  Accurate prediction of water temperature in streams is critical for
monitoring and understanding biogeochemical and ecological processes in
streams. Stream temperature is affected by weather patterns (such as solar
radiation) and water flowing through the stream network. Additionally, stream
temperature can be substantially affected by water releases from man-made
reservoirs to downstream segments. In this paper, we propose a heterogeneous
recurrent graph model to represent these interacting processes that underlie
stream-reservoir networks and improve the prediction of water temperature in
all river segments within a network. Because reservoir release data may be
unavailable for certain reservoirs, we further develop a data assimilation
mechanism to adjust the deep learning model states to correct for the
prediction bias caused by reservoir releases. A well-trained temporal modeling
component is needed in order to use adjusted states to improve future
predictions. Hence, we also introduce a simulation-based pre-training strategy
to enhance the model training. Our evaluation for the Delaware River Basin has
demonstrated the superiority of our proposed method over multiple existing
methods. We have extensively studied the effect of the data assimilation
mechanism under different scenarios. Moreover, we show that the proposed method
using the pre-training strategy can still produce good predictions even with
limited training data.
