A new model improves the representations of river networks in climate models across scales.

A PNNL innovation uses steam to recover heat from the high-temperature reactor effluent in the HTL process, substantially reducing the propensity for fouling and potentially reducing costs.

Researchers found that specific landscape metrics consistently outperform traditional population downscaling methods.

Interactions between fronts and the land-sea breeze lead to strong vertical exchange in the northern Gulf of Mexico.

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

A climate change study co-authored by PNNL scientist Hailong Wang inspired an artist to create an exhibit based on the research.

New report takes a comprehensive look at offshore aquaculture as a market for marine energy.

Soil transplant experiment gives new insights into coastal forest resilience, rising seas, and increasing storms.

ICON science is a Department of Energy-developed framework to enhance scientific outcomes via more intentional design of research efforts across all domains of science.

Soil pores are reorganized when newly thawed permafrost soils experience freeze-thaw cycling.

Ocean biogeochemical modeling software now available as open source to help researchers predict impacts of pollution, sea level rise, and climate change.

Principles derived from coastal wetlands to describe wetland channel cross-sections were applicable to the Columbia River estuary, but not the tidal river.

Pacific Northwest marsh carbon stocks are twice the global average. Long-term sea-level rise impacts will likely decrease ecosystem carbon stocks.

Scientists are exploring ways to derive more accurate predictions that can help with preparations for changing sea levels