Variations in the level of market globalization can greatly affect the amount of water required to meet future global demand for agricultural commodities.

Climate change and socioeconomic pressures are transforming passenger and freight transportation in the Arctic, producing effects that have yet to be fully understood.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.

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.

Small teams in the Biological Sciences Division at PNNL and at EMSL—the Environmental and Molecular Sciences Laboratory, an Office of Science user facility at PNNL—are pros at preparation.

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

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.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

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.

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

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

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.