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 …

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

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …

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 …

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 …

Neural networks have been shown to be extremely effective rainfall-runoff models, where
the river discharge is predicted from meteorological inputs. However, the question remains …

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

Human activities both aggravate and alleviate streamflow drought. Here we show that
aggravation is dominant in contrasting cases around the world analysed with a consistent …

Streamflow forecasting over gauged and ungauged basins play a vital role in water
resources planning, especially under the changing climate. Increased availability of large …