Abstract Model calibration and validation are critical in hydrological model robustness
assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data …

This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …

River flow forecasting models assist in the understanding, predicting, monitoring, and
managing of issues related to surface-water resources, such as water quality deterioration …

A data set of observed daily precipitation, maximum and minimum temperature, gridded to a
1/16 (~ 6 km) resolution, is described that spans the entire country of Mexico, the …

Near real-time quantitative precipitation estimates are required for many applications
including weather forecasting, flood forecasting, crop management, forest fire prevention …

Model intercomparison studies are carried out to test and compare the simulated outputs of
various model setups over the same study domain. The Great Lakes region is such a …

Climatic changes can profoundly alter hydrological conditions in river basins. Lake Baikal is
the deepest and largest freshwater reservoir on Earth, and has a unique ecosystem with …

Hydrological models are usually calibrated to in‐situ streamflow observations with
reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged …

Abstract High‐resolution, coupled, process‐based hydrology models, in which subsurface,
land‐surface, and energy budget processes are represented, have been applied at the …

Abstract Between January 2013 and December 2014, water levels on Lake Superior and
Lake Michigan‐Huron, the two largest lakes on Earth by surface area, rose at the highest …