transpiration_model: Transpiration model using plant optimization theory.

In oscarperezpriego/ETpartitioning: Partitioning of eddy covariance ET into flux components

Description

The model provide optimal estimates of transpiration rates using eddy covariance data.

Usage

transpiration_model(par, data, ColPhotos, ColH, ColVPD, ColTair, ColPair,
  ColQ, ColCa, ColUstar, ColWS, ColSW_in, Chi_o, WUE_o)

Arguments

par	A vector containing 4 parameters (a1,Do,To,beta)
data	Data.frame or matrix containing all required variables.
ColPhotos	Column name of numeric vector containing time series of photosynthesis data (umol CO2 m-2 s-1).
ColH	Column name of numeric vector containing time series of sensible heat flux (W m-2).
ColVPD	Column name of numeric vector containing time series of vapor pressure deficit (hPa).
ColTair	Column name of numeric vector containing time series of air temperature (deg C).
ColPair	Column name of numeric vector containing time series of atmospheric pressure (kPa).
ColQ	Column name of numeric vector containing time series of photosynthetic active radiation (umol m-2 s-1).
ColCa	Column name of numeric vector containing time series of atmospheric CO2 concentration (umol Co2 mol air-1).
ColUstar	Column name of numeric vector containing time series of wind friction velocity (m s-1).
ColWS	Column name of numeric vector containing time series of wind velocity (m s-1).
ColSW_in	Column name of numeric vector containing time series of incoming short-wave radiation (W m-2).
Chi_o	Long-term effective chi
WUE_o	Long-term effective WUE

Details

The transpiration is estimated according to gas-difusion equations:

transpiration_mod <- gw_bulk*VPD_plant/Pair

where gw_bulk represents the "bulk" surface conductance and accounts for the influence of stomata and aerodynamic properties.

Value

a numeric vector containing estimates of transpiration rates:

Note

The parameters (a1, Do, Topt and beta, see Perez-Priego et al., 2018) are estimated using a multi-constraint Markov Chain Monte Carlo (MCMC).The algortihm searches for those optimal solutions that maximize water use efficiency and according to the plant optimization theory.

References

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.

Examples

 ## Selecting a single day (e.g. 15-05-2011)
 tmp <-  EddySample[ EddySample$TIMESTAMP_START>  201105150000,]
 tmp <-  tmp[tmp$TIMESTAMP_START<  201105160000,]
 ## Defining parameter values
 par <- c(200, 0.2, 25, 0.6)

transpiration_model(
 par=par
 ,data=tmp
 ,ColPhotos="GPP_NT_VUT_MEAN"
 ,ColH="H_F_MDS"
 ,ColVPD="VPD_F"
 ,ColTair="TA_F"
 ,ColPair="PA_F"
 ,ColQ="PPFD_IN"
 ,ColCa="CO2_F_MDS"
 ,ColUstar="USTAR"
 ,ColWS="WS_F"
 ,ColSW_in="SW_IN_F"
 ,Chi_o = 0.88
 ,WUE_o = 24.25
)

oscarperezpriego/ETpartitioning documentation built on June 4, 2019, 12:13 a.m.