This vignette shows how to process ecosystem fluxes (CO2 and H2O) measured with the LI7500 tent setup presented in Halbritter et al. (2024). Because of field constraints, this setup differs from the smaller chamber in several points:
We import the data with licoread::import7500
.
library(licoread)
li7500_data <- import7500(
"ex_data/li7500",
plotinfo = c("site", "treatment", "date", "plot_id", "trial")
)
The licoread::import7500
function provides
f_start
and f_end
, meaning we could skip
fluxible::flux_match
. However, f_start
and
f_end
are based on the start and end of the files, which
would require a strict data collection routine. If such a strict routine
did not happen, there are two options to deal with that.
If f_start
and f_end
cannot be used as
start and end of measurement, we can recreate a record file (for example
in a spreadsheet) with the real start and end datetime:
licoread::import7500
flux_fitting
,
flux_quality
and flux_plot
)record
file with metadata
identifying the fluxes (filename
), start and end (note that
flux_match
requires datetime format)f_start
, f_end
and
f_fluxid
to avoid losing those informations
(flux_match
will overwrite them)flux_match
before flux_fitting
in
your workflow, and match the fluxes with the record file you
createdflux_plot
flux_calc
as normallyThat makes the processing non homogeneous but still reproducible.
If the start or the end are consistent but not the other, you can
change the direction of the cutting in flux_fitting
. By
default, flux_fitting
cuts the focus window as
start + start_cut
to end - end_cut
. The
argument cut_direction
allows to change that:
cut_direction = "from_start"
means the focus window
will be from start + start_cut
to
start + end_cut
cut_direction = "from_end"
means the focus window will
be from end - start_cut
to end - end_cut
fluxible
Wet air correction:
Fitting a linear model:
Using fluxible::flux_quality
to assess the quality of
the dataset.
li7500_flags_co2 <- flux_quality(li7500_fits_co2,
f_conc = `CO2 umol/mol_dry`,
rsquared_threshold = 0.5)
#>
#> Total number of measurements: 4
#>
#> ok 4 100 %
#> discard 0 0 %
#> zero 0 0 %
#> force_discard 0 0 %
#> start_error 0 0 %
#> no_data 0 0 %
#> force_ok 0 0 %
#> force_zero 0 0 %
#> force_lm 0 0 %
#> no_slope 0 0 %
Plotting:
flux_plot(li7500_flags_co2,
f_conc = `CO2 umol/mol_dry`,
f_ylim_upper = 500,
y_text_position = 450,
print_plot = TRUE)
flux_plot
for
CO2 fluxesCan be exported in pdf directly (much easier to handle with a lot of data) with:
flux_plot(li7500_flags_co2,
f_conc = `CO2 umol/mol_dry`,
f_ylim_upper = 500,
y_text_position = 450,
print_plot = FALSE,
output = "longpdf",
f_plotname = "li7500_co2")
If other data (PAR and co) need processing, that would happen here.
To “compress” environmental variables at a single value per flux, see
the arguments cols_ave
, cols_sum
, and
cols_med
in fluxible::flux_calc
. To keep the
raw data (gas concentration or anything else) in a nested column, see
the cols_nest
argument.
Now let’s calculate the fluxes with
fluxible::flux_calc
.
li7500_fluxes_co2 <- flux_calc(li7500_flags_co2,
slope_col = f_slope_corr,
temp_air_col = Temperature,
setup_volume = 2197,
atm_pressure = pressure_atm,
plot_area = 1.44,
conc_unit = "ppm",
flux_unit = "umol/m2/s",
cols_keep = c(
"site", "treatment", "date", "plot_id", "trial"
))
#> Cutting data according to 'keep_arg'...
#> Averaging air temperature for each flux...
#> Creating a df with the columns from 'cols_keep' argument...
#> Calculating fluxes...
#> R constant set to 0.082057 L * atm * K^-1 * mol^-1
#> Concentration was measured in ppm
#> Fluxes are in umol/m2/s
Wet air correction:
Fitting a linear model:
li7500_fits_h2o <- flux_fitting(li7500_data_h2o,
f_conc = `H2O mmol/mol_dry`,
fit_type = "linear",
start_cut = 0,
end_cut = 0)
Using fluxible::flux_quality
to assess the quality of
the dataset:
li7500_flags_h2o <- flux_quality(li7500_fits_h2o,
f_conc = `H2O mmol/mol_dry`,
rsquared_threshold = 0.5,
ambient_conc = 10, # the default is for CO2
error = 2)
#>
#> Total number of measurements: 4
#>
#> ok 4 100 %
#> discard 0 0 %
#> zero 0 0 %
#> force_discard 0 0 %
#> start_error 0 0 %
#> no_data 0 0 %
#> force_ok 0 0 %
#> force_zero 0 0 %
#> force_lm 0 0 %
#> no_slope 0 0 %
Plotting:
flux_plot(li7500_flags_h2o,
f_conc = `H2O mmol/mol_dry`,
print_plot = TRUE,
f_ylim_lower = 5,
f_ylim_upper = 15,
y_text_position = 12)
flux_plot
for
H2O fluxesOr directly exported as a pdf:
flux_plot(li7500_flags_h2o,
f_conc = `H2O mmol/mol_dry`,
print_plot = FALSE,
output = "longpdf",
f_plotname = "li7500_h2o",
f_ylim_lower = 5,
f_ylim_upper = 15,
y_text_position = 12)
Flux calculations:
li7500_fluxes_h2o <- flux_calc(li7500_flags_h2o,
slope_col = f_slope_corr,
temp_air_col = Temperature,
setup_volume = 2197,
atm_pressure = pressure_atm,
plot_area = 1.44,
conc_unit = "mmol/mol",
flux_unit = "mmol/m2/s",
cols_keep = c(
"site", "treatment", "date", "plot_id", "trial"
))
#> Cutting data according to 'keep_arg'...
#> Averaging air temperature for each flux...
#> Creating a df with the columns from 'cols_keep' argument...
#> Calculating fluxes...
#> R constant set to 0.082057 L * atm * K^-1 * mol^-1
#> Concentration was measured in mmol/mol
#> Fluxes are in mmol/m2/s
I’m glad you asked! The function fluxible::flux_diff
is
precisely made for that.
In order to calculate carbon and water use efficiencies, we need both H2O and CO2 fluxes in the same dataset.
library(tidyverse)
# to avoid confusion, we add a gas column
# this might be implemented in flux_calc in the future
li7500_fluxes_co2 <- li7500_fluxes_co2 |>
mutate(gas = "co2")
li7500_fluxes_h2o <- li7500_fluxes_h2o |>
mutate(gas = "h2o")
li7500_fluxes <- bind_rows(li7500_fluxes_co2, li7500_fluxes_h2o)
# Now the data are in a single long df
#> tibble [12 × 13] (S3: tbl_df/tbl/data.frame)
#> $ trial : chr [1:12] "GPP" "p" "r" "GPP" ...
#> $ f_flux : num [1:12] -5.59 -3.84 1.75 -6.57 -6.02 ...
#> $ f_fluxid : chr [1:12] "ACJ_C_10apr2019_plot1_p.txt" "AC"..
#> $ f_slope_corr : num [1:12] -0.0928 -0.0928 0.0423 -0.1452 -0...
#> $ f_temp_air_ave : num [1:12] 25.6 25.6 25 24.8 24.8 ...
#> $ f_atm_pressure_ave: num [1:12] 0.664 0.664 0.665 0.664 0.664 ...
#> $ f_datetime : POSIXct[1:12], format: "2019-04-10 15:45:11" ..
#> $ f_model : chr [1:12] "linear" "linear" "linear" "linea"..
#> $ site : chr [1:12] "ACJ" "ACJ" "ACJ" "ACJ" ...
#> $ treatment : chr [1:12] "C" "C" "C" "C" ...
#> $ date : chr [1:12] "10apr2019" "10apr2019" "10apr201"..
#> $ plot_id : chr [1:12] "plot1" "plot1" "plot1" "plot2" ...
#> $ gas : chr [1:12] "co2" "co2" "co2" "co2" ...