Type: Package
Title: Compute Soil Carbon Fluxes for the National Ecological Observatory Network Sites
Version: 1.0.0
Description: Acquires and synthesizes soil carbon fluxes at sites located in the National Ecological Observatory Network (NEON). Provides flux estimates and associated uncertainty as well as key environmental measurements (soil water, temperature, CO2 concentration) that are used to compute soil fluxes.
URL: https://github.com/jmzobitz/neonSoilFlux
BugReports: https://github.com/jmzobitz/neonSoilFlux/issues
License: GPL (≥ 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
Depends: R (≥ 2.10)
Imports: dplyr, ggplot2, lubridate, neonUtilities, purrr, stats, stringr, tibble, tidyr, tidyselect
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-05-23 23:21:28 UTC; zobitz
Author: John Zobitz ORCID iD [aut, cre], Edward Ayres ORCID iD [aut], Katie O'Rourke [ctb] (LinkedIn, <https://www.linkedin.com/in/katie-o-rourke-5a898b194/>), Zoey Werbin [ctb], Lajntxiag Lee [ctb], Ridwan Abdi [ctb] (LinkedIn, <https://www.linkedin.com/in/ridwaan-cabdi/>), Dijonë Mehmeti [ctb] (LinkedIn, <https://www.linkedin.com/in/dijon%C3%AB-mehmeti/>), Ly Xiong [ctb] (LinkedIn, <https://www.linkedin.com/in/ly-xiong-bb83ba1b2/>)
Maintainer: John Zobitz <zobitz@augsburg.edu>
Repository: CRAN
Date/Publication: 2024-05-25 17:10:02 UTC

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

Description

Acquires and synthesizes soil carbon fluxes at sites located in the National Ecological Observatory Network (NEON). Provides flux estimates and associated uncertainty as well as key environmental measurements (soil water, temperature, CO2 concentration) that are used to compute soil fluxes.

Author(s)

Maintainer: John Zobitz zobitz@augsburg.edu (ORCID)

Authors:

Other contributors:

See Also

Useful links:

Acquire NEON data for processing

Description

Given a site code and dates, apply the neonUtilities package to download the data from NEON API

Usage

acquire_neon_data(
  site_name,
  download_date,
  time_frequency = "30_minute",
  provisional = FALSE
)

Arguments

site_name

Required. NEON code for a particular site (a string)

download_date

Required. Date where we end getting NEON data. Format: YYYY-MM (can't specify day). So "2020-05" means it will grab data for the entire 5th month of 2020. (a string). Downloads data for a given month only

time_frequency

Required. Will you be using 30 minute ("30_minute") or 1 minute ("1_minute") recorded data? Defaults to 30 minutes.

provisional

Required. Should you use provisional data when downloading? Defaults to FALSE. See NEON Data Releases. Defaults to FALSE (similar to include.provisional in loadByProduct).

Value

A list containing stacked environmental data ('site_data') and soil properties ('site_megapit').

Author(s)

John Zobitz zobitz@augsburg.edu

Examples


out_env_data <- acquire_neon_data("SJER","2022-06")

Internal helper function to determine availability of data within a time interval

Description

Given a data measurement, determine when a monthly mean is used and the bulk QF flags. Helps to determine when the environmental measurements produced a QF value and to be used in subsequent flux calculations.

Usage

check_qf_flags(measurement_name, data)

Arguments

measurement_name

The name of the measurement (staPres, soilTemp, VSWC, soilCO2Concentration)

data

Data used to check the qf flags

Value

A data frame of startDateTime, horizontalPosition, and the associated QF flag.

# changelog and author contributions / copyrights

Author(s)

John Zobitz zobitz@augsburg.edu

Convert co2 concentration from ppm to µmol m-3 units

Description

Given a measurement of co2, convert it from ppm to umol m-3 based on temperature and pressure. Also compute associated error via quadrature.

Usage

co2_to_umol(
  temperature,
  pressure,
  co2,
  temperature_err,
  pressure_err,
  co2_err,
  zOffset
)

Arguments

temperature

Required. Soil temperature (degrees C)

pressure

Required. Barometric air pressure (kilopascal)

co2

Carbon dioxide in ppm

temperature_err

Required. Reported Soil temperature error (degrees C)

pressure_err

Required. Reported Barometric air pressure error (kilopascal)

co2_err

Required. Carbon dioxide in ppm error

zOffset

Required. Surface depth (m). Reported as a negative number.

Value

A value of the converted co2

Author(s)

John Zobitz zobitz@augsburg.edu

Examples

co2_to_umol(31,96.3,654,.15,.05,9,-.05)

Function to compute monthly means for a given month of NEON data.

Description

Given a NEON measurement data frame calculate the monthly mean values across all horizontal and vertical locations. Based off code from Zoey Werbin.

Usage

compute_monthly_mean(
  NEON_data,
  position_columns = c("horizontalPosition", "verticalPosition")
)

Arguments

NEON_data

Required. Input vector of neon measurements for a month

position_columns

Optional. Do we group by horizontalPosition, verticalPosition, and? Default is both. Added this option in case we just want to average across a given dimension.

Value

A data frame that reports for each horiztonal and vertical position the computed mean and standard deviation from sampling (similar to a bootstrap method) as well as the sample mean and sample standard deviation

Author(s)

John Zobitz zobitz@augsburg.edu

References

Zoey Werbin (@zoey-rw): original author https://github.com/zoey-rw/microbialForecasts/blob/caa7b1a8aa8a131a5ff9340f1562cd3a3cb6667b/data_construction/covariate_prep/soil_moisture/clean_NEON_sensor_moisture_data.r

Examples


# Download the NEON data directly - here this would be soil moisture
NEON_moist_30m_orig <- neonUtilities::loadByProduct(
  dpID = "DP1.00094.001",
  site = "WREF",
  startdate = "2022-06",
  enddate = "2022-06",
  timeIndex = "30",
  package = "expanded",
  check.size = FALSE,
  include.provisional = TRUE
)


# Then correct the swc
site_swc <- swc_correct(NEON_moist_30m_orig, "WREF","2022-06")

# Select the columns and the time frequency
time_frequency <- "30_minute"
column_selectors <- c("Mean", "Minimum", "Maximum", "ExpUncert", "StdErMean")

    swc <- site_swc |>
    purrr::pluck(paste0("SWS_",time_frequency)) |>
    dplyr::select(tidyselect::all_of(c("domainID","siteID",
    "horizontalPosition","verticalPosition","startDateTime","VSWCFinalQF")),
    tidyselect::matches(stringr::str_c("VSWC",column_selectors)))

  # Determine a data frame of the different horizontal and vertical positions
  swc_positions <- site_swc |>
  purrr::pluck(paste0("sensor_positions_","00094"))

  # Add on the positions for swc
  swc <- determine_position(swc_positions,swc)

Compute NEON fluxes at a site

Description

Given a site filename (from acquire_neon_data), process and compute fluxes. This file takes a saved data file from acquire: 1) Takes the needed components (QF and measurement flags) for soil water, temperature, co2, binding them together in a tidy data frame 2) Interpolates across the measurements 3) Merges air pressure data into this data frame 4) Does a final QF check so we should have only timeperiods where all measurements exist 5) Adds in the megapit data so we have bulk density, porosity measurements at the interpolated depth. 6) Saves the data

Usage

compute_neon_flux(input_site_env, input_site_megapit)

Arguments

input_site_env

Required. Input list of environmental data. Usually given from acquire_neon_data

input_site_megapit

Required. Input list of environmental soil data. Usually given from acquire_neon_data

Value

Data frame of fluxes and gradient from the timeperiod

Author(s)

John Zobitz zobitz@augsburg.edu based on code developed by Edward Ayres eayres@battelleecology.org

Examples


 # First acquire the NEON data at a given NEON site
out_env_data <- acquire_neon_data("SJER","2020-05")

# Then process and compute the fluxes:
 out_flux <- compute_neon_flux(input_site_env = sjer_env_data_2022_06,
 input_site_megapit = sjer_megapit_data_2022_06)

Internal fucntion to compute CO2 surface flux

Description

Computation function. Given a measurement of the co2 and diffusive flux at different levels, return the surface flux

Usage

compute_surface_flux(input_data)

Arguments

input_data

Required. A data frame containing zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors

Value

A value of the surface CO2 flux (umol m-2 s-1)

Author(s)

John Zobitz zobitz@augsburg.edu

Internal function that prepares downloaded NEON data for flux processing

Description

This file takes data frame from acquire_neon_data and: 1) Takes the needed components (QF and measurement flags) for soil water, temperature, co2, binding them together in a tidy data frame 2) Interpolates across the measurements 3) Merges air pressure data into this data frame

Usage

correct_env_data(input_data)

Arguments

input_data

Required. Nested data frame from acquire_neon_data.

Value

List of all QF flags over time period and Data frame of environmental measurements for flux computation

Author(s)

John Zobitz zobitz@augsburg.edu

Examples


# Note: you may need to first aqcuire the NEON data using acquire_neon_data
# Now correct existing environmental data:
corrected_data <- correct_env_data(sjer_env_data_2022_06)

Internal function to compute surface co2 flux at a given timepoint via De Jong and Schappert (1972)

Description

Given zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by estimating the surface concentration (through linear regression) and doing a gradient calculation. Modified from De Jong and Schappert (1972).

Usage

dejong_shappert_flux(zOffset, co2, co2_err, diffusive, diffusive_err)

Arguments

zOffset

Required. depths below surface - assumed to be positive in value. Important for directionality!

co2

Required. co2 at depth (umol m–1)

co2_err

Required. Associated errors with that value of co2

diffusive

Required. diffusivity at each depth

diffusive_err

Required Associated errors with diffusivity

Value

Data frame of fluxes associated error

Author(s)

John Zobitz zobitz@augsburg.edu

References

Jong, E. De, and H. J. V. Schappert. 1972. “Calculation of Soil Respiration and Activity from CO2 Profiles in the Soil.” Soil Science 113 (5): 328.

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.

See Also

[hirano_flux()], [tang_2003_flux()], [tang_2005_flux()] for other ways to compute surface fluxes.

Internal function to interpolate different depth measurements

Description

Definition function. Linearly interpolate a measurement across the different measurement depths

Usage

depth_interpolate(
  input_measurements,
  measurement_name,
  measurement_interpolate
)

Arguments

input_measurements

Required. Nested data frame (tibble of a months worth of data of co2, temperature, swc, pressure)

measurement_name

Required. Names of measurements we are interpolating. Currently only does one column at a time.

measurement_interpolate

Required. Names of measurement whose depth is used to interpolate (typically co2)

Value

A nested data frame with interpolated measurements.

Author(s)

John Zobitz zobitz@augsburg.edu based on code developed by Edward Ayres eayres@battelleecology.org

Internal function to determine the depth of a measurement

Description

Given a NEON measurement data frame and measurement depth, pull out the measurement depth for a measurement - because it may vary in time based on if a sensor is replaced.

Usage

determine_position(input_positions, input_measurement)

Arguments

input_positions

Required. Input vector of measurements depths

input_measurement

Required. Input vector of measurements.

Value

A data frame that reports the measurement depth for and associated environmental measurement.

Author(s)

John Zobitz zobitz@augsburg.edu

Compute soil diffusivity

Description

Given a tidied data frame of soil measurements (from interpolate.R), compute the diffusivity in a given soil layer

Usage

diffusivity(
  temperature,
  soil_water,
  pressure,
  temperature_err,
  soil_water_err,
  pressure_err,
  zOffset,
  porVol2To20
)

Arguments

temperature

Required. Soil temperature (degrees C)

soil_water

Required. Soil water content

pressure

Required. Barometric air pressure (kilopascal)

temperature_err

Required. Reported Soil temperature error (degrees C)

soil_water_err

Required. Reported Soil water content error

pressure_err

Required. Reported Barometric air pressure error (kilopascal)

zOffset

Required. Measurement level in cm.

porVol2To20

Required. Porosity of the 0-20 mm fraction (cm3 cm-3). Assumes no pores within rocks.

Value

A value of the computed diffusivity

Author(s)

John Zobitz zobitz@augsburg.edu

Examples

diffusivity(31,0.0102,96.3,.15,.2135,.05,-.05,0.45)

Helper function to plot QF results for environmental measurements.

Description

Given a flux measurement data frame, show when the environmental measurements produced a QF value

Usage

env_fingerprint_plot(input_fluxes)

Arguments

input_fluxes

data frame of computed fluxes

Value

A ggplot graph where we have ordered factors showing the QA values a given environmental measurement

Author(s)

John Zobitz zobitz@augsburg.edu

Examples

# Make a fingerprint plot for environmental variables:
env_fingerprint_plot(sjer_flux_2022_06)

Internal function that interpolates a soil measurement to different depths

Description

Definition function. Linearly interpolate a measurement across the different measurement depths

Usage

fit_function(
  input_depth,
  input_value,
  input_value_err,
  input_value_qf,
  interp_depth,
  measurement_special
)

Arguments

input_depth

Required. Vector of measurement depths.

input_value

Required. Vector of measured values (i.e. soil temperature and soil water) measured at depths input_depth

input_value_err

Required. Vector or reported measurement errors. Used to compute prediction error (via quadrature) when linear interpolation is used.

input_value_qf

Required. Vector of qf values from the smoothing 0 = no smoothing, 1 = mean value used (smoothing), 2 = NA value

interp_depth

Depths of the sensors required for interpolation

measurement_special

Flag if we want to just do linear interpolation for a given measurement

Value

A data frame of the depth and the measured column for the measurements and reported error

Author(s)

John Zobitz zobitz@augsburg.edu based on code developed by Edward Ayres eayres@battelleecology.org

Helper function to plot QF results for fluxes.

Description

Given a flux measurement data frame, show when the flux and diffusivity measurements produced a QF value

Usage

flux_fingerprint_plot(input_fluxes)

Arguments

input_fluxes

data frame of computed fluxes

Value

A ggplot graph where we have ordered factors showing the QA values a given flux computation

Author(s)

John Zobitz zobitz@augsburg.edu

Examples

# Make a fingerprint plot for computed flux values:
flux_fingerprint_plot(sjer_flux_2022_06)

Internal function to compute surface co2 flux at a given timepoint via Hirano et al 2005

Description

Given zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by estimating the surface concentration through linear regression and linear extrapolation of the bottom and top surface fluxes. Modified from Hirano et al (2005).

Usage

hirano_flux(zOffset, co2, co2_err, diffusive, diffusive_err)

Arguments

zOffset

Required. depths below surface - assumed to be positive in value. Important for directionality!

co2

Required. co2 at depth (umol m–1)

co2_err

Required. Associated errors with that value of co2

diffusive

Required. diffusivity at each depth

diffusive_err

Required Associated errors with diffusivity

Value

Data frame of fluxes associated error

Author(s)

John Zobitz zobitz@augsburg.edu

References

Hirano, Takashi, Honghyun Kim, and Yumiko Tanaka. 2003. “Long-Term Half-Hourly Measurement of Soil CO2 Concentration and Soil Respiration in a Temperate Deciduous Forest.” Journal of Geophysical Research: Atmospheres 108 (D20). https://doi.org/10.1029/2003JD003766.

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.

See Also

[dejong_shappert_flux()], [tang_2003_flux()], [tang_2005_flux()] for other ways to compute surface fluxes.

Internal function that inserts smoothed mean value of a measurement at a site

Description

Given a site measurement and monthly mean, insert in the monthly mean value when the QF flag fails.

Usage

insert_mean(data, monthly_mean, measurement_name)

Arguments

data

Required. input data to use

monthly_mean

Required. monthly mean of input data to use

measurement_name

Required. name of measurement

Value

Nested data frame of measurements

Author(s)

John Zobitz zobitz@augsburg.edu

Determine if a YYYY-MM string is inside a interval

Description

Determine if a YYYY-MM string is inside a interval

Usage

inside_interval(start, end, reference_time)

Arguments

start

starting interval time

end

ending interval time

reference_time

time we are comparing to - YYYY-MM string

Value

Logical indicating whether or not the reference time is inside the interval. We need this when working with downloaded NEON data which usually comes in a YYYY-MM string

Author(s)

John Zobitz zobitz@augsburg.edu

Examples

# Define starting and ending dates:
start_date <- as.POSIXct("2021-06-01 09:30:00",tz="UTC")
end_date <- as.POSIXct("2023-06-01 20:00:00",tz="UTC")

# Test, should return TRUE
inside_interval(start_date,end_date,"2022-06")

# Test, should return FALSE
inside_interval(start_date,end_date,"2020-06")

Internal function that makes sure for each time, position, and depth we have at least two data points for soil temp and soil h20, 3 for soil co2

Description

Given a merged data frame of co2, water, and temperature: 1) Filters on QF measurement flags 2) Filters if we have at least 2 soil h20 and temperature measurements, 3 co2 measurements at each time, horizontal position, and vertical depth 3) Filters if we have at least 3 distinct measurements at each time and horizontal position 4) Returns the resulting data frame.

This internal function is created to speed up data processing.

Usage

measurement_detect(input_data)

Arguments

input_data

Required. Nested data frame of merged soil water, temperature, co2, and pressure across different NEON locations and depths

Value

Data frame of fluxes from the timeperiod

Author(s)

John Zobitz zobitz@augsburg.edu based on code developed by Edward Ayres eayres@battelleecology.org

Internal function that filters environmental data for easier processing of fluxes.

Description

Internal function. Given a set of environmental measurements, create a merged data frame of measurements and positions where the QF_flag exists (either observed or a smoothed mean measurement).

Usage

measurement_merge(
  neon_data,
  data_code,
  data_product_id,
  measurement_name,
  qf_name
)

Arguments

neon_data

Required. A list of NEON data downloaded from the utilities

data_code

Required. Names of data product we are interpolating. (SWS = soil water, ST = soil temperature, SCO2C = soil CO2)

data_product_id

Name of the data product 00094 = soil water, 00041 = soil temperature, 00095 = soil CO2

measurement_name

Required. Names of column we are grabbing. (VSWCMean = soil water, soilTempMean = soil temperature, soilCO2concentrationMean = soil CO2)

qf_name

Required. Names of qf column we are grabbing. (VSWCFinalQF = soil water, finalQF = soil temperature, finalQF = soil CO2)

Value

A data frame of the requested data.

Author(s)

John Zobitz zobitz@augsburg.edu

Helper function to quickly compute the quadrature error

Description

Assume a derived quantity y is a function of inputs x_i: y = f(x_1,x_2,x_3, ...)

Given uncertainties (x_err) for each x_i, then this function will compute the corresponding y_err via quadrature. Inputs are the vector of partial derivaties df/dx_i, evaluated at (x_1,x_2,x_3,...).

Resulting y_err is the square root of the sum of (df/dx_1)^2 * (x_err)^2 + (df/dx_2)^2 * (x_err)^2 + (df/dx_3)^2 * (x_err)^2 ...

Usage

quadrature_error(x_pd, x_err)

Arguments

x_pd

Required. Input vector of partial derivatives for y = f(x), evaluated at x_i

x_err

Required. Error vector of measurements

Value

A value of quadrature error

Author(s)

John Zobitz zobitz@augsburg.edu

Examples

# Let's say we have 5 temperature measurements w/ error::
temperature <- c(31.108, 30.689, 30.463, 30.381, 30.250)
temperature_error <- c(0.1508,0.1507,0.1497,0.1496,0.1497)

# The sample mean is the sum of all measurements divided by the average:
sum(temperature)/5  # (Can also be computed with mean(temperature))

# The vector of partial derivatives is just 1/n for each measurement:
temperature_pd <- c(1/5,1/5,1/5,1/5,1/5)
quadrature_error(temperature_pd,temperature_error)
# Note: quadrature_error(1/5,temperature_error) is also allowed.

Measured environmental data at a NEON site

Description

A nested dataset containing environmental variables at the SJER site from June 2022. A convenience data frame to make processing fluxes easier for testing. Computed with the function acquire_neon_data. All the variables can be used to compute fluxes (along with megapit data).

Usage

data(sjer_env_data_2022_06)

Format

A nested 3 item list

Details

Computed flux values at a NEON site

Description

A nested dataset containing computed fluxes at the SJER site from June 2022. Fluxes were computed with using compute_neon_flux(sjer_env_data_2022_06,sjer_megapit_data_2022_06).

Usage

data(sjer_flux_2022_06)

Format

A nested data frame item list with 7200 rows and 8 columns

Details

Measured soil physical properties at a NEON site

Description

A nested dataset containing soil data properties at the SJER site from June 2022. A convenience data frame to make processing fluxes easier for testing. Computed with using the function acquire_neon_data. Essentially what is returned when data product DP1.00096.001 is loaded from loadByProduct.

Usage

data(sjer_megapit_data_2022_06)

Format

A nested 10 item list

Details

Source

From https://data.neonscience.org/data-products/DP1.00096.001

Internal function to correct depths for VSWC NEON data.

Description

Given the expanded SWC data, return a corrected version based on the values below

Usage

swc_correct(input_swc, curr_site, reference_time)

Arguments

input_swc

Required. input soil water content data from acquire_neon_data (as a list)

curr_site

Current site we are working with

reference_time

Current month we are working with

Value

A revised list of corrected soil water content and depths.

Author(s)

John Zobitz zobitz@augsburg.edu

Examples


# Download the soil water content data:
site_swc <- neonUtilities::loadByProduct(
dpID="DP1.00094.001",
site="SJER",
startdate="2020-05",
enddate="2020-05",
timeIndex = "30",
package="expanded",
check.size = FALSE,
include.provisional = TRUE
)

# Then correct the swc:
site_swc <- swc_correct(site_swc,"SJER","2020-05")

Corrected sensor locations for NEON soil water content data

Description

A dataset containing corrected NEON sensor location depths for soil water content data

Usage

swc_corrections

Format

A data frame with 2161 rows and 6 variables

Details

Source

<hhttps://data.neonscience.org/data-products/DP1.00094.001>

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

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).

Usage

tang_2003_flux(zOffset, co2, co2_err, diffusive, diffusive_err)

Arguments

zOffset

Required. depths below surface - assumed to be positive in value. Important for directionality!

co2

Required. co2 at depth (umol m–1)

co2_err

Required. Associated errors with that value of co2

diffusive

Required. diffusivity at each depth

diffusive_err

Required Associated errors with diffusivity

Value

Data frame of fluxes associated error

Author(s)

John Zobitz zobitz@augsburg.edu

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.

See Also

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

Internal function to compute surface co2 flux at a given timepoint via Tang et al 2005

Description

Given zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by linear extrapolation of surface fluxes. Modified from Tang et al (2005).

Usage

tang_2005_flux(zOffset, co2, co2_err, diffusive, diffusive_err)

Arguments

zOffset

Required. depths below surface - assumed to be positive in value. Important for directionality!

co2

Required. co2 at depth (umol m–1)

co2_err

Required. Associated errors with that value of co2

diffusive

Required. diffusivity at each depth

diffusive_err

Required Associated errors with diffusivity

Value

Data frame of fluxes associated error

Author(s)

John Zobitz zobitz@augsburg.edu

References

Tang, Jianwu, Laurent Misson, Alexander Gershenson, Weixin Cheng, and Allen H. Goldstein. 2005. “Continuous Measurements of Soil Respiration with and without Roots in a Ponderosa Pine Plantation in the Sierra Nevada Mountains.” Agricultural and Forest Meteorology 132 (3): 212–27. https://doi.org/10.1016/j.agrformet.2005.07.011.

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.

See Also

[dejong_shappert_flux()], [hirano_flux()], [tang_2003_flux()] for other ways to compute surface fluxes.