Description Usage Arguments Details Value References See Also
Model transpiration by optimizing short-term adaptation of ratio of leaf internal to ambiant CO2 concentration. (see Perez-Priego et al., 2018).
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data |
Data.frame with variables;
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... |
further arguments passed to both
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For each five-day window the short-term variation in leaf-internal water to CO2 ratio (chi) and the modifiers for photosynthesis fitted so to minimize not only the mismatch between observed and modeled GPP but also the unit cost of transpiration. The transpiration cost is specified by introducing a conditional factor demand (phi), which invokes the optimality hypothesis. The phi term is to be defined as the integrated cost of transpiration (i.e. transpiration_mod/photos_mod) over a time period (5 days) normalized by a factor describing the long-term effective water use efficiency (WUE_o).
These parameters are then used to predict transpiration.
Evaporation is computed as difference to ET from eddy-covariance.
The multi-objective function is defined as:
OF = sum((photos-photos_{mod})/photos_{unc})\^2)/n + phi
where phi invokes optimality theory by minimizing the following term
phi = (sum(transpiration_mod)/sum(photos_mod)*WUE_o
The 4 model parameters (a1, Do, Topt and beta, see Perez-Priego 2018) are
radiation curvature
empirical coef. related to response of stomatal closure to VPD.
optimum temperature
A plant state variable defining the carbon cost of water.
data with updated or added columns T and E in mm/hour.
Perez-Priego, O., G. Katul, M. Reichstein et al. Partitioning eddy covariance water flux components using physiological and micrometeorological approaches, Journal of Geophysical Research: Biogeosciences. In press
Reichstein, M., et al. (2005), On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11(9), 1424-1439.
calculate_longterm_leaf
,
predict_transpiration_opriego
,
priego_config
,
etpart_constants
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