Unveiling the temporal variability of gas transfer coefficients of streams based on high-frequency dissolved oxygen measurements

• Published in: Environmental research 2024-12, Vol.262 (Pt 2), p.119939, Article 119939
• Main Authors: Wang, Fang, Tian, Siyu, Yan, Weijin
• Format: Article
• Language: English
• Subjects: China; Dissolved oxygen; Dynamic; Environmental Monitoring - methods; Gas transfer coefficient; Greenhouse Gases - analysis; MCMC; Oxygen - analysis; Rivers - chemistry; Seasons; Stream and river; Temperature
• ISSN: 0013-9351; 1096-0953; 1096-0953
• DOI: 10.1016/j.envres.2024.119939

Greenhouse gas (GHG) emissions from streams and rivers are important sources of global GHG emissions. As a crucial parameter for estimating GHG emissions, the gas transfer coefficient (expressed as K600 at water temperature of 20 °C) has uncertainties. This study proposed a new approach for estimating K600 based on high-frequency dissolved oxygen (DO) data and an ecosystem metabolism model. This approach combines the numerical solution method with the Markov Chain Monte Carlo analysis. This study was conducted in the Chaohu Lake watershed in Southeastern China, using high-frequency data collected from six streams from 2021 to 2023. This study found: (1) The numerical solution of K600 demonstrated distinct dynamic variability for all streams, ranging from 0 to 111.39 cm h−1 (2) Streams with higher discharge (>10 m3 s−1) exhibited significant seasonal differences in K600 values. The monthly average discharge and water temperature were the two factors that determined the variation in K600 values. (3) K600 was a major source of uncertainty in CO2 emission fluxes, with a relative contribution of 53.72%. An integrated K600 model of riverine gas exchange was developed at the watershed scale and validated using the observed DO change. Our study stressed that K600 dynamics can better represent areal change to reduce uncertainty in estimating GHG emissions. •New approach to estimate riverine K600 was developed based on dissolved oxygen.•STRICT-K600 model performs better with good validation.•The K600 shows distinct temporal dynamic patterns.•Discharge and water temperature determine K600 dynamics.•K600 was a major source of uncertainty in GHG emission fluxes.