Large samples of catchments can provide insights into hydrological processes that cannot be obtained from small samples. This session aims to showcase recent data- and model-based efforts on large-sample hydrology, which advance the characterisation, understanding and modelling of hydrological diversity. We welcome abstracts from a wide range of fields, including catchment hydrology, land-surface modelling, eco-hydrology and groundwater hydrology, which look to explore the following aspects of large-sample hydrology:

1. Identification and characterisation of dominant hydrological processes with limited data (usually precipitation, temperature, streamflow) - how far can we get using hydrological signatures?
2. Landscape characterisation - hydrological processes are shaped by the interplay of landscape attributes (like topography, climate, vegetation, soil, geology), how to better understand this interplay using available data sets?
3. Generalisation from the catchment to continental scale - how can we use large samples of catchments to refine process understanding and modelling at the regional to global scale?
4. Hydrological similarity and catchment classification - including across continents
5. Quantification and synthesis of data quality and uncertainty - including across regional or national borders
6. Human intervention and land cover changes during the record period - how to characterise and account for those processes?
7. Hypotheses testing - using large samples to test the generality of existing hypotheses (particularly those originally formulated on small samples of catchments)

We encourage abstracts addressing any of these issues, in particular those aiming at reducing the geographical gap (i.e., contributing to a more balanced spatial distribution of large-sample data sets) and those making use of global data sources (e.g., remote-sensed data or reanalyses) to facilitate comparison between catchments from different parts of the globe.

In addition to this session, we are planning to organise a meeting at EGU 2018 to discuss and coordinate the production of large-sample data sets worldwide. If you are interested and would like to receive future updates, please email Nans Addor (N.Addor@uea.ac.uk) and Gemma Coxon (Gemma.Coxon@bristol.ac.uk).