Machine Learning for Postprocessing Ensemble Streamflow Forecasts
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Abstract (arXiv)
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.
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References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2018Neural networks for post-processing ensemble weather forecastscited by 95
- 2020Deep Learning for Post-Processing Ensemble Weather Forecastscited by 52
- 2017A trans-disciplinary review of deep learning research for water resources scientistscited by 47
- 2019Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scalescited by 27
- 2020A framework for probabilistic weather forecast post-processing across models and lead times using machine learningcited by 6
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