Branko Kerkez
University of Michigan, MI·Engineering
Flooding is the leading natural disaster for deaths and property damage in the United States, and storms are getting stronger and more frequent. Rural and low-income communities in low-lying areas are hit hardest.
- Fields
Engineering
Earth & Environmental Science
Social Science
- Topics
smart watersheds
flood mitigation
flooding
- Grants
- 1 known grant, through Aug 2027
What they work on
- Investigate how social capital (trust and relationships) shapes decision-making among rural and urban dam operators, and translate these insights into digital tools that promote coordinated dam releases.
- Develop methods linking river flows to fish habitat quality and set flow targets based on real-time data from a watershed sensor network.
- Create model-predictive control algorithms, which forecast and optimize future actions, to regulate dam releases stably and efficiently under varying levels of human operator participation.
More about the research
Dams along a river could work together to hold back floodwater, improve water quality and support fish habitat. In practice, though, operators in different towns often act alone. This project asks how sensors, data and control algorithms can help dam operators across a watershed coordinate.
Why it matters
Coordinated, sensor-guided dam releases could reduce flood risk, improve water quality and restore fish habitat. The main beneficiaries would be rural and low-income communities in flood-prone areas, along with dam operators and watershed planners. The digital watershed approach could serve as a scalable model for water management in other river basins across the country.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Bruno Sinopoli
Arizona State University Campus Immersion, AZ·Engineering
Establish closed-loop stability guarantees for REAP solvers, optimization methods that return a safe, feasible answer even when stopped early, and develop ways to implement them on discrete-time digital hardware.
Stefano Galelli
Cornell University, NY·Earth & Environmental Science
Characterize the structural uncertainty that comes from choosing one dam operation model over another, using a computational framework applied to 18 large river basins spanning every major U.S. hydrologic region and about 200 dams.
Emily Seelen
University of Alaska Fairbanks, AK·Earth & Environmental Science
Develop river-erosion forecasting tools for Alaskan permafrost rivers using satellite imagery analyzed with sub-pixel and machine-learning change detection, combined with theory on permafrost erosion and mud transport.
The school
University of Michigan: 124 labs on PI Pages, $2.1B a year on research, 963 research doctorates a year.
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