An intercomparison of approaches for improving operational seasonal streamflow forecasts

In basins where atmospheric tele-connection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak tele-connections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework.

Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions.

Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.