Researchers investigated the impact of using constant versus spatially varying crop parameters on carbon and energy fluxes in a realistic crop rotation scenario.

A new model uses machine learning to project electricity loads across the United States.

Watershed hydrological responses are sensitive to the spatial and temporal resolution of the gridded meteorological forcing used in simulations.

Future global warming-induced changes to watershed and coastal processes can significantly affect the magnitude of fluvial-coastal compound flooding.

Information encoded in streamflow reveals how the water-storage capacity of thawed ground changes as permafrost thaws.

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

Variations in burn severity are a key control on the chemical constituents of dissolved organic matter delivered to streams within a single burn perimeter.

Mesoscale convective systems that are clustered in time and space have broader impacts on flooding than individual systems.

Atmospheric rivers are increasingly reaching the Arctic in winter, slowing sea ice recovery and accounting for a third of winter sea ice decline from 1979-2021.

Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.

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.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

How a model treats gas transfer behavior onto dust and sea salt particles significantly influences nitrate aerosols and their effects on climate.

A comparison between model simulations and high-resolution satellite observations shows discrepancies in projections of convective storms.

With future warming, storms in the Western U.S. will be larger and produce more intense precipitation, particularly near the storm center, and increase flood risks.

PNNL welcomes new joint appointments to expand the research productivity and scientific impact of both PNNL and the university partners, broadening the base of expertise at each institution and helping to build interdisciplinary teams.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.

A new report, based on a community workshop and literature review, summarizes some of the biggest challenges in understanding and modeling Earth system and human–Earth system dynamics in the Puget Sound region of Washington State.

Improving a snow albedo model to account for non-spherical snow grain shapes and the mixing of dust inside snowflakes in an Earth system model.