Understanding and representing hydrological processes is the basis for developing and improving hydrological and Earth system models. Relevant hydrological data are becoming increasingly available globally, opening new avenues for modelling (model parametrization, evaluation, and application) and process representation. As a result, a variety of models are developed and trained by new quantitative and qualitative data at various temporal and spatial scales.
In this session, we welcome contributions on novel frameworks for model development, evaluation and parametrization across spatio-temporal scales.
Potential contributions could (but are not limited to):
(1) advance seamless modeling of spatial patterns in hydrology and land models using new data products and earth observations;
(2) improve model structure by representing often neglected processes in hydrological models such as human impacts or vegetation dynamics;
(3) provide novel concepts for improving the characterization of internal and external model fluxes and their spatio-temporal dynamics;
(4) introduce new approaches for model calibration and evaluation, especially to improve process representation, and/or to improve model predictions under changing conditions;
Songjun Wu