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1000 Titel
  • Resolving heterogeneous fluxes from tundra halves the growing season carbon budget
1000 Autor/in
  1. Ludwig, Sarah M. |
  2. Schiferl, Luke |
  3. Hung, Jacqueline |
  4. Natali, Susan M. |
  5. Commane, Roisin |
1000 Verlag
  • Copernicus Publications
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-03-18
1000 Erschienen in
1000 Quellenangabe
  • 21(5):1301-1321
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.5194/bg-21-1301-2024 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:p>Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. </jats:p>
1000 Liste der Beteiligten
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1000 Label
1000 Förderer
  1. National Aeronautics and Space Administration |
  2. Gordon and Betty Moore Foundation |
  3. Office of Polar Programs |
1000 Fördernummer
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1000 Förderprogramm
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1000 Dateien
1000 Förderung
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    1000 Förderer National Aeronautics and Space Administration |
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  2. 1000 joinedFunding-child
    1000 Förderer Gordon and Betty Moore Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer Office of Polar Programs |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
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1000 @id frl:6480289.rdf
1000 Erstellt am 2024-05-23T13:31:52.418+0200
1000 Erstellt von 322
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1000 Zuletzt bearbeitet 2024-05-27T11:44:52.879+0200
1000 Objekt bearb. Mon May 27 11:44:52 CEST 2024
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