Predictions of floods, droughts, and fast drought‐flood transitions are required at different
time scales to develop management strategies targeted at minimizing negative societal and …

Hydrological modeling, leveraging mathematical formulations to represent the hydrological
cycle, is a pivotal tool in representing the spatiotemporal dynamics and distribution patterns …

The rapid growth of data in water resources has created new opportunities to accelerate
knowledge discovery with the use of advanced deep learning tools. Hybrid models that …

While deep learning (DL) models exhibit superior simulation accuracy over traditional
distributed hydrological models (DHMs), their main limitations lie in opacity and the absence …

The diversity in hydrologic models has historically led to great controversy on the correct
approach to process-based hydrologic modeling, with debates centered on the adequacy of …

Hydrological model calibration combining Earth observations and in situ measurements is a
promising solution to overcome the limitations of the traditional streamflow‐only calibration …

The findings of hydrological modeling studies depend on which model was used. Although
hydrological model selection is a crucial step, experience suggests that hydrologists tend to …

In hydrology, two somewhat competing philosophies form the basis of most process-based
models. At one endpoint of this continuum are detailed, high-resolution descriptions of small …

The choice of hydrological model structure, that is, a model's selection of states and fluxes
and the equations used to describe them, strongly controls model performance and realism …

A general formulation of a surface water hydrological model is posed that enables multiple
numerical schemes, model structures, discretization schemes, and interpolation approaches …