Session 1: Future data for water future

Pressing water-related challenges, such as climate and land-use change, water scarcity and environmental pollution, require fact-based decision-making and intensive interdisciplinary efforts to secure and advance science-based monitoring of our ecosystems. At the same time, data is being produced at an ever increasing speed and volume, be it from satellites, sensor networks or model simulations. The challenge lies in the potential mismatch between these rapidly expanding data sources and the types of data our communities traditionally deal with, which are often sparse, irregularly spaced, spatially or temporally coarse grained, distributed or thematically incomplete, including e.g., historical observations, field surveys or laboratory data. These various, sometimes contrasting challenges can be addressed through collaborative research data management and knowledge extraction supported by new technologies including machine learning (ML) and AI. This session focuses on services and tools as well as efficient workflows and powerful data analysis frameworks for effective data exploitation, addressing discipline-specific approaches from the water research community, the question of appropriate standards and the relevance and implementation of the FAIR principles - bridging theory and practice.

Session 2: Water data and knowledge generation in times of AI and citizen science

While AI and ML are revolutionizing data analysis in water research, they are not a silver bullet for addressing the global water crisis; its effectiveness is limited by the quality and availability of ground-truth data and process-understanding required to understand complex hydrological systems. This session addresses the critical need for a dedicated focus on robust data generation through – for example – low-cost monitoring, integrated approaches and citizen science, moving beyond traditional, high-maintenance infrastructure toward scalable, distributed networks. We invite research which demonstrates that algorithms cannot replace a fundamental process understanding of hydrological systems emphasizing that the value of data lies in its scientific integrity and its ability to enhance process understanding. Thus, the session highlights the intrinsic value of data and empirical process understanding and the ongoing necessity of scientific rigor to transform raw measurements into actionable knowledge.

Session 3: Improved water balance quantification and process representation in the Anthropocene

In the Anthropocene era, human interventions are increasingly shaping the hydrological cycle, from intensive irrigation and urbanisation to large-scale water management. These interventions are exacerbated by the impacts of climate change. Accurately quantifying the components of the water balance in these heavily managed catchments remains a significant challenge, as traditional monitoring and modelling often struggle to capture the feedbacks between humans and water. Although remote sensing products such as MODIS for evapotranspiration and GRACE for storage provide valuable insights, there is an urgent need to move beyond these coarse-scale estimates to capture local complexities and processes. This session focuses on advancing the representation of hydrological processes in anthropogenic landscapes through novel monitoring techniques and improved model structures. We welcome contributions that integrate human activities, such as water abstraction and infrastructure, directly into hydrological models, or that leverage high-resolution data and multi-source fusion to overcome the limitations of standard satellite products. By bridging the gap between observation and process representation, this session aims to foster an interdisciplinary dialogue on closing the water balance in a world where 'natural' catchments are no longer the norm.

Session 4: “Shifting baselines” 

In times of climate change, land-use transformation, and evolving societal expectations, historical reference conditions are increasingly difficult to reconstruct and may no longer represent achievable or even desirable targets. This session addresses the concept of shifting baselines, a concept that encompasses how loss of information about former environmental conditions shifts assumptions about what is ‘natural’ over time, seeking to critically examine how past and present reference states are defined, measured, and interpreted. We invite contributions that integrate long-term data series, paleo-records, historical environmental archives, and retrospective analyses. As freshwater ecosystems are typically constituted in linked socio-ecological or biocultural systems, both ecological baselines and linked baselines of human-environment interactions, are important to contextualise past data to better understand the present and predict the future. Aquatic, especially freshwater, systems face additional challenges in that much ecological change happens below the surface, and can therefore be socially invisible, potentially influencing restoration targets. Particular emphasis is placed on the need to move beyond static benchmarks toward adaptive, function-oriented reference frameworks that acknowledge cultural values, governance contexts, and socio-hydrological feedbacks. By linking data, process understanding, and normative dimensions, the session contributes to developing scientifically robust and societally meaningful reference systems for shaping resilient water futures.