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 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.