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

Research in both ecology and AI strives for predictive understanding of complex systems,
where nonlinearities arise from multidimensional interactions and feedbacks across multiple …

Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and
cannot be applied to flows with large Reynolds numbers. Low-resolution large eddy …

Prediction of dynamic environmental variables in unmonitored sites remains a long-standing
challenge for water resources science. The majority of the world's freshwater resources have …

Physics-based models are widely used to study dynamical systems in a variety of scientific
and engineering problems. However, these models are necessarily approximations of reality …

Although groundwater discharge is a critical stream temperature control process, it is not
explicitly represented in many stream temperature models, an omission that may reduce …

Sea Surface Temperature (SST) prediction has attracted increasing attention due to its
critical role in climate change. Traditional SST prediction methods can be mainly divided into …

In the era of big data, there has been a surge in the availability of data containing rich spatial
and temporal information, offering valuable insights into dynamic systems and processes for …

This paper presents an overview of scientific modeling and discusses the complementary
strengths and weaknesses of ML methods for scientific modeling in comparison to process …