Reliable streamflow forecasts with associated uncertainty estimates are essential to manage
and make better use of Australia's scarce surface water resources. Here we present the …

Global overviews of upcoming flood and drought events are key for many applications,
including disaster risk reduction initiatives. Seasonal forecasts are designed to provide early …

Operational global-scale hydrological forecasting systems are widely used to help manage
hydrological extremes such as floods and droughts. The vast amounts of raw data that …

Seeking more accuracy and reliability, the hydrometeorological community has developed
several tools to decipher the different sources of uncertainty in relevant modeling processes …

For much of the last century, forecasting centers around the world have offered seasonal
streamflow predictions to support water management. Recent work suggests that the two …

Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological
forecasts, hydrological model structure, and parameterization, as well as in the observed …

Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in
a range of applications. However, it remains unclear whether DL models can produce …

The length of streamflow observations is generally limited to the last 50 years even in data-
rich countries like France. It therefore offers too small a sample of extreme low-flow events to …

This study develops a new error modelling method for ensemble short-term and real-time
streamflow forecasting, called error reduction and representation in stages (ERRIS). The …

Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly
used for the operational forecasting of floods by European hydrometeorological agencies …