Background/Question/Methods Air-water gas transfer influences fundamental processes of aquatic systems such as photosynthesis, respiration, and greenhouse gas emission. Quantifying greenhouse gas fluxes (e.g., CO2, CH4 and N2O), characterizing ecosystem metabolism regimes, and modeling water quality all require a reliable estimation of KL. Previous empirical models for predicting gas transfer velocity (KL) often only apply to a small range of streams and rivers. Two ‘universal’ scalings, KL ~ ¼ power of turbulent dissipation rate (ε1/4) and KL ~ shear velocity (U*), deduced from classic thin-film theory and surface renewal theory, have been reported for lake and marine systems, but have not been adequately tested for streams or rivers. In this study, we ask three questions: (1) Are the ‘universal’ scalings KL ~ (ε)1/4 and KL ~ U* applicable to streams and rivers? (2) how does KL vary with U*, ε1/4, and discharge across a wide variety of streams and rivers? And (3) do the main drivers of KL variation shift across streams and rivers of different sizes or types? To address these questions, we compiled 588 field measurements of KL, and used inverse modeling based on long-term high-frequency dissolved oxygen time series to estimate KL of 35 streams and rivers capturing a wide range of discharge (0 – 221 m3/s).
Results/Conclusions We found that the two physically based scalings held for both the KL estimated by inverse modeling and the KL from measurements, and outperformed 23 previous models in predicting KL. More importantly, we found the main driver of KL variation shifted from bottom friction to other factors with increasing stream/river size. In most streams and a few rivers, correlation between U* and KL is high, and discharge–KL relationships are strongly positive, indicating that turbulence generated by bottom friction dominates near-surface turbulence, a fundamental control of KL, thus, the two scaling models are applicable to these systems. However, most rivers (mean discharge > 10 m3/s) show low correlations between U* and KL, suggesting biochemical factors and winds likely override bottom friction in driving near-surface turbulence and KL. Based on these findings, we propose an integrative framework for better predicting KL in streams and rivers. Our study highlights the importance of simultaneously measuring near-surface turbulence dissipation rate, gas transfer velocity, biochemical factors, speed and direction of winds.
