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

Ganesh R. Ghimire, Witold F. Krajewski, Felipe Quintero

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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 sys-tematically, 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 1000 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.

Original languageEnglish
Pages (from-to)1931-1947
Number of pages17
JournalJournal of Hydrometeorology
Volume22
Issue number7
DOIs
StatePublished - Jul 2021
Externally publishedYes

Funding

Acknowledgments. This study did not receive any external funding. The study was funded by the Iowa Flood Center of the University of Iowa. The second author also acknowledges partial support from the Rose and Joseph Summers endowment. The authors are grateful to many colleagues at the IFC who facilitated the study by providing observational and computational support as well as fruitful discussions.

FundersFunder number
Rose and Joseph Summers endowment
University of Iowa

    Keywords

    • Ensembles
    • Forecast verification/skill
    • Hydrologic models
    • Model evaluation/performance
    • Operational forecasting
    • Probabilistic Quantitative Precipitation Forecasting (PQPF)
    • Short-range prediction

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