Drought prediction is of critical importance to early warning for drought managements. This
review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid …

Global climate models (GCMs) are developed to simulate past climate and produce
projections of climate in future. Their roles in ascertaining regional issues and possible …

Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws
inspiration from the intricate movement and flow patterns of water masses during flooding …

This study proposes a new hybrid model for monthly streamflow predictions by coupling a
physically based distributed hydrological model with a deep learning (DL) model …

Hydrological drought is not only caused by natural hydroclimate variability but can also be
directly altered by human interventions including reservoir operation, irrigation, groundwater …

Deep learning has been widely used in hydrological prediction such as monthly streamflow
and its performance is usually dependent on the abundance of training data. Even though …

Calibration is necessary for improving raw forecasts generated by global climate models
(GCMs) to fully utilize potential benefits of the forecasts in practical applications. Based on …

Multi-model ensemble forecasts are obtained by weighting multiple General Circulation
Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North …

This is the second paper of a two-part series on introducing an experimental seasonal
hydrological forecasting system over the Yellow River basin in northern China. While the …

The state of Iowa in the US Midwest is regularly affected by major floods and has seen a
notable increase in agricultural land cover over the twentieth century. We present a novel …