Package 'ConFluxPro'

Description

Alternate cfp_pfres() / cfp_fgres() models for sensitivity analysis and more.

Usage

alternate(
  x,
  f,
  run_map,
  return_raw = TRUE,
  error_funs = NULL,
  error_args = NULL
)

alternate_model(run_map, x, f)

Arguments

Details

alternate_model() is used internally to change and rerun one model, but can also be used to update a model with a given unique run_map, e.g. by filtering the best run_id from the original run_map.

Value

A list of type cfp_altres(), each entry an updated model.

Examples

PROFLUX <- ConFluxPro::base_dat |>
  filter(site == "site_a") |> # use only 'site_a' for example
  pro_flux()

# Create a cfp_run_map where TPS is changed between 90 % and 110 %
# of the original value for 2 runs.
my_run_map <-
cfp_run_map(
  PROFLUX,
  list("TPS" = c(0.9, 1.1)),
  "factor",
  n_runs = 2)

# run the new models by providing a function `f`
# that updates the soilphys data.frame.
alternate(
  x = PROFLUX,
  f = \(x) complete_soilphys(x, "a+AFPS^b", quiet = TRUE),
  run_map = my_run_map)

Description

An example cfp_dat() object that combines all other example data.

Usage

base_dat

Format

A cfp_dat() object as a list with

profiles

The profiles of the data.

gasdata

The gasdata object.

soilphys

The soilphys object.

layers_map

The layers_map object.

Description

Estimate model uncertainty

Usage

bootstrap_error(
  x,
  n_samples = 50,
  sd_x_ppm = NULL,
  n_replicates = NULL,
  sample_from = "gasdata",
  rep_cols = NULL
)

## S3 method for class 'cfp_altres'
bootstrap_error(
  x,
  n_samples = 50,
  sd_x_ppm = NULL,
  n_replicates = NULL,
  sample_from = "gasdata",
  rep_cols = NULL
)

## S3 method for class 'cfp_dat'
bootstrap_error(
  x,
  n_samples = 50,
  sd_x_ppm = NULL,
  n_replicates = NULL,
  sample_from = "gasdata",
  rep_cols = NULL
)

## S3 method for class 'cfp_fgmod'
bootstrap_error(
  x,
  n_samples = 50,
  sd_x_ppm = NULL,
  n_replicates = NULL,
  sample_from = "gasdata",
  rep_cols = NULL
)

## S3 method for class 'cfp_pfmod'
bootstrap_error(
  x,
  n_samples = 50,
  sd_x_ppm = NULL,
  n_replicates = NULL,
  sample_from = "gasdata",
  rep_cols = NULL
)

make_bootstrap_model(
  x,
  n_samples = 50,
  sd_x_ppm = NULL,
  n_replicates = NULL,
  sample_from = "gasdata",
  rep_cols = NULL
)

## S3 method for class 'cfp_pfmod'
make_bootstrap_model(
  x,
  n_samples = 50,
  sd_x_ppm = NULL,
  n_replicates = NULL,
  sample_from = "gasdata",
  rep_cols = NULL
)

calculate_bootstrap_error(x, y)

## S3 method for class 'cfp_pfmod'
calculate_bootstrap_error(x, y)

Arguments

Value

x with added columns DELTA_flux and DELTA_prod as an estimate of the error of of the corresponding columns in the same units.

General procedure

bootstrap_error() is mostly a wrapper around two functions that can also be run separately.

In make_bootstrap_model(), for sample_from = "gasdata" the gasdata concentration data is resampled for every depth and profile a total number of n_samples. This is done by randomly sampling the observations at each depth without changing the number of observations but while allowing replacing. If rep_cols are given, these columns are removed from the id_cols and the resulting profiles combined as one.

For sample_from = "soilphys", the soilphys data is combined using the rep_cols as repetitions. Among every remaining profile and depth, one observation across all repetitions is chosen for each of n_samples. sample_from = "both" applies both methods above. Each newly sampled profile is identifiable by the added bootstrap_id column which is also added to id_cols.

After this new model is run again, the bootstap error is calculated in calculate_bootstrap_error(). This is the standard deviation of the production and flux parameters across all bootstrapped model runs and is calculated for each profile and layer of the original model, or for each distinct profile in the new model without rep_cols. These are returned together with the mean values of prod, flux and F0 across all runs in the PROFLUX data.frame and can thereby be extracted by efflux() and production().

Artificial observations in gasdata

If there are not enough observations per depth (e.g.) because there is only one measurement per depth, it is possible to create artificial observations by providing n_replicates and sd_x_ppm. Here, every depth of every profile is first averaged to its mean (redundant if there is only one observation). Then, a random dataset of n_replicates observations is generated that is normally distributed around the mean with a standard deviation (in ppm) of sd_x_ppm. These observations are then resampled as described above. Note that this error should be representative of the sampling error in the field and not the measurement error of the measurement device, which is much lower.

Examples

PROFLUX <- pro_flux(ConFluxPro::base_dat)
PROFLUX_BSE <- bootstrap_error(PROFLUX)
efflux(PROFLUX_BSE)

PROFLUX_BSE <- bootstrap_error(PROFLUX, n_replicates = 5, sd_x_ppm = 25)
efflux(PROFLUX_BSE)

make_bootstrap_model(PROFLUX) # internal

Description

Apply a function to a list of cfp_pfres pr cfp_fgres objects stored in an cfp_altres object. This can be used to summarise alternate() results.

Usage

cfp_altapply(X, FUN, ...)

Arguments

Value

data.frame with the results of FUN bound together with added column run_id as identifier of the original list elements.

Examples

PROFLUX <- ConFluxPro::base_dat |> pro_flux()
model_list <- list('1' = PROFLUX, '2' = PROFLUX)

cfp_altapply(model_list, efflux)

Description

cfp_dat is the essential object class that binds all necessary input data to run a ConFluxPro model. It automatically combines the different datasets and checks them for validity. It may split soilphys layers to correspond with layers_map and gasdata depths.

Usage

cfp_dat(gasdata, soilphys, layers_map)

as_cfp_dat(x)

## S3 method for class 'cfp_dat'
as_cfp_dat(x)

Arguments

Value

A cfp_dat object with the following parameters:

gasdata

The gasdata object with added column "gd_id" that is unique for each profile.

soilphys

The soilphys object with added columns "sp_id" that is unique for each profile, "step_id" indicating the position of each step from the bottom up, "height" in m of each layer, "pmap" indicating which layer it belongs to from the bottom up. Potentially, some original steps were split to account for the depths within gasdata or layers_map.

layers_map

The layers_map object with added column "group_id" indicating each unique group of the same layer parameterization set by layers_map.

profiles

A data.frame where each row indicates one unique profile that is characterised by all id_cols present in the original input as well as the corresponding "gd_id", "sp_id", and "group_id". Each row has a unique identifier "prof_id".

id_cols

A character vector of all columns that identify a profile uniquely.

See Also

Other data formats: cfp_gasdata(), cfp_layered_profile(), cfp_layers_map(), cfp_profile(), cfp_soilphys()

Examples

gasdata <- cfp_gasdata(
  ConFluxPro::gasdata,
  id_cols = c("site", "Date"))
soilphys <- cfp_soilphys(
  ConFluxPro::soilphys,
  id_cols = c("site", "Date"))
layers_map <-
 cfp_layers_map(
   ConFluxPro::layers_map,
   gas = "CO2",
   lowlim = 0,
   highlim = 1000,
   id_cols = "site")
base_dat <- cfp_dat(gasdata, soilphys, layers_map)

### filter similar to dplyr::fliter
filter(base_dat, site == "site_a")
filter(base_dat, prof_id %in%  1:5)

### coersion from derived objects
PROFLUX <- pro_flux(base_dat)
as_cfp_dat(PROFLUX)

Description

An S3 class for fg_flux() models. The class inherits from cfp_dat and adds any model specific parameters.

Usage

cfp_fgmod(
  x,
  gases = unique_gases(x),
  modes = "LL",
  param = c("c_air", "DS"),
  funs = c("arith", "harm")
)

Arguments

Value

A cfp_fgmod object. This inherits from cfp_dat() and adds model specific parameters.

References

DAVIDSON, E. A., SAVAGE, K. E., TRUMBORE, S. E., & BORKEN, W. (2006). Vertical partitioning of CO2 production within a temperate forest soil. In Global Change Biology (Vol. 12, Issue 6, pp. 944–956). Wiley. https://doi.org/10.1111/j.1365-2486.2005.01142.x

See Also

Other model frames: cfp_altres(), cfp_fgres(), cfp_pfmod(), cfp_pfres()

Examples

cfp_fgmod(ConFluxPro::base_dat)

### coercion from other object types (internal)
fg_flux(ConFluxPro::base_dat) |>
  as_cfp_fgmod()

Description

A function to create an object of class cfp_fgres. This is the central result class generated by running fg_flux(). Intended for internal use only.

Usage

cfp_fgres(x, y)

Arguments

Value

A cfp_fgres object. This inherits from cfp_fgmod().

See Also

Other model frames: cfp_altres(), cfp_fgmod(), cfp_pfmod(), cfp_pfres()

Examples

FLUX <- fg_flux(ConFluxPro::base_dat)
cfp_fgres(
  cfp_fgmod(ConFluxPro::base_dat),
  FLUX$FLUX
)

Description

Create a cfp_gasdata object. This is a data.frame containing gas concentration data for one or multiple soil profiles. Each soil profile is uniquely identified by columns in the data.frame specified by the id_cols attribute.

Usage

cfp_gasdata(x, ...)

## S3 method for class 'data.frame'
cfp_gasdata(x, id_cols, ...)

## S3 method for class 'cfp_dat'
cfp_gasdata(x, ...)

Arguments

Value

A cfp_gasdata object.

See Also

Other data formats: cfp_dat(), cfp_layered_profile(), cfp_layers_map(), cfp_profile(), cfp_soilphys()

Examples

cfp_gasdata(
  ConFluxPro::gasdata,
  id_cols = c("site", "Date"))
### Also used to extract the gasdata object from cfp_dat
cfp_gasdata(ConFluxPro::base_dat)

Description

A subclass of cfp_profile() where each profile consists of layers that are defined by their upper and lower boundary without gaps or duplicates.

Usage

cfp_layered_profile(x, id_cols = NULL)

Arguments

Details

upper and lower define the upper and lower bounds of each layer in cm. Higher values lay on top of lower values.

Value

A cfp_layered_profile object. This is a [cfp_profile()] that is further subdivided into layers by the columns upper and lower.

See Also

Other data formats: cfp_dat(), cfp_gasdata(), cfp_layers_map(), cfp_profile(), cfp_soilphys()

Examples

df <- data.frame(
  site = rep(c("site_a", "site_b"), each = 2),
  upper = c(10, 0, 7, 0),
  lower = c(0, -100, 0, -100),
  variable = 1:4)

cfp_layered_profile(df, id_cols = "site")

Description

A function to create a cfp_layers_map object that defines the layers of both fg_flux() and pro_flux() models.

Usage

cfp_layers_map(x, ...)

## S3 method for class 'cfp_dat'
cfp_layers_map(x, ...)

## S3 method for class 'data.frame'
cfp_layers_map(
  x,
  id_cols,
  gas = NULL,
  lowlim = NULL,
  highlim = NULL,
  layer_couple = 0,
  ...
)

Arguments

Value

A cfp_layered_profile() data.frame with the columns described above as well as layer and pmap columns that identify each layer with an integer (ascending from bottom to top).

Add lowlim and highlim for multiple gases

Sometimes it is practical to model different gases with different limits. For example, it is a reasonable assumption that CO2 is not consumed in relevant amounts in most soils, whereas CH4 may be both produced or consumed. Therefore we may want to limit production rates of CO2 to only positive values, whereas allowing for negative CH4 production rates (i.e. consumption) as well.

To make this setup easy, you can provide a gas vector to the function together with highlim and lowlim vectors of the same length. The provided layers_map data.frame will then be replicated for each gas with the respective values of the production limits provided.

See Also

Other data formats: cfp_dat(), cfp_gasdata(), cfp_layered_profile(), cfp_profile(), cfp_soilphys()

Examples

cfp_layers_map(
   ConFluxPro::layers_map,
   gas = "CO2",
   lowlim = 0,
   highlim = 1000,
   id_cols = "site")

### add multiple gases at once
cfp_layers_map(
   ConFluxPro::layers_map,
   id_cols = "site",
   gas = c("CO2", "CH4"),
   lowlim = c(0, -1000),
   highlim = c(1000, 1000))

### Extract from an existing cfp_dat
cfp_layers_map(ConFluxPro::base_dat)

Description

Function to access parameter descriptions and units used in ConFluxPro

Usage

cfp_parameter(x = NULL)

Arguments

Value

A data.frame() with the name, description and unit of the parameter

Examples

#list parameters within an object
cfp_parameter(soilphys)
cfp_parameter(gasdata)

#list all paramters
cfp_parameter()

Description

An S3 class for pro_flux() models. The class inherits from cfp_dat and adds any model specific parameters.

Usage

cfp_pfmod(
  x,
  zero_flux = TRUE,
  zero_limits = c(-Inf, Inf),
  DSD0_optim = FALSE,
  evenness_factor = 0,
  known_flux_factor = 0
)

Arguments

Value

A cfp_pfmod object that inherits from cfp_dat()

See Also

Other model frames: cfp_altres(), cfp_fgmod(), cfp_fgres(), cfp_pfres()

Examples

cfp_pfmod(ConFluxPro::base_dat)

### coercion from other object types (internal)
pro_flux(ConFluxPro::base_dat) |>
  as_cfp_pfmod()

Description

A function to create an object of class cfp_pfres. This is the central result class generated by running pro_flux(). Intended for internal use only.

Usage

cfp_pfres(x, y)

Arguments

Value

A cfp_pfres object. This inherits from cfp_pfmod().

See Also

Other model frames: cfp_altres(), cfp_fgmod(), cfp_fgres(), cfp_pfmod()

Examples

PROFLUX <- pro_flux(ConFluxPro::base_dat)
cfp_pfres(
  cfp_pfmod(ConFluxPro::base_dat),
  PROFLUX$PROFLUX
)

Description

A central S3 class that defines a data.frame where columns given in id_cols define distinct soil profiles.

Usage

cfp_profile(x, id_cols = NULL)

Arguments

Value

A cfp_profile object. This is a data.frame with the id_cols attribute.

See Also

Other data formats: cfp_dat(), cfp_gasdata(), cfp_layered_profile(), cfp_layers_map(), cfp_soilphys()

Examples

df <- data.frame(
  site = rep(c("site_a", "site_b"), each = 2),
  variable = 1:4)

cfp_profile(df, id_cols = "site")

### multiple id_cols
df <- data.frame(
  site = rep(c("site_a", "site_b"), each = 4),
  replicate = rep(c(1,2), times = 4),
  variable = 1:8)

cfp_profile(df, id_cols = c("site", "replicate"))

Description

An S3 class cfp_run_map to be used in alternate(). Either create a new run map from a cfp_pfres or cfp_fgres model or extract an existing run_map from an cfp_altres object.

Usage

cfp_run_map(
  x,
  params = list(),
  type = NULL,
  method = NULL,
  n_runs = NULL,
  layers_different = FALSE,
  layers_from = "layers_map",
  layers_altmap = NULL,
  topheight_adjust = FALSE
)

Arguments

Value

An object of type cfp_run_map that can be used within alternate.

Examples

PROFLUX <- ConFluxPro::base_dat |> pro_flux()
# Create a cfp_run_map where TPS is changed between 90 % and 110 %
# of the original value for 50 runs.
cfp_run_map(
  PROFLUX,
  list("TPS" = c(0.9, 1.1)),
  "factor",
  n_runs = 50)

Description

Create a cfp_soilphys object. This is a data.frame containing layered data of soil physical properties, at the minimum of the air density c_air and diffusion coefficient DS for one or multiple soil profiles. Each soil profile is uniquely identified by columns in the data.frame specified by the id_cols attribute. Each profile is further subdivided into layers by columns upper and lower (see cfp_layered_profile).

Usage

cfp_soilphys(x, ...)

## S3 method for class 'cfp_dat'
cfp_soilphys(x, ...)

## S3 method for class 'data.frame'
cfp_soilphys(x, id_cols, ...)

Arguments

Value

A cfp_soilphys object.

See Also

Other data formats: cfp_dat(), cfp_gasdata(), cfp_layered_profile(), cfp_layers_map(), cfp_profile()

Examples

cfp_soilphys(
  ConFluxPro::soilphys,
  id_cols = c("site", "Date", "gas")
)
### Also used to extract an soilphys object from cfp_dat
cfp_soilphys(ConFluxPro::base_dat)

Description

This function analyses the soilphys dataframe before the flux calculation. It presents a warning, if there are variables missing and also looks for suspicious patterns that suggest an error in the interpolation made by discretize_depth. Mainly checks if certain columns are present and if they are missing, if they can be calculated from the data present. Looks for the following columns by default: "upper","lower","TPS","SWC","AFPS","t","p","DSD0","D0","DS"

Usage

check_soilphys(df, extra_vars = c(), id_cols)

Arguments

Value

data frame of 'suspicious' parameter/depth combinations, where all values are NA.

See Also

Other soilphys: complete_soilphys(), discretize_depth(), soilphys_layered()

Examples

check_soilphys(ConFluxPro::soilphys, id_cols = c("site", "Date"))

Description

Combinea list of multiple models or cfp_dat() objects into a single object.

Usage

combine_models(x)

## S3 method for class 'cfp_altres'
combine_models(x)

## S3 method for class 'list'
combine_models(x)

combine_models_by_reference(x_ref, x)

Arguments

Value

An object of the same type as the first object in x.

Examples

mod1 <- filter(base_dat, site == "site_a")
mod2 <- filter(base_dat, site == "site_b")
combine_models(list(mod1, mod2))

# use a reference model for coercion
combine_models_by_reference(mod1, list(mod1, mod2))

Description

This function completes the soilphys dataset by calculating different parameters if necessary, as long as all required parameters are available. Diffusion coefficients, as well as the air density are calculated if missing.

Usage

complete_soilphys(
  soilphys,
  DSD0_formula = NULL,
  gases = NULL,
  overwrite = TRUE,
  quiet = FALSE
)

Arguments

Value

A data.frame() with all necessary columns for cfp_soilphys.

See Also

D0_massman

Other soilphys: check_soilphys(), discretize_depth(), soilphys_layered()

Examples

soilphys_barebones <- ConFluxPro::soilphys |>
 dplyr::select(
   c("site",
     "Date",
     "upper",
     "lower",
     "depth",
     "t",
     "p",
     "TPS",
     "SWC",
     "a",
     "b")
     )

complete_soilphys(
  soilphys_barebones,
  DSD0_formula = "a*AFPS^b",
  gases = "CO2")

Description

This function calculates the free-air diffusion coefficients of different gases for a given temperature and pressure.

Usage

D0_massman(gas, t, p)

Arguments

Value

A numeric vector of D0 in m^2/s

References

Massman, W. J. A review of the molecular diffusivities of H2O, CO2, CH4, CO, O3, SO2, NH3, N2O, NO, and NO2 in air, O2 and N2 near STP. Atmospheric Environment 1998, 32(6), 1111–1127

Examples

D0_massman("CO2", 10, 1013)

Description

Extract the incoming and outgoing flux from below the deepest layer of a pro_flux() model. This returns zero, if zero_flux=TRUE.

Usage

deepflux(x, ...)

Arguments

Details

F0 represents the flux below the lowest layer defined in the cfp_pfres() model

Value

data.frame with F0 ($µmol / m^2 / s$)

Examples

PROFLUX <- ConFluxPro::base_dat |> pro_flux()

deepflux(PROFLUX)

Description

Get the unique layers or depths, i.e. the backbone of an object given a set of identifying columns.

Usage

depth_structure(x, id_cols = NULL, ...)

## S3 method for class 'cfp_layered_profile'
depth_structure(x, id_cols = NULL, ...)

## S3 method for class 'cfp_profile'
depth_structure(x, id_cols = NULL, ...)

## S3 method for class 'cfp_dat'
depth_structure(x, id_cols = NULL, structure_from = NULL, ...)

Arguments

Value

A cfp_profile with columns depth, or upper and lower.

Examples

depth_structure(cfp_soilphys(ConFluxPro::base_dat))
depth_structure(cfp_gasdata(ConFluxPro::base_dat))

Description

Interpolate and discretize data into a layered structure. The output is a data.frame where each profile is separated into layers that intersect at depths defined in the function call. See cfp_layered_profile().

There are different interpolation methods implemented, which might be more practical for different parameters or tasks.

• A 'linear' interpolation for continuous parameters, (e.g. soil temperature).

• The 'boundary' interpolation is only suitable for data that is already layered. It selects the value from the old layer that in which the new layer will lay in.

• A 'linspline' interpolation fits a linear spline model to the data with knots defined in knots

• 'nearest' finds the closest value to the new layer. You can define whether the closest value should be nearest to the top 1, or bottom 0 of the layer using int_depth

• 'harmonic' is similar to a linear interpolation but it uses the harmonic mean harm() using the distance in depth to each value as weights.

Multiple variables can be discretized at the same time by supplying multiple column names in param. It is also possible to use different method and controlling parameters int_depth and knots for each param. Just provide a list of settings the same length as param. If only one value is given, but multiple param the settings are reused for each parameter.

Usage

discretize_depth(
  df,
  param,
  method,
  depth_target,
  boundary_nearest = FALSE,
  boundary_average = "none",
  int_depth = 0.5,
  knots = NULL,
  ...
)

## S3 method for class 'cfp_profile'
discretize_depth(
  df,
  param,
  method,
  depth_target,
  boundary_nearest = FALSE,
  boundary_average = "none",
  int_depth = 0.5,
  knots = NULL,
  ...
)

## S3 method for class 'data.frame'
discretize_depth(
  df,
  param,
  method,
  depth_target,
  boundary_nearest = FALSE,
  boundary_average = "none",
  int_depth = 0.5,
  knots = NULL,
  id_cols = NULL,
  ...
)

Arguments

Value

A cfp_layered_profile() data.frame with the variables upper and lower defining the layers derived from depth_target. The column depth is the middle of each layer. And all variables from param

See Also

Other soilphys: check_soilphys(), complete_soilphys(), soilphys_layered()

Examples

data("soiltemp")
library(dplyr)

dt <- data.frame(
  site = rep(c("site_a", "site_b"), each = 12),
  depth = c(5, seq(0,-100,-10), 7, seq(0,-100,-10)))

discretize_depth(df = soiltemp,
                 param = "t",
                 method = "linear",
                 depth_target = dt,
                 id_cols = c(
                   "site","Date"))

Description

Different functions to estimate soil diffusivity from the air-filled pore space.

Usage

DSD0_millington_quirk(AFPS, TPS = NULL, tortuosity = NULL)

DSD0_moldrup(AFPS, AFPS_100, b_campbell)

DSD0_currie(AFPS, a_currie = 1.9, b_currie = 1.4)

DSD0_linear(AFPS, a_lin, b_lin)

Arguments

Details

• DSD0_millington_quirk() is of the form $D_s / D_0 = \Xi \cdot \epsilon$ where $\Xi$ is the tortuosity factor (tortuosity) calulcated as $\Xi = \frac{\epsilon^{(10/3)}}{\Phi^2}$ ; $\epsilon$ is the air-filled pore space (AFPS) and $\Phi$ is the porosity (TPS). From Millington & Quirk (1961).

• DSD0_moldrup() is of the form $D_s / D_0 = (2 \cdot \epsilon_{100}^3 + 0.04 \cdot \epsilon_{100}) \cdot (\frac{\epsilon}{\epsilon_{100}})^{(2 + \frac{3}{b_{campbell}})}$ where $\epsilon_{100}$ is the air-filled pore space at a matric potential head of -100 cm and $b_{campbell}$ is the slope of the water retention curve. From Moldrup et al. (2000).

• DSD0_currie() is of the form $D_s / D_0 = a \cdot \epsilon^b$ where $a$ and $b$ are fit parameter of an exponential model. From Currie (1960) with default values (a=1.9; b=1.4)from Troeh (1982).

• DSD0_linear() is a linear model of form $D_s / D_0 = a \cdot \epsilon + b$.

Value

A numeric vector of DSD0.

References

Millington, R. J., & Quirk, J. P. (1961). Permeability of porous solids. In Transactions of the Faraday Society (Vol. 57, p. 1200). Royal Society of Chemistry (RSC). https://doi.org/10.1039/tf9615701200

Moldrup, P., Olesen, T., Schjønning, P., Yamaguchi, T., & Rolston, D. E. (2000). Predicting the Gas Diffusion Coefficient in Undisturbed Soil from Soil Water Characteristics. In Soil Science Society of America Journal (Vol. 64, Issue 1, pp. 94–100). Wiley. https://doi.org/10.2136/sssaj2000.64194x

Currie, J. A. (1960). Gaseous diffusion in porous media. Part 2. - Dry granular materials. In British Journal of Applied Physics (Vol. 11, Issue 8, pp. 318–324). IOP Publishing. https://doi.org/10.1088/0508-3443/11/8/303

Troeh, F. R., Jabro, J. D., & Kirkham, D. (1982). Gaseous diffusion equations for porous materials. In Geoderma (Vol. 27, Issue 3, pp. 239–253). Elsevier BV. https://doi.org/10.1016/0016-7061(82)90033-7

Examples

DSD0_millington_quirk(0.2, 0.6)
DSD0_moldrup(0.2, 0.6, 1)
DSD0_currie(0.2)
DSD0_linear(0.2, a_lin = 1.4, b_lin = 0)

Description

Calculate or extract the soil/atmosphere efflux from cfp_pfres or cfp_fgres model results.

Usage

efflux(x, ...)

## S3 method for class 'cfp_pfres'
efflux(x, ...)

## S3 method for class 'cfp_fgres'
efflux(x, ..., method = "lm", layers = NULL)

## S3 method for class 'cfp_altres'
efflux(x, ...)

Arguments

Value

A data.frame with one row for each combination of id_cols and the column efflux in $µmol m^{-2}s^{-1}$.

Examples

my_dat <- ConFluxPro::base_dat |>
  filter(Date < "2021-03-01") #subset to speed up example
PROFLUX <- pro_flux(my_dat)
FLUX <- fg_flux(my_dat)

efflux(PROFLUX)
efflux(FLUX)

Description

A set of functions that can be called on an cfp_pfres object (the result of a call to pro_flux) to assess the quality of the model.

Usage

error_concentration(x, param_cols = NULL, normer = "sd")

## S3 method for class 'cfp_pfres'
error_concentration(x, param_cols = NULL, normer = "sd")

## S3 method for class 'cfp_fgres'
error_concentration(x, param_cols = NULL, normer = "sd")

## S3 method for class 'cfp_altres'
error_concentration(x, param_cols = NULL, normer = "sd")

error_efflux(x, param_cols, EFFLUX, normer = "sd", ...)

## S3 method for class 'cfp_pfres'
error_efflux(x, param_cols, EFFLUX, normer = "sd", ...)

## S3 method for class 'cfp_fgres'
error_efflux(x, param_cols, EFFLUX, normer = "sd", ...)

## S3 method for class 'cfp_altres'
error_efflux(x, param_cols, EFFLUX, normer = "sd", ...)

Arguments

Details

For error_concentration, the way the error parameter is calculated for cfp_fgres and cfp_pfres objects is entirely different and should not be used in comparison between the two. NRMSE of cfp_pfres objects are calculated as the mean of depth-wise NRMSEs of modelled versus input gas concentrations. 'NRMSE's of cfp_fgres objects simply calculate the mean of (dcdz_sd / dcdz_ppm) per group described in param_cols.

Value

The calculated error estimate for a single model, a list of models (cfp_altres) and for each parameter combination in param_cols

Examples

PROFLUX <- pro_flux(base_dat)

error_concentration(PROFLUX)
error_efflux(
  PROFLUX,
  EFFLUX = data.frame(efflux = 1),
  param_cols = c("site"))

Description

Evaluate the model runs produced by a call to alternate() with user-defined error functions.

Usage

evaluate_models(
  x,
  eval_funs = NULL,
  eval_weights = 1,
  param_cols,
  eval_cols,
  n_best = NULL,
  f_best = 0.01,
  scaling_fun = scale_min_median,
  ...
)

## S3 method for class 'cfp_altres'
evaluate_models(
  x,
  eval_funs = NULL,
  eval_weights = 1,
  param_cols = cfp_id_cols(cfp_layers_map(cfp_og_model(x))),
  eval_cols = NULL,
  n_best = NULL,
  f_best = 0.01,
  scaling_fun = scale_min_median,
  ...
)

Arguments

Value

A list with components best_runs the runs with the lowest model error (ME), model_error the model error for all runs, models_evaluated the raw values returned by error_funs and best_runs_runmap, a cfp_run_map() which can be used to rerun the best_runs model configurations. Note, that for best_runs_runmap the value of run_id is remapped to values 1:n_best.

Examples

PROFLUX <- pro_flux(base_dat |> filter(site == "site_a"))

run_map <-
 cfp_run_map(
   PROFLUX,
   params = list(TPS = c(0.9, 1.1)),
   type = "factor",
   n_runs = 5)

PF_alt <- alternate(
  PROFLUX,
  \(x) complete_soilphys(x, DSD0_formula = "a*AFPS^b", quiet = TRUE),
  run_map)

evaluate_models(
  PF_alt,
  eval_funs = list("NRMSE_conc" = error_concentration)
    )

Description

These functions extract components from different objects that can be created in ConFluxPro.

Usage

cfp_og_model(x)

## S3 method for class 'cfp_altres'
cfp_og_model(x)

cfp_id_cols(x)

cfp_gases(x)

cfp_modes(x)

cfp_param(x)

cfp_funs(x)

cfp_zero_flux(x)

cfp_zero_limits(x)

cfp_DSD0_optim(x)

cfp_evenness_factor(x)

cfp_known_flux_factor(x)

cfp_runmap_type(x)

cfp_params_df(x)

cfp_n_runs(x)

cfp_layers_different(x)

cfp_layers_from(x)

cfp_layers_altmap(x)

Arguments

Value

The extracted component, e.g. a data.frame() or character().

Examples

my_data <- ConFluxPro::base_dat |>
  filter(Date == "2021-01-01") # subset for example = faster runtime

### from cfp_dat objects (and derivatives)
cfp_id_cols(my_data)

cfp_gasdata(my_data) |> head()
cfp_soilphys(my_data) |> head()
cfp_layers_map(my_data) |> head()
my_data$profiles |> head()

### from cfp_pfmod or cfp_pfres objects
PROFLUX <- my_data |> pro_flux()
cfp_zero_flux(PROFLUX)
cfp_zero_limits(PROFLUX)
cfp_DSD0_optim(PROFLUX) #deprecated
cfp_evenness_factor(PROFLUX)
cfp_known_flux_factor(PROFLUX)
PROFLUX$PROFLUX |> head()

### from cfp_fgmod or cfp_fgres objects
FLUX <- my_data |> fg_flux()

cfp_gases(FLUX)
cfp_modes(FLUX)
cfp_param(FLUX)
cfp_funs(FLUX)
FLUX$FLUX |> head()

### from cfp_run_map
set.seed(42)
my_run_map <-
cfp_run_map(
  PROFLUX,
  list("TPS" = c(0.9, 1.1)),
  "factor",
  n_runs = 2)

cfp_params_df(my_run_map)
cfp_n_runs(my_run_map)
cfp_layers_from(my_run_map)
cfp_layers_different(my_run_map)
cfp_runmap_type(my_run_map)
cfp_layers_altmap(my_run_map)

### from cfp_altres
my_altres <-
alternate(
  x = PROFLUX,
  f = \(x) complete_soilphys(x, "a+AFPS^b", quiet = TRUE),
  run_map = my_run_map)

cfp_og_model(my_altres)
cfp_run_map(my_altres)

Description

fg_flux() implements different approaches to the flux-gradient method (FGM). It takes a valid input dataset from cfp_dat() and calculates for each layer defined in cfp_layers_map().

Usage

fg_flux(x, ...)

## S3 method for class 'cfp_dat'
fg_flux(x, ...)

## S3 method for class 'cfp_fgres'
fg_flux(x, ...)

## S3 method for class 'cfp_fgmod'
fg_flux(x, ...)

Arguments

Details

The model result contains the original data, but adds the dataset FLUX, which contains the calculated flux rates. You can use functions efflux and production to calculate different elements or access the raw result with model_result$FLUX.

Value

A cfp_fgres model result.

References

DAVIDSON, E. A., SAVAGE, K. E., TRUMBORE, S. E., & BORKEN, W. (2006). Vertical partitioning of CO2 production within a temperate forest soil. In Global Change Biology (Vol. 12, Issue 6, pp. 944–956). Wiley. https://doi.org/10.1111/j.1365-2486.2005.01142.x

See Also

Other flux models: pro_flux()

Examples

fg_flux(ConFluxPro::base_dat)

Description

Filter profiles by their id_cols or (where available) by their prof_id. This is built on dplyr::filter().

Usage

filter(.data, ..., .by = NULL, .preserve = FALSE)

## S3 method for class 'cfp_dat'
filter(.data, ..., .preserve = FALSE)

Arguments

Value

A subset of the original data.

Examples

base_dat |>
  filter(site == "site_a")

base_dat |>
  filter(Date > "2022-03-01")

Description

A function to either run fg_flux() or pro_flux() models from valid cfp_fgmod or cfp_pfmod objects.

Usage

flux(x)

Arguments

Value

Either a cfp_pfres or cfp_fgres model result.

Examples

FLUX <- ConFluxPro::base_dat |> fg_flux()
FLUX2 <- flux(FLUX)

all.equal(FLUX, FLUX2)

Description

A synthetic dataset of soil CO2 concentrations at two sites over a one-year period.

Usage

gasdata

Format

A tibble with 312 rows and 6 variables:

site

name of the site

Date

Date in the format "YYYY-MM-DD"

depth

depth from mineral soil in cm

repetition

id for which repetition in each depth

x_ppm

concentration, in ppm

gas

name of the gas

Description

This function calculates harmonic mean of a vector and can be used analogous to the base functions mean() or median()

Usage

harm(x, w = 1, na.rm = FALSE)

Arguments

Value

(numeric) harmonic mean of x

Examples

harm(c(1:10))
 harm(c(1:10),c(10:1))

Description

An example dataset for layers_map that devides each site into two layers.

Usage

layers_map

Format

A data.frame with 4 rows and 3 variables:

site

name of the site

upper

upper limit for layer in cm

lower

lower limit for layer in cm

Description

Get number of groups/profiles

Usage

n_groups(x)

n_profiles(x)

Arguments

Value

An integer giving the number of groups of the object.

An integer giving the number of profiles of the object.

Examples

n_groups(base_dat)

n_profiles(base_dat)
n_profiles(cfp_soilphys(base_dat))

Description

Plot vertical soil profiles of ConFluxPro objects using ggplot. This is mainly intended for diagnostic purposes and better understand the underlying data.

Supported objects:

cfp_pfres

Displays TPS, SWC and AFPS, as well as production and measured and modelled gas concentrations.

cfp_fgres

Displays TPS, SWC and AFPS, as well as the measured concentration profile, and concentration gradients for each layer.

cfp_soilphys

Displays TPS, SWC and AFPS, as well as values of Ds and Temperature.

cfp_gasdata

Displays the concentration profile.

cfp_layers_map

Displays the layer names, pmap and layer_couple, as well as the allowed production range.

Usage

plot_profile(x)

Arguments

Value

A ggplot2 plot with facets for each distinct profile. If more than 20 profiles are plotted a message is sent because this can take a long time.

Examples

data_subset <-  base_dat |>
  filter(Date == "2021-02-01")

plot_profile(cfp_soilphys(data_subset))
plot_profile(cfp_gasdata(data_subset))
plot_profile(cfp_layers_map(data_subset))

Description

This implements an inverse modeling approach which optimizes vertically resolved production (or consumption) of the gases in question to fit a modeled concentration profile to observed data.

One boundary condition of this model is, that there is no incoming or outgoing flux at the bottom of the lowest layer of the profile. If this boundary condition is not met, the flux must be optimised as well. This can be set in zero_flux.

Usage

pro_flux(x, ...)

## S3 method for class 'cfp_dat'
pro_flux(x, ...)

## S3 method for class 'cfp_pfres'
pro_flux(x, ...)

## S3 method for class 'cfp_pfmod'
pro_flux(x, ...)

Arguments

Value

A cfp_pfres() model result.

See Also

Other flux models: fg_flux()

Examples

soilphys <-
 cfp_soilphys(
   ConFluxPro::soilphys,
   id_cols = c("site", "Date")
 )

gasdata <-
 cfp_gasdata(
   ConFluxPro::gasdata,
   id_cols = c("site", "Date")
 )


lmap <-
 cfp_layers_map(
   ConFluxPro::layers_map,
   gas = "CO2",
   lowlim = 0,
   highlim = 1000,
   id_cols = "site"
 )

PROFLUX <-
 cfp_dat(gasdata,
         soilphys,
         lmap ) |>
 pro_flux()

Description

Easily extract the production of cfp_pfres() and cfp_fgres() models per layer defined in layers_map() and calculate the relative contribution per layer.

Usage

production(x, ...)

Arguments

Details

For a pro_flux() model, the extraction is straightforward and simply the product of the optimised production rate (per volume) multiplied by the height of the layer.

For fg_flux(), the assumption is made that the production of the layer $i$ is the difference of the flux in the layer above $F_{i+1}$ and the layer below $F_{i-1}$. The flux below the lowest layer is assumed to be zero and the flux above the topmost layer is the efflux. This approach has some uncertainties and it should be evaluated if it applies to your model.

If there are error estimates available from a call to bootstrap_error(), the errors are propagated as follows:

$\Delta prod_{rel} = |\Delta efflux \cdot \frac{prod_{abs}}{efflux^2}| + |\Delta prod_{abs}\cdot\frac{1}{efflux}|$

Value

data.frame with prod_abs ($µmol / m^2 / s$), efflux ($µmol / m^2 / s$) and prod_rel where $prod_{rel} = efflux / prod_{abs}$.

Examples

PROFLUX <- pro_flux(base_dat)

production(PROFLUX)

Description

(Normalized) root mean square error

Calculate the (normalized) root-mean-square-error of two vectors.

Usage

rmse(a, b)

nrmse(a, b, normer = "sd")

Arguments

Value

The (normalised) rmse of the provided vector.

Examples

set.seed(42)
a <- c(1, 2, 3, 4)
b <- a * rnorm(4, 1, 0.1)
rmse(a, b)
nrmse(a, b, normer = "sd")
nrmse(a, b, normer = "mean")

Description

run_map() was deprecated in favor of cfp_run_map for consistency.

Create a cfp_run_map for model alteration in alternate()

Usage

run_map(
  x,
  params = list(),
  type = NULL,
  method = NULL,
  n_runs = NULL,
  layers_different = FALSE,
  layers_from = "layers_map",
  layers_altmap = NULL,
  topheight_adjust = FALSE
)

Arguments

Value

An object of type cfp_run_map that can be used within alternate.

Examples

PROFLUX <- ConFluxPro::base_dat |> pro_flux()
# Create a cfp_run_map where TPS is changed between 90 % and 110 %
# of the original value for 50 runs.
cfp_run_map(
  PROFLUX,
  list("TPS" = c(0.9, 1.1)),
  "factor",
  n_runs = 50)

Description

Scale a vector between its minimum and median.

Usage

scale_min_median(x)

Arguments

Value

x scaled between min and median of x.

Examples

scale_min_median(1:10)

Description

A simple function to return a character (-vector) of the season from a Date (-vector). Months:

spring

3-5

summer

6-8

fall

9-11

winter

12-2

Usage

season(d)

Arguments

Value

A character vector the same length as d

Examples

season(as.Date(c("1955-01-15","1985-06-15","2015-10-15")))

Description

From any result parameter and its corresponding cfp_run_map calculate first-order and total sobol indices using the Azzini (2021) method.

Usage

sobol_calc_indices(Y, effect_cols, id_cols = character(), run_map)

Arguments

Details

This implements the approach outlined in Azzini et al (2021).

Value

A data.frame with the following columns

...

Any id_cols specified

param_id, param, pmap

Parameter identificators from the cfp_run_map used.

effect_param

The parameter for which the effect was calculated.

Vt, Vi, VY

Internal parameters for the indice calculation.

Si

First order sobol indice.

ST

Total order sobol indice.

References

Azzini, Ivano; Mara, Thierry A.; Rosati, Rossana: Comparison of two sets of Monte Carlo estimators of Sobol’ indices, Environmental Modelling & Software, Volume 144, 2021, 105167, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2021.105167

See Also

Other sobol: sobol_run_map()

Examples

PROFLUX <- pro_flux(base_dat)

sobol_map <- sobol_run_map(PROFLUX,
 params = list("TPS" = c(0.9, 1.1),
               "t" = c(0.9, 1.1)),
 type = c("factor", "factor"),
 n_runs = 10)

 PF_sobol <-
alternate(
  PROFLUX,
  \(x) complete_soilphys(x, DSD0_formula = "a*AFPS^b", quiet = TRUE),
  sobol_map)

sobol_calc_indices(efflux(PF_sobol), "efflux", c("site"), sobol_map)

Description

Modify an existing cfp_run_map for sobol indice estimation or create a new one from scratch.

Usage

sobol_run_map(x, ...)

## S3 method for class 'cfp_dat'
sobol_run_map(x, ...)

## S3 method for class 'cfp_run_map'
sobol_run_map(x, ...)

Arguments

Value

A cfp_run_map to be used in alternate for sensitivity analysis.

See Also

Other sobol: sobol_calc_indices()

Examples

PROFLUX <- pro_flux(base_dat)

sobol_run_map(PROFLUX,
 params = list("TPS" = c(0.9, 1.1),
               "t" = c(0.9, 1.1)),
 type = c("factor", "factor"),
 n_runs = 10)

Description

A synthetic dataset of soil total pore space and diffusion models after the general formula a*AFPS^b.

Usage

soildiff

Format

A tibble with 8 rows and 6 variables:

site

name of the site

upper

upper limit for layer in cm

lower

lower limit for layer in cm

TPS

total pore space as fraction of volume

a

diffusion-model fit parameter a

b

diffusion-model fit parameter b

Description

An example dataset for soilphys based on the sets soiltemp, soilwater and soildiff

Usage

soilphys

Format

A tibble with 120 rows and 4 variables:

site

name of the site

Date

Date in the format "YYYY-MM-DD"

depth

depth in cm

t

temperature in °C

Description

A synthetic dataset of soil temperature at discrete depths. The dates correspond to gasdata.

Usage

soiltemp

Format

A tibble with 120 rows and 4 variables:

site

name of the site

Date

Date in the format "YYYY-MM-DD"

depth

depth in cm

t

temperature in °C

Description

A synthetic dataset of soil water content in a layered structure. The dates correspond to gasdata.

Usage

soilwater

Format

A tibble with 180 rows and 5 variables:

site

name of the site

Date

Date in the format "YYYY-MM-DD"

upper

upper limit for layer in cm

lower

lower limit for layer in cm

SWC

soil water content as fraction of volume

Description

Get the gases from a CFP object.

Usage

unique_gases(x)

Arguments

Value

A character vector of gases in that object.

Examples

unique_gases(base_dat)

data.frame(gas = c("CO2", "CH4")) |>
cfp_profile(id_cols = "gas") |>
unique_gases()