Reservoirs play a significant role in water management by helping relieve water scarcity and catastrophic flooding. They also significantly impact the distribution of water across space and time. Reservoir operations affect depth-dependent temperature changes (stratification) in a reservoir that, in turn, influence downstream water temperature. Water temperature may affect aquatic habitats and the performance of thermal power plants. Scientists at the University of Houston and the U.S. Department of Energy’s Pacific Northwest National Laboratory developed a new module for use in Earth system models that better represents stratification. Modeling reservoir stratification contributes to an improved understanding of how people impact terrestrial hydrological, ecological, and biogeochemical cycles.
This research provides a framework for including reservoir temperature stratification in Earth system models to represent stream temperature at regional, continental, and global scales. Currently available reservoir stratification models are primarily suited for modeling individual reservoirs and lakes or require too much data to apply universally. In some cases, the inflow and outflow of water from the reservoir are ignored. By accounting for the impact of inflow, outflow, and other processes and including more realistic reservoir geometry, the new model provides a unique capability for modeling human influence on riverine processes.
The main target of this study was to develop a reservoir stratification model by building on the advantages of previously available models while addressing their shortcomings. The main process missing in previous stratification models is reservoir operation. Researchers represented the inflow and outflow (advection) processes of reservoir operations using a multi-layer vertical discretization. For better parametrization of this advection process, the researchers used a newly developed depth-area storage dataset and then applied the stratification model to 1400 reservoirs over the Continental U.S. They performed model evaluation using observation data for surface, profile (including reservoir depth), and reservoir outflow temperature data for 130 reservoirs with various reservoir operations. The model performance indicators were more than acceptable for about 77% of the validated reservoirs with a bias of -1.1°C.
By using the new reservoir storage-area depth dataset, the researchers were able to improve surface temperature simulation for ~70% of the validated reservoirs compared to using simplified reservoir geometry as in previously available models. A comprehensive and physically based representation of processes in reservoirs contributes to improving the understanding of how reservoir operations impact terrestrial, hydrological, ecological, and biogeochemical cycles.