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.

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

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

Future global warming-induced changes to watershed and coastal processes can significantly affect the magnitude of fluvial-coastal compound flooding.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Mesoscale convective systems that are clustered in time and space have broader impacts on flooding than individual systems.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

A new report, based on a community workshop and literature review, summarizes some of the biggest challenges in understanding and modeling Earth system and human–Earth system dynamics in the Puget Sound region of Washington State.

Building new climate outcome scenarios by stitching together pieces of existing ones.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

Researchers found that specific landscape metrics consistently outperform traditional population downscaling methods.

A systematic, multiple scenario approach was used to analyze the compounding impacts of demands for land for biofuels with increased land scarcity under a diverse set of uncertainties.

rmap enables users with limited to no geographic information system knowledge use R to visualize simple tabular data.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

The methods researchers use to estimate crop yield gaps determines the location of projected future agricultural “breadbasket” regions.