With future warming, storms in the Western U.S. will be larger and produce more intense precipitation, particularly near the storm center, and increase flood risks.
PNNL welcomes new joint appointments to expand the research productivity and scientific impact of both PNNL and the university partners, broadening the base of expertise at each institution and helping to build interdisciplinary teams.
Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
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.
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.
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.
Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.
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.
Scientists used a new analysis approach—coupled laser ablation sampling and capillary absorption spectroscopy—to gather more information on how the rhizosphere processes carbon.
