Improved Spring Peak-Flow Forecasting Using Ensemble Meteorological Predictions

Abstract

The potential of ensemble meteorological forecasts in improving ensemble spring peak flow prediction up to 14 days ahead is investigated for the Saguenay-Lac-Saint Jean watershed located in northeastern Canada. Large-scale ensemble meteorological forecasts (precipitation and temperature) generated by the National Center for Environmental Prediction’s (NCEP) Global Forecast System (GFS) are bias corrected for two meteorological stations in the watershed. The bias corrected NCEP ensemble meteorological forecast data are used as input in the hydrological model Hydrologiska Byråns Vattenbalan-avdelning (HBV) to simulate ensemble reservoir inflows and Serpent River flows up to 14 days ahead. The ensemble inflow and flow forecasts are compared with climatology as well as with the case in which only observed historical data are used for spring peak flow forecasting. The study results show that there is a significant improvement for the longer forecast range in the model forecast performance when bias-corrected NCEP forecast data are used. The improvement for forecasts for the spring season as well as for the entire year is revealed by Brier and rank probability skill score (BSS and RPSS, respectively) for reservoir inflow and Serpent River flow forecasts. Visual inspection of scatter plots between observed and simulated flows, and hydrographs of ensemble mean and ensemble members also reveal the potential of NCEP meteorological forecasts for improving spring flow forecasting.