Rivers shape ecosystems and economies, yet hydrologists have limited tools to study them. Enter Confluence—a groundbreaking, open-source framework leveraging NASA’s SWOT mission and HLS data to estimate river discharge and sediment levels worldwide. Hosted by PO.DAAC, Confluence delivers rapid, global insights, revolutionizing hydrology with cloud-based efficiency. A game changer for river monitoring.
Rivers and streams wrap around Earth in complex networks millions of miles long, driving trade, nurturing ecosystems, and stocking critical reserves of freshwater.
But the hydrologists who dedicate their professional lives to studying this immense web of waterways do so with a relatively limited set of tools. Around the world, a patchwork of just 3,000 or so river gauge stations supply regular, reliable data, making it difficult for hydrologists to detect global trends.
“The best way to study a river,” said Colin Gleason, Armstrong Professional Development Professor of Civil and Environmental Engineering at the University of Massachusetts, Amherst, “is to get your feet wet and visit it yourself. The second best way to study a river is to use a river gauge.”
Now, thanks to Gleason and a team of more than 30 researchers, there’s another option: ‘Confluence,’ an analytic collaborative framework that leverages data from NASA’s Surface Water and Ocean Topography (SWOT) mission and the Harmonized Landsat Sentinel-2 archive (HLS) to estimate river discharge and suspended sediment levels in every river on Earth wider than 50 meters. NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) hosts the software, making it open-source and free for users around the world.
By incorporating both altimetry data from SWOT which informs discharge estimates, and optical data from HLS, which informs estimates of suspended sediment data, Confluence marks the first time hydrologists can create timely models of river size and water quality at a global scale. Compared to existing workflows for estimating suspended sediment using HLS data, Confluence is faster by a factor of 30.
Nikki Tebaldi, a Cloud Adoption Engineer at NASA’s Jet Propulsion Laboratory (JPL) and Co-Investigator for Confluence, was the lead developer on this project. She said that while the individual components of Confluence have been around for decades, bringing them together within a single, cloud-based processing pipeline was a significant challenge.
“I’m really proud that we’ve pieced together all of these different algorithms, got them into the cloud, and we have them all executing commands and working,” said Tebaldi.
Suresh Vannan, former manager of PO.DAAC and a Co-Investigator for Confluence, said this new ability to produce timely, global estimates of river discharge and quality will have a huge impact on hydrological models assessing everything from the health of river ecosystems to snowmelt.
“There are a bunch of science applications that river discharge can be used for, because it’s pretty much taking a snapshot of what the river looks like, how it behaves. Producing that snapshot on a global scale is a game changer,” said Vannan.
While the Confluence team is still working with PO.DAAC to complete their software package, users can currently access the Confluence source code here. For tutorials, manuals, and other user guides, visit the PO.DAAC webpage here.
All of these improvements to the original Confluence algorithms developed for SWOT were made possible by NASA’s Advanced Intelligent Systems Technology (AIST) program, a part of the agency’s Earth Science Technology Office (ESTO), in collaboration with SWOT and PO.DAAC.
To learn more about opportunities to develop next-generation technologies for studying Earth from outer space, visit ESTO’s solicitation page here.
Project Lead: Colin Gleason / University of Massachusetts, Amherst
Sponsoring Organization: Advanced Intelligent Systems Technology program, within NASA’s Earth Science Technology Office
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