A new study explains why the arsenic that has accumulated in lake bottom sediments is more harmful to the lake ecosystems in shallow lakes.
A new study suggests that the commonly used split sample approach in hydrology, where time series are divided into a part for model calibration and a part for model validation, should be abandoned.
Using turbulent heat fluxes as an example, a new study shows that exchange of information between process-based models and deep learning methods may lead to improved predictions.
A clever combination of hydrologic modelling and discharge estimates from the Landsat satellite provides good discharge estimates throughout the Missouri river basin.
Around 16 percent of large-scale droughts over land originate above the ocean and these types of droughts are more extensive and severe than droughts that originate over land.
Machine learning and data on aquifer type, sediment thickness, and proxies for irrigation water use has been used to produce the most comprehensive map of land subsidence in the western U.S. to date.
Inhabitants of Bangladesh have deepened drinking water wells to avoid extracting arsenic-rich groundwater from shallow aquifers, but these may not be free from pollution either.
A new research effort has mapped 35 years of naturalized streamflow for 2.94 million river reaches worldwide: an invaluable dataset for hydrology, biogeochemistry, ecology, and remote sensing.
A unique set of high-frequency groundwater level monitoring reveals a loss of approximately ten million cubic meters of groundwater after a major earthquake.
Hydrological models are usually calibrated using observations of streamflow, but a new method uses remotely sensed land surface temperature for this purpose.