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 …

Predictive performance is one of the most important issues for practical applications of
ensemble hydroclimatic forecasts. While different forecasting studies tend to use different …

Estimating how much water is flowing through rivers at the global scale is challenging due to
a lack of observations in space and time. A way forward is to optimally combine the global …

Hydrological drought forecasts outperform meteorological ones, which is anticipated coming
from catchment memory. Yet, the importance of catchment memory in explaining …

In recent years, the growing impact of climate change on surface water bodies has made the
analysis and forecasting of streamflow rates essential for proper planning and management …

This study examines the spatial and temporal variability of flow intermittence over the period
1970–2018 across four countries (Australia, France, UK and the conterminous USA) …

This paper details the development and evaluation of the enhanced future FLows and
Groundwater (eFLaG) dataset of nationally consistent hydrological projections for the UK …

Abstract Machine-learning algorithms have shown promise for streamflow forecasts,
reservoir operations, and scheduling, but have exhibited lower accuracy in predicting …

Advancement in river flow prediction systems can greatly empower the operational river
management to make better decisions, practices, and policies. Machine learning methods …

The predictability of droughts in China was investigated using a series of statistical, dynamic
and hybrid models. The results indicate that, statistical models exhibit better skill in …