New methodological approach demonstrates how to assess the economic value, including non-traditional value streams, of converting non-powered dams to hydroelectric facilities.

A PNNL study developed a water management module for Xanthos that distinguishes between the operational characteristics of hydropower, irrigation, and flood control reservoirs.

Pacific Northwest National Laboratory launches the Training Outreach and Recruitment for Cybersecurity Hydropower program at the University of Texas at El Paso.

New research shows how cloud shapes affect the process of cloud evolution, resulting in better understanding of how clouds behave, improving weather forecasts, and enhancing comprehension of climate systems.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

As electricity grids incorporate renewables, several factors will constrain future output, according to a new paper.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

Scientists recently developed a flow direction model which produces high-quality flow routing parameters on any mesh system.

A new tool helps identify non-powered dams across the country that are top candidates for conversion to power-generating hydroelectric facilities.

Developed with community input, the new Hydropower Vision Roadmap lays out a path to achieve a shared vison for modern hydropower by 2050.

PNNL will lead support to one of six community teams chosen today by the U.S. Department of Energy to receive a total of up to $25 million.

Bradley Crowell with the U.S. Nuclear Regulatory Commission sees advanced materials integrity, radiological measurement, and environmental capabilities on his first visit to PNNL.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

Across the United States, water moving between the river and riverbed sediments does not overcome localized processes that govern organic matter chemistry.

New research simulates power demand, supply, and cost in a hotter, drier climate.