Abstract
Many large metropolitan areas are especially susceptible to floods generated by heavy, short-duration rainfall. The high density of people, buildings and infrastructure in these areas underline the importance of developing flood resilient cities and communities. An accurate real-time flood forecasting system can support decision making for launching preparedness and response actions in short-range (hours to days), and assist mitigating the disturbances caused by floods. This study explores the short-range predictive capability of a real-time flood forecast system by coupling the High-Resolution Rapid Refresh (HRRR) meteorological forecasted variables with a fully distributed hydrological model (WRF-Hydro). We provide a comprehensive analysis of short-range (36 h) forecasts for 19flood events generated by heavy rainfall in small urban and suburban watersheds (<200 km2) in the National Capital Region of the United States. Flood forecast performance are then assessed using different metrics based on observed data from U.S. Geological Survey stream gauges and reanalysis simulations using the StageIV quantitative precipitation estimates. Results show that despite the high variability of the HRRR cycle-to-cycle precipitation forecasts, the real-time flood forecast system can produce skillful forecasts with similar overall performance as the reanalysis simulations, achieving 65 % of flood detection rate. This variability had more impact on forecast skill in smaller subbasins, and flood forecasts were more consistent for longer duration and larger spatial extent rainfall. Hourly flood forecasts performed well even in smaller watersheds, correctly detecting flood occurrence with up to 34 h lead time and resulting in low peak flow magnitude and timing errors.
Published Version
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