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Abstract Dissolved Oxygen (DO) fluxes across the air‐water and sediment‐water interface (AWI and SWI) are two major processes that govern the amount of oxygen available to living organisms in aquatic ecosystems. Aquatic vegetation generates different scales of turbulence that change the flow structure and affect gas transfer mechanisms at AWI and SWI. A series of laboratory experiments with rigid cylinder arrays to mimic vegetation was conducted in a recirculating race‐track flume with a lightweight sediment bed. 2D Planar Particle Image Velocimetry was used to characterize the flow field under different submergence ratios and array densities to access the effect of vegetation‐generated turbulence on gas transfer. Gas transfer rate across AWI was determined by DO re‐aeration curves. The effective diffusion coefficient for gas transfer flux across SWI was estimated by the difference between near‐bed and near‐surface DO concentrations. When sediment begins to mobilize, near‐bed suspended sediment provides a negative buoyancy term that increases the critical Reynolds number for the surface gas transfer process according to a modified Surface Renewal model for vegetated flows. A new Reynolds number dependence model using near‐bed turbulent kinetic energy as an indicator is proposed to provide a universal prediction for the interfacial flux across SWI in flows with aquatic vegetation. This study provides critical information and useful models for future studies on water quality management and ecosystem restoration in natural water environments such as lakes, rivers, and wetlands.
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This content will become publicly available on November 1, 2025
Gas Transfer Across Air‐Water Interfaces in Inland Waters: From Micro‐Eddies to Super‐Statistics
Abstract In inland water covering lakes, reservoirs, and ponds, the gas exchange of slightly soluble gases such as carbon dioxide, dimethyl sulfide, methane, or oxygen across a clean and nearly flat air‐water interface is routinely described using a water‐side mean gas transfer velocity , where overline indicates time or ensemble averaging. The micro‐eddy surface renewal model predicts , where is the molecular Schmidt number, is the water kinematic viscosity, and is the waterside mean turbulent kinetic energy dissipation rate at or near the interface. While has been reported across a number of data sets, others report large scatter or variability around this value range. It is shown here that this scatter can be partly explained by high temporal variability in instantaneous around , a mechanism that was not previously considered. As the coefficient of variation in increases, must be adjusted by a multiplier that was derived from a log‐normal model for the probability density function of . Reported variations in with a macro‐scale Reynolds number can also be partly attributed to intermittency effects in . Such intermittency is characterized by the long‐range (i.e., power‐law decay) spatial auto‐correlation function of . That varies with a macro‐scale Reynolds number does not necessarily violate the micro‐eddy model. Instead, it points to a coordination between the macro‐ and micro‐scales arising from the transfer of energy across scales in the energy cascade.
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- PAR ID:
- 10574777
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 60
- Issue:
- 11
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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