Newly designed sensor network comprehensively monitors a range of coastal landscapes to better understand nutrient and elemental dynamics that drive ecosystem function.

Observations and models showed that tropical cyclone outer size increased in the western North Atlantic since 1979.

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

Exploring how shifting boundaries between upland and wetland areas influence soil processes and signal early ecosystem transformations.

A high-performance, scalable pipeline transforms raw high frequency coastal sensor data into research-ready datasets.

Artificial intelligence helps analyze images and reduce uncertainty for a key measure of watershed function.

Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.

Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.

Following wildfire, the transformation and mobility of phosphorus-containing compounds depend on the type of vegetation burned and the severity of the fire.

Tracking water age using a new precipitation tagging method within a hydrologic model generates key information to support hydropower energy and other water-dependent systems.

Higher temperatures and land-use pressures were associated with weaker chemical separation between river water and sediment dissolved organic matter across 93 sites.

PNNL researchers are bringing a data-driven approach to unleashing the potential of geothermal energy.

More rain and less snow shorten how long water stays in watersheds.

​​Tests show which low-cost 3D method best matches field measurements to minimize errors in mapping headwaters.​

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

A new PNNL game teaches players about obstacles that fish face during migration.

New partnership agreement to focus on AI, robotics, and advanced energy systems.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

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.

Replacing commercial acid with acidic waste enables researchers to improve nickel extraction efficiency, lower projected costs, and improve process economics.