Over the past decades, the scientific community has made significant efforts to simulate
flooding conditions using a variety of complex physically based models. Despite all …

A wide variety of processes controls the time of occurrence, duration, extent, and severity of
river floods. Classifying flood events by their causative processes may assist in enhancing …

Long short‐term memory (LSTM) networks represent one of the most prevalent deep
learning (DL) architectures in current hydrological modeling, but they remain black boxes …

Inferring the mechanisms causing river flooding is key to understanding past, present, and
future flood risk. However, a quantitative spatially distributed overview of the mechanisms …

Hydrometeorological flood generating processes (excess rain, short rain, long rain,
snowmelt, and rain‐on‐snow) underpin our understanding of flood behavior. Knowledge …

Climate change may systematically impact hydrometeorological processes and their
interactions, resulting in changes in flooding mechanisms. Identifying such changes is …

Flood is one of the most devastating natural hazards and is responsible for sizeable social-
economic losses and substantial fatalities. Flood events with diverse behavior …

Assessments of climate change impacts on runoff regimes are essential to climate change
adaptation and mitigation planning. Changing runoff regimes and thus changing seasonal …

Understanding hydrological variability is of crucial importance for water resource
management in sub-Saharan Africa (SSA). While existing studies typically focus on …

Abstract Machine learning (ML) algorithms are being increasingly used in Earth and
Environmental modeling studies owing to the ever‐increasing availability of diverse data …