The Earth's land surface is characterized by tremendous natural heterogeneity and human engineered modifications, both of which are significantly challenging to represent in land surface and hydrology regional-global scale models. While natural processes are included in most models, anthropogenic processes are rarely modelled. Satellite remote sensing monitor the hydrological cycle in its entirety, without differentiating whether natural or human driven processes cause its variability. Thus, remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. This session solicits innovative contributions on remote sensing approaches focused on capturing the impact of human footprint on the terrestrial water cycle. The session also encourages contributions on the development of data-driven modeling efforts that can provide alternatives to traditional model-based data assimilation efforts. Studies that quantify of the impact of anthropogenic processes through modeling and remote sensing on the water cycle are also encouraged.