Deadline for manuscript submissions: 1 June 2020.

Dear Colleagues,

Atmosphere dedicates this Special Issue to recent advances of coupled hydrological–vegetation–atmosphere modelling. In recent years, land surface modules of Earth system models have increased their complexity by introducing a detailed description of hydrological and vegetation dynamics, and their feedbacks with the atmosphere. Simlteneously, global or contintental scale hyperresolution hydrological models can be run operationally for the first time. Such advances have provided unprecedented knowledge on the global scale interactions between the land and the atmophere, and the terrestrial water and carbon cycles enhancing our prediction skill with respect to climate change projections, and natural hazard risk management, including floods and droughts. However, in spite of such advences, coupled hydrological–vegetation–atmosphere modelling is a highly challenging task, including large computational demand and limited data contraining model parameters. Advances in computer science and remote sensing provide the capabilty of overcoming such issues. The continuously increasing computational power enables, for the first time, the exporation of uncertainty in coupled Earth system dynamics. Remote sensing provides global scale data for hydrological, meteorological, and vegetation dynamics at fine spatial and temporal scales. The full potential of integrating the acievements of computer science and remote sensing with coupled models, in order to understand Earth system dynamics and their uncertainty in depth is yet to be achieved.

For this Special Issue, we invite you to contribute your research on new developments and applications of coupled hydrological–vegetation–atmosphere models. Contributions include but are not limited to: hyper-resolution models investigating the importance of the coupled water and carbon cycles on weather and climate and flood/drought forecasting, model-data fusion of new streams of data, such as satellite remote sensing and novel plant trait databases, and model uncertainty quantification.

Dr. Athanasios Paschalis
Guest Editor