Machine learning for postprocessing ensemble streamflow forecasts

Abstract

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 the application of machine learning as postprocessor for improving the quality of ensemble streamflow forecasts. 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 the warm season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts. HIGHLIGHTS We integrate numerical weather prediction ensembles and distributed hydrological models to generate ensemble streamflow forecasts.; We compare the performance of machine learning-based postprocessor with the standalone configurations of hydrometeorological modeling and machine learning.; Machine learning postprocessor improves both the skill and reliability of ensemble streamflow forecasts.;

Keywords

• distributed hydrologic model
• ensemble streamflow forecast
• lstm
• machine learning
• numerical weather prediction
• postprocessor