CIROH at UMN

Research leads: Melissa Kenney (IonE)

This project uses evidence-based social science to improve two emerging drought decision-support products: NOAA CPC’s probabilistic drought outlook and NIDIS’s Drought Planning Platform (DPP). In collaboration with drought stakeholders and product developers, the team will co-design and test probabilistic forecast visualizations to identify designs that support accurate interpretation and use. For the DPP, the team will develop user guidance and planning resources and scope an initial framework for evaluating usability and learning. The findings will guide product development and help people and communities use drought information in planning and preparedness.

Research leads: Melissa Kenney (IonE); Kristin Raub (Northeastern University)

This project fills critical gaps in understanding how Total Level Water forecasts can better support coastal decision making in Puerto Rico. Working with federal agencies and local partners in Puerto Rico, the team will explore how stakeholders use coastal flood information and whether current TWL visualizations and communication approaches meet their needs.The insights will inform the design of more usable coastal flood information services that support decision-making in island settings. 

Research leads: Melissa Kenney (IonE); Anne Jefferson (University of Vermont); Fredrika Moser (University of Maryland) 

This project will provide scientifically tested, actionable information that NOAA can use to improve the understandability and usability of Total Water Level, a new tool integrates tides, storm surge, sea level, waves, and inland flooding to predict the risk of compound coastal flooding in coastal communities.  

Research leads: Jim Nelson, Gus Williams, Riley Hales, James Halgren (Brigham Young University); Melissa Kenney (IonE); Anne Jefferson (University of Vermont); Ibrahim Demir (Tulane University); David Maidment, Andy Carter (University of Texas at Austin); Mike Follum, Joseph Gutenson (Follum Hydrologic Solutions, LLC)

This project improves the capabilities of Flood Inundation Maps (FIMs) to visualize probabilities of flooding, flood impacts on critical infrastructure, and flooding parameters such as water depth and velocity. The UMN team is leading the development and testing of evidence-based visualization methods for improving comprehension among a wide range of end users.
 

Research leads: Sagy Cohen, Arpita Patel (University of Alabama); Dinuke Munasinghe (University of Alabama); Melissa Kenney (IonE); Anthony Castronova (CUAHSI)

This project will design and develop a web-based usability testing platform emulating NOAA's National Water Prediction Service (NWPS) through a collaboration with front-end web developers at the University of Alabama with guidance from NOAA Office of Water Prediction partners. This platform mirrors the layout and interactivity of NOAA NWPS and its embedded Flood Inundation Mapping (FIM) services to provide a controlled testing environment for conducting usability studies of flood data and forecast information available on NWPS. We will use this platform to rigorously evaluate how design and communication interventions affect the usability of NWPS FIM products.

Research leads: Melissa Kenney (IonE); Anne Jefferson (University of Vermont); Michael Federoff (University of Alabama)

This project used visualization best practices and scientific testing to improve the usability, clarity and effectiveness of Flood Inundation Maps (FIMs) as decision support tools.
 

Research leads: Melissa Kenney (IonE); Zac McEachran, Tracy Twine (College of Food, Agricultural and Natural Resource Sciences, University of Minnesota)

This project supported stakeholder understanding of and preparation for future flood and drought risk by providing new modeling results about how streamflow will change along the mainstem of the Upper Mississippi River and building relationships with flood- and drought-impacted communities that face disproportionate risk.

Research leads: Melissa Kenney (IonE)

This project identified specific, actionable ways to deliver more impactful and effective decision support products while also directly supporting NOAA’s operational hydrologic forecasting visualizations by making them more intuitive, easier to interpret, and reducing cognitive overload when emergency responders must make critical decisions.