Using Deep Learning in Ensemble Streamflow Forecasting: Exploring the Predictive Value of Explicit Snowpack Information

Abstract

The Ensemble Streamflow Prediction (ESP) framework combines a probabilistic forecast structure with process-based models for water supply predictions. However, process-based models require computationally intensive parameter estimation, increasing uncertainties and limiting usability. Motivated by the strong performance of deep learning models, we seek to assess whether the Long Short-Term Memory (LSTM) model can provide skillful forecasts and replace process-based models within the ESP framework. Given challenges in implicitly capturing snowpack dynamics within LSTMs for streamflow prediction, we also evaluated the added skill of explicitly incorporating snowpack information to improve hydrologic memory representation. LSTM-ESPs were evaluated under four different scenarios: one excluding snow and three including snow with varied snowpack representations. The LSTM models were trained using information from 664 GAGES-II basins during WY1983–2000. During a testing period, WY2001–2010, 80% of basins exhibited Nash-Sutcliffe Efficiency (NSE) above 0.5 with a median NSE of around 0.70, indicating satisfactory utility in simulating seasonal water supply. LSTM-ESP forecasts were then tested during WY2011–2020 over 76 western US basins with operational Natural Resources Conservation Services (NRCS) forecasts. A key finding is that in high snow regions, LSTM-ESP forecasts using simplified ablation assumptions performed worse than those excluding snow, highlighting that snow data do not consistently improve LSTM-ESP performance. However, LSTM-ESP forecasts that explicitly incorporated past years' snow accumulation and ablation performed comparably to NRCS forecasts and better than forecasts excluding snow entirely. Overall, integrating deep learning within an ESP framework shows promise and highlights important considerations for including snowpack information in forecasting.