The function spwb_ldrOptimization optimizes the species root distribution within spwb, given the arguments x, meteo, soil and psi_crit. The optimization is based on the eco-hydrological equilibrium hypothesis (Eagleson, 1982), which is formulated here as the root distribution for which plant transpiration is maximized while the plant water potential is close to the species-defined critical value psi_crit (Cabon et al.,2018). Returns the optimized root distribution for each species, expressed as parameters of the function root_ldrDistribution.
spwb_ldrOptimization(x, soil, meteo, psi_crit, opt_mode = 1, RZmin = 301, RZmax = 4000,
V1min = 0.01, V1max = 0.94, resolution = 20, heat_stop = 0,
transformation = "identity", explore_out = FALSE,
verbose = FALSE, ...)An object of class spwbInput.
A list containing the description of the soil (see soil).
A data frame with daily meteorological data series. When using the 'Granier' transpiration mode the following columns are required:
DOY: Day of the year (Julian day).
Precipitation: Precipitation (in mm).
MeanTemperature: Mean temperature (in degrees Celsius).
PET: Potential evapotranspiration (in mm).
A numerical vector of length iqual to the number of species in the plot containing the species values of water potential inducing hydraulic failure (in MPa). Use NA values to skip optimization for particular plant cohorts.
Optimization mode:
opt_mode = 1 maximizes transpiration along the line of stress equal to psi_crit (Cabon et al. 2018).
opt_mode = 2 maximizes transpiration, subject to root construction constrains, among combinations with stress according to psi_crit).
The minimum value of RZ (the rooting depth) to be explored (in mm)
The maximum value of RZ (the rooting depth) to be explored (in mm)
The minimum value of V1 (the root proportion in the first soil layer) to be explored
The maximum value of V1 (the root proportion in the first soil layer) to be explored
An integer defining the number of values to obtain by discretization of the root parameters RZ and V1. The number of parameter combinations and therefore the computation cost increases increase with the square of resolution
Function to modify the size of Z intervals to be explored (by default, bins are equal).
An integer defining the number of days during to discard from the calculation of the optimal root distribution. Usefull if the soil water content initialization is not certain
A logical value. Are the values of average daily plant transpiration and minimum plant water potential to be returned?
A logical value. Print the internal messages of the function?
Additional parameters to function spwb.
If explore_out = FALSE returns a data frame with containing the species index used in medfate, psi_crit and the otpimized values of V1 and the LDR parameters Z50 and Z95 (see root_ldrDistribution) and as many rows as the number of species. If explore_out = TRUE returns a list containing optim, the aforementioned data frame, and explore_out, a list containing two array with the values of average daily plant transpiration and the minimum plant water potential for each species and parameter combination.
For each combination of the parameters RZ and V1 the function runs spwb, setting the total soil depth equal to RZ. The root proportion in each soil layer is derived from V1, the depth of the first soil layer (as defined in the argument soil) and RZ using the LDR root distribution model (Schenk and Jackson, 2002) and assuming that the depth containing 95 percent of the roots is equal to RZ. psi_crit, the species specific water potential inducing hydraulic failure, can be approached by the water potential inducing 50 percent of loss of conductance for the and gymnosperms and 88 percent for the angiosperms (Urli et al., 2013, Brodribb et al., 2010). Details of the hypothesis and limitations of the optimization method are given in Cabon et al. (under review).
Brodribb, T.J., Bowman, D.J.M.S., Nichols, S., Delzon, S., Burlett, R., 2010. Xylem function and growth rate interact to determine recovery rates after exposure to extreme water deficit. New Phytol. 188, 533<U+2013>542. doi:10.1111/j.1469-8137.2010.03393.x
Cabon, A., Mart<U+00ED>nez-Vilalta, J., Poyatos, R., Mart<U+00ED>nez de Arag<U+00F3>n, J., De C<U+00E1>ceres, M. (2018) Applying the eco-hydrological equilibrium hypothesis to estimate root ditribution in water-limited forests. Ecohydrology 11: e2015.
Eagleson, P.S., 1982. Ecological optimality in water-limited natural soil-vegetation systems: 1. Theory and hypothesis. Water Resour. Res. 18, 325<U+2013>340. doi:10.1029/WR018i002p00325
Schenk, H.J., Jackson, R.B., 2002. The Global Biogeography of Roots. Ecol. Monogr. 72, 311. doi:10.2307/3100092
Urli, M., Porte, A.J., Cochard, H., Guengant, Y., Burlett, R., Delzon, S., 2013. Xylem embolism threshold for catastrophic hydraulic failure in angiosperm trees. Tree Physiol. 33, 672<U+2013>683. doi:10.1093/treephys/tpt030
# NOT RUN {
#Load example daily meteorological data
data(examplemeteo)
#Load example plot plant data
data(exampleforest)
#Default species parameterization
data(SpParamsMED)
#Initialize soil with default soil params
examplesoil = soil(defaultSoilParams(2))
#Initialize control parameters
control = defaultControl()
#Initialize input
x = forest2spwbInput(exampleforest,examplesoil, SpParamsMED, control)
#Run optimization
spwb_ldrOptimization(x = x, soil = examplesoil, meteo = examplemeteo,
psi_crit = c(-2,-3,-4), elevation = 100)
# }
# NOT RUN {
# }
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