  Skillful streamflow forecasts can inform decisions in various areas of water
policy and management. We integrate numerical weather prediction ensembles,
distributed hydrological model and machine learning to generate ensemble
streamflow forecasts at medium-range lead times (1 - 7 days). We demonstrate a
case study for machine learning applications in postprocessing ensemble
streamflow forecasts in the Upper Susquehanna River basin in the eastern United
States. Our results show that the machine learning postprocessor can improve
streamflow forecasts relative to low complexity forecasts (e.g., climatological
and temporal persistence) as well as standalone hydrometeorological modeling
and neural network. The relative gain in forecast skill from postprocessor is
generally higher at medium-range timescales compared to shorter lead times;
high flows compared to low-moderate flows, and warm-season compared to cool
ones. Overall, our results highlight the benefits of machine learning in many
aspects for improving both the skill and reliability of streamflow forecasts.
