Modeling Variations in the Generation of Carbon Dioxide in the Riverbed across the Columbia River Basin
A newly developed basin-scale river corridor model can quantify how riverbed microbes drive respiration and the generation of carbon dioxide in the Columbia River Basin.
Wildfire Burn Severity Drives Short-Term Impacts on Water Chemistry
Variations in burn severity are a key control on the chemical constituents of dissolved organic matter delivered to streams within a single burn perimeter.
Measuring Permafrost Thaw with Streamflow
Information encoded in streamflow reveals how the water-storage capacity of thawed ground changes as permafrost thaws.
Using Topological Relationships in Mesh-Independent River Network Representations
A new model improves the representations of river networks in climate models across scales.
PNNL at EGU23
Join researchers from PNNL at EGU General Assembly 2023 as they present findings from their latest research in Earth science at the international event.
Navigating the Future of Global Water Use: A High-Resolution Analysis
Unveiling diverse scenarios and implications for water resource management in a rapidly changing world.
Downscaling a Large-Scale River Model Using Deep Learning
Developed a data-assimilation method based on physics-informed deep learning to resolve downscaled flow fields within a large-scale river model.
A Unique Coastal Forest Flooding Experiment
This study demonstrated that a large-scale flooding experiment in coastal Maryland, USA, aiming to understand how freshwater and saltwater floods may alter soil biogeochemical cycles and vegetation in a deciduous coastal forest.
Partitioning Ice Nucleating Particle Measurements Facilitates Improved Process Level Understanding in Models
Partitioning measured ice nucleating particle concentrations into individual particle types leads to a better understanding of the sources and model representations of these particles.
Convective Inhibition Explains Regional Differences in Tropical Precipitation
The roles of the various environmental variables in the transition from suppressed to active tropical precipitation regimes are characterized using statistical analysis and machine learning.