Calibrates SOFUN model parameters

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

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. Additional columns can optionally be provided to drivers to control e.g. the processing within a personalized cost function.

obs

A data frame containing observational data used for model calibration. See p_model_validation for a description of the data structure. Additional columns can optionally be provided to obs to control e.g. the processing within a personalized cost function.

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, simply passed on to the cost function.

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")
 )
#> 
 Running DEzs-MCMC, chain  1 iteration 51 of 51 . Current logp  -3669.919 -3238.148 -3965.871 . Please wait! 

#> runMCMC terminated after 1.584seconds