Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1029/2025JG009300 |
Licence  |
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| Title (Primary) | Phytoplankton and temperature control seasonal dynamics of greenhouse gases in a large river |
| Author | Koschorreck, M.; Schütze, C.; Koedel, U.; Bussmann, I.; Kamjunke, N. |
| Source Titel | Journal of Geophysical Research-Biogeosciences |
| Year | 2026 |
| Department | SEEFO; FLOEK; MET |
| Volume | 131 |
| Issue | 5 |
| Page From | e2025JG009300 |
| Language | englisch |
| Topic | T4 Coastal System |
| Supplements | Supplement 1 |
| Keywords | dissolve gases; river Elbe; CO2; CH4; N2O |
| Abstract | Rivers are a dynamic source of greenhouse gases (GHGs), yet the temporal variability and controlling mechanisms of their CO2:CH4:N2O ratios remain poorly constrained. We monitored the three GHGs in the German river Elbe over 5 years at two sites to identify seasonal controls as well as travel time related and site-specific mechanisms driving GHG concentration ratios and fluxes. CO2 concentrations ranged from 2.8 to 125 μmol L−1 and showed a clear seasonal pattern with minimum values below saturation in summer, mainly correlating with indicators of planktonic photosynthesis, light availability, and chlorophyll concentration. CH4 concentrations ranged from 0.0006 to 0.85 μmol L−1 and showed an opposite seasonal dynamic with maximum values in summer, correlating with temperature and particulate organic carbon. N2O concentrations were between 0.007 and 0.07 μmol L−1, mostly near saturation and mainly determined by temperature dependent solubility. We did not observe large differences between the two study sites except for elevated CH4 concentrations at the downstream site during summer. While CO2 was regulated by the metabolic balance of the water column, CH4 was more locally controlled, probably by hydrodynamic conditions affecting particle sedimentation. The total GHG-potential of the three gases in terms of CO2 equivalents was dominated by CO2 and its seasonal cycle. Higher CH4 emissions during summer were compensated by CO2 uptake. Data-driven models based on machine-learning methods revealed that in the Elbe River, it is probably possible to predict GHG concentrations based on seasonal indicators without the need for water quality parameters. |
| Koschorreck, M., Schütze, C., Koedel, U., Bussmann, I., Kamjunke, N. (2026): Phytoplankton and temperature control seasonal dynamics of greenhouse gases in a large river J. Geophys. Res.-Biogeosci. 131 (5), e2025JG009300 10.1029/2025JG009300 |
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