Anthropogenic activities, climate change and poor water resources management lead to insufficient water quality, both biological and chemical, and results in one of the world’s most urgent human health issues. Waterbodies are key for waterborne diseases and water surface water network for spreading of contaminants and diseases. The global health burden could be reduced by improving water supply, sanitation and management of water resources but also by improved understanding of the role of hydrology in transport of pathogens. This scientific session aims to explore the interdisciplinary facets of water cycle and human health in broad sense. By bringing together experts from hydrology, environmental pollution, microbiology, ecology, epidemiology and public health, this session seeks to foster a dialogue to effectively study hydrological processes related to spreading and transmission of diseases and emergent contaminants. The oral part of the session is composed of solicited presentations followed by a panel discussion.

Per- and polyfluoroalkyl substances (PFAS) are a group of man-made chemicals that have been extensively used worldwide for over 80 years due to their unique chemical properties, such as high stability and resistance to degradation. The widespread use of PFAS has led to pervasive contamination in terrestrial and aquatic environments, creating complex regulatory and environmental management challenges.

This session aims to contribute to a comprehensive understanding of PFAS pollution and to share effective strategies for their management and remediation/mitigation. Hence, we aim to bring together researchers and practitioners from diverse fields, including contaminant hydrogeology, environmental chemistry, toxicology, engineering, and policy, to share their insights on the occurrence, behaviour, and management of PFAS in the environment. We primarily seek contributions that explore the latest advancements in understanding PFAS pollution across different environmental matrices, including surface water, groundwater, and soils.

Topics of interest include, but are not limited to:
- The transport and fate of PFAS in terrestrial and aquatic environments,
- Modelling approaches to predict PFAS distribution and transport in various environmental settings,
- Innovative strategies and technologies for the treatment and remediation of PFAS-contaminated water, including drinking water and wastewater,
- Advancements and case studies on the successful application of PFAS remediation/mitigation techniques and their effectiveness in different environmental contexts,
- Ecotoxicological studies,
- Challenges and advancements in regulatory frameworks and policies for managing PFAS pollution, including approaches to identify and mitigate sources of PFAS contamination.

Given the complex nature of PFAS as "forever chemicals" and their ability to partition across different environmental media, this session emphasises the importance of interdisciplinary approaches and collaborative efforts to tackle the multifaceted challenges they present. We welcome studies that utilise laboratory research, field investigations, and modelling efforts, as well as contributions that discuss the implications of PFAS pollution on public and environmental health, ecological integrity, and regulatory landscapes.

The occurrence of pathogens and of an exponentially increasing number of contaminants in freshwater and estuary environments pose a serious problem to public health. This problem is likely to increase in the future due to more frequent and intense storm events, the intensification of agriculture, population growth and urbanization. Pathogens (e.g., pathogenic bacteria and viruses, antibiotic resistance bacteria) are introduced into surface water through the direct discharge of wastewater, by the release from animal manure or animal waste via overland flow, or, into groundwater through the transport from soil, which subsequently presents potential risks of infection when used for drinking, recreation or irrigation. Contaminants of emerging concern are released as diffuse sources from anthropogenic activities, as discharges from wastewater treatment plants (e.g., trace organic contaminants, PFAS), or occur due to microbial growth (e.g. cyanotoxins), posing a burden on human health. So far, the sources, pathways and transport mechanisms of fecal indicators, pathogens and emerging contaminants in water environments are poorly understood, and thus we lack a solid basis for quantitative risk assessment and selection of best mitigation measures. Innovative, interdisciplinary approaches are needed to advance this field of research. In particular, there is a need to better understand the dominant processes controlling fecal indicator, pathogen and contaminant fate and transport at larger scales.

This session aims to increase the understanding about the dominant processes controlling fecal indicator, pathogen and contaminant fate and transport at larger scales. Consequently, we welcome contributions that aim to close existing knowledge gaps and include both small and large-scale experiments, with the focus on
- the fate and transport of fecal indicators, pathogens, emerging contaminants including persistent and mobile organic trace substances (e.g. antibiotic resistance bacteria, cyanotoxins, PFAS) in rivers, soils, groundwater and estuaries
- Hydrological, physically based modelling approaches
- Methods for identifying the dominant processes and for transferring transport parameters of fecal indicators, pathogens and contaminants from the laboratory to the field or catchment scale
- Investigations of the implications of contamination of water resources for water safety management planning and risk assessment frameworks

Invited speaker: Prof. Gertjan Medema, KWR

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

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

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

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

This session is devoted to the study of fate and transport processes of micro and nanoplastic (MNP) particles in soil and groundwater systems. While MNPs in marine and aquatic environments have received considerable interest by the scientific community over the last decade, soil and groundwater environments are comparably understudied and MNP transport and fate in these compartments is less well understood.

We welcome contributions that provide a comprehensive overview on the problem of soil and groundwater MNP contamination from local to global scales. Additionally, we are looking forward to contributions around field sampling, lab processing and characterization techniques specific to MNP in soil and groundwater compartments, as well as to experiments and modelling studies that advance our theoretical understanding of how MNP as well as their leachtes interact with soil and groundwater ecosystems and influence their biochemistry.

With this session we strive to extend our knowledge on MNP fate, transport and interaction with soil and groundwater environments with a diverse range of hydrological and biochemical characteristics including soil type, grain size, hydraulic connectivity, flow velocity and groundwater recharge capacity, organic matter content and microbial activity or soil chemistry. We hope that a better understanding of MNP pathways through the subsurface will aid us in conceptualising potential exposure hazards and pollution risks of vital soil and groundwater resources.

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.

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

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.

The interaction between the soil-plant-atmosphere compartments and human activities is of paramount importance for the sustainable management and preservation of ecosystem functions and services. The functionality and services of terrestrial ecosystems are threatened by global climate change and human activities. The complexity and comprehensiveness of the impacts have so far proven challenging to assess due to the limitations of simplified experimental approaches and long-term observations, which often focus on a limited number of response variables.
Experimental systems such as lysimeters or ecotrons provide continuous, high-resolution and high-quality observations of detailed time series, which are crucial for a more accurate determination of the Earth's ecosystem services and functions and for promoting interdisciplinary ecosystem research.
This session will mainly focus on the diversity of ecosystem research using research platforms of lysimeters and ecotrons. We would also like to address the challenges of modelling ecosystem processes, comparison of metrics with other in situ instruments, upscaling approaches from such platforms to larger scales, validation studies (e.g. remote sensing), but also new developments in the field of lysimetry and further development of processing algorithms for interpretation of high temporal resolution lysimeter/ecotron weight data. We welcome contributions that (1) present novelties in the field of lysimeters, (2) assess and compare the functioning and services of terrestrial ecosystems, particularly in relation to climate change, (3) focus on water and nutrient transport processes (4) and greenhouse gases within the soil-plant-atmosphere continuum, (5) develop new techniques for the analysis of lysimeter and ecotron observations, (6) include ecosystem or hydrological modelling approaches using in situ observations from lysimeters or ecotrons.

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.

The Surface Water and Ocean Topography (SWOT) satellite mission, launched in December 2022, marks a significant advancement in hydrological sciences. It is the first satellite designed to investigate surface water in the global water cycle, and it provides the first comprehensive view of Earth's freshwater bodies from space. Using Ka-band radar interferometry, SWOT delivers, for the first time, simultaneous, high-resolution measurements of water surface elevation and inundation extent in rivers, lakes, reservoirs, and wetlands globally. This dataset will fundamentally transform our ability to understand surface water and reveal new insights into hydrologic processes. The hydrologic remote sensing community has worked for more than a decade to develop new methods and scientific understanding that are now allowing SWOT data to advance knowledge of global water fluxes. For this session, we solicit abstracts presenting recent advances enabling SWOT to unlock new frontiers in hydrology and enhance our understanding of Earth’s surface water.

The moment when hydrology became recognised and established as a science remains a topic of debate. Certainly, there is a long tradition of theories on the natural occurrence, distribution, and circulation of water on, in, and over the surface of the Earth (Horton, 1931). While some of these theories remain valid today, others have been replaced by more recent understandings, which reflect the evolution of hydrology as a science.
As a scientific hydrological community, we are committed to advancing our field. Progress in hydrology can greatly benefit from a solid historical foundation, enabling us to assess past achievements, identify research gaps, and learn from earlier missteps. Accordingly, the newly formed IAHS Working Group on the History of Hydrology aims to foster a culture of historical hydrological literacy to support the growth of hydrological science by connecting it to its roots.
For this session, we welcome contributions that examine the evolution of hydrological concepts over time, how overlooked methods might hold contemporary value, and reflect on the factors that have led to incorrect conclusions, i.e. learn from mistakes. Topics of interest include the history of hydrological models and modelling, including deterministic vs stochastic approaches, optimisation, and diagnostic metrics; land-mark hydrological projects, the management of historical datasets or experimental catchments and their management, and the significant contributions of scientists, especially female hydrologists and other under-represented groups, as well as institutes and organisations. We encourage contributions from countries that are underrepresented in the historical hydrological literature.

Prof. Günter Blöschl will provide an invited talk on the 'Evolution in the Understanding of the Characteristics of Extreme Hydrological Events'.

The effects of climate change highlight the importance of developing a resilient design approach for buildings, both in dense urban areas and rural communities. Nature-based solutions (NBSs) can help in this as an adaptation measure, providing multiple benefits at building scale. Increasing the applications of green walls and green-blue roofs can reduce heat stress, improve rainwater and wastewater management and drive the communities towards the concept of circular economy and self-subsistence.

This session aims to share and discuss the most recent advances in NBSs that increase building resilience and sustainability in the urban environment. Therefore, we aim for a session including researchers from different fields such as engineering and architecture, natural sciences such as microclimatology and meteorology, and social/psychological science. We encourage also those involved in policymaking to submit a contribution, to have an integrated approach to building development.

Our focus will primarily be on solutions that not only improve routine building management but also make meaningful contributions to the mitigation s of extreme events, like extreme urban heat stress (UHI/heat events) or extreme precipitation events and local flooding. Submissions may include (but not restricted to) contributions on:

- Laboratory, field measurements and numerical modelling studies (like microclimatic or hydrodynamic simulations) on green walls and green-blue roofs and other NBSs for rainwater management, wastewater treatment, thermal control, edible vegetation production, energy production
- Qualitative research like user- or agent-based approaches that investigate the potentials and effects of NBSs for climate change adaptation and improving thermal comfort, and further challenges of the water-energy nexus on this small/building scale.
- Urban areas mapping (e.g. GIS applications) or modelling for buildings urban management (BIM applications)
- Investment and cost return of NBS application to buildings
- Life-Cycle-Assessment (LCA) analysis
- Quantitative analysis on possible sanitary risks innovative wastewater treatment and reuse solutions at local scale
- Buildings retrofitting projects or real-scale applications
- NBS social acceptance
- Impact on human well-being and health

In essence, our session aims to explore the multifaceted aspects of NBSs in the context of building resilience, with particular emphasis on their impact, feasibility, and sustainability.

The approaches and methods we choose for a hydrological modelling study affect our modelling results and conclusions, and hence also their usefulness for decision support. As of today there is no common and consistently updated guidance on what good modelling practice is, and how we can achieve transparent, robust and reproducible workflows. While many useful practices such as scripted workflows, model benchmarking, controlled model comparisons, careful selection of 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 invite presentations of worked examples and software tools: What did(n’t) work? How were challenges overcome? How did developed workflows allow for detailed scrutiny of the techniques, assumptions, and interpretations of data, models, and their uncertainties? Contributions should aim to improve the scientific basis of (parts of) the modelling chain and put good modelling practice in focus again. This might include (but is not limited to) contributions on:

(1) Benchmarking to increase trust in model results
(2) Developing robust calibration and evaluation frameworks to improve transparency
(3) Going beyond common metrics in assessing model performance and realism
(4) Developing frameworks that enable hypothesis testing or consideration of alternative conceptual models
(5) Investigating subjectivity and documenting choices along the modelling chain
(6) Developing modelling protocols and/or scripted workflows to improve efficiency and reproducibility
(7) Examples of adopting the FAIR (Findable, Accessible, Interoperable and Reusable) principles in the modelling chain
(8) Methods for uncertainty analysis, data assimilation, and management optimization under uncertainty, e.g. in the decision-support context
(9) Communicating model results and their uncertainty to end users of model results
(10) Evaluating implications of model limitations and identifying priorities for future model development and data acquisition planning

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

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

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

Water in the snowpack and in glaciers represents an important component of the hydrological budget in many regions of the world, as well as a sustainment to life during dry seasons. Predicted impacts of climate change in catchments covered by snow or glaciers (including a shift from snowfall to rainfall, a modified total amount of precipitation, an earlier snowmelt, and a decrease in peak snow accumulation) will reflect on water resources availability for environment 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.

The generation of runoff 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 impact on runoff is 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;

This session will feature a solicited presentation by Prof. Bettina Schaefli from the University of Bern, Switzerland.

The session is linked to the IAHS HELPING working group on Droughts in Mountain Regions (https://iahs.info/Initiatives/Scientific-Decades/helping-working-groups/droughts-in-mountain-regions/)

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

This session will bring together the scientific community developing hydrology research on mountain ranges across the globe to share results and experiences. We invite contributions addressing past, present and future changes in mountain hydrology due to changes in climate and/or land use, how these changes affect local and downstream territories, and adaptation strategies to ensure the long-term sustainability of mountain ecosystem services, with a special focus on water cycle regulation and water resources generation. Example topics of interest to this session are:

- Sources of information for evaluating past and present conditions (in either surface and/or groundwater systems).
- Methods for differentiating climatic and anthropogenic drivers of hydrological change.
- Modelling approaches to assess hydrological change.
- Evolution, forecasting and impacts of extreme events.
- Case studies on adaptation to changing water resources availability.

Mountains receive and produce a high proportion of precipitation and runoff, forming the headwaters of many of the world’s major river systems and supplying water to at least half of humanity. These headwaters contain substantial snow and ice reserves and generally are undergoing amplified global warming resulting in rapid changes in landcover, permafrost, snowcover, glaciers, and hydrological regime. Because of the above, high mountain headwaters are the focus of global concern as exemplified by the UN International Year of Glaciers’ Preservation – 2025. Understanding and prediction of the mountain cryosphere and water cycle have been restricted by sparse observation networks, uncertainties in process representation and low model resolution, and substantial heterogeneity over small spatial scales. This session addresses the following questions: How can snow and ice hydrology best be measured in various alpine regions? How do land surface energy and water exchanges differ in various high mountain regions of the Earth? What improvements to high mountain hydrological predictability are possible in various alpine regions through improved process physics, representation of spatial variability, and incorporation of ground and remote observations? To what extent are existing model routines valid and transferrable amongst different alpine regions? Submissions that deal with observations and data, model application and diagnostic comparisons, new process understanding and insights, and better prediction of the changing mountain cryosphere and water cycle are welcome. This session is organized by and contributes to the International Network for Alpine Research Catchment Hydrology (INARCH; https://inarch.usask.ca/) of the World Climate Research Programme’s GEWEX Project.

The African continent is experiencing various impacts of climate induced sequential droughts, floods, heatwaves, and alteration between two extremes. These changes are causing water and food insecurity across the region. Advances in hydrological models, including process- and machine learning- based models, in better reproductions of observed variables such as streamflow and water availability are improving predictions of socio-economic risks of floods, droughts, and water stress. However, in data-sparse regions, the use of hydroclimatic models for disaster risk reduction still faces unsolved challenges.

This session aims to bring together communities working on different strands of African hydrology, climate risks, water and food security, and environmental risks. We welcome both fundamental and applied research in the areas of hydrological process understanding, monitoring, drought/flood forecasting, seasonal to decadal forecasting, water resources management, climate change and impact assessments including compound and multi-hazard risks. We particularly welcome interdisciplinary studies that combine the physical drivers of water-related risks and their socio-economic impacts. Science for solution initiatives contributing to the IAHS HELPING decade are welcome.

Transboundary waters encompass aquifers, lakes, and river systems shared by two or more countries. These waters do not adhere to political boundaries, meaning that water use, pollution or overexploitation in one region can have significant consequences downstream. Effective transboundary water management is thus crucial to address pressing issues from water scarcity and biodiversity protection to economic growth and peacekeeping.
Over half of the global population resides in transboundary basins. Given the diverse physical, political, and socio-economic contexts of these shared water bodies, integrated approaches and practices are needed to solve transboundary water problems, to foster cooperation and to ensure sustainable management.

We welcome contributions demonstrating:
(1) Modeling and inter-comparison of different models (ranging from traditional hydrological models to innovative AI approaches and hybrid applications) for simulating water balance components and water quality including climate change impact studies, sensitivity analyses, uncertainty evaluations.
(2) Evaluation of performance and uncertainty of transboundary datasets of climate and hydrological characteristics, including remote sensing products and climate projections. We seek contributions that explore how remote sensing can help close the transboundary water data gap, offering cost-effective, solutions for monitoring and assessing water resources across borders.
(3) Applications supporting sustainable management of transboundary water including water abstractions, water-savings or water retention solutions in agriculture and industry.
(4) Development and implementation of Joint Monitoring and Information Systems, such as GIS-based databases, that facilitate effective cooperation in water-related risk reduction and transboundary resilience modeling. These include experiences with joint problem definition, creating a common understanding, and evaluating the effectiveness of implemented strategies.
(5) Involvement of Multi-Level Stakeholder engagement in shared water management. This includes capacity development, voluntary data collection through citizen science, participatory modeling, trust-building, and science-policy-driven decision-making.

The session is organized by the Danube Water Balance (DRP0200156 Danube Region Interreg Programme) and GRANDE-U “Groundwater Resilience Assessment through iNtegrated Data Exploration for Ukraine” (NSF Awards No. 2409395 and 2409396) teams.

Understanding and representing hydrological processes is the basis for developing and improving hydrological and Earth system models. Relevant hydrological data are becoming globally available at an unprecedented rate, 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) introduce new global and regional data products into the modeling process;
(2) upscale experimental knowledge from smaller to larger scale for better usage in catchment models;
(3) advance seamless modeling of spatial patterns in hydrology and land models using distributed earth observations;
(4) improve model structure by representing often neglected processes in hydrological models such as human impacts, river regulations, irrigation, as well as vegetation dynamics;
(5) provide novel concepts for improving the characterization of internal and external model fluxes and their spatio-temporal dynamics;
(6) introduce new approaches for model calibration and evaluation, especially to improve process representation, and/or to improve model predictions under changing conditions;
(7) develop novel approaches and performance metrics for evaluating and constraining models in space and time

This session is organized as part of the grass-root modelling initiative on "Improving the Theoretical Underpinnings of Hydrologic Models" launched in 2016.

Stable and radioactive isotopes as well as other natural and artificial tracers are useful tools (i) to fingerprint the sources of water and solutes in catchments, (ii) to trace flow pathways or (iii) to quantify exchanges of water, solutes and particulates between hydrological compartments. We invite contributions that demonstrate novel applications and recent developments of isotope and other tracer techniques in hydrological field studies and 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.

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

Maintaining good water quality is essential for preserving the ecological, recreational, and industrial functions of our water resources. Water quality is mainly controlled by the catchment properties and hydro-meteorological conditions, with land use and climate change significantly altering the quantities and dynamics of particulate and solute concentrations at the outlet of catchments worldwide. This is especially true for agricultural catchments exposed to fertilization and increased erosion. However, catchments with other land use types are also prone to (negative) changes in water quality, for example, due to wastewater and road salt from urban areas or brownification in boreal forests. To address these diverse influences, water quality is typically monitored and assessed at the catchment scale. However, effective measures to prevent or reduce water quality deterioration are still hindered by our limited understanding of the underlying processes and causal relationships resulting from the complex interplay of hydrological, biogeochemical, and temporal factors.
Data-driven statistical analyses of discharge and concentration time series observed at catchment outlets (i.e., C-Q relationships) provide valuable insights into the underlying mechanisms, including process scaling and the effectiveness of measures.
The advantage of technologies and sensors for monitoring high spatiotemporal resolutions and the growing availability of long-term data can inform experimental and modeling studies, allowing us to progress from recognizing patterns to modeling and understanding processes. A profound understanding of solute and particulate mobilization, retention, and export mechanisms ultimately allows us to develop local or catchment-scale solutions to mitigate negative impacts on water quality and enhance sustainable land use management.

This session brings together contributions focused on analyzing or modelling solute and particulate export dynamics at the catchment scale with those focused on innovative monitoring techniques and the development of mitigation measures or other solutions to enhance or protect water quality.

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 the impacts of global change (climate change and extremes, land use change and socio-economic developments) on clean water availability across space and time is critically important for ensuring that there is enough water of suitable quality to meet 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. Thus, 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 address 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 other 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;
• 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 or water security (combining water quality & quantity) supply and demand in the context of a changing climate and other global change drivers;
• quantify the uncertainty of water quality model under drivers of global change;
• interpret and characterize uncertainties in machine-learning, AI and data-mining approaches that are trained on large, possibly high-resolution data sets;
• address the problem of temporal and spatial scaling (e.g. disparity of scales between processes, observations, model resolution and predictions) in water quality modelling;
• test transferability and generalizability of water quality findings;
• involve stakeholders in water quality model development to inform risk analysis and decision support;
• application of remote sensing 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. Notably, this session welcomes contributions focusing on circular economy principles, including case studies of emerging contaminants removal and recovery from water resources. The goal is to stimulate a dialogue that not only advances scientific knowledge but also promotes actionable outcomes that benefit society and the environment.
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. The session additionally contributes to disseminating the REMEDI project results (Grant ID: 956384). REMEDI focuses on X-ray contrast medium agents and trains early-stage researchers to address pharmaceutical water contamination, treatment and recovery.

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.

Floods and droughts have major impacts on society and ecosystems and are projected to increase in frequency and severity with climate change. These events at opposite ends of the hydrological spectrum are governed by different processes that operate on different spatial and temporal scales and require different approaches and indices to characterize them. However, there are also many similarities and links between the two types of extremes which are increasingly being studied.
This session on hydrological extremes aims to bring together the flood and drought communities to learn from the similarities and differences between flood and drought research. We aim to improve the understanding of the processes governing both types of 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.

Assessing the impact of climate variability and change on hydrological systems and water resources is important for society to better adapt to future shifts in water availability and extreme events such as floods and droughts. However, significant uncertainties remain in projecting these impacts, particularly those associated with internal/natural climate variability and hydrological response to changes in climatic forcing at multi-annual to multi-decadal timescales. Differences in model realizations, interannual to decadal variability, and the influence of major climate modes (e.g., ENSO, NAO, AMO) can substantially modify hydrological responses, potentially altering the expected effects of human-induced climate change. Understanding how these climate patterns interact with catchment properties (e.g., vegetation cover and groundwater support) and land-use changes is essential for improving hydrological predictions.

Catchments are complex systems that respond to climate forcing across multiple timescales. Yet, hydrological models often struggle to accurately capture these responses, suggesting potential misrepresentation or omission of key processes, timescales, and feedback. It is necessary to disentangle the interactions between hydrological, biophysical, and climatic drivers to enhance model realism and predictive skills.

This session invites contributions that advance our understanding of hydrological variability and extremes under climate variability and change, including:
1. The role of internal climate variability in shaping hydrological extremes and long-term water resource availability;
2. Advances in understanding hydrological and biophysical processes governing catchment responses to climate shifts;
3. Studies applying hydrological regularities (e.g., the Budyko hypothesis) to improve predictions under changing conditions;
4. Investigations of catchment “memory” and its representation in models;
5. Efforts to enhance the robustness of hydrological simulations under future climate variability and change through newly developed probabilistic and storyline impact scenarios.

The relationship between land cover, land use, and water resources is complex and bidirectional. Changes in land cover and use can dramatically alter water circulation, availability, quantity and quality, while water resources significantly shape land cover patterns and ecosystem health. Land cover changes are determined by many environmental factors, including water circulation, landscape quality, and ecosystems. Climate variability and land cover changes have been shown to alter the quality and availability of freshwater resources around the world at multiple scales. Human activity is the main driving force influencing land cover changes. Globally, land cover change is a dominant factor affecting ecosystems and the hydrological regime. Land use and land cover changes (LUCC) directly affect the magnitude of evapotranspiration, surface runoff, groundwater recharge by infiltration, and even precipitation. Generally, evapotranspiration, surface water storage, and groundwater recharge are interrelated processes that regulate the balance in the water dynamics of the entire basin. From the point of view of water management, the simulation of land use changes is very important because it provides future scenarios and patterns for water resources.
The main objective of the session is to discuss the role of land use/land cover changes in different regions and diverse scales in accelerating hydrological processes and altering water resources. The main topics should include the following problems:
1. Transitions of LULC changes in different landscapes,
2. Impact of LULC changes on ecosystems and the risk of floods and droughts,
3. Indicators of water resources including LULC changes,
4. Modelling of hydrological processes including LULC changes,
5. Projections of water resource changes affected by LULC and climate change,
6. New data sources to detect LULC changes

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.

Solicited talk by Noemi Vergopolan (Rice University): "Improving Forest Realism in Earth System Models through Satellite Observations"

Large data samples of catchments offer insights into the physiographic and hydroclimatic factors that shape hydrological processes. These datasets increasingly encompass a diverse range of hydrologic conditions, across time and space, facilitating research on a wide variety of topics. This includes testing hypotheses of hydrologic theories, exploring uncertainties in data and models, evaluating interactions within different hydrological model structures, and enabling predictions in ungauged basins.
We welcome abstracts that seek to accelerate progress on the following topics:
1. Development and improvement of large-sample data sets:
How can we address current challenges on the uneven geographical representation of catchments, quantification of uncertainty, catchment heterogeneities and human interventions for fair comparisons among datasets? Can we foster the harmonisation of large-sample data sets? 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 hydrologic theories (i.e. structural understanding) from well-monitored or experimental catchments to data-scarce catchments? Can currently available global datasets be used to draw improved perceptual models and better define hydrologic similarity?
3.Advance catchment modelling:
How can we improve process-based and machine learning modelling by using large samples of catchments? How can information and knowledge (i.e. functional understanding) be transferred between catchments and applied to data-scarce regions? Furthermore, how can we develop new models and workflows to more effectively leverage these models to infer hydrological response under changing environmental conditions, particularly those influenced by human activities?
4. Hydrologic 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? How can we improve our classification of catchments, of their connectivity and of their hydrologic processes?

Floods are extreme events that cause huge losses of lives and properties. As the consequences of climate change intensify, novel approaches in hydrology have become essential for developing robust flood risk mitigation strategies that aid in effective water resource management to foster sustainable development. Preparation, monitoring, and planning against the flood requires the generation of tools such as flood forecasting and predicting extreme flood events under climate change. The present session solicits novel contributions from the researchers to investigate and manifest revolutionary developments in the catchment hydrology by utilizing cutting-edge technologies such as Artificial Intelligence (AI), remote sensing, and process-based modeling. The combined use of these technologies is revolutionizing flood modeling and management methods and providing new avenues to analyze complicated hydrological processes that would improve the ability of ecosystems to adapt and recover from the impacts of climate change and challenges.

This session aims to bring together professionals from hydrology sciences and engineering to share their valuable and innovative insights, utilizing modern technologies on a variety of topics, including but not limited to the following:

• The paradigm shifts from conventional to modern learning approaches such as integrated, hybrid, and universal are crucial for the modeling of hydrological extremes.
• Regional modeling approach in hydrology for extreme event prediction in ungauged or poorly gauged basins.
• Real-time monitoring of extreme flood events using remote sensing, but not limited to optical, and Synthetic Aperture Radar (SAR).
• Explore the Physics-based AI, Generative AI (GAI), and Digital Twins including traditional AI in the field of flood risk mitigation.
• Discovering possibilities for integrated and innovative solutions using public participation for Food Risk Mitigation (FRM), including riverine, urban, and Glacial Lake Outburst Floods (GLOFs).
• Integrated watershed management (IWM) strategies that improve decision-making processes for flood mitigation and foster sustainable water resource management.

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 current and future state of our global and large scale water resources;

(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 global water policies.

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. Aquifers may span political and natural boundaries, but 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:
• Regional to global groundwater-related datasets and big-data assessments
• Transboundary and inter-catchment assessments of groundwater processes
• Identification of dominant controls on groundwater processes across large domains
• Recent methodological developments for 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)

Different approaches including global models, data-driven approaches, and machine learning are used to assess water balance components at the global, continental, and regional scale. 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) 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. 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.

A known challenge in hydrological science is the robust uncertainty analysis of physical processes through analysis of records from regional and global scale ground, coastal and marine observations (on point basis or gridded), satellite and reanalysis data, remote-sensed records, laboratory measurements and computational outputs. The stochastic analysis of analogies among hydroclimatic and hydrodynamic processes in a vast range of scales offers insights on coherence and uncertainty (marginal and dependence structures, intermittent and fractal behaviour, trends, irreversibility, etc.). Stochastic approaches can also serve as information for water-related management purposes, natural hazard assessment, and mitigation measures, e.g. in terms of hydrologic design estimation.
This session welcomes, but is not limited to, contributions on stochastic spatio-temporal analysis, modelling, simulation and prediction of hydrological-cycle and hydrodynamic processes (streamflow, precipitation, temperature, evapotranspiration, humidity, dew-point, soil moisture, groundwater, etc.), water-energy-food nexus processes (agricultural, financial and other related fields, solar radiation, wind speed, reservoir stage, etc.), laboratory measurements (i.e., small-scale models for large-scale applications), and computational outputs (e.g., concerning floods, droughts, climatic models, etc.).

In recent years, the field of geostatistics has seen significant advancements, yet the focus on more traditional approaches remains crucial. These methods are fundamental in understanding spatially and temporally variable hydrological and environmental processes, which are vital for risk assessment 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. Machine Learning approaches have specific and 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.

The complexity of hydrological and Earth systems poses significant challenges to their prediction and understanding capabilities. The advent of machine learning (ML) provides powerful tools for modeling these complex systems. However, realizing their full potential in this field is not just about algorithms and data, but requires a cooperative interaction between domain knowledge and data-driven power. This session aims to explore the frontier of this convergence and how it facilitates a deeper process understanding of various aspects of hydrological processes and their interactions with the atmosphere and biosphere across spatial and temporal scales.

We invite researchers working in the fields of explainable AI, physics-informed ML, hybrid Earth system modeling (ESM), and AI for causal and equation discovery in hydrology and Earth system sciences to share their methodologies, findings, and insights. Submissions are welcome on topics including, but not limited to:

- Explainability and transparency in ML/AI modeling of hydrological and Earth systems;
- Process and knowledge integration in ML/AI models;
- Data assimilation and hybrid ESM approaches;
- Causal learning and inference in ML models;
- Data-driven equation discovery in hydrological and Earth systems;
- Data-driven process understanding in hydrological and Earth systems;
- Challenges, limitations, and solutions related to hybrid models and XAI.

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 session will explore the integration of stakeholder engagement and advanced hydroinformatics techniques such as remote sensing, machine learning, AI, and numerical modeling to address key challenges in hydrological sciences and natural hazards. Emphasizing both theoretical advancements and practical applications, the session will attract contributions from diverse geographic regions and hydrological contexts, particularly those involving the co-development of research with decision-makers.

Key Themes:
1. Hydrological Extremes Monitoring and Prediction:
Utilizing remote sensing and global datasets to monitor and predict droughts, floods, and extreme climatic events, Developing machine learning models for forecasting hydrologic extremes across diverse basins, & Integrating land surface modeling with data assimilation of satellite and in-situ observations.

2. Process Dynamics in Complex Terrain
Modeling land surface hydrology across diverse topographies and climatic zones, Examining the influence of topographic variability on land surface dynamics and hydrological extremes, and Integrating local knowledge with high-resolution data for effective water resource management in mountainous regions.

3. Climate Change Impacts on Hydrology:
Evaluating the influence of climate change on hydrological processes and extremes, including floods and droughts & Applying integrated modeling approaches to understanding the combined effects of land use and climate change on hydrological regimes.

4. Equity in Water Resource Management:
Analyzing trade-offs in water distribution systems, focusing on equity and performance during scarcity & Case studies on the impacts of domestic water storage and rationing under varying climate conditions.

5. Technological Innovations in Hydroinformatics:
Leveraging AI and hydroinformatics tools to enhance water resource management and policymaking & Advances in data assimilation techniques to improve model predictions and decision support systems.

6. Participatory Research and Stakeholder Engagement
Collaborating with decision-makers to co-develop research methods and refine models, ensuring stakeholder-driven processes, Co-designing data analysis and visualization tools to support science-informed decision-making and enhance research relevance, & Case studies that highlight successful science-to-action projects through integrating stakeholder perspectives.

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.

We welcome abstracts on new observational systems for hydrosphere processes aiming to build digital twins for water management including atmosphere, soil, surface water or groundwater systems and related infrastructure. Improved monitoring of hydrological storages and fluxes is crucial for the development of new observational systems, modelling and analysis platforms. The processes measured on land, surface water and subsoil water, with novel technologies and integrated modelling platforms open new possibilities to observe the hydrosphere in various temporal scales, from sub-daily to long-term trends and extremities. Remotely sensed data including in-situ and mobile sensors are essential data source to support a digital revolution in hydrosphere processes studies and river basin management. We welcome presentations on digital twin development of different complexities and maturity levels for diverse water systems (river, groundwater, farmland, urban). Moreover, we welcome presentations on applications that integrate hydrological processes in natural and built environments for improved integrated water management using digital solutions to simulate reality and the various management challenges.

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

This session invites contributions that discuss advances, both in theory and/or application, in Bayesian 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) Analyses of over-parameterised models enabled by AI/ML techniques
3) Approaches to define meaningful priors for ML techniques in hydro(geo)logy
4) Novel methods for spatial and temporal evaluation/analysis of models
5) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data, etc.)
6) The role of SA in evaluating model consistency and reliability
7) Novel approaches and benchmarking efforts for parameter estimation
8) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)
9) Methods for detecting and characterizing model inadequacy

Drought and water scarcity affect many regions of the Earth, including areas generally considered water rich. The projected increase in the severity and frequency of droughts may lead to an increase of water scarcity, particularly in regions that are already water-stressed, and where overexploitation of available water resources can exacerbate the consequences droughts have. This may lead to (long-term) environmental and socio-economic impacts. Drought Monitoring and Forecasting are recognised as one of three pillars of effective drought management, and it is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability, and to develop innovative indicators and methodologies that translate the data and information to underpin effective drought early warning and risk management.

This session addresses statistical, remote sensing, physically-based techniques, as well as artificial intelligence and machine learning techniques; aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and water scarcity. These include, but are not limited to: precipitation, extreme temperatures, snow cover, soil moisture, streamflow, groundwater levels, and the propagation of drought through the hydrological cycle. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and integrating these with the needs and knowledges of water managers, policymakers and other stakeholders, are further issues that are addressed and are invited to submit to this session. Contributions focusing on the interrelationship and feedbacks between drought, low flows, and water scarcity, ; and the impacts these have on socio-economic sectors including agriculture, energy and ecosystems, are welcomed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the fields of water resources and drought risk management. Particularly welcome are applications and real-world case studies, both from regions that have long been exposed to significant water stress, as well as regions that are increasingly experiencing water shortages due to drought and where drought warning, supported by state-of-the-art monitoring and forecasting of water resources availability, is likely to become more important in the future.

This session brings together scientists, forecasters, practitioners and stakeholders interested in exploring the use of ensemble hydro-meteorological forecast and data assimilation techniques in hydrological applications: e.g., flood control and warning, reservoir operation for hydropower and water supply, transportation, and agricultural management. It will address the understanding of sources of predictability and quantification and reduction of predictive uncertainty of hydrological extremes in deterministic and ensemble hydrological forecasting. Uncertainty estimation in operational forecasting systems is becoming a more common practice. However, a significant research challenge and central interest of this session is to understand the sources of predictability and development of approaches, methods and techniques to enhance predictability (e.g. accuracy, reliability etc.) and quantify and reduce predictive uncertainty in general. Ensemble data assimilation, NWP preprocessing, multi-model approaches or hydrological postprocessing can provide important ways of improving the quality (e.g. accuracy, reliability) and increasing the value (e.g. impact, usability) of deterministic and ensemble hydrological forecasts. The models involved with the methods for predictive uncertainty, data assimilation, post-processing and decision-making may include machine learning models, ANNs, catchment models, runoff routing models, groundwater models, coupled meteorological-hydrological models as well as combinations (multimodel) of these. Demonstrations of the sources of predictability and subsequent quantification and reduction in predictive uncertainty at different scales through improved representation of model process (physics, parameterization, numerical solution, data support and calibration) and error, forcing and initial state are of special interest to the session.

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

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

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

Increased efforts are required to develop risk-informed and impact-based early warning systems, which can trigger life-saving early actions. This session focuses on advances in monitoring and impact-based forecasting of drought and rainfall-induced hazards. Heavy precipitation in catchments causes riverine floods or flash floods, erosion, sediment transport, debris flows, and shallow landslides, which may all result in catastrophic risks. Meteorological droughts (rainfall deficit) can lead to hydrological, agricultural, and socio-economic droughts, causing food insecurity and cross-sectoral impacts. For these rainfall-induced and drought-related hazards challenges exist regarding their temporal and spatial predictability. The rapid development of triggering rainfall events, scarce observations, and high variability and non-linearity of physical processes generate significant uncertainty. Also, the co-occurrence and compound effects of multiple hazards has to be understood. Droughts are insidious events, where the timing of the event in relation to the agricultural cycle is essential; thus, multi-timescale skilful forecasts are required. The substantial variability in societal exposure and the intricacies of socioeconomic vulnerability complicate the risk assessment. Valuable insights and best practices from the perspective of both knowledge producers and users will be presented. Contributions include: (1) Methods for translating forecasts into actionable impact-based information, such as risk modeling, inundation mapping, damage modeling, and impact modeling for the representation of societal vulnerability; (2) Action-oriented forecast verification and post-processing techniques to tailor forecasts for early action; (3) Monitoring and nowcasting of heavy precipitation events based on remote sensing to complement rain gauge networks; new direct and indirect observation techniques for the observation of rainfall-induced-hazards, and validation of forecasting approaches; (4) Short-range heavy precipitation forecasting (Numerical Weather Prediction), seamless forecasting strategies, and ensembles for the representation of uncertainties; development of integrated short-range hydro-meteorological forecasting chains and approaches for predicting rainfall-induced hazards in gauged and ungauged basins; (5) Understanding and modeling of surface water floods, flash floods, geomorphic processes, impacts, and their cascading effects, at appropriate space-time scales.

Climate services have a well-recognised potential to support decision makers in adapting and coping to extreme weather events, climate variability and change. In this context, predictions on sub-seasonal and seasonal-to-decadal timescales (i.e., horizons ranging from months to decades) are essential. Recent decades have seen significant advances towards forecasting systems that can deliver tangible impact to water sectors such as water supply, energy production and agriculture, empowering decision makers in taking climate-smart decisions.
Despite these advances, crossing the last-mile in climate services remains a challenge, and barriers remain to actual uptake and use. These include the lack of understanding of end-user needs and the options end-users have to respond; limited understanding of the decision-making processes of users, and a poor recognition of the local knowledge they hold. In parallel to advancing the science that underpins these services, this calls for more human-centred approaches and the integration of local and traditional knowledges within climate services co-creation to foster uptake and use.
This session aims to cover research and operational advances in climate services for predicting water availability that are useful, usable, and also used to support decision-making in water sectors. It welcomes, without being restricted to, presentations on:
• Advances in sub-seasonal, seasonal and decadal hydrological predictions of water availability, including through process-based, data-driven, machine learning and hybrid methods; seamless forecasting techniques.
• Hydro-climate forecasts and projections, including hydrological extremes, downscaling, bias correction, temporal disaggregation and spatial interpolation;
• Impact-based assessments of forecasts for decision-making; and perspectives on forecast value for end-users.
• Action-based, multi-disciplinary research; engagement and co-creation of climate services with local stakeholders and communities; and integration of local knowledge to foster uptake.
We encourage presentations that have demonstrable impacts through improved uptake of advanced services, leading to better preparedness for climate extremes and adaptation to climate change for water resources management, drinking water supply, transport, energy production, agriculture, disaster risk reduction, forestry, health, insurance, tourism and infrastructure.

One of the key issues in addressing flood-related disasters is the development of improved flood forecasting and early warning systems. With the advancements in hydro-meteorological measurement techniques through ground-based weather radar systems and satellite-based surrogate measurements in data scarce regions and data availability at different spatial and temporal scales, improved methods for forecasting with reasonable lead times can be developed. Advanced innovative methods and conceptual improvements in existing approaches are required to address the modeling and management of extreme floods' spatial and temporal complexity. The ensemble forecasting technique continues to be used for different lead time durations and for probabilistic flood forecasting. Different flood forecasting methods, including conceptually simple ones, are in use around the globe considering the complexity of the river basins, the cost of the development of the models, the lack of comprehensive hydro-meteorological monitoring networks, and other issues. One of the major factors of the cascading uncertainty through the hydrological models needs to be addressed in the probabilistic flood forecasting systems. This session aims to connect, identify, and publish the efforts of researchers globally to improve flood forecasting and issue early warnings ahead of catastrophic events. Research studies and case-study-specific studies dealing with the evaluation and verification of hydro-meteorological data, advanced forecasting methods, significant advances in ensemble forecasting techniques, and early warning systems in data-scarce and rich regions are appropriate for this session. The session also welcomes studies based on physics-based models and data-driven models.

In recent decades, there has been a growing focus on non-structural approaches, particularly early flood forecasting and warning systems, as effective means to mitigate the adverse impacts of floods. These systems have attracted attention for their ability to provide timely information to both citizens and authorities, enabling them to take necessary actions to safeguard their properties and infrastructure without the need for physical modifications or additional space. Real-time flood forecasting (RTFF) systems have gained popularity in early flood warning.
Data for RTFF can be sourced from various outlets, though sometimes access to these sources can be limited or challenging. RTFF necessitates the modeling of complex distributed systems with high spatial and temporal intricacies. This demands substantial computing resources and may leave limited time for timely early warnings. Significant breakthroughs have occurred in recent decades to address major challenges in the key stages of RTFF, including data collection and preparation, model development, performance assessment, and practical applications.
The objective of this session is to address challenges and advancements in the field by leveraging state-of-the-art techniques, new frameworks, equipment, software tools, hardware facilities, and the integration of existing methods with contemporary algorithms. We will also explore digital innovations and their applications in new pilot studies.
Specifically, this session will concentrate on the following research areas related to RTFF, with a focus on but not limited to:
● Hydrological data collection, analysis, imputation, assimilation and fusion taken from various data sources including ground stations, radar stations, remote sensing (aerial/satellite)
● RTFF modelling including physically/processed-based, conceptually-based, experimentally-based or data-driven modelling such as artificial Intelligence (AI), machine learning (ML)
● Application RTFF for flood alleviation or engagement with the public and authorities, such as early warning and early action systems, digital innovations such as digital twins (DT), or integrated with digital technologies such as augmented reality (AR) and virtual reality (VR).
● The broader implications of RTFF and early warning systems as soft engineering approaches, including their impact on flood risk management, insurance, capacity building, and community resilience.

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 water cycle, especially in urban and regulated catchments, leading to further changes in the frequency, magnitude, and timing of hydroclimatic extremes in a warming climate. These modifications include reservoir and dam constructions, drainage systems, urban land expansion, urban infrastructure development, water abstraction, wastewater discharge, and associated management aspects and contingency plans.

Despite recent advances in hydrology research and technology developments in the Anthropocene, our understanding of human–water interactions in both large- and local-scale hydrologic systems remains elusive. This knowledge gap is mainly due to the complexity of human influences, limited and patchy records on human activities, and the inadequacy of conventional modelling approaches that are often designed for natural catchments assuming stationarity conditions. As a result, the accuracy and reliability of hydrological forecasting in these human-influenced catchments are significantly affected. Given the large populations potentially affected by water-related hazards in these areas, there is an urgent need for more focused attention and research.

This session will explore recent advances in hydrological forecasting (e.g. floods, droughts, moisture-driven landslides, and compound or cascading hydro-hazards) for urban and regulated catchments. We invite abstracts focusing on (but not limited to) the following topics:
• Developments and applications of advanced statistical, process-based, and machine learning models for forecasting hydroclimatic extremes in urban or regulated catchment
• Recent developments in data acquisition for capturing human activities (or proxy data), including in-situ measurements, remote sensing, paleoclimatic record, and social media, as well as hydrological dataset analytics and integration
• Novel quantitative approaches for human impacts (of any type) on the water cycle and hydrological processes
• Impact-based assessment of water-related risks, such as economic impacts, human health and safety, social and community impacts, and environmental impacts
• Uncertainty quantifications and risk assessments of singular and compound hydro-hazards under climate non-stationarity
• Improved visualization and effective communication methods designed for early warnings and short-to-long-range predictions of rare and ‘record-breaking' extreme events.

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 storages such as dams and reservoirs have played a central role throughout history in ensuring a steady water supply during dry periods, supporting various sectors including domestic, industrial, and agricultural needs. However, the increasing water demands due to population growth coupled with the ongoing climate extremes with their impacts on drought and precipitation patterns accentuate the crucial need for efficient and sustainable water reservoirs management. Projected global warming is expected to influence the operation and storage efficiency of water reservoirs (e.g., via intensified evaporative losses) posing serious risks to a wide range of stakeholders. Considering the intensity and frequency of recent climate extremes (drought, heatwaves, intensive precipitations), it is more important than ever to develop sustainable and effective water management as well as strategies that incorporate various environmental and socio-economic drivers and pressures affecting water reservoirs. This session solicits theoretical and experimental analyses that investigate managing both natural and human-made water reservoirs under different local and global change scenarios and identify the associated risks to sustainable water reservoirs management. The session aims to unite various contributions that present effective strategies, tools, and technologies to enhance sustainable management and implementation of freshwater storages. This includes, but not limited to, remote sensing methods, in situ measurements, AI-based approaches, and hydrological models for investigating present and future dynamics of freshwater availability, risk assessments, storage efficiency enhancement, and water allocation policies to devise the necessary action plans and appropriate adaptation schemes to cope with water scarcity in a warming climate.

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

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

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

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. Here, 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.

We are happy to announce our two solicited speakers:
Heidi Kreibich and the IAHS Panta Rhei community about Panta Rhei: decade of progress in research on change in hydrology and society
and
Elisabeth Krueger and colleagues about Reframing resilience-oriented urban water management: Learning from social-ecological-technological system interactions and uncertainties in a water scarce city

This session welcomes abstracts that consider how to observe, analyse and model feedbacks of people and water, and the effects of social and environmental changes on hydrological systems. It 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.
Examples of relevant topics include:
• 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 including theoretical models to isolate, conceptualize and/or simulate feedbacks in human water systems
• Creation of databases describing hydrology in human-impacted systems
• Data analysis 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, disability status, primary language, nationality/refugee status and cultural background in the impacts of hydrological extremes, risk perception, and during/after crises and emergencies

Water sustains societies, economies, and ecosystem services locally and globally. Competition and conflict over access to and use of freshwater resources in many regions around the world is increasing as a result of changes in water demand, coupled with shifts in water availability due to climate change and variability. To address these challenges, integrative approaches to water management and policy are required to balance and manage trade-offs between social, economic, and environmental uses of water. In addition, there is an emerging need for adaptive and flexible solutions capable of updating decisions to changing climatic and socio-economic conditions to enhance the resilience of water systems. This session will provide 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 to the session that: (i) identify knowledge gaps and improvements to understanding about the critical interconnections, feedbacks, and risks between water system components, (ii) highlight development of new methods or tools for evaluating and monitoring trade-offs and performance in water allocation and management between different users and sectors, (iii) evaluate alternative technological, policy, and/or governance interventions to address the water-food-energy-environment nexus in different locations and at various scales (local, regional, and/or global), and (iv) advance methods to evaluate risks to water systems and identify solutions to enhance their 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 nexus.

Climate change presents one of the most pressing global challenges, with far-reaching implications for both natural and human systems. Among the myriad consequences of climate change, the rise in climate extreme events has been well-documented through observational and modelling studies. These extremes, ranging from heavy precipitation and floods to heatwaves, wildfires, and droughts, exert profound and often devastating impacts on agriculture and the society. In this context, understanding the intricate connectivity between agricultural and hydrological systems under the influence of climate change has emerged as a critical research imperative, and necessitates a multidisciplinary approach.

Recent advancements in remote sensing, machine learning, and the development of process-based models offer immense potential for in-depth investigations into agriculture and hydrological interactions in a changing climate. This session aims to solicit and showcase research contributions that employ diverse methodologies, particularly Earth Observations, machine learning approaches, and numerical/statistical models, to monitor, simulate, and predict the interaction and feedback between agricultural and hydrological systems, spanning various spatiotemporal scales.

We invite researchers to engage in this session and contribute to the collective understanding of how climate change impacts the intricate relationship between agricultural and hydrological systems. Topics of interest include, but are not limited to:

1. Observational insights: Present observational evidence and case studies that shed light on observed climate extremes, their effects on agricultural systems, and hydrological responses.
2. Machine learning applications: Innovative applications of machine learning algorithms to analyze and predict the impacts of climate extremes on agriculture and hydrology.
3. Numerical and Statistical Modeling: Utilize numerical and statistical models to simulate and project future scenarios of climate-induced changes in agricultural and hydrological systems.
4. Scaling Effects: Investigate the interconnectedness of agricultural and hydrological systems across various spatial and temporal scales, elucidating regional and global implications.
5. Adaptation and resilience: Discuss strategies for adapting agriculture and hydrology to climate extremes, emphasizing the importance of building resilience in these systems.

Land and water are mutually intertwined and each decision on land is also impacting water and water-related ecological processes. Land use and land cover (LULC) can alter hydrological processes and ecosystem dynamics and thereby affect critical biosphere functions, such as food production. These changes can emerge directly from anthropogenic interventions, or indirectly as the result of climate change.

Novel approaches are needed to assess the impact of LULC changes on the hydrological cycle (e.g. streamflow, groundwater quantity and quality, evaporation and transpiration, soil moisture, and rainfall interception) and its associated water-related ecosystem services (WES), including primarily the production of food. This includes the analysis of non-local and non-linear effects, incorporating socio-ecological systems, and identifying the feedback between land and water systems. In light of these interlinkages, new perspectives and interdisciplinary approaches, such as ecohydrology, agro-hydrology as well as socio-hydrology, are needed to inform effective and equitable water resource management.

This session welcomes studies that explore the impacts of LULC changes on all water resources, hydrological processes, and associated WES, primarily food production. More specifically, we welcome studies including, but not limited to:

• Advances in the quantification of hydrological impacts of LULC changes through agro- eco- and socio-hydrological modelling or the analysis of experimental data
• Advances in multi- and interdisciplinary methodologies for the assessment of the water-land nexus, including considerations on food and water security
• Analysis and evaluation of policy interventions to manage the land-water nexus, such as ecological restoration schemes and nature-based solutions, with respect to their effectiveness and feasibility
• Socio-hydrological and hydro-social approaches dealing with land, water, and ecosystem management, aiming also to highlight feedback loops between social and bio-geophysical dynamics
• Disentanglement of LULC and climate change impacts on water resources (surface and groundwater, green water, atmospheric water) hydrological processes, and associated WES.

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, 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/

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 of GI, Sustainable Drainage Systems (SuDS), Low Impact Developments (LIDs) or Nature-based Solutions (NbS), 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 GI implementation;

· Laboratory-, field- or GIS-based studies which examine the effectiveness or cost/benefit ratio of GI solutions in relation to their wider ecosystem potential;

· Methods for enhancing, optimising and maximising GI system potential;

· Innovative and integrated approaches or systems for issues including (but not limited to): 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 the opportunities of GI at multiple spatial scales;

· Rethinking urban design and sustainable and resilient recovery following crisis onset;

· Engagement and science communication of GI systems to enhance community resilience.

Urban watersheds face unique challenges amidst the need of water to support society as well as nature. The compounding effects of high percentages of impervious surface areas, degraded 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 will focus on all urban environmental science research, with a particular focus on urban hydrology and urban water challenges, including flood forecasting for extreme events. We focus on a wide breadth of studies on urban hydrology characterization and catchment functioning, runoff and pollution in urban watersheds, flood alert technologies and drought risks in urban or urbanized regions, compound hazards and risk assessment methodologies and communication, green-blue infrastructure and storm water management solutions, as well as community engagement and climate adaptation strategies in urban areas.

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

We invite presentations concerning remote sensing of soil moisture, field experiments, data assimilation, Cal/Val activities and fiducial reference measurements (FRMs).
Remote sensing has made tremendous progress to provide robust estimates of soil moisture at different depths and scales. 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 global scale, instruments such as SMMR (1978-1987), AMSR (2002-), ERS/SCAT (1992-2000) provided information on surface soil moisture. Current L-band sensors such as SMOS (2009-) and SMAP (2015-), and active C-band observations with the Metop/ASCAT series (2006-) and Sentinel-1, also enable an accurate quantification of the soil moisture. Operational programmes like Copernicus and novel developments will further enhance our capabilities to monitor soil moisture from agricultural to climate scales. Furthermore, research has put a new focus on establishing rigorous guidelines for the installation, calibration, operation, maintenance, and use of in situ soil moisture measurements using metrological practices, as well as on the development of advanced quality control procedures for in situ soil moisture measurement networks to obtain so-called fiducial reference measurements (FRMs).

We encourage submissions related to soil moisture remote sensing, including:
- Field experiments and theoretical advances in microwave modelling
- High spatial resolution soil moisture estimation based on, e.g., Sentinel observations, GNSS reflections, or using novel downscaling methods.
- Preparation of future missions including passive L-band high resolution concepts, CIMR, Metop-SG/SCA, NISAR....
- Root zone soil moisture retrieval and soil moisture data assimilation in land surface models, hydrological models and in Numerical Weather Prediction models.
- Evaluation and trend analysis of soil moisture climate data records.
- Inter-comparison and inter-validation between land surface models, remote sensing approaches and in-situ validation networks.
- Uncertainty characterization across scales and progress towards traceable uncertainty budgets in particular for data assimilation
- Soil moisture reference networks, especially the establishment of FRMs.
- Application of satellite soil moisture products for improving hydrological and other applications.

Snow constitutes a freshwater resource for over a billion people worldwide. A high percentage of this water resource mainly comes from seasonal snow. The ongoing warming poses a significant risk to snow water storages, potentially leading to a drastic reduction in water supply and causing adverse effects on the ecosystems.
Therefore, understanding seasonal snow dynamics, possible changes, and implications have become crucial for water resources management.

Remote sensing technology plays a crucial role in monitoring snow properties and their hydrological implications across spatial and temporal scales, allowing for a better understanding of snow dynamics (e.g., the interaction of snow with small-scale, quick snow changes within a day, rain on snow events, snow-vegetation interaction).

This session focuses on studies linking the use of 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 snow hydrological impacts and snow environmental effects. Works including technique and data from different technologies (time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies, or other new applications) across spatial (from the plot to the global) and temporal (from instantaneous to multiyear) scales are welcome.

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, recently lauched and commissioned, 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 SMASH, and covering forecasting.

The socio-economic impacts associated with floods are increasing. Floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 0.8 billion people were affected by inundations in the last decade, while the overall economic damage is estimated to be more than $300 billion.
In this context, remote sensing represents a valuable source of data and observations that may alleviate the decline in field surveys and gauging stations, especially in remote areas and developing countries. The implementation of remotely-sensed variables (such as digital elevation model, river width, flood extent, water level, flow velocities, land cover, etc.) in hydraulic modelling promises to considerably improve our process understanding and prediction. During the last decades, an increasing amount of research has been undertaken to better exploit the potential of current and future satellite observations, from both government-funded and commercial missions, as well as many datasets from airborne sensors carried on airplanes and drones. In particular, in recent years, the scientific community has shown how remotely sensed variables have the potential to play a key role in the calibration and validation of hydraulic models, as well as provide a breakthrough in real-time flood monitoring applications. With the proliferation of open data and models in Earth observation with higher data volumes than ever before, combined with the exponential growth in deep learning, this progress is expected to rapidly increase.
We invite presentations related to flood monitoring and mapping through remotely sensed data including but not limited to:
- Remote sensing data for flood hazard and risk mapping, including commercial satellite missions as well as airborne sensors (aircraft and drones);
- Remote sensing techniques to monitor flood dynamics;
- The use of remotely sensed data for the calibration, or validation, of hydrological or hydraulic models;
- Data assimilation of remotely sensed data into hydrological and hydraulic models;
- Improvement of river discretization and monitoring based on Earth observations;
- River flow estimation from remote sensing;
- Deep learning based flood monitoring or prediction
Early career and underrepresented scientists are particularly encouraged to participate.

Accurate monitoring of various hydrological cycle components (e.g., precipitation, evaporation, water storage, and runoff) and the anthropogenic fluxes which modify them, as well as the development of models to reproduce them are important for improving our understanding of hydrological processes. Acquiring this understanding is a crucial prerequisite to ameliorate resource management, optimize the development of infrastructure, and adjust land use practices to changing climate conditions and hazards such as floods and droughts, in particular irrigation management.
Agriculture is the largest consumer of water worldwide and huge differences exist between modern irrigation technology and traditional practices. However, reliable and organized data about water withdrawals for agricultural purposes are generally lacking worldwide, thus making irrigation a key missing variable to close the water budget over anthropized basins. Climate changes and increasing human pressure, together with traditional wasteful irrigation practices are enhancing the conflictual potential in water use, even in countries traditionally rich in water. Hence, saving irrigation water and improving irrigation efficiency on large areas with modern techniques is an urgent required action.
Several studies have recently explored the possibility of monitoring the natural and anthropogenic components of the water cycle by leveraging remote sensing information in combination with ground-based observations and/or hydrological modelling.
In this session, we will focus on:
-the use of approaches combining remote sensing data, hydrological modelling, and in-situ data to estimate variables in natural, agricultural, and anthropized systems (such as irrigation volumes and timing); and to analyse hydrological extremes
-the combination of satellite data and hydrological modelling to improve water management approaches such as irrigation water use efficiency and precision farming
- the performance of remotely sensed data in multi-variable calibration and spatial evaluation of hydrological and agricultural models

Please note: This is a merged session with multiple topics.

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 are looking for research to increase 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 impact 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 (physically based models or artificial intelligence techniques) or a combination of different sources of data (remote sensing and in situ) and different imagery types (satellite, airborne, drone). Finally, we wish to attract presentations on developments of user-friendly platforms (as open as possible), 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).

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.
- 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.

Significant empirical and theoretical advancements have revealed the departure of hydrometeorological processes from classical statistical models, highlighting the scaling behavior of their variables, especially extremes, across state, space, and time. These extremes, along with the general statistics of hydrometeorological processes, are crucial inputs for hydrological applications, which have increasing importance in the (re)insurance industry. Among the most common applications, catastrophe models are developed to manage risk accumulation; disaster response is used to prepare (re)insurers financially after major events; Real Disaster Scenarios are built to stress-test (re)insurers exposure both in the present-day and future climate.
For instance, in the context of a flood risk model, estimating design rainfall not only involves determining the absolute rainfall amount for a specific return period but also requires understanding the intra-event rainfall distribution, spatial extension, and rainfall intensities at neighboring stations. When these details are underestimated, it can easily turn into a poor risk assessment and weaker financial protection. Additionally, connections between hydrometeorological extremes and climatic oscillations, such as NAO or ENSO, and their evolution in a changing climate, provide insights for long-term risk management in the re-insurance sector, as required for regulatory purposes.
The integration of supporting information and the application of advanced AI approaches offer as well unprecedented opportunities to enhance these estimates. This session invites submissions, among others, on the following topics:
- Coupling stochastic approaches with deterministic hydrometeorological predictions to better represent predictive uncertainty.
- Developing robust statistics under non-stationary conditions for design purposes.
- Parsimonious models of hydrometeorological extremes across various spatial and temporal scales for risk analysis and hazard prediction.
- Improving the reliable estimation of extremes with high return periods, considering physical constraints.
- Linking underlying physics and hydroclimatic indices with the stochastics of hydrometeorological extremes.
- Exploring supporting data sets for additional stochastic information and utilizing novel AI and machine learning approaches.
- Applications carried out jointly by the (re)insurance industry and research institutions.

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 the spatially compounding nature of extreme events. Also, clustering of extremes in time 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. Key challenges in advancing our understanding of spatio-temporal extremes and in developing new modeling approaches include: the definition of multivariate events; the dealing with large dimensions; the quantification of spatial and temporal dependence, together with the introduction of flexible dependence structures; the identification of potential drivers for spatio-temporal dependence; the estimation of occurrence probabilities, and the linking of different spatial and temporal scales. This session invites contributions which help to better understand processes governing spatio-temporal extremes and/or propose new ways of describing and modeling compounding events at different scales.