R/tang_2003_flux.R

In neonSoilFlux: Compute Soil Carbon Fluxes for the National Ecological Observatory Network Sites

#' @title Internal function to compute surface co2 flux at a given timepoint via Tang et al 2003

#' @author
#' John Zobitz \email{zobitz@augsburg.edu}


#' @description
#' Given  zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by computing the gradient of co2 (slope from linear regression) and linear extrapolation of surface diffusivity. Modified from Tang et al (2003).

#' @param zOffset Required. depths below surface - assumed to be positive in value. Important for directionality!
#' @param co2 Required. co2 at depth (umol m--1)
#' @param co2_err Required. Associated errors with that value of co2
#' @param diffusive Required. diffusivity at each depth
#' @param diffusive_err Required Associated errors with diffusivity


#' @return Data frame of fluxes associated error

#' @seealso [dejong_shappert_flux()], [hirano_flux()], [tang_2005_flux()] for other ways to compute surface fluxes.

#' @references
#' Tang, Jianwu, Dennis D Baldocchi, Ye Qi, and Liukang Xu. 2003. “Assessing Soil CO2 Efflux Using Continuous Measurements of CO2 Profiles in Soils with Small Solid-State Sensors.” Agricultural and Forest Meteorology 118 (3): 207–20. https://doi.org/10.1016/S0168-1923(03)00112-6.
#'
#' Maier, M., and H. Schack-Kirchner. 2014. “Using the Gradient Method to Determine Soil Gas Flux: A Review.” Agricultural and Forest Meteorology 192–193 (July):78–95. https://doi.org/10.1016/j.agrformet.2014.03.006.



tang_2003_flux <- function(zOffset, co2, co2_err, diffusive, diffusive_err) {

  # changelog and author contributions / copyrights
  #   John Zobitz (2024-01-20)
  #     original creation
  #     2024-04-08: update to get namespaces correct



  # Assume zOffset is positive, so we want the flux to be negative (opposite the direction of the flux)
  # Tang03: estimate slope from linear regression of z and C, also then Da0

  # Define the output vector:
  out_flux <- tibble::tibble(flux = NA, flux_err = NA, method = "tang_2003")

  if (any(is.na(co2)) | any(is.na(co2_err)) | any(is.na(diffusive)) | any(is.na(diffusive_err))) {
    return(out_flux)
  } else {
    n_obs <- length(co2)
    mean_z <- mean(zOffset)

    mean_co2 <- mean(co2) # pd: 1/n_obs
    pd_mean_co2 <- 1 / n_obs

    slope_co2 <- sum((co2 - mean_co2) * (zOffset - mean_z)) / (sum((zOffset - mean_z)^2))
    pd_slope_co2 <- (1 - pd_mean_co2) * (zOffset - mean_z) / (sum((zOffset - mean_z)^2))

    # Compute the quadrature error
    slope_err <- quadrature_error(pd_slope_co2, co2_err)

    # Now do the same for the intercept of Diffusivity
    mean_diffus <- mean(diffusive) # pd: 1/n_obs
    pd_mean_diffus <- 1 / n_obs

    slope_diffus <- sum((diffusive - mean_diffus) * (zOffset - mean_z)) / (sum((zOffset - mean_z)^2))
    pd_slope_diffus <- (1 - pd_mean_diffus) * (zOffset - mean_z) / (sum((zOffset - mean_z)^2))


    slope_diffus_err <- quadrature_error(pd_slope_diffus, diffusive_err)

    d0 <- mean_diffus - slope_diffus * mean_z # intercept of linear regression
    pd_d0 <- pd_mean_diffus - pd_slope_diffus * mean_z

    d0_err <- quadrature_error(pd_d0, diffusive_err)

    surface_flux <- -d0 * slope_co2 # To get partial derivative we use the product rule

    surface_flux_pd <- c(-d0, -slope_co2)
    surface_flux_tot_err <- c(slope_err, d0_err)

    surface_flux_err <- quadrature_error(surface_flux_pd, surface_flux_tot_err)


    if (surface_flux < 0) {
      out_flux$flux <- -surface_flux
      out_flux$flux_err <- surface_flux_err
    }

    return(out_flux)
  }
}