Machine Learning for Postprocessing Ensemble Streamflow Forecasts

TL;DR

This paper demonstrates how machine learning can enhance medium-range ensemble streamflow forecasts by integrating weather predictions and hydrological models, leading to improved accuracy and reliability in water management decisions.

Contribution

It introduces a machine learning postprocessing approach that significantly improves forecast skill over traditional methods at medium-range timescales.

Findings

Machine learning improves forecast skill more at medium-range lead times.

Higher gains are observed for high flows and during warm seasons.

Postprocessing enhances both the skill and reliability of 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 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.