Qingyun Duan, the new Editor-in-Chief of Reviews of Geophysics, shares his scientific interests and vision for the journal in the coming years.
Modeling
Posted inEditors' Highlights
What Happens to Nutrients After They Leave Agricultural Fields?
To better quantify the fate of nutrients after they are released from agricultural fields, scientists examine storage and nitrate export regimes in agricultural hydrology systems.
Posted inEditors' Vox
Accounting for Small-Scale Processes in Large-Scale Models
A new book explores how fast processes can be better represented in atmospheric models to improve weather and climate prediction.
Posted inScience Updates
Decoding the Dialogue Between Clouds and Land
New research is challenging established assumptions about how clouds form and interact with Earth’s surface. One result may be better weather forecasts.
Posted inEditors' Highlights
Going Through a Rough Patch: Modeling Sediment Moving in Rivers
Irregularities of the rocky surface due to bumps and sediment patches are key to capturing the movement of sediment grains in rivers.
Posted inScience Updates
Five Key Needs for Addressing Flood Injustice
Better data and assessment metrics—and improved researcher involvement in communities—are needed to understand and redress inequitable vulnerabilities to and recoveries from flooding.
Posted inEditors' Highlights
Rainstorm Intensity Drives Desert Landscape Evolution
New mathematical models show that the persistence of near-vertical cliffs in arid landscapes is maintained by infrequent, but high-intensity rain storms.
Posted inResearch Spotlights
The Escalating Impact of Global Warming on Atmospheric Rivers
Climate change is set to intensify atmospheric rivers and exacerbate extreme rainfall worldwide.
Posted inNews
Antarctica’s Ocean Acidity Set to Rise Rapidly by Century’s End
New research shows acidity levels could as much as double by 2100, imperiling fragile ecosystems in the frigid Southern Ocean.
Posted inEditors' Highlights
Machine Learning for Geochemists Who Don’t Want to Code
Geochemistry π is an easy-to-use step-by-step interface to carry out common machine learning tasks on geochemical data, including regression, clustering, classification, and dimension-reduction.