, occurrence and fate of plastic pollution (macro-, micro- and nano-plastics) across aquatic environments, soil and groundwater. While we aim to place special emphasis on laboratory experiments and modelling approaches, we also welcome presentations employing field work, and contributions focused on theoretical concepts.

The presentations will revolve around understanding and characterising plastic movement and distribution, considering governing factors like particle size, shape, density, and environmental conditions such as flow, turbulence, temperature, salinity and suspended sediment. Relevant biological and chemical processes will be taken into account such as biofouling, aggregation and fragmentation, as well as plastic's role as transport vectors for other emerging contaminants in subsurface environments, adding to their transport complexity and substantially impacts the health of subsurface environments.

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 modelling approaches tailored to simulate the intricate transport dynamics of plastics in the environment 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.

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 and distribution attributed to secular trends in relevant processes and/or to anthropogenic activities, such as irrigation, groundwater pumping, dam and reservoir operations. For example, studies have investigated changes in the rate of exchange across terrestrial water storages and land-atmosphere interactions 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 understanding the role of climatic change and the human water management in influencing land-atmosphere interactions and resulting in accelerating or decelerating water cycle processes, (ii) uncertainties and open questions related to this critically important research topic. Studies focused on change analysis of individual or integrated water cycle components and land atmosphere interactions using one or more of the above-mentioned data types and modeling techniques are encouraged, with either a regional or global focus. We are interested in studies that highlight existing uncertainties in the state of knowledge regarding the magnitude or direction of these changes, including changes due to human management. We particularly encourage studies that highlight existing uncertainties in the magnitude or direction of water cycle changes and those that utilize diverse sources of observations and modeling approaches and foster interdisciplinary collaboration to inform sustainable water management strategies.

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.

Extreme hydrometeorological events, such as floods and droughts, are changing significantly under a warming climate and are amplifying risks to infrastructure, ecosystems, economies, and society. These extremes, including their compound occurrences, are driven by complex climate–land–atmosphere processes, making their characterization, prediction, and risk assessment challenging. Addressing these challenges requires advances in monitoring, modeling, detection, attribution, and uncertainty quantification to strengthen resilience at global, regional, and local scales. This session focuses on investigating changes in the frequency, intensity, duration, severity, spatial extent, and clustering of extreme events,like floods and droughts, under climate variability and change. We welcome studies on understanding the role of climate drivers, land–atmosphere feedbacks, land-use/land-cover change, and evapotranspiration dynamics in shaping the evolution and propagation of floods and droughts.. Submissions employing innovative datasets, high-resolution observations, advanced indices, machine learning approaches, and integrated modeling frameworks to improve detection, attribution, prediction, and early warning of extreme and compound events are welcome. Studies linking extremes to risk, vulnerability, exposure, adaptive capacity, and decision-making across water, agricultural, ecological, and socio-economic systems are also welcome.

In an era of climate uncertainty and evolving human influence on natural environments, understanding the dynamics of long-term hydrological change has become critical to properly simulate and predict the multifaceted implications of climate change on water availability and aquatic environments. 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. This session focusses on better understanding hydrological responses to changes in climatic forcing at multi-annual to multi-decadal timescales, on improving the representation of such responses in hydrological models, and on tangible examples of climate change impact assessments on hydrological and related socio-ecological systems. Submissions are encouraged across the full spectrum of available techniques, such as site-specific investigations or large-scale studies, using both in-situ and/or remotely sensed data or novel modelling frameworks. We hope to showcase research across diverse geographical regions and varied contexts to facilitate sharing of methods, insights and lessons learned. Therefore, 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;
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 and change;
5. So-called “bottom-up” approaches to decision making under deep uncertainty, novel modelling paradigms, innovative risk assessment frameworks, or characterisations of multiple (compound) sources of risk in 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.

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.

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.

This session focuses on advancing probabilistic hydro-meteorological forecasting from research to operations (and operations to research) across spatial scales and time horizons. It aims to illustrate current progress in monitoring, modeling, and forecasting of rainfall-induced hazards (including their impacts) and discuss how advanced hydrometeorological ensemble approaches are to be scientifically robust while also being user-centric, impactful, and effectively communicating uncertainty to support decisions.
Topics of interest include:
- 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.
- 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.
- Advanced computational science for scalable ensemble generation, data assimilation, and post-processing.
- Observation, understanding and prediction of societal vulnerability.
- Impact analysis and inclusive forecasting, linking forecasts to diverse applications and sectors.
- Standardized evaluation frameworks for ensembles, including verification, benchmarking, and uncertainty quantification.
- Science communication to improve understanding of probabilistic forecasts and uncertainty.
- Behavioral science and crisis management, understanding how people act on forecasts, especially during extremes.
- Research-to-operations (R2O) and Operations-to-Research (O2R).

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.

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 stakeholders. This includes 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.

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);
• RTFF modelling including physically/processed/conceptually/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 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 twins (DT), augmented reality (AR), virtual reality (VR), and mobile apps.
• RTFF: impact on flood risk management, insurance, capacity building, vulnerability assessment, and community resilience.
• 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;
• Impact-based assessments of forecasts for decision-making
• 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 early warning, 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.

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 is an escalating challenge in transboundary river basins and shared aquifers under climate change and broader global transformations. Declining precipitation, increasing drought frequency and severity, glacier retreat, groundwater depletion, and growing sectoral water demands are intensifying pressure on shared water resources. At the same time, data gaps and uncertainties in climate projections continue to hinder effective, cooperative transboundary water governance. Arid and semi-arid regions with long-standing exposure to water scarcity, such as the Middle East, Central Asia, and Southern Africa, offer valuable insights into hydrological processes under persistent stress, data-limited environments, modeling strategies, and adaptive management responses. Meanwhile, river basins traditionally considered water-abundant, including the Danube River Basin, are increasingly experiencing droughts, prolonged low-flow conditions, and rising abstractions. These emerging dynamics underscore the need for improved understanding of evolving scarcity patterns and for proactive adaptation strategies. Addressing these complex challenges requires a holistic and integrative approach that explicitly incorporates the Water–Energy–Food-Ecosystem (WEFE) Nexus to support benefit-sharing and cooperation among riparian states. Recent advances in data-driven decision-making, particularly through remote sensing, hydrological and hydro-climatic modeling, and uncertainty assessment, offer significant opportunities to reduce data gaps and strengthen transboundary water management. Equally important is human and institutional capacity building, which remains central to translating scientific knowledge into effective policy and practice. This session aims to foster cross-sectoral and transboundary collaboration, enhance data accessibility, and promote science-based strategies for securing water resources in shared basins under climate change. Contributions are invited that address spatial and temporal patterns and changes in components of the water cycle, and that present innovative approaches and methods for strengthening transboundary cooperation in applied hydrology. Particular emphasis is placed on observations, remote sensing, modeling, uncertainty analysis, and integrated water resources management.

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.

This session addresses the growing challenges of water resources management under accelerating climate change and intensifying anthropogenic pressures, with a primary focus on Africa and Mediterranean region, complemented by insights from other climate-vulnerable contexts. Climate variability, rapid urbanization, agricultural expansion, ecosystem degradation, and population growth are placing increasing pressure on already stressed hydrological systems, underscoring the urgent need for equitable, cooperative, and climate-resilient water governance frameworks.

The session explores how action-oriented research, innovation, modelling, capacity development, and multi-stakeholder cooperation can support more adaptive and inclusive water management pathways. It emphasizes translating scientific knowledge into institutional strengthening, system thinking, policy processes, and practical implementation, while moving beyond monosectoral approaches toward integrated strategies that address equity, allocation trade-offs, and governance under a changing hydrological cycle.

We invite interdisciplinary and transdisciplinary contributions advancing water management under climate stress, with broader relevance through comparative or transferable lessons. Contributions may include:
- Climate-resilient and equitable water management case studies,
- Capacity development initiatives in water governance and climate adaptation,
- Cross-sectoral partnerships and co-production processes,
- Innovative tools and methodologies, including monitoring, Earth observation, modelling, digital twin, and decision-support systems,
- Governance and policy instruments promoting fairness and resilience.

By combining insights from African and Mediterranean experiences, the session seeks to identify scalable and context-sensitive pathways for cooperative and sustainable water resources management. Through dialogue and synthesis, it aims to generate actionable insights for water policy and practice and contribute to global debates on water governance and climate resilience.
All session participants are invited to attend a townhall (splinter) meeting associated with this session. Further details will be communicated in due course.

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/

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.

The increased societal attention to climate change, drought and flood early warning systems, ecosystem monitoring and biodiversity conservation has led to a large demand for estimating, modelling, mapping, and forecasting evapotranspiration (ET) as a key water flux at the soil-vegetation-atmosphere interface. Cutting-edge techniques, which increase the efficiency of processing large datasets, such as artificial intelligence (AI), data fusion, sharpening algorithms, and the integration of physical- and process-based models with empirical and statistical approaches, including machine learning, are essential for bridging different spatial and temporal scales while addressing and communicating method-specific uncertainties.

This session will focus on various ET estimation methods, including sap flow or soil heat pulse sensors, lysimeters, eddy covariance stations, scintillometers, and remote sensing-based methods. We will also explore emerging techniques such as AI, data fusion, sharpening algorithms, machine learning, and cloud computing. Additionally, we will cover detailed evaluations of scale dependencies, approaches for handling uncertainties and systematic biases, and assessment of the representativeness of the estimates.

We welcome contributions that (1) assess and compare various in-situ and remote sensing methods, (2) analyse trends and spatio-temporal patterns in ET data, including sources of error and uncertainty, (3) bridge scales between different in-situ measurements, modelled and remotely sensed ET, addressing validation, calibration, and upscaling challenges, and (4) evaluate the challenges and opportunities associated with fusing data products, improving processing pipelines, applying AI methods, cloud computing and new technologies.

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 the shifting hydrological processes on the earth’s surface in the 21st century. This continuous increase of remote sensing data sources has opened new or enhanced applications in hydrology, especially in the framework of water flux modelling. Their integration with long-term in-situ data, for both regional and large-scale analyses still needs skilled assessment in many scientific and operational applications. For example, the scale effects of a coarse mapping of such variables can strongly affect the accuracy of hydrological models, and pose a constraint for harnessing remote sensing data in forecasting schemes or scenario assessments.

The session presents and discusses recent advances in the remote sensing of hydrological cycle components as well as the application of remote sensing in hydrological modeling, often enhanced through the use of in-situ data collection, to better understand gaps, opportunities, and challenges in the study and simulation of hydrological processes in the 21st century. In particular, we encourage submissions focused on:
* Advanced remote sensing of water cycle components such as evapotranspiration, infiltration, and water abstraction for agricultural use
* Hydrological extremes such as floods and droughts
* Improving monitoring in poorly gauged and ungauged basins
* Developing novel methods of gathering and combining in-situ benchmark data with remote sensing approaches
*Optimal integration of remote sensing and hydrological modelling and the performance of using remotely sensed data in driving hydrological models, multi-variable calibration and spatial evaluation
* Tools tailored for end-users out of the remote sensing community
* Integrating remote sensing into operational systems (flood alerts, drought risks, …)
*The added-value of spatially downscaling remotely sensed data to improve hydrological modelling

This session is co-organised by the International Commission of Remote Sensing (ICRS) of the International Association of Hydrological Sciences (IAHS) in the context of the HELPING scientific decade (Hydrology Engaging Local People IN one Global world).

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 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. Improved representation of spatial–temporal dependence, clustering, and uncertainty is also critical for robust hazard and risk assessments, with direct implications for infrastructure design, disaster preparedness, climate adaptation strategies, and risk management in the (re)insurance sector.
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.

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, land degradation, and biodiversity loss increasingly threatening groundwater recharge, availability, and quality. Managed aquifer recharge (MAR) and ecosystem restoration offer complementary pathways to enhance groundwater sustainability and resilience under current and future climate conditions.
MAR has emerged as a key adaptation strategy to reinforce sustainable groundwater management by maintaining water storage, improving water quality, supporting groundwater-dependent ecosystems, counteracting land subsidence and salinization, and creating hydraulic barriers against seawater intrusion or contaminant migration. At the same time, ecosystem restoration measures can improve soil structure, reduce sediment and pollutant loads, enhance infiltration, and promote natural groundwater replenishment while delivering co-benefits for biodiversity, carbon sequestration, and ecosystem services.
For both MAR and restoration-based recharge enhancement to achieve long-term sustainability, it is essential to adapt approaches to local hydrogeological, climatic, ecological, and socio-economic conditions, supported by robust monitoring, modelling, and evaluation frameworks. This joint session aims to highlight advances, applications, and lessons learned at the interface of engineered and nature-based solutions for sustainable groundwater management under climate change.
We welcome contributions focusing on:
• Ecosystem restoration interventions that enhance groundwater recharge, such as soil restoration, erosion control, river, floodplain, and wetland rehabilitation.
• Impacts of restoration measures on groundwater quantity, chemical and microbiological quality, and groundwater safety.
• Environmental and ecosystem co-benefits of restoration.
• Innovations in MAR methods.
• Case studies and lessons learned from MAR schemes in different hydrogeological and climate settings.
• MAR and water quality issues, including impacts on groundwater quality, treatment system effectiveness, and clogging challenges.
• Monitoring, modelling, and assessment approaches for evaluating recharge, water quality, and long-term sustainability.
• Socio-hydrological, governance, and economic aspects, including cost-effectiveness and decision-support tools.
This joint session merges the former sessions HS8.1.3 Sustainable water management with managed aquifer recharge (MAR) and HS8.1.7 Ecosystem restoration for aquifer recharge, groundwater quality, and climate resilience.

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.

Emerging contaminants (inorganic particles, biocolloids, micro- and nanoplastics, PFAS, pharmaceuticals) in the subsurface 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 other dissolved substances 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. Although there is extended experience in dealing with the colloidal domain in subsurface media, new particles pose new challenges and one has to acknowledge that the analytical window to the colloidal domain is nowadays wide open: some of the "old" concepts likely need to be reevaluated.

Recent research on PFAS has raised concerns and led to stricter regulation in many countries. PFAS combines aqueous mobility, extreme recalcitrance and adverse health effects at very low concentrations. This requires immediate actions to reduce their release and spreading, better understand their transport and associated risks, and to remove them from the environment. The unique properties of PFAS also pose many additional challenges for groundwater management, risk assessment and remediation. Many processes in both the groundwater and vadose zones need to be better understood and there is an urgent need for improved remediation and mitigation methods. Field testing and upscaling findings from laboratory batch and column testing conducted under idealized soil conditions to natural conditions at field sites is critical.

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 and PFAS in aquatic and terrestrial systems. The presented papers will provide better process understanding through laboratory and field research, modeling, and site characterization to address new challenges and solutions associated with contamination of the soil-groundwater system by PFAS and particles as well as unsolved challenges related to other emerging or traditional contaminants.

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.

Understanding the flows, connectivity, and residence times of water and sediments is critical to quantifying the hydrological and biogeochemical functioning in surface and subsurface systems. Tracers provide a powerful means to investigate transport processes over a wide range of spatial and temporal scales, from rapid event-driven dynamics to long-term groundwater storage and ecosystem memory. This session brings together classical and emerging tracer approaches, including stable and radioactive isotopes, environmental DNA (eDNA), geochemical and artificial tracers, and modelling techniques, to explore how physical, chemical, and biological signals can be jointly used to unravel flow pathways, transit time distributions, and system connectivity.
We welcome contributions that use single or multi-tracer strategies to investigate groundwater residence times, groundwater–surface water interactions, sediment transport, fracture and aquifer connectivity, and responses to hydrological extremes and environmental change. eDNA offers a rapidly developing and complementary tracer of transport and connectivity, providing spatially resolved biological signals that reflect hydrological pathways, sediment dynamics, degradation processes, and biogeochemical controls. Together with established tracers, eDNA opens new opportunities to link physical transport processes with ecological patterns and ecosystem functioning.
The session particularly encourages studies that integrate tracers spanning different temporal and spatial scales, combine field observations with laboratory experiments and numerical modelling, or exploit sediment archives to connect present-day dynamics with longer-term records. Contributions presenting innovative sampling strategies, automated or distributed monitoring systems, novel sensors, and citizen science approaches are also welcome.
By uniting hydrogeochemical, isotopic, modelling, and biological tracer communities, this session aims to foster interdisciplinary exchange and advance our ability to characterise water and sediment pathways across complex hydrological systems. It provides a platform to explore how novel tracer or multi-tracer frameworks can improve understanding of transport processes, system vulnerability, recovery times, and sustainable management of water-dependent ecosystems.

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.

This session will sponsor a Special Issue in Natural Hazards and Earth System Sciences (NHESS) titled “Integrated Water Resources Management and Resilience in Coastal Aquifers under Climate and Human-induced Changes.” While presenters from the session are strongly encouraged to submit their work, the Special Issue is open to all relevant contributions, and we invite the broader scientific community to submit full papers for consideration.