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Thermal stratification in reservoirs is a critical process that regulates downstream riverine energy and biogeochemical cycling. Current stratification models either simplify vertical energy process, reservoir geometry or neglecting the effects of reservoir operation. Here we present a new multilayer reservoir stratification model that can be applied for reservoir and stream temperature simulation at regional or global scale. With a multilayer vertical discretization, we introduce a newly developed storage-area-depth dataset to improve parameterization of advection processes in and out of the reservoir. The new model better represents vertical temperature gradient and subsequently temperature of water released to downstream. The stratification model is applied to 1,400 reservoirs over the contiguous United States and validated against observed surface, profile, and outflow temperature data over 130 reservoirs subjected to various levels of regulation. The Nash-Sutcliffe values are higher than 0.5 for about 77% of the validated reservoirs using surface temperature while the average values of root mean square error and bias are 3.6 °C and -1.1 °C, respectively. Using the new reservoir storage-area-depth dataset improves the simulation of surface temperature at over 69% of the validated reservoirs compared to using simplified reservoir geometry. The reservoir stratification model contributes to improving predictive understanding of anthropogenic impact on terrestrial hydrological, ecological, and biogeochemical cycles. [ABSTRACT FROM AUTHOR]

A new large-scale stream temperature model has been developed within the Community Earth System Model (CESM) framework. The model is coupled with the Model for Scale Adaptive River Transport (MOSART) that represents river routing and a water management model (WM) that represents the effects of reservoir operations and water withdrawals on flow regulation. The coupled models allow the impacts of reservoir operations and withdrawals on stream temperature to be explicitly represented in a physically based and consistent way. The models have been applied to the Contiguous United States driven by observed meteorological forcing. Including water management in the models improves the agreement between the simulated and observed streamflow at a large number of stream gauge stations. It is then shown that the model is capable of reproducing stream temperature spatiotemporal variation satisfactorily by comparing against the observed data from over 320 USGS stations. Both climate and water management are found to have important influence on the spatiotemporal patterns of stream temperature. Furthermore, it is quantitatively estimated that reservoir operation could cool down stream temperature in the summer low-flow season (August-October) by as much as 1∼2°C due to enhanced low-flow conditions, which have important implications to aquatic ecosystems. Sensitivity of the simulated stream temperature to input data and reservoir operation rules used in the WM model motivates future directions to address some limitations in the current modeling framework. [ABSTRACT FROM AUTHOR]