Function spwb_day performs water balance for a single day and growth_day performs water and carbon balance for a single day.

growth_day(
  x,
  date,
  meteovec,
  latitude,
  elevation,
  slope,
  aspect,
  runon = 0,
  modifyInput = TRUE
)

spwb_day(
  x,
  date,
  meteovec,
  latitude,
  elevation,
  slope,
  aspect,
  runon = 0,
  modifyInput = TRUE
)

Arguments

x

An object of class spwbInput or growthInput.

date

Date as string "yyyy-mm-dd".

meteovec

A named numerical vector with weather data. See variable names in parameter meteo of spwb.

latitude

Latitude (in degrees).

elevation, slope, aspect

Elevation above sea level (in m), slope (in degrees) and aspect (in degrees from North).

runon

Surface water amount running on the target area from upslope (in mm).

modifyInput

Boolean flag to indicate that the input x object is allowed to be modified during the simulation.

Value

Function spwb_day() returns a list of class spwb_day with the following elements:

  • "cohorts": A data frame with cohort information, copied from spwbInput.

  • "topography": Vector with elevation, slope and aspect given as input.

  • "weather": A vector with the input weather.

  • "WaterBalance": A vector of water balance components (rain, snow, net rain, infiltration, ...) for the simulated day, equivalent to one row of 'WaterBalance' object given in spwb.

  • "Soil": A data frame with results for each soil layer:

    • "HerbTranspiration": Water extracted by herbaceous plants from each soil layer (in mm).

    • "HydraulicInput": Water entering each soil layer from other layers, transported via plant hydraulic network (in mm) (only for transpirationMode = "Sperry").

    • "HydraulicOutput": Water leaving each soil layer (going to other layers or the transpiration stream) (in mm) (only for transpirationMode = "Sperry").

    • "PlantExtraction": Water extracted by woody plants from each soil layer (in mm).

    • "psi": Soil water potential (in MPa).

  • "Stand": A named vector with with stand values for the simulated day, equivalent to one row of 'Stand' object returned by spwb.

  • "Plants": A data frame of results for each plant cohort (see transp_transpirationGranier or transp_transpirationSperry).

The following items are only returned when transpirationMode = "Sperry" or transpirationMode = "Cochard":

  • "EnergyBalance": Energy balance of the stand (see transp_transpirationSperry).

  • "RhizoPsi": Minimum water potential (in MPa) inside roots, after crossing rhizosphere, per cohort and soil layer.

  • "SunlitLeaves" and "ShadeLeaves": For each leaf type, a data frame with values of LAI, Vmax298 and Jmax298 for leaves of this type in each plant cohort.

  • "ExtractionInst": Water extracted by each plant cohort during each time step.

  • "PlantsInst": A list with instantaneous (per time step) results for each plant cohort (see transp_transpirationSperry).

  • "LightExtinction": A list of information regarding radiation balance through the canopy, as returned by function light_instantaneousLightExtinctionAbsortion.

  • "CanopyTurbulence": Canopy turbulence (see wind_canopyTurbulence).

Details

The simulation functions allow using three different sub-models of transpiration and photosynthesis:

  • The sub-model corresponding to 'Granier' transpiration mode is illustrated by function transp_transpirationGranier and was described in De Caceres et al. (2015), and implements an approach originally described in Granier et al. (1999).

  • The sub-model corresponding to 'Sperry' transpiration mode is illustrated by function transp_transpirationSperry and was described in De Caceres et al. (2021), and implements a modelling approach originally described in Sperry et al. (2017).

  • The sub-model corresponding to 'Cochard' transpiration mode is illustrated by function transp_transpirationCochard and was described for model SurEau-Ecos v2.0 in Ruffault et al. (2022).

Simulations using the 'Sperry' or 'Cochard' transpiration mode are computationally much more expensive than 'Granier'.

References

De Cáceres M, Martínez-Vilalta J, Coll L, Llorens P, Casals P, Poyatos R, Pausas JG, Brotons L. (2015) Coupling a water balance model with forest inventory data to predict drought stress: the role of forest structural changes vs. climate changes. Agricultural and Forest Meteorology 213: 77-90 (doi:10.1016/j.agrformet.2015.06.012).

De Cáceres M, Mencuccini M, Martin-StPaul N, Limousin JM, Coll L, Poyatos R, Cabon A, Granda V, Forner A, Valladares F, Martínez-Vilalta J (2021) Unravelling the effect of species mixing on water use and drought stress in holm oak forests: a modelling approach. Agricultural and Forest Meteorology 296 (doi:10.1016/j.agrformet.2020.108233).

Granier A, Bréda N, Biron P, Villette S (1999) A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands. Ecol Modell 116:269–283. https://doi.org/10.1016/S0304-3800(98)00205-1.

Ruffault J, Pimont F, Cochard H, Dupuy JL, Martin-StPaul N (2022) SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level. Geoscientific Model Development 15, 5593-5626 (doi:10.5194/gmd-15-5593-2022).

Sperry, J. S., M. D. Venturas, W. R. L. Anderegg, M. Mencuccini, D. S. Mackay, Y. Wang, and D. M. Love. 2017. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. Plant Cell and Environment 40, 816-830 (doi: 10.1111/pce.12852).

Author

  • Miquel De Cáceres Ainsa, CREAF

  • Nicolas Martin-StPaul, URFM-INRAE

Examples

#Load example daily meteorological data
data(examplemeteo)

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

#Initialize control parameters
control <- defaultControl("Granier")

# Day to be simulated
d <- 100
meteovec <- unlist(examplemeteo[d,-1])
date <- as.character(examplemeteo$dates[d])

#Simulate water balance one day only (Granier mode)
examplesoil <- soil(defaultSoilParams(4))
x1 <- forest2spwbInput(exampleforest,examplesoil, SpParamsMED, control)
sd1 <- spwb_day(x1, date, meteovec,  
                latitude = 41.82592, elevation = 100, slope=0, aspect=0) 

#Simulate water balance for one day only (Sperry mode)
control <- defaultControl("Sperry")
x2 <- forest2spwbInput(exampleforest, examplesoil, SpParamsMED, control)
sd2 <-spwb_day(x2, date, meteovec,
              latitude = 41.82592, elevation = 100, slope=0, aspect=0)

#Plot plant transpiration (see function 'plot.swb.day()')
plot(sd2)


#Simulate water balance for one day only (Cochard mode)
control <- defaultControl("Cochard")
x3 <- forest2spwbInput(exampleforest, examplesoil, SpParamsMED, control)
sd3 <-spwb_day(x3, date, meteovec,
              latitude = 41.82592, elevation = 100, slope=0, aspect=0)


#Simulate water and carbon balance for one day only (Granier mode)
control <- defaultControl("Granier")
x4  <- forest2growthInput(exampleforest,examplesoil, SpParamsMED, control)
sd4 <- growth_day(x4, date, meteovec,
                latitude = 41.82592, elevation = 100, slope=0, aspect=0)