On the biases of empirical riverine gas transfer velocity models at high submergence

Quantifying gas transfer velocity in rivers, a key step in characterising riverine ecosystems, carbon cycling, and greenhouse gas emissions, typically relies on empirical or semi-empirical models. Although fluid mechanics theory predicts a direct influence of the depth-to-bed-roughness-size ratio (relative submergence) on gas transfer velocity, the submergence is rarely reported in gas exchange datasets, and empirical models do not include it as an explanatory parameter. We employed a well-established two-scale bed-friction model to reconstruct the missing submergence information in the largest known gas transfer velocity dataset, revealing a lack of data from high-submergence rivers. According to the theoretical scaling, we predict that the current empirical models, calibrated on low-submergence streams and small rivers, may be overestimating gas exchange fluxes by more than 100 % in large, high-submergence (typically lowland) rivers. These biases may have significant impacts on the calculation of ecosystem metabolism and global greenhouse gas budgets.