Details

Autor(en) / Beteiligte

• Lokupitiya, E.
• Denning, A. S.
• Schaefer, K.
• Ricciuto, D.
• Anderson, R.
• Arain, M. A.
• Baker, I.
• Barr, A. G.
• Chen, G.
• Chen, J. M.
• Ciais, P.
• Cook, D. R.
• Dietze, M.
• Maayar, M. El
• Fischer, M.
• Grant, R.
• Hollinger, D.
• Izaurralde, C.
• Jain, A.
• Kucharik, C.
• Li, Z.
• Liu, S.
• Li, L.
• Matamala, R.
• Peylin, P.
• Price, D.
• Running, S. W.
• Sahoo, A.
• Sprintsin, M.
• Suyker, A. E.
• Tian, H.
• Tonitto, C.
• Torn, M.
• Verbeeck, Hans
• Verma, S. B.
• Xue, Y.

Titel

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Ist Teil von

• Biogeochemistry, 2016-08, Vol.129 (1/2), p.53-76

Ort / Verlag

Cham: Springer Science + Business Media

Erscheinungsjahr

2016

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO₂ seasonal uptake over agricultural regions.