Details

Autor(en) / Beteiligte

• Martinsen, Kenneth Thorø
• Kragh, Theis
• Sand‐Jensen, Kaj

Titel

Carbon Dioxide Partial Pressure and Emission Throughout the Scandinavian Stream Network

Ist Teil von

• Global biogeochemical cycles, 2020-12, Vol.34 (12), p.n/a

Ort / Verlag

Washington: Blackwell Publishing Ltd

Erscheinungsjahr

2020

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Stream networks transport and emit substantial volumes of carbon dioxide (CO2) into the atmosphere. We gathered open monitoring data from streams in three Scandinavian countries and estimated CO2 partial pressure (pCO2) at 2,298 sites. Most of the sites (87%) were supersaturated when averaged across the year with an overall mean pCO2 of 1,464 μatm (range: 17–15,646). Using remote sensing data, we modeled a realistic stream network including streams above ~2.5 m wide and calculated catchment averages of multiple variables associated with geomorphometry, stream network proximity, and land cover. We compared the ability of eight machine learning models to predict pCO2 and found that the Random Forest model achieved the highest accuracy, with a root‐mean‐square error of 0.22 (log10(pCO2)) and R2 of 0.66. Mean catchment elevation, slope, and permanent water cover were the most important predictor variables. We used the predictive model to create a high‐resolution (25‐m resolution) map with predicted stream pCO2 throughout the 268.807 km stream network in Denmark, Sweden, and Finland. Predicted pCO2 averaged 1,134 μatm (range: 154–8,174). We used surface runoff, air temperature, and stream channel slope to estimate gas transfer velocity and CO2 flux throughout the network. Mean stream CO2 fluxes ranged from 1.0 and 1.2 in Sweden and Finland, respectively, 3 to 3.2 g C m−2 day−1 in Denmark. Better‐performing models improve our ability to predict pCO2 in stream networks and reduce the uncertainty of upscaling estimates of carbon emissions from inland waters to countries and continents. Key Points Carbon dioxide partial pressure in streams can be predicted from catchment characteristics Prediction of carbon dioxide partial pressure can be improved using machine learning Prediction of carbon dioxide partial pressure, followed by estimation of fluxes, can be performed across large stream networks