From Sky to Land and Back Again

By Megan Johnson, NCSA
A close up view of plant cells transpiring - the plant version of sweating.

Evapotranspiration over Czech forests. Evaporation of water from spruce forests near Liberec. Mists can be seen rising above the treetops.
In this image, you can see the moisture rising from the trees as mist. This is evapotranspiration at work.

Rain and other precipitation deliver essential water for life on Earth, but what comes down must also go up for the cycle to continue in a harmonious way. How much water we have for planting, drinking and many other activities depends on how much falls, but what returns to the atmosphere is just as important. Evaporated water, either from puddles on the sidewalk, water in a lake, river, or ocean and water that a plant gives off in transpiration or soil, are all part of a process called evapotranspiration (ET). In order to accurately study how much water there is to go around, scientists need to know the difference between precipitation and ET. Measuring how much water falls is far simpler than measuring how much rises back into the atmosphere, so researchers from the University of Illinois Urbana-Champaign (U. of I.) have created a computer model that uses artificial intelligence (AI) to assist them in these measurements. Those types of models require so much processing power that supercomputers become necessary to complete the research in a reasonable amount of time. For this research, the team reached out to the U.S. National Science Foundation’s ACCESS program to get an allocation on NCSA’s Delta supercomputer.

The research team is comprised of a cross-discipline group of experts, including Jeongho Han, a doctoral student in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at U. of I. and co-author Jorge Guzman, research assistant professor in ABE. This project is also part of a larger project on soil erosion funded by the USDA National Institute of Food and Agriculture. Maria Chu, an associate professor in ABE, is the principal investigator on that project and co-author of the new paper.

Co-authors of the paper from left to right, Maria L. Chu, Jeongho Han, Jorge A. Guzman
Co-authors of the paper from left to right, Maria L. Chu, Jeongho Han, Jorge A. Guzman

“The process of requesting an allocation through ACCESS was smooth and easy,” said Jorge Guzman, speaking on behalf of the team. “The interface is amicable and easy to navigate. The system provided clear instructions and support throughout the allocation process.

“We used the NCSA Delta system to research, develop, and implement the Dynamic Land Cover Evapotranspiration Model Algorithm (DyLEMa),” said Guzman, speaking on behalf of the team. “Evapotranspiration (ET), the fluxes of water moving from the soil to the atmosphere, is challenging to measure or predict. In our approach, we used NASA, USGS, NOAA and USDA data for training and implementing machine learning models to predict unavailable remote sensing daily ET across Illinois for 20 years (from 2000 to 2020). To complete this data, we developed a computational framework that worked across ET’s spatial and temporal scales, requiring ample storage and rapid data access (data-intensive) and vast parallelized computation (computing-intensive). That is where the NCSA Delta system came to play an essential role in our work. Using the high-performance computing (HPC) resources at NCSA, we efficiently handled these data-intensive and computation-intensive tasks.”

An animation of the ET prediction for six months in 2020 for the state of Illinois. Credit: Jorge Guzman

Guzman and Chu have used high-performance computing (HPC) resources prior to this project and consider themselves very familiar with using them in their research. That wasn’t the case for the entire team. “Our graduate students are commonly unfamiliar with these systems,” said Guzman. However, they were all impressed with how well ACCESS supported the various levels of expertise. For some of their team, this was the first time using HPC.

“NCSA, the ACCESS program, and Delta provide a fantastic infrastructure and support for Jeongho to help him rapidly learn the HPC environment and navigate it to resolve issues while developing his research goals,” said Guzman. “As he had no prior experience using HPC and NCSA resources, it was initially challenging, but NCSA provided good support and documentation, making the learning process more effective. Additionally, the support team from NCSA and ACCESS were always available and helped resolve the issues rapidly. Their support was invaluable when we encountered problems setting up model runs and transferring data, especially resolving the issues where small details arose impacting HPC performance.”

An image of the WERL research team. They are looking down at a camera from above. A skyscraper can be seen behind them.
The Watershed-Ecosystems Research Laboratory (WERL) group.

Supercomputers are a tremendous tool for researchers, hastening results in ways traditional computing can’t. “Utilizing HPC resources was significant in achieving and accelerating our research,” said Guzman. “Although we have dedicated computing and data nodes in our group, the goal of this research task was overwhelming. We end up using more than 400,000 core hours on Delta and perhaps another similar on the Illinois Campus Cluster Program (ICCP) and dedicated resources. Compared to traditional computing resources, HPC allowed us to complete our goal in a shorter time by several orders of magnitude, which would otherwise take years.”

The group recently published their paper “Dynamic land cover evapotranspiration model algorithm: DyLEMa,” in Computers and Electronics in Agriculture. The US Department of Agriculture – National Institute for Food and Agriculture (NIFA) award number 2019-67019-29884, NSF’s ACCESS program and the Illinois Campus Cluster provided funding for this research.

We would highly recommend using ACCESS for HPC resources to other researchers. ACCESS’s computing capacity and low-latency storage can significantly enhance research productivity and outcomes, allowing the development of new research goals crossing computing limitations.

–Jorge Guzman, speaking on behalf of his research team

To read more in-depth details about the research, you can find the original story here on the College of Agricultural, Consumer and Environmental Sciences (ACES) website: Illinois researchers develop an AI model to reduce uncertainty in evapotranspiration prediction and on NCSA’s website here: The Rising Mists

Project Details

Resource Provider Institution(s): National Center for Supercomputing Applications (NCSA)
Affiliations: University of Illinois Urbana-Champaign, USDA National Institute of Food and Agriculture
Funding Agency: NSF, NIFA
Grant or Allocation Number(s): ACCESS Allocation: EES220062 Also funded by the US Department of Agriculture – National Institute for Food and Agriculture (NIFA) award number 2019-67019-29884, and the Illinois Campus Cluster

The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

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