spwb_ldrOptimization.RdFunctions spwb_ldrExploration and spwb_ldrOptimization are used to
find optimum the species root distribution within spwb, given the arguments
x, meteo and psi_crit.
spwb_ldrExploration(
x,
meteo,
cohorts = NULL,
RZmin = 301,
RZmax = 4000,
V1min = 0.01,
V1max = 0.94,
resolution = 10,
heat_stop = 0,
transformation = "identity",
verbose = FALSE,
...
)
spwb_ldrOptimization(y, psi_crit, opt_mode = 1)An object of class spwbInput.
A data frame with daily meteorological data series (see spwb).
A character string with the names of cohorts to be explored. If NULL then all cohorts are explored.
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
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
Function to modify the size of Z intervals to be explored (by default, bins are equal).
A logical value. Print the internal messages of the function?
Additional parameters to function spwb.
The result of calling spwb_ldrExploration.
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). 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).
opt_mode = 2 maximizes transpiration among combinations with stress according to psi_crit).
opt_mode = 3 maximizes photosynthesis among combinations with stress according to psi_crit).
opt_mode = 4 maximizes transpiration, subject to root construction constrains, among combinations with stress according to psi_crit).
opt_mode = 5 maximizes photosynthesis, subject to root construction constrains, among combinations with stress according to psi_crit).
Function spwb_ldrExploration returns a list containing a list containing
the explored RZ and V1 combinations as well as arrays with the values of average daily plant transpiration,
average daily net photosynthesis and the minimum plant water potential for each cohort and parameter combination.
Function spwb_ldrOptimization returns a data frame with containing
the species index used in medfate, psi_crit and the optimized values of V1
and the LDR parameters Z50 and Z95 (see root_ldrDistribution)
and as many rows as the number of species.
For each combination of the parameters RZ and V1 the function spwb_ldrExploration 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 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.
Function spwb_ldrOptimization takes the result of the exploration and tries
to find optimum root distribution parameters. 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. (2019).
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, 533–542. doi:10.1111/j.1469-8137.2010.03393.x
Cabon, A., Martínez-Vilalta, J., Poyatos, R., Martínez de Aragón, J., De Cáceres, 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, 325–340. 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, 672–683. doi:10.1093/treephys/tpt030
spwb, soil, root_ldrDistribution
# \donttest{
#Load example daily meteorological data
data(examplemeteo)
#Load example plot plant data
data(exampleforestMED)
#Default species parameterization
data(SpParamsMED)
#Initialize soil with default soil params
examplesoil <- soil(defaultSoilParams(2))
#Initialize control parameters
control <- defaultControl("Granier")
#Initialize input
x <- forest2spwbInput(exampleforestMED,examplesoil, SpParamsMED, control)
#Run exploration (weather subset for faster computation)
y <- spwb_ldrExploration(x = x, meteo = examplemeteo[1:50,],
elevation = 100, latitude = 41.82592)
#> Error in spwb_ldrExploration(x = x, meteo = examplemeteo[1:50, ], elevation = 100, latitude = 41.82592): could not find function "spwb_ldrExploration"
#Optimization under different modes
spwb_ldrOptimization(y = y, psi_crit = c(-2,-3,-4), opt_mode = 1)
#> Error in spwb_ldrOptimization(y = y, psi_crit = c(-2, -3, -4), opt_mode = 1): could not find function "spwb_ldrOptimization"
# }