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 …

Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or
storms have devastating effects each year. One of the key challenges for society is …

This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …

High-quality datasets are essential to support hydrological science and modeling. Several
CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist …

An intensified hydrological cycle with global warming is expected to increase the intensity
and frequency of extreme precipitation events. However, whether and to what extent the …

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 …

There is a drastic geographic imbalance in available global streamflow gauge and
catchment property data, with additional large variations in data characteristics. As a result …

Climate change and increasing water use associated with socio-economic growth have
exacerbated the water crisis in many parts of the world. Many regional studies rely on Earth …

The accurate representation of spatio-temporal patterns of precipitation is an essential input
for numerous environmental applications. However, the estimation of precipitation patterns …

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 …