Is This Rainfall Forecast Good or Bad? For Flood Forecasting, the Answer Is Scale Dependent

Abstract

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 National Water Model (NWM) streamflow output is examined at 7162 gauging stations operated by the U.S. 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 Hillslope Link Model (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 a function of lead time and spatial scale. Results demonstrate the marginal benefit of the HRRR for streamflow forecast especially for basins smaller than 1000 km2. SIGNIFICANCE STATEMENT: The authors analyzed several years of real-time streamflow forecasts generated by the National Water Model over the CONUS that used high-resolution quantitative precipitation forecasts. They concluded that QPF uncertainty is the dominant source of errors in streamflow forecasts. The forecasting skill for both streamflow and rainfall increases with the basin scale but is poor for basins smaller than about 1000 km2 which represent half of the gauged basins. The study, limited to 18 h of lead time, also shows that forecasting skill quickly decreases with time. The results offer a hydrologic perspective that explains why so many deadly flash floods lack site-specific forecasts. The authors argue for a balanced approach to allocating resources for rainfall estimation and forecasting and hydrologic modeling.