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,
conc_col,
datetime_col,
f_start = f_start,
f_end = f_end,
f_fluxid = f_fluxid,
start_cut = 0,
end_cut = 0,
cz_window = 15,
b_window = 10,
a_window = 10,
roll_width = 15,
t_zero = 0,
fit_type
)
Arguments
- conc_df
dataframe of gas concentration over time
- conc_col
column with gas concentration data
- datetime_col
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
- 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 (exponential fit)
- b_window
window to estimate b. It is an interval after tz where it is assumed that the model fits the data perfectly (exponential fit)
- a_window
window at the end of the flux to estimate a (exponential fit)
- roll_width
width of the rolling mean for CO2 when looking for
tz
, ideally same ascz_window
(exponential fit)- t_zero
time at which the slope should be calculated (for
quadratic
andexp_tz
fits)- fit_type
exp_zhao18
,exp_tz
,exp_hm
,quadratic
orlinear.
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 fixt_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) \(C(t) = C~m~ + (C~z~ - C~m~) \exp(-b * t)\)
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
exponential
is equal to exp_zhao18
, for backwards compatibility
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>, …