Ecohydrology encompasses the interactions of the hydrological, biogeochemical, and ecosystem processes. Understanding and representing these processes accurately in ecohydrological models is essential in the context of rapidly changing climate and environmental systems. With the advancement of computing technology, AI/ML tools, and hydrological data, models are being improved and developed for various applications. This session aims to collate the ideas for novel frameworks for model development, improvement of existing models, improvement in parameterization methods, and uncertainty quantification. Potential contributions include (but not limited to):
1. Introduce experimental knowledge into watershed-scale models
2. Improvement in model structure and coupling between modelling systems
3. Improving parameterization schemes for ecohydrological models
4. Model evaluation strategies and criterion
5. Improvement in modelling in data-scarce regions.