Unveiling diverse scenarios and implications for water resource management in a rapidly changing world.

Using a combination of satellite data and modeling to study the temperatures and humidity people might feel in urban areas, researchers have pinpointed who in the U.S. is most vulnerable to heat stress.

Quantifying the long-term destination of anthropogenic emissions and robustness of global carbon sinks.

New study identifies crucial factors for more accurate hydrological predictions in the Community Land Model version 5.

Groundwater modeling of irrigation return flow dynamics is important for improving estimates of water availability.

Team brought experience in nuclear waste forms and regulatory policies to the Federally Funded Research and Development Center’s report, which was reviewed by a National Academies’ committee.

The Great Plains and Great Lakes regions are the major sources of moisture for the precipitation that falls in the Great Lakes region.

A computationally efficient calibration procedure helps constrain simulated runoff in an Earth system model.

The complex interactions between the land and atmosphere can affect cloud growth.

Existing clouds affect the growth of neighboring clouds.

Oluwayemi Garuba brings Arctic expertise to panel on climate modeling.

Daniel Deng, mechanical engineer and Laboratory Fellow at PNNL, has been named 2022 PNNL Inventor of the Year.

Yun Qian has been selected as an editor of the Journal of Geophysical Research: Atmospheres, a prominent atmospheric science journal.

PNNL researchers explore the feasibility of using low-flying drones to survey sites for low levels of radiation.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

A review describes the latest techniques for simulating hydropower at large scales to inform power grid planning and operations.

Hydropower generation estimated using water proxy data leads to a revision of the nation’s historical seasonal hydropower output.

The frequency and intensity of storms affecting U.S. coastal areas will likely increase in a warming world.

Researchers investigated the impact of using constant versus spatially varying crop parameters on carbon and energy fluxes in a realistic crop rotation scenario.

A new model uses machine learning to project electricity loads across the United States.