Help for package fluxible

Title:

Ecosystem Gas Fluxes Calculations for Closed Loop Chamber Setup

Version:

1.2.6

Date:

2025-06-22

Description:

Processes the raw data from closed loop flux chamber (or tent) setups into ecosystem gas fluxes usable for analysis. It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Functions provided include different models (exponential as described in Zhao et al (2018) <doi:10.1016/j.agrformet.2018.08.022>, quadratic and linear) to estimate the fluxes from the raw data, quality assessment, plotting for visual check and calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...).

License:

GPL (≥ 3)

Encoding:

UTF-8

RoxygenNote:

7.3.2

Suggests:

knitr, rmarkdown, testthat (≥ 3.0.0), vdiffr, forcats, tidyverse, fs

Config/testthat/edition:

Imports:

broom, dplyr, ggforce, ggplot2, haven, lubridate, rlang, purrr, stats, stringr, tidyr, zoo, progress, purrrlyr, tidyselect, lifecycle

Depends:

R (≥ 4.1)

LazyData:

true

URL:

https://plant-functional-trait-course.github.io/fluxible/, https://github.com/Plant-Functional-Trait-Course/fluxible

VignetteBuilder:

knitr

BugReports:

https://github.com/Plant-Functional-Trait-Course/fluxible/issues

NeedsCompilation:

Packaged:

2025-06-24 11:20:16 UTC; jga051

Author:

Joseph Gaudard

[aut, cre], Richard James Telford

[aut]

Maintainer:

Joseph Gaudard <joseph.gaudard@pm.me>

Repository:

CRAN

Date/Publication:

2025-06-24 11:50:02 UTC

fluxible: Ecosystem Gas Fluxes Calculations for Closed Loop Chamber Setup

Description

Processes the raw data from closed loop flux chamber (or tent) setups into ecosystem gas fluxes usable for analysis. It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Functions provided include different models (exponential as described in Zhao et al (2018) doi:10.1016/j.agrformet.2018.08.022, quadratic and linear) to estimate the fluxes from the raw data, quality assessment, plotting for visual check and calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...).

Author(s)

Maintainer: Joseph Gaudard joseph.gaudard@pm.me (ORCID)

Authors:

Richard James Telford richard.telford@uib.no (ORCID)

CO2 concentration

Description

CO2 concentration with measurements meta data

Usage

co2_conc

Format

A tibble with 1251 rows and 13 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.
turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
f_start: Datetime at which the measurement was started.
f_end: Datetime at which the measurement ended.
f_fluxid: Unique ID for each flux.
f_n_conc: Number of data point per flux.
f_ratio: Ratio of n_conc over length of the measurement (in seconds).
f_flag_match: Data quality flags.

Examples

co2_conc

CO2 concentration with missing data

Description

CO2 concentration with measurements meta data and missing data in the middle of the measurements

Usage

co2_conc_mid_missing

Format

A tibble with 1251 rows and 13 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.
turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
f_start: Datetime at which the measurement was started.
f_end: Datetime at which the measurement ended.
f_fluxid: Unique ID for each flux.
f_n_conc: Number of data point per flux.
f_ratio: Ratio of n_conc over length of the measurement (in seconds).
f_flag_match: Data quality flags.

Examples

co2_conc_mid_missing

CO2 concentration

Description

CO2 concentration with measurements meta data, with missing data.

Usage

co2_conc_missing

Format

A tibble with 668 rows and 13 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.
turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
f_start: Datetime at which the measurement was started.
f_end: Datetime at which the measurement ended.
f_fluxid: Unique ID for each flux.
f_n_conc: Number of data point per flux.
f_ratio: Ratio of n_conc over length of the measurement (in seconds).
f_flag_match: Data quality flags.

Examples

co2_conc_missing

CO2 concentration with missing data

Description

Continuous CO2 concentration as measured on the field, with missing data.

Usage

co2_df_missing

Format

A tibble with 1148 rows and 5 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.

Examples

co2_df_missing

CO2 concentration

Description

Continuous CO2 concentration as measured on the field

Usage

co2_df_short

Format

A tibble with 1801 rows and 5 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.

Examples

co2_df_short

CO2 fluxes

Description

Manually calculated CO2 fluxes for testing purpose. df_short and record_short were used, with a zhao18 fit.

Usage

co2_fluxes

Format

A tibble with 6 rows and 11 variables

f_fluxid: Unique ID for each flux.
f_slope_tz: Slope of C(t) at t zero.
f_temp_air_ave: Air temperature inside the flux chamber in Celsius averaged over the flux measurement.
f_flux: CO2 flux in mmol/sqm/hour.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm averaged over the flux measurement.
temp_soil: Ground temperature inside the flux chamber in Celsius averaged over the flux measurement.
turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
f_start: Datetime at which the measurement started.
temp_fahr: Air temperature inside the flux chamber in Fahrenheit averaged over the flux measurement.
temp_kelvin: Air temperature inside the flux chamber in Kelvin averaged over the flux measurement.

Examples

co2_fluxes

CO2 fluxes with PAR values

Description

CO2 fluxes with photosynthetically active radiation (PAR) for testing and examples of light response curves (LRC)

Usage

co2_fluxes_lrc

Format

A tibble with 257 rows and 5 variables

f_flux: CO2 flux in mmol/sqm/hour.
datetime: Time and date of the measurement.
PAR_ave: Photosynthetically active radiation inside the chamber in micromol/s/sqm averaged over the flux measurement.
type: Type of measurement: ecosystems respiration (ER), net ecosystem exchange (NEE), or light response curve (LRC).
warming: Treatment: control or warming.

Examples

co2_fluxes_lrc

CO2 concentration at Liahovden

Description

CO2 concentration at Liahovden site, used in example in readme file

Usage

co2_liahovden

Format

A tibble with 89692 rows and 5 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.

Examples

co2_liahovden

Calculates ecosystem gas fluxes

Description

Calculates a flux based on the rate of change of gas concentration over time

Usage

flux_calc(
  slopes_df,
  slope_col,
  f_datetime = f_datetime,
  temp_air_col,
  chamber_volume = deprecated(),
  setup_volume,
  atm_pressure,
  plot_area,
  f_fluxid = f_fluxid,
  conc_unit,
  flux_unit,
  cols_keep = c(),
  cols_ave = c(),
  cols_sum = c(),
  cols_med = c(),
  cols_nest = "none",
  tube_volume = deprecated(),
  temp_air_unit = "celsius",
  f_cut = f_cut,
  keep_arg = "keep",
  cut = TRUE,
  fit_type = c()
)

Arguments

slopes_df

dataframe of flux slopes

slope_col

column containing the slope to calculate the flux

f_datetime

column containing the datetime of each gas concentration measurements in slopes_df. The first one after cutting will be kept as datetime of each flux in the output.

temp_air_col

column containing the air temperature used to calculate fluxes. Will be averaged with NA removed.

chamber_volume

see setup_volume

setup_volume

volume of the flux chamber and instrument together in L, can also be a column in case it is a variable

atm_pressure

atmospheric pressure in atm, can be a constant (numerical) or a variable (column name)

plot_area

area of the plot in m^2, can also be a column in case it is a variable

f_fluxid

column containing the flux IDs

conc_unit

unit in which the concentration of gas was measured ppm, ppb, or ppt

flux_unit

desired units for the calculated fluxes. Has to be of the form amount/surface/time. Amount can be mol, mmol, umol, nmol or pmol. Time can be d (day), h (hour), mn (minute) or s (seconds). Surface can be m2, dm2 or cm2.

cols_keep

columns to keep from the input to the output. Those columns need to have unique values for each flux, as distinct is applied.

cols_ave

columns with values that should be averaged for each flux in the output. Note that NA are removed in mean calculation. Those columns will get the ⁠_ave⁠ suffix in the output.

cols_sum

columns with values for which is sum is provided for each flux in the output. Those columns will get the ⁠_sum⁠ suffix in the output.

cols_med

columns with values for which is median is provided for each flux in the output. Note that NA are removed in median calculation. Those columns will get the ⁠_med⁠ suffix in the output.

cols_nest

columns to nest in nested_variables for each flux in the output. Can be character vector of column names, "none" (default) selects none, or "all" selects all the column except those in cols_keep.

tube_volume

see setup_volume

temp_air_unit

units in which air temperature was measured. Has to be either celsius (default), fahrenheit or kelvin.

f_cut

column containing cutting information

keep_arg

name in f_cut of data to keep

cut

if 'TRUE' (default), the measurements will be cut according to 'f_cut' before calculating fluxes. This has no influence on the flux itself since the slope is provided from flux_fitting, but it will influence the values of the variables in cols_ave, cols_cum, and cols_med.

fit_type

(optional) model used in flux_fitting. Will be automatically filled if slopes_df was produced using flux_fitting.

Value

a dataframe containing flux IDs, datetime of measurements' starts, fluxes in mmol * m^-2 * h^-1 or micromol * m^-2 * h^-1 (f_flux) according to flux_unit, temperature average for each flux in Kelvin (f_temp_ave), the model used in flux_fitting, any column specified in cols_keep, any column specified in cols_ave, cols_med or cols_sum with their values treated accordingly over the measurement after cuts, and a column nested_variables with the variables specified in cols_nest.

Examples

data(co2_conc)
slopes <- flux_fitting(co2_conc, conc, datetime, fit_type = "exp_zhao18")
flux_calc(slopes,
f_slope,
datetime,
temp_air,
conc_unit = "ppm",
flux_unit = "mmol/m2/h",
setup_volume = 24.575,
atm_pressure = 1,
plot_area = 0.0625)

check the items inside `flux_fun_check`

Description

check the items inside flux_fun_check

Usage

flux_check_item(arg, fn, msg, narg, df_name = NA)

Arguments

arg

argument to be checked by fn

fn

function to check arg

msg

message to display in case arg is the wrong class

narg

name of arg

df_name

name of arg in case it is a data frame

Author(s)

Adam Klimes

filter cut data before calculating fluxes

Description

filter cut data before calculating fluxes

Usage

flux_cut(slopes_df, cut_col, keep_arg)

Arguments

slopes_df

dataset containing slopes and cut column

cut_col

column containing cutting information

keep_arg

name in cut_col of data to keep

to check the type of fit

Description

extracts the type of fit that was applied in flux_fitting or checks that the fit_type provided by the user is compatible with Fluxible

Usage

flux_fit_type(
  df,
  fit_type = c(),
  fit_type_list = c("exp_hm", "exp_tz", "exp_zhao18", "exponential", "linear",
    "quadratic")
)

Arguments

df

any dataframe

fit_type

type of fit that was applied in flux_fitting. Needs to be filled only if the df was produced outside of the Fluxible workflow.

fit_type_list

list of fit types in use with Fluxible.

Fitting a model to concentration data and estimating the slope

Description

Fits gas concentration over time data with a model (exponential, quadratic or linear) and provides the slope later used to calculate gas fluxes with flux_calc

Usage

flux_fitting(
  conc_df,
  f_conc = f_conc,
  f_datetime = f_datetime,
  f_start = f_start,
  f_end = f_end,
  f_fluxid = f_fluxid,
  fit_type,
  start_cut = 0,
  end_cut = 0,
  t_zero = 0,
  cz_window = 15,
  b_window = 10,
  a_window = 10,
  roll_width = 15
)

Arguments

conc_df

dataframe of gas concentration over time

f_conc

column with gas concentration data

f_datetime

column with datetime of each concentration measurement Note that if there are duplicated datetime in the same f_fluxid only the first row will be kept

f_start

column with datetime when the measurement started (ymd_hms)

f_end

column with datetime when the measurement ended (ymd_hms)

f_fluxid

column with ID of each flux

fit_type

exp_zhao18, exp_tz, exp_hm, quadratic or linear. exp_zhao18 is using the exponential model C(t) = C_m + a (t - t_z) + (C_z - C_m) exp(-b (t - t_z)) from Zhao et al (2018). expt_tz is a modified version which allows the user to fix t_zero: C(t) = C_m + a * t + (C_z - C_m) exp(-b * t) exp_hm is using the HM model (Pedersen et al., 2010; Hutchinson and Mosier, 1981) C(t) = C_m + (C_z - C_m) exp(-b * t) exponential is equal to exp_zhao18, for backwards compatibility

start_cut

time to discard at the start of the measurements (in seconds)

end_cut

time to discard at the end of the measurements (in seconds)

t_zero

time at which the slope should be calculated (for quadratic, exp_tz and exp_hm fits)

cz_window

window used to calculate Cz, at the beginning of cut window (exp_zhao18 and exp_tz fits)

b_window

window to estimate b. It is an interval after tz where it is assumed that the model fits the data perfectly (exp_zhao18 and exp_tz fits)

a_window

window at the end of the flux to estimate a (exp_zhao18 and exp_tz fits)

roll_width

width of the rolling mean for gas concentration when looking for tz, ideally same as cz_window (exp_zhao18 and exp_tz fits)

Value

a dataframe with the slope at t zero (f_slope), a datetime column of t zero (f_start_z), a factor column indicating the cuts (f_cut), the time in seconds since the start of the measurement (f_time), the modeled fit (f_fit), the modeled slope (f_fit_slope), the parameters of the fit depending on the model used, and any columns present in the input. The type of fit is added as an attribute for use by the other functions.

References

Pedersen, A.R., Petersen, S.O., Schelde, K., 2010. A comprehensive approach to soil-atmosphere trace-gas flux estimation with static chambers. European Journal of Soil Science 61, 888–902. https://doi.org/10.1111/j.1365-2389.2010.01291.x

Hutchinson, G.L., Mosier, A.R., 1981. Improved Soil Cover Method for Field Measurement of Nitrous Oxide Fluxes. Soil Science Society of America Journal 45, 311–316. https://doi.org/10.2136/sssaj1981.03615995004500020017x

Zhao, P., Hammerle, A., Zeeman, M., Wohlfahrt, G., 2018. On the calculation of daytime CO2 fluxes measured by automated closed transparent chambers. Agricultural and Forest Meteorology 263, 267–275. https://doi.org/10.1016/j.agrformet.2018.08.022

Examples

data(co2_conc)
flux_fitting(co2_conc, conc, datetime, fit_type = "exp_zhao18")
flux_fitting(co2_conc, conc, datetime,  fit_type = "quadratic",
t_zero = 10, end_cut = 30)

Fitting a model to the gas concentration curve and estimating the slope over time, using a modified version of the model from Zhao et al (2018) that allows the user to fix `t_zero`.

Description

Fits the exponential expression to the concentration evolution ⁠C(t) = C_m + a * t + (C_z - C_m) exp(-b * t)⁠

Usage

flux_fitting_exptz(
  conc_df_cut,
  conc_df,
  f_conc,
  f_start,
  f_fluxid,
  start_cut,
  cz_window,
  b_window,
  a_window,
  roll_width,
  t_zero
)

Arguments

conc_df_cut

dataframe of gas concentration over time, cut

conc_df

dataframe of gas concentration over time

f_conc

column with gas concentration

f_start

column with datetime when the measurement started

f_fluxid

column with ID of each flux

start_cut

time to discard at the start of the measurements (in seconds)

cz_window

window used to calculate Cz, at the beginning of cut window

b_window

window to estimate b. It is an interval after tz where it is assumed that C fits the data perfectly

a_window

window at the end of the flux to estimate a

roll_width

width of the rolling mean for CO2 when looking for tz, ideally same as cz_window

t_zero

time at which the slope should be calculated (for quadratic fit)

Value

a dataframe with the slope at t zero, modeled concentration over time and exponential expression parameters

References

Fitting a model to the gas concentration curve and estimating the slope over time, using the HM model (Pedersen et al., 2010; Hutchinson and Mosier, 1981).

Description

Fits the exponential expression to the concentration evolution ⁠C(t) = C_m + (C_z - C_m) exp(-b * t)⁠

Usage

flux_fitting_hm(
  conc_df_cut,
  conc_df,
  f_conc,
  f_start,
  f_fluxid,
  start_cut,
  cz_window,
  b_window,
  roll_width,
  t_zero
)

Arguments

conc_df_cut

dataframe of gas concentration over time, cut

conc_df

dataframe of gas concentration over time

f_conc

column with gas concentration

f_start

column with datetime when the measurement started

f_fluxid

column with ID of each flux

start_cut

time to discard at the start of the measurements (in seconds)

cz_window

window used to calculate Cz, at the beginning of cut window

b_window

window to estimate b. It is an interval after tz where it is assumed that C fits the data perfectly

roll_width

width of the rolling mean for CO2 when looking for tz, ideally same as cz_window

t_zero

time at which the slope should be calculated (for quadratic fit)

Value

a dataframe with the slope at t zero, modeled concentration over time and exponential expression parameters

References

linear fit to gas concentration over time

Description

fits a linear model to the gas concentration over time

Usage

flux_fitting_lm(conc_df_cut, conc_df, f_conc, f_fluxid, start_cut)

Arguments

conc_df_cut

dataframe of gas concentration over time, cut

conc_df

dataframe of gas concentration over time

f_conc

column with gas concentration

f_fluxid

column with ID of each flux

start_cut

time to discard at the start of the measurements (in seconds)

Value

a df with the modeled gas concentration, slope, intercept, std error, r square and p value of the linear model

quadratic fit to gas concentration over time

Description

fits a quadratic model to the gas concentration over time

Usage

flux_fitting_quadratic(
  conc_df_cut,
  conc_df,
  f_conc,
  f_start,
  f_fluxid,
  start_cut,
  t_zero
)

Arguments

conc_df_cut

dataframe of gas concentration over time, cut

conc_df

dataframe of gas concentration over time

f_conc

column with gas concentration

f_start

column with datetime when the measurement started

f_fluxid

column with ID of each flux

start_cut

time to discard at the start of the measurements (in seconds)

t_zero

time at which the slope should be calculated

Value

a df with the modeled gas concentration, slope, intercept, std error, r square and p value of the quadratic model

Fitting a model to the gas concentration curve and estimating the slope over time, using the exponential model from Zhao et al (2018)

Description

Fits an exponential expression to the concentration evolution

Usage

flux_fitting_zhao18(
  conc_df_cut,
  conc_df,
  f_conc,
  f_start,
  f_fluxid,
  start_cut,
  cz_window,
  b_window,
  a_window,
  roll_width
)

Arguments

conc_df_cut

dataframe of gas concentration over time, cut

conc_df

dataframe of gas concentration over time

f_conc

column with gas concentration

f_start

column with datetime when the measurement started

f_fluxid

column with ID of each flux

start_cut

time to discard at the start of the measurements (in seconds)

cz_window

window used to calculate Cz, at the beginning of cut window

b_window

window to estimate b. It is an interval after tz where it is assumed that C fits the data perfectly

a_window

window at the end of the flux to estimate a

roll_width

width of the rolling mean for CO2 when looking for tz, ideally same as cz_window

Value

a dataframe with the slope at t zero, modeled concentration over time and exponential expression parameters

References

Counts quality flags

Description

Provides a table of how many fluxes were attributed which quality flag. This function is incorporated in flux_quality as a message, but can be used alone to extract a dataframe with the flag count.

Usage

flux_flag_count(
  flags_df,
  f_fluxid = f_fluxid,
  f_quality_flag = f_quality_flag,
  f_flags = c("ok", "discard", "zero", "force_discard", "start_error", "no_data",
    "force_ok", "force_zero", "force_lm", "no_slope")
)

Arguments

flags_df

dataframe of flux slopes

f_fluxid

column containing fluxes unique ID

f_quality_flag

column containing the quality flags

f_flags

list of flags used in the dataset (if different from default from flux_quality). If not provided, it will list only the flags that are present in the dataset (no showing 0).

Value

a dataframe with the number of fluxes for each quality flags and their proportion to the total

Author(s)

Vincent Belde

Examples

data(co2_conc)
slopes <- flux_fitting(co2_conc, conc, datetime, fit_type = "exp_zhao18")
slopes_flag <- flux_quality(slopes, conc)
flux_flag_count(slopes_flag)

checking that arguments and columns are in the correct class

Description

checking that arguments and columns are in the correct class

Usage

flux_fun_check(args, fn, msg, name_df = NA)

Arguments

args

list of arguments or dataframe to check

fn

list of functions used to check (is.numeric, is.character, ...)

msg

list of messages to return in case of failed check

name_df

in case args is a df with selected columns to check origdf is the original df to take the name from for a more obvious error message

Author(s)

Adam Klimes

Calculates GEP

Description

flux_gep was renamed flux_gpp out of consistancy with the litterature.

Calculate gross ecosystem production (GEP) from net ecosystem (NEE) exchange and ecosystem respiration (ER) as GEP = NEE - ER. Datetime and other variables to keep will be taken from the NEE measurement. Fluxes presents in the dataset that are neither NEE nor ER (soilR, LRC or other) are not lost.

Usage

flux_gep(
  fluxes_df,
  type_col,
  f_datetime,
  f_flux = f_flux,
  id_cols,
  nee_arg = "NEE",
  er_arg = "ER",
  cols_keep = "none"
)

Arguments

fluxes_df

a dataframe containing NEE and ER

type_col

column containing type of flux (NEE or ER)

f_datetime

column containing start of measurement as datetime

f_flux

column containing flux values

id_cols

columns used to identify each pair of ER and NEE

nee_arg

argument designating NEE fluxes in type column

er_arg

argument designating ER fluxes in type column

cols_keep

columns to keep from fluxes_df. Values from NEE row will be filled in GEP row. none (default) keeps only the columns in id_cols, flux, type and datetime columns; all keeps all the columns; can also be a vector of column names.

Value

a dataframe with $GEP = NEE - ER$ in long format with GEP, NEE, and ER as flux type, datetime, and any column specified in cols_keep. Values of datetime and columns in cols_keep for GEP row are taken from NEE measurements.

Examples

data(co2_fluxes)
flux_gep(co2_fluxes, type, f_start, id_cols = "turfID",
cols_keep = c("temp_soil"))

Calculates GPP

Description

to calculate gross primary production (GPP) from net ecosystem (NEE) exchange and ecosystem respiration (ER) as GPP = NEE - ER. Datetime and other variables to keep will be taken from the NEE measurement. Fluxes presents in the dataset that are neither NEE nor ER (soilR, LRC or other) are not lost.

Usage

flux_gpp(
  fluxes_df,
  type_col,
  f_datetime,
  f_flux = f_flux,
  id_cols,
  nee_arg = "NEE",
  er_arg = "ER",
  cols_keep = "none"
)

Arguments

fluxes_df

a dataframe containing NEE and ER

type_col

column containing type of flux (NEE or ER)

f_datetime

column containing start of measurement as datetime

f_flux

column containing flux values

id_cols

columns used to identify each pair of ER and NEE

nee_arg

argument designating NEE fluxes in type column

er_arg

argument designating ER fluxes in type column

cols_keep

columns to keep from fluxes_df. Values from NEE row will be filled in GPP row. none (default) keeps only the columns in id_cols, flux, type and datetime columns; all keeps all the columns; can also be a vector of column names.

Value

a dataframe with $GPP = NEE - ER$ in long format with GPP, NEE, and ER as flux type, datetime, and any column specified in cols_keep. Values of datetime and columns in cols_keep for GPP row are taken from NEE measurements.

Examples

data(co2_fluxes)
flux_gpp(co2_fluxes, type, f_start, id_cols = "turfID",
cols_keep = c("temp_soil"))

Standardizes CO2 fluxes at fixed PAR values

Description

Calculates light response curves for CO2 fluxes and standardizes CO2 fluxes according to the LRC

Usage

flux_lrc(
  fluxes_df,
  type_col,
  par_ave = par_ave,
  f_flux = f_flux,
  lrc_arg = "LRC",
  nee_arg = "NEE",
  er_arg = "ER",
  lrc_group = c(),
  par_nee = 300,
  par_er = 0
)

Arguments

fluxes_df

a dataframe containing NEE, ER and LRC measurements

type_col

column containing type of flux (NEE, ER, LRC)

par_ave

column containing the PAR value for each flux

f_flux

column containing flux values

lrc_arg

argument designating LRC fluxes in type column

nee_arg

argument designating NEE fluxes in type column

er_arg

argument designating ER fluxes in type column

lrc_group

character vector of columns to use to group the LRC (campaign, site, treatment), if applicable

par_nee

PAR value to correct the NEE fluxes to

par_er

PAR value to correct the ER fluxes to

Details

The light response curves are calculated with a quadratic of the form flux(PAR) = a * PAR² + b * PAR + c

Value

the same dataframe with the additional column PAR_corrected_flux

Examples

data(co2_fluxes_lrc)
flux_lrc(
fluxes_df = co2_fluxes_lrc,
type_col = type,
par_ave = PAR_ave,
f_flux = f_flux,
lrc_arg = "LRC",
nee_arg = "NEE",
er_arg = "ER",
lrc_group = c("warming"),
par_nee = 300,
par_er = 0
)

Matching continuously measured fluxes with measurement IDs and meta data

Description

Matching a dataframe of continuously measured gas concentration data with measurement metadata from another dataframe. Measurements are paired with their metadata based on datetime. Extra variables in both dataframes are kept in the output.

Usage

flux_match(
  raw_conc,
  field_record,
  f_datetime,
  start_col,
  end_col,
  measurement_length,
  fixed_length = TRUE,
  time_diff = 0,
  startcrop = 0,
  ratio_threshold = deprecated(),
  f_conc = deprecated()
)

Arguments

raw_conc

dataframe of CO2 concentration measured continuously. Has to contain at least a datetime column in ymd_hms format and a gas concentration column as double.

field_record

dataframe recording which measurement happened when. Has to contain at least a column containing the start of each measurement, and any other column identifying the measurements.

f_datetime

datetime column in raw_conc (ymd_hms format)

start_col

start column in field_record (ymd_hms format)

end_col

end columne in field_record (ymd_hms format). Only needed if fixed_length = "FALSE".

measurement_length

length of the measurement (in seconds) from the start specified in the field_record

fixed_length

if TRUE (default), the measurement_length is used to create the end column. If FALSE, end_col has to be provided.

time_diff

time difference (in seconds) between the two datasets. Will be added to the datetime column of the raw_conc dataset. For situations where the time was not synchronized correctly.

startcrop

startcrop is no longer supported. Please use start_cut in flux_fitting instead.

ratio_threshold

ratio_threshold is no longer supported. Please use ratio_threshold in flux_quality instead.

f_conc

f_conc is no longer required

Value

a dataframe with concentration measurements, corresponding datetime, flux ID (f_fluxid), measurements start (f_start) and end (f_end).

Examples

data(co2_df_short, record_short)
flux_match(co2_df_short, record_short, datetime, start,
measurement_length = 180)

Using an already existing end column to slice measurements

Description

Provides the f_end column for flux_match

Usage

flux_match_col(field_record, start_col, end_col, name_field_record)

Arguments

field_record

dataframe recording which measurement happened when. Has to contain at least a column containing the start of each measurement, and any other column identifying the measurements.

start_col

start column in field_record (ymd_hms format)

end_col

end columne in field_record (ymd_hms format)

name_field_record

name of the df (for error message)

Using a fixed measurement length to slice the measurements

Description

Provides the f_end column for flux_match

Usage

flux_match_fixed(field_record, start_col, measurement_length)

Arguments

field_record

dataframe recording which measurement happened when. Has to contain at least a column containing the start of each measurement, and any other column identifying the measurements.

start_col

start column in field_record (ymd_hms format)

measurement_length

length of the measurement (in seconds) from the start specified in the field_record

prepares text to print for flux_plot function

Description

creates a df with quality flags and quality diagnostics to print on the plots produced by flux_plot. flux_param_lm is for fit in the lm family (linear and quadratic) flux_param_exp is for the exponential fit

Usage

flux_param_exp(slopes_df, f_conc)

Arguments

slopes_df

the slopes_df that is being provided to flux_plot

f_conc

column with gas concentration

prepares text to print for flux_plot function

Description

Usage

flux_param_kappamax(slopes_df, f_conc)

Arguments

slopes_df

the slopes_df that is being provided to flux_plot

f_conc

column with gas concentration

prepares text to print in flux_plot

Description

Usage

flux_param_lm(slopes_df, f_conc)

Arguments

slopes_df

the slopes_df that is being provided to flux_plot

f_conc

column with gas concentration

prepares text to print in flux_plot

Description

Usage

flux_param_qua(slopes_df, f_conc)

Arguments

slopes_df

the slopes_df that is being provided to flux_plot

f_conc

column with gas concentration

Plotting fluxes for visual evaluation

Description

Plots the fluxes, fit and slope in facets with color code indicating quality flags This function takes time to run and is optional in the workflow, but it is still highly recommended to use it to visually check the measurements. Note that 'flux_plot' is specific to the fluxible package and will work best with datasets produced following a fluxible workflow.

Usage

flux_plot(
  slopes_df,
  f_conc = f_conc,
  f_datetime = f_datetime,
  color_discard = "#D55E00",
  color_cut = "#D55E00",
  color_ok = "#009E73",
  color_zero = "#CC79A7",
  scale_x_datetime_args = list(date_breaks = "1 min", minor_breaks = "10 sec",
    date_labels = "%e/%m \n %H:%M"),
  f_ylim_upper = 800,
  f_ylim_lower = 400,
  f_plotname = "",
  facet_wrap_args = list(ncol = 4, nrow = 3, scales = "free"),
  y_text_position = 500,
  print_plot = "FALSE",
  output = "print_only",
  ggsave_args = list()
)

Arguments

slopes_df

dataset containing slopes, with flags produced by flux_quality

f_conc

column with gas concentration

f_datetime

column with datetime of each data point

color_discard

color for fits with a discard quality flag

color_cut

color for the part of the flux that is cut

color_ok

color for fits with an ok quality flag

color_zero

color for fits with a zero quality flag

scale_x_datetime_args

list of arguments for scale_x_datetime

f_ylim_upper

y axis upper limit

f_ylim_lower

y axis lower limit

f_plotname

filename for the extracted pdf file; if empty, the name of slopes_df will be used

facet_wrap_args

list of arguments for facet_wrap_paginate

y_text_position

position of the text box

print_plot

logical, if TRUE it prints the plot as a ggplot object but will take time depending on the size of the dataset

output

pdfpages, the plots are saved as A4 landscape pdf pages; ggsave, the plots can be saved with the ggsave function; print_only (default) prints the plot without creating a file (independently from print_plot being TRUE or FALSE)

ggsave_args

list of arguments for ggsave (in case output = "ggsave")

Value

plots of fluxes, with raw concentration data points, fit, slope, and color code indicating quality flags and cuts. The plots are organized in facets according to flux ID, and a text box display the quality flag and diagnostics of each measurement. The plots are returned as a ggplot object if print_plot = TRUE; if print_plot = FALSE it will not return anything but will produce a file according to the output argument.

Examples

data(co2_conc)
slopes <- flux_fitting(co2_conc, conc, datetime, fit_type = "exp_zhao18")
slopes_flag <- flux_quality(slopes, conc)
flux_plot(slopes_flag, conc, datetime)

plotting fluxes with exponential fit

Description

plots the fluxes that were fitted with an exponential model

Usage

flux_plot_exp(slopes_df, f_conc, f_datetime, y_text_position)

Arguments

slopes_df

dataset containing slopes

f_conc

column with gas concentration

f_datetime

column with datetime of each data point

y_text_position

position of the text box

creates the flag column to be used by flux_plot

Description

creates a column with quality flags (from flux_quality) for the part of the rows to be kept, and cut flag for rows to be discarded

Usage

flux_plot_flag(slopes_df, param_df)

Arguments

slopes_df

as provided in flux_plot

param_df

as provided by flux_param

plotting fluxes with linear fit

Description

plots the fluxes that were fitted with a linear model

Usage

flux_plot_lin(slopes_df, f_conc, f_datetime, y_text_position)

Arguments

slopes_df

dataset containing slopes

f_conc

column with gas concentration

f_datetime

column with datetime of each data point

y_text_position

position of the text box

plotting fluxes with a quadratic fit

Description

specific part of flux_plot for quadratic fit

Usage

flux_plot_quadratic(slopes_df, f_conc, f_datetime, y_text_position)

Arguments

slopes_df

dataset containing slopes

f_conc

column with gas concentration

f_datetime

column with datetime of each data point

y_text_position

position of the text box

Assessing the quality of the fits

Description

Indicates if the slopes provided by flux_fitting should be discarded or replaced by 0 according to quality thresholds set by user

Usage

flux_quality(
  slopes_df,
  f_conc = f_conc,
  f_fluxid = f_fluxid,
  f_slope = f_slope,
  f_time = f_time,
  f_start = f_start,
  f_end = f_end,
  f_fit = f_fit,
  f_cut = f_cut,
  f_pvalue = f_pvalue,
  f_rsquared = f_rsquared,
  f_slope_lm = f_slope_lm,
  f_fit_lm = f_fit_lm,
  f_b = f_b,
  force_discard = c(),
  force_ok = c(),
  force_zero = c(),
  force_lm = c(),
  force_exp = c(),
  ratio_threshold = 0.5,
  gfactor_threshold = 10,
  fit_type = c(),
  ambient_conc = 421,
  error = 100,
  pvalue_threshold = 0.3,
  rsquared_threshold = 0.7,
  rmse_threshold = 25,
  cor_threshold = 0.5,
  b_threshold = 1,
  cut_arg = "cut",
  instr_error = 5,
  kappamax = FALSE
)

Arguments

slopes_df

dataset containing slopes

f_conc

column containing the measured gas concentration (exponential fits)

f_fluxid

column containing unique IDs for each flux

f_slope

column containing the slope of each flux (as calculated by the flux_fitting function)

f_time

column containing the time of each measurement in seconds (exponential fits)

f_start

column with datetime of the start of the measurement (after cuts)

f_end

column with datetime of the end of the measurement (after cuts)

f_fit

column containing the modeled data (exponential fits)

f_cut

column containing the cutting information

f_pvalue

column containing the p-value of each flux (linear and quadratic fits)

f_rsquared

column containing the r squared of each flux (linear and quadratic fits)

f_slope_lm

column containing the linear slope of each flux (as calculated by the flux_fitting function)

f_fit_lm

column with the fit of the linear model. (as calculated by the flux_fitting function)

f_b

column containing the b parameter of the exponential expression (exponential fits)

force_discard

vector of fluxIDs that should be discarded by the user's decision

force_ok

vector of fluxIDs for which the user wants to keep the calculated slope despite a bad quality flag

force_zero

vector of fluxIDs that should be replaced by zero by the user's decision

force_lm

vector of fluxIDs for which the linear slope should be used by the user's decision

force_exp

vector of fluxIDs for which the exponential slope should be used by the user's decision (kappamax method)

ratio_threshold

ratio of gas concentration data points over length of measurement (in seconds) below which the measurement will be considered as not having enough data points to be considered for calculations

gfactor_threshold

threshold for the g-factor. Defines a window with its opposite outside which the flux will be flagged discard (exponential quadratic fits).

fit_type

model fitted to the data, linear, quadratic or exponential. Will be automatically filled if slopes_df was produced using flux_fitting

ambient_conc

ambient gas concentration in ppm at the site of measurement (used to detect measurement that started with a polluted setup)

error

error of the setup, defines a window outside of which the starting values indicate a polluted setup

pvalue_threshold

threshold of p-value below which the change of gas concentration over time is considered not significant (linear and quadratic fits)

rsquared_threshold

threshold of r squared value below which the linear model is considered an unsatisfactory fit (linear and quadratic fits)

rmse_threshold

threshold for the RMSE of each flux above which the fit is considered unsatisfactory (exponential fits)

cor_threshold

threshold for the correlation coefficient of gas concentration with time below which the correlation is considered not significant (exponential fits)

b_threshold

threshold for the b parameter. Defines a window with its opposite inside which the fit is considered good enough (exponential fits)

cut_arg

argument defining that the data point should be cut out

instr_error

error of the instrument, in the same unit as the gas concentration

kappamax

logical. If TRUE the kappamax method will be applied.

Details

the kappamax method (Hüppi et al., 2018) selects the linear slope if |b| > kappamax, with kappamax = |f_slope_lm / instr_error|. The original kappamax method was applied to the HMR model (Pedersen et al., 2010; Hutchinson and Mosier, 1981), but here it can be applied to any exponential fit.

Value

a dataframe with added columns of quality flags (f_quality_flag), the slope corrected according to the quality flags (f_slope_corr), and any columns present in the input. It will also print a summary of the quality flags. This summary can also be exported as a dataframe using flux_flag_count

References

Hüppi, R., Felber, R., Krauss, M., Six, J., Leifeld, J., Fuß, R., 2018. Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates. PLOS ONE 13, e0200876. https://doi.org/10.1371/journal.pone.0200876

Hutchinson, G.L., Mosier, A.R., 1981. Improved Soil Cover Method for Field Measurement of Nitrous Oxide Fluxes. Soil Science Society of America Journal 45, 311–316.

Examples

data(co2_conc)
slopes <- flux_fitting(co2_conc, conc, datetime, fit_type = "exp_zhao18")
flux_quality(slopes, conc)

quality assessment for the slopes estimated by flux_fitting

Description

indicates if fluxes should be discarded or replaced by 0 according to parameters set by user. flux_quality_lm is for the model of the lm family. flux_quality_exp is for the exponential model.

Usage

flux_quality_exp(
  slopes_df,
  f_conc,
  f_fluxid,
  f_slope,
  f_time,
  f_fit,
  f_slope_lm,
  f_b,
  force_discard,
  force_ok,
  force_zero,
  force_lm,
  gfactor_threshold,
  rmse_threshold,
  cor_threshold,
  b_threshold,
  name_df
)

Arguments

slopes_df

dataset containing slopes, fluxID, and parameters of the exponential expression

f_conc

column with gas concentration

f_fluxid

column of ID for each measurement

f_slope

column containing the slope of each flux (as calculated by the flux_fitting function)

f_time

column containing the time of each measurement in seconds

f_fit

column containing the modeled data

f_slope_lm

column containing the linear slope of each flux (as calculated by the flux_fitting function)

f_b

column containing the b parameter of the exponential expression

force_discard

vector of fluxIDs that should be discarded by the user's decision

force_ok

vector of fluxIDs for which the user wants to keep the calculated slope despite a bad quality flag

force_zero

vector of fluxIDs that should be replaced by zero by the user's decision

force_lm

vector of fluxIDs for which the linear slope should be used by the user's decision

gfactor_threshold

threshold for the g-factor. Defines a window with its opposite outside which the flux will be flagged discard.

rmse_threshold

threshold for the RMSE of each flux above which the fit is considered unsatisfactory

cor_threshold

threshold for the correlation coefficient of gas concentration with time below which the correlation is considered non significant

b_threshold

threshold for the b parameter. Defines a window with its opposite inside which the fit is considered good enough.

name_df

name of slopes_df

Value

same dataframe with added flag and corrected slopes columns

selecting linear slope with kappamax method

Description

selecting linear slope with kappamax method

Usage

flux_quality_kappamax(
  slopes_df,
  f_slope,
  f_fit,
  f_fluxid,
  f_slope_lm,
  f_fit_lm,
  f_b,
  force_exp,
  fit_type,
  instr_error,
  name_df
)

Arguments

slopes_df

dataset containing slopes

f_slope

column containing the slope of each flux (as calculated by the flux_fitting function)

f_fit

column containing the modeled data (exponential fits)

f_fluxid

column of ID for each measurement

f_slope_lm

column containing the linear slope of each flux

f_fit_lm

column with the fit of the linear model.

f_b

column containing the b parameter of the exponential expression

force_exp

vector of fluxIDs for which the exponential slope should be used by the user's decision (kappamax method)

fit_type

model fitted to the data, linear, quadratic or exponential. Will be automatically filled if slopes_df was produced using flux_fitting

instr_error

error of the instrument, in the same unit as the gas concentration

name_df

name of slopes_df

quality assessment for the slopes estimated by flux_fitting

Description

indicates if fluxes should be discarded or replaced by 0 according to parameters set by user. flux_quality_lm is for the model of the lm family. flux_quality_exp is for the exponential model.

Usage

flux_quality_lm(
  slopes_df,
  f_fluxid,
  f_slope,
  f_pvalue,
  f_rsquared,
  force_discard,
  force_ok,
  force_zero,
  pvalue_threshold,
  rsquared_threshold,
  name_df
)

Arguments

slopes_df

dataset containing slopes, fluxID, p.value and r.squared

f_fluxid

column of ID for each measurement

f_slope

column containing the slope of each flux (as calculated by the flux_fitting function)

f_pvalue

column containing the p-value of each flux

f_rsquared

column containing the r squared to be used for the quality assessment

force_discard

vector of fluxIDs that should be discarded by the user's decision

force_ok

vector of fluxIDs for which the user wants to keep the calculated slope despite a bad quality flag

force_zero

vector of fluxIDs that should be replaced by zero by the user's decision

pvalue_threshold

threshold of p-value below which the change of gas concentration over time is considered not significant (user decided)

rsquared_threshold

threshold of r squared value below which the linear model is considered an unsatisfactory fit

name_df

name of slopes_df (used for error message)

Value

same dataframe with added flag and corrected slopes columns

quality assessment for the slopes estimated by flux_fitting

Description

indicates if fluxes should be discarded or replaced by 0 according to parameters set by user. flux_quality_lm is for the model of the lm family. flux_quality_exp is for the exponential model.

Usage

flux_quality_qua(
  slopes_df,
  f_fluxid,
  f_slope,
  f_pvalue,
  f_rsquared,
  f_slope_lm,
  force_discard,
  force_ok,
  force_zero,
  force_lm,
  gfactor_threshold,
  pvalue_threshold,
  rsquared_threshold,
  name_df
)

Arguments

slopes_df

dataset containing slopes, fluxID, p.value and r.squared

f_fluxid

column of ID for each measurement

f_slope

column containing the slope of each flux (as calculated by the flux_fitting function)

f_pvalue

column containing the p-value of each flux

f_rsquared

column containing the r squared to be used for the quality assessment

f_slope_lm

column containing the linear slope of each flux (as calculated by the flux_fitting function)

force_discard

vector of fluxIDs that should be discarded by the user's decision

force_ok

vector of fluxIDs for which the user wants to keep the calculated slope despite a bad quality flag

force_zero

vector of fluxIDs that should be replaced by zero by the user's decision

force_lm

vector of fluxIDs for which the linear slope should be used by the user's decision

gfactor_threshold

threshold for the g-factor. Defines a window with its opposite outside which the flux will be flagged discard.

pvalue_threshold

threshold of p-value below which the change of gas concentration over time is considered not significant (user decided)

rsquared_threshold

threshold of r squared value below which the linear model is considered an unsatisfactory fit

name_df

name of slopes_df (used for error message)

Value

same dataframe with added flag and corrected slopes columns

Unit conversion coefficient for fluxes

Description

calculates the conversion coefficient for flux_calc

Usage

flux_units(
  flux_units,
  conc_units,
  conc_units_list = c("ppm", "ppb", "ppt"),
  amount_units = c("mol", "mmol", "umol", "nmol", "pmol"),
  surface_units = c("m2", "dm2", "cm2"),
  time_units = c("d", "h", "mn", "s")
)

Arguments

flux_units

desired units for the calculated fluxes. Has to be of the form amount/time/surface. Amount can be mol, mmol, umol, nmol or pmol. Time can be d (day), h (hour), mn (minute) or s (seconds). Surface can be m2, dm2 or cm2.

conc_units

units of gas concentration ppm, ppb or ppt.

conc_units_list

list of possible units for gas concentration.

amount_units

list of possible units for amount.

surface_units

list of possible units for surface.

time_units

list of possible units for time.

Details

The conversion is done from umol/s/m2 and gas concentration measured in ppm.

Value

A single numerical to multiply flux values with to convert units.

Examples

flux_units("mol/m2/mn", "ppm")

CO2 and CH4 concentration

Description

CO2 and CH4 measured simultaneously

Usage

raw_twogases

Format

A tibble with 21681 rows and 4 variables

co2_conc: CO2 concentration in ppm
ch4_conc: CH4 concentration in ppb
datetime: Datetime on the datapoint
temp_air: Air temperature inside the chamber in Celsius

Examples

raw_twogases

Measurements meta data at Liahovden

Description

Measurements meta data as recorded on the field at site Liahovden

Usage

record_liahovden

Format

A tibble with 138 rows and 3 variables

turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
round: Round of measurement.
start: Datetime at which the measurement was started.

Examples

record_liahovden

Measurements meta data

Description

Measurements meta data as recorded on the field

Usage

record_short

Format

A tibble with 6 rows and 3 variables

turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
start: Datetime at which the measurement was started.

Examples

record_short

Slopes for each flux

Description

Slopes of C(t) for each flux with air temperature in various units.

Usage

slopes0_temp

Format

A tibble with 1251 rows and 29 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.
turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
f_start: Datetime at which the measurement was started.
f_end: Datetime at which the measurement ended.
f_fluxid: Unique ID for each flux.
f_ratio: Ratio of number of datapoints over length of measurement in seconds.
f_flag_match: Flags from flux_match.
f_time: Time variable of the flux in seconds.
f_cut: Indicating if the measurement should be kept (keep) or discarded (cut).
f_Cz: Cz parameter of the C(t) function.
f_Cm: Cm parameter of the C(t) function, calculated by optim() with Cm_est as starting point.
f_a: a parameter of the C(t) function, calculated by optim() with a_est as starting point.
f_b: b parameter of the C(t) function, calculated by optim() with b_est as starting point.
f_tz: tz parameter of the C(t) function, calculated by optim() with tz_est as starting point.
f_slope: Slope of C(t) at tz
f_fit: C(t), modeled CO2 concentration as a function of time.
f_fit_slope: Output of linear model of CO2 concentration passing by C(tz) and a slope of slope_tz.
f_start_z: Datetime format of tz
f_cor_coef: Correlation coeffecient of concentration over time.
f_RMSE: RMSE of the fit.
f_quality_flag: Quality flags according to flux_quality.
f_slope_corr: Slope as advised by quality flags.
temp_fahr: Air temperature inside the flux chamber in Fahrenheit averaged over the flux measurement.
temp_kelvin: Air temperature inside the flux chamber in Kelvin averaged over the flux measurement.

Examples

slopes0_temp

From raw gas concentration over time to clean fluxes

Description

Wrapper function for the Fluxible workflow. We recommand using the step-by-step workflow for more control over the process.

Usage

stupeflux(
  raw_conc,
  field_record,
  f_datetime,
  start_col,
  end_col,
  f_conc,
  setup_volume,
  measurement_length,
  fit_type,
  temp_air_col,
  atm_pressure,
  plot_area,
  conc_unit,
  flux_unit,
  fixed_length = TRUE,
  cols_keep = c(),
  cols_ave = c(),
  cols_sum = c(),
  cols_med = c(),
  ratio_threshold = 0.5,
  time_diff = 0,
  start_cut = 0,
  end_cut = 0,
  cz_window = 15,
  b_window = 10,
  a_window = 10,
  roll_width = 15,
  t_zero = 0,
  force_discard = c(),
  force_ok = c(),
  force_zero = c(),
  ambient_conc = 421,
  error = 100,
  pvalue_threshold = 0.3,
  rsquared_threshold = 0.7,
  rmse_threshold = 25,
  cor_threshold = 0.5,
  b_threshold = 1,
  temp_air_unit = "celsius",
  cut = TRUE,
  slope_correction = TRUE
)

Arguments

raw_conc

dataframe of CO2 concentration measured continuously. Has to contain at least a datetime column in ymd_hms format and a gas concentration column as double.

field_record

dataframe recording which measurement happened when. Has to contain at least a column containing the start of each measurement, and any other column identifying the measurements.

f_datetime

datetime column in raw_conc (dmy_hms format)

start_col

start column in field_record (dmy_hms format)

end_col

end columne in field_record (ymd_hms format)

f_conc

concentration column in raw_conc

setup_volume

volume of the flux chamber and instrument together in L, can also be a column in case it is a variable

measurement_length

length of the measurement (in seconds) from the start specified in the field_record

fit_type

temp_air_col

column containing the air temperature used to calculate fluxes. Will be averaged with NA removed.

atm_pressure

atmospheric pressure, can be a constant (numerical) or a variable (column name)

plot_area

area of the plot in m^2, can also be a column in case it is a variable

conc_unit

unit in which the concentration of gas was measured ppm or ppb

flux_unit

unit in which the calculated flux will be mmol outputs fluxes in mmol * m^-2 * h^-1; micromol outputs fluxes in micromol * m^-2 * h^-1

fixed_length

if TRUE (default), the measurement_length is used to create the end column. If FALSE, end_col has to be provided.

cols_keep

columns to keep from the input to the output. Those columns need to have unique values for each flux, as distinct() is applied.

cols_ave

columns with values that should be averaged for each flux in the output. Note that NA are removed in mean calculation.

cols_sum

columns with values for which is sum is provided for each flux in the output. Note that NA are removed in sum calculation.

cols_med

columns with values for which is median is provided for each flux in the output. Note that NA are removed in median calculation.

ratio_threshold

ratio of gas concentration data points over length of measurement (in seconds) below which the measurement will be considered as not having enough data points to be considered for calculations

time_diff

time difference (in seconds) between the two datasets. Will be added to the datetime column of the raw_conc dataset. For situations where the time was not synchronized correctly.

start_cut

time to discard at the start of the measurements (in seconds)

end_cut

time to discard at the end of the measurements (in seconds)

cz_window

window used to calculate Cz, at the beginning of cut window (exp_zhao18 and exp_tz fits)

b_window

window to estimate b. It is an interval after tz where it is assumed that the model fits the data perfectly (exp_zhao18 and exp_tz fits)

a_window

window at the end of the flux to estimate a (exp_zhao18 and exp_tz fits)

roll_width

width of the rolling mean for CO2 when looking for tz, ideally same as cz_window (exp_zhao18 and exp_tz fits)

t_zero

time at which the slope should be calculated (for quadratic and exp_tz fits)

force_discard

vector of fluxIDs that should be discarded by the user's decision

force_ok

vector of fluxIDs for which the user wants to keep the calculated slope despite a bad quality flag

force_zero

vector of fluxIDs that should be replaced by zero by the user's decision

ambient_conc

ambient gas concentration in ppm at the site of measurement (used to detect measurement that started with a polluted setup)

error

error of the setup, defines a window outside of which the starting values indicate a polluted setup

pvalue_threshold

threshold of p-value below which the change of gas concentration over time is considered not significant (linear and quadratic fit)

rsquared_threshold

threshold of r squared value below which the linear model is considered an unsatisfactory fit (linear and quadratic fit)

rmse_threshold

threshold for the RMSE of each flux above which the fit is considered unsatisfactory (exp_zhao18 and exp_tz fits)

cor_threshold

threshold for the correlation coefficient of gas concentration with time below which the correlation is considered not significant (exp_zhao18 and exp_tz fits)

b_threshold

threshold for the b parameter. Defines a window with its opposite inside which the fit is considered good enough (exp_zhao18 and exp_tz fits)

temp_air_unit

units in which air temperature was measured. Has to be either celsius (default), fahrenheit or kelvin.

cut

slope_correction

logical. If TRUE, the flux will be calculated with the slope corrected according to the recommandations of the quality flags.

Value

a dataframe containing flux IDs, datetime of measurements' starts, fluxes in mmol * m^-2 * h^-1 or micromol * m^-2 * h^-1 (f_flux) according to flux_unit, temperature average for each flux in Kelvin (f_temp_ave), the total volume of the setup for each measurement (f_volume_setup), the model used in flux_fitting, any column specified in cols_keep, any column specified in cols_ave with their value averaged over the measurement after cuts and discarding NA.

References

Examples

data(co2_df_short)
data(record_short)
stupeflux(
raw_conc = co2_df_short,
field_record = record_short,
f_datetime = datetime,
start_col = start,
f_conc = conc,
measurement_length = 180,
fit_type = "exp_zhao18",
temp_air_col = temp_air,
conc_unit = "ppm",
flux_unit = "mmol",
setup_volume = 24.575,
atm_pressure = 1,
plot_area = 0.0625
)

Two gases field record

Description

Two gases field record

Usage

twogases_record

Format

A tibble with 12 rows and 1 variable

start: Start datetime of each flux measurement

Examples

twogases_record

fluxible: Ecosystem Gas Fluxes Calculations for Closed Loop Chamber Setup

Description

Author(s)

See Also

CO2 concentration

Description

Usage

Format

Examples

CO2 concentration with missing data

Description

Usage

Format

Examples

CO2 concentration

Description

Usage

Format

Examples

CO2 concentration with missing data

Description

Usage

Format

Examples

CO2 concentration

Description

Usage

Format

Examples

CO2 fluxes

Description

Usage

Format

Examples

CO2 fluxes with PAR values

Description

Usage

Format

Examples

CO2 concentration at Liahovden

Description

Usage

Format

Examples

Calculates ecosystem gas fluxes

Description

Usage

Arguments

Value

Examples

check the items inside flux_fun_check

Description

Usage

Arguments

Author(s)

filter cut data before calculating fluxes

Description

Usage

Arguments

to check the type of fit

Description

Usage

Arguments

Fitting a model to concentration data and estimating the slope

Description

Usage

Arguments

Value

References

Examples

Fitting a model to the gas concentration curve and estimating the slope over time, using a modified version of the model from Zhao et al (2018) that allows the user to fix t_zero.

Description

Usage

Arguments

Value

References

Fitting a model to the gas concentration curve and estimating the slope over time, using the HM model (Pedersen et al., 2010; Hutchinson and Mosier, 1981).

Description

Usage

Arguments

check the items inside `flux_fun_check`

Fitting a model to the gas concentration curve and estimating the slope over time, using a modified version of the model from Zhao et al (2018) that allows the user to fix `t_zero`.