Open AccessLandscape process domains drive patterns of CO 2 evasion from river networks
Author(s) -
RocherRos Gerard,
Sponseller Ryan A.,
Lidberg William,
Mörth CarlMagnus,
Giesler Reiner
Publication year - 2019
Publication title -
limnology and oceanography letters
Language(s) - English
Resource type - Journals
ISSN - 2378-2242
DOI - 10.1002/lol2.10108
Subject(s) - environmental science , streams , evasion (ethics) , predictability , hydrology (agriculture) , proxy (statistics) , computer science , geology , mathematics , immune system , immunology , biology , computer network , statistics , geotechnical engineering , machine learning
Streams are important emitters of CO 2 but extreme spatial variability in their physical properties can make upscaling very uncertain. Here, we determined critical drivers of stream CO 2 evasion at scales from 30 to 400 m across a 52.5 km 2 catchment in northern Sweden. We found that turbulent reaches never have elevated CO 2 concentrations, while less turbulent locations can potentially support a broad range of CO 2 concentrations, consistent with global observations. The predictability of stream p CO 2 is greatly improved when we include a proxy for soil‐stream connectivity. Catchment topography shapes network patterns of evasion by creating hydrologically linked “domains” characterized by high water‐atmosphere exchange and/or strong soil‐stream connection. This template generates spatial variability in the drivers of CO 2 evasion that can strongly bias regional and global estimates. To overcome this complexity, we provide the foundations of a mechanistic framework of CO 2 evasion by considering how landscape process domains regulate transfer and supply.