About this article

Any process-based model of forest functioning and dynamics needs information on climate, vegetation and soils of the forest stand to be simulated. Moreover, since medfate allows simulating cohorts belonging to different species, species-specific parameters are also needed. Finally, simulation control parameters may need to be changed, depending on the goals of the simulation. This article explains data structures required as input to run simulations using the package so that the user can understand them. A companion article Preparing model inputs provides a practical example to illustrate how to create model inputs and some common problems encountered.

Species parameter tables

Simulation models in medfate require a data frame with species (taxon) parameter values. The package includes default data sets to be readily used. The values of the parameter table were obtained from global trait data bases, bibliographic searches, fit to empirical data or expert-based guesses:

data("SpParamsMED") # For the Spanish forest inventory (including taxon groups)
data("SpParamsES") # For the Spanish forest inventory (full taxon list)
data("SpParamsFR") # For the French forest inventory 
data("SpParamsUS") # For the US forest inventory (FIA) 

A large number of parameters (columns) can be found in species parameter tables. Not all parameters are needed for all models. You can find parameter definitions in table SpParamsDefinition, which we reproduce below:

ParameterName Definition Type Units
Name Plant names (species binomials, genus or other) used in vegetation data String NA
SpIndex Internal species codification (0,1,2,) Integer NA
AcceptedName Accepted scientific name of a taxon (genus, species, subspecies or variety) used for parameterization String NA
Species Taxonomic species of accepted name String NA
Genus Taxonomic genus of accepted name String NA
Family Taxonomic family of accepted name String NA
Order Taxonomic order of accepted name String NA
Group Either “Gymnosperm” or “Angiosperm” String NA
GrowthForm Growth form: Either “Shrub”, “Tree” or “Tree/Shrub” String Categorical
LifeForm Raunkiaer life form String Categorical
LeafShape Broad/Needle/Linear/Scale/Spines/Succulent String Categorical
LeafSize Either “Small” (< 225 mm), “Medium” (> 225 mm & < 2025 mm) or “Large” (> 2025 mm) String Categorical
PhenologyType Leaf phenology type, either “oneflush-evergreen” (new leaves develop in spring-summer), “progressive-evergreen” (new leaves develop during any season), “winter-deciduous” (leaf senescence in autumn, new leaves in spring-summer) or “winter-semideciduous” (same as before, but abscission of senescent leaves occurs when new leaves are produced). String Categorical
DispersalType Dispersal type, either wind-dispersed or animal-dispersed String Categorical
Hmed Median plant height Numeric cm
Hmax Maximum plant height Numeric cm
Z50 Depth corresponding to 50% of fine roots Numeric mm
Z95 Depth corresponding to 95% of fine roots Numeric mm
fHDmin Minimum value of height-diameter ratio Numeric NA
fHDmax Maximum value of height-diameter ratio Numeric NA
a_ash Allometric coefficient for shrub area as function of height Numeric NA
b_ash Allometric coefficient for shrub area as function of height Numeric NA
a_bsh Allometric coefficient for fine fuel shrub biomass (dry weight) Numeric NA
b_bsh Allometric coefficient for fine fuel shrub biomass (dry weight) Numeric NA
a_btsh Allometric coefficient for total fuel shrub biomass (dry weight) Numeric NA
b_btsh Allometric coefficient for total fuel shrub biomass (dry weight) Numeric NA
cr Proportion of total height corresponding to the crown (i.e. Crown length divided by total height) Numeric [0-1]
BTsh Shrub bark thickness Numeric mm
a_fbt Regression coefficient for tree foliar biomass Numeric NA
b_fbt Regression coefficient for tree foliar biomass Numeric NA
c_fbt Regression coefficient for tree foliar biomass Numeric NA
a_cr Regression coefficient for crown ratio Numeric NA
b_1cr Regression coefficient for crown ratio Numeric NA
b_2cr Regression coefficient for crown ratio Numeric NA
b_3cr Regression coefficient for crown ratio Numeric NA
c_1cr Regression coefficient for crown ratio Numeric NA
c_2cr Regression coefficient for crown ratio Numeric NA
a_cw Regression coefficient for crown width Numeric NA
b_cw Regression coefficient for crown width Numeric NA
a_bt Regression coefficient for bark thickness (mm) as function of DBH (cm) Numeric NA
b_bt Regression coefficient for bark thickness (mm) as function of DBH (cm) Numeric NA
LeafDuration Duration of leaves in year Numeric years
t0gdd Date to start the accumulation of degree days Numeric days
Sgdd Degree days for leaf budburst Numeric Degrees C
Tbgdd Base temperature for the calculation of degree days to leaf budburst Numeric Degrees C
Ssen Degree days corresponding to senescence Numeric Degrees C
Phsen Photoperiod corresponding to start counting senescence Numeric hours
Tbsen Base temperature for the calculation of degree days to senescence Numeric Degrees C
xsen Discrete values, to allow for any absent/proportional/more than proportional effects of temperature on senescence Integer {0,1,2}
ysen Discrete values, to allow for any absent/proportional/more than proportional effects of photoperiod on senescence Integer {0,1,2}
SLA Specific leaf area (mm2/mg = m2/kg) Numeric m2/kg
LeafDensity Density of leaf tissue (dry weight over volume) Numeric g/cm3
WoodDensity Wood tissue density (at 0% humidity!) Numeric g/cm3
FineRootDensity Density of fine root tissue (dry weight over volume). Numeric g/cm3
conduit2sapwood Proportion of sapwood corresponding to conducive elements (vessels or tracheids) as opposed to parenchymatic tissue. Numeric [0,1]
r635 Ratio of foliar (photosynthetic) + small branches (<6.35 mm) dry biomass to foliar (photosynthetic) dry biomass Numeric >=1
pDead Proportion of total fine fuels that are dead Numeric [0,1]
Al2As Leaf area to sapwood area ratio Numeric m2 / m2
Ar2Al Root area to leaf area ratio Numeric m2 / m2
LeafWidth Leaf width Numeric cm
SRL Specific root length Numeric cm/g
RLD Fine root length density (density of root length per soil volume) Numeric cm/cm3
maxFMC Maximum fuel moisture (in percent of dry weight) Numeric %
minFMC Minimum fuel moisture (in percent of dry weight) Numeric %
LeafPI0 Osmotic potential at full turgor of leaves Numeric Mpa
LeafEPS Modulus of elasticity (capacity of the cell wall to resist changes in volume in response to changes in turgor) of leaves Numeric Mpa
LeafAF Apoplastic fraction (proportion of water outside the living cells) in leaves Numeric %
StemPI0 Osmotic potential at full turgor of symplastic xylem tissue Numeric 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 Numeric Mpa
StemAF Apoplastic fraction (proportion of water outside the living cells) in stem xylem Numeric %
SAV Surface-area-to-volume ratio of the small fuel (1h) fraction (leaves and branches < 6.35mm) Numeric m2/m3
HeatContent High fuel heat content Numeric kJ/kg
LigninPercent Percent of lignin+cutin over dry weight in leaves Numeric %
LeafAngle The angle between the leaf plane and the horizontal plane (i.e. leaf zenith angle) Numeric degrees
LeafAngleSD Standard deviation of the leaf angle Numeric degrees
ClumpingIndex Canopy clumping index Numeric [0-1]
gammaSWR Reflectance (albedo) coefficient for SWR (gammaPAR is 0.8*gammaSWR) Numeric unitless
alphaSWR Absorbance coefficient for SWR (alphaPAR is alphaSWR*1.35) Numeric unitless
kPAR Light extinction coeficient for PAR (extinction coefficient for SWR is kPAR/1.35) Numeric unitless
g Canopy water storage capacity per LAI unit Numeric mm/LAI
Tmax_LAI Empirical coefficient relating LAI with the ratio of maximum transpiration over potential evapotranspiration. Numeric NA
Tmax_LAIsq Empirical coefficient relating squared LAI with the ratio of maximum transpiration over potential evapotranspiration. Numeric NA
Psi_Extract Water potential corresponding to 50% reduction of transpiration Numeric MPa
Exp_Extract Parameter of the Weibull function regulating transpiration reduction Numeric NA
WUE Daily water use efficiency (gross photosynthesis over transpiration) under no light, water or CO2 limitations and VPD = 1kPa Numeric g C * mm H2O-1
WUE_par Coefficient regulating the influence of % PAR on gross photosynthesis Numeric NA
WUE_co2 Coefficient regulating the influence of atmospheric CO2 concentration on gross photosynthesis Numeric NA
WUE_vpd Coefficient regulating the influence of vapor pressure deficit (VPD) on gross photosynthesis Numeric NA
Gswmin Minimum leaf conductance (cuticular+incomplete closure) at 20C Numeric mol H2O * s-1 * m-2
Gswmax Maximum stomatal conductance to water vapour Numeric mol H2O * s-1 * m-2
Gs_Toptim Temperature corresponding to maximal stomatal conductance Numeric Degrees C
Gs_Tsens Stomatal sensitivity to temperature Numeric NA
Gs_P50 Water potential causing 50% reduction in stomatal conductance Numeric MPa
Gs_slope Rate of decrease in stomatal conductance at Gs_P50 Numeric %/MPa
VCleaf_kmax Maximum leaf hydraulic conductance Numeric mmol H2O * s-1 * m-2 * MPa-1
VCleaf_P12 12% of maximum conductance of the leaf vulnerability curve Numeric MPa
VCleaf_P50 50% of maximum conductance of the leaf vulnerability curve Numeric MPa
VCleaf_P88 88% of maximum conductance of the leaf vulnerability curve Numeric MPa
VCleaf_slope Slope of the rate of leaf embolism spread at VCleaf_P50 Numeric %/MPa
Kmax_stemxylem Maximum sapwood-specific hydraulic conductivity of stem xylem Numeric kg H2O * s-1 * m-1 * Mpa-1
VCstem_P12 12% of maximum conductance of the stem vulnerability curve Numeric MPa
VCstem_P50 50% of maximum conductance of the stem vulnerability curve Numeric MPa
VCstem_P88 88% of maximum conductance of the stem vulnerability curve Numeric MPa
VCstem_slope Slope of the rate of stem embolism spread at VCleaf_P50 Numeric %/MPa
Kmax_rootxylem Maximum sapwood-specific hydraulic conductivity of root xylem Numeric kg H2O * s-1 * m-1 * Mpa-1
VCroot_P12 12% of maximum conductance of the root vulnerability curve Numeric MPa
VCroot_P50 50% of maximum conductance of the root vulnerability curve Numeric MPa
VCroot_P88 88% of maximum conductance of the root vulnerability curve Numeric MPa
VCroot_slope Slope of the rate of root embolism spread at VCleaf_P50 Numeric %/MPa
Vmax298 Maximum Rubisco carboxilation rate Numeric mmol CO2 * s-1* m-2
Jmax298 Maximum rate of electron transport at 298K Numeric mmol electrons * s-1 * m-2
Nleaf Nitrogen mass per leaf dry mass Numeric mg N / g dry
Nsapwood Nitrogen mass per sapwood dry mass Numeric mg N / g dry
Nfineroot Nitrogen mass per fine root dry mass Numeric mg N / g dry
WoodC Wood carbon content per dry mass Numeric g C / g dry
RERleaf Maintenance respiration rates for leaves. Numeric g gluc * g dry-1 * day-1
RERsapwood Maintenance respiration rates for living cells of sapwood. Numeric g gluc * g dry-1 * day-1
RERfineroot Maintenance respiration rates for fine roots. Numeric g gluc * g dry-1 * day-1
CCleaf Leaf construction costs Numeric g gluc * g dry-1
CCsapwood Sapwood construction costs Numeric g gluc * g dry-1
CCfineroot Fine root construction costs Numeric g gluc * g dry-1
RGRleafmax Maximum leaf relative growth rate Numeric m2/cm2/day
RGRsapwoodmax Maximum sapwood growth rate relative to sapwood area (for shrubs) Numeric cm2/cm2/day
RGRcambiummax Maximum sapwood growth rate relative to cambium perimeter (for trees) Numeric cm2/cm/day
RGRfinerootmax Maximum fineroot relative growth rate Numeric g dry/g dry/day
SRsapwood Sapwood daily senescence rate Numeric Day-1
SRfineroot Fine root daily senescence rate Numeric Day-1
RSSG Minimum relative starch for sapwood growth Numeric [0-1]
MortalityBaselineRate Deterministic proportion or probability specifying the baseline reduction of cohort’s density occurring in a year Numeric Year-1
SurvivalModelStep Time step in years of the empirical survival model depending on stand basal area (e.g. 10) Numeric Year
SurvivalB0 Intercept of the logistic baseline survival model depending on stand basal area Numeric NA
SurvivalB1 Slope of the logistic baseline survival model depending on stand basal area Numeric NA
SeedProductionHeight Minimum height for seed production Numeric cm
SeedMass Seed dry mass Numeric mg
SeedLongevity Seedbank average longevity Numeric yr
DispersalDistance Distance parameter for dispersal kernel Numeric m
DispersalShape Shape parameter for dispersal kernel Numeric NA
ProbRecr Probability of recruitment within the bioclimatic envelope Numeric [0-1]
MinTempRecr Minimum average temperature of the coldest month for successful recruitment Numeric Degrees C
MinMoistureRecr Minimum value of the moisture index (annual precipitation over annual PET) for successful recruitment Numeric unitless
MinFPARRecr Minimum percentage of PAR at the ground level for successful recruitment Numeric %
RecrTreeDBH Recruitment tree dbh (typically 1 cm) Numeric cm
RecrTreeHeight Recruitment tree (sapling) height Numeric cm
RecrShrubHeight Recruitment shrub height Numeric cm
RecrTreeDensity Recruitment tree (sapling) density Numeric ind/ha
RecrShrubCover Recruitment shrub cover Numeric %
RecrZ50 Recruitment depth corresponding to 50% of fine roots Numeric mm
RecrZ95 Recruitment depth corresponding to 95% of fine roots Numeric mm
RespFire Probability of resprouting after fire disturbance Numeric [0-1]
RespDist Probability of resprouting after undefined disturbance (typically desiccation) Numeric [0-1]
RespClip Probability of resprouting after clipping Numeric [0-1]
IngrowthTreeDensity Tree density when reaching DBH of ingrowth Numeric cm
IngrowthTreeDBH Tree DBH of ingrowth (typically 7.5 cm) Numeric ind/ha

In order to understand the role of parameters in the model, you should read the details of model design and formulation included in the medfatebook. Details regarding how the species parameter tables are build can be found in article Species parameterization.

Vegetation

Forest objects

Models included in medfate were primarily designed to be ran on forest inventory plots. In this kind of data, the vegetation of a sampled area is often described by several records of woody plants (trees and shrubs) along with their size and species identity. Forest plots in medfate are assumed to be in a data structure that follows closely the Spanish national forest inventory, but is simple enough to so that other forest sampling schemes can be mapped onto it.

Each forest plot is represented in an object of class forest, a list that contains several elements. Among them, the most important items are two data frames, treeData (for trees) and shrubData (for shrubs):

data(exampleforest)
exampleforest
## $treeData
##            Species   N   DBH Height Z50  Z95
## 1 Pinus halepensis 168 37.55    800 100  600
## 2     Quercus ilex 384 14.60    660 300 1000
## 
## $shrubData
##             Species Cover Height Z50  Z95
## 1 Quercus coccifera  3.75     80 200 1000
## 
## $herbCover
## [1] 10
## 
## $herbHeight
## [1] 20
## 
## $seedBank
## [1] Species Percent
## <0 rows> (or 0-length row.names)
## 
## attr(,"class")
## [1] "forest" "list"

Trees are expected to be primarily described in terms of species, diameter (DBH; cm) and height (cm), whereas shrubs are described in terms of species, percent cover (%) and mean height (cm). Root distribution has to be specified for both growth forms, in terms of the depths (mm) corresponding to 50% and 95% of cumulative fine root distribution. Functions are provided in package medfateutils to map variables in user data frames into tables treeData and shrubData. Information about the herb layer may be either absent or included in an aggregated way (i.e. without distinguishing cohorts).

While the former example illustrates the standard structure of a forest object, users may use an alternative description, based on leaf area index and crown ratio of woody cohorts and the herb layer:

data(exampleforest2)
exampleforest2
## $treeData
##            Species  N DBH Height Z50  Z95 LAI CrownRatio
## 1 Pinus halepensis NA  NA    800 100  600 0.8       0.66
## 2     Quercus ilex NA  NA    660 300 1000 0.5       0.60
## 
## $shrubData
##             Species Cover Height Z50  Z95  LAI CrownRatio
## 1 Quercus coccifera    NA     80 200 1000 0.03        0.8
## 
## $herbCover
## [1] NA
## 
## $herbHeight
## [1] 20
## 
## $herbLAI
## [1] 0.25
## 
## $seedBank
## [1] Species Percent
## <0 rows> (or 0-length row.names)
## 
## attr(,"class")
## [1] "forest" "list"

This alternative forest form is suitable for water balance simulations, but does not allow simulating forest dynamics.

Single-cohort forests

Although medfate has been designed to perform simulations on multi-cohort forests, it can also handle simulations where vegetation is described using a single cohort. Functions tree2forest() and shrub2forest() allow defining single-cohort forests from attributes. For example a holm oak (Quercus ilex) forest of 4-m height and having a leaf area index of \(2\, m^2\cdot m^{-2}\) can be defined using:

oak_forest <-tree2forest("Quercus ilex", Height= 400, LAI = 2)

The function will return a forest object where most attributes are empty:

oak_forest
## $treeData
##        Species DBH Height  N Z50 Z95 LAI
## 1 Quercus ilex  NA    400 NA  NA  NA   2
## 
## $shrubData
## [1] Species Height  Cover   Z50     Z95    
## <0 rows> (or 0-length row.names)
## 
## $herbCover
## [1] NA
## 
## $herbHeight
## [1] NA
## 
## $seedBank
## [1] Species Percent
## <0 rows> (or 0-length row.names)
## 
## attr(,"class")
## [1] "forest" "list"

Since density and diameter have not been provided, simulations in this case will be restricted to water balance. Moreover, note that when defining single-cohort forests all possible interactions with functionally distinct plants are neglected.

Aboveground and belowground data

We can use some functions to inspect how above-ground and below-ground information is represented in medfate.

For example, we can use function forest2aboveground() on the object exampleforest to show how medfate completes above-ground information:

above = forest2aboveground(exampleforest, SpParamsMED)
above
##         SP        N   DBH Cover   H        CR   LAI_live LAI_expanded LAI_dead
## T1_148 148 168.0000 37.55    NA 800 0.6605196 0.84874773   0.84874773        0
## T2_168 168 384.0000 14.60    NA 660 0.6055642 0.70557382   0.70557382        0
## S1_165 165 749.4923    NA  3.75  80 0.8032817 0.03062604   0.03062604        0

Note that the call to forest2aboveground() included the species parameter table, because species-specific allometric coefficients are needed to calculate leaf area from tree size or shrub percent cover and height. Moreover, note that the plant cohorts were given unique codes that tell us whether they correspond to trees (‘T’) or shrubs (‘S’).

Columns N, DBH and Cover describe forest structure and are required for simulating growth, but not for soil water balance, which only requires columns SP, H (in cm), CR (i.e. the crown ratio), LAI_live, LAI_expanded and LAI_dead. Therefore, one could use alternative forest description as starting point, i.e.:

above2 = forest2aboveground(exampleforest2, SpParamsMED)
above2
##         SP  N DBH Cover   H   CR LAI_live LAI_expanded LAI_dead
## T1_148 148 NA  NA    NA 800 0.66     0.80         0.80        0
## T2_168 168 NA  NA    NA 660 0.60     0.50         0.50        0
## S1_165 165 NA  NA    NA  80 0.80     0.03         0.03        0

Of course, the resulting data frame has missing values, whereas the other values are directly copied from forest.

Aboveground leaf area distribution (with or without distinguishing among cohorts) can be examined by calling function vprofile_leafAreaDensity():

vprofile_leafAreaDensity(exampleforest, SpParamsMED, byCohorts = F)

vprofile_leafAreaDensity(exampleforest, SpParamsMED, byCohorts = T)

Belowground data

Regarding belowground information, we need vectors with depths corresponding to 50% and 95% of fine roots, which we simply concatenate from our forest data:

Z50 = c(exampleforest$treeData$Z50, exampleforest$shrubData$Z50)
Z95 = c(exampleforest$treeData$Z95, exampleforest$shrubData$Z95)

These parameters specify a continuous distribution of fine roots. Users can visually inspect the distribution of fine roots of forest objects by calling function vprofile_rootDistribution():

vprofile_rootDistribution(exampleforest, SpParamsMED)

Soils

Soil physical description

Simulation models in medfate require information on the physical attributes of soil, namely soil depth, texture, bulk density and rock fragment content. Soil physical attributes can be initialized to default values, for a given number of layers, using function defaultSoilParams():

spar = defaultSoilParams(2)
print(spar)
##   widths clay sand om  bd rfc
## 1    300   25   25 NA 1.5  25
## 2    700   25   25 NA 1.5  45

where widths are soil layer widths in mm; clay and sand are the percentage of clay and sand, in percent of dry weight, om stands for organic matter, bd is bulk density (in \(g \cdot cm^{-3}\)) and rfc the percentage of rock fragments. Because soil properties vary strongly at fine spatial scales, ideally soil physical attributes should be measured on samples taken at the forest stand to be simulated. For those users lacking such data, soil properties modelled at larger scales are available via SoilGrids.org (see function soilgridsParams() in package medfateutils).

Soil input object

The soil input for simulations is an object of class soil (a list) that is created using a function with the same name:

examplesoil = soil(spar)
class(soil)
## [1] "function"

In addition to the physical soil description, this object contains soil parameters needed for soil water balance simulations:

names(examplesoil)
##  [1] "SoilDepth"    "W"            "SWE"          "Temp"         "Gsoil"       
##  [6] "dVec"         "sand"         "clay"         "om"           "VG_alpha"    
## [11] "VG_n"         "VG_theta_res" "VG_theta_sat" "Ksat"         "macro"       
## [16] "bd"           "rfc"

For example, macro specifies the macroporosity of each layer; Gsoil and Ksoil are parameters needed to model the process of evaporation from the bare soil. The meaning of all elements in the soil object can be found in the help page for function soil().

At any time, one can show the characteristics and status of the soil object using its print function:

print(examplesoil, model = "SX")
## Soil depth (mm): 1000 
## 
## Layer  1  [ 0  to  300 mm ] 
##     clay (%): 25 silt (%): 50 sand (%): 25 organic matter (%): NA [ Silt loam ]
##     Rock fragment content (%): 25 Macroporosity (%): 5 
##     Theta WP (%): 14 Theta FC (%): 30 Theta SAT (%): 49 Theta current (%) 30 
##     Vol. WP (mm): 32 Vol. FC (mm): 68 Vol. SAT (mm): 111 Vol. current (mm): 68 
##     Temperature (Celsius): NA 
## 
## Layer  2  [ 300  to  1000 mm ] 
##     clay (%): 25 silt (%): 50 sand (%): 25 organic matter (%): NA [ Silt loam ]
##     Rock fragment content (%): 45 Macroporosity (%): 5 
##     Theta WP (%): 14 Theta FC (%): 30 Theta SAT (%): 49 Theta current (%) 30 
##     Vol. WP (mm): 55 Vol. FC (mm): 117 Vol. SAT (mm): 190 Vol. current (mm): 117 
##     Temperature (Celsius): NA 
## 
## Total soil saturated capacity (mm): 300 
## Total soil water holding capacity (mm): 185 
## Total soil extractable water (mm): 116 
## Total soil current Volume (mm): 185 
## 
## Snow pack water equivalent (mm): 0 
## Soil water table depth (mm): 1000

Importantly, the soil object is used to store the degree of moisture of each soil layer. In particular, element W contains the state variable that represents moisture content - the proportion of moisture relative to field capacity - which is normally initialized to 1 for each layer:

examplesoil$W
## [1] 1 1

Advanced soil plant energy and water balance modelling requires considering the temperature of soil. Hence, Temp contains the temperature (in degrees) of soil layers:

examplesoil$Temp
## [1] NA NA

Soil layer temperatures are initialized to missing values, so that at the first time step they will be set to atmospheric temperature. While simple water balance modeling can be run using either Saxton’s or Van Genuchten’s equations as water retention curves, Van Genuchten’s model is forced for advanced modelling.

Water retention curves

The modelled moisture content of the soil depends on the water retention curve used to represent the relationship between soil volumetric water content (\(\theta\); %) and soil water potential (\(\Psi\); MPa). By default the Saxton (model = "SX") equations are used to model the water retention curve, but the user may choose to follow Van Genuchten - Mualem equations, which will give slightly different values for the same texture:

print(examplesoil, model="VG")
## Soil depth (mm): 1000 
## 
## Layer  1  [ 0  to  300 mm ] 
##     clay (%): 25 silt (%): 50 sand (%): 25 organic matter (%): NA [ Silt loam ]
##     Rock fragment content (%): 25 Macroporosity (%): 5 
##     Theta WP (%): 13 Theta FC (%): 30 Theta SAT (%): 42 Theta current (%) 30 
##     Vol. WP (mm): 29 Vol. FC (mm): 68 Vol. SAT (mm): 95 Vol. current (mm): 68 
##     Temperature (Celsius): NA 
## 
## Layer  2  [ 300  to  1000 mm ] 
##     clay (%): 25 silt (%): 50 sand (%): 25 organic matter (%): NA [ Silt loam ]
##     Rock fragment content (%): 45 Macroporosity (%): 5 
##     Theta WP (%): 13 Theta FC (%): 30 Theta SAT (%): 42 Theta current (%) 30 
##     Vol. WP (mm): 49 Vol. FC (mm): 117 Vol. SAT (mm): 163 Vol. current (mm): 117 
##     Temperature (Celsius): NA 
## 
## Total soil saturated capacity (mm): 258 
## Total soil water holding capacity (mm): 185 
## Total soil extractable water (mm): 123 
## Total soil current Volume (mm): 185 
## 
## Snow pack water equivalent (mm): 0 
## Soil water table depth (mm): 1000

While Saxton equations use texture and organic matter as inputs, the Van Genuchten-Mualem equations need other parameters, which are estimated using pedotransfer functions and their names start with VG_ (two alternative options are provided in function soil to estimate Van Genuchten parameters). The following code calls function soil_retentionCurvePlot() to illustrate the difference between the two water retention models in this soil:

soil_retentionCurvePlot(examplesoil, model="both")

Low-level functions, such as soil_psi2thetaSX() and soil_psi2thetaVG() (and their counterparts soil_theta2psiSX() and soil_theta2psiVG()), can be used to calculate volumetric soil moisture from the water potential (and viceversa) using the two models. When simulating soil water balance, the user can choose among the two models (see control parameters below).

Meteorological forcing

All simulations in the package require daily weather inputs. The minimum weather variables that are required are minimum/maximum temperature, minimum/maximum relative humidity, precipitation and radiation. Other variables like wind speed are recommended but not required. Here we show an example of meteorological forcing data.

data(examplemeteo)
head(examplemeteo)
##        dates MinTemperature MaxTemperature Precipitation MinRelativeHumidity
## 1 2001-01-01     -0.5934215       6.287950      4.869109            65.15411
## 2 2001-01-02     -2.3662458       4.569737      2.498292            57.43761
## 3 2001-01-03     -3.8541036       2.661951      0.000000            58.77432
## 4 2001-01-04     -1.8744860       3.097705      5.796973            66.84256
## 5 2001-01-05      0.3288287       7.551532      1.884401            62.97656
## 6 2001-01-06      0.5461322       7.186784     13.359801            74.25754
##   MaxRelativeHumidity Radiation WindSpeed
## 1           100.00000  12.89251  2.000000
## 2            94.71780  13.03079  7.662544
## 3            94.66823  16.90722  2.000000
## 4            95.80950  11.07275  2.000000
## 5           100.00000  13.45205  7.581347
## 6           100.00000  12.84841  6.570501

Simulation models in medfate have been designed to work along with data generated from package meteoland. The user is strongly recommended to resort to this package to obtain suitable weather input for medfate simulations.

Simulation control

Apart from data inputs, the behaviour of simulation models can be controlled using a set of global parameters. The default parameterization is obtained using function defaultControl():

control = defaultControl()
names(control)
##   [1] "fillMissingRootParams"            "fillMissingSpParams"             
##   [3] "fillMissingWithGenusParams"       "verbose"                         
##   [5] "subdailyResults"                  "standResults"                    
##   [7] "soilResults"                      "plantResults"                    
##   [9] "leafResults"                      "temperatureResults"              
##  [11] "fireHazardResults"                "fireHazardStandardWind"          
##  [13] "fireHazardStandardDFMC"           "transpirationMode"               
##  [15] "soilFunctions"                    "defaultWindSpeed"                
##  [17] "defaultCO2"                       "defaultRainfallIntensityPerMonth"
##  [19] "snowpack"                         "leafPhenology"                   
##  [21] "bareSoilEvaporation"              "unlimitedSoilWater"              
##  [23] "interceptionMode"                 "infiltrationMode"                
##  [25] "rhizosphereOverlap"               "unfoldingDD"                     
##  [27] "verticalLayerSize"                "windMeasurementHeight"           
##  [29] "segmentedXylemVulnerability"      "cavitationRefillStem"            
##  [31] "cavitationRefillLeaves"           "hydraulicRedistributionFraction" 
##  [33] "ndailysteps"                      "nsubsteps"                       
##  [35] "taper"                            "multiLayerBalance"               
##  [37] "sapFluidityVariation"             "TPhase_gmin"                     
##  [39] "Q10_1_gmin"                       "Q10_2_gmin"                      
##  [41] "rootRadialConductance"            "averageFracRhizosphereResistance"
##  [43] "thermalCapacityLAI"               "boundaryLayerSize"               
##  [45] "refillMaximumRate"                "sunlitShade"                     
##  [47] "numericParams"                    "leafCavitationEffects"           
##  [49] "stomatalSubmodel"                 "plantCapacitance"                
##  [51] "cavitationFlux"                   "soilDisconnection"               
##  [53] "leafCuticularTranspiration"       "stemCuticularTranspiration"      
##  [55] "C_SApoInit"                       "C_LApoInit"                      
##  [57] "k_SSym"                           "fractionLeafSymplasm"            
##  [59] "gs_NightFrac"                     "JarvisPAR"                       
##  [61] "fTRBToLeaf"                       "gCrown0"                         
##  [63] "subdailyCarbonBalance"            "allowDessication"                
##  [65] "allowStarvation"                  "sinkLimitation"                  
##  [67] "shrubDynamics"                    "herbDynamics"                    
##  [69] "allocationStrategy"               "phloemConductanceFactor"         
##  [71] "nonSugarConcentration"            "equilibriumOsmoticConcentration" 
##  [73] "minimumRelativeStarchForGrowth"   "constructionCosts"               
##  [75] "senescenceRates"                  "maximumRelativeGrowthRates"      
##  [77] "mortalityMode"                    "mortalityBaselineRate"           
##  [79] "mortalityRelativeSugarThreshold"  "mortalityRWCThreshold"           
##  [81] "recrTreeDBH"                      "recrTreeDensity"                 
##  [83] "ingrowthTreeDBH"                  "ingrowthTreeDensity"             
##  [85] "allowSeedBankDynamics"            "allowRecruitment"                
##  [87] "allowResprouting"                 "recruitmentMode"                 
##  [89] "removeEmptyCohorts"               "minimumTreeCohortDensity"        
##  [91] "minimumShrubCohortCover"          "dynamicallyMergeCohorts"         
##  [93] "seedRain"                         "seedProductionTreeHeight"        
##  [95] "seedProductionShrubHeight"        "probRecr"                        
##  [97] "minTempRecr"                      "minMoistureRecr"                 
##  [99] "minFPARRecr"                      "recrTreeHeight"                  
## [101] "recrShrubCover"                   "recrShrubHeight"                 
## [103] "recrTreeZ50"                      "recrShrubZ50"                    
## [105] "recrTreeZ95"                      "recrShrubZ95"

Control parameters should normally be left to their default value until their effect on simulations is fully understood.

Input objects for simulation functions

Simulation functions spwb() and growth() (and similar functions) require first combining forest, soil, species-parameter and simulation control inputs into a single input object (of class spwbInput or growthInput) that is then used as input to the corresponding simulation function along with weather data. The combination of vegetation, soil and control inputs is done via functions forest2spwbInput() and forest2growthInput(). While it complicates the code, having this additional step is handy because cohort-level parameters and state variables initialized can then be modified by the user (or an automated calibration algorithm) before calling the actual simulation functions. The input objects for functions spwb() and growth() are presented in more detail in articles Basic water balance and Forest growth, respectively.

Function fordyn() is different from the other two simulation functions, in the sense that the user enters forest, soil, species-parameter and simulation control inputs directly into the simulation function (in fact, fordyn() internally calls forest2growthInput() to initialize the input object to function growth()).