The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-
BMA methods have been widely used to quantify flood forecast uncertainty. This study …

Extreme climatic events have considerable impacts on society, and their prediction is an
essential tool for climate change adaptation. A reliable forecast of dry and wet extremes is …

The calibration of macroscale hydrological models is often challenged by the lack of
adequate observations of river discharge and infrastructure operations. This modeling …

Hydrological climate variables such as precipitation, temperature, humidity,
evapotranspiration, infiltration, and streamflow provide useful information for the effective …

Streamflow flash droughts (SFDs) occur due to a rapid decline in streamflow and cause
major challenges associated with water availability for downstream ecosystems, hydropower …

Flood early warning systems (FEWS) are crucial for flood risk management; however,
several catchments in the developing world are still far behind in all aspects of FEWS and …

Pakistan suffered from severe floods in the past, but in August 2022, the country
experienced exceptional extreme rainfall events that caused widespread and catastrophic …

Process-based hydrology models are critical for understanding streamflow and water supply
under global change. However, these models require parameterization which introduces …

The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-
BMA methods have been widely used to quantify flood forecast uncertainty. This study, for …

Climate change projections, essential for water resource management, are dominated by
multiple sources of uncertainty, including scenario uncertainty and Internal Climate …