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For much of the last century, forecasting centers around the world\udhave offered seasonal streamflow predictions to support water management.\udRecent work suggests that the two major avenues to advance seasonal\udpredictability are improvements in the estimation of initial hydrologic\udconditions (IHCs) and the incorporation of climate information. This study\udinvestigates the marginal benefits of a variety of methods using IHCs and/or\udclimate information, focusing on seasonal water supply forecasts (WSFs) in\udfive case study watersheds located in the US Pacific Northwest region. We\udspecify two benchmark methods that mimic standard operational approaches –\udstatistical regression against IHCs and model-based ensemble streamflow\udprediction (ESP) – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using\udclimate information either from standard indices or from climate reanalysis\udvariables and (ii) several hybrid/hierarchical approaches harnessing both\udland surface and climate predictability. In basins where atmospheric\udteleconnection signals are strong, and when watershed predictability is low,\udclimate information alone provides considerable improvements. For those\udbasins showing weak teleconnections, custom predictors from reanalysis fields\udwere more effective in forecast skill than standard climate indices. ESP\udpredictions tended to have high correlation skill but greater bias compared\udto other methods, and climate predictors failed to substantially improve\udthese deficiencies within a trace weighting framework. Lower complexity\udtechniques were competitive with more complex methods, and the hierarchical\udexpert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP) provided a robust\udalternative for skillful and reliable water supply forecasts at all\udinitialization times. Three key findings from this effort are (1) objective\udapproaches supporting methodologically consistent hindcasts open\udthe door to a broad range of beneficial forecasting strategies; (2) the use\udof climate predictors can add to the seasonal forecast skill available from\udIHCs; and (3) sample size limitations must be handled rigorously to avoid\udover-trained forecast solutions. Overall, the results suggest that despite a\udrich, long heritage of operational use, there remain a number of compelling\udopportunities to improve the skill and value of seasonal streamflow\udpredictions

An intercomparison of approaches for improving operational seasonal streamflow forecasts