Package 'rsofun'

rsofun BiomeE driver data (Leuning photosynthesis model)

Description

Small dataset representing the driver to run the BiomeE-model at the CH-LAE site using the Leuning photosynthesis specification (and half-hourly time step) It can also be used together with leaf trait data from CH-LAE (biomee_validation) to optimize model parameters.

Usage

biomee_gs_leuning_drivers

Format

A tibble of driver data.

sitename

Site name

params_siml

Simulation parameters as a data.frame, including the following data:

spinup

Flag indicating whether this simulation does spin-up.

spinupyears

Number of spin-up years.

recycle

Length of standard recycling period (years).

firstyeartrend

First transient year.

nyeartrend

Number of transient years.

steps_per_day

Time resolution (day-1).

do_U_shaped_mortality

Flag indicating whether U-shaped mortality is used.

update_annualLAImax

Flag indicating whether updating LAImax according to mineral N in soil.

do_closedN_run

Flag indicating whether doing N closed runs to recover N balance enforcing 0.2 kg N m-2 in the inorganic N pool.

code_method_photosynth

String specifying the method of photosynthesis used in the model, either "pmodel" or "gs_leuning".document()

code_method_mortality

String indicating the type of mortality in the model. One of the following: "dbh" is size-dependent mortality, "const_selfthin" is constant self thinning (in development), "cstarvation" is carbon starvation, and "growthrate" is growth rate dependent mortality.

site_info

Site meta info in a data.frame. This data structure can be freely used for documenting the dataset, but must include at least the following data:

lon

Longitude of the site location.

lat

Latitude of the site location.

elv

Elevation of the site location, in meters.

forcing

Forcing data.frame used as input

ppfd

Photosynthetic photon flux density (mol s-1 m-2)

tair

Air temperature (deg C)

vpd

Vapor pressure deficit (Pa)

rain

Precipitation (kgH2O m-2 s-1 == mm s-1)

wind

Wind velocity (m s-1)

pair

Atmospheric pressure (pa)

co2

CO2 atmospheric concentration (ppm)

params_tile

Tile-level model parameters, into a single row data.frame, including the following data:

soiltype

Integer indicating the type of soil: Sand = 1, LoamySand = 2, SandyLoam = 3, SiltLoam = 4, FrittedClay = 5, Loam = 6, Clay = 7.

FLDCAP

Field capacity (vol/vol). Water remaining in a soil after it has been thoroughly saturated and allowed to drain freely.

WILTPT

Wilting point (vol/vol). Water content of a soil at which plants wilt and fail to recover.

K1

Fast soil C decomposition rate (year$^{-1}$).

K2

Slow soil C decomposition rate (year$^{-1}$).

K_nitrogen

Mineral Nitrogen turnover rate (year$^{-1}$).

MLmixRatio

Ratio of C and N returned to litters from microbes.

etaN

N loss rate through runoff (organic and mineral) (year$^{-1}$).

LMAmin

Minimum LMA, leaf mass per unit area, kg C m$^{-2}$.

fsc_fine

Fraction of fast turnover carbon in fine biomass.

fsc_wood

Fraction of fast turnover carbon in wood biomass.

GR_factor

Growth respiration factor.

l_fract

Fraction of the carbon retained after leaf drop.

retransN

Retranslocation coefficient of nitrogen.

f_initialBSW

Coefficient for setting up initial sapwood.

f_N_add

Re-fill of N for sapwood.

tf_base

Calibratable scalar for respiration, used to increase LUE levels.

par_mort

Canopy mortality parameter.

par_mort_under

Parameter for understory mortality.

params_species

A data.frame containing species-specific model parameters, with one species per row, including the following data:

lifeform

Integer set to 0 for grasses and 1 for trees.

phenotype

Integer set to 0 for deciduous and 1 for evergreen.

pt

Integer indicating the type of plant according to photosynthesis: 0 for C3; 1 for C4

alpha_FR

Fine root turnonver rate (year$^{-1}$).

rho_FR

Material density of fine roots (kg C m$^{-3}$).

root_r

Radius of the fine roots, in m.

root_zeta

e-folding parameter of root vertical distribution, in m.

Kw_root

Fine root water conductivity (mol m$^{-2}$ s$^{-1}$ MPa$^{-1}$).

leaf_size

Characteristic leaf size.

Vmax

Max RuBisCo rate, in mol m$^{-2}$ s$^{-1}$.

Vannual

Annual productivity per unit area at full sun (kg C m$^{-2}$ year$^{-2}$).

wet_leaf_dreg

Wet leaf photosynthesis down-regulation.

m_cond

Factor of stomatal conductance.

alpha_phot

Photosynthesis efficiency.

gamma_L

Leaf respiration coefficient, in year$^{-1}$.

gamma_LN

Leaf respiration coefficient per unit N.

gamma_SW

Sapwood respiration rate, in kg C m$^{-2}$ year$^{-1}$.

gamma_FR

Fine root respiration rate, kg C kg C$^{-1}$ year$^{-1}$.

tc_crit

Critical temperature triggerng offset of phenology, in Kelvin.

tc_crit_on

Critical temperature triggerng onset of phenology, in Kelvin.

gdd_crit

Critical value of GDD5 for turning ON growth season.

betaON

Critical soil moisture for phenology onset.

betaOFF

Critical soil moisture for phenology offset.

seedlingsize

Initial size of seedlings, in kg C per individual.

LNbase

Basal leaf N per unit area, in kg N m$^{-2}$.

lAImax

Maximum crown LAI (leaf area index).

Nfixrate0

Reference N fixation rate (kg N kg C$^{-1}$ root).

NfixCost0

Carbon cost of N fixation (kg C kg N$^{-1}$).

phiCSA

Ratio of sapwood area to leaf area.

mortrate_d_c

Canopy tree mortality rate (year$^{-1}$).

mortrate_d_u

Understory tree mortality rate (year$^{-1}$).

maturalage

Age at which trees can reproduce (years).

v_seed

Fraction of G_SF to G_F.

fNSmax

Multiplier for NSNmax as sum of potential bl and br.

LMA

Leaf mass per unit area (kg C m$^{-2}$).

rho_wood

Wood density (kg C m$^{-3}$).

alphaBM

Coefficient for allometry (biomass = alphaBM * DBH ** thetaBM).

thetaBM

Coefficient for allometry (biomass = alphaBM * DBH ** thetaBM).

kphio

Quantum yield efficiency $\varphi_0$, in mol mol$^{-1}$.

phiRL

Ratio of fine root to leaf area.

LAI_light

Maximum LAI limited by light.

init_cohort

A data.frame of initial cohort specifications, including the following data:

init_cohort_species

Index of a species described in param_species.

init_cohort_nindivs

Initial individual density, in individuals per m$^{2}$.

init_cohort_bsw

Initial biomass of sapwood, in kg C per individual.

init_cohort_bHW

Initial biomass of heartwood, in kg C per tree.

init_cohort_nsc

Initial non-structural biomass.

init_soil

A data.frame of initial soil pools, including the following data:

init_fast_soil_C

Initial fast soil carbon, in kg C m$^{-2}$.

init_slow_soil_C

Initial slow soil carbon, in kg C m$^{-2}$.

init_Nmineral

Mineral nitrogen pool, in kg N m$^{-2}$.

N_input

Annual nitrogen input to soil N pool, in kg N m$^{-2}$ year$^{-1}$.

---

rsofun BiomeE (gs_leuning) output data

Description

Example output dataset from a BiomeE-model run (gs_leuning)

Usage

biomee_gs_leuning_output

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1 rows and 2 columns.

---

rsofun BiomeE driver data (P-model photosynthesis model)

Description

Small dataset representing the driver to run the BiomeE-model at the CH-LAE site using the P-model photosynthesis specification (and daily time step). It can also be used together with leaf trait data from CH-LAE (biomee_validation) to optimize model parameters.

Usage

biomee_p_model_drivers

Format

See biomee_gs_leuning_drivers

---

rsofun BiomeE (P-model) output data

Description

Example output dataset from a BiomeE-model run (p-model)

Usage

biomee_p_model_output

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1 rows and 2 columns.

---

rsofun BiomeE targets validation data

Description

Small example dataset of target observations (leaf trait data) at the CH-LAE site to optimize model parameters with the function calib_sofun

Usage

biomee_validation

Format

A tibble of validation data:

sitename

site name

data

validation data

Source

Lukas Hörtnagl, Werner Eugster, Nina Buchmann, Eugenie Paul-Limoges, Sophia Etzold, Matthias Haeni, Peter Pluess, Thomas Baur (2004-2014) FLUXNET2015 CH-Lae Laegern, Dataset. https://doi.org/10.18140/FLX/1440134

---

Calibrates SOFUN model parameters

Description

This is the main function that handles the calibration of SOFUN model parameters.

Usage

calib_sofun(drivers, obs, settings, optim_out = TRUE, ...)

Arguments

drivers	A data frame with driver data. See p_model_drivers for a description of the data structure.
obs	A data frame containing observational data used for model calibration. See p_model_validation for a description of the data structure.
settings	A list containing model calibration settings. See the 'P-model usage' vignette for more information and examples. method A string indicating the optimization method, either 'GenSA' or 'BayesianTools'. par A list of model parameters. For each parameter, an initial value and lower and upper bounds should be provided. The calibratable parameters include model parameters 'kphio', 'kphio_par_a', 'kphio_par_b', 'soilm_thetastar', 'soilm_betao', 'beta_costunitratio', 'rd_to_vcmax', 'tau_acclim', 'kc_jmax' and 'rootzone_whc' , and (if doing Bayesian calibration) error parameters for each target variable, named for example 'err_gpp'. This list must match the input parameters of the calibration metric and the parameters should be given in the order above. metric A cost function. See the 'Cost functions for parameter calibration' vignette for examples. control A list of arguments passed on to the optimization function. If method = 'GenSA', see GenSA. If method = 'BayesianTools' the list should include at least settings and sampler, see BayesianTools::runMCMC.
optim_out	A logical indicating whether the function returns the raw output of the optimization functions (defaults to TRUE).
...	Optional arguments passed on to the cost function specified as settings$metric. .

Value

A named list containing the calibrated parameter vector 'par' and the output object from the optimization 'mod'. For more details on this output and how to evaluate it, see runMCMC (also this post) and GenSA.

Examples

# Fix model parameters that won't be calibrated
params_fix <- list(
  kphio_par_a        = 0,
  kphio_par_b        = 1.0,
  soilm_thetastar    = 0.6*240,
  soilm_betao        = 0.01,
  beta_unitcostratio = 146,
  rd_to_vcmax        = 0.014,
  tau_acclim         = 30,
  kc_jmax            = 0.41
)

# Define calibration settings
settings <- list(
  method = "BayesianTools",
  par = list(
    kphio = list(lower=0.04, upper=0.09, init=0.05),
    err_gpp = list(lower = 0.01, upper = 4, init = 2)
  ),
  metric = rsofun::cost_likelihood_pmodel,
  control = list(
    sampler = "DEzs",
    settings = list(
      nrChains = 1,
      burnin = 0,        
      iterations = 50     # kept artificially low
    )
  )
 )
 
 # Run the calibration for GPP data
 calib_output <- rsofun::calib_sofun(
   drivers = rsofun::p_model_drivers,
   obs = rsofun::p_model_validation,
   settings = settings,
   # extra arguments for the cost function
   par_fixed = params_fix,
   targets = c("gpp")
 )

---

Log-likelihood cost function for BiomeE with different targets

Description

Cost function for parameter calibration, which computes the log-likelihood for the biomee model fitting several target variables for a given set of parameters.

Usage

cost_likelihood_biomee(par, obs, drivers, targets)

Arguments

par	A vector containing parameter values for 'phiRL', 'LAI_light', 'tf_base', 'par_mort' in that order, and for the error terms corresponding to the target variables, e.g. 'err_GPP' if GPP is a target. Make sure that the order of the error terms in par coincides with the order provided in the targets argument.
obs	A nested data frame of observations, following the structure of biomee_validation, for example.
drivers	A nested data frame of driver data, for example biomee_gs_leuning_drivers.
targets	A character vector indicating the target variables for which the optimization will be done. This should be a subset of c("GPP", "LAI", "Density", "Biomass").

Details

The cost function performs a BiomeE model run for the value of par given as argument. The likelihood is calculated assuming that the predicted targets are independent, normally distributed and centered on the observations. The optimization should be run using BayesianTools, so the likelihood is maximized.

Value

The log-likelihood of the simulated targets by the biomee model versus the observed targets.

Examples

# Compute the likelihood for a set of
# BiomeE model parameter values
# and the example data
cost_likelihood_biomee(
 par = c(3.5, 3.5, 1, 1,    # model params
         0.5),              # err_GPP
 obs = biomee_validation,
 drivers = biomee_gs_leuning_drivers,
 targets = c("GPP")
)

---

Cost function computing a log-likelihood for calibration of P-model parameters

Description

The cost function performs a P-model run for the input drivers and model parameter values, and computes the outcome's normal log-likelihood centered at the input observed values and with standard deviation given as an input parameter (calibratable).

Usage

cost_likelihood_pmodel(
  par,
  obs,
  drivers,
  targets,
  par_fixed = NULL,
  parallel = FALSE,
  ncores = 2
)

Arguments

par	A vector of values for the parameters to be calibrated, including a subset of model parameters (described in runread_pmodel_f), in order, and error terms for each target variable (for example 'gpp_err'), in the same order as the targets appear in targets.
obs	A nested data.frame of observations, with columns 'sitename' and 'data' (see p_model_validation or p_model_validation_vcmax25 to check their structure).
drivers	A nested data.frame of driver data. See p_model_drivers for a description of the data structure.
targets	A character vector indicating the target variables for which the optimization will be done and the RMSE computed. This string must be a column name of the data data.frame belonging to the validation nested data.frame (for example 'gpp').
par_fixed	A named list of model parameter values to keep fixed during the calibration. These should complement the input par such that all model parameters are passed on to runread_pmodel_f.
parallel	A logical specifying whether simulations are to be parallelised (sending data from a certain number of sites to each core). Defaults to FALSE.
ncores	An integer specifying the number of cores used for parallel computing. Defaults to 2.

Details

To run the P-model, all model parameters must be given. The cost function uses arguments par and par_fixed such that, in the calibration routine, par can be updated by the optimizer and par_fixed are kept unchanged throughout calibration.

If the validation data contains a "date" column (fluxes), the simulated target time series is compared to the observed values on those same dates (e.g. for GPP). Otherwise, there should only be one observed value per site (leaf traits), and the outputs (averaged over the growing season, weighted by predicted GPP) will be compared to this single value representative of the site (e.g. Vcmax25). As an exception, when the date of a trait measurement is available, it will be compared to the trait value predicted on that date.

Value

The log-likelihood of the observed target values, assuming that they are independent, normally distributed and centered on the predictions made by the P-model run with standard deviation given as input (via 'par' because the error terms are estimated through the calibration with 'BayesianTools', as shown in the "Parameter calibration and cost functions" vignette).

Examples

# Compute the likelihood for a set of 
# model parameter values involved in the
# temperature dependence of kphio 
# and example data
cost_likelihood_pmodel(
 par = c(0.05, -0.01, 1,     # model parameters
         2),                # err_gpp
 obs = p_model_validation,
 drivers = p_model_drivers,
 targets = c('gpp'),
 par_fixed = list(
  soilm_thetastar    = 0.6 * 240,  # old setup with soil moisture stress
  soilm_betao        = 0.0,
  beta_unitcostratio = 146.0,
  rd_to_vcmax        = 0.014,      # from Atkin et al. 2015 for C3 herbaceous
  tau_acclim         = 30.0,
  kc_jmax            = 0.41
 )
)

---

RMSE cost function for BiomeE

Description

Cost function for parameter calibration, which computes the root mean squared error (RMSE) between BiomeE simulations (using the input set of parameters) and observed target variables. Cost function for parameter calibration, which computes the RMSE for the biomee model fitting target variables 'GPP','LAI','Density' and 'Biomass' for a given set of parameters.

Usage

cost_rmse_biomee(par, obs, drivers)

Arguments

par	A vector containing parameter values for 'phiRL', 'LAI_light', 'tf_base', 'par_mort' in that order.
obs	A nested data frame of observations, following the structure of biomee_validation, for example.
drivers	A nested data frame of driver data, for example biomee_gs_leuning_drivers.

Value

The root mean squared error (RMSE) between the observed and simulated values of 'GPP','LAI','Density' and 'Biomass' (all variables have the same weight). Relative errors (difference divided by observed values) are used instead of absolute errors. The cost function performs a BiomeE model run for parameter values par and model drivers drivers given as arguments, producing the simulated values used to compute the RMSE.

Examples

# Compute RMSE for a set of
# model parameter values
# and example data
cost_rmse_biomee(
 par = c(3.5, 3.5, 1, 1),
 obs = biomee_validation,
 drivers = biomee_gs_leuning_drivers
)

---

Cost function computing RMSE for calibration of P-model parameters

Description

The cost function performs a P-model run for the input drivers and parameter values, and compares the output to observations of various targets by computing the root mean squared error (RMSE).

Usage

cost_rmse_pmodel(
  par,
  obs,
  drivers,
  targets,
  par_fixed = NULL,
  target_weights = NULL,
  parallel = FALSE,
  ncores = 2
)

Arguments

par	A vector of values for the parameters to be calibrated (a subset of those described in runread_pmodel_f, in order).
obs	A nested data.frame of observations, with columns 'sitename' and 'data' (see p_model_validation or p_model_validation_vcmax25 to check their structure).
drivers	A nested data.frame of driver data. See p_model_drivers for a description of the data structure.
targets	A character vector indicating the target variables for which the optimization will be done and the RMSE computed. This string must be a column name of the data data.frame belonging to the validation nested data.frame (for example 'gpp').
par_fixed	A named list of model parameter values to keep fixed during the calibration. These should complement the input par such that all model parameters are passed on to runread_pmodel_f.
target_weights	A vector of weights to be used in the computation of the RMSE if using several targets. By default (target_weights = NULL) the RMSE is computed separately for each target and then averaged. The provided weights are used to compute a weighted average of RMSE across targets.
parallel	A logical specifying whether simulations are to be parallelised (sending data from a certain number of sites to each core). Defaults to FALSE.
ncores	An integer specifying the number of cores used for parallel computing. Defaults to 2.

Details

To run the P-model, all model parameters must be given. The cost function uses arguments par and par_fixed such that, in the calibration routine, par can be updated by the optimizer and par_fixed are kept unchanged throughout calibration.

If the validation data contains a "date" column (fluxes), the simulated target time series is compared to the observed values on those same dates (e.g. for GPP). Otherwise, there should only be one observed value per site (leaf traits), and the outputs (averaged over the growing season, weighted by predicted GPP) will be compared to this single value representative of the site (e.g. Vcmax25). As an exception, when the date of a trait measurement is available, it will be compared to the trait value predicted on that date.

Value

The root mean squared error (RMSE) between observed values and P-model predictions. The RMSE is computed for each target separately and then aggregated (mean or weighted average).

Examples

# Compute RMSE for a set
# of model parameter values
# and example data
cost_rmse_pmodel(
 par = c(0.05, -0.01, 0.5),  # kphio related parameters
 obs = p_model_validation,
 drivers = p_model_drivers,
 targets = c('gpp'),
 par_fixed = list(
  soilm_thetastar    = 0.6 * 240,  # old setup with soil moisture stress
  soilm_betao        = 0.0,
  beta_unitcostratio = 146.0,
  rd_to_vcmax        = 0.014,      # from Atkin et al. 2015 for C3 herbaceous
  tau_acclim         = 30.0,
  kc_jmax            = 0.41
 )
)

---

Initialises a tibble with dates

Description

Creates a tibble with rows for each date from 'yrstart' to 'yrend' in 'yyyy-mm-dd' format. Intervals of dates are specified by argument 'freq'. ddf <- init_dates_dataframe(2000, 2003, startmoy=1, startdoy=1, freq="days", endmoy=12, enddom=31, noleap=FALSE)

Usage

init_dates_dataframe(
  yrstart,
  yrend,
  startmoy = 1,
  startdoy = 1,
  freq = "days",
  endmoy = 12,
  enddom = 31,
  noleap = FALSE
)

Arguments

yrstart	An integer defining the start year of dates covered by the dataframe.
yrend	An integer defining the end year of dates covered by the dataframe.
startmoy	An integer defining the start month-of-year of dates covered by the dataframe. Defaults to 1.
startdoy	An integer defining the start day-of-year of dates covered by the dataframe. Defaults to 1.
freq	A character string specifying the time steps of dates (in rows). Defaults to "days". Any of "days", "months", "years". If freq = "months" the 15$^{th}$ day of the months is used as date, and if freq = "years" the 1$^{st}$ of January of each year is returned.
endmoy	An integer defining the end month-of-year of dates covered by the dataframe. Defaults to 12.
enddom	An integer defining the end day-of-year of dates covered by the dataframe. Defaults to 31.
noleap	Whether leap years are ignored, that is, whether the 29$^{th}$ of February is removed. Defaults to FALSE.

Value

A tibble with dates.

---

rsofun P-model driver data

Description

Small dataset representing the driver to run the P-model at the FR-Pue site. It can also be used together with daily GPP flux time series data from CH-LAE (p_model_validation) to optimize model parameters. To optimize model parameters to leaf traits data use the datasets p_model_drivers_vcmax25 and p_model_validation_vcmax25.

Usage

p_model_drivers

Format

A tibble of driver data:

sitename

A character string containing the site name.

forcing

A tibble of a time series of forcing climate data, including the following data:

date

Date of the observation in YYYY-MM-DD format.

temp

Daytime average air temperature in $^\circ$C.

vpd

Daytime average vapour pressure deficit in Pa.

ppfd

Photosynthetic photon flux density (PPFD) in mol m$^{-2}$ s$^{-1}$. If all values are NA, it indicates that PPFD should be calculated by the SPLASH model.

netrad

Net radiation in W m$^{-2}$. This is currently ignored as a model forcing.

patm

Atmospheric pressure in Pa.

snow

Snow in water equivalents mm s$^{-1}$.

rain

Rain as precipitation in liquid form in mm s$^{-1}$.

tmin

Daily minimum air temperature in $^\circ$C.

tmax

Daily maximum air temperature in $^\circ$C.

fapar

Fraction of photosynthetic active radiation (fAPAR), taking values between 0 and 1.

co2

Atmospheric CO$_2$ concentration.

ccov

Cloud coverage in %. This is only used when either PPFD or net radiation are not prescribed.

params_siml

A tibble of simulation parameters, including the following data:

spinup

A logical value indicating whether this simulation does spin-up.

spinupyears

Number of spin-up years.

recycle

Length of standard recycling period, in years.

outdt

An integer indicating the output periodicity.

ltre

A logical value, TRUE if evergreen tree.

ltne

A logical value, TRUE if evergreen tree and N-fixing.

ltrd

A logical value, TRUE if deciduous tree.

ltnd

A logical value, TRUE if deciduous tree and N-fixing.

lgr3

A logical value, TRUE if grass with C3 photosynthetic pathway.

lgn3

A logical value, TRUE if grass with C3 photosynthetic pathway and N-fixing.

lgr4

A logical value, TRUE if grass with C4 photosynthetic pathway.

site_info

A tibble containing site meta information. This data structure can be freely used for documenting the dataset, but must include at least the following data:

lon

Longitude of the site location in degrees east.

lat

Latitude of the site location in degrees north.

elv

Elevation of the site location, in meters above sea level.

whc

A numeric value for the rooting zone water holding capacity (in mm)

Source

Pastorello, G., Trotta, C., Canfora, E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020). https://doi.org/10.1038/s41597-020-0534-3

University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681

Weedon, G. P., G. Balsamo, N. Bellouin,S. Gomes, M. J. Best, and P. Viterbo(2014), The WFDEI meteorologicalforcing data set: WATCH Forcing Datamethodology applied to ERA-Interimreanalysis data, Water Resour. Res.,50,7505–7514, doi:10.1002/2014WR015638.

Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

---

rsofun P-model driver data (for leaf traits)

Description

Small dataset representing the driver to run the P-model at four separate sites. It can also be used together with leaf traits data from these four sites (p_model_validation_vcmax25) to optimize model parameters. To optimize model parameters to GPP flux data use the datasets p_model_drivers and p_model_validation.

Usage

p_model_drivers_vcmax25

Format

See p_model_drivers

Source

Atkin, O. K., Bloomfield, K. J., Reich, P. B., Tjoelker, M. G., Asner, G. P., Bonal, D., et al. (2015). Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. New Phytol. 206 (2), 614–636. doi:10.1111/nph.13253

University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681

Weedon, G. P., G. Balsamo, N. Bellouin,S. Gomes, M. J. Best, and P. Viterbo(2014), The WFDEI meteorologicalforcing data set: WATCH Forcing Datamethodology applied to ERA-Interimreanalysis data, Water Resour. Res.,50,7505–7514, doi:10.1002/2014WR015638.

Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

C.D. Keeling, R.B. Bacastow, A.E. Bainbridge, C.A. Ekdahl, P.R. Guenther, and L.S. Waterman, (1976), Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii, Tellus, vol. 28, 538-551

---

rsofun P-model output data

Description

Example output dataset from a p-model run using p_model_drivers

Usage

p_model_output

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1 rows and 3 columns.

---

rsofun P-model output data (using vcmax25 drivers)

Description

Example output dataset from a p-model run using p_model_drivers_vcmax25

Usage

p_model_output_vcmax25

Format

An object of class tbl_df (inherits from tbl, data.frame) with 4 rows and 3 columns.

---

rsofun P-model GPP validation data

Description

Small example dataset of target observations (daily GPP flux data) to optimize model parameters with the function calib_sofun

Usage

p_model_validation

Format

A tibble of validation data:

sitename

A character string containing the site name (e.g. 'FR-Pue').

data

A tibble [ 2,920 x 3 ] with time series for the following variables:

date

Date vector with format YYYY-MM-DD.

gpp

The observed Gross Primary Productivity (GPP) for each time stamp (in gC m$^{-2}$ d$^{-1}$).

gpp_unc

The uncertainty of the GPP (in gC m$^{-2}$ d$^{-1}$).

Source

Pastorello, G., Trotta, C., Canfora, E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020). https://doi.org/10.1038/s41597-020-0534-3

Examples

require(ggplot2); require(tidyr)
p_model_validation %>% tidyr::unnest(data)

---

rsofun P-model Vcmax25 validation data

Description

Small example dataset of target observations (leaf trait data) to optimize model parameters with the function calib_sofun

Usage

p_model_validation_vcmax25

Format

A tibble of validation data:

sitename

A character string containing the site names (e.g. 'Reichetal_Colorado').

data

A tibble [ 1 x 2 ] with observations for the following variables:

vcmax25

The observed maximum rate of carboxylation (Vcmax), normalised to 25$^o$ C (in mol C m$^{-2}$ d$^{-1}$), aggregated over different plant species in each site.

vcmax25_unc

The uncertainty of the Vcmax25 (in mol C m$^{-2}$ d$^{-1}$), calculated as the standard deviation among Vcmax25 observations for several species per site or as the total standard deviation across sites for single-plant-species sites.

Source

Atkin, O. K., Bloomfield, K. J., Reich, P. B., Tjoelker, M. G., Asner, G. P., Bonal, D., et al. (2015). Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. New Phytol. 206 (2), 614–636. doi:10.1111/nph.13253

Examples

require(ggplot2); require(tidyr)
p_model_validation_vcmax25 %>% tidyr::unnest(data)

---

Run BiomeE (R wrapper)

Description

Run BiomeE Fortran model on single site.

Usage

run_biomee_f_bysite(
  sitename,
  params_siml,
  site_info,
  forcing,
  params_tile,
  params_species,
  init_cohort,
  init_soil,
  makecheck = TRUE
)

Arguments

sitename	Site name.
params_siml	Simulation parameters. See examples biomee_gs_leuning_drivers or biomee_p_model_drivers
site_info	Site meta info in a data.frame. See examples biomee_gs_leuning_drivers or biomee_p_model_drivers
forcing	Forcing data.frame used as input. See examples biomee_gs_leuning_drivers or biomee_p_model_drivers
params_tile	Tile-level model parameters, into a single row data.frame. See examples biomee_gs_leuning_drivers or biomee_p_model_drivers
params_species	A data.frame containing species-specific model parameters, with one species per row. See examples biomee_gs_leuning_drivers or biomee_p_model_drivers
init_cohort	A data.frame of initial cohort specifications. See examples biomee_gs_leuning_drivers or biomee_p_model_drivers
init_soil	A data.frame of initial soil pools. See examples biomee_gs_leuning_drivers or biomee_p_model_drivers
makecheck	Flag specifying whether checks are performed to verify model inputs and parameters.

Value

Model output is provided as a list, with elements:

output_hourly_tile

A data.frame containing hourly predictions .

year

Year of the simulation.

doy

Day of the year.

hour

Hour of the day.

rad

Radiation, in W m$^{-2}$.

Tair

Air temperature, in Kelvin.

Prcp

Precipitation, in mm m$^{-2}$.

GPP

Gross primary production (kg C m$^{-2}$ hour$^{-1}$).

Resp

Plant respiration (kg C m$^{-2}$ hour$^{-1}$).

Transp

Transpiration (mm m$^{-2}$).

Evap

Evaporation (mm m$^{-2}$).

Runoff

Water runoff (mm m$^{-2}$).

Soilwater

Soil water content in root zone (kg m$^{-2}$).

wcl

Volumetric soil water content for each layer (vol/vol).

FLDCAP

Field capacity (vol/vol).

WILTPT

Wilting point (vol/vol).

output_daily_tile

A data.frame with daily outputs at a tile level.

year

Year of the simulation.

doy

Day of the year.

Tc

Air temperature (Kelvin).

Prcp

Precipitation (mm m$^{-2}$).

totWs

Soil water content in root zone (kg m$^{-2}$).

Trsp

Transpiration (mm m$^{2-}$).

Evap

Evaporation (mm m$^{-2}$).

Runoff

Water runoff (mm m$^{-2}$).

ws1

Volumetric soil water content for layer 1.

ws2

Volumetric soil water content for layer 2.

ws3

Volumetric soil water content for layer 3.

LAI

Leaf area index (m$^2$/m$^2$).

GPP

Gross primary production (kg C m$^{-2}$ day$^{-1}$).

Rauto

Plant autotrophic respiration (kg C m$^{-2}$ day$^{-1}$).

Rh

Heterotrophic respiration (kg C m$^{-2}$ day$^{-1}$).

NSC

Non-structural carbon (kg C m$^{-2}$).

seedC

Biomass of seeds (kg C m$^{-2}$).

leafC

Biomass of leaves (kg C m$^{-2}$).

rootC

Biomass of fine roots (kg C m$^{-2}$).

SW_C

Biomass of sapwood (kg C m$^{-2}$).

HW_C

biomass of heartwood (kg C m$^{-2}$).

NSN

Non-structural N pool (kg N m$^{-2}$).

seedN

Nitrogen of seeds (kg N m$^{-2}$).

leafN

Nitrogen of leaves (kg N m$^{-2}$).

rootN

Nitrogen of roots (kg N m$^{-2}$).

SW_N

Nitrogen of sapwood (kg N m$^{-2}$).

HW_N

Nitrogen of heartwood (kg N m$^{-2}$).

McrbC

Microbial carbon (kg C m$^{-2}$).

fastSOM

Fast soil carbon pool (kg C m$^{-2}$).

slowSOM

Slow soil carbon pool (kg C m$^{-2}$).

McrbN

Microbial nitrogen (kg N m$^{-2}$).

fastSoilN

Fast soil nitrogen pool (kg N m$^{-2}$).

slowSoilN

Slow soil nitrogen pool (kg N m$^{-2}$).

mineralN

Mineral nitrogen pool (kg N m$^{-2}$).

N_uptk

Nitrogen uptake (kg N m$^{-2}$).

output_daily_cohorts

A data.frame with daily predictions for each canopy cohort.

year

Year of the simulation.

doy

Day of the year.

hour

Hour of the day.

cID

An integer indicating the cohort identity.

PFT

An integer indicating the Plant Functional Type.

layer

An integer indicating the crown layer, numbered from top to bottom.

density

Number of trees per area (trees ha$^{-1}$).

f_layer

Fraction of layer area occupied by this cohort.

LAI

Leaf area index (m$^2$/m$^2$).

gpp

Gross primary productivity (kg C tree$^{-1}$ day$^{-1}$).

resp

Plant autotrophic respiration (kg C tree$^{-1}$ day$^{-1}$).

transp

Transpiration (mm tree$^{-1}$ day$^{-1}$).

NPPleaf

Carbon allocated to leaves (kg C tree$^{-1}$ day$^{-1}$).

NPProot

Carbon allocated to fine roots (kg C tree$^{-1}$ day$^{-1}$).

NPPwood

Carbon allocated to wood (kg C tree$^{-1}$ day$^{-1}$).

NSC

Nonstructural carbohydrates of a tree in this cohort (kg C tree$^{-1}$).

seedC

Seed biomass of a tree in this cohort (kg C tree$^{-1}$).

leafC

Leaf biomass of a tree in this cohort (kg C tree$^{-1}$).

rootC

Fine root biomass of a tree in this cohort (kg C tree$^{-1}$).

SW_C

Sapwood biomass of a tree in this cohort (kg C tree$^{-1}$).

HW_C

Heartwood biomass of a tree in this cohort (kg C tree$^{-1}$).

NSN

Nonstructural nitrogen of a tree in this cohort (kg N tree$^{-1}$).

seedN

Seed nitrogen of a tree in this cohort (kg N tree$^{-1}$).

leafN

Leaf nitrogen of a tree in this cohort (kg N tree$^{-1}$).

rootN

Fine root nitrogen of a tree in this cohort (kg N tree$^{-1}$).

SW_N

Sapwood nitrogen of a tree in this cohort (kg N tree$^{-1}$).

HW_N

Heartwood nitrogen of a tree in this cohort (kg N tree$^{-1}$).

output_annual_tile

A data.frame with annual outputs at tile level.

year

Year of the simulation.

CAI

Crown area index (m$^2$/m$^2$).

LAI

Leaf area index (m$^2$/m$^2$).

Density

Number of trees per area (trees ha$^{-1}$).

DBH

Diameter at tile level (cm).

Density12

Tree density for trees with DBH > 12 cm (individuals ha$^{-1}$).

DBH12

Diameter at tile level considering trees with DBH > 12 cm (cm).

QMD12

Quadratic mean diameter at tile level considering trees with DBH > 12 cm (cm).

NPP

Net primary productivity (kg C m$^{-2}$ yr$^{-1}$).

GPP

Gross primary productivity (kg C m$^{-2}$ yr$^{-1}$).

Rauto

Plant autotrophic respiration (kg C m$^{-2}$ yr$^{-1}$).

Rh

Heterotrophic respiration (kg C m$^{-2}$ yr$^{-1}$).

rain

Annual precipitation (mm m$^{-2}$ yr$^{-1}$).

SoilWater

Soil water content in root zone (kg m$^{-2}$).

Transp

Transpiration (mm m$^{-2}$ yr$^{-1}$).

Evap

Evaporation (mm m$^{-2}$ yr$^{-1}$).

Runoff

Water runoff (mm m$^{-2}$ yr$^{-1}$).

plantC

Plant biomass (kg C m$^{-2}$).

soilC

Soil carbon (kg C m$^{-2}$).

plantN

Plant nitrogen (kg N m$^{-2}$).

soilN

Soil nitrogen (kg N m$^{-2}$).

totN

Total nitrogen in plant and soil (kg N m$^{-2}$).

NSC

Nonstructural carbohydrates (kg C m$^{-2}$).

SeedC

Seed biomass (kg C m$^{-2}$).

leafC

Leaf biomass (kg C m$^{-2}$).

rootC

Fine root biomass (kg C m$^{-2}$).

SapwoodC

Sapwood biomass (kg C m$^{-2}$).

WoodC

Heartwood biomass (kg C m$^{-2}$).

NSN

Nonstructural nitrogen (kg N m$^{-2}$).

SeedN

Seed nitrogen (kg N m$^{-2}$).

leafN

Leaf nitrogen (kg N m$^{-2}$).

rootN

Fine root nitrogen (kg N m$^{-2}$).

SapwoodN

Sapwood nitrogen (kg N m$^{-2}$).

WoodN

Heartwood nitrogen (kg N m$^{-2}$).

McrbC

Microbial carbon (kg C m$^{-2}$).

fastSOM

Fast soil carbon pool (kg C m$^{-2}$).

SlowSOM

Slow soil carbon pool (kg C m$^{-2}$).

McrbN

Microbial nitrogen (kg N m$^{-2}$).

fastSoilN

Fast soil nitrogen pool (kg N m$^{-2}$).

slowsoilN

Slow soil nitrogen pool (kg N m$^{-2}$).

mineralN

Mineral nitrogen pool (kg N m$^{-2}$).

N_fxed

Nitrogen fixation (kg N m$^{-2}$).

N_uptk

Nitrogen uptake (kg N m$^{-2}$).

N_yrMin

Annual available nitrogen (kg N m$^{-2}$).

N_P25

Annual nitrogen from plants to soil (kg N m$^{-2}$).

N_loss

Annual nitrogen loss (kg N m$^{-2}$).

totseedC

Total seed carbon (kg C m$^{-2}$).

totseedN

Total seed nitrogen (kg N m$^{-2}$).

Seedling_C

Total carbon from all compartments but seeds (kg C m$^{-2}$).

Seeling_N

Total nitrogen from all compartments but seeds (kg N m$^{-2}$).

MaxAge

Age of the oldest tree in the tile (years).

MaxVolume

Maximum volumne of a tree in the tile (m$^3$).

MaxDBH

Maximum DBH of a tree in the tile (m).

NPPL

Growth of a tree, including carbon allocated to leaves (kg C m$^{-2}$ year$^{-1}$).

NPPW

Growth of a tree, including carbon allocated to sapwood (kg C m$^{-2}$ year$^{-1}$).

n_deadtrees

Number of trees that died (trees m$^{-2}$ year$^{-1}$).

c_deadtrees

Carbon biomass of trees that died (kg C m$^{-2}$ year$^{-1}$).

m_turnover

Continuous biomass turnover (kg C m$^{-2}$ year$^{-1}$).

c_turnover_time

Carbon turnover rate, calculated as the ratio between plant biomass and NPP (year$^{-1}$).

output_annual_cohorts

A data.frame of annual outputs at the cohort level.

year

Year of the simulation.

cID

An integer indicating the cohort identity.

PFT

An integer indicating the Plant Functional Type.

layer

An integer indicating the crown layer, numbered from top to bottom.

density

Number of trees per area (trees ha$^{-1}$).

f_layer

Fraction of layer area occupied by this cohort.

dDBH

Diameter growth of a tree in this cohort (cm year$^{-1}$).

dbh

Tree diameter (cm).

height

Tree height (m).

age

Age of the cohort (years).

Acrow

Crown area of a tree in this cohort (m$^2$).

wood

Sum of sapwood and heartwood biomass of a tree in this cohort (kg C tree$^{-1}$).

nsc

Nonstructural carbohydrates in a tree (kg C tree$^{-1}$).

NSN

Nonstructural nitrogen of a tree (kg N tree$^{-1}$).

NPPtr

Total growth of a tree, including carbon allocated to seeds, leaves, fine roots, and sapwood (kg C tree$^{-1}$ year$^{-1}$).

seed

Fraction of carbon allocated to seeds to total growth.

NPPL

Fraction of carbon allocated to leaves to total growth.

NPPR

Fraction of carbon allocated to fine roots to total growth.

NPPW

Fraction of carbon allocated to sapwood to total growth.

GPP_yr

Gross primary productivity of a tree (kg C tree$^{-1}$ year$^{-1}$).

NPP_yr

Net primary productivity of a tree (kg C tree$^{-1}$ year$^{-1}$).

Rauto

Plant autotrophic respiration (kg C tree$^{-1}$ yr$^{-1}$).

N_uptk

Nitrogen uptake (kg N tree$^{-1}$ yr$^{-1}$).

N_fix

Nitrogen fixation (kg N tree$^{-1}$ yr$^{-1}$).

maxLAI

Maximum leaf area index for a tree (m$^2$ m$^{-2}$).

Volume

Tree volume (m$^3$).

n_deadtrees

Number of trees that died (trees yr$^{-1}$).

c_deadtrees

Carbon biomass of trees that died (kg C yr$^{-1}$).

deathrate

Mortality rate of this cohort (yr$^{-1}$).

Examples

# Example BiomeE model run

# Use example drivers data
drivers <- biomee_gs_leuning_drivers

# Run BiomeE for the first site
mod_output <- run_biomee_f_bysite(
 sitename = drivers$sitename[1],
 params_siml = drivers$params_siml[[1]],
 site_info = drivers$site_info[[1]],
 forcing = drivers$forcing[[1]],
 params_tile = drivers$params_tile[[1]],
 params_species = drivers$params_species[[1]],
 init_cohort = drivers$init_cohort[[1]],
 init_soil = drivers$init_soil[[1]]
)

---

R wrapper for SOFUN P-model

Description

Call to the Fortran P-model

Usage

run_pmodel_f_bysite(
  sitename,
  params_siml,
  site_info,
  forcing,
  params_modl,
  makecheck = TRUE,
  verbose = TRUE
)

Arguments

sitename	Site name.
params_siml	Simulation parameters. spinup A logical value indicating whether this simulation does spin-up. spinupyears Number of spin-up years. recycle Length of standard recycling period, in years. outdt An integer indicating the output periodicity. ltre A logical value, TRUE if evergreen tree. ltne A logical value, TRUE if evergreen tree and N-fixing. ltrd A logical value, TRUE if deciduous tree. ltnd A logical value, TRUE if deciduous tree and N-fixing. lgr3 A logical value, TRUE if grass with C3 photosynthetic pathway. lgn3 A logical value, TRUE if grass with C3 photosynthetic pathway and N-fixing. lgr4 A logical value, TRUE if grass with C4 photosynthetic pathway.
site_info	A list of site meta info. Required: lon Longitude of the site location. lat Latitude of the site location. elv Elevation of the site location, in meters. whc A numeric value for the total root zone water holding capacity (in mm), used for simulating the soil water balance.
forcing	A data frame of forcing climate data, used as input (see p_model_drivers for a detailed description of its structure and contents).
params_modl	A named list of free (calibratable) model parameters. kphio The quantum yield efficiency at optimal temperature $\varphi_0$, in mol mol$^{-1}$. When temperature dependence is used, it corresponds to the multiplicative parameter $c$ (see Details). kphio_par_a The shape parameter $a$ of the temperature-dependency of quantum yield efficiency (see Details). To disable the temperature dependence, set kphio_par_a = 0. kphio_par_b The optimal temperature parameter $b$ of the temperature dependent quantum yield efficiency (see Details), in $^o$C. soilm_thetastar The threshold parameter $\theta^{*}$ in the soil moisture stress function (see Details), given in mm. To turn off the soil moisture stress, set soilm_thetastar = 0. soilm_betao The intercept parameter $\beta_{0}$ in the soil moisture stress function (see Details). This is the parameter calibrated in Stocker et al. 2020 GMD. beta_unitcostratio The unit cost of carboxylation, corresponding to $\beta = b / a'$ in Eq. 3 of Stocker et al. 2020 GMD. rd_to_vcmax Ratio of Rdark (dark respiration) to Vcmax25. tau_acclim Acclimation time scale of photosynthesis, in days. kc_jmax Parameter for Jmax cost ratio (corresponding to c$^*$ in Stocker et al. 2020 GMD).
makecheck	A logical specifying whether checks are performed to verify forcings and model parameters. TRUE by default.
verbose	A logical specifying whether to print warnings. Defaults to TRUE.

Details

Depending on the input model parameters, it's possible to run the different P-model setups presented in Stocker et al. 2020 GMD. The P-model version implemented in this package allows more flexibility than the one presented in the paper, with the following functions:

The temperature dependence of the quantum yield efficiency is given by:
$\varphi_0 (T) = c (1 + a (T - b)^2 )$ if $0 < c (1 + a (T - b)^2 ) < 1$,
$\varphi_0 (T) = 0$ if $c (1 + a (T - b)^2 ) \leq 0$, and
$\varphi_0 (T) = 1$ if $c (1 + a (T - b)^2 ) \geq 1$.
The ORG setup can be reproduced by setting kphio_par_a = 0 and calibrating the kphio parameter only. The BRC setup (which calibrates $c_L = \frac{a_L b_L}{4}$ in Eq. 18) is more difficult to reproduce, since the temperature-dependency has been reformulated and a custom cost function would be necessary for calibration. The new parameters are related to $c_L$ as follows:
$a = -0.0004919819$
$b = 32.35294$
$c = 0.6910823 c_L$

The soil moisture stress is implemented as
$\beta(\theta) = \frac{\beta_0 - 1}{{\theta^{*}}^2} (\theta - \theta^{*})^2 + 1$ if $0 \leq \theta \leq \theta^{*}$ and
$\beta(\theta) = 1$ if $\theta > \theta^{*}$.
In Stocker et al. 2020 GMD, the threshold plant-available soil water is set as $\theta^{*}$ = 0.6 * whc where whc is the site's water holding capacity. Also, the $\beta$ reduction at low soil moisture ($\beta_0 = \beta(0)$) was parameterized as a linear function of mean aridity (Eq. 20 in Stocker et al. 2020 GMD) but is considered a constant model parameter in this package. Hence, the FULL calibration setup cannot be exactly replicated.

Value

Model output is provided as a tidy dataframe, with columns:

date

Date of the observation in YYYY-MM-DD format.

year_dec

Decimal representation of year and day of the year (for example, 2007.000 corresponds to 2007-01-01 and 2007.003 to 2007-01-02.

fapar

Fraction of photosynthetic active radiation (fAPAR), taking values between 0 and 1.

gpp

Gross Primary Productivity (GPP) for each time stamp (in gC m$^{-2}$ d$^{-1}$).

aet

Actual evapotranspiration (AET), calculated by SPLASH following Priestly-Taylor (in mm d$^{-1}$).

le

Latent heat flux (in J m$^{-2}$ d$^{-1}$).

pet

Potential evapotranspiration (PET), calculated by SPLASH following Priestly-Taylor (in mm d$^{-1}$).

vcmax

Maximum rate of RuBisCO carboxylation (Vcmax) (in mol C m$^{-2}$ d$^{-1}$).

jmax

Maximum rate of electron transport for RuBP regeneration (in mol CO$_2$ m$^{-2}$ s$^{-1}$).

vcmax25

Maximum rate of carboxylation (Vcmax), normalised to 25$^o$C (in mol C m$^{-2}$ d$^{-1}$).

jmax25

Maximum rate of electron transport, normalised to 25$^o$C (in mol C m$^{-2}$ s$^{-1}$).

gs_accl

Acclimated stomatal conductance (in mol C m$^{-2}$ d$^{-1}$ Pa$^{-1}$).

wscal

Relative soil water content, between 0 (permanent wilting point, PWP) and 1 (field capacity, FC).

chi

Ratio of leaf-internal to ambient CO$_{2}$, ci:ca (unitless).

iwue

Intrinsic water use efficiency (iWUE) (in Pa).

rd

Dark respiration (Rd) in gC m$^{-2}$ d$^{-1}$.

tsoil

Soil temperature, in $^{o}$C.

netrad

Net radiation, in W m$^{-2}$. WARNING: this is currently ignored as a model forcing. Instead, net radiation is internally calculated by SPLASH.

wcont

Soil water content, in mm.

snow

Snow water equivalents, in mm.

cond

Water input by condensation, in mm d$^{-1}$

Examples

# Define model parameter values from previous work
params_modl <- list(
  kphio              = 0.04998,    # setup ORG in Stocker et al. 2020 GMD
  kphio_par_a        = 0.0,        # disable temperature-dependence of kphio
  kphio_par_b        = 1.0,
  soilm_thetastar    = 0.6 * 240,  # old setup with soil moisture stress
  soilm_betao        = 0.0,
  beta_unitcostratio = 146.0,
  rd_to_vcmax        = 0.014,      # from Atkin et al. 2015 for C3 herbaceous
  tau_acclim         = 30.0,
  kc_jmax            = 0.41
)

# Run the Fortran P-model 
mod_output <- run_pmodel_f_bysite(
  # unnest drivers example data
  sitename = p_model_drivers$sitename[1],
  params_siml = p_model_drivers$params_siml[[1]],
  site_info = p_model_drivers$site_info[[1]],
  forcing = p_model_drivers$forcing[[1]],
  params_modl = params_modl
 )

---

Run BiomeE

Description

Runs BiomeE model for multiple sites.

Usage

runread_biomee_f(drivers, makecheck = TRUE, parallel = FALSE, ncores = 2)

Arguments

drivers	A nested data frame with one row for each site and columns named according to the arguments of function 'runread_biomee_f_bysite()'. See '?run_biomee_f_bysite' for the list of parameters and forcing data required.
makecheck	Flag specifying whether checks are performed to verify forcings.
parallel	Flag specifying whether simulations are to be parallelised (sending data from a certain number of sites to each core). Defaults to FALSE.
ncores	An integer specifying the number of cores used for parallel computing. Defaults to 2.

Value

A tibble with one row for each site and model outputs stored in the nested column data. See '?run_biomee_f_bysite' for a description of the BiomeE output variables.

Examples

# Example BiomeE model run

runread_biomee_f(
  drivers = biomee_gs_leuning_drivers
)
runread_biomee_f(
  drivers = biomee_p_model_drivers
)

---

Run the P-model

Description

Runs the P-model and loads output in once.

Usage

runread_pmodel_f(drivers, par, makecheck = TRUE, parallel = FALSE, ncores = 1)

Arguments

drivers	A nested data frame with one row for each site and columns named according to the arguments of function run_pmodel_f_bysite, namely sitename, params_siml, site_info and forcing.
par	A named list of free (calibratable) model parameters. kphio The quantum yield efficiency at optimal temperature $\varphi_0$, in mol mol$^{-1}$. When temperature dependence is used, it corresponds to the multiplicative parameter $c$ (see Details). kphio_par_a The shape parameter $a$ of the temperature-dependency of quantum yield efficiency (see Details). To disable the temperature dependence, set kphio_par_a = 0. kphio_par_b The optimal temperature parameter $b$ of the temperature dependent quantum yield efficiency (see Details), in $^o$C. soilm_thetastar The threshold parameter $\theta^{*}$ in the soil moisture stress function (see Details), given in mm. To turn off the soil moisture stress, set soilm_thetastar = 0. soilm_betao The intercept parameter $\beta_{0}$ in the soil moisture stress function (see Details). This is the parameter calibrated in Stocker et al. 2020 GMD. beta_unitcostratio The unit cost of carboxylation, corresponding to $\beta = b / a'$ in Eq. 3 of Stocker et al. 2020 GMD. rd_to_vcmax Ratio of Rdark (dark respiration) to Vcmax25. tau_acclim Acclimation time scale of photosynthesis, in days. kc_jmax Parameter for Jmax cost ratio (corresponding to c$^*$ in Stocker et al. 2020 GMD).
makecheck	A logical specifying whether checks are performed to verify forcings. Defaults to TRUE.
parallel	A logical specifying whether simulations are to be parallelised (sending data from a certain number of sites to each core). Defaults to FALSE.
ncores	An integer specifying the number of cores used for parallel computing (by default ncores = 2).

Details

Depending on the input model parameters, it's possible to run the different P-model setups presented in Stocker et al. 2020 GMD. The P-model version implemented in this package allows more flexibility than the one presented in the paper, with the following functions:

The temperature dependence of the quantum yield efficiency is given by:
$\varphi_0 (T) = c (1 + a (T - b)^2 )$ if $0 < c (1 + a (T - b)^2 ) < 1$,
$\varphi_0 (T) = 0$ if $c (1 + a (T - b)^2 ) \leq 0$, and
$\varphi_0 (T) = 1$ if $c (1 + a (T - b)^2 ) \geq 1$.
The ORG setup can be reproduced by setting kphio_par_a = 0 and calibrating the kphio parameter only. The BRC setup (which calibrates $c_L = \frac{a_L b_L}{4}$ in Eq. 18) is more difficult to reproduce, since the temperature-dependency has been reformulated and a custom cost function would be necessary for calibration. The new parameters are related to $c_L$ as follows:
$a = -0.0004919819$
$b = 32.35294$
$c = 0.6910823 c_L$

The soil moisture stress is implemented as
$\beta(\theta) = \frac{\beta_0 - 1}{{\theta^{*}}^2} (\theta - \theta^{*})^2 + 1$ if $0 \leq \theta \leq \theta^{*}$ and
$\beta(\theta) = 1$ if $\theta > \theta^{*}$.
In Stocker et al. 2020 GMD, the threshold plant-available soil water is set as $\theta^{*}$ = 0.6 * whc where whc is the site's water holding capacity. Also, the $\beta$ reduction at low soil moisture ($\beta_0 = \beta(0)$) was parameterized as a linear function of mean aridity (Eq. 20 in Stocker et al. 2020 GMD) but is considered a constant model parameter in this package. Hence, the FULL calibration setup cannot be exactly replicated.

Value

A data frame (tibble) with one row for each site, site information stored in the nested column site_info and outputs stored in the nested column data. See run_pmodel_f_bysite for a detailed description of the outputs.

Examples

# Define model parameter values from previous work
params_modl <- list(
  kphio              = 0.04998,    # setup ORG in Stocker et al. 2020 GMD
  kphio_par_a        = 0.0,        # disable temperature-dependence of kphio
  kphio_par_b        = 1.0,
  soilm_thetastar    = 0.6 * 240,  # old setup with soil moisture stress
  soilm_betao        = 0.0,
  beta_unitcostratio = 146.0,
  rd_to_vcmax        = 0.014,      # from Atkin et al. 2015 for C3 herbaceous
  tau_acclim         = 30.0,
  kc_jmax            = 0.41
)

# Run the model for these parameters and the example drivers
output <- rsofun::runread_pmodel_f(
  drivers = rsofun::p_model_drivers,
  par = params_modl)
output_vcmax25 <- rsofun::runread_pmodel_f(
  drivers = rsofun::p_model_drivers_vcmax25,
  par = params_modl)

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