Journal article
Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting
Journal of hydrometeorology, Vol.22(7), pp.1931-1947
05/19/2021
DOI: 10.1175/JHM-D-20-0297.1
Abstract
AbstractIncorporating 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.
Details
- Title: Subtitle
- Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting
- Creators
- Ganesh R Ghimire - University of IowaWitold F Krajewski - University of IowaFelipe Quintero - University of IowaOak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Resource Type
- Journal article
- Publication Details
- Journal of hydrometeorology, Vol.22(7), pp.1931-1947
- DOI
- 10.1175/JHM-D-20-0297.1
- ISSN
- 1525-755X
- eISSN
- 1525-7541
- Language
- English
- Date published
- 05/19/2021
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984202251602771
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