Journal
JOURNAL OF HYDROLOGY
Volume 612, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jhydrol.2022.128168
Keywords
Quantitative precipitation forecast; Flood; Deep learning; Hydrologic -hydraulic model; Forecasts; Hurricane Harvey
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Flood prediction techniques have advanced and this study showcases a comprehensive flood prediction for Hurricane Harvey using high-resolution quantitative precipitation forecasts and deep learning nowcasts. The results indicate the strengths and limitations of different methods in terms of accuracy and precipitation intensity prediction.
A flood is one of the most hazardous natural disasters, and it commonly causes fatalities and socioeconomic damages. The advances of modeling techniques and observation data in flood prediction have found success in field operations. This paper presents a comprehensive flood prediction of Hurricane Harvey in 1-hour lead-time that is not limited to 1D streamflow forecast but also 2D flood extent and 3D inundation depth. It uses highresolution quantitative precipitation forecasts (QPFs, from operational Rapid Refresh-RAP, and High Resolution Rapid Refresh-HRRR models) and deep learning nowcasts (AI nowcasts). The results show that the QPFs have a well-known displacement issue and the AI nowcast cannot predict the precipitation intensity, and an attempt to combine the two methods (AI hybrid) failed to improve the overall accuracy. However, the 2D flood extent predictions with the HRRR and AI hybrid forcings can provide information indicating the future flooded area with about 50% accuracy (hit rate) and stream flow prediction showed that the HRRR QPF can provide relatively accurate flood peak prediction (-8%). In contrast, the AI nowcast reveals minimal displacement errors but underpredicts precipitation intensity. The deep learning method also indicates that the binary tests with low threshold, which are commonly employed in the deep learning field, neglect the importance of precipitation intensity errors for extreme event studies.
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