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 …

Hydrologic signatures are quantitative metrics or indices that describe statistical or
dynamical properties of hydrologic data series, primarily streamflow. Hydrologic signatures …

Researchers have attempted to use machine learning algorithms to replace physically
based models for streamflow prediction. Although existing studies have contributed to …

Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …

The machine learning algorithm 'random forest'has been applied in many areas of water
resources research including discharge simulation. Due to low setup and operation cost …

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 …

Deep learning is becoming an increasingly important way to produce accurate hydrological
predictions across a wide range of spatial and temporal scales. Uncertainty estimations are …

We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-
GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates …

Large-sample datasets containing hydrometeorological time series and catchment attributes
for hundreds of catchments in a country, many of them known as “CAMELS”(Catchment …

Hydrology plays a central role in applied and fundamental environmental sciences, but it is
well known to suffer from an overwhelming diversity of models, particularly to simulate …