Large-sample hydrology (LSH) is essential for understanding and predicting hydrological variability and change across spatiotemporal scales. The field has advanced significantly in recent decades, supported by the growing availability of datasets including a large variety of hydrological systems. The session highlights recent advances in LSH, with a focus on understanding hydrological variability by making use of large-sample datasets, developing and improving these datasets, and working towards catchment classification and characterization. We welcome abstracts that contribute to these topics, particularly (but not exclusively) related to the following sub-topics:
1. Enhance process understanding based on LSH:
How can we use large-sample datasets to improve and synthesize hydrological process understanding and to transfer knowledge obtained in well-monitored catchments to data scarce regions? How can we bridge and consolidate findings from large-sample analyses with our understanding of hydrological processes from well-monitored or experimental catchments?
2. Develop and improve large-sample datasets:
How can we address current challenges, e.g., uneven spatial coverage, representativeness of catchment descriptors, dataset harmonization, and uncertainty quantification? How can we expand large-sample datasets by, e.g., including data at higher spatial and temporal resolution or real-time data? How can we ensure data quality? What new, unconventional data sources might be exploited? How can we (systematically) represent anthropogenic influences in large-sample datasets?
3. Advance hydrologic modeling:
How can we improve process-based and data-driven modeling by using LSH? How can functional knowledge obtained from large-sample studies be transferred to ungauged basins or data-scarce regions through modeling? How can we develop new models and workflows to evaluate hydrological response under change, also with regard to human impacts? How can processes that extend beyond the topographic catchment boundaries, e.g., groundwater or water quality, be integrated into large-sample hydrological models?
4. Improve catchment classification and characterization:
How can we use static and dynamic catchment descriptors available in large sample datasets to derive groups of catchments with similar hydrologic behavior? How can we improve catchment characterization, e.g., by developing and including new descriptors or replacing static with dynamic attributes?