A new study shows why fine sediments in rivers are not simply proportional to the water flow across the United States.
Years of daily readings provide an unprecedented view into how a submerged aquatic meadow kept nitrogen from reaching the St. Lawrence Estuary as well as insights on how climate change may alter it.
Extrapolation or not? Big data may help deep learning to go places where it has not been before by transferring learned hydrologic relationships.
Mounting evidence suggests the need for improved water planning strategies and revamped hydrological models.
Researchers tracked long-term sediment dynamics in Canada’s Quesnel Lake following the 2014 failure of a dam that spilled record-breaking amounts of contaminated mining waste.
In northern Bangladesh, residents are losing their livelihoods, homes, and personal safety when water carries sand and gravel into their communities.
Data from 45 burned sites help researchers better understand climate change and wildfires’ impact on snowpack.
The implications of nature not conforming to statistical assumptions can be devastating; researchers describe why extreme floods may be bigger than we assume.
The new graph convolutional recurrent neural network (GCRNN) will enable water utilities to forecast water use, even if some sensors fail.
Benefits might accrue for both wildlife and climate resiliency if more floodplains along the lower Missouri River were allowed to flood.