Predicting induced sediment oxygen flux in oxygenated lakes and reservoirs

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Virginia Tech


Bubble plume oxygenation systems are commonly used to mitigate anoxia and its deleterious effects on water quality in thermally stratified lakes and reservoirs. Following installation, increases in sediment oxygen flux (JO2) are typically observed during oxygenation and are positively correlated with the bubble plume gas flow rate. Studies show that JO2 is controlled by the thickness of the diffusive boundary layer (DBL) at the sediment-water interface (SWI), which is in turn controlled by turbulence. As a result, JO2 can be quite spatially and temporally variable.

Accurately predicting oxygenation-induced JO2 is vitally important for ensuring successful oxygenation system design and operation. Yet despite the current understanding of physical and chemical controls on JO2, methods for predicting oxygenation-induced JO2 are still based on empirical correlations and factors of safety. As hypolimnetic oxygenation becomes more widely used as a lake management tool for improving and maintaining water quality, there is a need to move from the current empirically based approach to a mechanistic approach and improve the ability to predict induced JO2. This work details field campaigns to investigate and identify appropriate models of oxygen supply to the SWI and oxygen demand exerted from the sediment, with the intent to use these models to predict oxygenation-induced JO2.

Oxygen microprofiles across the SWI and near-sediment velocity measurements were collected in situ during three field campaigns on two oxygenated lakes, providing simultaneous measurements of JO2 and turbulence. Field observations show that oxygenation can increase JO2 by increasing bulk hypolimnetic oxygen concentrations, which increases the concentration gradient across the SWI. Oxygenation can also enhance turbulence, which decreases the DBL thickness and increases JO2. Existing models of interfacial flux were compared to field measurements to determine which model best predicted the observed JO2. Models based on the Batchelor scale, friction velocity, and film-renewal theory all agree reasonably well with field observations in both lakes. Additionally, the oxygen microprofiles were used to fit a transient model of oxygen kinetics in lake sediment and determine the appropriate kinetic model. Oxygen microprofiles in both lakes can be described using zero-order kinetics, rather than first-order kinetics.

The interfacial flux and sediment kinetic models are incorporated into a coupled bubble plume and 3-D hydrodynamic lake model, allowing for spatial and temporal variation in simulated JO2. This comprehensive model was calibrated and validated to field data from two separate field campaigns on Carvin's Cove Reservoir, Virginia. Simulated temperature profiles agreed quite well with field observations, while simulated oxygen profiles differed from observed profiles, particularly in the bottom 1 m of the water column. The model overestimates oxygen concentrations near the sediment, which results in higher simulated JO2 than was observed during the field campaigns. These discrepancies are attributed to oxygen-consuming chemical processes, such as oxidation of soluble metals, which are not accounted for in the hydrodynamic model. Despite this, the model is still able to capture the impact of bubble plume operation on JO2, as simulated JO2 is higher when the diffusers are operating. With some additional improvements to the water quality modeling aspects of the model, as well as further calibration and validation, the model should be able to reproduce observed JO2 provided oxygen concentrations near the SWI are accurately reproduced as well. The current work is an attempt to push toward a comprehensive lake oxygenation model. A comprehensive model such as this should improve the ability to predict oxygenation-induced JO2 and lead to improvements in the design and operation of hypolimnetic oxygenation systems.



bubble plume, hypolimnetic oxygenation, oxygen flux, sediment kinetics, hydrodynamic modeling, water quality modeling, Si3D