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Fitting a model to concentration data and estimating the slope

Source: R/flux_fitting.R
flux_fitting.Rd

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 = "exp_zhao18",
  start_cut = 0,
  end_cut = 0,
  t_zero = 0,
  cut_direction = "none",
  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)

cut_direction

"none" (default) means that the focus window is f_start + start_cut to f_end - end_cut; "from_start" makes it f_start + start_cut to f_start + end_cut; "from_end" makes it f_end - start_cut to f_end - end_cut. Bug fix since v1.3.4: "from_start" was doing f_start + start_cut to f_start + start_cut + end_cut

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")
#> Cutting measurements...
#> Estimating starting parameters for optimization...
#> Optimizing fitting parameters...
#> Calculating fits and slopes...
#> Done.
#> Warning: 
#>  fluxID 5 : slope was estimated on 205 points out of 210 seconds
#>  fluxID 6 : slope was estimated on 206 points out of 210 seconds
#> # A tibble: 1,251 × 29
#>    datetime            temp_air temp_soil  conc   PAR turfID       type 
#>    <dttm>                 <dbl>     <dbl> <dbl> <dbl> <fct>        <fct>
#>  1 2022-07-28 23:43:35    NA         NA    447. NA    156 AN2C 156 ER   
#>  2 2022-07-28 23:43:36     7.22      10.9  447.  1.68 156 AN2C 156 ER   
#>  3 2022-07-28 23:43:37    NA         NA    448. NA    156 AN2C 156 ER   
#>  4 2022-07-28 23:43:38    NA         NA    449. NA    156 AN2C 156 ER   
#>  5 2022-07-28 23:43:39    NA         NA    449. NA    156 AN2C 156 ER   
#>  6 2022-07-28 23:43:40    NA         NA    450. NA    156 AN2C 156 ER   
#>  7 2022-07-28 23:43:41    NA         NA    451. NA    156 AN2C 156 ER   
#>  8 2022-07-28 23:43:42    NA         NA    451. NA    156 AN2C 156 ER   
#>  9 2022-07-28 23:43:43    NA         NA    453. NA    156 AN2C 156 ER   
#> 10 2022-07-28 23:43:44    NA         NA    453. NA    156 AN2C 156 ER   
#> # ℹ 1,241 more rows
#> # ℹ 22 more variables: f_start <dttm>, f_end <dttm>, f_fluxid <fct>,
#> #   f_ratio <dbl>, f_flag_match <chr>, f_time <dbl>, f_cut <fct>,
#> #   f_rsquared_lm <dbl>, f_adj_rsquared_lm <dbl>, f_slope_lm <dbl>,
#> #   f_intercept_lm <dbl>, f_pvalue_lm <dbl>, f_fit_lm <dbl>, f_Cz <dbl>,
#> #   f_Cm <dbl>, f_a <dbl>, f_b <dbl>, f_tz <dbl>, f_slope <dbl>, f_fit <dbl>,
#> #   f_fit_slope <dbl>, f_start_z <dttm>
flux_fitting(co2_conc, conc, datetime,  fit_type = "quadratic",
t_zero = 10, end_cut = 30)
#> # A tibble: 1,251 × 30
#>    datetime            temp_air temp_soil  conc   PAR turfID       type 
#>    <dttm>                 <dbl>     <dbl> <dbl> <dbl> <fct>        <fct>
#>  1 2022-07-28 23:43:35    NA         NA    447. NA    156 AN2C 156 ER   
#>  2 2022-07-28 23:43:36     7.22      10.9  447.  1.68 156 AN2C 156 ER   
#>  3 2022-07-28 23:43:37    NA         NA    448. NA    156 AN2C 156 ER   
#>  4 2022-07-28 23:43:38    NA         NA    449. NA    156 AN2C 156 ER   
#>  5 2022-07-28 23:43:39    NA         NA    449. NA    156 AN2C 156 ER   
#>  6 2022-07-28 23:43:40    NA         NA    450. NA    156 AN2C 156 ER   
#>  7 2022-07-28 23:43:41    NA         NA    451. NA    156 AN2C 156 ER   
#>  8 2022-07-28 23:43:42    NA         NA    451. NA    156 AN2C 156 ER   
#>  9 2022-07-28 23:43:43    NA         NA    453. NA    156 AN2C 156 ER   
#> 10 2022-07-28 23:43:44    NA         NA    453. NA    156 AN2C 156 ER   
#> # ℹ 1,241 more rows
#> # ℹ 23 more variables: f_start <dttm>, f_end <dttm>, f_fluxid <fct>,
#> #   f_ratio <dbl>, f_flag_match <chr>, f_time <dbl>, f_cut <fct>,
#> #   f_rsquared_lm <dbl>, f_adj_rsquared_lm <dbl>, f_slope_lm <dbl>,
#> #   f_intercept_lm <dbl>, f_pvalue_lm <dbl>, f_fit_lm <dbl>, f_param1 <dbl>,
#> #   f_param2 <dbl>, f_rsquared <dbl>, f_adj_rsquared <dbl>, f_intercept <dbl>,
#> #   f_pvalue <dbl>, f_slope <dbl>, f_fit <dbl>, f_fit_slope <dbl>, …