Large-Sample Hydrology: Advancing dataset developments, enhancing process understanding, and unifying insights through catchment modeling

Convener: Thiago NascimentoECSECS | Co-conveners: Martina Kauzlaric, Paul C. AstagneauECSECS, Adriane HövelECSECS, Ryoko ArakiECSECS

Abstract submission

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