Is this rainfall forecast good or bad? For flood forecasting, the answer is scale-dependent

Quantitative Precipitation Forecasting benefits real-time streamflow forecasts by extending the lead time horizon. Uncertainties in QPF compromise these benefits. This study examined the performance of the short-term QPF product known as High Resolution Rapid Refresh, used as the input to hydrologic models for streamflow forecasting. The models are the National Water Model operated by the National Water Center and the Hillslope Link Model used by the Iowa Flood Center to provide real-time forecasts for Iowa. The NWM streamflow output is examined at 7162 gauging stations operated by the United States Geological Survey. Results of three analyses are discussed. The first analysis compares HRRR QPF to the corresponding Quantitative Precipitation Estimation product known as Multi Radar Multi Sensor. Both the QPF and the QPE products represent hourly rainfall accumulations. The comparison is performed in the context of river basins with boundaries defined by the USGS gauging stations using several performance criteria. The second analysis represents a categorical evaluation of the ability of the QPF-driven NWM to detect floods, defined as discharge exceeding the Mean Annual Peak value. The third analysis is limited to the USGS-gauged basins located in Iowa using the HLM. The HLM is driven by the QPF for the 18 separate lead times in an open-loop configuration mimicking traditional hydrologic model simulation. A control simulation uses the MRMS QPE as the driving input. All analyses are conducted as function of lead time and spatial scale. Results demonstrate marginal benefit of the HRRR for streamflow forecast especially for basins smaller than 1,000 km 2 .