High-resolution hydrodynamic-sediment modeling shows that inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics drive sediment deposition in Amazonian floodplains.

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.

This study used historical data, remote sensing, and aquatic sensors to measure how far wildfire impacts propagated through the watershed after the 2022 Hermit’s Peak/Calf Canyon fire, New Mexico’s largest wildfire in history.

The prediction of rainfall over the Amazon rainforest by weather and climate models is highly uncertain, particularly for large rainstorms which are commonly seen during the wet season, from March to May.

Extensive in situ and remote sensing measurements were collected to address data gaps and better understand the interactions of convective clouds and the surrounding environment.

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

PNNL scientist James Stegen and an international team of collaborators recently published a comprehensive review of variably inundated ecosystems (VIEs).

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.

PNNL researchers received an award from the Materials, Metals & Materials Society (TMS) for a publication on friction riveting.

A new study led by PNNL researcher TC Chakraborty provides some answers about how irrigated agricultural land near cities affects urban heat stress.

PNNL is hosting a scientific conference focused directly on understanding and predicting the collection of an organism’s traits.

Circadian rhythms of blue-green microbes offer new window into predictive phenomics—how to predict and control the traits of an organism.

Researchers at PNNL are pursuing new approaches to understand, predict and control the phenome—the collection of biological traits within an organism shaped by its genes and interactions with the environment.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

This week at AAAS, PNNL scientists discuss phenomics and the factors that influence DNA instructions to bring about the world around us.

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

Andrew Gettelman at PNNL elected as president of the Atmospheric Sciences section of the American Geophysical Union.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.