An Alternative Ensemble Streamflow Prediction Approach Using Improved Subseasonal Precipitation Forecasts from the North America Multi-Model Ensemble Phase II

Journal Article · Tue Mar 04 00:00:00 EST 2025 · Journal of Hydrometeorology

DOI:https://doi.org/10.1175/jhm-d-24-0048.1· OSTI ID:2538488

Zhang, Lujun ^[1]; Yang, Tiantian ^[1]; Gao, Shang ^[2]; Fan, Ming ^[3]; Lu, Dan ^[3]; Xu, Hongmei ^[4]; Xiao, Chan ^[4]

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1. University of Oklahoma, Norman, OK (United States)
2. University of Oklahoma, Norman, OK (United States); University of Arizona, Tucson, AZ (United States)
3. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
4. China Meteorological Administration, Beijing (China)

In this article, streamflow forecasting at a subseasonal time scale (10–30 days into the future) is important for various human activities. The ensemble streamflow prediction (ESP) is a widely applied technique for subseasonal streamflow forecasting. However, ESP’s reliance on the randomly resampled historical precipitation limits its predictive capability. Available dynamical subseasonal precipitation forecasts provide an alternative to the randomly resampled precipitation in ESP. Prior studies found the predictive performance of raw subseasonal precipitation forecast is limited in many regions such as the central south of the United States, which raises questions about its effectiveness in assisting streamflow forecasting. To further assess the hydrologic applicability of dynamical subseasonal precipitation forecasts, we test the subseasonal precipitation forecast from North America Multi-Model Ensemble Phase II (NMME-2) at four watersheds in the central south region of the United States. The subseasonal precipitation forecasts are postprocessed with bias correction and spatial disaggregation (BCSD) to correct bias and improve spatial resolution before replacing the randomly resampled precipitation in ESP for streamflow predictions. The performance of the resulting streamflow predictions is benchmarked with ESP. Evaluation is conducted using Kling–Gupta Efficiency (KGE), continuous ranked probability score (CRPS), probability of detection (POD), false alarm ratios (FARs), as well as reliability diagrams. Our results suggest that BCSD-corrected subseasonal precipitation forecasts lead to overall improved streamflow predictions due to added skills in winter and spring. Our results also suggest that BCSD-corrected subseasonal precipitation forecasts lead to improved predictions on the occurrence of high-percentile streamflow values above 75%. Overall, BCSD-corrected subseasonal precipitation has shown promising performance, highlighting its potential broader applications for river and flood forecasting.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: National Key R&D Program of China; National Science Foundation (NSF); USDOD; USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 2538488
• Journal Information: Journal of Hydrometeorology, Journal Name: Journal of Hydrometeorology Journal Issue: 3 Vol. 26; ISSN 1525-755X
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Forecasting
North America
Precipitation
Streamflow
Subseasonal variability