Hierarchical Temporal Scale Data-driven Reservoir Operation Modeling
- Qianqiu Longyang,
- Ruijie Zeng

Ruijie Zeng

Arizona State University
Corresponding Author:ruijie.zeng.1@asu.edu
Author ProfileAbstract
As an important anthropogenic interference on the water 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
represent reservoir operation rules by capturing relationships embedded
in historical records. Similar to hydrologic processes vary across
temporal scales, reservoir operations manifest themselves at different
timescales, prioritizing different targets to mitigate streamflow
variability at a given time scale. To capture interactions of reservoir
operations 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. Machine learning models were constructed to simulate
reservoir operation at daily, weekly, and monthly scales, where
decisions at short-term scales interact with long-term decisions. We
found that the hierarchical temporal scale configuration better captures
reservoir releases than models constructed at a single time scale,
especially for reservoirs with multiple operation targets. 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 are commonly
faced with complicated trade-offs to serve multiple purposes. The
proposed hierarchical temporal scale approach is flexible to incorporate
various data-driven models and decision variables to derive reservoir
operation rule, providing a robust framework to understand the feedbacks
between natural streamflow variability and human interferences across
time scales.