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Titel Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO2 and H2O fluxes at five European maize (Zea mays L.) sites
VerfasserIn L. Li, N. Vuichard, N. Viovy, P. Ciais, T. Wang, E. Ceschia, W. Jans, M. Wattenbach, P. Beziat, T. Gruenwald, S. Lehuger, C. Bernhofer
Medientyp Artikel
Sprache Englisch
ISSN 1726-4170
Digitales Dokument URL
Erschienen In: Biogeosciences ; 8, no. 6 ; Nr. 8, no. 6 (2011-06-29), S.1721-1736
Datensatznummer 250005966
Publikation (Nr.) Volltext-Dokument vorhandencopernicus.org/bg-8-1721-2011.pdf
 
Zusammenfassung
This paper is a modelling study of crop management impacts on carbon and water fluxes at a range of European sites. The model is a crop growth model (STICS) coupled with a process-based land surface model (ORCHIDEE). The data are online eddy-covariance observations of CO2 and H2O fluxes at five European maize cultivation sites. The results show that the ORCHIDEE-STICS model explains up to 75 % of the observed daily net CO2 ecosystem exchange (NEE) variance, and up to 79 % of the latent heat flux (LE) variance at five sites. The model is better able to reproduce gross primary production (GPP) variations than terrestrial ecosystem respiration (TER) variations. We conclude that structural deficiencies in the model parameterizations of leaf area index (LAI) and TER are the main sources of error in simulating CO2 and H2O fluxes. A number of sensitivity tests, with variable crop variety, nitrogen fertilization, irrigation, and planting date, indicate that any of these management factors is able to change NEE by more than 15 %, but that the response of NEE to management parameters is highly site-dependent. Changes in management parameters are found to impact not only the daily values of NEE and LE, but also the cumulative yearly values. In addition, LE is shown to be less sensitive to management parameters than NEE. Multi-site model evaluations, coupled with sensitivity analysis to management parameters, thus provide important information about model errors, which helps to improve the simulation of CO2 and H2O fluxes across European croplands.
 
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