Paper No. JAWRA-10-0191-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.
Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1
Article first published online: 28 FEB 2012
© 2012 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 48, Issue 4, pages 643–655, August 2012
How to Cite
Harshburger, B. J., Walden, V. P., Humes, K. S., Moore, B. C., Blandford, T. R. and Rango, A. (2012), Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. JAWRA Journal of the American Water Resources Association, 48: 643–655. doi: 10.1111/j.1752-1688.2012.00642.x
- Issue published online: 1 AUG 2012
- Article first published online: 28 FEB 2012
- Received November 2, 2010; accepted December 19, 2011.
- snow hydrology;
- water supply;
- surface water hydrology;
- quantitative modeling;
- ensemble streamflow forecasting
Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643-655. DOI: 10.1111/j.1752-1688.2012.00642.x
Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1-15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt-dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak-flow value is typically low. The model performance was assessed by computing the coefficient of determination (R2), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R2 value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.