Parameter calibration and cost functions

The rsofun package allows to calibrate parameters of the pmodel and biomee models via the calib_sofun() function. The implementation of the calibration is fairly flexible and can be adapted to a specific use-case via a cost function (used as metrics for the optimization routines in calib_sofun()). The package provides a set of standard cost functions named cost_*, which can be used for a variety of calibrations (different sets of model parameters, using various target variables, etc.). Alternatively, it’s possible to write a more specific new cost function to be used together with calib_sofun().

In this vignette, we go over some examples on how to use the rsofun cost functions for parameter calibration and how to write your own custom one from scratch.

Calibration to GPP using RMSE and GenSA optimizer

A simple approach to parameter calibration is to find the parameter values that lead to the best prediction performance, in terms of the RMSE (root mean squared error). The function cost_rmse_pmodel() runs the P-model internally to calculate the RMSE between predicted target values (in this case GPP) and the corresponding observations.

The implementation of cost_rmse_pmodel() allows flexibility in various ways. We can simultaneously calibrate a subset of model parameters and also replicate the different calibration setups in Stocker et al., 2020 GMD. For example, following the ORG setup, only parameter kphio is calibrated. Furthermore, the standard cost functions allow to calibrate to several targets (fluxes and leaf traits predicted by the P-model) simultaneously and to parallelize the simulations. Since the P-model is run internally to make predictions, we must always specify which values the model parameters should take, i.e. the parameters that aren’t calibrated (via argument par_fixed).

The syntax to run the calibration routine is as follows:

# Define calibration settings and parameter ranges from previous work
settings_rmse <- list(
  method = 'GenSA',                   # minimizes the RMSE
  metric = cost_rmse_pmodel,          # our cost function
  control = list(                     # control parameters for optimizer GenSA
    maxit = 100),                     
  par = list(                         # bounds for the parameter space
    kphio = list(lower=0.02, upper=0.2, init=0.05)
  )
)

# Calibrate the model and optimize the free parameters using
# demo datasets
pars_calib_rmse <- calib_sofun(
  # calib_sofun arguments:
  drivers = p_model_drivers,  
  obs = p_model_validation,
  settings = settings_rmse,
  # extra arguments passed to the cost function:
  par_fixed = list(         # fix all other parameters
    kphio_par_a        = 0.0,        # set to zero to disable temperature-dependence 
                                     # of kphio, setup ORG
    kphio_par_b        = 1.0,
    soilm_thetastar    = 0.6 * 240,  # to recover paper setup with soil moisture stress
    soilm_betao        = 0.0,
    beta_unitcostratio = 146.0,
    rd_to_vcmax        = 0.014,      # value from Atkin et al. 2015 for C3 herbaceous
    tau_acclim         = 30.0,
    kc_jmax            = 0.41
  ),
  targets = "gpp"           # define target variable GPP
)

The output of calib_sofun() is a list containing the calibrated parameter values and the raw optimization output from the optimizer (here from GenSA or, as we see next, from BayesianTools::runMCMC).

Calibration to GPP using a simple likelihood function and BayesianTools

Let’s calibrate the parameters involved in the temperature dependency of the quantum yield efficiency, kphio, kphio_par_a and kphio_par_b, taking a Bayesian calibration approach. We assume that the target variable ('gpp') follows a normal distribution centered at the observations and with its standard deviation being a new calibratable parameter ('err_gpp'). We also assume a uniform prior distribution for all calibratable parameters. By maximizing the normal log-likelihood, the MAP (maximum a posteriori) estimators for all 4 parameters are computed. With the function cost_likelihood_pmodel(), we can easily perform this calibration, as follows:

# Define calibration settings
settings_likelihood <- list(
  method = 'BayesianTools',
  metric = cost_likelihood_pmodel,            # our cost function
  control = list(                             # optimization control settings for 
    sampler = 'DEzs',                           # BayesianTools::runMCMC
    settings = list(
      burnin = 1500,
      iterations = 3000
    )),
  par = list(
    kphio = list(lower = 0, upper = 0.2, init = 0.05),
    kphio_par_a = list(lower = -0.5, upper = 0.5, init = -0.1),
    kphio_par_b = list(lower = 10, upper = 40, init =25),
    err_gpp = list(lower = 0.1, upper = 4, init = 0.8)
  )
)

# Calibrate the model and optimize the free parameters using
# demo datasets
pars_calib_likelihood <- calib_sofun(
  # calib_sofun arguments:
  drivers = p_model_drivers,
  obs = p_model_validation,
  settings = settings_likelihood,
  # extra arguments passed ot the cost function:
  par_fixed = list(         # fix all other parameters
    soilm_thetastar    = 0.6 * 240,  # to recover paper setup with soil moisture stress
    soilm_betao        = 0.0,
    beta_unitcostratio = 146.0,
    rd_to_vcmax        = 0.014,      # value from Atkin et al. 2015 for C3 herbaceous
    tau_acclim         = 30.0,
    kc_jmax            = 0.41
  ),
  targets = "gpp"
)

Furthermore, there are equivalent cost functions available for the BiomeE model. Check out the reference pages for more details on how to use cost_likelihood_biomee() and cost_rmse_biomee().

Calibration to GPP and Vcmax25 using the joint log-likelihood and BayesianTools

You may be interested in calibrating the model to different target variables simultaneously, like flux and leaf trait measurements. Here we present an example, where we use cost_likelihood_pmodel() to compute the joint normal likelihood of all the targets specified (that is, by summing the log-likelihoods of GPP and Vcmax25) and ultimately calibrate the kc_jmax parameter. It would be possible to follow this workflow for several-target calibration also with RMSE as the optimization metric, using cost_rmse_pmodel() and GenSA optimization.

# Define calibration settings for two targets
settings_joint_likelihood <- list(
  method = "BayesianTools",
  metric = cost_likelihood_pmodel,
  control = list(
    sampler = "DEzs",
    settings = list(
      burnin = 1500,             # kept artificially low
      iterations = 3000
    )),
  par = list(kc_jmax = list(lower = 0.2, upper = 0.8, init = 0.41),  # uniform priors
             err_gpp = list(lower = 0.001, upper = 4, init = 1),
             err_vcmax25 = list(lower = 0.000001, upper = 0.0001, init = 0.00001))
)

# Run the calibration on the concatenated data
par_calib_join <- calib_sofun(
  drivers = rbind(p_model_drivers,
                  p_model_drivers_vcmax25), 
  obs = rbind(p_model_validation,
              p_model_validation_vcmax25), 
  settings = settings_joint_likelihood,
  # arguments for the cost function
  par_fixed = list(         # fix parameter value from previous calibration
    kphio              = 0.041,
    kphio_par_a        = 0.0,
    kphio_par_b        = 16,
    soilm_thetastar    = 0.6 * 240,  # to recover paper setup with soil moisture stress
    soilm_betao        = 0.0,
    beta_unitcostratio = 146.0,
    rd_to_vcmax        = 0.014,      # value from Atkin et al. 2015 for C3 herbaceous
    tau_acclim         = 30.0
  ),    
  targets = c('gpp', 'vcmax25')
)

Note that GPP predictions are directly compared to GPP observations on that day, but Vcmax25 predicted by the P-model (being a leaf trait) is averaged over the growing season and compared to a single Vcmax25 observation taken per site. The cost functions provided in the package tell apart fluxes and leaf traits by the presence of a "date" column in the nested validation data frames p_model_validation and p_model_validation_vcmax25.

Write your custom cost function

If the RMSE or normal log-likelihood (for one or several targets) cost functions that we provide do not fit your use case, you can easily write a custom one. In this section, we drive you through the main ideas with an example.

All cost functions must take at least three arguments:

par: A vector of calibratable model parameters. In each iteration of the optimization, a new set of values of par is used to run the model and compute the cost.
obs: A data frame of observations, against which to compare the simulation results.
drivers: A data frame of driver data, used to run the simulations.
Additional optional arguments can be used, like for example the model parameter values that should be fixed across simulations, etc.

Since we are calibrating the parameters based on model outputs, the cost function runs the P-model and compare its output to observed validation data.

function(par, obs, drivers){
  # Your code
}

In the optimization procedure, the cost function only takes as argument the parameters par that are fed to calib_sofun() via settings$par (see previous sections). Nevertheless, within the cost function we call runread_pmodel_f() and this function needs a full set of model parameters. Therefore, the parameters that aren’t being calibrated must be hard coded inside the cost function (or passed as an argument like the rsofun cost functions). In this example, we only want to calibrate the soil moisture stress parameters.

function(par, obs, drivers){
  
  # Set values for the list of calibrated and non-calibrated model parameters
  params_modl <- list(
    kphio              = 0.09423773,
    kphio_par_a        = 0.0,
    kphio_par_b        = 25,
    soilm_thetastar    = par[1],
    soilm_betao        = par[2],
    beta_unitcostratio = 146.0,
    rd_to_vcmax        = 0.014,
    tau_acclim         = 30.0,
    kc_jmax            = 0.41
  )
  
  # Run the model
  df <- runread_pmodel_f(
    drivers,
    par = params_modl,
    makecheck = TRUE,
    parallel = FALSE
  )
  
  # Your code to compute the cost
}

The following chunk defines the final function. We clean the observations and model output and align the data according to site and date, to compute the mean absolute error (MAE) on GPP. Finally, the function should return a scalar value, in this case the MAE, which we want to minimize. Keep in mind that the GenSA optimization will minimize the cost, but with the BayesianTools method the cost is always maximized.

cost_mae <- function(par, obs, drivers){

  # Set values for the list of calibrated and non-calibrated model parameters
  params_modl <- list(
    kphio              = 0.09423773,
    kphio_par_a        = 0.0,
    kphio_par_b        = 25,
    soilm_thetastar    = par[1],
    soilm_betao        = par[2],
    beta_unitcostratio = 146.0,
    rd_to_vcmax        = 0.014,
    tau_acclim         = 30.0,
    kc_jmax            = 0.41
  ) # Set values for the list of calibrated and non-calibrated model parameters
  
  
  # Run the model
  df <- runread_pmodel_f(
    drivers = drivers,
    par = params_modl,
    makecheck = TRUE,
    parallel = FALSE
  )
  
  # Clean model output to compute cost
  df <- df %>%
    dplyr::select(sitename, data) %>%
    tidyr::unnest(data)
    
  # Clean validation data to compute cost
  obs <- obs %>%
    dplyr::select(sitename, data) %>%
    tidyr::unnest(data) %>%
    dplyr::rename('gpp_obs' = 'gpp') # rename for later
    
  # Left join model output with observations by site and date
  df <- dplyr::left_join(df, obs, by = c('sitename', 'date'))
  
  # Compute mean absolute error
  cost <- mean(abs(df$gpp - df$gpp_obs), na.rm = TRUE)
  
  # Return the computed cost
  return(cost)
}

As a last step, let’s verify that the calibration procedure runs using this cost function.

# Define calibration settings and parameter ranges
settings_mae <- list(
  method = 'GenSA',
  metric = cost_mae, # our cost function
  control = list(
    maxit = 100),
  par = list(
    soilm_thetastar = list(lower=0.0, upper=3000, init=0.6*240),
    soilm_betao = list(lower=0, upper=1, init=0.2)
  )
)

# Calibrate the model and optimize the free parameters
pars_calib_mae <- calib_sofun(
  drivers = p_model_drivers,
  obs = p_model_validation,
  settings = settings_mae
)

Parameter calibration and cost functions

Josefa Arán

Calibration to GPP using RMSE and GenSA optimizer

Calibration to GPP using a simple likelihood function and BayesianTools

Calibration to GPP and Vcmax25 using the joint log-likelihood and BayesianTools

Write your custom cost function