Closing the gap between theoretical reservoir operation and the real-world implementation remains a challenge in contemporary reservoir operations. Past research has focused on optimization algorithms and establishing optimal policies for reservoir operations. In this research, we attempt to understand operators' release decisions by investigating historical release data from 79 reservoirs in California and the Great Plains, using a data-mining approach. We use information theory--specifically, mutual information--to measure the quality of inference between a set of classic indicators and observed releases at the monthly and weekly timescales. Then we investigate the results obtained from the data mining step to discover knowledge about the behavior of reservoir operators under the various conditions. The central aim of this step is to understand the human behavior of reservoir operators in the face of many factors with particular attention to the value of hydrologic information and uncertainty. We also expand the state of art technique to determine the most relevant suite of hydrologic inputs to release datasets by introducing an input selection algorithm to measure the information relevance between a suite of inputs and a single output. We apply the proposed algorithm to 22 reservoirs in California to predict daily release based on a set from a 121 potential input variables. Results indicate that the proposed algorithm is a reliable measure of the competence of model inputs as reflected in enhanced model performance. Finally we couple the data mining procedure with conventional reservoir optimization techniques to build an enhanced stochastic dynamic programming (SDP) model. The enhanced SDP model is applied to the Shelbyville Reservoir, IL, and then compared to two classic SDP formulations. From a data mining procedure, past month's inflow, current month's inflow, past month's release, and past month's Palmer drought severity index are found to be important state variables in the enhanced SDP model formulations for Shelbyville Reservoir. The study indicates that the enhanced SDP model resembles historical records more closely yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions). [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]