# P-model usage

#### Koen Hufkens, Josefa Arán, Benjamin Stocker

The `rsofun` package and framework includes two distinct simulation models. The `p-model` and `biomee` (which in part relies on p-model component). Here we give a short example on how to run the `p-model` on the included demo datasets to familiarize yourself with both the data structure and the outputs.

## Demo data

The package includes two demo datasets to run and validate p-model 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
    #> # A tibble: 1 × 4
    #>   sitename params_siml       site_info        forcing
    #>   <chr>    <list>            <list>           <list>
    #> 1 FR-Pue   <tibble [1 × 11]> <tibble [1 × 4]> <tibble [2,190 × 13]>

    p_model_validation
    #> # A tibble: 1 × 2
    #>   sitename data
    #>   <chr>    <list>
    #> 1 FR-Pue   <tibble [2,190 × 3]>

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 `p-model` 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
    #> # A tibble: 4 × 4
    #>   sitename             params_siml       site_info        forcing
    #>   <chr>                <list>            <list>           <list>
    #> 1 Reichetal_Colorado   <tibble [1 × 11]> <tibble [1 × 6]> <tibble [365 × 13]>
    #> 2 Reichetal_New_Mexico <tibble [1 × 11]> <tibble [1 × 6]> <tibble [365 × 13]>
    #> 3 Reichetal_Venezuela  <tibble [1 × 11]> <tibble [1 × 6]> <tibble [365 × 13]>
    #> 4 Reichetal_Wisconsin  <tibble [1 × 11]> <tibble [1 × 6]> <tibble [365 × 13]>

    p_model_validation_vcmax25
    #> # A tibble: 4 × 2
    #> # Groups:   sitename [4]
    #>   sitename             data
    #>   <chr>                <list>
    #> 1 Reichetal_Colorado   <tibble [1 × 2]>
    #> 2 Reichetal_New_Mexico <tibble [1 × 2]>
    #> 3 Reichetal_Venezuela  <tibble [1 × 2]>
    #> 4 Reichetal_Wisconsin  <tibble [1 × 2]>

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.

## Running model

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.
    # Correspond to maximum a posteriori estimates from Bayesian calibration in
    # analysis/02-bayesian-calibration.R.
    params_modl <- list(
      kphio              = 5.000000e-02, # chosen to be too high for demonstration
      kphio_par_a        =-2.289344e-03,
      kphio_par_b        = 1.525094e+01,
      soilm_thetastar    = 1.577507e+02,
      soilm_betao        = 1.169702e-04,
      beta_unitcostratio = 146.0,
      rd_to_vcmax        = 0.014,
      tau_acclim         = 20.0,
      kc_jmax            = 0.41
      )

    # Run the model for these parameters.
    output <- rsofun::runread_pmodel_f(
      p_model_drivers,
      par = params_modl
      )

### Plotting output

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 <- output |>
      tidyr::unnest(data) |>
      dplyr::select(date, gpp_mod = gpp) |>
      dplyr::left_join(
        p_model_validation |>
          tidyr::unnest(data) |>
          dplyr::select(date, gpp_obs = gpp),
        by = "date"
        ) |>

      # Plot only first year
      dplyr::slice(1:365)

    # Plot GPP
    ggplot(data = df_gpp_plot) +
      geom_point(
        aes(
          x = date,
          y = gpp_obs,
          color = "Observations"
        ),
      ) +
      geom_line(
        aes(
          x = date,
          y = gpp_mod,
          color = "P-model"
        )
      ) +
      theme_classic() +
      theme(
        panel.grid.major.y = element_line(),
        legend.position = "bottom"
        ) +
      labs(
        x = 'Date',
        y = expression(paste("GPP (g C m"^-2, "s"^-1, ")"))
      ) +
      scale_color_manual(
        NULL,
        breaks = c("Observations",
                   "P-model"),
        values = c("black", "tomato"))
    #> Warning: Removed 42 rows containing missing values or values outside the scale range
    #> (`geom_point()`).

## Calibrating model parameters

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 <- df_gpp_plot |>
      left_join(
        output_new |>
          unnest(data) |>
          select(date, gpp_calib = gpp),
        by = "date"
      )

    # Plot GPP
    ggplot(data = df_gpp_plot) +
      geom_point(
        aes(
          x = date,
          y = gpp_obs,
          color = "Observations"
        ),
      ) +
      geom_line(
        aes(
          x = date,
          y = gpp_mod,
          color = "P-model (uncalibrated)"
        )
      ) +
      geom_line(
        aes(
          x = date,
          y = gpp_calib,
          color = "P-model (calibrated)"
        )
      ) +
      theme_classic() +
      theme(
        panel.grid.major.y = element_line(),
        legend.position = "bottom"
        ) +
      labs(
        x = 'Date',
        y = expression(paste("GPP (g C m"^-2, "s"^-1, ")"))
      ) +
      scale_color_manual(
        NULL,
        breaks = c("Observations",
                   "P-model (uncalibrated)",
                   "P-model (calibrated)"),
        values = c("black", "grey", "tomato"))
    #> Warning: Removed 42 rows containing missing values or values outside the scale range
    #> (`geom_point()`).

For details on the optimization settings we refer to the manuals of GenSA and BayesianTools.

## One-step P-model call

For certain applications, the core theory of the P-model, predicting the acclimation of photosynthesis at the leaf-level, may be all that’s needed for simulations. {rsofun} also provides a stripped-down version of “one-step” P-model call. That is, without considering forcing (and output) time series, delayed acclimation, ecosystem-level up-scaled quantities, and the coupling with the water balance and related water limitation on photosynthesis. Applications of the stripped-down version may include simulations of leaf-level photosynthetic traits. The function `run_pmodel_onestep_f_bysite()` provides this functionality and further improves computational efficiency over a call to `run_pmodel_f_bysite()`. This is particularly useful when calibrating the model to photosynthetic traits data.

    run_pmodel_onestep_f_bysite(
      lc4 = FALSE,
      forcing = data.frame(
        temp  = 20,           # temperature, deg C
        vpd   = 1000,         # Pa,
        ppfd  = 300/10^6,     # mol/m2/s
        co2   = 400,          # ppm,
        patm  = 101325        # Pa
      ),
      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,
        beta_unitcostratio = 146.0,
        rd_to_vcmax        = 0.014,      # from Atkin et al. 2015 for C3 herbaceous
        kc_jmax            = 0.41
      ),
      makecheck = TRUE
    )
    #> # A tibble: 1 × 9
    #>       vcmax      jmax   vcmax25    jmax25 gs_accl   chi    iwue      rd bigdelta
    #>       <dbl>     <dbl>     <dbl>     <dbl>   <dbl> <dbl>   <dbl>   <dbl>    <dbl>
    #> 1 0.0000177 0.0000400 0.0000275 0.0000518 0.00158 0.694 7.64e-5 3.11e-6     19.4
    #
    #
    # make sure to check ?run_pmodel_onestep_f_bysite for units of output quantities.

`run_pmodel_onestep_f_bysite()` uses the same low-level code module as `run_pmodel_f_bysite()` (`photosynth_pmodel.mod.f90`) and therefore provides fully consistent outputs with the latter. Using `run_pmodel_onestep_f_bysite()` instead of `rpmodel::rpmodel()` guarantees consistency of the implementation across time-series and one-step model applications within the same package. Note that unit transformations as shown below is needed to obtain identical outputs.

    library(rpmodel)
    out_rpmodel <- rpmodel(
      tc             = 20,           # temperature, deg C
      vpd            = 1000,         # Pa,
      co2            = 400,          # ppm,
      fapar          = 1,            # fraction,
      ppfd           = 300/10^6,     # ~~mol/m2/d~~ or mol/m2/s
                                     # rpmodel docs state that units of ppfd define
                                     # output units of: lue, gpp, vcmax, rd
                                     # (as well as vcmax25, gs)
      patm           = 101325,       # Pa
      kphio          = 0.04998,      # quantum yield efficiency as calibrated
                                     # for setup ORG by Stocker et al. 2020 GMD,
      beta           = 146.0,        # unit cost ratio a/b,
      c4             = FALSE,
      method_jmaxlim = "wang17",
      do_ftemp_kphio = FALSE,        # corresponding to setup ORG
      do_soilmstress = FALSE,        # corresponding to setup ORG
      verbose        = TRUE
      )

    # out_rpmodel
    tidyr::as_tibble(out_rpmodel) |>
      dplyr::select(vcmax, jmax, vcmax25, jmax25, gs, chi, iwue, rd) |>
      # bring units to same as output of run_pmodel_onestep_f_bysite()
      dplyr::mutate(gs   = gs/1/(300/10^6), # divide by fapar (-) and by ppfd (mol/m2/s)
                    iwue = iwue/101325,     # divide by patm (Pa)
                    rd   = rd*12            # mutliply by c_molmass (gC/mol)
      )
    #> # A tibble: 1 × 8
    #>       vcmax      jmax   vcmax25    jmax25      gs   chi      iwue         rd
    #>       <dbl>     <dbl>     <dbl>     <dbl>   <dbl> <dbl>     <dbl>      <dbl>
    #> 1 0.0000177 0.0000400 0.0000279 0.0000547 0.00160 0.694 0.0000764 0.00000338