|Rango, Albert - Al|
Submitted to: Journal of the American Water Resources Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/19/2011
Publication Date: 8/1/2012
Publication URL: http://handle.nal.usda.gov/10113/57179
Citation: Harshburger, B.J., Walden, V.P., Humes, K.S., Moore, B.C., Blandford, T.R., Rango, A. 2012. Generation of ensemble streamflow forecasts using an enhanced version of the snowmelt runoff model. Journal of the American Water Resources Association. 48(4):643-655. Interpretive Summary: Water demand is increasing in the western United States while at the same time that water supply is diminishing because of climate change and other factors. In these situations, improved forecasts are needed, especially short-term (15 day) forecasts. The well-known Snowmelt Runoff Model (SRM) was modified to operate for 1-15 day periods of time using input from ensemble forecasts of temperature and precipitation obtained from the Global Forecasting System model produced by the National Center for Environmental Prediction. Results were very good for up to 7 days and adequate for 7 to 15 days. The modified SRM performed well in average to above average runoff years. This modified SRM would be useful for state water agencies as well as federal agencies needing a forecasting approach for short time periods.
Technical 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.