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Riding the Jetstream to the treetops

Speed read
  • Climate change threatens Rocky Mountain forests
  • Scientists use Jetstream to develop geographic information systems remotely
  • Research relies on scalable datasets

The forests surrounding Boulder Creek, Colorado, resemble a scene from a dream.

Illuminated walking paths cut through the forest, offering beautiful vistas of the Rocky Mountains to passersby, engulfing each visitor in a sea of green leaves.

However, these forests are threatened by many factors linked to climate change, including temperature increases and periods of drought.<strong><a href ='http://criticalzone.org/boulder/'>Critical Zone Observatory. </a></strong> Researchers harnessed Jetstream to examine the effects of climate on carbon loading in Como Creek, Gordon Gulch, and Betasso Preserve, three catchments in the Boulder Creek watershed. Courtesy Tyson Swetnam, et al.

Addressing these factors is essential for scientists. If temperatures rise just one degree Celsius, streamflow in the area will be reduced by five to seven percent, potentially impacting viability for the trees.

How can scientists successfully manage this vital ecosystem in the face of creeping global temperatures?

Tyson Swetnam, a science informatician at CyVerse, recently led a research project which analyzed forest biomass data from 513,029 trees in the Boulder Creek watershed.

To speed things up, he used Jetstream, the first National Science Foundation-supported cloud resource for science and engineering research.

By running large instances on Jetstream, I can parallelize tasks and complete processes in one tenth of the time it would take on a conventional laptop computer. ~ Tyson Swetnam.

One of the advantages Jetstream offers Swetnam and his team is the ability to develop interactive geographic information systems (GIS) that can be accessed by researchers anywhere, regardless of geographic location. This allows Swetnam’s team to analyze data and create models as quickly as possible.

“With Web-OpenDroneMap running on Jetstream, I can upload imagery collected from small, unmanned aerial systems using a wifi- or 4G- connected mobile device to make 3D models,” Swetnam says. “This provides useful, actionable data in real time.”

Jetstream also creates the opportunity for Swetnam to work with scalable spatial data infrastructures (SDI), which is a critical part of his work at CyVerse and an important feature in the future of computational science as data sets become larger and more complex.

“By utilizing resources like Jetstream, CyVerse will be able to provide a utilitarian, horizontally scalable SDI to the life sciences community,” Swetnam says.

“Researchers will be able to use this service to extend their analytical capabilities and answer previously unassailable research questions.”

Crowd in the cloud. Cloud-based virtualization means more scientists are able to bring their research into the high-performance computing era. Courtesy UITS at Indiana University.

Swetnam says that some scientists may be intimidated by working with advanced computing networks, but that Jetstream offers services to help train new users to the system.

“Some people fear working with cyberinfrastructure because of the presumed complexities of learning to code,” Swetnam says. “Online tutorials and learning apps are available for new users to quickly pick up basic coding and GIS skills and apply them to their research.”

As places like the Rocky Mountains face an increasing number of threats from climate change, the timely efficiency of Jetstream-enabled research is more critical than ever.

The research project was partially funded by the US Department of Energy (DOE).

Funded by the NSF, Jetstream results from a partnership between Indiana University’s Pervasive Technology Institute, the University of Texas at Austin’s Texas Advanced Computing Center (TACC), the Computation Institute at the University of Chicago, the iPlant Collaborative at the University of Arizona, and the University of Texas, San Antonio.

It is managed through the XSEDE Resource Allocation System.

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