Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands

Croplands are man-made ecosystems that have high net primary productivity during the growing season of crops, thus impacting carbon and other exchanges with the atmosphere. These exchanges play a major role in nutrient cycling and climate change related issues. An accurate representation of crop phe...

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Bibliographic Details
Main Authors: E. Lokupitiya, S. Denning, K. Paustian, I. Baker, K. Schaefer, S. Verma, T. Meyers, C. J. Bernacchi, A. Suyker, M. Fischer
Format: Article
Language:English
Published: Copernicus Publications 2009-06-01
Series:Biogeosciences
Online Access:http://www.biogeosciences.net/6/969/2009/bg-6-969-2009.pdf
Description
Summary:Croplands are man-made ecosystems that have high net primary productivity during the growing season of crops, thus impacting carbon and other exchanges with the atmosphere. These exchanges play a major role in nutrient cycling and climate change related issues. An accurate representation of crop phenology and physiology is important in land-atmosphere carbon models being used to predict these exchanges. To better estimate time-varying exchanges of carbon, water, and energy of croplands using the Simple Biosphere (SiB) model, we developed crop-specific phenology models and coupled them to SiB. The coupled SiB-phenology model (SiBcrop) replaces remotely-sensed NDVI information, on which SiB originally relied for deriving Leaf Area Index (LAI) and the fraction of Photosynthetically Active Radiation (fPAR) for estimating carbon dynamics. The use of the new phenology scheme within SiB substantially improved the prediction of LAI and carbon fluxes for maize, soybean, and wheat crops, as compared with the observed data at several AmeriFlux eddy covariance flux tower sites in the US mid continent region. SiBcrop better predicted the onset and end of the growing season, harvest, interannual variability associated with crop rotation, day time carbon uptake (especially for maize) and day to day variability in carbon exchange. Biomass predicted by SiBcrop had good agreement with the observed biomass at field sites. In the future, we will predict fine resolution regional scale carbon and other exchanges by coupling SiBcrop with RAMS (the Regional Atmospheric Modeling System).
ISSN:1726-4170
1726-4189