| 摘 要： |
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 K-600 at water temperature of 20 degrees C) has uncertainties. This study proposed a new approach for estimating K-600 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 K-600 demonstrated distinct dynamic variability for all streams, ranging from 0 to 111.39 cm h(-1) (2) Streams with higher discharge (>10 m(3) s(-1)) exhibited significant seasonal differences in K-600 values. The monthly average discharge and water temperature were the two factors that determined the variation in K-600 values. (3) K-600 was a major source of uncertainty in CO2 emission fluxes, with a relative contribution of 53.72%. An integrated K-600 model of riverine gas exchange was developed at the watershed scale and validated using the observed DO change. Our study stressed that K-600 dynamics can better represent areal change to reduce uncertainty in estimating GHG emissions. |
在线留言
联系我们
所长信箱