Preparing inputs

Species-specific parameters

Simulation models in medfate require a data frame with species parameter values. The package provides a default data set of parameter values for a set of Mediterranean species. The set of species are reported in the Spanish National Forest Inventory. Hence they represent woody taxa found in Spain, but may not be sufficient for other areas. 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")

A large number of parameters (columns) can be found in SpParamsMED:

names(SpParamsMED)

##   [1] "Name"                 "IFNcodes"             "SpIndex"             
##   [4] "Genus"                "Order"                "Family"              
##   [7] "Group"                "GrowthForm"           "LifeForm"            
##  [10] "LeafShape"            "LeafSize"             "PhenologyType"       
##  [13] "Hmed"                 "Hmax"                 "Z50"                 
##  [16] "Z95"                  "fHDmin"               "fHDmax"              
##  [19] "a_ash"                "b_ash"                "a_bsh"               
##  [22] "b_bsh"                "a_btsh"               "b_btsh"              
##  [25] "cr"                   "a_fbt"                "b_fbt"               
##  [28] "c_fbt"                "d_fbt"                "a_cr"                
##  [31] "b_1cr"                "b_2cr"                "b_3cr"               
##  [34] "c_1cr"                "c_2cr"                "a_cw"                
##  [37] "b_cw"                 "LeafDuration"         "t0gdd"               
##  [40] "Sgdd"                 "Tbgdd"                "Ssen"                
##  [43] "Phsen"                "Tbsen"                "xsen"                
##  [46] "ysen"                 "SLA"                  "LeafDensity"         
##  [49] "WoodDensity"          "FineRootDensity"      "conduit2sapwood"     
##  [52] "r635"                 "pDead"                "Al2As"               
##  [55] "LeafWidth"            "SRL"                  "RLD"                 
##  [58] "maxFMC"               "minFMC"               "LeafPI0"             
##  [61] "LeafEPS"              "LeafAF"               "StemPI0"             
##  [64] "StemEPS"              "StemAF"               "SAV"                 
##  [67] "HeatContent"          "LigninPercent"        "gammaSWR"            
##  [70] "alphaSWR"             "kPAR"                 "g"                   
##  [73] "Tmax_LAI"             "Tmax_LAIsq"           "Psi_Extract"         
##  [76] "Psi_Critic"           "WUE"                  "pRootDisc"           
##  [79] "Gswmin"               "Gswmax"               "VCleaf_kmax"         
##  [82] "VCleaf_c"             "VCleaf_d"             "Kmax_stemxylem"      
##  [85] "VCstem_c"             "VCstem_d"             "Kmax_rootxylem"      
##  [88] "VCroot_c"             "VCroot_d"             "Narea"               
##  [91] "Vmax298"              "Jmax298"              "WoodC"               
##  [94] "RERleaf"              "RERsapwood"           "RERfineroot"         
##  [97] "RGRleafmax"           "RGRsapwoodmax"        "RGRfinerootmax"      
## [100] "SRsapwood"            "SRfineroot"           "SeedProductionHeight"
## [103] "RecrTreeDBH"          "RecrTreeHeight"       "RecrShrubHeight"     
## [106] "RecrTreeDensity"      "RecrShrubCover"       "RecrZ50"             
## [109] "RecrZ95"              "MinTempRecr"          "MinMoistureRecr"     
## [112] "MinFPARRecr"

Not all parameters are needed for all models. The user can find parameter definitions in SpParamsDefinition:

data("SpParamsDefinition")
knitr::kable(SpParamsDefinition[,-2])

ParameterName	Definition	Type	Units
Name	Taxon names (species binomials or genus)	String	NA
IFNcodes	Codes in the forest inventory, separated by ‘/’	String	NA
SpIndex	Species index 0,1,2,…	Integer	NA
Genus	Taxonomic genus	String	NA
Order	Taxonomical order	String	NA
Family	Taxonomical family	String	NA
Group	Either “Gymnosperm” or “Angiosperm”	String	NA
GrowthForm	Growth form: Either “Shrub”, “Tree” or “Tree/Shrub”	String	NA
LifeForm	Raunkiaer life form	String	NA
LeafShape	Broad/Needle/Linear/Scale/Spines/Succulent	String	NA
LeafSize	Either “Small” (< 225 mm), “Medium” (> 225 mm & < 2025 mm) or “Large” (> 2025 mm)	String	NA
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).	NA	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]
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
d_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
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	ºC
Tbgdd	Base temperature for the calculation of degree days to leaf budburst	Numeric	ºC
Ssen	Degree days corresponding to senescence	Numeric	ºC
Phsen	Photoperiod corresponding to start counting senescence	Numeric	hours
Tbsen	Base temperature for the calculation of degree days to senescence	Numeric	ºC
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·m-2
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	%
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 stomatal closure (aprox.)	Numeric	Mpa
Psi_Critic	Water potential corresponding to 50% of stem cavitation (~ 50% PLC)	Numeric	Mpa
WUE	Water use efficiency (gross photosynthesis over transpiration)	Numeric	g C · mm H2O-1
pRootDisc	Relative root conductance leading to hydraulic disconnection from a soil layer	Numeric	[0-1]
Gswmin	Minimum stomatal conductance to water vapour	Numeric	mol H2O·s-1·m-2
Gswmax	Maximum stomatal conductance to water vapour	Numeric	mol H2O·s-1·m-2
VCleaf_kmax	Maximum leaf hydraulic conductance	Numeric	mmol H2O·s-1·m-2·MPa-1
VCleaf_c	Parameter c of the leaf vulnerability curve	Numeric	NA
VCleaf_d	Parameter d of the leaf vulnerability curve	Numeric	Mpa
Kmax_stemxylem	Maximum sapwood-specific hydraulic conductivity of stem xylem	Numeric	kg H2O·s-1·m-1·MPa-1
VCstem_c	Parameter c of the stem xylem vulnerability curve	Numeric	NA
VCstem_d	Parameter d of the stem xylem vulnerability curve	Numeric	Mpa
Kmax_rootxylem	Maximum sapwood-specific hydraulic conductivity of root xylem	Numeric	kg H2O·s-1·m-1·MPa-1
VCroot_c	Parameter c of the root xylem vulnerability curve	Numeric	NA
VCroot_d	Parameter d of the root xylem vulnerability curve	Numeric	Mpa
Narea	Nitrogen mass per leaf area	Numeric	g N·m-2
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
WoodC	Wood carbon content per dry weight	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
RGRleafmax	Maximum leaf relative growth rate	Numeric	m2/cm2/day
RGRsapwoodmax	Maximum sapwood relative growth rate (in basal area or sapwood area)	Numeric	cm2/cm2/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
SeedProductionHeight	Minimum height for seed production	Numeric	cm
RecrTreeDBH	Recruitment tree dbh	Numeric	cm
RecrTreeHeight	Recruitment tree height	Numeric	cm
RecrShrubHeight	Recruitment shrub height	Numeric	cm
RecrTreeDensity	Recruitment tree 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
MinTempRecr	Minimum average temperature of the coldest month for successful recruitment	Numeric	ºC
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	%

To fully understand the role of parameters in the model, the user should read the details of model design and formulation at https://emf-creaf.github.io/medfatebook/index.html. An example of how to define and populate a species parameter table for a given region is given in article ‘Species parameterization for Spain’.

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 format that follows closely the Spanish national forest inventory. 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(exampleforestMED)
exampleforestMED

## $ID
## [1] "1"
## 
## $patchsize
## [1] 10000
## 
## $treeData
##   Species   N   DBH Height Z50  Z95
## 1     148 168 37.55    800 750 3000
## 2     168 384 14.60    660 750 3000
## 
## $shrubData
##   Species Cover Height Z50  Z95
## 1     165  3.75     80 300 1500
## 
## $herbCover
## [1] 10
## 
## $herbHeight
## [1] 20
## 
## 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 to map variables in user data frames into tables treeData and shrubData (see function forest_mapWoodyTables()).

Aboveground and belowground data

We recommend users to define forest objects as starting point for simulations with medfate. However, simulation functions in medfate allow starting in a more general way using two data frames, one with aboveground information (i.e. the leave area and size of plants) and the other with belowground information (i.e. root distribution). The aboveground data frame does not distinguish between trees and shrubs. It includes, for each plant cohort to be considered in rows, its species identity, height and leaf area index (LAI). While users can build their input data themselves, we use function forest2aboveground() on the object exampleforestMED to show how should the data look like:

above = forest2aboveground(exampleforestMED, 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 1.00643723   1.00643723        0
## T2_168 168 384.0000 14.60    NA 660 0.6055642 0.92661573   0.92661573        0
## S1_165 165 749.4923    NA  3.75  80 0.8032817 0.03965932   0.03965932        0

Note that the call to forest2aboveground() included species parameters, because species-specific values are needed to calculate leaf area from tree diameters or shrub cover. Columns N, DBH and Cover 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. Here plant cohorts are given unique codes that tell us whether they correspond to trees or shrubs, but the user can use other row identifiers as long as they are unique. In practice, the user only needs to worry to calculate the values for LAI_live. LAI_live and LAI_expanded can contain the same LAI values, and LAI_dead is normally zero. This is so because models update LAI_expanded and LAI_dead according to the leaf phenology of species.

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

vprofile_leafAreaDensity(above, byCohorts = F)

vprofile_leafAreaDensity(above, byCohorts = T)

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(exampleforestMED$treeData$Z50, exampleforestMED$shrubData$Z50)
Z95 = c(exampleforestMED$treeData$Z95, exampleforestMED$shrubData$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(exampleforestMED, 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"         "Ksoil"       
##  [6] "Gsoil"        "dVec"         "sand"         "clay"         "om"          
## [11] "VG_alpha"     "VG_n"         "VG_theta_res" "VG_theta_sat" "Ksat"        
## [16] "Kdrain"       "macro"        "bd"           "rfc"

For example, macro specifies the macroporosity of each layer; Gsoil and Ksoil are parameters needed to model the process of water infiltration into the 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 (%): 29 Theta SAT (%): 42 Theta current (%) 29 
##     Vol. WP (mm): 30 Vol. FC (mm): 64 Vol. SAT (mm): 95 Vol. current (mm): 64 
##     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): 181 
## Total soil extractable water (mm): 118 
## Total soil current Volume (mm): 181 
## 
## 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 weather variables that are required depend on the complexity of model we are using. In the simplest case, only mean temperature, precipitation and potential evapotranspiration is required, but the more complex simulation model also requires radiation, wind speed, min/max temparature and relative humitidy. Here we show an example of meteorological forcing data.

data(examplemeteo)
head(examplemeteo)

##            MeanTemperature MinTemperature MaxTemperature Precipitation
## 2001-01-01      3.57668969     -0.5934215       6.287950      4.869109
## 2001-01-02      1.83695972     -2.3662458       4.569737      2.498292
## 2001-01-03      0.09462563     -3.8541036       2.661951      0.000000
## 2001-01-04      1.13866156     -1.8744860       3.097705      5.796973
## 2001-01-05      4.70578690      0.3288287       7.551532      1.884401
## 2001-01-06      4.57036721      0.5461322       7.186784     13.359801
##            MeanRelativeHumidity MinRelativeHumidity MaxRelativeHumidity
## 2001-01-01             78.73709            65.15411           100.00000
## 2001-01-02             69.70800            57.43761            94.71780
## 2001-01-03             70.69610            58.77432            94.66823
## 2001-01-04             76.89156            66.84256            95.80950
## 2001-01-05             76.67424            62.97656           100.00000
## 2001-01-06             89.01940            74.25754           100.00000
##            Radiation WindSpeed WindDirection       PET
## 2001-01-01  12.89251  2.000000           172 1.3212770
## 2001-01-02  13.03079  7.662544           278 2.2185985
## 2001-01-03  16.90722  2.000000           141 1.8045176
## 2001-01-04  11.07275  2.000000           172 0.9200627
## 2001-01-05  13.45205  7.581347           321 2.2914449
## 2001-01-06  12.84841  6.570501           141 1.7255058

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] "modifyInput"                         "fillMissingSpParams"                
##  [3] "verbose"                             "subdailyResults"                    
##  [5] "transpirationMode"                   "soilFunctions"                      
##  [7] "defaultWindSpeed"                    "snowpack"                           
##  [9] "leafPhenology"                       "rockyLayerDrainage"                 
## [11] "unlimitedSoilWater"                  "plantWaterPools"                    
## [13] "unfoldingDD"                         "verticalLayerSize"                  
## [15] "windMeasurementHeight"               "cavitationRefill"                   
## [17] "ndailysteps"                         "nsubsteps"                          
## [19] "cochard"                             "capacitance"                        
## [21] "taper"                               "multiLayerBalance"                  
## [23] "gainModifier"                        "costModifier"                       
## [25] "costWater"                           "klatstem"                           
## [27] "klatleaf"                            "numericParams"                      
## [29] "fracLeafResistance"                  "fracRootResistance"                 
## [31] "averageFracRhizosphereResistance"    "Catm"                               
## [33] "thermalCapacityLAI"                  "boundaryLayerSize"                  
## [35] "refillMaximumRate"                   "allowDessication"                   
## [37] "allowStarvation"                     "allowDefoliation"                   
## [39] "sinkLimitation"                      "shrubDynamics"                      
## [41] "allocationStrategy"                  "nonStomatalPhotosynthesisLimitation"
## [43] "phloemConductanceFactor"             "nonSugarConcentration"              
## [45] "equilibriumOsmoticConcentration"     "minimumRelativeSugarForGrowth"      
## [47] "respirationRates"                    "senescenceRates"                    
## [49] "constructionCosts"                   "maximumRelativeGrowthRates"         
## [51] "mortalityMode"                       "mortalityBaselineRate"              
## [53] "mortalityRelativeSugarThreshold"     "mortalityRWCThreshold"              
## [55] "recruitmentMode"                     "removeDeadCohorts"                  
## [57] "minimumCohortDensity"                "seedRain"                           
## [59] "seedProductionTreeHeight"            "seedProductionShrubHeight"          
## [61] "minTempRecr"                         "minMoistureRecr"                    
## [63] "minFPARRecr"                         "recrTreeDBH"                        
## [65] "recrTreeDensity"                     "recrTreeHeight"                     
## [67] "recrShrubCover"                      "recrShrubHeight"                    
## [69] "recrTreeZ50"                         "recrShrubZ50"                       
## [71] "recrTreeZ95"                         "recrShrubZ95"

These parameters should normally be left to their default value under 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 inputs is done via functions spwbInput() and growthInput(), respectively, or the more convenient 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 then vignettes corresponding to each model.

Function fordyn() is different from the other two models, 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()).