  This paper proposes a new data-driven method for predicting water temperature
in stream networks with reservoirs. The water flows released from reservoirs
greatly affect the water temperature of downstream river segments. However, the
information of released water flow is often not available for many reservoirs,
which makes it difficult for data-driven models to capture the impact to
downstream river segments. In this paper, we first build a state-aware graph
model to represent the interactions amongst streams and reservoirs, and then
propose a parallel learning structure to extract the reservoir release
information and use it to improve the prediction. In particular, for reservoirs
with no available release information, we mimic the water managers' release
decision process through a pseudo-prospective learning method, which infers the
release information from anticipated water temperature dynamics. For reservoirs
with the release information, we leverage a physics-based model to simulate the
water release temperature and transfer such information to guide the learning
process for other reservoirs. The evaluation for the Delaware River Basin shows
that the proposed method brings over 10\% accuracy improvement over existing
data-driven models for stream temperature prediction when the release data is
not available for any reservoirs. The performance is further improved after we
incorporate the release data and physical simulations for a subset of
reservoirs.
