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Chen_2018_Environ._Res._Lett._13_075005.pdf 3,22MB
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1000 Titel
  • Great uncertainties in modeling grazing impact on carbon sequestration: a multi-model inter-comparison in temperate Eurasian Steppe
1000 Autor/in
  1. Chen, Yizhao |
  2. Yuwen, Tao |
  3. Cheng, Yuan |
  4. ju, weimin |
  5. Ye, Jingyi |
  6. Hickler, Thomas |
  7. Liao, Cuijuan |
  8. Feng, Lan |
  9. Ruan, Honghua |
1000 Erscheinungsjahr 2018
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2018-07-06
1000 Erschienen in
1000 Quellenangabe
  • 13(7):075005
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2018
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1088/1748-9326/aacc75 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The impact of grazing activity on terrestrial carbon (C) sequestration has been noticed and studied worldwide. Recent efforts have been made to incorporate the disturbance into process-based land models. However, the performance of grazing models has not been well investigated at large scales. In this study, we performed a spatially explicit model uncertainty assessment in the world's largest pasture ecosystem, the temperate Eurasian Steppe. Five grazing models were explicitly incorporated into a single terrestrial biogeochemical model to simulate regional C consumption from grazing activity (Cgraze). First, we summarized the underlying mechanisms and explicitly compared the general functions used to describe the processes in different models. Then, the models (five models with 12 simulations) were run in parallel using the same forcing data and livestock distribution map in 2006. Results indicated that the modeled regional Cgraze varied from 0.1–16.1 gC m−2 for the year. The corresponding ratios of Cgraze to aboveground net primary productivity ANPP and net primary productivity (NPP) ranged from 0.08%–24.6% and 0.028%–11.2%, respectively. Parameter sensitivity was further analyzed. Model outputs are highly sensitive to the intake rate (i.e. feeding rate of livestock per day), half maximum intake rate, and initial livestock weight. Our results indicate that great uncertainty exists in simulating Cgraze. We ascribed the major uncertainty to the different process description and poor parameterization. This study calls for more efforts to the comprehensive synthesis of usable dataset, the foundation of a standard observation system and the observe-based inter-comparison to evaluate models, which would facilitate more accurate assessment of C sequestration by pasture ecosystems and lead to better representation in earth system models.
1000 Sacherschließung
lokal model uncertainty
lokal temperate Eurasian steppe
lokal C sequestration
lokal remote sensing
lokal grazing model
lokal grassland management
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-9218-6679|https://orcid.org/0000-0003-1807-9370|https://frl.publisso.de/adhoc/uri/Q2hlbmcsIFl1YW4=|https://orcid.org/0000-0002-0010-7401|https://frl.publisso.de/adhoc/uri/WWUsIEppbmd5aQ==|https://orcid.org/0000-0002-4668-7552|https://orcid.org/0000-0003-0128-4423|https://frl.publisso.de/adhoc/uri/RmVuZywgTGFu|https://orcid.org/0000-0002-6075-474X
1000 Label
1000 Förderer
  1. National Basic Research Program of China (973 Program) |
  2. National Natural Science Foundation of China |
  3. Priority Academic Program Development of Jiangsu Higher Education Institutions |
1000 Fördernummer
  1. 2016YFA0600202
  2. 41701227
  3. -
1000 Förderprogramm
  1. -
  2. National Youth Science Fund
  3. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer National Basic Research Program of China (973 Program) |
    1000 Förderprogramm -
    1000 Fördernummer 2016YFA0600202
  2. 1000 joinedFunding-child
    1000 Förderer National Natural Science Foundation of China |
    1000 Förderprogramm National Youth Science Fund
    1000 Fördernummer 41701227
  3. 1000 joinedFunding-child
    1000 Förderer Priority Academic Program Development of Jiangsu Higher Education Institutions |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
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1000 @id frl:6424768.rdf
1000 Erstellt am 2020-12-16T14:12:55.001+0100
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1000 Oai Id
  1. oai:frl.publisso.de:frl:6424768 |
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