Machine Learning for Postprocessing Ensemble Streamflow Forecasts

Skillful streamflow forecasting informs decisions in various areas of water policy and management. We integrate dynamical modeling with machine learning to demonstrate the enhanced quality of streamflow forecasts at short-to medium-range timescales (1 - 7 days). Dynamical modeling generates ensemble streamflow forecasts by forcing a hydrological model with numerical weather prediction model outputs. We employ a Long Short-Term Memory (LSTM) neural network to correct forecast biases in raw ensemble streamflow forecasts obtained from dynamical modeling. For forecast verification, we use different metrics such as skill score and reliability diagram conditioned upon the lead time, flow threshold, and season. The verification results show that the LSTM can improve streamflow forecasts relative to climatological, temporal persistence, deterministic, and raw ensemble forecasts. The LSTM demonstrates improvement across all lead times, flow thresholds, and seasons. As compared to the raw ensembles, relative gain in forecast skill from LSTM is generally higher at medium-range timescales compared to initial lead time; high flows compared to low-moderate flows; and warm-season compared to the cool ones. Overall, our results highlight the benefits of LSTM for improving both the skill and reliability of streamflow forecasts.