Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

Developed with community input, the new Hydropower Vision Roadmap lays out a path to achieve a shared vison for modern hydropower by 2050.

A PNNL study that evaluated the use of friction stir technology on stainless steel has shown that the steel resists erosion more than three times that of its unprocessed counterpart.

A new paper found that hydropower turbines with composite blades generate about 20 percent more power than turbines with traditional stainless steel blades at the same flow rate.

Researchers at PNNL are now leading research to mitigate biofouling and its impacts on the environment and industry