Predicting lake dissolved organic carbon at a global scale

The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different e...

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Bibliographic Details
Published in:Scientific reports 2020-05, Vol.10 (1), p.8471-8471, Article 8471
Main Authors: Toming, Kaire, Kotta, Jonne, Uuemaa, Evelyn, Sobek, Sebastian, Kutser, Tiit, Tranvik, Lars J.
Format: Article
Language:English
Subjects:
704/286
704/47/4113
Aquatic ecosystems
Biogeochemistry
Carbon
Carbon cycle
Dissolved organic carbon
Ecosystems
Environmental factors
Humanities and Social Sciences
Inland waters
Lake morphometry
Lakes
Learning algorithms
Machine learning
Morphometry
multidisciplinary
Prognosis
Science
Science (multidisciplinary)
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Summary:The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of in situ DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km 2 . Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L −1 . The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-65010-3