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 predictions, 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 present and future state of water resources worldwide and within large-scale systems, including hydrological extremes and uncertainty assessment.
(ii) the use of global Earth observations and in-situ datasets for large-scale hydrology and data assimilation techniques for large-scale hydrological models, including machine learning and hybrid modelling approaches.
(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.
(v) understanding interactions and feedbacks between hydrological processes, water management, and human intervention activities (e.g. reservoirs, dams, irrigation) across large-scale systems.