Machine-learning algorithms have shown promise for streamflow forecasts, reservoir operations, and scheduling, but have exhibited lower accuracy in predicting extended time horizons of peak storage volume (PSV). Deep learning algorithms exhibited improved inflow forecasting accuracy, but existing research has been mostly limited to real-time operation and short-term planning. We evaluate a new approach based on a hybrid ResCNN-LSTM Encoder-Decoder algorithm, enabling long-term multi-step reservoir forecasts. The proposed approach provides a three-month, weekly averaged prediction of reservoir storage volume (RSV) during the runoff season based on historical snow water equivalent (SWE). The optimal architecture and hyper-parameters for the model are configured through five-fold cross validation resulting in a twelve-layered residual convolutional neural network (ResCNN) as the encoder and a four-layered long short-term memory (LSTM) neural network as the decoder. We evaluate the algorithm using 30 years of RSV and SWE data at the Upper Stillwater Reservoir located in Utah. The most accurate long-term predictions occurred during periods of large runoff (in excess of 28,000 ac-ft). The periods where the model performed the worst were during small runoff and late-season SWE accumulation. We find that the ResCNN-LSTM consistently outperforms three widely used statistical models, with an average PSV absolute percent error of 2.66% for the proposed algorithm compared to SARIMA (14.22%), TBATS (13.82%), and VAR (18.14%).