Type:	Package
Title:	Mediterranean Forest Simulation
Version:	4.8.1
Date:	2025-04-22
Description:	Simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>; De Caceres et al. (2021) <doi:10.1016/j.agrformet.2020.108233>].
License:	GPL-2 | GPL-3 | LGPL-3 [expanded from: GPL (≥ 2) | LGPL (≥ 3)]
URL:	https://emf-creaf.github.io/medfate/, https://github.com/emf-creaf/medfate
BugReports:	https://github.com/emf-creaf/medfate/issues
LazyLoad:	yes
Depends:	R (≥ 3.5.0)
Imports:	ggplot2 (≥ 3.0.0), meteoland (≥ 2.0.0), Rcpp (≥ 1.0.6), shiny
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
LinkingTo:	Rcpp, meteoland
Encoding:	UTF-8
NeedsCompilation:	yes
RoxygenNote:	7.3.2
Config/testthat/edition:	3
LazyData:	true
Packaged:	2025-04-23 06:53:24 UTC; miquel
Author:	Miquel De Cáceres [aut, cre, cph], Nicolas Martin-StPaul [aut], Víctor Granda [aut], Antoine Cabon [aut], Arsène Druel [aut], Julien Ruffault [aut], Jordi Martínez-Vilalta [ctb], Maurizio Mencuccini [ctb], François Pimont [ctb], Hervé Cochard [ctb], Aitor Améztegui [ctb], Léa Veuillen [ctb], Shengli Huang [ctb], John Burkardt [cph] (Copyright holder of C++ code in 'incgamma.cpp')
Maintainer:	Miquel De Cáceres <miquelcaceres@gmail.com>
Repository:	CRAN
Date/Publication:	2025-04-23 07:40:02 UTC

medfate: Mediterranean Forest Simulation

Description

Simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation

Author(s)

Maintainer: Miquel De Cáceres miquelcaceres@gmail.com ORCID

Authors:

• Nicolas Martin-StPaul ORCID

• Víctor Granda ORCID

• Antoine Cabon ORCID

• Arsène Druel ORCID

• Julien Ruffault ORCID

Contributors:

• Jordi Martínez-Vilalta ORCID

• Maurizio Mencuccini ORCID

• François Pimont ORCID

• Hervé Cochard ORCID

• Aitor Améztegui ORCID

• Léa Veuillen ORCID

• Shengli Huang ORCID

References

• De Cáceres et al. (2015) (doi:10.1016/j.agrformet.2015.06.012)

• De Cáceres et al. (2021) (doi:10.1016/j.agrformet.2020.108233)

• Ruffault et al. (2022) (doi:10.5194/gmd-15-5593-2022)

See Also

Useful links:

• https://emf-creaf.github.io/medfate/

• https://emf-creaf.github.io/medfatebook/

Parameter average values

Description

Internal data set with parameter averages for taxonomic families. This is used by input initialization functions to provide suitable parameter values when missing from species parameter tables.

Format

Data frame trait_family_means has taxonomic families in rows and parameter names as columns.

Source

Same sources as SpParamsMED

See Also

SpParamsMED, spwbInput

Examples

medfate::trait_family_means

Standard fuel models (Albini 1976, Scott & Burgan 2005)

Description

Standard fuel models converted to metric system. Copied from package 'Rothermel' (Giorgio Vacchiano, Davide Ascoli).

Format

A data frame including standard fuel models as in Albini (1976) and Scott and Burgan (2005), to be used as input of fire_Rothermel function. All values converted to metric format.

Fuel_Model_Type

A factor with levels D (for dynamic) or S (for static).

Load_1h

Loading of 1h fuel class [t/ha].

Load_10h

Loading of 10h fuel class [t/ha].

Load_100h

Loading of 100h fuel class [t/ha]

Load_Live_Herb

Loading of herbaceous fuels [t/ha]

Load_Live_Woody

Loading of woody fuels [t/ha]

‘⁠SA/V_1h⁠’

Surface area to volume ratio of 1h fuel class [m2/m3]

‘⁠SA/V_10h⁠’

Surface area to volume ratio of 10h fuel class [m2/m3]

‘⁠SA/V_100h⁠’

Surface area to volume ratio of 100h fuel class [m2/m3]

‘⁠SA/V_Live_Herb⁠’

Surface area to volume ratio of herbaceous fuels [m2/m3]

‘⁠SA/V_Live_Woody⁠’

Surface area to volume ratio of woody fuels [m2/m3]

Fuel_Bed_Depth

Fuel bed depth [cm]

Mx_dead

Dead fuel moisture of extinction [percent]

Heat_1h

Heat content of 1h fuel class [kJ/kg]

Heat_10h

Heat content of 10h fuel class [kJ/kg]

Heat_100h

Heat content of 100h fuel class [kJ/kg]

Heat_Live_Herb

Heat content of herbaceous fuels [kJ/kg]

Heat_Live_Woody

Heat content of woody fuels [kJ/kg]

Source

Albini, F. A. (1976). Computer-based models of wildland fire behavior: A users' manual. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

Scott, J., and Burgan, R. E. (2005). A new set of standard fire behavior fuel models for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRSGTR-153. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

See Also

fire_Rothermel

Examples

data(SFM_metric)

Data tables with species parameter definitions and values

Description

A data sets of species parameter definition and values, the latter resulting from existing databases, fit to empirical data or expert-based guesses.

Format

• Data frame SpParamsDefinition has parameters in rows and columns 'ParameterName', 'ParameterGroup', 'Definition', 'Type' and 'Units'.

• Data frames SpParamsMED has species or genus as rows and column names equal to parameter names in SpParamsDefinition.

Details

SpParamsMED was the official species parameter for package versions up to v.4.0.0, but will not be maintained in the future. Additional species parameter tables for different countries are distributed via package traits4models.

Examples

data(SpParamsDefinition)
data(SpParamsMED)

Simulation in agricultural areas

Description

Function aspwb_day performs water balance for a single day in an agriculture location. Function aspwb performs water balance for multiple days in an agriculture location.

Usage

aspwbInput(crop_factor, control, soil)

aspwb_day(
  x,
  date,
  meteovec,
  latitude,
  elevation,
  slope = NA_real_,
  aspect = NA_real_,
  runon = 0,
  lateralFlows = NULL,
  waterTableDepth = NA_real_,
  modifyInput = TRUE
)

aspwb(
  x,
  meteo,
  latitude,
  elevation,
  slope = NA_real_,
  aspect = NA_real_,
  waterTableDepth = NA_real_
)

Arguments

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwbInput, spwb

Examples

control <- defaultControl()
examplesoil <- defaultSoilParams(4)

x <- aspwbInput(0.75, control, examplesoil)

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

#Call simulation function for a single days
sd <- aspwb_day(x, date, meteovec,  
               latitude = 41.82592, elevation = 100) 

#Call simulation function for multiple days
S <- aspwb(x, examplemeteo, latitude = 41.82592, elevation = 100)

Internal communication

Description

Functions for internal communication. Not to be called by users.

Usage

aspwb_day_inner(
  internalCommunication,
  x,
  date,
  meteovec,
  latitude,
  elevation,
  slope = NA_real_,
  aspect = NA_real_,
  runon = 0,
  lateralFlows = NULL,
  waterTableDepth = NA_real_,
  modifyInput = TRUE
)

copy_model_output(internalCommunication, x, model)

general_communication_structures(
  numCohorts,
  nlayers,
  ncanlayers,
  ntimesteps,
  model
)

instance_communication_structures(x, model)

growth_day_inner(
  internalCommunication,
  x,
  date,
  meteovec,
  latitude,
  elevation,
  slope = NA_real_,
  aspect = NA_real_,
  runon = 0,
  lateralFlows = NULL,
  waterTableDepth = NA_real_,
  modifyInput = TRUE
)

spwb_day_inner(
  internalCommunication,
  x,
  date,
  meteovec,
  latitude,
  elevation,
  slope = NA_real_,
  aspect = NA_real_,
  runon = 0,
  lateralFlows = NULL,
  waterTableDepth = NA_real_,
  modifyInput = TRUE
)

Arguments

Physical and biophysical utility functions

Description

Internal utility functions for the calculation of biophysical variables.

Usage

biophysics_radiationDiurnalPattern(t, daylength)

biophysics_temperatureDiurnalPattern(
  t,
  tmin,
  tmax,
  tminPrev,
  tmaxPrev,
  tminNext,
  daylength
)

biophysics_leafTemperature(absRad, airTemperature, u, E, leafWidth = 1)

biophysics_leafTemperature2(
  SWRabs,
  LWRnet,
  airTemperature,
  u,
  E,
  leafWidth = 1
)

biophysics_leafVapourPressure(leafTemp, leafPsi)

biophysics_irradianceToPhotonFlux(I, lambda = 546.6507)

biophysics_waterDynamicViscosity(temp)

Arguments

Details

Functions biophysics_leafTemperature and biophysics_leafTemperature2 calculate leaf temperature according to energy balance equation given in Campbell and Norman (1988).

Function biophysics_radiationDiurnalPattern follows the equations given in Liu and Jordan (1960).

Function biophysics_temperatureDiurnalPattern determines diurnal temperature pattern assuming a sinusoidal pattern with T = Tmin at sunrise and T = (Tmin+Tmax)/2 at sunset and a linear change in temperature between sunset and Tmin of the day after (McMurtrie et al. 1990).

Function biophysics_waterDynamicViscosity calculates water dynamic viscosity following the Vogel (1921) equation.

Value

Values returned for each function are:

• biophysics_leafTemperature and biophysics_leafTemperature2: leaf temperature (in ºC)

• biophysics_leafVapourPressure: leaf vapour pressure (in kPa)

• biophysics_radiationDiurnalPattern: the proportion of daily radiation corresponding to the input time in seconds after sunrise.

• biophysics_temperatureDiurnalPattern: diurnal pattern of temperature.

• biophysics_waterDynamicViscosity: Water dynamic viscosity relative to 20ºC.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Campbell, G. S., and J. M. Norman. 1998. An introduction to environmental biophysics: 2nd edition. (eqns. 14.1 & 14.3)

B. Y. H. Liu and R. C. Jordan, “The interrelationship and characteristic distribution of direct, diffuse and total solar radiation,” Solar Energy, vol. 4, no. 3, pp. 1–19, 1960.

McMurtrie, R. E., D. A. Rook, and F. M. Kelliher. 1990. Modelling the yield of Pinus radiata on a site limited by water and nitrogen. Forest Ecology and Management 30:381–413.

H. Vogel, "Das Temperaturabhangigkeitsgesetz der Viskositat von Flussigkeiten", Physikalische Zeitschrift, vol. 22, pp. 645–646, 1921.

See Also

spwb

Carbon-related functions

Description

Low-level functions used in the calculation of carbon balance.

Usage

carbon_sugarStarchDynamicsLeaf(sugarConc, starchConc, eqSugarConc)

carbon_sugarStarchDynamicsStem(sugarConc, starchConc, eqSugarConc)

carbon_osmoticWaterPotential(sugarConc, temp, nonSugarConc)

carbon_sugarConcentration(osmoticWP, temp, nonSugarConc)

carbon_relativeSapViscosity(sugarConc, temp)

carbon_leafStructuralBiomass(LAI, N, SLA)

carbon_leafStarchCapacity(LAI, N, SLA, leafDensity)

carbon_sapwoodStructuralBiomass(SA, H, L, V, woodDensity)

carbon_sapwoodStructuralLivingBiomass(
  SA,
  H,
  L,
  V,
  woodDensity,
  conduit2sapwood
)

carbon_sapwoodStarchCapacity(SA, H, L, V, woodDensity, conduit2sapwood)

carbon_carbonCompartments(x, biomassUnits = "g_m2")

Arguments

Value

Values returned for each function are:

• carbon_leafStarchCapacity: Capacity of storing starch in the leaf compartment (mol gluc/ind.).

• carbon_leafStructuralBiomass: Leaf structural biomass (g dry/ind.)

• carbon_sapwoodStarchCapacity: Capacity of storing starch in the sapwood compartment (mol gluc/ind.).

• carbon_sapwoodStructuralBiomass: Sapwood structural biomass (g dry/ind.)

• carbon_sapwoodStructuralLivingBiomass: Living sapwood (parenchyma) structural biomass (g dry/ind.)

• carbon_sugarConcentration: Sugar concentration (mol gluc/l)

• carbon_osmoticWaterPotential: Osmotic component of water potential (MPa)

• carbon_relativeSapViscosity: Relative viscosity of sapwood with respect to pure water (according to Forst et al. (2002)).

• carbon_sugarStarchDynamicsLeaf: Rate of conversion from sugar to starch in leaf (mol gluc/l/s).

• carbon_sugarStarchDynamicsStem: Rate of conversion from sugar to starch in leaf (mol gluc/l/s).

• carbon_carbonCompartments: A data frame with the size of compartments for each plant cohort, in the specified units.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Forst P, Wermer F, Delgado A (2002). On the pressure dependence of the viscosity of aqueous sugar solutions. Rheol Acta 41: 369–374 DOI 10.1007/s00397-002-0238-y

See Also

growth

Control parameters for simulation models

Description

Creates a list control parameters default values for simulations

Usage

defaultControl(transpirationMode = "Granier", soilDomains = "buckets")

Arguments

Details

The function returns a list with default parameters. Users can change those defaults that need to be set to other values and use the list as input for model functions. The relevant parameters are different for each model function.

Value

A list, with the following options (default values in brackets):

General:

• verbose [= TRUE]: Boolean flag to indicate console output during calculations. In function fordyn verbose is always set to FALSE.

• fillMissingRootParams [= TRUE]: Boolean flag to indicate that initializing functions should provide estimates for Z50 and Z95 if these are missing in the forest data. Note that if fillMissingRootParams is set to FALSE then simulations may fail if the user does not provide values for Z50 and Z95 in tree or shrub data.

• fillMissingSpParams [= TRUE]: Boolean flag to indicate that initializing functions should provide estimates for functional parameters if these are missing in the species parameter table SpParams. Note that if fillMissingSpParams is set to FALSE then simulations may fail if the user does not provide values for required parameters.

• fillMissingWithGenusParams [=TRUE]: Boolean flag to indicate that initializing functions should provide estimates from genus value, if species-level values are missing in the species parameter table SpParams but genus-level ones are not.

• standResults [= TRUE]: Boolean flag to keep stand-level results (in a data frame called 'Stand').

• soilResults [= TRUE]: Boolean flag to keep soil-level results (in a list called 'Soil').

• snowResults [= TRUE]: Boolean flag to keep snow results (in a data frame called 'Snow').

• plantResults [= TRUE]: Boolean flag to keep plant-level water/energy/photosynthesis results (in a list called 'Plants').

• labileCarbonBalanceResults [= TRUE]: Boolean flag to keep plant-level labile carbon balance results (in a list called 'LabileCarbonBalance').

• plantStructureResults [= TRUE]: Boolean flag to keep plant-level structure results (in a list called 'PlantStructure').

• growthMortalityResults [= TRUE]: Boolean flag to keep plant-level growth and mortality results (in a list called 'GrowthMortality').

• leafResults [= TRUE]: Boolean flag to keep leaf-level results (in elements called 'SunlitLeaves' and 'ShadeLeaves').

• temperatureResults [= TRUE]: Boolean flag to keep temperature results (in elements called 'Temperature' and 'TemperatureLayers').

• subdailyResults [= FALSE]: Boolean flag to force subdaily results to be stored (as a list called 'subdaily' of spwb_day objects, one by simulated date) in calls to spwb. In function fordyn subdailyResults is always set to FALSE.

• fireHazardResults [= FALSE]: Boolean flag to force calculation of daily fire hazard.

• fireHazardStandardWind [= NA]: Wind speed (in m/s) for fire-hazard estimation. If missing, actual wind-speed is used.

• fireHazardStandardDFMC [= NA]: Dead fuel moisture content for fire-hazard estimation. If missing, estimation from current weather is used.

Water balance (functions spwb, pwb or spwb_day):

• transpirationMode [= "Granier"]: Transpiration model (either 'Granier', 'Sperry' or 'Sureau'). See spwbInput.

• soilFunctions [= "VG"]: Soil water retention curve and conductivity functions, either 'SX' (for Saxton) or 'VG' (for Van Genuchten). If transpirationMode is 'Sperry' or 'Sureau' then soilFunctions is forced to 'VG'. Only simulations with 'Granier' are allowed to use Saxton functions.

• VG_PTF: String indicating the pedotransfer functions for van Genuchten parameters (either 'Toth' or 'Carsel').

• ndailysteps [= 24]: Number of steps into which each day is divided for determination of soil water balance, stomatal conductance, transpiration and photosynthesis (24 equals 1-hour intervals).

• max_nsubsteps_soil [= 300]: Maximum number of substeps for soil water balance solving.

• defaultWindSpeed [= 2.5]: Default wind speed value (in m/s) to be used when missing from data.

• defaultCO2 [= 386]: Default atmospheric (abovecanopy) CO2 concentration (in micromol·mol-1 = ppm). This value will be used whenever CO2 concentration is not specified in the weather input.

• defaultRainfallIntensityPerMonth [= c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 5.6, 5.6, 5.6, 5.6, 5.6, 1.5)]: A vector of twelve values indicating the rainfall intensity (mm/h) per month. By default synoptic storms (1.5 mm/h) are assumed between December and June, and convective storms (5.6 mm/h) are assumed between July and November.

• leafPhenology [= TRUE]: Boolean flag to indicate the simulation of leaf phenology for winter-deciduous species.

• bareSoilEvaporation [= TRUE]: Boolean flag to indicate the simulation of evaporation from bare soil.

• unlimitedSoilWater [= FALSE]: Boolean flag to indicate the simulation of plant transpiration assuming that soil water is always at field capacity.

• unfoldingDD [= 300]: Degree-days for complete leaf unfolding after budburst has occurred.

• interceptionMode [= "Gash1995"]: Infiltration model, either "Gash1995" or "Liu2001".

• infiltrationMode [= "GreenAmpt1911"]: Infiltration model, either "GreenAmpt1911" or "Boughton1989".

• infiltrationCorrection [= 5.0]: Factor to correct infiltration amount in the GreenAmpt1911 model in single-domain simulations.

• soilDomains [= "buckets"]: Either 'buckets' (for multi-bucket model), 'single' (for single-domain Richards model) or 'dual' (for dual-permeability model). See hydrology_soilWaterBalance.

• rhizosphereOverlap [= "total"]: A string indicating the degree of rhizosphere spatial overlap between plant cohorts:

  • "none" - no overlap (independent water pools).

  • "partial" - partial overlap determined by coarse root volume.

  • "total" - total overlap (plants extract from common soil pools).

• verticalLayerSize [= 100]: Size of vertical layers (in cm) for the calculation of light extinction (and photosynthesis).

• windMeasurementHeight [= 200]: Height (in cm) over the canopy corresponding to wind measurements.

• segmentedXylemVulnerability [= TRUE/FALSE]: If FALSE leaf and root vulnerability curves will be equal to those of stem. By default, segmentedXylemVulnerability = TRUE for transpirationMode = "Sperry" and segmentedXylemVulnerability = FALSE for transpirationMode = "Sureau".

• leafCavitationEffects, stemCavitationEffects [= FALSE/TRUE]: A flag indicating whether cavitation effects on conductance of leaves and stem are applied. Only relevant for transpirationMode = "Sperry".

• leafCavitationRecovery, stemCavitationRecovery [= "annual"]: A string indicating how recovery of previous cavitation leaf/stem xylem is done (only relevant for functions spwb and spwb_day):

  • "none" - no recovery.

  • "annual" - every first day of the year.

  • "rate" - following a rate of new leaf or sapwood formation.

  • "total" - instantaneous complete recovery.

• cavitationRecoveryMaximumRate [= 0.05]: Maximum rate of daily refilling of embolized conduits as sapwood area per leaf area (in cm2·m-2·day-1).

• lfmcComponent [= "fine"]: Plant component used to estimate LFMC, either "leaf" or "fine" (for fine fuel).

Water balance (functions spwb, pwb or spwb_day when traspirationMode = "Granier" only):

• hydraulicRedistributionFraction [= 0.1]: Fraction of plant transpiration corresponding to hydraulic redistribution.

Water balance (functions spwb, pwb or spwb_day when traspirationMode = "Sperry" or traspirationMode = "Sureau"):

• nsubsteps_canopy [= 3600]: Number of substeps into which each step is divided for multi-layer canopy energy balance solving.

• multiLayerBalance [= FALSE]: Flag to indicate multiple canopy energy balance. If FALSE, canopy is considered a single layer for energy balance.

• sapFluidityVariation [= TRUE]: Flag to indicate that temperature affects sap fluidity (and indirectly plant conductance).

• TPhase_gmin [= 37.5]: Temperature for transition phase of gmin.

• Q10_1_gmin [= 1.2]: Temperature dependance of gmin when T less than or equal to TPhase.

• Q10_2_gmin [= 4.8]: Temperature dependance of gmin when T greater than TPhase.

• taper [= TRUE]: Whether taper of xylem conduits is accounted for when calculating aboveground stem conductance from xylem conductivity.

• thermalCapacityLAI [= 1000000]: Thermal canopy capacitance per LAI unit.

• rootRadialConductance [= 4]: Radial conductance in roots (mmol·s-1·m-2·MPa-1).

• averageFracRhizosphereResistance [= 0.15]: Fraction to total continuum (leaf+stem+root+rhizosphere) resistance that corresponds to rhizosphere (averaged across soil water potential values).

• boundaryLayerSize [= 2000]: Size of the boundary layer (in cm) over the canopy (relevant for multi-layer canopy energy balance).

Water balance (functions spwb, pwb or spwb_day when traspirationMode = "Sperry" only):

• numericParams: A list with the following elements:

  • maxNsteps [= 400]: Maximum number of steps in supply function.

  • ntrial [= 200]: Number of iteration trials when finding root of equation system.

  • psiTol [= 0.0001]: Tolerance value for water potential.

  • ETol [= 0.0001]: Tolerance value for flow.

Water balance (functions spwb, pwb or spwb_day when traspirationMode = "Sureau" only):

• plantCapacitance [= TRUE]: Whether the effect of (symplasmic) plant water compartments is considered in simulations.

• cavitationFlux [= TRUE]: Whether the effect of water flux generated by cavitation of apoplasmic tissues is considered in simulations.

• soilDisconnection [= FALSE]: Whether the ability of the plants to physically disconnect their root system from the soil is considered in simulations.

• leafCuticularTranspiration [= TRUE]: Whether the effect of leaf cuticular transpiration is considered in simulations.

• stemCuticularTranspiration [= FALSE]: Whether the effect of stem cuticular transpiration is considered in simulations.

• C_SApoInit [= 2.0e-5]: Maximum capacitance of the stem apoplasm (mmol·m-2).

• C_LApoInit [= 1.0e-5]: Maximum capacitance of the leaf apoplasm (mmol·m-2).

• k_SSym [= 0.26]: Conductance from stem apoplasm to stem symplasm (mmol·s-1·m-2·MPa-1).

• fractionLeafSymplasm [= 0.5]: Fraction of the leaf resistance from leaf apoplasm to leaf symplasm ([0-1]).

• gs_NightFrac [= 0.05]: Stomatal conductance at night as fraction of maximum stomatal conductance ([0-1]).

• stomatalSubmodel [= "Baldocchi"]: Stomatal regulation sub-model, either "Jarvis" or "Baldocchi".

• JarvisPAR [= 0.003]: Parameter regulating the response of stomatal conductance to light (PAR) in the Jarvis model.

• fTRBToLeaf [= 0.8]: Fraction of surface of bark exposed to air per leaf area.

Forest growth (functions growth or growth_day):

• subdailyCarbonBalance [= FALSE]: Boolean flag to indicate that labile carbon balance should be conducted at sub-daily steps (applies only to transpirationMode = "Sperry").

• allowDessication [= TRUE]: Boolean flag to indicate that mortality by dessication is allowed.

• allowStarvation [= TRUE]: Boolean flag to indicate that mortality by starvation is allowed.

• sinkLimitation [= TRUE]: Boolean flag to indicate that temperature and turgor limitations to growth are applied.

• shrubDynamics [= TRUE]: Boolean flag to allow the application of demographic processes to shrubs.

• herbDynamics [= TRUE]: Boolean flag to allow dynamic herb leaf area as a function of shading due to leaf area of woody cohorts.

• allocationStrategy [= "Al2As"]: Strategy for allocation (either "Plant_kmax", for constant maximum plant conductance, or "Al2As" for constant Huber value).

• phloemConductanceFactor [= 0.2]): Factor to transform stem xylem conductance to stem phloem conductance (only for transpirationMode = "Sperry").

• nonSugarConcentration [= 0.25]: Non-sugar (inorganic) solute concentration (mol·l-1) in cells.

• equilibriumOsmoticConcentration [= c(leaf = 0.8, sapwood = 0.6)]: Equilibrium osmotic concentrations (mol·l-1) for leaf and sapwood cells. The difference between leaf and sapwood values helps maintaining phloem transport. The equilibrium sugar concentration is equilibriumOsmoticConcentration - nonSugarConcentration defaults to [= c(leaf = 0.55, sapwood = 0.35)].

• minimumRelativeStarchForGrowth [= 0.50]: Default minimum concentration of storage carbon (starch), relative to the maximum storage capacity, for sapwood growth to occur, when not specified via SpParams (RSSG).

• constructionCosts [= c(leaf = 1.5, sapwood = 1.47, fineroot = 1.30)]: Default construction costs, including respiration and structural carbon, per dry weight of new tissue (g gluc · g dry -1) when not specified via SpParams (CCleaf, CCsapwood and CCfineroot).

• senescenceRates [= c(sapwood = 0.0001261398, fineroot = 0.001897231)]: Default senescence rates (day-1) for sapwood and fineroots when not specified via SpParams (SRsapwood and SRfineroot). Defaults are equivalent to 9%, 5% and 50% annual turnover for gymnosperm sapwood, angiosperm sapwood and fine roots, respectively.

• maximumRelativeGrowthRates [= c(leaf = 0.09, cambium = 0.005, sapwood = 0.002, fineroot = 0.1)]: Default maximum relative growth rates for leaves (m2 leaf ·cm-2 sapwood· day-1), tree sapwood (cm2 sapwood· cm-1 cambium · day-1), shrub sapwood (cm2 sapwood ·cm-2 sapwood· day-1) and fine roots (g dw · g dw -1 · day -1) when not specified via SpParams (RGRleafmax, RGRcambiummax , RGRsapwoodmax and RGRfinerootmax, respectively).

• mortalityMode [= "density/deterministic"]: String describing how mortality is applied. Current accepted values are combinations of "cohort" vs "density" (for whole-cohort mortality vs reduction of stem density) and "deterministic" vs. "stochastic".

• mortalityBaselineRate [= 0.0015]: Default deterministic proportion or probability specifying the baseline reduction of cohort's density occurring in a year (for mortalityMode = "density/deterministic" or "density/stochastic").

• mortalityRelativeSugarThreshold [= 0.4]: Threshold of stem sugar concentration relative to the equilibrium sugar concentration, resulting in an increased starvation mortality rate/probability whenever levels are below.

• mortalityRWCThreshold [= 0.4]: Threshold of stem relative water content resulting in increased mortality rate/probability whenever levels are below.

• recrTreeDBH [= 1]: Default DBH (cm) for recruited trees (when species parameter RecrTreeDBH is missing).

• recrTreeDensity [= 3000]: Default density (ind·ha-1) for recruited trees (when species parameter RecrTreeDensity is missing).

• ingrowthTreeDBH [= 7.5]: Default DBH (cm) for ingrowth trees (when species parameter RecrTreeDBH is missing).

• ingrowthTreeDensity [= 127]: Default density (ind·ha-1) for ingrowth trees (when species parameter RecrTreeDensity is missing).

Forest dynamics (function fordyn):

• allowSeedBankDynamics [= TRUE]: Boolean flag to indicate that seed production and seed bank dynamics is simulated.

• allowRecruitment [= TRUE]: Boolean flag to indicate that recruitment from seeds is allowed.

• allowResprouting [= TRUE]: Boolean flag to indicate that resprouting is allowed.

• recruitmentMode [= "stochastic"]: String describing how recruitment from seeds is applied. Current accepted values are "deterministic" or "stochastic".

• removeEmptyCohorts [= TRUE]: Boolean flag to indicate the removal of cohorts whose density is too low.

• minimumTreeCohortDensity [= 1]: Threshold of tree density resulting in cohort removal.

• minimumShrubCohortCover [= 0.01]: Threshold of shrub cover resulting in cohort removal.

• dynamicallyMergeCohorts [= TRUE]: Boolean flag to indicate that cohorts should be merged when possible. This option speeds up calculations but results in a loss of cohort identity and reinitialization of many state variables.

• keepCohortsWithID [= TRUE]: Boolean flag to indicate that cohorts having a non-missing value in a column "ID" (if present) should not be merged or removed.

• seedRain [= NULL]: Vector of species names whose seed rain is to be added to seed bank, regardless of local seed production.

• seedProductionTreeHeight [= 300]: Default minimum tree height for producing seeds (when species parameter SeedProductionHeight is missing).

• seedProductionShrubHeight [= 30]: Default minimum shrub height for producing seeds (when species parameter SeedProductionHeight is missing).

• probRecr [= 0.05]: Default annual probability of seed-recruitment (when species parameter ProbRecr is missing).

• minTempRecr [= 0]: Default threshold of minimum average temperature of the coldest month necessary for recruiting from seeds (when species parameter MinTempRecr is missing).

• minMoistureRecr [= 0.3]: Default threshold of minimum moisture index (annual precipitation over annual ETP) necessary for seed-recruiting (when species parameter MinMoistureRecr is missing).

• minFPARRecr [= 10]: Default threshold of minimum fraction of PAR (in %) reaching the ground necessary for recruiting (when species parameter MinFPARRecr is missing).

• recrTreeHeight [= 620]: Default height (cm) for recruited trees (when species parameter RecrTreeHeight is missing).

• recrShrubCover [= 1]: Default cover (%) for shrubs recruited from seed (when species parameter RecrShrubCover is missing).

• recrShrubHeight [= 25]: Default height (cm) for recruited shrubs (when species parameter RecrShrubHeight is missing).

• recrTreeZ50 [= 100]: Default value for Z50 (mm) in seed-recruited trees (when species parameter RecrZ50 is missing).

• recrShrubZ50 [= 50]: Default value for Z50 (mm) in seed-recruited shrubs (when species parameter RecrZ50 is missing).

• recrTreeZ95 [= 1000]: Default value for Z95 (mm) in seed-recruited trees (when species parameter RecrZ50 is missing).

• recrShrubZ50 [= 500]: Default value for Z95 (mm) in seed-recruited shrubs (when species parameter RecrZ50 is missing).

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwbInput, spwb, growth, fordyn

Default forest management actions

Description

Function defaultManagementFunction implements actions for 'regular' and 'irregular' management models of monospecific or mixed stands, whereas function defaultManagementArguments returns a list with default values for the parameters regulating management. Both functions are meant to be used in simulations with fordyn.

Usage

defaultManagementFunction(x, args, verbose = FALSE)

defaultManagementArguments()

Arguments

Details

This function implements silvicultural actions following either 'regular' or 'irregular' management models. Irregular models are implemented by executing thinning operations only, whereas regular models include both thinning and a set of final cuts. Thinning occurs anytime a stand-level metric (e.g. basal area) crosses a given threshold, and different kinds of thinning operations are allowed. Unrealistic high frequency thinning can be avoided by setting a minimum number of years to happen between thinning operations. Final cuts start whenever mean DBH exceeds a given threshold, and may include different cuts separated a number of years. The function can be applied to target management of specific taxa (instead of assuming a monospecific stand), but the thresholds that determine thinning operations apply to stand-level metrics. Mean DBH will be calculated for the target species only. Planting is only allowed under regular management models, and is applied after the last final cut. Understory clearings are assumed to occur anytime there is an intervention on trees, an only a residual shrub cover is left.

Thinning types:

• above: Extract largest trees (according to DBH) until thinning objective is met.

• below: Extract smallest trees (according to DBH) until thinning objective is met.

• systematic: Extract equally from all size classes until thinning objective is met.

• above-systematic: Extract half the objective as systematic thinning and the other hald as above thinning.

• below-systematic: Extract half the objective as systematic thinning and the other hald as below thinning.

• free string: A string specifying the proportion of tree cuts from size classes, with size classes separated by "/" and each one composed of a number specifying the upper limit and a number indicating its proportion, separated by "-" (e.g. "10-50/40-30/60-20").

Value

Function defaultManagementFunction returns a list with the following items:

• "action": A string identifying the action performed (e.g. "thinning").

• "N_tree_cut": A vector with the density of trees removed.

• "Cover_shrub_cut": A vector with the cover of shrubs removed.

• "planted_forest": An object of class forest with the new plant cohorts resulting from tree/shrub planting.

• "management_args": A list of management arguments to be used in the next call to the management function.

Function defaultManagementArguments returns a list with default arguments:

• "type": Management model, either 'regular' or 'irregular'.

• "targetTreeSpecies": Either "all" for unspecific cuttings or a numeric vector of target tree species to be selected for cutting operations.

• "thinning": Kind of thinning to be applied in irregular models or in regular models before the final cuts. Options are 'below', 'above', 'systematic', 'below-systematic', 'above-systematic' or a string with the proportion of cuts to be applied to different diameter sizes (see details).

• "thinningMetric": The stand-level metric used to decide whether thinning is applied, either 'BA' (basal area), 'N' (density) or 'HB' (Hart-Becking index).

• "thinningThreshold": The threshold value of the stand-level metric causing the thinning decision.

• "thinningPerc": Percentage of stand's basal area to be removed in thinning operations.

• "minThinningInterval": Minimum number of years between thinning operations.

• "yearsSinceThinning": State variable to count the years since the last thinning ocurred.

• "finalMeanDBH": Mean DBH threshold to start final cuts.

• "finalPerc": String with percentages of basal area to be removed in final cuts, separated by '-' (e.g. "40-60-100").

• "finalPreviousStage": Integer state variable to store the stage of final cuts ('0' before starting final cuts).

• "finalYearsBetweenCuts": Number of years separating final cuts.

• "finalYearsToCut": State variable to count the years to be passed before new final cut is applied.

• "plantingSpecies": Species code to be planted. If missing, planting does not occur and only natural regeneration is allowed.

• "plantingDBH": Initial DBH (cm) of planted species.

• "plantingHeight": Initial height (cm) of planted species.

• "plantingDensity": Initial density (ind./ha) of the planted species.

• "understoryMaximumCover": Percentage of overall shrub cover to be left after any silvicultural intervention. If missing, shrub cover will not be left unmodified.

Author(s)

Miquel De Cáceres Ainsa, CREAF

Aitor Améztegui, UdL

Jose-Ramon Gonzalez Olabarria, CTFC

See Also

fordyn

Examples

# Load example forest object
data(exampleforest)
  
# Define arguments
args = defaultManagementArguments()
  
# Call management function
f = defaultManagementFunction(exampleforest, args)
  
#list names
names(f)
  
# Action performed
f$action
  
# Number of trees cut for each cohort
f$N_tree_cut
  
# Percent cover of shrubs removed
f$Cover_shrub_cut
  

Default soil parameters

Description

Creates a data frame with default soil physical description for model functions

Usage

defaultSoilParams(n = 4)

Arguments

Details

The function returns a data frame with default physical soil description, with soil layers in rows. Users can change those that need to be set to other values and use the list as input for function soil.

Value

A data frame with layers in rows and the following columns (and default values):

• widths (= c(300,700,1000,2000): Width of soil layers (in mm).

• clay (= 25): Clay percentage for each layer (in %).

• sand (= 25): Sand percentage for each layer (in %).

• om (= NA): Organic matter percentage for each layer (in %) (optional).

• nitrogen (= NA): Sum of total nitrogen (ammonia, organic and reduced nitrogen) for each layer (in g/kg) (optional).

• bd (= 1.5): Bulk density for each layer (in g/cm3).

• rfc (= c(20,40,60,85)): Percentage of rock fragment content (volume basis) for each layer.

Note

While this function is limited to five soil layers, user defined data frames can discretize soils using an unlimited number of soil layers.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

soil, soil_redefineLayers, defaultControl, SpParamsMED

Examples

defaultSoilParams(4)

Drought stress indicators

Description

Calculates plant drought stress indices, at different temporal scales, from simulation results.

Usage

droughtStress(x, index = "NDD", freq = "years", bySpecies = FALSE, draw = TRUE)

Arguments

Details

The currently available drought stress indices are:

• "ADS": Average of daily drought stress values for the period considered.

• "MDS": Maximum daily drought stress during the period considered.

• "DI": Drought intensity, as defined in De Cáceres et al. (2015).

• "NDD": Number of drought days, as defined in De Cáceres et al. (2015).

• "WSI": Water stress integral, as defined in Myers (1988).

Value

A data frame with periods (e.g., years or months) in rows and plant cohorts (or species) in columns. Values are the calculated stress index. If draw=TRUE a ggplot is returned instead.

Author(s)

Miquel De Cáceres Ainsa, CREAF

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).

Myers BJ (1988) Water stress integral - a link between short-term stress and long-term growth. Tree Physiology 4: 315–323 (doi: 10.1093/treephys/4.4.315)

See Also

summary.spwb, waterUseEfficiency

Creation of an empty forest

Description

Creates an empty forest object.

Usage

emptyforest(ntree = 0, nshrub = 0, nseed = 0)

Arguments

Value

An empty forest object.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

forest, tree2forest, summary.forest, forest_mapWoodyTables, forest_mergeTrees, plot.forest

Examples

# Initializes forest with 2 tree cohorts and 1 shrub cohort
emptyforest(ntree = 2, nshrub = 1)

Evaluation of simulations results

Description

Functions to compare model predictions against observed values.

Usage

evaluation_table(
  out,
  measuredData,
  type = "SWC",
  cohort = NULL,
  temporalResolution = "day"
)

evaluation_stats(
  out,
  measuredData,
  type = "SWC",
  cohort = NULL,
  temporalResolution = "day"
)

evaluation_plot(
  out,
  measuredData,
  type = "SWC",
  cohort = NULL,
  temporalResolution = "day",
  plotType = "dynamics"
)

evaluation_metric(
  out,
  measuredData,
  type = "SWC",
  cohort = NULL,
  temporalResolution = "day",
  metric = "loglikelihood"
)

Arguments

Details

Users should provide the appropriate columns in measuredData, depending on the type of output to be evaluated:

• "SWC", "RWC", "REW": A column with the same name should be present. By default, the first soil layer is compared. Evaluation can be done for specific soil layers, for example using "RWC.2" for the relative water content of the second layer.

• "ETR" or "SE+TR": A column named "ETR" should be present, containing stand's evapotranspiration in mm/day (or mm/week, mm/month, etc, depending on the temporal resolution). If type="ETR" observed values will be compared against modelled evapotranspiration (i.e. sum of transpiration, soil evaporation and interception loss), whereas if type= "SE+TR" observed values will be compared against the sum of transpiration and soil evaporation only.

• "LE": A column named "LE" should be present containing daily latent heat turbulent flux in MJ/m2.

• "H": A column named "H" should be present containing daily sensible heat turbulent flux in MJ/m2.

• "E": For each plant cohort whose transpiration is to be evaluated, a column starting with "E_" and continuing with a cohort name (e.g. "E_T1_68") with transpiration in L/m2/day on a leaf area basis (or L/m2/week, L/m2/month, etc, depending on the temporal resolution).

• "GPP": A column named "GPP" should be present containing daily gross primary productivity in gC/m2.

• "LFMC": For each plant cohort whose transpiration is to be evaluated, a column starting with "LFMC_" and continuing with a cohort name (e.g. "LFMC_T1_68") with fuel moisture content as percent of dry weight.

• "WP": For each plant cohort whose transpiration is to be evaluated, two columns, one starting with "PD_" (for pre-dawn) and the other with "MD_" (for midday), and continuing with a cohort name (e.g. "PD_T1_68"). They should contain leaf water potential values in MPa. These are compared against sunlit water potentials.

• "BAI": For each plant cohort whose growth is to be evaluated, a column starting with "BAI_" and continuing with a cohort name (e.g. "BAI_T1_68") with basal area increment in cm2/day, cm2/week, cm2/month or cm2/year, depending on the temporal resolution.

• "DI": For each plant cohort whose growth is to be evaluated, a column starting with "DI_" and continuing with a cohort name (e.g. "DI_T1_68") with basal area increment in cm/day, cm/week, cm/month or cm/year, depending on the temporal resolution.

• "DBH": For each plant cohort whose growth is to be evaluated, a column starting with "DBH_" and continuing with a cohort name (e.g. "DBH_T1_68") with DBH values in cm.

• "Height": For each plant cohort whose growth is to be evaluated, a column starting with "Height_" and continuing with a cohort name (e.g. "Height_T1_68") with Height values in cm.

Additional columns may exist with the standard error of measured quantities. These should be named as the referred quantity, followed by "_err" (e.g. "PD_T1_68_err"), and are used to draw confidence intervals around observations.

Row names in measuredData indicate the date of measurement (in the case of days). Alternatively, a column called "dates" can contain the measurement dates. If measurements refer to months or years, row names should also be in a "year-month-day" format, although with "01" for days and/or months (e.g. "2001-02-01" for february 2001, or "2001-01-01" for year 2001).

Value

• Function evaluation_table returns a data frame with dates, observed and predicted values.

• Function evaluation_stats returns evaluation statistics (a vector or a data frame depending on type):

  • Bias: Mean deviation (positive values correspond to model overestimations).

  • Bias.rel: Bias in relative terms (%).

  • MAE: Mean absolute error.

  • MAE.rel: Mean absolute error in relative terms (%).

  • r: Pearson's linear correlation coefficient.

  • NSE: Nash-Sutcliffe model efficiency coefficient.

  • NSE.abs: Modified Nash-Sutcliffe model efficiency coefficient (L1 norm) (Legates & McCabe 1999).

• Function evaluation_plot returns a ggplot object.

• Function evaluation_metric returns a scalar with the desired metric.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Legates, D.R., McCabe, G.J., 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241.

See Also

spwb, growth, optimization, exampleobs

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)

#Call simulation function
S1 <- spwb(x1, examplemeteo, latitude = 41.82592, elevation = 100)

#Load observed data (in this case the same simulation results with some added error)  
data(exampleobs)

#Evaluation statistics for soil water content
evaluation_stats(S1, exampleobs)

#NSE only
evaluation_metric(S1, exampleobs, metric="NSE")

#Comparison of temporal dynamics
evaluation_plot(S1, exampleobs)

#Loglikelihood value
evaluation_metric(S1, exampleobs)

Example daily meteorology data

Description

Example data set of meteorological input.

Format

A data frame containing daily meteorology of a location in Catalonia (Spain) for year 2001:

dates

Vector of Date objects.

MinTemperature

Minimum daily temperature (in degrees Celsius).

MaxTemperature

Maximum daily temperature (in degrees Celsius).

Precipitation

Daily precipitation (in mm of water).

MinRelativeHumidity

Minimum daily relative humidity (in percent).

MaxRelativeHumidity

Maximum daily relative humidity (in percent).

Radiation

Incoming radiation (in MJ/m2).

WindSpeed

Wind speed (in m/s).

Source

Interpolated from weather station data (Spanish and Catalan meteorology agencies) using package 'meteoland'.

See Also

spwb

Examples

 data(examplemeteo)

Example observed data

Description

Example (fake) data set of variables measured in a plot.

Format

A data frame containing daily 'observed' values for year 2001:

dates

Measurement dates.

SWC

Soil moisture content (in m3/m3).

ETR

Total evapotranspiration (mm).

E_T1_148

Transpiration of Pinus halepensis cohort 'T1_148' (L/m2 of leaf area).

E_T2_168

Transpiration of Quercus ilex cohort 'T2_168' (L/m2 of leaf area).

FMC_T1_148

Fuel moisture content of Pinus halepensis cohort 'T1_148' (in percent).

FMC_T2_168

Fuel moisture content of Quercus ilex cohort 'T2_168' (in percent).

BAI_T1_148

Basal area increment for Pinus halepensis cohort 'T1_148' (in cm2).

BAI_T2_168

Basal area increment for Quercus ilex cohort 'T2_168' (in cm2).

DI_T1_148

Diameter increment for Pinus halepensis cohort 'T1_148' (in cm).

DI_T2_168

Diameter increment for Quercus ilex cohort 'T2_168' (in cm).

Source

This data set was actually created by running a simulation and adding some gaussian error to the outputs.

See Also

evaluation

Examples

data(exampleobs)

Extracts model outputs

Description

Function extract() extracts daily or subdaily output and returns it as a tidy data frame.

Usage

extract(
  x,
  level = "forest",
  output = NULL,
  vars = NULL,
  dates = NULL,
  subdaily = FALSE
)

Arguments

Details

When subdaily = FALSE, parameter output is used to restrict the section in x where variables are located. For example output = "Plants" will correspond to variables "LAI", "LAIlive", "Transpiration", "StemPLC",... as returned by a call names(x$Plants).

Option subdaily = TRUE only works when simulations have been carried using control option 'subdailyResults = TRUE' (see defaultControl). When using subdaily = TRUE, parameter output is not taken into account, and options for parameter vars are the following:

• Variables for level = "forest" or level = "soillayer": Not allowed. An error is raised.

• Variables for level = "cohort": "E","Ag","An","dEdP","RootPsi","StemPsi","LeafPsi","StemPLC","StemRWC","LeafRWC","StemSympRWC","LeafSympRWC","PWB".

• Variables for level = "shadeleaf" and level="sunlitleaf": "Abs_SWR","Abs_PAR","Net_LWR","E","Ag","An","Ci","Gsw","VPD","Temp","Psi","iWUE".

Value

Function extract() returns a data frame:

• If level = "forest", columns are "date" and variable names.

• If level = "soillayer", columns are "date", "soillayer" and variable names.

• If level = "cohort", level = "sunlitleaf" or level = "shadeleaf", columns are "date", "cohorts", "species" and variable names.

• If subdaily = TRUE, columns are "datetime", "cohorts", "species" and variable names.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

summary.spwb

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
x <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)

#Call simulation function (ten days)
S1<-spwb(x, examplemeteo[1:10, ], latitude = 41.82592, elevation = 100)

#Extracts daily forest-level output as a data frame
extract(S1, level = "forest")

#Extracts daily soil layer-level output as a data frame
extract(S1, level = "soillayer")

#Extracts daily cohort-level output as a data frame
extract(S1, level = "cohort")

#Select the output tables/variables to be extracted
extract(S1, level ="cohort", output="Plants", vars = c("PlantStress", "StemPLC"))

Fire hazard

Description

Estimates potential fire behaviour at each daily step of a simulation

Usage

fireHazard(
  x,
  SpParams,
  forest = NULL,
  standardConditions = FALSE,
  freq = "days",
  fun = "max"
)

Arguments

Details

Live fuel moisture of shrub and canopy layers is estimated from plant water status. Dead fuel moisture is estimated following Resco-de-Dios et al. (2015).

Value

A matrix with fire behaviour variables (columns) for each simulated day (rows) or coarser time steps if summaries are requested.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Resco de Dios, V., A. W. Fellows, R. H. Nolan, M. M. Boer, R. A. Bradstock, F. Domingo, and M. L. Goulden. 2015. A semi-mechanistic model for predicting the moisture content of fine litter. Agricultural and Forest Meteorology 203:64–73.

Ruffault J, Limousin JM, Pimont F, Dupuy JL, De Cáceres M, Cochard H, Mouillot F, Blackman C, Torres-Ruiz JM, Parsons R, Moreno M, Delzon S, Jansen S, Olioso A, Choat B, Martin-StPaul N. 2023. Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought. New Phytologist. (10.1111/nph.18614).

See Also

spwb, fuel_FCCS, fire_FCCS

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)

#Call simulation function
S1 <- spwb(x1, examplemeteo, latitude = 41.82592, elevation = 100)

#Evaluate fire hazard
F1 <- fireHazard(S1, SpParamsMED, exampleforest)

Fire behaviour functions

Description

Function fire_FCCS() implements a modification of the fire behavior models described for the Fuel Characteristics Classification System (FCCS) in Prichard et al. (2013). Function fire_Rothermel() implements Rothermel's (1972) fire behaviour model (modified from package 'Rothermel' (Giorgio Vacchiano, Davide Ascoli)).

Usage

fire_FCCS(
  FCCSpropsSI,
  MliveSI = as.numeric(c(90, 90, 60)),
  MdeadSI = as.numeric(c(6, 6, 6, 6, 6)),
  slope = 0,
  windSpeedSI = 11
)

fire_Rothermel(
  modeltype,
  wSI,
  sSI,
  delta,
  mx_dead,
  hSI,
  mSI,
  u,
  windDir,
  slope,
  aspect
)

Arguments

Details

Default moisture, slope and windspeed values are benchmark conditions used to calculate fire potentials (Sandberg et al. 2007) and map vulnerability to fire.

Value

Both functions return list with fire behavior variables.

In the case of fire_FCCS, the function returns the variables in three blocks (lists SurfaceFire, CrownFire and FirePotentials), and the values are:

• SurfaceFire$`midflame_WindSpeed [m/s]`: Midflame wind speed in the surface fire.

• SurfaceFire$phi_wind: Spread rate modifier due to wind.

• SurfaceFire$phi_slope: Spread rate modifier due to slope.

• SurfaceFire$`I_R_surf [kJ/m2/min]`: Intensity of the surface fire reaction.

• SurfaceFire$`I_R_litter [kJ/m2/min]`: Intensity of the litter fire reaction.

• SurfaceFire$`q_surf [kJ/m2]`: Heat sink of the surface fire.

• SurfaceFire$`q_litter [kJ/m2]`: Heat sink of the litter fire.

• SurfaceFire$xi_surf: Propagating flux ratio of the surface fire.

• SurfaceFire$xi_litter: Propagating flux ratio of the litter fire.

• SurfaceFire$`ROS_surf [m/min]`: Spread rate of the surface fire(without accounting for faster spread in the litter layer).

• SurfaceFire$`ROS_litter [m/min]`: Spread rate of the litter fire.

• SurfaceFire$`ROS_windslopecap [m/min]`: Maximum surface fire spread rate according to wind speed.

• SurfaceFire$`ROS [m/min]`: Final spread rate of the surface fire.

• SurfaceFire$`I_b [kW/m]`: Fireline intensity of the surface fire.

• SurfaceFire$`FL [m]`: Flame length of the surface fire.

• CrownFire$`I_R_canopy [kJ/m2/min]`: Intensity of the canopy fire reaction.

• CrownFire$`I_R_crown [kJ/m2/min]`: Intensity of the crown fire reaction (adding surface and canopy reactions).

• CrownFire$`q_canopy [kJ/m2]`: Heat sink of the canopy fire.

• CrownFire$`q_crown [kJ/m2]`: Heat sink of the crown fire (adding surface and canopy heat sinks).

• CrownFire$xi_surf: Propagating flux ratio of the crown fire.

• CrownFire$`canopy_WindSpeed [m/s]`: Wind speed in the canopy fire (canopy top wind speed).

• CrownFire$WAF: Wind speed adjustment factor for crown fires.

• CrownFire$`ROS [m/min]`: Spread rate of the crown fire.

• CrownFire$Ic_ratio: Crown initiation ratio.

• CrownFire$`I_b [kW/m]`: Fireline intensity of the crown fire.

• CrownFire$`FL [m]`: Flame length of the crown fire.

• FirePotentials$RP: Surface fire reaction potential ([0-9]).

• FirePotentials$SP: Surface fire spread rate potential ([0-9]).

• FirePotentials$FP: Surface fire flame length potential ([0-9]).

• FirePotentials$SFP: Surface fire potential ([0-9]).

• FirePotentials$IC: Crown initiation potential ([0-9]).

• FirePotentials$TC: Crown-to-crown transmission potential ([0-9]).

• FirePotentials$RC: Crown fire spread rate potential ([0-9]).

• FirePotentials$CFC: Crown fire potential ([0-9]).

Note

Default moisture, slope and windspeed values are benchmark conditions used to calculate fire potentials (Sandberg et al. 2007) and map vulnerability to fire.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Albini, F. A. (1976). Computer-based models of wildland fire behavior: A users' manual. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

Rothermel, R. C. 1972. A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service Research Paper INT USA.

Prichard, S. J., D. V Sandberg, R. D. Ottmar, E. Eberhardt, A. Andreu, P. Eagle, and K. Swedin. 2013. Classification System Version 3.0: Technical Documentation.

See Also

fuel_FCCS

Examples

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

#Calculate fuel properties according to FCCS
fccs <- fuel_FCCS(exampleforest, SpParamsMED)
fccs
  
#Calculate fire behavior according to FCCS
fire_FCCS(fccs)
  
 

Fire severity functions

Description

Functions to estimate fire effects on foliage, buds and cambium, based on the model by Michaletz & Johnson (2008)

Usage

fire_plumeTemperature(Ib_surf, z, T_air = 25, rho_air = 1.169)

fire_barkThermalDiffusivity(fmc_bark, rho_bark = 500, T_air = 25)

fire_radialBoleNecrosis(
  Ib_surf,
  t_res,
  bark_diffusivity,
  T_air = 25,
  rho_air = 1.169,
  T_necrosis = 60
)

fire_leafThermalFactor(SLA, h = 130, c = 2500)

fire_necrosisCriticalTemperature(
  t_res,
  thermal_factor,
  T_air = 25,
  T_necrosis = 60
)

fire_necrosisHeight(
  Ib_surf,
  t_res,
  thermal_factor,
  T_air = 25,
  rho_air = 1.169,
  T_necrosis = 60
)

Arguments

Value

• Function fire_plumeTemperature returns the plume temperature at a given height.

• Function fire_barkThermalDiffusivity returns the bark thermal diffusivity given a bark moisture value.

• Function fire_radialBoleNecrosis returns the depth of radial bole necrosis in cm.

• Function fire_leafThermalFactor returns the thermal factor of leaves as a function of specific leaf area.

• Function fire_necrosisCriticalTemperature returns the (plume) temperature yielding necrosis for a given residence time and tissue thermal factor.

• Function fire_necrosisHeight returns the height (in m) of necrosis for tissues with given thermal factor.

References

Michaletz, S.T., and Johnson, E.A. 2006. A heat transfer model of crown scorch in forest fires. Can. J. For. Res. 36: 2839–2851. doi:10.1139/X06-158.

Michaletz ST, Johnson EA. 2008. A biophysical process model of tree mortality in surface fires. Canadian Journal of Forest Research 38: 2013–2029.

Forest dynamics

Description

Function fordyn implements a forest dynamics model that simulates growth, mortality, recruitment and (optionally) management actions in a given forest stand during a period specified in the input climatic data.

Usage

fordyn(
  forest,
  soil,
  SpParams,
  meteo,
  control,
  latitude,
  elevation = NA,
  slope = NA,
  aspect = NA,
  CO2ByYear = numeric(0),
  management_function = NULL,
  management_args = NULL
)

Arguments

Details

Function fordyn simulates forest dynamics for annual time steps, building on other simulation functions. For each simulated year, the function performs the following steps:

1. Calls function growth to simulate daily water/carbon balance, growth and mortality processes and update the forest object.

2. If required, calls function management_function, using as parameters the forest object and management_args, which may result in a density reduction for existing plant cohorts and/or a set of new planted cohorts.

3. Simulate natural recruitment (for species present in the stand or given in a seed rain input).

4. Prepares the input of function growth for the next annual time step.

5. Store forest status, management arguments, and summaries.

To enable forest management, the user needs to provide a function that implements it, which is passed to fordyn via its argument management_function. Such function should have the following arguments:

• "x": the forest object representing the stand to be managed.

• "args": a list of parameters regulating the behavior of the management function.

• "verbose": a logical flag to enable console output during the execution of the management function.

and return a list with the following elements:

• "action": A string identifying the action performed (e.g. "thinning").

• "N_tree_cut": A vector with the density of trees removed.

• "Cover_shrub_cut": A vector with the cover of shrubs removed.

• "planted_forest": An object of class forest with the new plant cohorts resulting from tree/shrub planting.

• "management_args": A list of management arguments to be used in the next call to the management function.

An example of management function is provided in defaultManagementFunction.

Value

A list of class 'fordyn' with the following elements:

• "StandSummary": A data frame with stand-level summaries (tree basal area, tree density, shrub cover, etc.) at the beginning of the simulation and after each simulated year.

• "SpeciesSummary": A data frame with species-level summaries (tree basal area, tree density, shrub cover, etc.) at the beginning of the simulation and after each simulated year.

• "CohortSummary": A data frame with cohort-level summaries (tree basal area, tree density, shrub cover, etc.) at the beginning of the simulation and after each simulated year.

• "TreeTable": A data frame with tree-cohort data (species, density, diameter, height, etc.) at the beginning of the simulation (if any) and after each simulated year.

• "DeadTreeTable": A data frame with dead tree-cohort data (species, density, diameter, height, etc.) at the beginning of the simulation and after each simulated year.

• "CutTreeTable": A data frame with cut tree data (species, density, diameter, height, etc.) after each simulated year.

• "ShrubTable": A data frame with shrub-cohort data (species, density, cover, height, etc.) at the beginning of the simulation and after each simulated year.

• "DeadShrubTable": A data frame with dead shrub-cohort data (species, density, cover, height, etc.) at the beginning of the simulation (if any) and after each simulated year.

• "CutShrubTable": A data frame with cut shrub data (species, density, cover, height, etc.) after each simulated year.

• "ForestStructures": A list with the forest object of the stand at the beginning of the simulation and after each simulated year.

• "GrowthResults": A list with the results of calling function growth for each simulated year.

• "ManagementArgs": A list of management arguments to be used in another call to fordyn.

• "NextInputObject": An object of class growthInput to be used in a subsequent simulation.

• "NextForestObject": An object of class forest to be used in a subsequent simulation.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

De Cáceres M, Molowny-Horas R, Cabon A, Martínez-Vilalta J, Mencuccini M, García-Valdés R, Nadal-Sala D, Sabaté S, Martin-StPaul N, Morin X, D'Adamo F, Batllori E, Améztegui A (2023) MEDFATE 2.9.3: A trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales. Geoscientific Model Development 16: 3165-3201 (https://doi.org/10.5194/gmd-16-3165-2023).

See Also

growth, regeneration, plot.growth, defaultManagementFunction

Examples

#Load example daily meteorological data
data(examplemeteo)
#Prepare a two-year meteorological data with half precipitation during 
#the second year
meteo2001 <- examplemeteo
meteo2002 <- examplemeteo
meteo2002$Precipitation <- meteo2002$Precipitation/2
meteo2002$dates <- seq(as.Date("2002-01-01"), 
                           as.Date("2002-12-31"), by="day")
meteo_01_02 <- rbind(meteo2001, meteo2002)

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

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

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

#Call simulation function
fd<-fordyn(exampleforest, examplesoil, 
           SpParamsMED, meteo_01_02, control,
           latitude = 41.82592, elevation = 100)

#Stand-level summaries
fd$StandSummary

#Tree table by annual steps
fd$TreeTable

#Dead tree table by annual steps
fd$DeadTreeTable

Description of a forest stand.

Description

exampleforest is an example of forest stand description, whereas exampleforest2 is an alternative forest description where leaf area index and crown ratio are supplied instead of structural (density, DBH and cover) parameters.

Format

An object of class forest contains the description of the woody (tree or shrub) cohorts and herb layer of a forest patch. It has the following structure (see details):

• treeData: A data frame of tree cohorts (in rows) and the following columns:

  • Species: String with species (taxon) name or a non-negative integer for tree species identity (i.e., 0,1,2,...) matching SpParams.

  • Height: Total tree height (in cm).

  • DBH: Tree diameter at breast height (in cm).

  • N: Density (number of individuals/hectare) that the measured tree represents.

  • Z50: Depth (in mm) corresponding to 50% of fine roots.

  • Z95: Depth (in mm) corresponding to 95% of fine roots.

• shrubData: A data frame of shrub cohorts (in rows) and the following columns:

  • Species: String with species (taxon) name or a non-negative integer for shrub species identity (i.e., 0,1,2,...) matching SpParams.

  • Height: Average total height of plants (in cm).

  • Cover: Percent cover.

  • Z50: Depth (in mm) corresponding to 50% of fine roots.

  • Z95: Depth (in mm) corresponding to 95% of fine roots.

• herbCover: Percent cover of the herb layer (optional).

• herbHeight: Mean height (in cm) of the herb layer (optional).

• seedBank: A data frame containing seed bank information with the following columns:

  • Species: String with species (taxon) name or a non-negative integer for tree species identity (i.e., 0,1,2,...) matching SpParams.

  • Percent: Amount of seeds in relation to full seed bank (in %).

Details

The structure presented above for forest objects corresponds to the required data elements. A forest object can contain additional information when this is available. Data frames treeData and shrubData can contain additional columns:

• LAI: Leaf area index (m2/m2).

• FoliarBiomass: Standing dry biomass of leaves (kg/m2).

• FuelLoading: Fine fuel loading (kg/m2).

• CrownRatio: The ratio between crown length and total height (between 0 and 1)

• Z100: Depth (in mm) corresponding to 100% of fine roots (to specify a truncated root distribution).

• ObsID: A string identifying plant cohorts at the stage of forest sampling. Used to track the fate of particular plant cohorts through simulations. In fordyn, the use of these identifiers can be combined with the control option keepCohortsWithObsID so that these cohorts are not merged or removed during simulations.

Similarly, one can define forest list elements herbLAI, herbFoliarBiomass or herbFuelLoading. All these values are used to override allometry-based estimates of those variables when initializing inputs for functions spwb or spwb_day. Note that leaf area index, foliar biomass and fuel loading are related entities, and they are treated as such in medfate. Therefore, users are expected to supply one or the other, and not all of them at the same time.

Source

DGCN (2005). Tercer Inventario Forestal Nacional (1997-2007): Catalunya. Dirección General de Conservación de la Naturaleza, Ministerio de Medio Ambiente, Madrid.

See Also

forest, spwb, spwbInput

summary.forest, emptyforest, plot.forest

Examples

data(exampleforest)
data(exampleforest2)

Input for simulation models (deprecated)

Description

Functions forest2spwbInput() and forest2growthInput() take an object of class forest and a soil data input to create input objects for simulation functions spwb (or pwb) and growth, respectively. Function forest2aboveground() calculates aboveground variables such as leaf area index. Function forest2belowground() calculates belowground variables such as fine root distribution.

Usage

forest2aboveground(x, SpParams, gdd = NA_real_, loading = FALSE)

forest2belowground(x, soil, SpParams)

forest2spwbInput(x, soil, SpParams, control)

forest2growthInput(x, soil, SpParams, control)

Arguments

Details

Function forest2aboveground() extracts height and species identity from plant cohorts of x, and calculate leaf area index and crown ratio.

IMPORTANT NOTE: Function names forest2spwbInput() and forest2growthInput() are now internal and deprecated, but they can still be used for back-compatibility. They correspond to functions spwbInput and growthInput

Value

Function forest2aboveground() returns a data frame with the following columns (rows are identified as specified by function plant_ID):

• SP: Species identity (an integer) (first species is 0).

• N: Cohort density (ind/ha) (see function plant_density).

• DBH: Tree diameter at breast height (cm).

• H: Plant total height (cm).

• CR: Crown ratio (crown length to total height) (between 0 and 1).

• LAI_live: Live leaf area index (m2/m2) (one-side leaf area relative to plot area), includes leaves in winter dormant buds.

• LAI_expanded: Leaf area index of expanded leaves (m2/m2) (one-side leaf area relative to plot area).

• LAI_dead: Dead leaf area index (m2/m2) (one-side leaf area relative to plot area).

• Loading: Fine fuel loading (kg/m2), only if loading = TRUE.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwbInput, soil, forest, SpParamsMED, defaultSoilParams, plant_ID

Examples

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

# Aboveground parameters
forest2aboveground(exampleforest, SpParamsMED)

# Example of aboveground parameters taken from a forest
# described using LAI and crown ratio
data(exampleforest2)
forest2aboveground(exampleforest2, SpParamsMED)

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

# Bewowground parameters (distribution of fine roots)
forest2belowground(exampleforest, examplesoil, SpParamsMED)

Map forest plot data

Description

Mapping functions to facilitate building forest objects from forest plot data

Usage

forest_mapTreeTable(x, mapping_x, SpParams, plot_size_x = NULL)

forest_mapShrubTable(y, mapping_y, SpParams, plot_size_y = NULL)

forest_mapWoodyTables(
  x = NULL,
  y = NULL,
  mapping_x = NULL,
  mapping_y = NULL,
  SpParams,
  plot_size_x = NULL,
  plot_size_y = NULL
)

Arguments

Value

Functions forest_mapTreeTable and forest_mapShrubTable return a data frame with the structure of treeData and shrubData from forest objects. Function forest_mapWoodyTable returns directly a forest object.

Author(s)

Miquel De Cáceres Ainsa, EMF-CREAF

See Also

forest, poblet_trees, forest_mergeTrees, tree2forest

Examples

# Load species parameters
data(SpParamsMED)

# Create an empty forest object
f <- emptyforest()

# (1) Mapping tree data
# Load Poblet tree data
data(poblet_trees)

# Subset control plot
x <- subset(poblet_trees, Plot.Code=="POBL_CTL")

# Estimate sampled area (15-m radius plot)
sampled_area <- pi*15^2

# Define mapping
mapping_x <- c("Species.name" = "Species", "DBH" = "Diameter.cm")

# Map tree data for plot 'POBL_CTL'
f$treeData <- forest_mapTreeTable(x,
                    mapping_x = mapping_x, SpParams = SpParamsMED,
                    plot_size_x = sampled_area)

# (2) Mapping shrub individual data
#
# Create the individual shrub data frame
species <- c("Erica arborea","Cistus albidus", "Erica arborea", "Cistus albidus", "Cistus albidus")
H <- c(200,50,100,40,30)
D1 <- c(140,40,100, 35,30)
D2 <- D1
y <- data.frame(species, H, D1, D2)

# Define mapping (D1 and D2 map to variables with the same name)
mapping_y <- c("Species.name"= "species", "Height" ="H", "D1", "D2")

# Map individual shrub data to cover data (here each individual becomes a cohort)
# assuming that the sampled area was 4 m2
f$shrubData <- forest_mapShrubTable(y,
                     mapping_y = mapping_y, SpParams = SpParamsMED,
                     plot_size_y = 4)

# (3) Print forest attributes
summary(f, SpParamsMED)

# (4) Forest initialization in a single step
f <- forest_mapWoodyTables(x, y,
                           mapping_x = mapping_x, mapping_y = mapping_y,
                           SpParams = SpParamsMED,
                           plot_size_x = sampled_area, plot_size_y = 4)
summary(f, SpParamsMED)

Forest complexity reduction

Description

Functions forest_mergeTrees and forest_mergeShrubs merge cohorts of a forest object. Function forest_reduceToDominant performs a strongest simplification of plant cohorts (see details).

Usage

forest_mergeTrees(x, byDBHclass = TRUE, keepCohortsWithObsID = FALSE)

forest_mergeShrubs(x, byHeightclass = TRUE, keepCohortsWithObsID = FALSE)

forest_reduceToDominant(x, SpParams)

Arguments

Details

Tree DBH classes are defined in 5-cm intervals, whereas shrub height classes are defined in 10-cm intervals. Tree DBH and shrub height classes are defined up to a specific size (i.e. larger plants are not merged) corresponding to 52.5 cm and 90 cm, respectively.

Function forest_reduceToDominant simplifies the input forest to the tree cohort of highest LAI, among those of the tree species with highest LAI. The leaf area index of the whole tree layer will be attributed to the chosen cohort. The same is performed for the shrub layer.

Value

Another forest object with simplified structure/composition, depending on the function.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwb, forest, forest_mapWoodyTables, fordyn, summary.forest

Examples

# Example forest data
data("exampleforest")

# Reduce to dominant tree and dominant shrub
reduced <- forest_reduceToDominant(exampleforest, SpParamsMED)

# Check that overall LAI does not change
stand_LAI(exampleforest, SpParamsMED)
stand_LAI(reduced, SpParamsMED)

Fuel stratification and fuel characteristics

Description

Function fuel_stratification provides a stratification of the stand into understory and canopy strata. Function fuel_FCCS calculates fuel characteristics from a forest object following an adaptation of the protocols described for the Fuel Characteristics Classification System (Prichard et al. 2013).

Usage

fuel_stratification(
  object,
  SpParams,
  gdd = NA_real_,
  heightProfileStep = 10,
  maxHeightProfile = 5000,
  bulkDensityThreshold = 0.05
)

fuel_FCCS(
  object,
  SpParams,
  cohortFMC = as.numeric(c()),
  loadingOffset = as.numeric(c(0, 0, 0, 0, 0)),
  gdd = NA_real_,
  heightProfileStep = 10,
  maxHeightProfile = 5000,
  bulkDensityThreshold = 0.05,
  depthMode = "crownaverage"
)

Arguments

Value

Function fuel_FCCS returns a data frame with five rows corresponding to fuel layers: canopy, shrub, herb, woody and litter. Columns correspond fuel properties:

• w: Fine fuel loading (in kg/m2).

• cover: Percent cover.

• hbc: Height to base of crowns (in m).

• htc: Height to top of crowns (in m).

• delta: Fuel depth (in m).

• rhob: Fuel bulk density (in kg/m3).

• rhop: Fuel particle density (in kg/m3).

• PV: Particle volume (in m3/m2).

• beta: Packing ratio (unitless).

• betarel: Relative packing ratio (unitless).

• etabetarel: Reaction efficiency (unitless).

• sigma: Surface area-to-volume ratio (m2/m3).

• pDead: Proportion of dead fuels.

• FAI: Fuel area index (unitless).

• h: High heat content (in kJ/kg).

• RV: Reactive volume (in m3/m2).

• MinFMC: Minimum fuel moisture content (as percent over dry weight).

• MaxFMC: Maximum fuel moisture content (as percent over dry weight).

• ActFMC: Actual fuel moisture content (as percent over dry weight). These are set to NA if parameter cohortFMC is empty.

Function fuel_stratification returns a list with the following items:

• surfaceLayerBaseHeight: Base height of crowns of shrubs in the surface layer (in cm).

• surfaceLayerTopHeight: Top height of crowns of shrubs in the surface layer (in cm).

• understoryLAI: Cumulated LAI of the understory layer (i.e. leaf area comprised between surface layer base and top heights).

• canopyBaseHeight: Base height of tree crowns in the canopy (in cm).

• canopyTopHeight: Top height of tree crowns in the canopy (in cm).

• canopyLAI: Cumulated LAI of the canopy (i.e. leaf area comprised between canopy base and top heights).

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Prichard, S. J., D. V Sandberg, R. D. Ottmar, E. Eberhardt, A. Andreu, P. Eagle, and K. Swedin. 2013. Classification System Version 3.0: Technical Documentation.

Reinhardt, E., D. Lutes, and J. Scott. 2006. FuelCalc: A method for estimating fuel characteristics. Pages 273–282.

See Also

fire_FCCS, spwb

Examples

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

#Show stratification of fuels
fuel_stratification(exampleforest, SpParamsMED)
  
#Calculate fuel properties according to FCCS
fccs <- fuel_FCCS(exampleforest, SpParamsMED)
fccs

Wind adjustment factor for Rothermel's model

Description

Function fuel_windAdjustmentFactor determines the adjustment factor of wind for surface fires, according to Andrews (2012).

Usage

fuel_windAdjustmentFactor(
  topShrubHeight,
  bottomCanopyHeight,
  topCanopyHeight,
  canopyCover
)

Arguments

Value

A scalar value between 0 and 1

References

Andrews, P. L. 2012. Modeling wind adjustment factor and midflame wind speed for Rothermel’s surface fire spread model. USDA Forest Service - General Technical Report RMRS-GTR:1–39.

Examples

#Load example plot plant data
 data(exampleforest)
  
#Default species parameterization
data(SpParamsMED)

#Calculate fuel properties according to FCCS
fccs <- fuel_FCCS(exampleforest, SpParamsMED)

# Estimate wind adjustment factor
fuel_windAdjustmentFactor(fccs$htc[2], fccs$hbc[1], fccs$htc[1], fccs$cover[1])

Forest growth

Description

Function growth is a process-based model that performs energy, water and carbon balances; and determines changes in water/carbon pools, functional variables (leaf area, sapwood area, root area) and structural ones (tree diameter, tree height, shrub cover) for woody plant cohorts in a given forest stand during a period specified in the input climatic data.

Usage

growth(
  x,
  meteo,
  latitude,
  elevation,
  slope = NA_real_,
  aspect = NA_real_,
  CO2ByYear = numeric(0),
  waterTableDepth = NA_real_
)

Arguments

Details

Detailed model description is available in the medfate book. Simulations using the 'Sperry' or 'Sureau' transpiration modes are computationally much more expensive than those using the 'Granier' transpiration mode.

Value

A list of class 'growth' with the following elements:

• "latitude": Latitude (in degrees) given as input.

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

• "weather": A copy of the input weather data frame.

• "growthInput": A copy of the object x of class growthInput given as input.

• "growthOutput": An copy of the final state of the object x of class growthInput.

• "WaterBalance": A data frame where different water balance variables (see spwb).

• "EnergyBalance": A data frame with the daily values of energy balance components for the soil and the canopy (only for transpirationMode = "Sperry" or transpirationMode = "Sureau"; see spwb).

• "CarbonBalance": A data frame where different stand-level carbon balance components (gross primary production, maintenance respiration, synthesis respiration and net primary production), all in g C · m-2.

• "BiomassBalance": A data frame with the daily values of stand biomass balance components (in g dry · m-2.

• "Temperature": A data frame with the daily values of minimum/mean/maximum temperatures for the atmosphere (input), canopy and soil (only for transpirationMode = "Sperry" or transpirationMode = "Sureau"; see spwb).

• "Soil": A data frame where different soil variables (see spwb).

• "Stand": A data frame where different stand-level variables (see spwb).

• "Plants": A list of daily results for plant cohorts (see spwb).

• "SunlitLeaves" and "ShadeLeaves": A list with daily results for sunlit and shade leaves (only for transpirationMode = "Sperry" or transpirationMode = "Sureau"; see spwb).

• "LabileCarbonBalance": A list of daily labile carbon balance results for plant cohorts, with elements:

  • "GrossPhotosynthesis": Daily gross photosynthesis per dry weight of living biomass (g gluc · g dry-1).

  • "MaintentanceRespiration": Daily maintenance respiration per dry weight of living biomass (g gluc · g dry-1).

  • "GrowthCosts": Daily growth costs per dry weight of living biomass (g gluc · g dry-1).

  • "RootExudation": Root exudation per dry weight of living biomass (g gluc · g dry-1).

  • "LabileCarbonBalance": Daily labile carbon balance (photosynthesis - maintenance respiration - growth costs - root exudation) per dry weight of living biomass (g gluc · g dry-1).

  • "SugarLeaf": Sugar concentration (mol·l-1) in leaves.

  • "StarchLeaf": Starch concentration (mol·l-1) in leaves.

  • "SugarSapwood": Sugar concentration (mol·l-1) in sapwood.

  • "StarchSapwood": Starch concentration (mol·l-1) in sapwood.

  • "SugarTransport": Average instantaneous rate of carbon transferred between leaves and stem compartments via floem (mol gluc·s-1).

• "PlantBiomassBalance": A list of daily plant biomass balance results for plant cohorts, with elements:

  • "StructuralBiomassBalance": Daily structural biomass balance (g dry · m-2).

  • "LabileBiomassBalance": Daily labile biomass balance (g dry · m-2).

  • "PlantBiomassBalance": Daily plant biomass balance, i.e. labile change + structural change (g dry · m-2).

  • "MortalityBiomassLoss": Biomass loss due to mortality (g dry · m-2).

  • "CohortBiomassBalance": Daily cohort biomass balance (including mortality) (g dry · m-2).

• "PlantStructure": A list of daily area and biomass values for compartments of plant cohorts, with elements:

  • "LeafBiomass": Daily amount of leaf structural biomass (in g dry) for an average individual of each plant cohort.

  • "SapwoodBiomass": Daily amount of sapwood structural biomass (in g dry) for an average individual of each plant cohort.

  • "FineRootBiomass": Daily amount of fine root biomass (in g dry) for an average individual of each plant cohort.

  • "LeafArea": Daily amount of leaf area (in m2) for an average individual of each plant cohort.

  • "SapwoodArea": Daily amount of sapwood area (in cm2) for an average individual of each plant cohort.

  • "FineRootArea": Daily amount of fine root area (in m2) for an average individual of each plant cohort.

  • "HuberValue": The ratio of sapwood area to (target) leaf area (in cm2/m2).

  • "RootAreaLeafArea": The ratio of fine root area to (target) leaf area (in m2/m2).

  • "DBH": Diameter at breast height (in cm) for an average individual of each plant cohort.

  • "Height": Height (in cm) for an average individual of each plant cohort.

• "GrowthMortality": A list of daily growth and mortality rates for plant cohorts, with elements:

  • "LAgrowth": Leaf area growth (in m2·day-1) for an average individual of each plant cohort.

  • "SAgrowth": Sapwood area growth rate (in cm2·day-1) for an average individual of each plant cohort.

  • "FRAgrowth": Fine root area growth (in m2·day-1) for an average individual of each plant cohort.

  • "StarvationRate": Daily mortality rate from starvation (ind/d-1).

  • "DessicationRate": Daily mortality rate from dessication (ind/d-1).

  • "MortalityRate": Daily mortality rate (any cause) (ind/d-1).

• "subdaily": A list of objects of class growth_day, one per day simulated (only if required in control parameters, see defaultControl).

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

De Cáceres M, Molowny-Horas R, Cabon A, Martínez-Vilalta J, Mencuccini M, García-Valdés R, Nadal-Sala D, Sabaté S, Martin-StPaul N, Morin X, D'Adamo F, Batllori E, Améztegui A (2023) MEDFATE 2.9.3: A trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales. Geoscientific Model Development 16: 3165-3201 (https://doi.org/10.5194/gmd-16-3165-2023).

See Also

growthInput, growth_day, plot.growth

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

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

#Call simulation function
G1 <- growth(x1, examplemeteo, latitude = 41.82592, elevation = 100)
 
#Switch to 'Sperry' transpiration mode
control <- defaultControl("Sperry")

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

#Call simulation function
G2 <-growth(x2, examplemeteo, latitude = 41.82592, elevation = 100)

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

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

#Call simulation function
G3 <-growth(x3, examplemeteo, latitude = 41.82592, elevation = 100)

      

Single-day simulation

Description

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

Usage

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

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

Arguments

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 'Sureau' transpiration mode is illustrated by function transp_transpirationSureau and was described for model SurEau-Ecos v2.0 in Ruffault et al. (2022).

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

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:

  • "Psi": Soil water potential (in MPa) at the end of the day.

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

  • "HydraulicInput": Water entering each soil layer from other layers, transported via plant roots (in mm).

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

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

• "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 = "Sureau":

• "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).

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

spwbInput, spwb, plot.spwb_day, growthInput, growth, plot.growth_day

Examples

#Load example daily meteorological data
data(examplemeteo)

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

#Define soil parameters
examplesoil <- defaultSoilParams(4)

# 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)
control <- defaultControl("Granier")
x1 <- spwbInput(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 <- spwbInput(exampleforest, examplesoil, SpParamsMED, control)
sd2 <-spwb_day(x2, date, meteovec,
              latitude = 41.82592, elevation = 100, slope=0, aspect=0)

#Simulate water balance for one day only (Sureau mode)
control <- defaultControl("Sureau")
x3 <- spwbInput(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  <- growthInput(exampleforest,examplesoil, SpParamsMED, control)
sd4 <- growth_day(x4, date, meteovec,
                latitude = 41.82592, elevation = 100, slope=0, aspect=0)

#Simulate water and carbon balance for one day only (Sperry mode)
control <- defaultControl("Sperry")
x5  <- growthInput(exampleforest,examplesoil, SpParamsMED, control)
sd5 <- growth_day(x5, date, meteovec,
                latitude = 41.82592, elevation = 100, slope=0, aspect=0)

#Simulate water and carbon balance for one day only (Sureau mode)
control <- defaultControl("Sureau")
x6  <- growthInput(exampleforest,examplesoil, SpParamsMED, control)
sd6 <- growth_day(x6, date, meteovec,
                latitude = 41.82592, elevation = 100, slope=0, aspect=0)

Herbaceous description functions

Description

Functions to calculate attributes of the herbaceous component of a forest object

Usage

herb_foliarBiomass(x, SpParams)

herb_fuelLoading(x, SpParams)

herb_LAI(x, SpParams)

Arguments

Value

A single scalar:

• herb_foliarBiomass: Herbaceous biomass of leaves (in kg/m2).

• herb_fuelLoading: Herbaceous fine fuel loading (in kg/m2).

• herb_LAI: Herbaceous leaf area index (m2/m2).

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwb, forest, plant_basalArea, summary.forest

Hydraulic confuctance functions

Description

Set of functions used in the calculation of soil and plant hydraulic conductance.

Usage

hydraulics_psi2K(psi, psi_extract, exp_extract = 3)

hydraulics_K2Psi(K, psi_extract, exp_extract = 3)

hydraulics_averagePsi(psi, v, exp_extract, psi_extract)

hydraulics_xylemConductance(psi, kxylemmax, c, d)

hydraulics_xylemConductanceSigmoid(psi, kxylemmax, P50, slope)

hydraulics_xylemPsi(kxylem, kxylemmax, c, d)

hydraulics_psiCrit(c, d, pCrit = 0.001)

hydraulics_vanGenuchtenConductance(psi, krhizomax, n, alpha)

hydraulics_correctConductanceForViscosity(kxylem, temp)

hydraulics_psi2Weibull(psi50, psi88 = NA_real_, psi12 = NA_real_)

hydraulics_vulnerabilityCurvePlot(
  x,
  soil = NULL,
  type = "leaf",
  vulnerabilityFunction = "Weibull",
  psiVec = seq(-0.1, -8, by = -0.01),
  relative = FALSE,
  speciesNames = FALSE,
  draw = TRUE,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL
)

Arguments

Details

Details of plant hydraulic models are given the medfate book. Function hydraulics_vulnerabilityCurvePlot draws a plot of the vulnerability curves for the given soil object and network properties of each plant cohort in x.

Value

Values returned for each function are:

• hydraulics_psi2K: Whole-plant relative conductance (0-1).

• hydraulics_K2Psi: Soil water potential (in MPa) corresponding to the given whole-plant relative conductance value (inverse of hydraulics_psi2K()).

• hydraulics_averagePsi: The average water potential (in MPa) across soil layers.

• hydraulics_vanGenuchtenConductance: Rhizosphere conductance corresponding to an input water potential (soil vulnerability curve).

• hydraulics_xylemConductance: Xylem conductance (flow rate per pressure drop) corresponding to an input water potential (plant vulnerability curve).

• hydraulics_xylemPsi: Xylem water potential (in MPa) corresponding to an input xylem conductance (flow rate per pressure drop).

• hydraulics_psi2Weibull: Parameters of the Weibull vulnerability curve that goes through the supplied psi50 and psi88 values.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Sperry, J. S., F. R. Adler, G. S. Campbell, and J. P. Comstock. 1998. Limitation of plant water use by rhizosphere and xylem conductance: results from a model. Plant, Cell and Environment 21:347–359.

Sperry, J. S., and D. M. Love. 2015. What plant hydraulics can tell us about responses to climate-change droughts. New Phytologist 207:14–27.

See Also

hydraulics_supplyFunctionPlot, hydraulics_maximumStemHydraulicConductance, spwb, soil

Examples

#Manual display of vulnerability curve
kstemmax = 4 # in mmol·m-2·s-1·MPa-1
stemc = 3 
stemd = -4 # in MPa
psiVec = seq(-0.1, -7.0, by =-0.01)
kstem = unlist(lapply(psiVec, hydraulics_xylemConductance, kstemmax, stemc, stemd))
plot(-psiVec, kstem, type="l",ylab="Xylem conductance (mmol·m-2·s-1·MPa-1)", 
     xlab="Canopy pressure (-MPa)", lwd=1.5,ylim=c(0,kstemmax))

#Load example dataset
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

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

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

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

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

#Leaf vulnerability curves
hydraulics_vulnerabilityCurvePlot(x, type="leaf")

#Stem vulnerability curves
hydraulics_vulnerabilityCurvePlot(x, type="stem")
             

Hydraulic-related defoliation

Description

Functions to calculate the proportion of crown defoliation due to hydraulic disconnection.

Usage

hydraulics_proportionDefoliationSigmoid(
  psiLeaf,
  P50,
  slope,
  PLC_crit = 0.88,
  P50_cv = 10
)

hydraulics_proportionDefoliationWeibull(
  psiLeaf,
  c,
  d,
  PLC_crit = 0.88,
  P50_cv = 10
)

Arguments

Details

The functions assume that crowns are made of a population of leaves whose hydraulic vulnerability (i.e. the water potential corresponding to 50% loss of conductance) follows a Gaussian distribution centered on the input P50 and with a known coefficient of variation (P50_cv). The slope parameter (or the c exponent in the case of a Weibull function) is considered constant. Leaves are hydraulically disconnected, and shedded, when their embolism rate exceeds a critical value (PLC_crit).

Value

The proportion of crown defoliation.

Author(s)

Hervé Cochard, INRAE

Miquel De Cáceres Ainsa, CREAF

See Also

hydraulics_conductancefunctions

Scaling from conductivity to conductance

Description

Functions used to scale from tissue conductivity to conductance of different elements of the continuum.

Usage

hydraulics_maximumSoilPlantConductance(krhizomax, krootmax, kstemmax, kleafmax)

hydraulics_soilPlantResistancesSigmoid(
  psiSoil,
  psiRhizo,
  psiStem,
  PLCstem,
  psiLeaf,
  PLCleaf,
  krhizomax,
  n,
  alpha,
  krootmax,
  root_P50,
  root_slope,
  kstemmax,
  stem_P50,
  stem_slope,
  kleafmax,
  leaf_P50,
  leaf_slope
)

hydraulics_soilPlantResistancesWeibull(
  psiSoil,
  psiRhizo,
  psiStem,
  PLCstem,
  psiLeaf,
  PLCleaf,
  krhizomax,
  n,
  alpha,
  krootmax,
  rootc,
  rootd,
  kstemmax,
  stemc,
  stemd,
  kleafmax,
  leafc,
  leafd
)

hydraulics_averageRhizosphereResistancePercent(
  krhizomax,
  n,
  alpha,
  krootmax,
  rootc,
  rootd,
  kstemmax,
  stemc,
  stemd,
  kleafmax,
  leafc,
  leafd,
  psiStep = -0.01
)

hydraulics_findRhizosphereMaximumConductance(
  averageResistancePercent,
  n,
  alpha,
  krootmax,
  rootc,
  rootd,
  kstemmax,
  stemc,
  stemd,
  kleafmax,
  leafc,
  leafd,
  initialValue = 0
)

hydraulics_taperFactorSavage(height)

hydraulics_terminalConduitRadius(height)

hydraulics_referenceConductivityHeightFactor(refheight, height)

hydraulics_maximumStemHydraulicConductance(
  xylemConductivity,
  refheight,
  Al2As,
  height,
  taper = FALSE
)

hydraulics_rootxylemConductanceProportions(L, V)

Arguments

Details

Details of the hydraulic model are given in the medfate book

Value

Values returned for each function are:

• hydraulics_maximumSoilPlantConductance: The maximum soil-plant conductance, in the same units as the input segment conductances.

• hydraulics_averageRhizosphereResistancePercent: The average percentage of resistance due to the rhizosphere, calculated across water potential values.

• hydraulics_findRhizosphereMaximumConductance: The maximum rhizosphere conductance value given an average rhizosphere resistance and the vulnerability curves of rhizosphere, root and stem elements.

• hydraulics_taperFactorSavage: Taper factor according to Savage et al. (2010).

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Christoffersen, B. O., M. Gloor, S. Fauset, N. M. Fyllas, D. R. Galbraith, T. R. Baker, L. Rowland, R. A. Fisher, O. J. Binks, S. A. Sevanto, C. Xu, S. Jansen, B. Choat, M. Mencuccini, N. G. McDowell, and P. Meir. 2016. Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro). Geoscientific Model Development Discussions 9: 4227–4255.

Savage, V. M., L. P. Bentley, B. J. Enquist, J. S. Sperry, D. D. Smith, P. B. Reich, and E. I. von Allmen. 2010. Hydraulic trade-offs and space filling enable better predictions of vascular structure and function in plants. Proceedings of the National Academy of Sciences of the United States of America 107:22722–7.

Olson, M.E., Anfodillo, T., Rosell, J.A., Petit, G., Crivellaro, A., Isnard, S., León-Gómez, C., Alvarado-Cárdenas, L.O., and Castorena, M. 2014. Universal hydraulics of the flowering plants: Vessel diameter scales with stem length across angiosperm lineages, habits and climates. Ecology Letters 17: 988–997.

See Also

hydraulics_psi2K, hydraulics_supplyFunctionPlot, spwb, soil

Hydraulic supply functions

Description

Set of functions used in the implementation of hydraulic supply functions (Sperry and Love 2015).

Usage

hydraulics_EXylem(
  psiPlant,
  psiUpstream,
  kxylemmax,
  c,
  d,
  allowNegativeFlux = TRUE,
  psiCav = 0
)

hydraulics_E2psiXylem(E, psiUpstream, kxylemmax, c, d, psiCav = 0)

hydraulics_E2psiXylemUp(E, psiDownstream, kxylemmax, c, d, psiCav = 0)

hydraulics_EVanGenuchten(psiRhizo, psiSoil, krhizomax, n, alpha, l = 0.5)

hydraulics_ECrit(psiUpstream, kxylemmax, c, d, pCrit = 0.001)

hydraulics_E2psiVanGenuchten(
  E,
  psiSoil,
  krhizomax,
  n,
  alpha,
  psiStep = -1e-04,
  psiMax = -10
)

hydraulics_E2psiTwoElements(
  E,
  psiSoil,
  krhizomax,
  kxylemmax,
  n,
  alpha,
  c,
  d,
  psiCav = 0,
  psiStep = -1e-04,
  psiMax = -10
)

hydraulics_E2psiBelowground(E, hydraulicNetwork, psiIni = as.numeric(c(0)))

hydraulics_E2psiAboveground(E, psiRootCrown, hydraulicNetwork)

hydraulics_E2psiNetwork(E, hydraulicNetwork, psiIni = as.numeric(c(0)))

hydraulics_supplyFunctionOneXylem(
  psiSoil,
  v,
  kstemmax,
  stemc,
  stemd,
  psiCav = 0,
  maxNsteps = 200L,
  dE = 0.01
)

hydraulics_supplyFunctionTwoElements(
  Emax,
  psiSoil,
  krhizomax,
  kxylemmax,
  n,
  alpha,
  c,
  d,
  psiCav = 0,
  dE = 0.1,
  psiMax = -10
)

hydraulics_supplyFunctionThreeElements(
  Emax,
  psiSoil,
  krhizomax,
  kxylemmax,
  kleafmax,
  n,
  alpha,
  stemc,
  stemd,
  leafc,
  leafd,
  psiCav = 0,
  dE = 0.1,
  psiMax = -10
)

hydraulics_supplyFunctionBelowground(
  hydraulicNetwork,
  minFlow = 0,
  pCrit = 0.001
)

hydraulics_supplyFunctionAboveground(
  Erootcrown,
  psiRootCrown,
  hydraulicNetwork
)

hydraulics_supplyFunctionNetwork(hydraulicNetwork, minFlow = 0, pCrit = 0.001)

hydraulics_regulatedPsiXylem(E, psiUpstream, kxylemmax, c, d, psiStep = -0.01)

hydraulics_regulatedPsiTwoElements(
  Emax,
  psiSoil,
  krhizomax,
  kxylemmax,
  n,
  alpha,
  c,
  d,
  dE = 0.1,
  psiMax = -10
)

hydraulics_initSperryNetworks(x)

hydraulics_supplyFunctionPlot(
  x,
  draw = TRUE,
  type = "E",
  speciesNames = FALSE,
  ylim = NULL
)

Arguments

Details

Function hydraulics_supplyFunctionPlot draws a plot of the supply function for the given soil object and network properties of each plant cohort in x. Function hydraulics_vulnerabilityCurvePlot draws a plot of the vulnerability curves for the given soil object and network properties of each plant cohort in x.

Value

Values returned for each function are:

• hydraulics_E2psiXylem: The plant (leaf) water potential (in MPa) corresponding to the input flow, according to the xylem supply function and given an upstream (soil or root) water potential.

• hydraulics_E2psiVanGenuchten: The root water potential (in MPa) corresponding to the input flow, according to the rhizosphere supply function and given a soil water potential.

• hydraulics_E2psiTwoElements: The plant (leaf) water potential (in MPa) corresponding to the input flow, according to the rhizosphere and plant supply functions and given an input soil water potential.

• hydraulics_E2psiNetwork: The rhizosphere, root crown and plant (leaf water potential (in MPa) corresponding to the input flow, according to the vulnerability curves of rhizosphere, root and stem elements in a network.

• hydraulics_Ecrit: The critical flow according to the xylem supply function and given an input soil water potential.

• hydraulics_EVanGenuchten: The flow (integral of the vulnerability curve) according to the rhizosphere supply function and given an input drop in water potential (soil and rhizosphere).

• hydraulics_EXylem: The flow (integral of the vulnerability curve) according to the xylem supply function and given an input drop in water potential (rhizosphere and plant).

• hydraulics_supplyFunctionOneXylem, hydraulics_supplyFunctionTwoElements and hydraulics_supplyFunctionNetwork: A list with different numeric vectors with information of the two-element supply function:

  • E: Flow values (supply values).

  • FittedE: Fitted flow values (for hydraulics_supplyFunctionTwoElements).

  • Elayers: Flow values across the roots of each soil layer (only for hydraulics_supplyFunctionNetwork).

  • PsiRhizo: Water potential values at the root surface (only for hydraulics_supplyFunctionNetwork).

  • PsiRoot: Water potential values inside the root crown (not for hydraulics_supplyFunctionOneXylem).

  • PsiPlant: Water potential values at the canopy (leaf).

  • dEdP: Derivatives of the supply function.

• hydraulics_supplyFunctionPlot: If draw = FALSE a list with the result of calling hydraulics_supplyFunctionNetwork for each cohort.

• hydraulics_regulatedPsiXylem: Plant water potential after regulation (one-element loss function) given an input water potential.

• hydraulics_regulatedPsiTwoElements: Plant water potential after regulation (two-element loss function) given an input soil water potential.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Sperry, J. S., F. R. Adler, G. S. Campbell, and J. P. Comstock. 1998. Limitation of plant water use by rhizosphere and xylem conductance: results from a model. Plant, Cell and Environment 21:347–359.

Sperry, J. S., and D. M. Love. 2015. What plant hydraulics can tell us about responses to climate-change droughts. New Phytologist 207:14–27.

See Also

hydraulics_psi2K, hydraulics_maximumStemHydraulicConductance, spwb, soil

Examples

kstemmax = 4 # in mmol·m-2·s-1·MPa-1
stemc = 3 
stemd = -4 # in MPa
psiVec = seq(-0.1, -7.0, by =-0.01)

#Vulnerability curve
kstem = unlist(lapply(psiVec, hydraulics_xylemConductance, kstemmax, stemc, stemd))
plot(-psiVec, kstem, type="l",ylab="Xylem conductance (mmol·m-2·s-1·MPa-1)", 
     xlab="Canopy pressure (-MPa)", lwd=1.5,ylim=c(0,kstemmax))

Soil infiltration

Description

Soil infiltration functions:

• Function hydrology_infiltrationBoughton calculates the amount of water that infiltrates into the topsoil, according to the USDA SCS curve number method (Boughton 1989).

• Function hydrology_infiltrationGreenAmpt calculates the amount of water that infiltrates into the topsoil, according to the model by Green & Ampt (1911).

• Function hydrology_infiltrationAmount uses either Green & Ampt (1911) or Boughton (1989) to estimate infiltration.

• Function hydrology_infiltrationRepartition distributes infiltration among soil layers depending on macroporosity.

Usage

hydrology_infiltrationBoughton(input, Ssoil)

hydrology_infiltrationGreenAmpt(t, psi_w, Ksat, theta_sat, theta_dry)

hydrology_infiltrationRepartition(I, widths, macro, a = -0.005, b = 3)

hydrology_infiltrationAmount(
  rainfallInput,
  rainfallIntensity,
  soil,
  soilFunctions,
  model = "GreenAmpt1911",
  K_correction = 1
)

Arguments

Details

When using function hydrology_infiltrationGreenAmpt, the units of Ksat, t and psi_wat have to be in the same system (e.g. cm/h, h and cm).

Value

Functions hydrology_infiltrationBoughton, hydrology_infiltrationGreenAmpt and hydrology_infiltrationAmount return the daily amount of water that infiltrates into the soil (in mm of water).

Function hydrology_infiltrationRepartition returns the amount of infiltrated water that reaches each soil layer.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Boughton (1989). A review of the USDA SCS curve number method. - Australian Journal of Soil Research 27: 511-523.

Green, W.H. and Ampt, G.A. (1911) Studies on Soil Physics, 1: The Flow of Air and Water through Soils. The Journal of Agricultural Science, 4, 1-24.

See Also

spwb, hydrology_waterInputs

Rainfall interception

Description

Function hydrology_rainInterception calculates the amount of rainfall intercepted daily by the canopy, given a rainfall and canopy characteristics. Two canopy interception models are currently available: the sparse Gash (1995) model and the Liu (2001) model. In both cases the current implementation assumes no trunk interception.

Usage

hydrology_rainfallIntensity(month, prec, rainfallIntensityPerMonth)

hydrology_rainInterception(Rainfall, Cm, p, ER = 0.05, model = "Gash1995")

hydrology_interceptionPlot(
  x,
  SpParams,
  ER = 0.05,
  gdd = NA,
  throughfall = FALSE,
  model = "Gash1995"
)

Arguments

Details

Function hydrology_rainInterception can accept either vectors or scalars as parameters Cm, p and ER. If they are supplied as vectors they should be of the same length as Rainfall.

Function hydrology_rainfallIntensity estimates the rainfall intensity (mm/h) for input values of rainfall and seasonal variation in rainfall intensity (mm/h).

Value

Function hydrology_rainInterception returns a vector of the same length as Rainfall containing intercepted rain values.

Function hydrology_rainfallIntensity returns a scalar with the rainfall intensity.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Liu (2001). Evaluation of the Liu model for predicting rainfall interception in forests world-wide. - Hydrol. Process. 15: 2341-2360.

Gash (1979). An analytical model of rainfall interception by forests. - Quarterly Journal of the Royal Meteorological Society.

Gash et al. (1995). Estimating sparse forest rainfall interception with an analytical model. - Journal of Hydrology.

See Also

spwb

Examples

#Load example plot plant data
data(exampleforest)

#Default species parameterization
data(SpParamsMED)

#Draw rainfall interception for two values of the E/R ratio
hydrology_interceptionPlot(exampleforest, SpParamsMED, ER = c(0.05, 0.2))

Water vertical inputs

Description

High-level functions to define water inputs into the soil of a stand:

• Function hydrology_waterInputs performs canopy water interception and snow accumulation/melt.

• Function hydrology_snowMelt estimates snow melt using a simple energy balance, according to Kergoat (1998).

Usage

hydrology_snowMelt(tday, rad, LgroundSWR, elevation)

hydrology_waterInputs(
  x,
  prec,
  rainfallIntensity,
  pet,
  tday,
  rad,
  elevation,
  Cm,
  LgroundPAR,
  LgroundSWR,
  modifyInput = TRUE
)

Arguments

Details

The function simulates different vertical hydrological processes, which are described separately in other functions. If modifyInput = TRUE the function will modify the x object (including both soil moisture and the snowpack on its surface) as a result of simulating hydrological processes.

Value

Function hydrology_waterInputs returns a named vector with the following elements, all in mm:

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Kergoat L. (1998). A model for hydrological equilibrium of leaf area index on a global scale. Journal of Hydrology 212–213: 268–286.

See Also

spwb_day, hydrology_rainInterception, hydrology_soilEvaporation

Bare soil evaporation and herbaceous transpiration

Description

Functions:

• Function hydrology_soilEvaporationAmount calculates the amount of evaporation from bare soil, following Ritchie (1972).

• Function hydrology_soilEvaporation calculates the amount of evaporation from bare soil and distributes it among soil layers.

• Function hydrology_herbaceousTranspiration calculates the amount of transpiration due to herbaceous plants.

Usage

hydrology_soilEvaporationAmount(DEF, PETs, Gsoil)

hydrology_soilEvaporation(
  soil,
  snowpack,
  soilFunctions,
  pet,
  LgroundSWR,
  modifySoil = TRUE
)

hydrology_herbaceousTranspiration(
  pet,
  LherbSWR,
  herbLAI,
  soil,
  soilFunctions,
  modifySoil = TRUE
)

Arguments

Value

Function hydrology_soilEvaporationAmount returns the amount of water evaporated from the soil.

Function hydrology_soilEvaporation returns a vector of water evaporated from each soil layer.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Ritchie (1972). Model for predicting evaporation from a row crop with incomplete cover. - Water resources research.

See Also

spwb, hydrology_waterInputs, hydrology_infiltration

Soil water balance

Description

Function hydrology_soilWaterBalance estimates water balance of soil layers given water inputs/outputs, including the simulation of water movement within the soil.

Usage

hydrology_soilWaterBalance(
  soil,
  soilFunctions,
  rainfallInput,
  rainfallIntensity,
  snowmelt,
  sourceSink,
  runon = 0,
  lateralFlows = NULL,
  waterTableDepth = NA_real_,
  infiltrationMode = "GreenAmpt1911",
  infiltrationCorrection = 5,
  soilDomains = "buckets",
  nsteps = 24L,
  max_nsubsteps = 3600L,
  modifySoil = TRUE
)

Arguments

Details

The multi-bucket model adds/substracts water to each layer and if content is above field capacity the excess percolates to the layer below. If there is still an excess for the bottom layer, the model will progressively fill upper layers (generating saturation excess if the first layer becomes saturated). Every day the some layers are over field capacity, the model simulates deep drainage.

The single-domain model simulates water flows by solving Richards's equation using the predictor-corrector method, as described in Bonan et al. (2019).

The dual-permeability model is an implementation of the model MACRO 5.0 (Jarvis et al. 1991; Larsbo et al. 2005).

Both the multi-bucket and the single-domain model can apply a correction to the infiltration rate to account for macroporosity in infiltration. In the dual-permeability model extra infiltration through macropores is simulated explicitly.

Value

Returns a named vector with different elements, depending on soilDomains.

If soilDomains == "buckets":

• Snowmelt: Snowmelt input (mm).

• Source/sinks: Sum of source/sink input across layers (mm).

• Infiltration: Water infiltrated into the soil (mm).

• InfiltrationExcess: Excess infiltration in the topmost layer (mm) leading to an increase in runoff.

• SaturationExcess: Excess saturation in the topmost layer (mm) leading to an increase in runoff.

• Runoff: Surface runoff generated by saturation excess or infiltration excess (mm).

• DeepDrainage: Water draining from the bottom layer (mm). This quantity is corrected to close the water balance.

• CapillarityRise: Water entering the soil via capillarity rise (mm) from the water table, if waterTableDepth is supplied.

If soilDomains == "single" the named vector contains the following additional elements:

• Correction: Amount of water (mm) added to deep drainage to correct the water balance.

• VolumeChange: Change in soil water volume (mm).

• Substep: Time step of the moisture solving (seconds).

If soilDomains == "dual" the named vector contains the following additional elements:

• Lateral flows: Sum of water circulating between micropores and macropores, positive when filling micropores (mm).

• InfiltrationMatrix: Water infiltrated into the soil matrix (mm).

• InfiltrationMacropores: Water infiltrated into the soil macropore domain (mm).

• InfiltrationExcessMatrix/InfiltrationExcessMacropores: Excess infiltration in the topmost layer (mm) leading to an increase in runoff.

• SaturationExcessMatrix/SaturationExcessMacropores: Excess saturation in the topmost layer (mm) leading to an increase in runoff.

• DrainageMatrix: Water draining from the bottom layer of the matrix domain (mm). This quantity is corrected to close water balance in the micropore domain.

• DrainageMacropores: Water draining from the bottom layer of the macropore domain (mm). This quantity is corrected to close the water balance in the macropore domain.

• CorrectionMatrix: Amount of water (mm) added to deep drainage of soil matrix to correct the water balance.

• CorrectionMacropores: Amount of water (mm) added to deep drainage of macropores to correct the water balance.

• MatrixVolumeChange: Change in soil water volume in the soil matrix domain (mm).

• MacroporeVolumeChange: Change in soil water volume in the macropore domain (mm).

Author(s)

Miquel De Cáceres Ainsa, CREAF

María González Sanchís, UPV-CTFC

References

Bonan, G. (2019). Climate change and terrestrial ecosystem modeling. Cambridge University Press, Cambridge, UK.

Jarvis, N.J., Jansson, P‐E., Dik, P.E. & Messing, I. (1991). Modelling water and solute transport in macroporous soil. I. Model description and sensitivity analysis. Journal of Soil Science, 42, 59–70.

Larsbo, M., Roulier, S., Stenemo, F., Kasteel, R. & Jarvis, N. (2005). An Improved Dual‐Permeability Model of Water Flow and Solute Transport in the Vadose Zone. Vadose Zone Journal, 4, 398–406.

See Also

spwb, hydrology_waterInputs, hydrology_infiltration

Examples

# Define soil parameters
spar <- defaultSoilParams(4)

# Initializes soil hydraulic parameters
examplesoil <- soil(spar)

# Water balance in a multi-bucket model
hydrology_soilWaterBalance(examplesoil, "VG", 10, 5, 0, c(-1,-1,-1,-1), 
                           soilDomains = "buckets", modifySoil = FALSE)
                           
# Water balance in a single-domain model (Richards equation)
hydrology_soilWaterBalance(examplesoil, "VG", 10, 5, 0, c(-1,-1,-1,-1), 
                           soilDomains = "single", modifySoil = FALSE)
                    
# Water balance in a dual-permeability model (MACRO)
hydrology_soilWaterBalance(examplesoil, "VG", 10, 5, 0, c(-1,-1,-1,-1), 
                           soilDomains = "dual", modifySoil = FALSE)
  

Advanced radiation transfer functions

Description

Functions light_layerDirectIrradianceFraction and light_layerDiffuseIrradianceFraction calculate the fraction of above-canopy direct and diffuse radiation reaching each vegetation layer. Function light_layerSunlitFraction calculates the proportion of sunlit leaves in each vegetation layer. Function light_cohortSunlitShadeAbsorbedRadiation calculates the amount of radiation absorbed by cohort and vegetation layers, while differentiating between sunlit and shade leaves.

Usage

light_leafAngleCDF(leafAngle, p, q)

light_leafAngleBetaParameters(leafAngle, leafAngleSD)

light_directionalExtinctionCoefficient(p, q, solarElevation)

light_layerDirectIrradianceFraction(
  LAIme,
  LAImd,
  LAImx,
  kb,
  ClumpingIndex,
  alpha,
  gamma,
  trunkExtinctionFraction = 0.1
)

light_layerDiffuseIrradianceFraction(
  LAIme,
  LAImd,
  LAImx,
  K,
  ClumpingIndex,
  ZF,
  alpha,
  gamma,
  trunkExtinctionFraction = 0.1
)

light_cohortSunlitShadeAbsorbedRadiation(
  Ib0,
  Id0,
  LAIme,
  LAImd,
  LAImx,
  kb,
  K,
  ClumpingIndex,
  ZF,
  alpha,
  gamma,
  trunkExtinctionFraction = 0.1
)

light_layerSunlitFraction(LAIme, LAImd, kb, ClumpingIndex)

light_instantaneousLightExtinctionAbsortion(
  LAIme,
  LAImd,
  LAImx,
  p,
  q,
  ClumpingIndex,
  alphaSWR,
  gammaSWR,
  ddd,
  ntimesteps = 24L,
  trunkExtinctionFraction = 0.1
)

light_longwaveRadiationSHAW(
  LAIme,
  LAImd,
  LAImx,
  LWRatm,
  Tsoil,
  Tair,
  trunkExtinctionFraction = 0.1
)

Arguments

Details

Functions for short-wave radiation are adapted from Anten & Bastiaans (2016), whereas long-wave radiation balance follows Flerchinger et al. (2009). Vegetation layers are assumed to be ordered from bottom to top.

Value

Functions light_layerDirectIrradianceFraction, light_layerDiffuseIrradianceFraction and light_layerSunlitFraction return a numeric vector of length equal to the number of vegetation layers.

Function light_cohortSunlitShadeAbsorbedRadiation returns a list with two elements (matrices): I_sunlit and I_shade.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Anten, N.P.R., Bastiaans, L., 2016. The use of canopy models to analyze light competition among plants, in: Hikosaka, K., Niinemets, U., Anten, N.P.R. (Eds.), Canopy Photosynthesis: From Basics to Application. Springer, pp. 379–398.

Flerchinger, G. N., Xiao, W., Sauer, T. J., Yu, Q. 2009. Simulation of within-canopy radiation exchange. NJAS - Wageningen Journal of Life Sciences 57 (1): 5–15. https://doi.org/10.1016/j.njas.2009.07.004.

See Also

spwb, light_basic

Examples

solarElevation <- 0.67 # in radians
SWR_direct <- 1100
SWR_diffuse <- 300
PAR_direct <- 550
PAR_diffuse <- 150

LAI <- 2
nlayer <- 10
LAIlayerlive <- matrix(rep(LAI/nlayer,nlayer),nlayer,1)
LAIlayerdead <- matrix(0,nlayer,1)
meanLeafAngle <- 60 # in degrees
sdLeafAngle <- 20

beta <- light_leafAngleBetaParameters(meanLeafAngle*(pi/180), sdLeafAngle*(pi/180))

## Extinction coefficients
kb <- light_directionalExtinctionCoefficient(beta["p"], beta["q"], solarElevation)
kd_PAR <- 0.5
kd_SWR <- kd_PAR/1.35

Radiation extinction functions used in basic transpiration sub-model

Description

Radiation extinction functions used in basic transpiration sub-model

Usage

light_PARcohort(x, SpParams, gdd = NA_real_)

light_PARground(x, SpParams, gdd = NA_real_)

light_SWRground(x, SpParams, gdd = NA_real_)

light_cohortAbsorbedSWRFraction(z, x, SpParams, gdd = NA_real_)

Arguments

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwb, light_advanced

Input for simulation models

Description

Functions spwbInput() and growthInput() take an object of class forest and a soil data input to create input objects for simulation functions spwb (or pwb) and growth, respectively.

Usage

spwbInput(x, soil, SpParams, control)

growthInput(x, soil, SpParams, control)

Arguments

Details

Functions spwbInput() and growthInput() initialize inputs differently depending on control parameters.

IMPORTANT NOTE: Older function names forest2spwbInput and forest2growthInput are now deprecated, but they can still be used for back-compatibility.

Value

Function spwbInput() returns a list of class spwbInput with the following elements (rows of data frames are identified as specified by function plant_ID):

• control: List with control parameters (see defaultControl).

• soil: A data frame with initialized soil parameters (see soil).

• snowpack: The amount of snow (in mm) in the snow pack over the soil.

• canopy: A list of stand-level state variables.

• cohorts: A data frame with cohort information, with columns SP and Name.

• above: A data frame with columns H, CR and LAI (see function forest2aboveground).

• below: A data frame with columns Z50, Z95. If control$transpirationMode = "Sperry" additional columns are fineRootBiomass and coarseRootSoilVolume.

• belowLayers: A list. If control$transpirationMode = "Granier" it contains elements:

  • V: A matrix with the proportion of fine roots of each cohort (in rows) in each soil layer (in columns).

  • L: A matrix with the length of coarse roots of each cohort (in rows) in each soil layer (in columns).

  • Wpool: A matrix with the soil moisture relative to field capacity around the rhizosphere of each cohort (in rows) in each soil layer (in columns).

  If control$transpirationMode = "Sperry" or control$transpirationMode = "Sureau" there are the following additional elements:

  • VGrhizo_kmax: A matrix with maximum rhizosphere conductance values of each cohort (in rows) in each soil layer (in columns).

  • VGroot_kmax: A matrix with maximum root xylem conductance values of each cohort (in rows) in each soil layer (in columns).

  • RhizoPsi: A matrix with the water potential around the rhizosphere of each cohort (in rows) in each soil layer (in columns).

• paramsPhenology: A data frame with leaf phenology parameters:

  • PhenologyType: Leaf phenology type.

  • LeafDuration: Leaf duration (in years).

  • Sgdd: Degree days needed for leaf budburst (for winter decideous species).

  • Tbgdd: Base temperature for the calculation of degree days to leaf budburst.

  • Ssen: Degree days corresponding to leaf senescence.

  • Phsen: Photoperiod corresponding to start counting senescence degree-days.

  • Tbsen: Base temperature for the calculation of degree days to leaf senescence.

• paramsAnatomy: A data frame with plant anatomy parameters for each cohort:

  • Hmax: Maximum plant height (cm).

  • Hmed: Median plant height (cm).

  • Al2As: Leaf area to sapwood area ratio (in m2·m-2).

  • Ar2Al: Fine root area to leaf area ratio (in m2·m-2).

  • SLA: Specific leaf area (mm2/mg = m2/kg).

  • LeafWidth: Leaf width (in cm).

  • LeafDensity: Density of leaf tissue (dry weight over volume).

  • WoodDensity: Density of wood tissue (dry weight over volume).

  • FineRootDensity: Density of fine root tissue (dry weight over volume).

  • SRL: Specific Root length (cm·g-1).

  • RLD: Root length density (cm·cm-3).

  • r635: Ratio between the weight of leaves plus branches and the weight of leaves alone for branches of 6.35 mm.

• paramsInterception: A data frame with rain interception and light extinction parameters for each cohort:

  • kPAR: PAR extinction coefficient.

  • g: Canopy water retention capacity per LAI unit (mm/LAI).

  If control$transpirationMode = "Sperry" or control$transpirationMode = "Sureau" additional columns are:

  • gammaSWR: Reflectance (albedo) coefficient for SWR .

  • alphaSWR: Absorbance coefficient for SWR .

• paramsTranspiration: A data frame with parameters for transpiration and photosynthesis. If control$transpirationMode = "Granier", columns are:

  • Gswmin: Minimum stomatal conductance to water vapor (in mol H2O·m-2·s-1).

  • Tmax_LAI: Coefficient relating LAI with the ratio of maximum transpiration over potential evapotranspiration.

  • Tmax_LAIsq: Coefficient relating squared LAI with the ratio of maximum transpiration over potential evapotranspiration.

  • Psi_Extract: Water potential corresponding to 50% relative transpiration (in MPa).

  • Exp_Extract: Parameter of the Weibull function regulating transpiration reduction.

  • VCstem_c, VCstem_d: Parameters of the stem xylem vulnerability curve (Weibull).

  • WUE: Daily water use efficiency (gross photosynthesis over transpiration) under no light, water or CO2 limitations and VPD = 1kPa (g C/mm water).

  • WUE_par: Coefficient regulating the influence of % PAR on gross photosynthesis.

  • WUE_co2: Coefficient regulating the influence of atmospheric CO2 concentration on gross photosynthesis.

  • WUE_vpd: Coefficient regulating the influence of vapor pressure deficit (VPD) on gross photosynthesis.

  If control$transpirationMode = "Sperry" columns are:

  • Gswmin: Minimum stomatal conductance to water vapor (in mol H2O·m-2·s-1).

  • Gswmax: Maximum stomatal conductance to water vapor (in mol H2O·m-2·s-1).

  • Vmax298: Maximum Rubisco carboxilation rate at 25ºC (in micromol CO2·s-1·m-2).

  • Jmax298: Maximum rate of electron transport at 25ºC (in micromol photons·s-1·m-2).

  • Kmax_stemxylem: Sapwood-specific hydraulic conductivity of stem xylem (in kg H2O·s-1·m-1·MPa-1).

  • Kmax_rootxylem: Sapwood-specific hydraulic conductivity of root xylem (in kg H2O·s-1·m-1·MPa-1).

  • VCleaf_kmax: Maximum leaf hydraulic conductance (in mmol H2O·s-1·m-2·MPa-1).

  • VCleaf_c, VCleaf_d: Parameters of the leaf vulnerability curve (Weibull).

  • VCstem_kmax: Maximum stem xylem conductance (in mmol H2O·s-1·m-2·MPa-1).

  • VCstem_c, VCstem_d: Parameters of the stem xylem vulnerability curve (Weibull).

  • VCroot_c, VCroot_d: Parameters of the root xylem vulnerability curve (Weibull).

  • Plant_kmax: Maximum whole-plant conductance (in mmol H2O·s-1·m-2·MPa-1).

  • FR_leaf, FR_stem, FR_root: Fraction of whole-plant resistance corresponding to each segment.

  If control$transpirationMode = "Sureau" columns are:

  • Gswmin: Minimum stomatal conductance to water vapor (in mol H2O·m-2·s-1).

  • Gswmax: Maximum stomatal conductance to water vapor (in mol H2O·m-2·s-1).

  • Gsw_AC_slope: Slope of the Gsw vs Ac/Cs relationship (see photo_photosynthesisBaldocchi).

  • Gs_P50, Gs_slope: Parameters of the curve describing the decrease in stomatal conductance as a function of leaf water potential (sigmoid).

  • Vmax298: Maximum Rubisco carboxylation rate at 25ºC (in micromol CO2·s-1·m-2).

  • Jmax298: Maximum rate of electron transport at 25ºC (in micromol photons·s-1·m-2).

  • Kmax_stemxylem: Sapwood-specific hydraulic conductivity of stem xylem (in kg H2O·s-1·m-1·MPa-1).

  • Kmax_rootxylem: Sapwood-specific hydraulic conductivity of root xylem (in kg H2O·s-1·m-1·MPa-1).

  • VCleaf_kmax: Maximum leaf hydraulic conductance (in mmol H2O·s-1·m-2·MPa-1).

  • VCleaf_c, VCleaf_d: Parameters of the leaf vulnerability curve (Weibull).

  • VCleaf_P50, VCleaf_slope: Parameters of the leaf vulnerability curve (sigmoid).

  • VCstem_kmax: Maximum stem xylem conductance (in mmol H2O·s-1·m-2·MPa-1).

  • VCstem_c, VCstem_d: Parameters of the stem xylem vulnerability curve (Weibull).

  • VCstem_P50, VCstem_slope: Parameters of the stem xylem vulnerability curve (sigmoid).

  • VCroot_c, VCroot_d: Parameters of the root xylem vulnerability curve (Weibull).

  • VCroot_P50, VCroot_slope: Parameters of the root xylem vulnerability curve (sigmoid).

  • Plant_kmax: Maximum whole-plant conductance (in mmol H2O·s-1·m-2·MPa-1).

  • FR_leaf, FR_stem, FR_root: Fraction of whole-plant resistance corresponding to each segment.

• paramsWaterStorage: A data frame with plant water storage parameters for each cohort:

  • LeafPI0: Osmotic potential at full turgor of leaves (MPa).

  • LeafEPS: Modulus of elasticity (capacity of the cell wall to resist changes in volume in response to changes in turgor) of leaves (MPa).

  • LeafAF: Apoplastic fraction (proportion of water outside the living cells) in leaves.

  • Vleaf: Storage water capacity in leaves, per leaf area (L/m2).

  • StemPI0: Osmotic potential at full turgor of symplastic xylem tissue (MPa).

  • StemEPS: Modulus of elasticity (capacity of the cell wall to resist changes in volume in response to changes in turgor) of symplastic xylem tissue (Mpa).

  • StemAF: Apoplastic fraction (proportion of water outside the living cells) in stem xylem.

  • Vstem: Storage water capacity in sapwood, per leaf area (L/m2).

• internalPhenology and internalWater: data frames to store internal state variables.

• internalFCCS: A data frame with fuel characteristics, according to fuel_FCCS (only if fireHazardResults = TRUE, in the control list).

Function growthInput() returns a list of class growthInput with the same elements as spwbInput, but with additional information.

• Element above includes the following additional columns:

  • LA_live: Live leaf area per individual (m2/ind).

  • LA_dead: Dead leaf area per individual (m2/ind).

  • SA: Live sapwood area per individual (cm2/ind).

• paramsGrowth: A data frame with growth parameters for each cohort:

  • RERleaf: Maintenance respiration rates (at 20ºC) for leaves (in g gluc·g dry-1·day-1).

  • RERsapwood: Maintenance respiration rates (at 20ºC) for sapwood (in g gluc·g dry-1·day-1).

  • RERfineroot: Maintenance respiration rates (at 20ºC) for fine roots (in g gluc·g dry-1·day-1).

  • CCleaf: Leaf construction costs (in g gluc·g dry-1).

  • CCsapwood: Sapwood construction costs (in g gluc·g dry-1).

  • CCfineroot: Fine root construction costs (in g gluc·g dry-1).

  • RGRleafmax: Maximum leaf relative growth rate (in m2·cm-2·day-1).

  • RGRsapwoodmax: Maximum sapwood relative growth rate (in cm2·cm-2·day-1).

  • RGRfinerootmax: Maximum fine root relative growth rate (in g dry·g dry-1·day-1).

  • SRsapwood: Sapwood daily senescence rate (in day-1).

  • SRfineroot: Fine root daily senescence rate (in day-1).

  • RSSG: Minimum relative starch for sapwood growth (proportion).

  • fHDmin: Minimum value of the height-to-diameter ratio (dimensionless).

  • fHDmax: Maximum value of the height-to-diameter ratio (dimensionless).

  • WoodC: Wood carbon content per dry weight (g C /g dry).

• paramsMortalityRegeneration: A data frame with mortality/regeneration parameters for each cohort:

  • MortalityBaselineRate: Deterministic proportion or probability specifying the baseline reduction of cohort's density occurring in a year.

  • SurvivalModelStep: Time step in years of the empirical survival model depending on stand basal area (e.g. 10).

  • SurvivalB0: Intercept of the logistic baseline survival model depending on stand basal area.

  • SurvivalB1: Slope of the logistic baseline survival model depending on stand basal area.

  • RecrTreeDensity: Density of tree recruits from seeds.

  • IngrowthTreeDensity: Density of trees reaching ingrowth DBH.

  • RecrTreeDBH: DBH for tree recruits from seeds or resprouting (e.g. 1 cm).

  • IngrowthTreeDBH: Ingrowth DBH for trees (e.g. 7.5 cm).

• paramsAllometry: A data frame with allometric parameters for each cohort:

  • Aash: Regression coefficient relating the square of shrub height with shrub area.

  • Absh, Bbsh: Allometric coefficients relating phytovolume with dry weight of shrub individuals.

  • Acr, B1cr, B2cr, B3cr, C1cr, C2cr: Regression coefficients used to calculate crown ratio of trees.

  • Acw, Bcw: Regression coefficients used to calculated crown width of trees.

• internalAllocation: A data frame with internal allocation variables for each cohort:

  • allocationTarget: Value of the allocation target variable.

  • leafAreaTarget: Target leaf area (m2) per individual.

  • sapwoodAreaTarget: Target sapwood area (cm2) per individual.

  • fineRootBiomassTarget: Target fine root biomass (g dry) per individual.

  • crownBudPercent: Percentage of the crown with buds.

• internalCarbon: A data frame with the concentration (mol·gluc·l-1) of metabolic and storage carbon compartments for leaves and sapwood.

• internalMortality: A data frame to store the cumulative mortality (density for trees and cover for shrubs) predicted during the simulation, also distinguishing mortality due to starvation or dessication.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

resetInputs, spwb, soil, forest, SpParamsMED, defaultSoilParams, plant_ID

Examples

#Load example plot plant data
data(exampleforest)

# Example of aboveground parameters taken from a forest
# described using LAI and crown ratio
data(exampleforest2)

#Default species parameterization
data(SpParamsMED)


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

# Initialize control parameters using 'Granier' transpiration mode
control <- defaultControl("Granier")

# Prepare spwb input
spwbInput(exampleforest, examplesoil, SpParamsMED, control)
                
# Prepare input for 'Sperry' transpiration mode
control <- defaultControl("Sperry")
spwbInput(exampleforest,examplesoil,SpParamsMED, control)

# Prepare input for 'Sureau' transpiration mode
control <- defaultControl("Sureau")
spwbInput(exampleforest,examplesoil,SpParamsMED, control)

# Example of initialization from a forest 
# described using LAI and crown ratio
control <- defaultControl("Granier")
spwbInput(exampleforest2, examplesoil, SpParamsMED, control)

Modify parameters

Description

Routines to modify species parameter table or model input objects

Usage

modifySpParams(SpParams, customParams, subsetSpecies = TRUE)

modifyCohortParams(x, customParams, verbose = TRUE)

modifyInputParams(x, customParams, verbose = TRUE)

Arguments

Details

When calling function modifySpParams, customParams should be a data frame with as many rows as species and as many columns as parameters to modify, plus a column called 'Name' or 'Species' to match species names between the two tables. In both cases, the function will match input strings with column 'Name' of x. Alternatively, customParams can contain a column 'SpIndex' for matching of species indices, but this is deprecated.

When calling modifyCohortParams, customParams can be a data frame with as many rows as cohorts and as many columns as parameters to modify, plus a column called 'Cohort' which will be matched with the cohort names given by spwbInput or growthInput. Alternatively, customParams can be a named list or named numeric vector as for modifyInputParams.

When calling modifyInputParams, customParams must be either a named list or a named numeric vector. Cohort parameters are specified using the syntax "[cohortName]/[paramName]" for names (e.g. "T2_176/Z50" to modify parameter 'Z50' of cohort 'T2_176'). Soil layer parameters are specified using the syntax "[paramName]@#layer" for names, where #layer is the layer index (e.g. "rfc@1" will modify the rock fragment content of soil layer 1). Control parameters are specified using either "[paramName]" (e.g "phloemConductanceFactor") or "[paramName]$[subParamName]" (e.g "maximumRelativeGrowthRates$leaf"). It may seem unnecessary to modify soil or control parameters via a function, but modifyInputParams is called from optimization functions (see optimization).

Value

Function modifySpParams returns a modified species parameter data frame.

Functions modifyCohortParams and modifyInputParams return a modified spwbInput or growthInput object. Note that modifications may affect other parameters beyond those indicated in customParams, as a result of parameter dependencies (see examples).

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwbInput, SpParamsMED, optimization

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)

# Cohort name for Pinus halepensis
PH_coh <- paste0("T1_", SpParamsMED$SpIndex[SpParamsMED$Name=="Pinus halepensis"])
PH_coh 

# Modify Z50 and Z95 of Pinus halepensis cohort 
customParams <- c(200,2000)
names(customParams) <- paste0(PH_coh,c("/Z50", "/Z95"))
x1m <- modifyInputParams(x1, customParams)

# Inspect original and modified objects 
x1$below
x1m$below

# Inspect dependencies: fine root distribution across soil layers
x1$belowLayers$V
x1m$belowLayers$V

# Modify rock fragment content and sand proportion of soil layer 1
x1s <- modifyInputParams(x1, c("rfc@1" = 5, "sand@1" = 10))

# Inspect original and modified soils 
x1$soil
x1s$soil

# When modifying growth input objects dependencies increase
x1 <- growthInput(exampleforest,examplesoil, SpParamsMED, control)
customParams <- c(2000,2)
names(customParams) <- paste0(PH_coh,c("/Al2As", "/LAI_live"))
x1m <- modifyInputParams(x1, customParams)

Tissue moisture functions

Description

Set of functions used to calculate tissue moisture from water potential and viceversa.

Usage

moisture_sapwoodWaterCapacity(Al2As, height, V, L, wd)

moisture_leafWaterCapacity(SLA, ld)

moisture_turgorLossPoint(pi0, epsilon)

moisture_symplasticRWC(psiSym, pi0, epsilon)

moisture_symplasticPsi(RWC, pi0, epsilon)

moisture_apoplasticRWC(psiApo, c, d)

moisture_apoplasticPsi(RWC, c, d)

moisture_tissueRWC(psiSym, pi0, epsilon, psiApo, c, d, af)

plant_water(x)

moisture_pressureVolumeCurvePlot(
  x,
  segment = "stem",
  fraction = "all",
  psiVec = seq(-0.1, -8, by = -0.01),
  speciesNames = FALSE
)

Arguments

Value

Values returned for each function are:

• moisture_symplasticRWC: Relative water content [0-1] of the symplastic fraction.

• moisture_apoplasticRWC: Relative water content [0-1] of the apoplastic fraction.

• moisture_symplasticWaterPotential: Water potential (in MPa) of the symplastic fraction.

• moisture_apoplasticWaterPotential: Water potential (in MPa) of the apoplastic fraction.

• moisture_turgorLossPoint: Water potential (in MPa) corresponding to turgor loss point.

• moisture_segmentRWC: Segment relative water content [0-1].

• water_plant: A vector of water content (mm) per plant cohort.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Bartlett, M.K., Scoffoni, C., Sack, L. 2012. The determinants of leaf turgor loss point and prediction of drought tolerance of species and biomes: a global meta-analysis. Ecology Letters 15: 393–405.

Hölttä, T., Cochard, H., Nikinmaa, E., Mencuccini, M. 2009. Capacitive effect of cavitation in xylem conduits: Results from a dynamic model. Plant, Cell and Environment 32: 10–21.

Martin-StPaul, N., Delzon, S., Cochard, H. 2017. Plant resistance to drought depends on timely stomatal closure. Ecology Letters 20: 1437–1447.

See Also

hydraulics_psi2K, hydraulics_supplyFunctionPlot, spwb, soil

Examples

psi = seq(-10,0, by=0.1)
rwc_s = rep(NA, length(psi))
for(i in 1:length(psi)) rwc_s[i] = moisture_symplasticRWC(psi[i],-3,12)
plot(psi, rwc_s, type="l", xlab="Water potential (MPa)", ylab = "Symplasmic RWC")

Mortality

Description

A simple sigmoid function to determine a daily mortality likelihood according to the value of a stress variable.

Usage

mortality_dailyProbability(stressValue, stressThreshold)

Arguments

Value

Returns a probability (between 0 and 1)

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

growth, regeneration

Multiple model runs and function factories for optimization routines

Description

Function factories to generate functions to be used in model calibration, uncertainty or sensitivity analysis.

Usage

multiple_runs(
  parMatrix,
  x,
  meteo,
  latitude,
  elevation = NA,
  slope = NA,
  aspect = NA,
  summary_function = NULL,
  args = NULL,
  verbose = TRUE
)

optimization_function(
  parNames,
  x,
  meteo,
  latitude,
  elevation = NA,
  slope = NA,
  aspect = NA,
  summary_function,
  args = NULL
)

optimization_evaluation_function(
  parNames,
  x,
  meteo,
  latitude,
  elevation = NA,
  slope = NA,
  aspect = NA,
  measuredData,
  type = "SWC",
  cohorts = NULL,
  temporalResolution = "day",
  metric = "loglikelihood"
)

optimization_multicohort_function(
  cohortParNames,
  cohortNames,
  x,
  meteo,
  latitude,
  otherParNames = NULL,
  elevation = NA,
  slope = NA,
  aspect = NA,
  summary_function,
  args = NULL
)

optimization_evaluation_multicohort_function(
  cohortParNames,
  cohortNames,
  x,
  meteo,
  latitude,
  otherParNames = NULL,
  elevation = NA,
  slope = NA,
  aspect = NA,
  measuredData,
  type = "SWC",
  cohorts = cohortNames,
  temporalResolution = "day",
  metric = "loglikelihood"
)

Arguments

Details

See evaluation for details regarding how to specify measured data.

Functions produced by these function factories should be useful for sensitivity analyses using package 'sensitivity'.

Parameter naming (i.e. parNames) should follow the rules specified in section details of modifyInputParams. The exception to the naming rules applies when multiple cohorts are to be modified to the same values with functions optimization_multicohort_function and optimization_evaluation_multicohort_function. Then, only a vector of parameter names is supplied for cohortParNames.

Value

Function multiple_runs returns a list, whose elements are either the result of calling simulation models or the result of calling summary_function afterwards.

Function optimization_function returns a function whose parameters are parameter values and whose return is a prediction scalar (e.g. total transpiration).

Function optimization_evaluation_function returns a function whose parameters are parameter values and whose return is an evaluation metric (e.g. loglikelihood of the data observations given model predictions). If evaluation data contains information for different cohorts (e.g. plant water potentials or transpiration rates) then the evaluation is performed for each cohort and the metrics are averaged.

Function optimization_multicohorts_function returns a function whose parameters are parameter values and whose return is a prediction scalar (e.g. total transpiration). The difference with optimization_function is that multiple cohorts are set to the same parameter values.

Function optimization_evaluation_multicohort_function returns a function whose parameters are parameter values and whose return is an evaluation metric (e.g. loglikelihood of the data observations given model predictions). If evaluation data contains information for different cohorts (e.g. plant water potentials or transpiration rates) then the evaluation is performed for each cohort and the metrics are averaged. The difference with optimization_evaluation_function is that multiple cohorts are set to the same parameter values.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

evaluation_metric, modifyInputParams, spwb, growth

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)

# Cohort name for Pinus halepensis
PH_coh <- paste0("T1_", SpParamsMED$SpIndex[SpParamsMED$Name=="Pinus halepensis"])
PH_coh 

#Parameter names of interest
parNames <- c(paste0(PH_coh,"/Z50"), paste0(PH_coh,"/Z95"))

#Specify parameter matrix
parMatrix <- cbind(c(200,300), c(500,1000))
colnames(parMatrix) <- parNames

#Define a summary function as the total transpiration over the simulated period
sf<-function(x) {sum(x$WaterBalance$Transpiration, na.rm=TRUE)}

#Perform two runs and evaluate the summary function
multiple_runs(parMatrix, 
              x1, examplemeteo, latitude = 42, elevation = 100,
              summary_function = sf)

#Load observed data (in this case the same simulation results with some added error)  
# Generate a prediction function for total transpiration over the simulated period
# as a function of parameters "Z50" and "Z95" for Pinus halepensis cohort 
of<-optimization_function(parNames = parNames,
                          x = x1,
                          meteo = examplemeteo, 
                          latitude = 41.82592, elevation = 100,
                          summary_function = sf)

# Evaluate for the values of the parameter matrix
of(parMatrix[1, ])
of(parMatrix)


# Generate a loglikelihood function for soil water content
# as a function of parameters "Z50" and "Z95" for Pinus halepensis cohort 
data(exampleobs)
oef<-optimization_evaluation_function(parNames = parNames,
                                      x = x1,
                                      meteo = examplemeteo, latitude = 41.82592, elevation = 100,
                                      measuredData = exampleobs, type = "SWC", 
                                      metric = "loglikelihood")

# Loglikelihood for the values of the parameter matrix
oef(parMatrix[1, ])
oef(parMatrix)

Leaf phenology

Description

Function pheno_leafDevelopmentStatus returns the expanded status (0 to 1) of leaves according to the growth degree days required to start bud burst and leaf unfolding, as dictated by a simple ecodormancy (one-phase) model (Chuine et al. 2013). Function pheno_leafSenescenceStatus returns the 0/1 senescence status of leaves according to the one-phase senescence model of Delpierre et al. (2009) on the basis of photoperiod and temperature. Function pheno_updateLeaves updates the status of expanded leaves and dead leaves of object x given the photoperiod, temperature and wind of a given day. It applies the development model for 1 < doy < 180 and the senescence model for 181 > doy > 365.

Usage

pheno_leafDevelopmentStatus(Sgdd, gdd, unfoldingDD = 300)

pheno_leafSenescenceStatus(Ssen, sen)

pheno_updatePhenology(x, doy, photoperiod, tmean)

pheno_updateLeaves(x, wind, fromGrowthModel)

Arguments

Value

Function pheno_leafDevelopmentStatus returns a vector of values between 0 and 1, whereas function pheno_leafSenescenceStatus returns a vector of 0 (senescent) and 1 (expanded) values. The other two functions do not return any value (see note).

Note

Functions pheno_updatePhenology and pheno_updateLeaves modify the input object x. The first modifies phenological state and the second modifies the leaf area accordingly.

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Chuine, I., De Cortazar-Atauri, I.G., Kramer, K., Hänninen, H., 2013. Plant development models. Phenology: An Integrative Environmental Science. Springer, pp. 275–293.

Delpierre N, Dufrêne E, Soudani K et al (2009) Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric For Meteorol 149:938–948. doi:10.1016/j.agrformet.2008.11.014

See Also

spwb, spwbInput

Photosynthesis submodel functions

Description

Set of functions used in the calculation of photosynthesis

Usage

photo_GammaTemp(Tleaf)

photo_KmTemp(Tleaf, Oi = 209)

photo_VmaxTemp(Vmax298, Tleaf)

photo_JmaxTemp(Jmax298, Tleaf)

photo_electronLimitedPhotosynthesis(Q, Ci, GT, Jmax)

photo_rubiscoLimitedPhotosynthesis(Ci, GT, Km, Vmax)

photo_photosynthesis(Q, Catm, Gc, Tleaf, Vmax298, Jmax298, verbose = FALSE)

photo_photosynthesisBaldocchi(
  Q,
  Catm,
  Tleaf,
  u,
  Vmax298,
  Jmax298,
  leafWidth,
  Gsw_AC_slope,
  Gsw_AC_intercept
)

photo_leafPhotosynthesisFunction(
  E,
  psiLeaf,
  Catm,
  Patm,
  Tair,
  vpa,
  u,
  absRad,
  Q,
  Vmax298,
  Jmax298,
  leafWidth = 1,
  refLeafArea = 1,
  verbose = FALSE
)

photo_leafPhotosynthesisFunction2(
  E,
  psiLeaf,
  Catm,
  Patm,
  Tair,
  vpa,
  u,
  SWRabs,
  LWRnet,
  Q,
  Vmax298,
  Jmax298,
  leafWidth = 1,
  refLeafArea = 1,
  verbose = FALSE
)

photo_sunshadePhotosynthesisFunction(
  E,
  psiLeaf,
  Catm,
  Patm,
  Tair,
  vpa,
  SLarea,
  SHarea,
  u,
  absRadSL,
  absRadSH,
  QSL,
  QSH,
  Vmax298SL,
  Vmax298SH,
  Jmax298SL,
  Jmax298SH,
  leafWidth = 1,
  verbose = FALSE
)

photo_multilayerPhotosynthesisFunction(
  E,
  psiLeaf,
  Catm,
  Patm,
  Tair,
  vpa,
  SLarea,
  SHarea,
  u,
  absRadSL,
  absRadSH,
  QSL,
  QSH,
  Vmax298,
  Jmax298,
  leafWidth = 1,
  verbose = FALSE
)

Arguments

Details

Details of the photosynthesis submodel are given in the medfate book

Value

Values returned for each function are:

• photo_GammaTemp: CO2 compensation concentration (micromol * mol-1).

• photo_KmTemp: Michaelis-Menten coefficients of Rubisco for Carbon (micromol * mol-1) and Oxigen (mmol * mol-1).

• photo_VmaxTemp: Temperature correction of Vmax298.

• photo_JmaxTemp: Temperature correction of Jmax298.

• photo_electronLimitedPhotosynthesis: Electron-limited photosynthesis (micromols-1m-2) following Farquhar et al. (1980).

• photo_rubiscoLimitedPhotosynthesis: Rubisco-limited photosynthesis (micromols-1m-2) following Farquhar et al. (1980).

• photo_photosynthesis: Calculates gross photosynthesis (micromols-1m-2) following (Farquhar et al. (1980) and Collatz et al (1991).

• photo_leafPhotosynthesisFunction: Returns a data frame with the following columns:

  • LeafTemperature: Leaf temperature (ºC).

  • LeafVPD: Leaf vapor pressure deficit (kPa).

  • LeafCi: Internal CO2 concentration (micromol * mol-1).

  • Gsw: Leaf stomatal conductance to water vapor (mol * s-1 * m-2).

  • GrossPhotosynthesis: Gross photosynthesis (micromols-1m-2).

  • NetPhotosynthesis: Net photosynthesis, after discounting autotrophic respiration (micromols-1m-2).

• photo_sunshadePhotosynthesisFunction: Returns a data frame with the following columns:

  • GrossPhotosynthesis: Gross photosynthesis (micromols-1m-2).

  • NetPhotosynthesis: Net photosynthesis, after discounting autotrophic respiration (micromols-1m-2).

  • LeafCiSL: Sunlit leaf internal CO2 concentration (micromol * mol-1).

  • LeafCiSH: Shade leaf internal CO2 concentration (micromol * mol-1).

  • LeafTempSL: Sunlit leaf temperature (ºC).

  • LeafTempSH: Shade leaf temperature (ºC).

  • LeafVPDSL: Sunlit leaf vapor pressure deficit (kPa).

  • LeafVPDSH: Shade leaf vapor pressure deficit (kPa).

• photo_multilayerPhotosynthesisFunction: Return a data frame with the following columns:

  • GrossPhotosynthesis: Gross photosynthesis (micromols-1m-2).

  • NetPhotosynthesis: Net photosynthesis, after discounting autotrophic respiration (micromols-1m-2).

Author(s)

Miquel De Cáceres Ainsa, CREAF

References

Bernacchi, C. J., E. L. Singsaas, C. Pimentel, A. R. Portis, and S. P. Long. 2001. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant, Cell and Environment 24:253–259.

Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry. 1991. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest Meteorology 54:107–136.

Farquhar, G. D., S. von Caemmerer, and J. A. Berry. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149:78–90.

Leuning, R. 2002. Temperature dependence of two parameters in a photosynthesis model. Plant, Cell and Environment 25:1205–1210.

Sperry, J. S., M. D. Venturas, W. R. L. Anderegg, M. Mencuccini, D. S. Mackay, Y. Wang, and D. M. Love. 2016. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. Plant Cell and Environment.

See Also

hydraulics_supplyFunctionNetwork, biophysics_leafTemperature, spwb

Woody plant cohort description functions

Description

Functions to calculate attributes of woody plants in a forest object.

Usage

plant_ID(x, SpParams, treeOffset = 0L, shrubOffset = 0L)

plant_basalArea(x, SpParams)

plant_largerTreeBasalArea(x, SpParams, self_proportion = 0.5)

plant_cover(x, SpParams)

plant_species(x, SpParams)

plant_speciesName(x, SpParams)

plant_density(x, SpParams)

plant_height(x, SpParams)

plant_individualArea(x, SpParams)

plant_crownRatio(x, SpParams)

plant_crownBaseHeight(x, SpParams)

plant_crownLength(x, SpParams)

plant_foliarBiomass(x, SpParams, gdd = NA_real_, competitionEffect = TRUE)

plant_fuelLoading(x, SpParams, gdd = NA_real_, includeDead = TRUE)

plant_equilibriumLeafLitter(x, SpParams, AET = 800)

plant_equilibriumSmallBranchLitter(
  x,
  SpParams,
  smallBranchDecompositionRate = 0.81
)

plant_phytovolume(x, SpParams)

plant_LAI(
  x,
  SpParams,
  gdd = NA_real_,
  bounded = TRUE,
  competitionEffect = TRUE
)

plant_characterParameter(x, SpParams, parName)

plant_parameter(x, SpParams, parName, fillMissing = TRUE, fillWithGenus = TRUE)

Arguments

Value

A vector with values for each woody plant cohort of the input forest object:

• plant_basalArea: Tree basal area (m2/ha).

• plant_largerTreeBasalArea: Basal area (m2/ha) of trees larger (in diameter) than the tree. Half of the trees of the same record are included.

• plant_characterParameter: The parameter values of each plant, as strings.

• plant_cover: Shrub cover (in percent).

• plant_crownBaseHeight: The height corresponding to the start of the crown (in cm).

• plant_crownLength: The difference between crown base height and total height (in cm).

• plant_crownRatio: The ratio between crown length and total height (between 0 and 1).

• plant_density: Plant density (ind/ha). Tree density is directly taken from the forest object, while the shrub density is estimated from cover and height by calculating the area of a single individual.

• plant_equilibriumLeafLitter: Litter biomass of leaves at equilibrium (in kg/m2).

• plant_equilibriumSmallBranchLitter: Litter biomass of small branches (< 6.35 mm diameter) at equilibrium (in kg/m2).

• plant_foliarBiomass: Standing biomass of leaves (in kg/m2).

• plant_fuelLoading: Fine fuel load (in kg/m2).

• plant_height: Total height (in cm).

• plant_ID: Cohort coding for simulation functions (concatenation of 'T' (Trees) or 'S' (Shrub), cohort index and species index).

• plant_LAI: Leaf area index (m2/m2).

• plant_individualArea: Area (m2) occupied by a shrub individual.

• plant_parameter: The parameter values of each plant, as numeric.

• plant_phytovolume: Shrub phytovolume (m3/m2).

• plant_species: Species identity integer (indices start with 0).

• plant_speciesName: String with species taxonomic name (or a functional group).

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwb, forest, summary.forest

Examples

#Default species parameterization
data(SpParamsMED)

#Load example plot
data(exampleforest)

#A plant-level way to obtain stand basal area
sum(plant_basalArea(exampleforest, SpParamsMED), na.rm=TRUE)

#The analogous plant-level function for LAI
sum(plant_LAI(exampleforest, SpParamsMED))
  
#The analogous plant-level function for fuel loading
sum(plant_fuelLoading(exampleforest, SpParamsMED))
      
#Summary function for 'forest' objects can be also used
summary(exampleforest, SpParamsMED)

#Cohort IDs in the models
plant_ID(exampleforest, SpParamsMED)
      

Plot forest attributes

Description

Convenient wrappers for vertical forest profiles (see vprofile_leafAreaDensity).

Usage

## S3 method for class 'forest'
plot(
  x,
  SpParams,
  type = "LeafAreaDensity",
  byCohorts = FALSE,
  bySpecies = FALSE,
  includeHerbs = FALSE,
  ...
)

## S3 method for class 'forest'
shinyplot(x, SpParams, ...)

Arguments

Value

A ggplot or a shiny application, depending on the function.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

forest, summary.forest, vprofile_leafAreaDensity

Examples

data(exampleforest)
data(SpParamsMED)
plot(exampleforest, SpParamsMED)

Plots simulation results

Description

Function plot plots time series of the results of the soil plant water balance model (see spwb), plant water balance model (see pwb), the forest growth model (see growth) or the forest dynamics model (see fordyn).

Usage

## S3 method for class 'spwb'
plot(
  x,
  type = "PET_Precipitation",
  cohorts = NULL,
  bySpecies = FALSE,
  dates = NULL,
  subdaily = FALSE,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  summary.freq = NULL,
  ...
)

## S3 method for class 'pwb'
plot(
  x,
  type = "PlantTranspiration",
  cohorts = NULL,
  bySpecies = FALSE,
  dates = NULL,
  subdaily = FALSE,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  summary.freq = NULL,
  ...
)

## S3 method for class 'growth'
plot(
  x,
  type = "PET_Precipitation",
  cohorts = NULL,
  bySpecies = FALSE,
  dates = NULL,
  subdaily = FALSE,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  summary.freq = NULL,
  ...
)

## S3 method for class 'fordyn'
plot(
  x,
  type = "StandBasalArea",
  cohorts = NULL,
  bySpecies = FALSE,
  dates = NULL,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  summary.freq = NULL,
  ...
)

Arguments

Details

The following plots are currently available for spwb (most of them also for pwb):

• "PET_Precipitation": Potential evapotranspiration and Precipitation.

• "PET_NetRain": Potential evapotranspiration and Net rainfall.

• "Snow": Snow precipitation and snowpack dynamics.

• "Export": Water exported through deep drainage and surface runoff.

• "Evapotranspiration": Plant transpiration and soil evaporation.

• "SoilPsi": Soil water potential.

• "SoilRWC": Soil relative water content (in percent of field capacity).

• "SoilTheta": Soil moisture water content (in percent volume).

• "SoilVol": Soil water volumetric content (in mm).

• "PlantExtraction": Water extracted by plants from each soil layer.

• "HydraulicRedistribution": Water added to each soil layer coming from other soil layers, transported through the plant hydraulic network.

• "LAI": Expanded and dead leaf area index of the whole stand.

• "PlantLAI": Plant cohort leaf area index (expanded leaves).

• "PlantLAIlive": Plant cohort leaf area index ("live" leaves).

• "PlantStress": Plant cohort average daily drought stress.

• "PlantTranspiration": Plant cohort transpiration.

• "TranspirationPerLeaf": Plant cohort transpiration per leaf area.

• "PlantGrossPhotosynthesis": Plant cohort photosynthesis.

• "GrossPhotosynthesisPerLeaf": Plant cohort photosynthesis per leaf area.

• "StemRWC": Average daily stem relative water content.

• "LeafRWC": Average daily leaf relative water content.

• "LFMC": Live fuel moisture content.

The following plots are available for spwb and pwb only if transpirationMode = "Granier":

• "PlantPsi": Plant cohort water potential.

• "FPAR": Fraction of PAR at the canopy level of each plant cohort.

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

The following plots are available for spwb and pwb only if transpirationMode = "Sperry":

• "SoilPlantConductance": Average instantaneous overall soil plant conductance (calculated as the derivative of the supply function).

• "LeafPsiMin": Midday leaf water potential.

• "LeafPsiMax": Pre-dawn leaf water potential.

• "LeafPsiRange": Range of leaf water potential.

• "LeafPsiMin_SL": Minimum water potential of sunlit leaves.

• "LeafPsiMax_SL": Maximum water potential of sunlit leaves.

• "LeafPsiMin_SH": Minimum water potential of shade leaves.

• "LeafPsiMax_SH": Maximum water potential of shade leaves.

• "TempMin_SL": Minimum temperature of sunlit leaves.

• "TempMax_SL": Maximum temperature of sunlit leaves.

• "TempMin_SH": Minimum temperature of shade leaves.

• "TempMax_SH": Maximum temperature of shade leaves.

• "GSWMin_SL": Minimum stomatal conductance of sunlit leaves.

• "GSWMax_SL": Maximum stomatal conductance of sunlit leaves.

• "GSWMin_SH": Minimum stomatal conductance of shade leaves.

• "GSWMax_SH": Maximum stomatal conductance of shade leaves.

• "StemPsi": Midday (upper) stem water potential.

• "RootPsi": Midday root crown water potential.

• "PlantNetPhotosynthesis": Plant cohort net photosynthesis.

• "NetPhotosynthesisPerLeaf": Plant cohort net photosynthesis per leaf area.

• "PlantWUE": Plant cohort daily water use efficiency.

• "PlantAbsorbedSWR": Plant cohort absorbed short wave radiation.

• "AbsorbedSWRPerLeaf": Plant cohort absorbed short wave radiation per leaf area.

• "PlantNetLWR": Plant cohort net long wave radiation.

• "NetLWRPerLeaf": Plant cohort net long wave radiation per leaf area.

• "AirTemperature": Minimum/maximum/mean daily temperatures above canopy.

• "CanopyTemperature": Minimum/maximum/mean daily temperatures inside canopy.

• "SoilTemperature": Minimum/maximum/mean daily temperatures inside the first soil layer.

• "CanopyEnergyBalance": Canopy energy balance components.

• "SoilEnergyBalance": Soil energy balance components.

In addition to the former, the following plots are available for objects growth or fordyn:

• "CarbonBalance": Stand-level carbon balance components.

• "BiomassBalance": Stand-level biomass balance components.

• "GrossPhotosynthesis": Gross photosynthesis rate per dry weight.

• "MaintenanceRespiration": Maintenance respiration cost per dry weight.

• "PhotosynthesisMaintenanceRatio": The ratio of gross photosynthesis over maintenance respiration.

• "RootExudation": Root exudation rate per dry weight.

• "LabileCarbonBalance": Labile carbon balance per dry weight.

• "SugarLeaf": Sugar concentration in leaves.

• "StarchLeaf": Starch concentration in leaves.

• "SugarSapwood": Sugar concentration in sapwood.

• "StarchSapwood": Starch concentration in sapwood.

• "SugarTransport": Phloem sugar transport rate.

• "StructuralBiomassBalance": Daily structural biomass balance (g dry · ind-2).

• "LabileBiomassBalance": Daily labile biomass balance (g dry · ind-2).

• "PlantBiomassBalance": Daily plant biomass balance, i.e. labile change + structural change (g dry · ind-2).

• "MortalityBiomassLoss": Biomass loss due to mortality (g dry · m-2).

• "PlantBiomassBalance": Daily cohort biomass balance (including mortality) (g dry · m-2).

• "LeafBiomass": Leaf structural dry biomass per individual.

• "SapwoodBiomass": Sapwood dry biomass per individual.

• "FineRootBiomass": Fine root dry biomass per individual.

• "SapwoodArea": Sapwood area per individual.

• "LeafArea": Leaf area per individual.

• "FineRootArea": Fine root area per individual (only for transpirationMode = "Sperry" or transpirationMode = "Sureau").

• "DBH": Diameter at breast height (in cm) for an average individual of each plant cohort.

• "Height": Height (in cm) for an average individual of each plant cohort.

• "SAgrowth": Sapwood area growth rate.

• "LAgrowth": Leaf area growth rate.

• "FRAgrowth": Fine root area growth rate (only for transpirationMode = "Sperry" or transpirationMode = "Sureau").

• "HuberValue": Ratio of leaf area to sapwood area.

• "RootAreaLeafArea": Ratio of fine root area to leaf area (only for transpirationMode = "Sperry" or transpirationMode = "Sureau").

Finally, the following plots are only available for fordyn simulation results:

• "StandBasalArea": Stand basal area of living trees.

• "StandDensity": Stand density of living trees.

• "SpeciesBasalArea": Basal area of living trees by species.

• "SpeciesDensity": Density of living trees by species.

• "CohortBasalArea": Basal area of living trees by plant cohort.

• "CohortDensity": Density of living trees by plant cohort.

Value

An ggplot object

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwb, pwb, growth, fordyn, summary.spwb

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
x <- spwbInput(exampleforest,examplesoil, SpParamsMED, control)

#Call simulation function
S1 <- spwb(x, examplemeteo, latitude = 41.82592, elevation = 100)

#Plot results
plot(S1)

Plots simulation results for one day

Description

Functions to plot the sub-daily simulation results of spwb_day, growth_day or the transpiration calculations of transp_transpirationSperry or transp_transpirationSureau.

Usage

## S3 method for class 'spwb_day'
plot(
  x,
  type = "PlantTranspiration",
  bySpecies = FALSE,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  ...
)

## S3 method for class 'growth_day'
plot(
  x,
  type = "PlantTranspiration",
  bySpecies = FALSE,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  ...
)

## S3 method for class 'pwb_day'
plot(
  x,
  type = "PlantTranspiration",
  bySpecies = FALSE,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  ...
)

Arguments

Details

The following plots are available for spwb_day and pwb_day:

• "LeafPsi": Leaf water potential (for shade and sunlit leaves).

• "LeafPsiAverage": Average leaf water potential.

• "RootPsi": Root crown water potential.

• "StemPsi": Stem water potential.

• "StemPLC": (Average) percentage of loss conductance in the stem conduits.

• "StemRWC": (Average) relative water content in the stem.

• "LeafRWC": Relative water content in the leaf.

• "StemSympRWC": (Average) relative water content in the stem symplasm.

• "LeafSympRWC": Relative water content in the leaf symplasm.

• "SoilPlantConductance": Overall soil plant conductance (calculated as the derivative of the supply function).

• "PlantExtraction": Water extracted from each soil layer.

• "PlantTranspiration": Plant cohort transpiration per ground area.

• "TranspirationPerLeaf": Plant cohort transpiration per leaf area.

• "PlantGrossPhotosynthesis": Plant cohort gross photosynthesis per ground area.

• "GrossPhotosynthesisPerLeaf": Plant cohort gross photosynthesis per leaf area.

• "PlantNetPhotosynthesis": Plant cohort net photosynthesis per ground area.

• "NetPhotosynthesisPerLeaf": Plant cohort net photosynthesis per leaf area.

• "LeafTranspiration": Instantaneous transpiration per leaf area (differentiates sunlit and shade leaves).

• "LeafGrossPhotosynthesis": Instantaneous gross photosynthesis per leaf area (differentiates sunlit and shade leaves).

• "LeafNetPhotosynthesis": Instantaneous net photosynthesis per leaf area (differentiates sunlit and shade leaves).

• "LeafAbsorbedSWR": Absorbed short wave radiation per leaf area (differentiates sunlit and shade leaves).

• "LeafAbsorbedPAR": Absorbed photosynthetically-active radiation per leaf area (differentiates sunlit and shade leaves).

• "LeafNetLWR": Net long wave radiation per leaf area (differentiates sunlit and shade leaves).

• "LeafCi": Leaf intercellular CO2 concentration (differentiates sunlit and shade leaves).

• "LeafIntrinsicWUE": Leaf intrinsic water use efficiency, i.e. the ratio between instantaneous photosynthesis and stomatal conductance (differentiates sunlit and shade leaves).

• "LeafVPD": Leaf vapour pressure deficit (differentiates sunlit and shade leaves).

• "LeafStomatalConductance": Leaf stomatal conductance to water vapour (differentiates sunlit and shade leaves).

• "LeafTemperature": Leaf temperature (differentiates sunlit and shade leaves).

• "Temperature": Above-canopy, inside-canopy and soil temperature.

• "CanopyEnergyBalance": Canopy energy balance components.

• "SoilEnergyBalance": Soil energy balance components.

• "PlantWaterBalance": Difference between water extraction from the soil and transpired water per ground area.

• "WaterBalancePerLeaf": Difference between water extraction from the soil and transpired water per leaf area.

And the following plots are additionally available for growth_day:

• "GrossPhotosynthesis": Gross photosynthesis rate per dry weight.

• "MaintenanceRespiration": Maintenance respiration cost per dry weight.

• "RootExudation": Root exudation rate per dry weight.

• "LabileCarbonBalance": Labile carbon balance per dry weight.

• "SugarLeaf": Sugar concentration in leaves.

• "StarchLeaf": Starch concentration in leaves.

• "SugarSapwood": Sugar concentration in sapwood.

• "StarchSapwood": Starch concentration in sapwood.

• "SugarTransport": Phloem sugar transport rate.

Value

An ggplot object

Note

Only for soil plant water balance simulations using transpirationMode = "Sperry" or transpirationMode = "Sureau". This function can be used to display subdaily dynamics of corresponding to single days on spwb runs, if control option subdailyResults is set to TRUE. See also option subdaily in plot.spwb.

Author(s)

Miquel De Cáceres Ainsa, CREAF

See Also

spwb_day, growth_day, plot.spwb

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 (2 layers)
examplesoil <- defaultSoilParams(4)

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

#Enable subdaily results for plotting
control["subdailyResults"] <- TRUE

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

#Simulate one day only
d <- 100
date <- examplemeteo$dates[d]
meteovec <- unlist(examplemeteo[d,])
sd2 <- spwb_day(x2, date, meteovec,
              latitude = 41.82592, elevation = 100, slope= 0, aspect = 0)

#Display transpiration for subdaily steps
plot(sd2, "PlantTranspiration")

Example forest inventory data

Description

Example data to illustrate the creation of forest objects from inventory data, coming from a forest inventory survey, used to illustrate the general function forest_mapTreeTable:

• poblet_trees - Data frame with example tree plot data from Poblet, Catalonia (717 observations and 4 variables).

  • Plot.Code - Plot ID (character)

  • Indv.Ref - Tree individual (integer)

  • Species - Species name (character)

  • Diameter.cm - Tree diameter at breast height (cm)

Source

• Data table poblet_trees corresponds to field data sampled by the Catalan Forest Ownership Center (Centre de la Propietat Forestal; CPF).

See Also

forest_mapTreeTable

Plant regeneration

Description

Functions to simulate annual plant regeneration from seed recruitment or from resprouting

Usage

regeneration_seedproduction(forest, SpParams, control)

regeneration_seedrefill(seedBank, refillSpecies, refillPercent = NULL)

regeneration_seedmortality(seedBank, SpParams, minPercent = 1)

regeneration_recruitment(
  forest,
  SpParams,
  control,
  minMonthTemp,
  moistureIndex,
  verbose = FALSE
)

regeneration_resprouting(
  forest,
  internalMortality,
  SpParams,
  control,
  management_results = NULL
)

Arguments