Watershed-based representation discretized into topographic subgrid units better captured small-scale land cover, precipitation, temperature, and snow variability across multiple spatial scales.

Physics-informed and data-driven neural network models enhance Earth system models in simulating compound flooding at river-ocean interfaces.

Over 115 million environmental observations are now publicly available to enhance research of coastal forest ecosystems.

Using multiple specialized techniques, IDREAM researchers gained a better understanding of how trace impurities within gibbsite affect the hydrogen yield.

Five PNNL innovations earned 2026 R&D 100 Awards for advances in biotechnology, manufacturing, critical minerals, and nuclear security.

PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.

RemPlex 2025 Global Summit on Environmental Remediation attendees share knowledge about cleanup and monitoring of complex sites worldwide; more than 100 presentations are posted online.

Nanosize platelets of an aluminum material slide and join in a staggered orientation to form larger crystals.

IDREAM researchers uncovered how dimeric complexes control how aluminum hydroxide minerals dissolve.

From developing new energy storage materials to revealing patterns of Earth’s complex systems, studies led by PNNL researchers are recognized for their innovation and influence.

The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.

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

Trace amounts of iron and chromium can incorporate into gibbsite, affecting how the mineral dissolves.

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.

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.

Increasing concentrations of ions slows the rate of particle aggregation in boehmite suspensions.

PNNL chemical engineer Mariefel Olarte shares passion for mentoring students and the next-generation workforce.