RemPlex provides a global forum committed to fostering technical leadership, collaborative research, and professional development that facilitates the cost-effective remediation of complex sites.

PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.

A report on the projected effects of climate change on federal hydropower through the end of the twenty-first century.

Pacific Northwest National Laboratory, supported by the Department of Energy’s Grid Deployment Office, is leading a technical assistance initiative to help utilities test and deploy alternate GPS timing solutions.

New report examining multiple decades of climate and hydropower generation data provides insights into resilience of the western hydropower fleet.

PNNL’s integrated software systems (FRAMES, MEPAS, MetView, APGEMS, CAPP) allow users to assess the environmental fate and transport of contaminants—and the potential impacts on humans and the environment—in a systematic, holistic approach.

PNNL is a leader in the integration of aberration-corrected electron microscopy, in-situ techniques, and atom probe tomography to address challenges in nuclear materials, environmental remediation, energy storage, and national security.

The Flow Tradeoff Tool is a free, comprehensive software toolkit designed to evaluate trade-offs between hydropower energy production and environmental flows.

A new set of resources from PNNL helps guide dam owners and operators through response and recovery actions in the wake of cybersecurity or unusual incidents.

HydroWIRES initiative helps water stakeholders reap the full benefit of hydropower’s superpower: versatility

Researchers are bridging hydropower data gaps for unrivaled forecasting precision to support grid resilience.

PNNL deploys advanced drone systems to tackle the nation’s most pressing challenges in energy, environmental remediation, and national security.

The Isotope Program at PNNL supports scientific advances in the production and use of radioisotopes for research, medicine, and industrial applications.

Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.