A modeling study shows that adding batteries to a dam could decrease the wear and tear on hydropower turbines and open up new opportunities for dam operators to earn revenue.
Distributed science is thriving at PNNL, where scientists share data and collaborate with researchers around the world to increase the impact of the work.
High-resolution hydrodynamic-sediment modeling shows that inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics drive sediment deposition in Amazonian floodplains.
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.
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.
Zhiqun (Daniel) Deng, Lab Fellow at PNNL, has been named a fellow of the American Society of Mechanical Engineers, an honor that recognizes outstanding engineering achievements.
PNNL’s science and technology helps hydropower operators detect, prevent and recover from cyberattacks while protecting a source of electricity that enhances grid reliability and resilience.
Armed with some of the world’s most advanced instrumentation, researchers at PNNL are working to analyze huge amounts of data and uncover hidden biological connections.
This project sought to assure that research activities centered around different sampling and monitoring efforts in northwest Ohio would not disturb any historical cultural resources.