Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

Predictions of floods, droughts, and fast drought‐flood transitions are required at different
time scales to develop management strategies targeted at minimizing negative societal and …

The management of water resources depends heavily on hydrological prediction, and
advances in machine learning (ML) present prospects for improving predictive modelling …

Groundwater-sustained baseflow is a vital source of river flow, especially during dry
seasons. The proportion of river flow sustained by baseflow—the baseflow index—is …

Groundwater is by far the largest unfrozen freshwater resource on the planet. It plays a
critical role as the bottom of the hydrologic cycle, redistributing water in the subsurface and …

The turbulent surfaces of rivers and streams are natural hotspots of biogeochemical
exchange with the atmosphere. At the global scale, the total river-atmosphere flux of trace …

Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …

Abstract Earth System Models (ESMs) are essential tools for understanding and predicting
global change, but they cannot explicitly resolve hillslope‐scale terrain structures that …

Between the land and ocean, diverse coastal ecosystems transform, store, and transport
material. Across these interfaces, the dynamic exchange of energy and matter is driven by …

Tropical ecosystems have the highest levels of biodiversity, cycle more water and absorb
more carbon than any other terrestrial ecosystem on Earth. Consequently, these ecosystems …