Water Engineering Research

Korea Water Resources Association (한국수자원학회)
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FLASH FLOOD FORECASTING USING REMOTELY SENSED INFORMATION AND NEURAL NETWORKS PART II : MODEL APPLICATION

  • Kim, Gwang-seob (Department of Civil Engineering, Kyungpook National University) ;
  • Lee, Jong-Seok (Utah Water Research Laboratory, College of Engineering, Utah State University, Logan, U.S.A.)
  • Published : 2002.04.01
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Abstract

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

Keywords

References

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