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

Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

Global vegetation and associated ecosystem services critically depend on soil moisture
availability which has decreased in many regions during the last three decades. While …

The response of vegetation physiology to drought at large spatial scales is poorly
understood due to a lack of direct observations. Here, we study vegetation drought …

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-
relevant scales is critical to mitigating climate change and ensuring sustainable food …

The hydrological cycle is an important process that controls how and where water is
distributed on Earth. It includes processes including transpiration, evaporation …

Deep learning methods have frequently outperformed conceptual hydrologic models in
rainfall-runoff modelling. Attempts of investigating such deep learning models internally are …

Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …

Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short‐Term
Memory (LSTM) is widely used to for such predictions, owing to its excellent migration …

Current modeling approaches in hydrology often rely on either physics-based or data-
science methods, including Machine Learning (ML) algorithms. While physics-based models …