Understanding and representing hydrological processes is the basis for developing and improving hydrological and Earth system models. Hydrological observations, experiments, Earth observations, and new data products are becoming increasingly available across spatial and temporal scales, opening new opportunities to improve the representation, parametrization, evaluation, and understanding of hydrological processes in models.
In this session, we welcome contributions that advance hydrological modelling through new approaches to process representation, model development, parametrization, and evaluation across spatio-temporal scales. We particularly encourage contributions that use new observations, experiments, or modelling concepts to improve our understanding and representation of hydrological processes, and that provide insights or approaches that can be transferred beyond individual study sites or applications.
Potential contributions could (but are not limited to):
1. Advance seamless modelling of spatial patterns in hydrology and land models using new data products and Earth observations, particularly where these data provide new constraints on process representation;
2. Improve model structure by representing often neglected or insufficiently represented processes in hydrological models, such as human impacts, river regulation, irrigation, and vegetation dynamics;
3. Provide novel concepts for improving the characterization of internal and external model fluxes and their spatio-temporal dynamics, including approaches that enable better representation and understanding of hydrological processes across scales;
4. Introduce new approaches for model calibration and evaluation, especially approaches that diagnose deficiencies in process representation, improve understanding of model behaviour, and/or improve model predictions under changing conditions;
5. Upscale experimental and process-based knowledge from plot, hillslope, or other small scales to catchment, regional, or larger-scale models;
We particularly encourage contributions that go beyond the application of established models to individual study sites and instead provide new insights, methods, or frameworks for improving process representation. Contributions demonstrating how observations, experiments, or novel modelling approaches can lead to improved and transferable process understanding and model representation are especially welcome.