A Hierarchical Temporal Scale Framework for Data-Driven Reservoir Release Modeling

As an important anthropogenic interference in the hydrologic cycle, reservoir operation behavior remains challenging to be properly represented in hydrologic models, thus limiting the capability of predicting streamflow under the interactions between hydrologic variability and operational preferences. Data-driven models provide a promising approach to capture relationships embedded in historical records. Similar to hydrologic processes that vary across temporal scales, reservoir operations manifest themselves at different timescales, prioritizing different operation targets to mitigate streamflow variability at a given time scale. To capture the interaction of reservoir operation across time scales, we proposed a hierarchical temporal scale framework to investigate the behaviors of over 300 major reservoirs across the Contiguous United States with a wide range of streamflow conditions. Data-driven models were constructed to simulate reservoir releases at monthly, weekly, and daily scales, where decisions at short-term scales interact with long-term decisions. We found that the hierarchical temporal scale configuration could compensate for the absence of key explanatory variables as model inputs, thereby efficiently capturing the release decisions of reservoirs situated in the west. Model-based sensitivity analysis shows that for more than one-third of the studied reservoirs, the release schemes, as a function of decision variables, vary at different time scales, suggesting that operators commonly face complicated trade-offs to serve multiple designed purposes. The proposed hierarchical temporal scale approach is flexible to incorporate various data-driven models and decision variables to derive reservoir operation rules, providing a robust framework to understand the feedback between natural streamflow variability and human interferences across time scales.