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

Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of
catchments to go beyond individual case studies and derive robust conclusions on …

A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe
efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is …

Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …

The goal of this commentary is to critically evaluate the use of popular performance metrics
in hydrologic modeling. We focus on the Nash‐Sutcliffe Efficiency (NSE) and the Kling …

Predictions of hydrologic variables across the entire water cycle have significant value for
water resources management as well as downstream applications such as ecosystem and …

Deep learning methods have recently shown a broad application prospect in rainfall-runoff
modeling. However, the lack of physical mechanism becomes a major limitation in using …

Hydrological signatures are now used for a wide range of purposes, including catchment
classification, process exploration, and hydrological model calibration. The recent boost in …

The findings of hydrological modeling studies depend on which model was used. Although
hydrological model selection is a crucial step, experience suggests that hydrologists tend to …

The choice of hydrological model structure, that is, a model's selection of states and fluxes
and the equations used to describe them, strongly controls model performance and realism …