Scale-Dependent Worth of QPF for Real-Time Streamflow Forecasting

Abstract

<p>The authors explore uncertainty associated with the quantitative precipitation forecasts (QPF) and its implication to the predictability of real-time streamflow forecasts. Including rainfall forecasts into real-time streamflow forecasting system extends the forecast lead time. As rainfall is a key driver of rainfall-runoff models both past and future rainfall estimates should be used in streamflow and flood forecasting. Since both QPE and QPF are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. Particularly QPF is notorious for its significant uncertainty with respect to location, timing and magnitude. Operational hydrologic services often limit their use of the QPF to one or two days into the future. The authors study this problem systematically using operational models and QPF. Their focus is on scale-dependence of the trade-off between the QPF time horizon and streamflow accuracy. To address this question, the authors first perform comprehensive independent evaluation of QPF at about 140 basins with wide range of spatial scales (10 - 40000 km2) corresponding to U.S Geological Survey (USGS) streamflow monitoring stations over the state of Iowa in Midwestern United States. High Resolution Rapid Refresh (HRRR) is an hourly short-medium range rainfall forecast of up to 18 hours updated every hour with spatial resolution of about 3 km by 3 km. Six-hourly rainfall forecasts are available for up to seven days ahead. Since basins are hydrologically relevant, the authors perform HRRR skill verification for the years 2016-2019 using conventional verification techniques and mean areal precipitation (basin scale rainfall volume) with respect to multi-radar</p><p>multi-sensor (MRMS) QPE (gauge-corrected) rainfall. The authors show that the QPF errors/uncertainties are scale-dependent. The QPF skills show increase as the basin scale and lead time of the forecast increases at short-medium range. In the second part of the study, both QPE and QPFs are forced separately to the hydrologic model called hillslope-link model (HLM) used at the Iowa Flood Center for real-time streamflow forecasting for Iowa. The objective is to understand the contribution of QPF uncertainty structure on the skill of streamflow forecasts. Since real-time streamflow observations (15 minutes resolution) are available at USGS sites, the authors incorporate them using a simple data assimilation framework. Several scenarios of forecasts, such as open-loop combined with QPF, persistence-based approach (using streamflow observations) combined with QPF, and open-loop combined with QPF for more than 18 hours horizon is explored. The authors report the contribution of QPF errors on hydrologic predictions across scales and suggest a forecasting scenario that shows the most enhanced predictability of streamflows.</p>