calib_sofun: Calibrates SOFUN model parameters

In rsofun: The P-Model and BiomeE Modelling Framework

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")
 )

rsofun documentation built on Nov. 2, 2023, 6:02 p.m.