 |
| Titel |
Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation |
| VerfasserIn |
Y. Song, A. K. Jain, G. F. McIsaac |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1726-4170
|
| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 10, no. 12 ; Nr. 10, no. 12 (2013-12-09), S.8039-8066 |
| Datensatznummer |
250085465
|
| Publikation (Nr.) |
 copernicus.org/bg-10-8039-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| Worldwide expansion of agriculture is impacting the earth's climate by
altering carbon, water, and energy fluxes, but the climate in turn is
impacting crop production. To study this two-way interaction and its impact
on seasonal dynamics of carbon, water, and energy fluxes, we implemented
dynamic crop growth processes into a land surface model, the Integrated
Science Assessment Model (ISAM). In particular, we implemented crop-specific
phenology schemes and dynamic carbon allocation schemes. These schemes
account for light, water, and nutrient stresses while allocating the
assimilated carbon to leaf, root, stem, and grain pools. The dynamic
vegetation structure simulation better captured the seasonal variability in
leaf area index (LAI), canopy height, and root depth. We further implemented
dynamic root distribution processes in soil layers, which better simulated
the root response of soil water uptake and transpiration. Observational data
for LAI, above- and belowground biomass, and carbon, water, and energy fluxes
were compiled from two AmeriFlux sites, Mead, NE, and Bondville, IL, USA, to
calibrate and evaluate the model performance. For the purposes of calibration
and evaluation, we use a corn–soybean (C4–C3) rotation system over the
period 2001–2004. The calibrated model was able to capture the diurnal and
seasonal patterns of carbon assimilation and water and energy fluxes for the
corn–soybean rotation system at these two sites. Specifically, the
calculated gross primary production (GPP), net radiation fluxes at the top of
the canopy, and latent heat fluxes compared well with observations. The
largest bias in model results was in sensible heat flux (SH) for corn and
soybean at both sites. The dynamic crop growth simulation better captured the
seasonal variability in carbon and energy fluxes relative to the static
simulation implemented in the original version of ISAM. Especially, with
dynamic carbon allocation and root distribution processes, the model's
simulated GPP and latent heat flux (LH) were in much better agreement with
observational data than for the static root distribution simulation. Modeled
latent heat based on dynamic growth processes increased by 12–27% during
the growing season at both sites, leading to an improvement in modeled GPP by
13–61% compared to the estimates based on the original version of the
ISAM. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|