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The Intelligent Digital Watershed

IIHR Research Engineer Jerry Schnoor (left) and grad student Sudipta Mishra diagram the intelligent digital watershed.

IIHR Research Engineer Jerry Schnoor (left) and grad student Sudipta Mishra diagram the intelligent digital watershed.

People make decisions based on information at hand. Armed with better information, would Iowans make better land-use choices affecting water quality?

That is the hope of the IIHR researchers who are developing the “Intelligent Digital Watershed” (IDW), but the verdict is still out, says IIHR Research Engineer Jerry Schnoor, the project’s principal investigator. Schnoor, who is also the Allen S. Henry Chair in Engineering, is leading research to improve water pollution monitoring,
supported by a three-year National Science Foundation grant of almost $1 million.

What, exactly, is an intelligent digital watershed? “It’s an end-to-end system for the monitoring, modeling, and prediction of water and water quality within a watershed,” Schnoor explains. The IDW is an electronic representation of the watershed as captured by data, with a series of interconnected numerical models. The IDW can simulate different scenarios in the watershed with regard to land-use choices, weather, commodity prices, and more. It includes surface waters, groundwater, snowpack, and soil water.

The computing power needed for this system of linked models is formidable. The IDW uses the latest technology in sensors, wireless and broadband communication, and high-performance computing.

“It is intelligent because the system can learn as it processes greater and greater amounts of digital information—or hydroinformatics—in real time,” Schnoor says.

The Human Factor

Schnoor and his team built a local watershed observatory at Clear Creek near Amana, Iowa. Their observatory takes into account the human interactions with the watershed, and how the decisions people make can affect water quality. Schnoor calls it an “agent-based model,” or ABM. “We have linked so-called ‘agent-based
models’ with water quality and agricultural cropgrowth models,” he explains. “‘Agents’ in this case are the farmers and landowners who make decisions that affect water quality, such as which crops to plant, how much fertilizer to apply, and how to till the soil.”

The team, which includes IIHR Research Engineer Marian Muste, surveyed farmers and landowners in the Clear Creek Watershed to gather their opinions and to develop a model that makes decisions much as its human counterparts would, based on economic issues and environmental values. “We hooked the water-quality model to the ABM model,” Schnoor says, “so that farmers’ decisions are used to set the land management practices in the water-quality model.”

Better Data = Smarter Choices

Schnoor hopes that the IDW will provide useful feedback to landowners, who will have more accurate information on the results of their land-use choices. Besides the income from their crop, they will see soil loss due to erosion, effects on water quality due to runoff, and more.

“The beauty of our project and philosophy is that we share all information with everyone via the Internet,” Schnoor explains. “As we share the results with real farmers, they too could learn and may change their decisions on how to run their farms for better income and agricultural production, as well as for environmental conservation of soil and water.”

Learning from Each Other

IIHR graduate student Sudipta Mishra played an important role in building the intelligent digital watershed. Mishra, who is from India, says the work builds on his previous experience in the hydrological processes in agriculturally dominated watersheds. “We try to understand the links between shifts in land use, soil conservation practices, and the resulting water quantity and quality,” Mishra explains.

He appreciates the multidisciplinary approach the project requires. “Working with people from other disciplines, ranging from economics to social science and computer science, is really exciting. Understanding their perspectives is often challenging, too.”

Mishra adds, “I have learned many valuable lessons on how to work in a multidisciplinary team through this work.”

Collaborator Andrew Kusiak, professor of mechanical and industrial engineering, leads the UI Intelligent Systems Laboratory. His team has been able to make use of the data assembled for the intelligent digital watershed project using data mining techniques. “Collaboration is worth the minor inconveniences of getting familiar with a new research domain that could produce immeasurable benefits,” Kusiak says. “Collaboration is a discovery enabler and a research progress accelerator.”

Learning from each other is one of the key concepts underpinning this effort. Schnoor hopes that the intelligent digital watershed helps us understand the balance between economic and environmental goals in a truly useful way.

“That is our goal,” he says. “People make decisions based on information. We are trying to provide farmers with better information on the effects of their decisions on water quality and their income. Using this information, they may choose to modify their behavior, depending on how they value the trade-offs.”

Learning from Each Other

IIHR graduate student Sudipta Mishra played an important role in building the intelligent digital watershed. Mishra, who is from India, says the work builds on his previous experience in the hydrological processes in agriculturally dominated watersheds. “We try to understand the links between shifts in land use, soil conservation
practices, and the resulting water quantity and quality,” Mishra explains.

He appreciates the multidisciplinary approach the project requires. “Working with people from other disciplines, ranging from economics to social science and computer science, is really exciting. Understanding their perspectives is often challenging, too.”

Mishra adds, “I have learned many valuable lessons on how to work in a multidisciplinary team through this work.”

Collaborator Andrew Kusiak, professor of mechanical and industrial engineering, leads the UI Intelligent Systems Laboratory. His team has been able to make use of the data assembled for the intelligent digital watershed project using data mining techniques. “Collaboration is worth the minor inconveniences of getting familiar with a new research domain that could produce immeasurable benefits,” Kusiak says. “Collaboration is a discovery enabler and a research progress accelerator.”

Learning from each other is one of the key concepts underpinning this effort. Schnoor hopes that the intelligent digital watershed helps us understand the balance between economic and environmental goals in a truly useful way.

“That is our goal,” he says. “People make decisions based on information. We are trying to provide farmers with better information on the effects of their decisions on water quality and their income. Using this information, they may choose to modify their behavior, depending on how they value the trade-offs.”

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Last modified on June 25th, 2015
Posted on March 6th, 2012

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