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

Journal Article · · Journal of Hydrometeorology
 [1];  [2];  [2]
  1. Univ. of Iowa, Iowa City, IA (United States). Iowa Flood Center and IIHR-Hydroscience & Engineering; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Iowa, Iowa City, IA (United States). Iowa Flood Center and IIHR-Hydroscience & Engineering
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Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) 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. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km2) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km2. The space-time scale, or reference time tr) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1811378
Journal Information:
Journal of Hydrometeorology, Journal Name: Journal of Hydrometeorology Journal Issue: 7 Vol. 22; ISSN 1525-755X
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English

References (1)

NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive
  • Commerce, National Centers For Environmental Prediction/National Weather Service/NOAA/U. S. Department Of
  • UCAR/NCAR - Research Data Archive https://doi.org/10.5065/d65d8pwk
dataset January 2015

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