New apps help researchers with statistical analyses of data.

In soil, microbes produce and consume methane. Using a technique called pool dilution, researchers can separate the rate of methane production and consumption from the net rate.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

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.

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.

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.

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

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.

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