Aanika Tipirneni, SDSC Communications, contributed to this story

Professor Ripendra Awal, who teaches an array of classes, including environmental soil science, at Texas’ Prairie View A&M University, focuses a great deal of his research on efficient irrigation and often uses ACCESS resources, including Jetstream2 at Indiana University and Jetstream2 at the Texas Advanced Computing Center. Most recently, Awal presented his studies on IrrigWise at the 2023 Water for Global Food Conference.

“We used our ACCESS allocations on Jetstream and Jetstream2 to develop IrrigWise – a web-based tool that calculates plant irrigation needs – using data from several weather stations from different weather station networks publically available across Texas, USDA-NRCS Soil Survey Geographic Database and the National Weather Service,” Awal said. “IrrigWise specifically offers versatile irrigation scheduling – that is, it can calculate at what time and for how long a crop at any location across Texas needs to be watered.” 

Awal also uses the system to show his Prairie View A&M students how supercomputers can be used to improve irrigation management of commonly found crops in Texas. 

We are really excited about continuing our work on ACCESS tools to determine more ways that we can  improve irrigation management of crops and urban landscape and provide safeguards against water scarcity and serious groundwater depletion.

–Ripendra Awal, Prairie View A&M University

“Due to changes in data policy of some weather station networks and frequent changes in data format, the coverage of application is reducing,” Awal said. “We will update our gridded climate data-based irrigation scheduling tool, IrrigWise-PRISM, in the coming days.” 

Project Details

Resource Provider Institution(s): Indiana U (Jetstream2), Texas Advanced Computing Center (TACC)
Affiliations: Prairie View A&M
Funding Agency: NSF
Grant or Allocation Number(s): This work was supported by USDA National Institute of Food and Agriculture CBG (grant no. 2017-38821-26410/project accession no. 1012198). Computational research was supported by ACCESS (grant no. TG-ENG170027).

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