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Inundation Prediction

Run in tangent with UConn’s Hydrometeorology and Hydrologic Remote Sensing Group, our inundation prediction team works to improve predictability of the water cycle and its extremes. Currently, hydro-meteorological forecast systems use ground stations to obtain hydrological and meteorological observations. But in many parts of the world data is unreliable or missing.

Data sets on weather parameters–including precipitation, remote sensing capabilities, and land surface, hydrologic, and hydrodynamic models are combined to improve water cycle predictability. The critical question within the scope of the project is to address is how much flood risk can be caused by the co-occurrence of both river flow and storm surges.

Method and Result Updates

The primary scope of this project is to develop a systems to predict in short-term the compound flood-inundation risk in critical coastal locations. Figure 1 and Figure 2 demonstrate the result of one high flow event at one substation.

Images

Team Members

  • Xinyi Shen, Assistant Research Professor of Civil and Environmental Engineering, University of Connecticut
  • Guilia Sofia, Assistant Research Professor of Civil and Environmental Engineering, University of Connecticut
  • Emmanouil Anagnostou, Professor of Civil and Environmental Engineering, University of Connecticut

Eversource Energy Center | Innovation Partnership Building: 159 Discovery Drive, Unit 5276, Storrs, CT 06269-5276 | E-Mail: eversourceenergycenter@uconn.edu