knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.align = "center", fig.width = 7, fig.height = 5 ) library(rsofun) library(dplyr) library(ggplot2) # fake variable as optimization isn't run pars <- list() pars$par["kphio"] <- 0.04478049
The rsofun
package and framework includes two main models. The pmodel
and biomee
(which in part relies on P-model components). Here we give a short example on how to run the pmodel
on the included demo datasets to familiarize yourself with both the data structure and the outputs.
The package includes two demo datasets to run and validate pmodel output using GPP observations. These files can be directly loaded into your workspace by typing:
library(rsofun) # this is to deal with an error p_model_drivers.rds not being found p_model_drivers p_model_validation
These are real data from the French FR-Pue fluxnet site. Information about data structure, variable names, and their meaning and units can be found in the reference pages of p_model_drivers
and p_model_validation
. We can use these data to run the model, together with observations of GPP we can also calibrate pmodel
parameters.
Another two datasets are provided as an example to validate the model against leaf traits data, rather than fluxes. Measurements of Vcmax25 (aggregated over species) for a subset of 4 sites from the GlobResp database (Atkin et al., 2015) are given in p_model_validation_vcmax25
and the corresponding forcing for the P-model is given in p_model_drivers_vcmax25
. Since leaf traits are only measured once per site, the forcing used is a single year of average climate (the average measurements between 2001 and 2015 on each day of the year).
p_model_drivers_vcmax25 p_model_validation_vcmax25
For the remainder of this vignette, we will use the GPP flux datasets. The workflow is exactly the same for leaf traits data.
To get your raw data into the structure used within rsofun
, please see R packages ingestr and FluxDataKit.
With all data prepared we can run the P-model using runread_pmodel_f()
. This function takes the nested data structure and runs the model site by site, returning nested model output results matching the input drivers.
# 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, # set to zero to disable temperature-dependence of kphio kphio_par_b = 1.0, soilm_thetastar = 0.6 * 240, # to recover old 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 ) # run the model for these parameters output <- rsofun::runread_pmodel_f( p_model_drivers, par = params_modl )
We can now visualize both the model output and the measured values together.
# Load libraries for plotting library(dplyr) library(tidyr) library(ggplot2) # Create data.frame for plotting df_gpp_plot <- rbind( output |> filter(sitename == "FR-Pue") |> unnest(data) |> select(date, gpp) |> mutate(type = "P-model output"), p_model_validation |> filter(sitename == "FR-Pue") |> unnest(data) |> select(date, gpp) |> mutate(type = "Observed") ) df_gpp_plot$type <- factor(df_gpp_plot$type, levels = c('P-model output', 'Observed')) # Plot GPP ggplot(data = df_gpp_plot) + geom_line( aes(x = date, y = gpp, color = type), alpha = 0.7 ) + scale_color_manual(values = c( 'P-model output'='grey70', 'Observed'='black')) + theme_classic() + theme(panel.grid.major.y = element_line()) + labs( x = 'Date', y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), colour = "" )
To optimize new parameters based upon driver data and a validation dataset we must first specify an optimization strategy and settings, as well as a cost function and parameter ranges.
settings <- list( method = "GenSA", metric = cost_rmse_pmodel, control = list( maxit = 100), par = list( kphio = list(lower=0.02, upper=0.2, init = 0.05) ) )
rsofun
supports both optimization using the GenSA
and BayesianTools
packages. The above statement provides settings for a GenSA
optimization approach. For this example the maximum number of iterations is kept artificially low. In a real scenario you will have to increase this value orders of magnitude. Keep in mind that optimization routines rely on a cost function, which, depending on its structure influences parameter selection. A limited set of cost functions is provided but the model structure is transparent and custom cost functions can be easily written. More details can be found in the "Parameter calibration and cost functions" vignette.
In addition starting values and ranges are provided for the free parameters in the model. Free parameters include: parameters for the quantum yield efficiency kphio
, kphio_par_a
and kphio_par_b
, soil moisture stress parameters soilm_thetastar
and soilm_betao
, and also beta_unitcostratio
, rd_to_vcmax
, tau_acclim
and kc_jmax
(see ?runread_pmodel_f
). Be mindful that with newer versions of rsofun
additional parameters might be introduced, so re-check vignettes and function documentation when updating existing code.
With all settings defined the optimization function calib_sofun()
can be called with driver data and observations specified. Extra arguments for the cost function (like what variable should be used as target to compute the root mean squared error (RMSE) and previous values for the parameters that aren't calibrated, which are needed to run the P-model).
# calibrate the model and optimize free parameters pars <- calib_sofun( drivers = p_model_drivers, obs = p_model_validation, settings = settings, # extra arguments passed to the cost function: targets = "gpp", # define target variable GPP par_fixed = params_modl[-1] # fix non-calibrated parameters to previous # values, removing kphio )
When successful the optimized parameters can be used to run subsequent modelling efforts, in this case slightly improving the model fit over a more global parameter set.
# Update the parameter list with calibrated value params_modl$kphio <- pars$par["kphio"] # Run the model for these parameters output_new <- rsofun::runread_pmodel_f( p_model_drivers, par = params_modl ) # Update data.frame for plotting df_gpp_plot <- rbind( df_gpp_plot, output_new |> filter(sitename == "FR-Pue") |> unnest(data) |> select(date, gpp) |> mutate(type = "P-model output (calibrated)") ) df_gpp_plot$type <- factor(df_gpp_plot$type, levels = c('P-model output', 'P-model output (calibrated)', 'Observed')) # Plot GPP ggplot(data = df_gpp_plot) + geom_line( aes(x = date, y = gpp, color = type), alpha = 0.7 ) + scale_color_manual(values = c( 'P-model output'='grey70', 'P-model output (calibrated)'='grey40', 'Observed'='black')) + theme_classic() + theme(panel.grid.major.y = element_line()) + labs( x = 'Date', y = expression(paste("GPP (g C m"^-2, "s"^-1, ")")), colour = "" )
For details on the optimization settings we refer to the manuals of GenSA and BayesianTools.
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