The role of hydrologic information in reservoir operation - Learning from historical releases

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 study, 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. The 79 reservoirs are classified by hydrological regions, intra-annual seasons, average annual precipitation (climate), ratio of maximum reservoir capacity to average annual inflow (size ratio), hydrologic uncertainty associated with inflows, and reservoirs' main usage. 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. Several general trends are found to explain which sources of hydrologic information dictate reservoir release decisions under different conditions. Current inflow is the most important indicator during wet seasons, while previous releases are more relevant during dry seasons and in weekly data (as compared with monthly data). Inflow forecasting is the least important indicator in release decision making, but its importance increases linearly with hydrologic uncertainty and decreases logarithmically with reservoir size. No single hydrologic indicator is dominant across all reservoirs in either of the two regions.