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| Titel |
Modeling sugarcane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values |
| VerfasserIn |
A. Valade, P. Ciais, N. Vuichard, N. Viovy, A. Caubel, N. Huth, F. Marin, J.-F. Martiné |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 3 ; Nr. 7, no. 3 (2014-06-30), S.1225-1245 |
| Datensatznummer |
250115639
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| Publikation (Nr.) |
 copernicus.org/gmd-7-1225-2014.pdf |
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| Zusammenfassung |
| Agro-land surface models (agro-LSM) have been developed from the integration
of specific crop processes into large-scale generic land surface models that
allow calculating the spatial distribution and variability of energy, water
and carbon fluxes within the soil–vegetation–atmosphere continuum. When
developing agro-LSM models, particular attention must be given to the effects
of crop phenology and management on the turbulent fluxes exchanged with the
atmosphere, and the underlying water and carbon pools. A part of the
uncertainty of agro-LSM models is related to their usually large number of
parameters. In this study, we quantify the parameter-values uncertainty in
the simulation of sugarcane biomass production with the agro-LSM
ORCHIDEE–STICS, using a multi-regional approach with data from sites in
Australia, La Réunion and Brazil. In ORCHIDEE–STICS, two models are
chained: STICS, an agronomy model that calculates phenology and management,
and ORCHIDEE, a land surface model that calculates biomass and other
ecosystem variables forced by STICS phenology. First, the parameters that
dominate the uncertainty of simulated biomass at harvest date are determined
through a screening of 67 different parameters of both STICS and ORCHIDEE on
a multi-site basis. Secondly, the uncertainty of harvested biomass
attributable to those most sensitive parameters is quantified and
specifically attributed to either STICS (phenology, management) or to
ORCHIDEE (other ecosystem variables including biomass) through distinct Monte
Carlo runs. The uncertainty on parameter values is constrained using
observations by calibrating the model independently at seven sites. In a
third step, a sensitivity analysis is carried out by varying the most
sensitive parameters to investigate their effects at continental scale. A
Monte Carlo sampling method associated with the calculation of partial ranked
correlation coefficients is used to quantify the sensitivity of harvested
biomass to input parameters on a continental scale across the large regions
of intensive sugarcane cultivation in Australia and Brazil. The ten
parameters driving most of the uncertainty in the ORCHIDEE–STICS modeled
biomass at the 7 sites are identified by the screening procedure. We found
that the 10 most sensitive parameters control phenology (maximum rate of
increase of LAI) and root uptake of water and nitrogen (root profile and root
growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal
temperature of photosynthesis, optimal carboxylation rate), radiation
interception (extinction coefficient), and transpiration and respiration
(stomatal conductance, growth and maintenance respiration coefficients) in
ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis
temperature parameters contribute most to the uncertainty in harvested
biomass simulations at site scale. The spatial variation of the ranked
correlation between input parameters and modeled biomass at harvest is well
explained by rain and temperature drivers, suggesting different climate-mediated
sensitivities of modeled sugarcane yield to the model parameters,
for Australia and Brazil. This study reveals the spatial and temporal
patterns of uncertainty variability for a highly parameterized agro-LSM and
calls for more systematic uncertainty analyses of such models. |
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