Coupling Machine Learning and Physically Based Hydrological Models for Reservoir-Based Streamflow Forecasting

High-accuracy streamflow forecasting with long lead times can help promote the efficient utilization of water resources. However, the construction of cascade reservoirs has allowed the evolution of natural continuous rivers into multi-block rivers. The existing streamflow forecasting methods fail to consider the impact of reservoir operation. Thus, a novel short-term streamflow forecasting method for multi-block watersheds was proposed by integrating machine learning and hydrological models. Firstly, based on IMERG precipitation, the forecast precipitation product’s error is corrected by the long short-term memory neural network (LSTM). Secondly, coupling convolutional LSTM (ConvLSTM) and LSTM, operation rules for cascade reservoirs are extracted. Thirdly, a short-term deterministic streamflow forecasting model was built for multi-block watersheds. Finally, according to the sources of forecasting errors, probabilistic streamflow forecasting models based on the Gaussian mixture model (GMM) were proposed, and their performances were compared. Taking the Yalong River as an example, the main results are as follows: (1) Deep learning models (ConvLSTM and LSTM) show good performance in forecast precipitation correction and reservoir operation rule extraction, contributing to streamflow forecasting accuracy. (2) The proposed streamflow deterministic forecasting method has good forecasting performance with NSE above 0.83 for the following 1–5 days. (3) The GMM model, using upstream evolutionary forecasted streamflow, interval forecasted streamflow, and downstream forecasted streamflow as the input–output combination, has good probabilistic forecasting performance and can adequately characterize the “non-normality” and “heteroskedasticity” of forecasting uncertainty.