<p> Stream networks with reservoirs provide a particularly hard modeling challenge because reservoirs can decouple physical processes (e.g., water temperature dynamics in streams) from atmospheric signals. Including observed reservoir releases as inputs to models can improve water temperature predictions below reservoirs, but many reservoirs are not well-observed. This data release contains predictions from stream temperature models described in Jia et al. 2022, which describes different deep learning and process-guided deep learning model architectures that were developed to handle scenarios of missing reservoir releases. The spatial extent of this modeling effort was restricted to two spatially disjointed regions in the Delaware River Basin. The first region included streams above the Delaware River at Lordville, NY, and included the West Branch of the Delaware River above and below the Cannonsville Reservoir and the East Branch of the Delaware River above and below the Pepacton Reservoir. Additionally, the Neversink River which flows into the Delaware River at Port Jervis, New York, was included and contains river reaches above and below the Neversink Reservoir. For each model, there are test period predictions from 2006-12-26 through 2020-06-22. Model input, training, and validation data can be found in Oliver et al. (2021). <p>The publication associated with this data release is Jia X., Chen S., Xie Y., Yang H., Appling A., Oliver S., Jiang Z. 2022. Modeling reservoir release in stream temperature prediction using pseudo-prospective learning and physical simulations, SIAM International Conference on Data Mining (SDM). DOI: https://doi.org/10.1137/1.9781611977172.11</p>;