Variations in the level of market globalization can greatly affect the amount of water required to meet future global demand for agricultural commodities.
A team of researchers at PNNL has created a publicly available Hydropower eLibrary to improve access to information that could help streamline the FERC environmental review and licensing process.
Climate change and socioeconomic pressures are transforming passenger and freight transportation in the Arctic, producing effects that have yet to be fully understood.
Testing the assumption that different future socio-economic development patterns, which result in different land-use changes, can be paired with different future climate outcomes for risk assessments in a multi-model framework.
Incorporating spatially explicit land characteristics in a global model illustrates the complex effects of applying uniform regional protection assumptions in a global analysis.
PNNL is honoring its postdoctoral researchers as part of the fourteenth annual National Postdoc Appreciation Week with seven profiles of postdocs from around the Laboratory.
PNNL is supporting the floating offshore wind industry to enable gigawatt-scale development of floating offshore wind in the United States while minimizing environmental impacts and supporting local workforces.
PNNL led one of five Pathway Summer School programs nationwide, with a specific focus on engaging students from Native American or Indigenous backgrounds.
Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.
The Distributed Wind Market Report provides market statistics and analysis, along with insights into market trends and characteristics of wind technologies used as distributed energy resources.
PNNL-Sequim scientists will spend the next year testing a new technology that could allow the ocean to soak up more carbon dioxide without contributing to ocean acidification.
Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.
A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.