transp_modes: Transpiration modes

In medfate: Mediterranean Forest Simulation

Transpiration modes

Description

High-level sub-models representing transpiration, plant hydraulics, photosynthesis and water relations within plants.

Usage

transp_transpirationSperry(
  x,
  meteo,
  day,
  latitude,
  elevation,
  slope,
  aspect,
  canopyEvaporation = 0,
  snowMelt = 0,
  soilEvaporation = 0,
  herbTranspiration = 0,
  stepFunctions = NA_integer_,
  modifyInput = TRUE
)

transp_transpirationSureau(
  x,
  meteo,
  day,
  latitude,
  elevation,
  slope,
  aspect,
  canopyEvaporation = 0,
  snowMelt = 0,
  soilEvaporation = 0,
  herbTranspiration = 0,
  modifyInput = TRUE
)

transp_transpirationGranier(
  x,
  meteo,
  day,
  latitude,
  elevation,
  slope,
  aspect,
  modifyInput = TRUE
)

Arguments

x	An object of class spwbInput or growthInput, built using the 'Granier', 'Sperry' or 'Sureau' transpiration modes.
meteo	A data frame with daily meteorological data series (see spwb).
day	An integer to identify a day (row) within the meteo data frame.
latitude	Latitude (in degrees).
elevation, slope, aspect	Elevation above sea level (in m), slope (in degrees) and aspect (in degrees from North).
canopyEvaporation	Canopy evaporation (from interception) for day (mm).
snowMelt	Snow melt values for day (mm).
soilEvaporation	Bare soil evaporation for day (mm).
herbTranspiration	Transpiration of herbaceous plants for day (mm).
stepFunctions	An integer to indicate a simulation step for which photosynthesis and profit maximization functions are desired.
modifyInput	Boolean flag to indicate that the input x object is allowed to be modified during the simulation.

Details

Three sub-models are available:

• Sub-model in function transp_transpirationGranier was described in De Cáceres et al. (2015), and implements an approach originally described in Granier et al. (1999).

• Sub-model in function transp_transpirationSperry was described in De Cáceres et al. (2021), and implements a modelling approach originally described in Sperry et al. (2017).

• Sub-model in function transp_transpirationSureau was described for SurEau-Ecos v2.0 model in Ruffault et al. (2022).

Value

A list with the following elements:

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

• "Stand": A vector of stand-level variables.

• "Plants": A data frame of results for each plant cohort. When using transp_transpirationGranier, element "Plants" includes:

  • "LAI": Leaf area index of the plant cohort.

  • "LAIlive": Leaf area index of the plant cohort, assuming all leaves are unfolded.

  • "AbsorbedSWRFraction": Fraction of SWR absorbed by each cohort.

  • "Transpiration": Transpirated water (in mm) corresponding to each cohort.

  • "GrossPhotosynthesis": Gross photosynthesis (in gC/m2) corresponding to each cohort.

  • "psi": Water potential (in MPa) of the plant cohort (average over soil layers).

  • "DDS": Daily drought stress [0-1] (relative whole-plant conductance).

  When using transp_transpirationSperry or transp_transpirationSureau, element "Plants" includes:

  • "LAI": Leaf area index of the plant cohort.

  • "LAIlive": Leaf area index of the plant cohort, assuming all leaves are unfolded.

  • "Extraction": Water extracted from the soil (in mm) for each cohort.

  • "Transpiration": Transpirated water (in mm) corresponding to each cohort.

  • "GrossPhotosynthesis": Gross photosynthesis (in gC/m2) corresponding to each cohort.

  • "NetPhotosynthesis": Net photosynthesis (in gC/m2) corresponding to each cohort.

  • "RootPsi": Minimum water potential (in MPa) at the root collar.

  • "StemPsi": Minimum water potential (in MPa) at the stem.

  • "StemPLC": Proportion of conductance loss in stem.

  • "LeafPsiMin": Minimum (predawn) water potential (in MPa) at the leaf (representing an average leaf).

  • "LeafPsiMax": Maximum (midday) water potential (in MPa) at the leaf (representing an average leaf).

  • "LeafPsiMin_SL": Minimum (predawn) water potential (in MPa) at sunlit leaves.

  • "LeafPsiMax_SL": Maximum (midday) water potential (in MPa) at sunlit leaves.

  • "LeafPsiMin_SH": Minimum (predawn) water potential (in MPa) at shade leaves.

  • "LeafPsiMax_SH": Maximum (midday) water potential (in MPa) at shade leaves.

  • "dEdP": Overall soil-plant conductance (derivative of the supply function).

  • "DDS": Daily drought stress [0-1] (relative whole-plant conductance).

  • "StemRWC": Relative water content of stem tissue (including symplasm and apoplasm).

  • "LeafRWC": Relative water content of leaf tissue (including symplasm and apoplasm).

  • "LFMC": Live fuel moisture content (in percent of dry weight).

  • "WaterBalance": Plant water balance (extraction - transpiration).

• "Extraction": A data frame with mm of water extracted from each soil layer (in columns) by each cohort (in rows).

  The remaining items are only given by transp_transpirationSperry or transp_transpirationSureau:

• "EnergyBalance": A list with the following elements:

  • "Temperature": A data frame with the temperature of the atmosphere ('Tatm'), canopy ('Tcan') and soil ('Tsoil.1', 'Tsoil.2', ...) for each time step.

  • "CanopyEnergyBalance": A data frame with the components of the canopy energy balance (in W/m2) for each time step.

  • "SoilEnergyBalance": A data frame with the components of the soil energy balance (in W/m2) for each time step.

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

• "Sunlitleaves" and "ShadeLeaves": Data frames for sunlit leaves and shade leaves and the following columns per cohort:

  • "LAI": Cumulative leaf area index of sunlit/shade leaves.

  • "Vmax298": Average maximum carboxilation rate for sunlit/shade leaves.

  • "Jmax298": Average maximum electron transport rate for sunlit/shade leaves.

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

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

  • "E": A data frame with the cumulative transpiration (mm) for each plant cohort during each time step.

  • "Ag": A data frame with the cumulative gross photosynthesis (gC/m2) for each plant cohort during each time step.

  • "An": A data frame with the cumulative net photosynthesis (gC/m2) for each plant cohort during each time step.

  • "Sunlitleaves" and "ShadeLeaves": Lists with instantaneous (for each time step) results for sunlit leaves and shade leaves and the following items:

    • "Abs_SWR": A data frame with instantaneous absorbed short-wave radiation (SWR).

    • "Net_LWR": A data frame with instantaneous net long-wave radiation (LWR).

    • "An": A data frame with instantaneous net photosynthesis (in micromol/m2/s).

    • "Ci": A data frame with instantaneous intercellular CO2 concentration (in ppm).

    • "GW": A data frame with instantaneous stomatal conductance (in mol/m2/s).

    • "VPD": A data frame with instantaneous vapour pressure deficit (in kPa).

    • "Temp": A data frame with leaf temperature (in degrees Celsius).

    • "Psi": A data frame with leaf water potential (in MPa).

  • "dEdP": A data frame with the slope of the plant supply function (an estimation of whole-plant conductance).

  • "RootPsi": A data frame with root crown water potential (in MPa) for each plant cohort during each time step.

  • "StemPsi": A data frame with stem water potential (in MPa) for each plant cohort during each time step.

  • "LeafPsi": A data frame with leaf (average) water potential (in MPa) for each plant cohort during each time step.

  • "StemPLC": A data frame with the proportion loss of conductance [0-1] for each plant cohort during each time step.

  • "StemRWC": A data frame with the (average) relative water content of stem tissue [0-1] for each plant cohort during each time step.

  • "LeafRWC": A data frame with the relative water content of leaf tissue [0-1] for each plant cohort during each time step.

  • "StemSympRWC": A data frame with the (average) relative water content of symplastic stem tissue [0-1] for each plant cohort during each time step.

  • "LeafSympRWC": A data frame with the relative water content of symplastic leaf tissue [0-1] for each plant cohort during each time step.

  • "PWB": A data frame with plant water balance (extraction - transpiration).

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

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

• "SupplyFunctions": If stepFunctions is not missing, a list of supply functions, photosynthesis functions and profit maximization functions.

Author(s)

• Miquel De Cáceres Ainsa, CREAF

• Nicolas Martin-StPaul, URFM-INRAE

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).

See Also

spwb_day, plot.spwb_day

Examples

#Load example daily meteorological data
data(examplemeteo)

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

#Define soil with default soil params (4 layers)
examplesoil <- defaultSoilParams(4)

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

#Initialize input
x1 <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)

# Transpiration according to Granier's model, plant water potential 
# and plant stress for a given day
t1 <- transp_transpirationGranier(x1, examplemeteo, 1, 
                                 latitude = 41.82592, elevation = 100, slope = 0, aspect = 0, 
                                 modifyInput = FALSE)

#Switch to 'Sperry' transpiration mode
control <- defaultControl("Sperry")

#Initialize input
x2 <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)

# Transpiration according to Sperry's model
t2 <- transp_transpirationSperry(x2, examplemeteo, 1, 
                                latitude = 41.82592, elevation = 100, slope = 0, aspect = 0,
                                modifyInput = FALSE)
                                
#Switch to 'Sureau' transpiration mode
control <- defaultControl("Sureau")

#Initialize input
x3 <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)

# Transpiration according to Sureau model
t3 <- transp_transpirationSureau(x3, examplemeteo, 1, 
                                  latitude = 41.82592, elevation = 100, slope = 0, aspect = 0,
                                  modifyInput = FALSE)
                                

medfate documentation built on April 4, 2025, 2:01 a.m.