Hydrological models are formulations of hypotheses about natural systems' behavior. These formulations are constructed and refined to maximize the model realism, i.e., the agreement of the model with the reality. To enhance the model realism, both quantitative data and qualitative expert knowledge known as soft data, gained by experimentalists and modelers over the course of the last decades, play crucial role. However, increasing model realism, which potentially increases the model degrees of freedom as more details and processes are modelled, may come at the cost of increasing the model predictive uncertainty. Considering this potential contradiction, this session invites contributions that improve our understanding, simulation, and predictive capability of hydrologic systems, across a range of scales from hillslope to meso- and larger-scales. Potential contributions may include (but not limited to): (1) improving model structural adequacy, (2) introducing new formulations for model components (constitutive functions) capturing the internal and external model fluxes or their overall behavior, (3) upscaling and applying the experimentalists' knowledge at catchment scale, (4) investigating the added value of any new source of data and any new model formulation, such as formally differentiating the notions of velocity and celerity with help of tracers, (6) better accounting for biological processes affecting the hydrological fluxes such as transpiration and (7) formally accounting for hysteresis in different hydrologic processes including the development of hysteretic model components.

This session is organized as part of the grass-root modelling initiative on "Improving the Theoretical Underpinnings of Hydrologic Models" launched in 2016.

Invited Speaker: James Kirchner