Newly designed sensor network comprehensively monitors a range of coastal landscapes to better understand nutrient and elemental dynamics that drive ecosystem function.

Exploring how shifting boundaries between upland and wetland areas influence soil processes and signal early ecosystem transformations.

A high-performance, scalable pipeline transforms raw high frequency coastal sensor data into research-ready datasets.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

This research helps predict how coastal forests will respond to future sea level changes which is crucial for protecting these ecosystems.

Due to their inherent variability and complexity over space and time, scientists are challenged to understand the complex interactions among soil, vegetation, and water along coastal terrestrial-aquatic interfaces.

The Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field, Measurements, and Experiments project established a network of observational field sites across Chesapeake Bay and western Lake Erie.

This study characterized above- and below-ground properties to explore the spatial heterogeneity of the terrestrial aquatic interface ecosystem within the Chesapeake Bay area and evaluate the major drivers of soil respiration.

This study evaluated the sensitivity of multiple geophysical methods to measure and evaluate the spatiotemporal variability of select soil properties across terrestrial–aquatic interfaces.

Researchers integrated field measurements, lab experiments, and model simulations to study oxygen consumption dynamics in soils along a coastal gradient.

This research explores how changes in groundwater levels affect the chemistry of underground water, especially in areas where land meets water, like wetlands.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

The 2024 State of the Science Report on environmental effects of marine renewable energy research has been released.

The Delaware River Basin is projected to experience a regional increase in flood severity under future climate scenarios.

Study explores Exploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments, a consortium of scientists interested in the exchange between water and land in coastal systems.

Models have identified a new source of anthropogenic emissions driving the observed weakening of the Atlantic Meridional Overturning Circulation.

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.

PNNL signs memorandum of understanding with Oregon State University to support marine science and technology advancements.