Hybrid hydrological modelling of the biophysical impacts of earth’s greening on streamflow

Vegetation plays an important but complicated role in modulating land-atmosphere interactions and the water cycle. Under global change, increasing vegetation greenness trends have been observed, which further complicate the control of vegetation in the earth system. Despite growing interest in the role of vegetation in the hydrological processes, large uncertainties still exist, particularly when it comes to the underexplored response of streamflow to vegetation greening. In this study, we explore the watershed-relevant biophysical controls of vegetation greening on streamflow. In order to do so, we develop a hybrid ecohydrological model. This model adheres to the water balance principles, while it simultaneously has a flexible structure that enables integrating physical insights from observational data. The multi-task learning optimization ensures physical consistency across a range of processes and temporal frequencies, which allows us to investigate the cascading impacts of vegetation changes across the water cycle, leading up to the streamflow as an end-process. Ecohydrological insights are directly derived from observational data, while physically meaningful model parameters reflect how ecosystem functions and hydrological processes respond to vegetation changes. We find that the marked change in streamflow can be attributed to vegetation change controls on diverse biophysical processes. Our research highlights the potential of hybrid models to capture complex earth system processes by exploiting multiple observational data streams, machine learning and physical constraints.