Package 'REddyProc' reference manual

Title:	Post Processing of (Half-)Hourly Eddy-Covariance Measurements
Description:	Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
Authors:	Department for Biogeochemical Integration at MPI-BGC, Jena, Germany [cph], Thomas Wutzler [aut, cre], Markus Reichstein [aut], Antje Maria Moffat [aut, trl], Olaf Menzer [ctb], Mirco Migliavacca [aut], Kerstin Sickel [ctb, trl], Ladislav <U+0160>igut [ctb]
Maintainer:	Thomas Wutzler <[email protected]>
License:	GPL (>= 2)
Version:	1.3.3
Built:	2024-11-25 15:30:59 UTC
Source:	https://github.com/earthyscience/reddyproc

Post Processing of (Half-)Hourly Eddy-Covariance Measurements

Description

Standard and extensible Eddy-Covariance data post-processing including uStar-filtering, gap-filling, and flux-partitioning (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>).

The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>).

This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>).

Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>).

Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).

A general description and an online tool based on this package can be found here: https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb.

Details

A detailed example of the processing can be found in the useCase vignette.

A first overview of the REddyProc functions:

These functions help with the preparation of your data for the analysis:

Loading text files into dataframes: fLoadTXTIntoDataframe
Preparing a proper time stamp: help_DateTimes
Calculating latent variables, e.g. VPD: fCalcVPDfromRHandTair

Then the data can be processed with the sEddyProc-class R5 reference class:

Initializing the R5 reference class: sEddyProc_initialize
Estimating the turbulence criterion, Ustar threshold, for omitting data from periods of low turbulence: Functions sEddyProc_sEstUstarThreshold and sEddyProc_sEstUstarThresholdDistribution .
Gap filling: sEddyProc_sMDSGapFill and sEddyProc_sMDSGapFillAfterUstar.
Flux partitioning based on Night-Time: sEddyProc_sMRFluxPartition
Flux partitioning based on Day-Time: sEddyProc_sGLFluxPartition

Processing across different scenarios of u* threshold estimate is supported by

Estimating the turbulence criterion, Ustar threshold, for omitting data from periods of low turbulence: sEddyProc_sEstimateUstarScenarios and associated
- query the thresholds to be used sEddyProc_sGetUstarScenarios
- set the thresholds to be used sEddyProc_sSetUstarScenarios
- query the estimated thresholds all different aggregation levels sEddyProc_sGetEstimatedUstarThresholdDistribution
Gap-Filling: sEddyProc_sMDSGapFillUStarScens
Flux partitioning based on Night-Time (Reichstein 2005): sEddyProc_sMRFluxPartitionUStarScens
Flux partitioning based on Day-Time (Lasslop 2010): sEddyProc_sGLFluxPartitionUStarScens
Flux partitioning based on modified Day-Time (Keenan 2019): sEddyProc_sTKFluxPartitionUStarScens

Before or after processing, the data can be plotted:

Fingerprint: sEddyProc_sPlotFingerprint
Half-hourly fluxes and their daily means: sEddyProc_sPlotHHFluxes
Daily sums (and their uncertainties): sEddyProc_sPlotDailySums
Diurnal cycle: sEddyProc_sPlotDiurnalCycle

For exporting data and results see help_export.

A complete list of REddyProc functions be viewed by clicking on the Index link at the bottom of this help page.

Also have a look at the package vignettes.

Author(s)

Department for Biogeochemical Integration at MPI-BGC, Jena, Germany

References

Reichstein M, Falge E, Baldocchi D et al. (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11, 1424-1439.

BerkeleyJulianDateToPOSIXct

Description

convert JulianDate format used in Berkeley release to POSIXct

Usage

BerkeleyJulianDateToPOSIXct(julianDate, tz = "UTC", 
    ...)BerkeleyJulianDateToPOSIXct(julianDate, tz = "UTC", 
    ...)

Arguments

`julianDate`	numeric vector representing times (see details for format)
`tz`	time zone used to represent the dates
`...`	further arguments to `strptime`

Details

In the Berkeley-Release of the Fluxnet data, the time is stored as an number with base10-digits representing YYYYMMddhhmm

Author(s)

TW, Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

Eddy covariance data from Gebesee crop site, Germany

Description

The data frame 'DEGebExample' contains half-hourly eddy covariance measurements from Gebesee of the years 2004 to 2006.

Usage

data(DEGebExample)data(DEGebExample)

Format

For each column, the attributes 'varnames' for the variable names and 'units' for the variable units are provided.

Time stamp: DateTime: POSIXct-time of the end of the half-hour period, Use as.POSIXlt(DateTime)$year to get hour, day of year, ...
Flux measurements: NEE
Meteo measurements: Rg, Tair, rH, VPD, Ustar

For processing of the example data see vignette("DEGebExample").

Details

DISCLAIMER: This example dataset should only be used for test purposes of the REddyProc R package. For other uses, the data is openly available through the European Fluxes Database (http://www.europe-fluxdata.eu/home/site-details?id=3) and upon registration the current version can be downloaded there.

Source

The data was downloaded from http://www.europe-fluxdata.eu at date 2016-01-25.

Estimate VPD from assuming dewpoint at daily minimum temperature

Description

VPD is required for daytime NEE flux partitioning. Hence, it is necessary to estimate VPD also for long gaps in data. With two assumptions, VPD can be estimated from temperature 1). The change of water mass in air is negligible during the day. VPD is the difference of actual vapour pressure to saturation vapour pressure. 2.) At morning minimum temperature, vapour pressure is at minimum in many cases at saturation. Hence

$VPD = Esat(Tair) - E \approx Esat(Tair) - Esat_{daymin} \approx Esat(Tair) - Esat(Tair_{min})$

Usage

estimate_vpd_from_dew(df, pNonMissing = 0.1)
estimate_vpd_from_dew(df, pNonMissing = 0.1)

Arguments

`df`	data.frame with columns DateTime, VPD, Tair, and Tair_f
`pNonMissing`	numeric scalar of the necessary fraction of finite VPD and Tair. If fraction is lower then a warning is thrown.

Details

Since sometimes Esat_daymin is lower than Esat(Tair_min) the estimated VPDfromDew is underestimated. This function applies a linear model of the existing VPD and estimated VPD to correct for this bias: VPD ~ 0 + VPDfromDew * Tair_f * hourOfDay * TminOftheDay * TRangeDay

Value

numeric vector of length(nrow(data)) of estimated VPD

Eddy covariance data from Tharandt, Germany

Description

The data frame 'EddyData.F' contains half-hourly eddy covariance measurements from Tharandt of the year 1998.

Usage

data(Example_DETha98)data(Example_DETha98)

Format

For each column, the attributes 'varnames' for the variable names and 'units' for the variable units are provided.

Time stamp: Year - Year provided with century 1998.
DoY - Day of year provided as 1 to 365 (or 1 to 366 in leap years).
Hour - Hour provided as decimal 0.0 to 23.5.
Flux measurements: NEE, LE, H
Meteo measurements: Rg, Tair, Tsoil, rH, VPD, Ustar

For processing of the example data see useCase vignette.

Source

The data originates from the CARBODATA CD.

extract processing results with columns corresponding to Fluxnet15 release

Description

extract processing results with columns corresponding to Fluxnet15 release

Usage

extract_FN15(
  EProc = .self,
  is_export_nonfilled = TRUE,
  keep_other_cols = FALSE
)
extract_FN15(
  EProc = .self,
  is_export_nonfilled = TRUE,
  keep_other_cols = FALSE
)

Arguments

`EProc`	sEddyProc class with uncertainty also in meteo variables and both nighttime and daytime partitioning columns present
`is_export_nonfilled`	set to FALSE to not export columns before gapfilling
`keep_other_cols`	set to TRUE to report also other columns

Value

data.frame with columns names of Fluxnet15. Timestamps are in ISO string format POSIXctToBerkeleyJulianDate

fCalcAVPfromVMFandPress

Description

Calculate AVP from VMF and Press

Usage

fCalcAVPfromVMFandPress(VMF = VMF.V.n, Press = Press.V.n, 
    VMF.V.n, Press.V.n)fCalcAVPfromVMFandPress(VMF = VMF.V.n, Press = Press.V.n, 
    VMF.V.n, Press.V.n)

Arguments

`VMF`	Vapor mole fraction (VMF, mol / mol)
`Press`	Atmospheric pressure (Press, hPa)
`VMF.V.n`	deprecated
`Press.V.n`	deprecated

Value

Data vector of actual vapor pressure (AVP, hPa (mbar))

Author(s)

AMM Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

fCalcETfromLE

Description

Calculate ET from LE and Tair

Usage

fCalcETfromLE(LE = LE.V.n, Tair = Tair.V.n, 
    LE.V.n, Tair.V.n)fCalcETfromLE(LE = LE.V.n, Tair = Tair.V.n, 
    LE.V.n, Tair.V.n)

Arguments

`LE`	Data vector of latent heat (LE, W m-2)
`Tair`	Data vector of air temperature (Tair, degC)
`LE.V.n`	deprecated
`Tair.V.n`	deprecated

Value

Data vector of evapotranspiration (ET, mmol H20 m-2 s-1)

Author(s)

fCalcExtRadiation

Description

Calculate the extraterrestrial solar radiation with the eccentricity correction

Usage

fCalcExtRadiation(DoY = DoY.V.n, DoY.V.n)fCalcExtRadiation(DoY = DoY.V.n, DoY.V.n)

Arguments

`DoY`	Data vector with day of year (DoY)
`DoY.V.n`	deprecated, use DoY

Value

Data vector of extraterrestrial radiation (ExtRad, W_m-2)

Author(s)

fCalcPotRadiation

Description

Calculate the potential radiation

Usage

fCalcPotRadiation(DoY = DoY.V.n, Hour = Hour.V.n, 
    LatDeg = Lat_deg.n, LongDeg = Long_deg.n, 
    TimeZone = TimeZone_h.n, useSolartime = TRUE, 
    DoY.V.n, Hour.V.n, Lat_deg.n, Long_deg.n, 
    TimeZone_h.n, useSolartime.b = TRUE)fCalcPotRadiation(DoY = DoY.V.n, Hour = Hour.V.n, 
    LatDeg = Lat_deg.n, LongDeg = Long_deg.n, 
    TimeZone = TimeZone_h.n, useSolartime = TRUE, 
    DoY.V.n, Hour.V.n, Lat_deg.n, Long_deg.n, 
    TimeZone_h.n, useSolartime.b = TRUE)

Arguments

`DoY`	Data vector with day of year (DoY), same length as Hour or length 1
`Hour`	Data vector with time as decimal hour of local time zone
`LatDeg`	Latitude in (decimal) degrees
`LongDeg`	Longitude in (decimal) degrees
`TimeZone`	Time zone (in hours)
`useSolartime`	by default corrects hour (given in local winter time) for latitude to solar time (where noon is exactly at 12:00). Set this to FALSE to directly use local winter time
`DoY.V.n`	deprecated
`Hour.V.n`	deprecated
`Lat_deg.n`	deprecated
`Long_deg.n`	deprecated
`TimeZone_h.n`	deprecated
`useSolartime.b`	deprecated

Value

Data vector of potential radiation (PotRad, W_m-2)

Author(s)

Examples

hour <- seq(8, 16, by = 0.1)
potRadSolar <- fCalcPotRadiation(160, hour, 39.94, -5.77, TimeZone = +1)
potRadLocal <- fCalcPotRadiation(160, hour, 39.94, -5.77, TimeZone = +1
	  , useSolartime = FALSE)
plot(potRadSolar ~ hour, type = 'l')
abline(v = 13, lty = "dotted")
lines(potRadLocal ~  hour, col = "blue")
abline(v = 12, col = "blue", lty = "dotted")
legend("bottomright", legend = c("solar time", "local winter time")
	, col = c("black", "blue"), inset = 0.05, lty = 1)
hour <- seq(8, 16, by = 0.1)
potRadSolar <- fCalcPotRadiation(160, hour, 39.94, -5.77, TimeZone = +1)
potRadLocal <- fCalcPotRadiation(160, hour, 39.94, -5.77, TimeZone = +1
	  , useSolartime = FALSE)
plot(potRadSolar ~ hour, type = 'l')
abline(v = 13, lty = "dotted")
lines(potRadLocal ~  hour, col = "blue")
abline(v = 12, col = "blue", lty = "dotted")
legend("bottomright", legend = c("solar time", "local winter time")
	, col = c("black", "blue"), inset = 0.05, lty = 1)

fCalcRHfromAVPandTair

Description

Calculate relative humidity from actual vapour pressure and air temperature

Usage

fCalcRHfromAVPandTair(AVP = AVP.V.n, Tair = Tair.V.n, 
    AVP.V.n, Tair.V.n)fCalcRHfromAVPandTair(AVP = AVP.V.n, Tair = Tair.V.n, 
    AVP.V.n, Tair.V.n)

Arguments

`AVP`	Data vector of actual vapour pressure (AVP, hPa (mbar))
`Tair`	Data vector of air temperature (Tair, degC)
`AVP.V.n`	Data vector of actual vapour pressure (AVP, hPa (mbar))
`Tair.V.n`	Data vector of air temperature (Tair, degC)

Value

Data vector of relative humidity (rH, %)

Author(s)

fCalcSVPfromTair

Description

Calculate SVP (of water) from Tair

Usage

fCalcSVPfromTair(Tair = Tair.V.n, Tair.V.n)fCalcSVPfromTair(Tair = Tair.V.n, Tair.V.n)

Arguments

`Tair`	Data vector of air temperature (Tair, degC)
`Tair.V.n`	deprecated

Value

Data vector of saturation vapor pressure (SVP, hPa (mbar))

Author(s)

fCalcVPDfromRHandTair

Description

Calculate VPD from rH and Tair

Usage

fCalcVPDfromRHandTair(rH = RH.V.n, Tair = Tair.V.n, 
    RH.V.n, Tair.V.n)fCalcVPDfromRHandTair(rH = RH.V.n, Tair = Tair.V.n, 
    RH.V.n, Tair.V.n)

Arguments

`rH`	Data vector of relative humidity (rH, %)
`Tair`	Data vector of air temperature (Tair, degC)
`RH.V.n`	deprecated
`Tair.V.n`	deprecated

Value

Data vector of vapour pressure deficit (VPD, hPa (mbar))

Author(s)

fCheckHHTimeSeries

Description

Check half-hourly time series data

Usage

fCheckHHTimeSeries(Time = Time.V.p, DTS = DTS.n, 
    CallFunction = if (!missing(CallFunction.s)) CallFunction.s else "", 
    Time.V.p, DTS.n, CallFunction.s)fCheckHHTimeSeries(Time = Time.V.p, DTS = DTS.n, 
    CallFunction = if (!missing(CallFunction.s)) CallFunction.s else "", 
    Time.V.p, DTS.n, CallFunction.s)

Arguments

`Time`	Time vector in POSIX format
`DTS`	Number of daily time steps (24 or 48)
`CallFunction`	Name of function called from
`Time.V.p`	deprecated
`DTS.n`	deprecated
`CallFunction.s`	deprecated

Details

The number of steps per day can be 24 (hourly) or 48 (half-hourly).

The time stamp needs to be provided in POSIX time format,

equidistant half-hours,

and stamped on the half hour.

The sEddyProc procedures require at least three months of data.

Full days of data are preferred: the total amount of data rows should be a multiple of the daily time step, and

in accordance with FLUXNET standards, the dataset is spanning from the end of the first (half-)hour (0:30 or 1:00, respectively) and to midnight (0:00).

Value

Function stops on errors.

Author(s)

fConvertCtoK

Description

Convert degree Celsius to degree Kelvin

Usage

fConvertCtoK(Celsius = Celsius.V.n, Celsius.V.n)fConvertCtoK(Celsius = Celsius.V.n, Celsius.V.n)

Arguments

`Celsius`	Data vector in Celsius (degC)
`Celsius.V.n`	deprecated way of specifying Celsius

Value

Data vector in temperature Kelvin (Temp_K, degK)

Author(s)

fConvertGlobalToVisible

Description

Partition global (solar) radiation into only visible (the rest is UV and infrared)

Usage

fConvertGlobalToVisible(Global = Global.V.n, 
    Global.V.n)fConvertGlobalToVisible(Global = Global.V.n, 
    Global.V.n)

Arguments

`Global`	Data vector of global radiation (W m-2)
`Global.V.n`	deprecated

Value

Data vector of visible part of solar radiation (VisRad, W m-2)

Author(s)

fConvertKtoC

Description

Convert degree Kelvin to degree Celsius

Usage

fConvertKtoC(Kelvin = Kelvin.V.n, Kelvin.V.n)fConvertKtoC(Kelvin = Kelvin.V.n, Kelvin.V.n)

Arguments

`Kelvin`	Data vector in Kelvin (degK)
`Kelvin.V.n`	deprecated, use Kelvin instead

Value

Data vector in temperature Celsius (Temp_C, degC)

Author(s)

fConvertTimeToPosix

Description

Convert different time formats to POSIX

Usage

fConvertTimeToPosix(Data.F, TFormat = TFormat.s, 
    Year = if (!missing(Year.s)) Year.s else "none", 
    Month = if (!missing(Month.s)) Month.s else "none", 
    Day = if (!missing(Day.s)) Day.s else "none", 
    Hour = if (!missing(Hour.s)) Hour.s else "none", 
    Min = if (!missing(Min.s)) Min.s else "none", 
    TName = if (!missing(TName.s)) TName.s else "DateTime", 
    TFormat.s, Year.s, Month.s, Day.s, Hour.s, 
    Min.s, TName.s, tz = "GMT")fConvertTimeToPosix(Data.F, TFormat = TFormat.s, 
    Year = if (!missing(Year.s)) Year.s else "none", 
    Month = if (!missing(Month.s)) Month.s else "none", 
    Day = if (!missing(Day.s)) Day.s else "none", 
    Hour = if (!missing(Hour.s)) Hour.s else "none", 
    Min = if (!missing(Min.s)) Min.s else "none", 
    TName = if (!missing(TName.s)) TName.s else "DateTime", 
    TFormat.s, Year.s, Month.s, Day.s, Hour.s, 
    Min.s, TName.s, tz = "GMT")

Arguments

`Data.F`	Data frame with time columns to be converted
`TFormat`	Abbreviation for implemented time formats, see details
`Year`	Column name of year
`Month`	Column name of month
`Day`	Column name of day
`Hour`	Column name of hour
`Min`	Column name of min
`TName`	Column name of new column
`TFormat.s`	deprecated
`Year.s`	deprecated
`Month.s`	deprecated
`Day.s`	deprecated
`Hour.s`	deprecated
`Min.s`	deprecated
`TName.s`	deprecated
`tz`	timezone used to store the data. Advised to keep GMT to avoid daytime shifting issues

Details

The different time formats are converted to POSIX (GMT) and a 'TimeDate' column is prefixed to the data frame

Implemented time formats:

YDH: year, day of year, hour in decimal (e.g. 1998, 1, 10.5). The day (of year) format is (1-365 or 1-366 in leap years). The hour format is decimal time (0.0-23.5).
YMDH: year, month, day of month, hour in decimal (e.g. 1998, 1, 1, 10.5) The month format is (1-12) The day (of month) format is (1-31).
YMDHM: year, month, day of month, integer hour, minute (e.g. 1998, 1, 1, 10, 30) The hour format is (0-23) The minute format is (0-59)

Value

Data frame with prefixed POSIX time column.

Author(s)

AMM, TW Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

Examples

# See unit test in test_fConvertTimeToPosix for example
# See unit test in test_fConvertTimeToPosix for example

fConvertVisibleWm2toPhotons

Description

Convert units of visible radiation from irradiance to photons flux

Usage

fConvertVisibleWm2toPhotons(Wm2 = Wm2.V.n, 
    Wm2.V.n)fConvertVisibleWm2toPhotons(Wm2 = Wm2.V.n, 
    Wm2.V.n)

Arguments

`Wm2`	Data vector in units of irradiance (W m-2)
`Wm2.V.n`	deprecated

Value

Data vector in units of photons flux (PPFD, umol photons m-2 s-1)

Author(s)

Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

filter entire days

Description

Omit records before the start of the first full day and the end of the last full day

Usage

filter_entire_days(df, col_time = "DateTime")filter_entire_days(df, col_time = "DateTime")

Arguments

`df`	data.frame with column col_time of equidistant
`col_time`	Name of the column with the equidistant timesteps

Details

Column attributes such as 'units' are kept.

Author(s)

filter years eop

Description

Subset data.frame to given years respecting the end-of-period convention

Usage

filter_years_eop(df, years, col_time = "DateTime")filter_years_eop(df, years, col_time = "DateTime")

Arguments

`df`	data.frame with column col_time of equidistant
`years`	integer vector of years of the form `c(1998, 1998)`
`col_time`	Name of the column with the equidistant timesteps

Details

The end-of-period (usually end-of-half-hour) convention in the Fluxnet community results in midnight and new-year being the last record of the previous day or the year respectively, although POSIXt function will report the next day or year respectively.

Column attributes such as 'units' are kept.

Author(s)

filterLongRuns

Description

replace runs, i.e sequences of numerically equal values, by NA

Usage

filterLongRuns(data, colNames, ...)filterLongRuns(data, colNames, ...)

Arguments

`data`	data.frame with columns to filter
`colNames`	string vector of names indicating which columns to filter
`...`	further arguments to `filterLongRunsInVector` such as `minNRunLength`.

Details

Longer runs, i.e. sequences of numerically identical values, in a series of measurements hint to problems during a noisy measurement, e.g. by sensor malfunction due to freezing. This function, replaces such values in such runs to indicate missing values.

Value

data.frame ans with long runs in specified columns replaced by NA

Author(s)

filterLongRunsInVector

Description

replace runs of numerically equal values by NA

Usage

filterLongRunsInVector(x, minNRunLength = 8, 
    replacement = NA, na.rm = TRUE)filterLongRunsInVector(x, minNRunLength = 8, 
    replacement = NA, na.rm = TRUE)

Arguments

`x`	vector in which to replace long runs
`minNRunLength`	minimum length of a run to replace. Defaults to 4 hours in half-hourly spaced data.
`replacement`	value replacing the original values in long run
`na.rm`	set to FALSE if NA values interrupt runs

Value

vector x with long runs replaced by NA

Author(s)

fKeepColumnAttributes

Description

Copy column attributes after processing a data.frame

Usage

fKeepColumnAttributes(x, FUN, ...)fKeepColumnAttributes(x, FUN, ...)

Arguments

`x`	data.frame to be processed
`FUN`	`function(x::data.frame, ...) -> data.frame` to be applied
`...`	additional arguments to FUN

Details

The columns of the resulting data.frame that match a column name in x will get the same attributes as in x.

Value

result of function(x, ...) with column attributes preserved

Author(s)

Temperature dependence of soil respiration

Description

Temperature dependence of soil respiration after Equation 11 in Lloyd & Taylor (1994)

Usage

fLloydTaylor(RRef = R_ref.n, E0 = E_0.n, 
    TSoil = Tsoil.n, TRef = if (missing(T_ref.n)) 273.15 + 
        10 else T_ref.n, T0 = if (missing(T_0.n)) 227.13 else T_0.n, 
    R_ref.n, E_0.n, Tsoil.n, T_ref.n, T_0.n)fLloydTaylor(RRef = R_ref.n, E0 = E_0.n, 
    TSoil = Tsoil.n, TRef = if (missing(T_ref.n)) 273.15 + 
        10 else T_ref.n, T0 = if (missing(T_0.n)) 227.13 else T_0.n, 
    R_ref.n, E_0.n, Tsoil.n, T_ref.n, T_0.n)

Arguments

`RRef`	Respiration rate at reference temperature
`E0`	Temperature sensitivity ("activation energy") in Kelvin (degK)
`TSoil`	Soil temperature in Kelvin (degK)
`TRef`	Reference temperature of 10 degC in Kelvin (degK)
`T0`	Regression temperature as fitted by LloydTaylor (1994) in Kelvin (degK)
`R_ref.n`	deprecated way to specify RRef
`E_0.n`	deprecated way to specify E0
`Tsoil.n`	deprecated way to specify Tsoil
`T_ref.n`	deprecated way to specify TRef
`T_0.n`	deprecated way to specify T0

Value

Data vector of soil respiration rate (R, umol CO2 m-2 s-1)

Author(s)

AMM reference<< Lloyd J, Taylor JA (1994) On the temperature dependence of soil respiration. Functional Ecology, 8, 315-323. Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

Examples

  T <- c(-10:30)
  resp <- fLloydTaylor(10, 330, T + 273.15)
plot(resp ~ T)
T <- c(-10:30)
  resp <- fLloydTaylor(10, 330, T + 273.15)
plot(resp ~ T)

Read basic variables from Ameriflux standard (as of 2022) files

Description

Reads Variables from file into data.frame from file and passes it to read_from_ameriflux22.

Usage

fLoadAmeriflux22(file_path, ...)
fLoadAmeriflux22(file_path, ...)

Arguments

`file_path`	scalar string: the path to the csv file
`...`	further arguments to `read_csv`

fLoadEuroFlux16

Description

reads a sequence of annual files in the format of Europe-fluxdata 2016

Usage

fLoadEuroFlux16(siteName, dirName = "", additionalColumnNames = character(0))fLoadEuroFlux16(siteName, dirName = "", additionalColumnNames = character(0))

Arguments

`siteName`	scalar string: the name of the site, i.e. start of the filename before _<year>_
`dirName`	scalar string: the directory where the files reside
`additionalColumnNames`	character vector: column names to read in addition to c("Month", "Day", "Hour", "NEE_st", "qf_NEE_st", "ustar", "Ta", 'Rg')

Details

The filenames should correspond to the pattern <sitename>_<YYYY>_. * .txt And hold columns c("Month", "Day", "Hour", "NEE_st", "qf_NEE_st", "ustar", "Ta", 'Rg'). By default only those columns are read and reported only c("DateTime", "NEE", "Ustar", "Tair", "Rg", "qf_NEE_st" (Note the renaming). NEE is set to NA for all values with "qf_NEE_st != 0. Values of -9999.0 are replaced by NA

Author(s)

TW Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

Read a file in the format of Fluxnet 2015 release

Description

Assigns default units to the columns and keeps variable name attributes as in original file.

Usage

fLoadFluxnet15(
  file_path,
  additional_columns = character(0),
  colname_NEE = "NEE",
  ...
)
fLoadFluxnet15(
  file_path,
  additional_columns = character(0),
  colname_NEE = "NEE",
  ...
)

Arguments

`file_path`	scalar string: the path to the csv file
`additional_columns`	character vector of columns to read in addition of standard columns of `read_from_fluxnet15`. Can be a character vector or a object return by `cols`
`colname_NEE`	name (scalar string) of column that reports NEE observations
`...`	further arguments to `read_csv`

Examples

  ds_fn15 <- Example_DETha98 %>%
     fConvertTimeToPosix('YDH',Year = 'Year',Day = 'DoY', Hour = 'Hour') %>%
     dplyr::mutate(
        TIMESTAMP_END = POSIXctToBerkeleyJulianDate(DateTime),
        season = factor(199801)
     ) %>%
     dplyr::rename(SW_IN = "Rg", TA = "Tair", USTAR = "Ustar") %>%
     dplyr::select(dplyr::one_of(c(
       "TIMESTAMP_END","NEE","SW_IN","TA","VPD","USTAR","season")))
  head(ds_fn15)
  fname <- tempfile()
  readr::write_csv(ds_fn15, fname)

  # standard columns are renamed to REddyProc defaults
  ds_eproc <- fLoadFluxnet15(fname)
  head(ds_eproc)
  EProc <- sEddyProc$new("DE-Tha", ds_eproc)
  head(EProc$sExportData())

  # Additional columns can be specified, e.g. factor column season
  ds_eproc <- fLoadFluxnet15(fname,
    additional_columns = readr::cols(season = readr::col_factor()))
  head(ds_eproc)
  EProc <- sEddyProc$new("DE-Tha", ds_eproc,
    c("NEE", "Rg", "Tair", "VPD", "Ustar","season"),
    ColNamesNonNumeric = "season"
    )
  head(EProc$sExportData())
ds_fn15 <- Example_DETha98 %>%
     fConvertTimeToPosix('YDH',Year = 'Year',Day = 'DoY', Hour = 'Hour') %>%
     dplyr::mutate(
        TIMESTAMP_END = POSIXctToBerkeleyJulianDate(DateTime),
        season = factor(199801)
     ) %>%
     dplyr::rename(SW_IN = "Rg", TA = "Tair", USTAR = "Ustar") %>%
     dplyr::select(dplyr::one_of(c(
       "TIMESTAMP_END","NEE","SW_IN","TA","VPD","USTAR","season")))
  head(ds_fn15)
  fname <- tempfile()
  readr::write_csv(ds_fn15, fname)

  # standard columns are renamed to REddyProc defaults
  ds_eproc <- fLoadFluxnet15(fname)
  head(ds_eproc)
  EProc <- sEddyProc$new("DE-Tha", ds_eproc)
  head(EProc$sExportData())

  # Additional columns can be specified, e.g. factor column season
  ds_eproc <- fLoadFluxnet15(fname,
    additional_columns = readr::cols(season = readr::col_factor()))
  head(ds_eproc)
  EProc <- sEddyProc$new("DE-Tha", ds_eproc,
    c("NEE", "Rg", "Tair", "VPD", "Ustar","season"),
    ColNamesNonNumeric = "season"
    )
  head(EProc$sExportData())

Load text file with one header and one unit row into data frame

Description

If gaps with the flag -9999.0 exist, these are set to NA.

Usage

fLoadTXTIntoDataframe(FileName = FileName.s, 
    Dir = if (!missing(Dir.s)) Dir.s else "", 
    FileName.s, Dir.s = "")fLoadTXTIntoDataframe(FileName = FileName.s, 
    Dir = if (!missing(Dir.s)) Dir.s else "", 
    FileName.s, Dir.s = "")

Arguments

`FileName`	File name as a character string
`Dir`	Directory as a character string
`FileName.s`	deprecated
`Dir.s`	deprecated way of specifying Dir

Details

Function fLoadFluxNCIntoDataframe, which loads data from NetCDF-Files, has been moved to add-on package REddyProcNCDF. In addition, fLoadEuroFlux16 loads data from several annual files in format corresponding to Europe-fluxdata 2016.

For using only part of the records, use fFilterAttr to keep units attributes.

Value

Data frame with data from text file.

Author(s)

Examples


examplePath <- getExamplePath('Example_DETha98.txt', TRUE)
EddyData.F <- fLoadTXTIntoDataframe(examplePath)
examplePath <- getExamplePath('Example_DETha98.txt', TRUE)
EddyData.F <- fLoadTXTIntoDataframe(examplePath)

fSplitDateTime

Description

Replace Column DateTime by columns Year, DoY, and Hour in a data.frame

Usage

fSplitDateTime(df)fSplitDateTime(df)

Arguments

`df`	data.frame with column DateTime

Details

This function helps exporting to the format required by the REddyProc web interface with columns Year, DoY, and Hour

Author(s)

fWriteDataframeToFile

Description

Write data frame to ASCII tab-separated text file

Usage

fWriteDataframeToFile(Data.F, FileName = FileName.s, 
    Dir = if (!missing(Dir.s)) Dir.s else "", 
    Digits = if (!missing(Digits.n)) Digits.n else 5, 
    isSplitDatetime = FALSE, FileName.s, 
    Dir.s, Digits.n)fWriteDataframeToFile(Data.F, FileName = FileName.s, 
    Dir = if (!missing(Dir.s)) Dir.s else "", 
    Digits = if (!missing(Digits.n)) Digits.n else 5, 
    isSplitDatetime = FALSE, FileName.s, 
    Dir.s, Digits.n)

Arguments

`Data.F`	Data frame
`FileName`	File base name as a string
`Dir`	Directory as a string
`Digits`	(integer) number of digits, i.e. precision, for numeric values
`isSplitDatetime`	set to TRUE to create columns Year, DoY and Hour
`FileName.s`	deprecated
`Dir.s`	deprecated
`Digits.n`	deprecated

Details

Missing values are flagged as -9999.0

Value

Output of data frame written to file of specified type.

Author(s)

AMM, KS, TW Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

Examples

(Dir <- tempdir())   # directory where output is written to
fWriteDataframeToFile(Example_DETha98, 'OutputTest.txt', Dir = Dir)
(Dir <- tempdir())   # directory where output is written to
fWriteDataframeToFile(Example_DETha98, 'OutputTest.txt', Dir = Dir)

fWriteFrench23

Description

Write data frame to ASCII comma-separated text file

Usage

fWriteFrench23(data, filename, isSplitDatetime = FALSE, 
    digits = 5)fWriteFrench23(data, filename, isSplitDatetime = FALSE, 
    digits = 5)

Arguments

`data`	Data frame to be exported, with unit attributes attached to columns
`filename`	(string) name (including path) of the output file
`isSplitDatetime`	set to TRUE to create columns Year, DoY and Hour
`digits`	(integer) number of digits, i.e. precision, for numeric values

Details

Writes data.frame as comma-separated file after two header rows.

The first header row contains the column names, and the second units.

Spaces in column names are replaced by underscore and % is replaced by the word percent.

Author(s)

get day boundaries

Description

Return the first timestamp at (end_of_first_record_in_day) and the last at midnight

Usage

get_day_boundaries(dt)get_day_boundaries(dt)

Arguments

`dt`	vector of equidistant POSIXt timestamps with several records a day, usually 48

Author(s)

Get the timestep in fractional hours

Description

Get the timestep in fractional hours

Usage

get_timestep_hours(x)
get_timestep_hours(x)

Arguments

`x`	Vector of POSIX timestamps of at least length 2.

Value

Numeric scalar of the time difference of the first two entries in fraction hours.

getAmerifluxToBGC05VariableNameMapping

Description

map Ameriflux variable names to REddyProc defaults to names

Usage

getAmerifluxToBGC05VariableNameMapping(map = character(), 
    mapDefault = c(YEAR = "Year", DOY = "DoY", 
        NEE = "NEE", LE = "LE", H = "H", 
        SW_IN = "Rg", TA = "Tair", TS = "Tsoil", 
        RH = "rH", VPD = "VPD", USTAR = "Ustar", 
        NEE_PI = "NEE_orig", H_PI = "H_orig", 
        LE_PI = "LE_orig", NEE_F = "NEE_f", 
        H_F = "H_f", LE_F = "LE_f", NEE_QC = "NEE_fqc", 
        H_QC = "H_fqc", LE_QC = "LE_fqc"))getAmerifluxToBGC05VariableNameMapping(map = character(), 
    mapDefault = c(YEAR = "Year", DOY = "DoY", 
        NEE = "NEE", LE = "LE", H = "H", 
        SW_IN = "Rg", TA = "Tair", TS = "Tsoil", 
        RH = "rH", VPD = "VPD", USTAR = "Ustar", 
        NEE_PI = "NEE_orig", H_PI = "H_orig", 
        LE_PI = "LE_orig", NEE_F = "NEE_f", 
        H_F = "H_f", LE_F = "LE_f", NEE_QC = "NEE_fqc", 
        H_QC = "H_fqc", LE_QC = "LE_fqc"))

Arguments

`map`	named character vector: additional mapping, that extends or overwrites defaults in `mapDefault`
`mapDefault`	named character vector: default mapping

Details

Get a mapping of variable names of Ameriflux (Berkley 2016 Fluxnet release) to of REddyProc defaults to names

Author(s)

getBGC05ToAmerifluxVariableNameMapping

Description

map REddyProc names the Berkeley 2016 release of the Fluxnet data

Usage

getBGC05ToAmerifluxVariableNameMapping(map = character(), 
    mapDefault = c(Year = "YEAR", DoY = "DOY", 
        Rg = "SW_IN", Tair = "TA", Tsoil = "TS", 
        rH = "RH", VPD = "VPD", Ustar = "USTAR", 
        NEE_orig = "NEE_PI", H_orig = "H_PI", 
        LE_orig = "LE_PI", NEE_f = "NEE_F", 
        H_f = "H_F", LE_f = "LE_F", NEE_fqc = "NEE_QC", 
        H_fqc = "H_QC", LE_fqc = "LE_QC"))getBGC05ToAmerifluxVariableNameMapping(map = character(), 
    mapDefault = c(Year = "YEAR", DoY = "DOY", 
        Rg = "SW_IN", Tair = "TA", Tsoil = "TS", 
        rH = "RH", VPD = "VPD", Ustar = "USTAR", 
        NEE_orig = "NEE_PI", H_orig = "H_PI", 
        LE_orig = "LE_PI", NEE_f = "NEE_F", 
        H_f = "H_F", LE_f = "LE_F", NEE_fqc = "NEE_QC", 
        H_fqc = "H_QC", LE_fqc = "LE_QC"))

Arguments

`map`	named character vector: additional mapping, that extends or overwrites defaults in `mapDefault`
`mapDefault`	named character vector: default mapping

Details

Get a mapping of variable names of REddyProc defaults to names of the Berkeley 2016 release of the Fluxnet data

Author(s)

Examples

# adding mapping of foo, and overwriting mapping of DoY
getBGC05ToAmerifluxVariableNameMapping(c(foo = "FOO", DoY = "doy"))
# adding mapping of foo, and overwriting mapping of DoY
getBGC05ToAmerifluxVariableNameMapping(c(foo = "FOO", DoY = "doy"))

getExamplePath

Description

checks if example filename is existing and if not tries to download it.

Usage

getExamplePath(filename = "Example_DETha98.txt", 
    isTryDownload = FALSE, exampleDir = getREddyProcExampleDir(), 
    remoteDir = "")getExamplePath(filename = "Example_DETha98.txt", 
    isTryDownload = FALSE, exampleDir = getREddyProcExampleDir(), 
    remoteDir = "")

Arguments

`filename`	the name of the example file
`isTryDownload`	scalar logical whether to try downloading the file to package or tmp directory. Because of CRAN checks, need to explicitly set to TRUE
`exampleDir`	directory where examples are looked up and downloaded to
`remoteDir`	the URL do download from

Details

Example input text data files are not distributed with the package, because it exceeds allowed package size. Rather, the example files will be downloaded when required from github by this function.

The remoteDir (github) must be reachable, and the writing directory must be writeable.

Value

the full path name to the example data or if not available an zero-length character. Allows to check for if (length(getExamplePath()) ) ...

Author(s)

getFilledExampleDETha98Data

Description

Get or create the gapfilled version of the Example_DETha98 example data

Usage

getFilledExampleDETha98Data(exampleDir = getREddyProcExampleDir())getFilledExampleDETha98Data(exampleDir = getREddyProcExampleDir())

Arguments

exampleDir

the directory where the cached filled example data is stored

Value

example data.frame Example_DETha98 processed by gapfilling.

Author(s)

getREddyProcExampleDir

Description

get the example directory inside temporary directory

Usage

getREddyProcExampleDir(isPreferParentDir = identical(Sys.getenv("NOT_CRAN"), 
    "true"), subDir = "REddyProcExamples")getREddyProcExampleDir(isPreferParentDir = identical(Sys.getenv("NOT_CRAN"), 
    "true"), subDir = "REddyProcExamples")

Arguments

`isPreferParentDir`	logical scalar, whether to prefer temp parent directory instead of the R-session temp-Directory. See details.
`subDir`	the name of the subdirectory inside the tmp directory, where examples are stored

Details

If isPreferParentDir = FALSE (the default), the examples will be downloaded again for each new R-session in a session specific directory as given by tempdir. This corresponds to CRAN policy. IF TRUE, the parent of tempdir will be used, so that downloads of examples are preserved across R-sessions. This is the default if environment variable "NOT_CRAN" is defined, when running from testthat::check.

Author(s)

getTZone

Description

extracts the timezone attribute from POSIXct with default on missing

Usage

getTZone(x, default = "GMT")getTZone(x, default = "GMT")

Arguments

`x`	POSIXct vector
`default`	time zone returned, if x has not timezone associated or attribute is the zero string

Author(s)

Examples

getTZone(as.POSIXct("2010-07-01 16:00:00", tz = "etc/GMT-1") )
getTZone(as.POSIXct("2010-07-01 16:00:00") )
# printed with local time zone, but actually has no tz attribute
getTZone(Sys.time())
getTZone(as.POSIXct("2010-07-01 16:00:00", tz = "etc/GMT-1") )
getTZone(as.POSIXct("2010-07-01 16:00:00") )
# printed with local time zone, but actually has no tz attribute
getTZone(Sys.time())

globalDummyVars

Description

Dummy global variables with the same name as fields in R5 classes have been defined.

Reason: Class methods have been defined as plain functions, so that they can be better documented. However, the assignment operator <<- has no meaning in it and therefore R CMD check complains. As a workaround they have been defined as global variable. Do not use them.

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

help DateTimes

Description

Overview of functions helping with Timestamps and Dates

Usage

help_DateTimes()help_DateTimes()

Details

Functions helping with preparing and subsetting timestamps:

Convert different time formats to POSIX: fConvertTimeToPosix
Convert JulianDate format used in Berkeley release to POSIXct: BerkeleyJulianDateToPOSIXct
Return the first timestamp at (end_of_first_record_in_day) and the last at midnight: get_day_boundaries
Omit records before the start of the first full day and the end of the last full day: filter_entire_days
Subset data.frame to given years respecting the end-of-period convention: filter_years_eop

Back to REddyProc-package.

Author(s)

help export

Description

Overview of functions helping with exporting Data and Results

Usage

help_export()help_export()

Details

Functions helping with exporting data

Export Input data from REddyProc class: sEddyProc_sExportData
Export Computed results from REddyProc class: sEddyProc_sExportResults
Write data frame to ASCII tab-separated text file: fWriteDataframeToFile
Write data frame to ASCII comma-separated text file with units in header row: fWriteFrench23

Writing a file that can be supplied to the REddyProc webservice at MPI-BGC Jena can be done by exporting data from REddyProc class EProc.

df <- EProc$sExportData()
fWriteDataframeToFile(df, "myfilename.txt", isSplitDatetime = TRUE)

For preparing minimal working examples also consider

Omit records before the start of the first full day and the end of the last full day: df <- filter_entire_days(df)
Subset data.frame to one or two years: df <- filter_years_eop(df, c(1998))

There are several functions that import from file of different formats.

Load text file with one header and one unit row into data frame: fLoadTXTIntoDataframe
Reads sequence of annual files in the format of Europe-fluxdata 2016: fLoadEuroFlux16
Read basic variables from Ameriflux standard (as of 2022) files: fLoadAmeriflux22
Read NetCDF files -> moved to separate package REddyProcNCDF (https://github.com/bgctw/REddyProcNCDF)

Back to REddyProc-package.

Author(s)

LightResponseCurveFitter

Description

Constructs an instance of class LightResponseCurveFitter-class. However, better construct specialized descendants.

Usage

LightResponseCurveFitter(...)LightResponseCurveFitter(...)

Arguments

...

not used

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

LightResponseCurveFitter computeCost

Description

Computing residual sum of squares for predictions vs. data of NEE

Usage

LightResponseCurveFitter_computeCost(thetaOpt, 
    theta, iOpt, flux, sdFlux, parameterPrior, 
    sdParameterPrior, ...)LightResponseCurveFitter_computeCost(thetaOpt, 
    theta, iOpt, flux, sdFlux, parameterPrior, 
    sdParameterPrior, ...)

Arguments

`thetaOpt`	parameter vector with components of theta0 that are optimized
`theta`	parameter vector with positions as in argument of `LightResponseCurveFitter_getParameterNames`
`iOpt`	position in theta that are optimized
`flux`	numeric: NEP (-NEE) or GPP time series [umolCO2 / m2 / s], should not contain NA
`sdFlux`	numeric: standard deviation of Flux [umolCO2 / m2 / s], should not contain NA
`parameterPrior`	numeric vector along theta: prior estimate of parameter (range of values)
`sdParameterPrior`	standard deviation of parameterPrior
`...`	other arguments to `LightResponseCurveFitter_predictLRC`, such as VPD0, fixVPD

Author(s)

LightResponseCurveFitter computeLRCGradient

Description

Gradient of LightResponseCurveFitter_predictLRC

Usage

LightResponseCurveFitter_computeLRCGradient(theta, 
    Rg, VPD, Temp, VPD0 = 10, fixVPD = (k == 
        0), TRef = 15)LightResponseCurveFitter_computeLRCGradient(theta, 
    Rg, VPD, Temp, VPD0 = 10, fixVPD = (k == 
        0), TRef = 15)

Arguments

`theta`	theta [numeric] -> parameter vector (theta[1] = k (k), theta[2] = beta (beta), theta[3] = alpha, theta[4] = RRef (rb), theta[4] = E0)
`Rg`	ppfd [numeric] -> photosynthetic flux density [umol / m2 / s] or Global Radiation
`VPD`	VPD [numeric] -> Vapor Pressure Deficit [hPa]
`Temp`	Temp [degC] -> Temperature [degC]
`VPD0`	VPD0 [hPa] -> Parameters VPD0 fixed to 10 hPa according to Lasslop et al 2010
`fixVPD`	boolean scalar or vector of nrow(theta): fixVPD if TRUE the VPD effect is not considered and VPD is not part of the computation
`TRef`	numeric scalar of Temperature (degree Celsius) for reference respiration RRef

Author(s)

LightResponseCurveFitter fitLRC

Description

Optimize rectangular hyperbolic light response curve in one window

Usage

LightResponseCurveFitter_fitLRC(dsDay, E0, 
    sdE0, RRefNight, controlGLPart = partGLControl(), 
    lastGoodParameters = rep(NA_real_, 7L))LightResponseCurveFitter_fitLRC(dsDay, E0, 
    sdE0, RRefNight, controlGLPart = partGLControl(), 
    lastGoodParameters = rep(NA_real_, 7L))

Arguments

`dsDay`	data.frame with columns NEE, Rg, Temp_C, VPD, and no NAs in NEE
`E0`	temperature sensitivity of respiration
`sdE0`	standard deviation of E_0.n
`RRefNight`	basal respiration estimated from night time data
`controlGLPart`	further default parameters (see `partGLControl`)
`lastGoodParameters`	numeric vector returned by last reasonable fit

Details

Optimization is performed for three initial parameter sets that differ by beta0 (* 1.3, * 0.8). From those three, the optimization result is selected that yielded the lowest misfit. Starting values are: k = 0, beta = interpercentileRange(0.03, 0.97) of respiration, alpha = 0.1, R_ref from nightTime estimate. E0 is fixed to the night-time estimate, but varies for estimating parameter uncertainty.

If controlGLPart$nBootUncertainty == 0L then the covariance matrix of the parameters is estimated by the Hessian of the LRC curve at optimum. Then, the additional uncertainty and covariance with uncertainty E0 is neglected.

If controlGLPart.l$nBootUncertainty > 0L then the covariance matrix of the parameters is estimated by a bootstrap of the data. In each draw, E0 is drawn from N ~ (E_0, sdE_0).

If there are no estimates for more than 20% of the bootstrapped samples The an NA-result with convergence code 1001L is returned.

Value

a list, If none of the optimizations from different starting conditions converged, the parameters are NA.

`thetaOpt`	numeric vector of optimized parameters including the fixed ones and E0
`iOpt`	index of parameters that have been optimized, here including E0, which has been optimized prior to this function.
`thetaInitialGuess`	the initial guess from data
`covParms`	numeric matrix of the covariance matrix of parameters, including E0
`convergence`	integer code specifying convergence problems: \ 0: good convergence \ , 1-1000: see `optim` \ , 1001: too few bootstraps converged \ , 1002: fitted parameters were outside reasonable bounds \ , 1003: too few valid records in window \ , 1004: near zero covariance in bootstrap indicating bad fit \ , 1005: covariance from curvature of fit yielded negative variances indicating bad fit \ , 1006: prediction of highest PAR in window was far from saturation indicating insufficient data to constrain LRC \ , 1010: no temperature-respiration relationship found \ , 1011: too few valid records in window (from different location: partGLFitLRCOneWindow) \

Author(s)

TW, MM Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

LightResponseCurveFitter getOptimizedParameterPositions

Description

get the positions of the parameters to optimize for given Fixed

Usage

LightResponseCurveFitter_getOptimizedParameterPositions(isUsingFixedVPD, 
    isUsingFixedAlpha)LightResponseCurveFitter_getOptimizedParameterPositions(isUsingFixedVPD, 
    isUsingFixedAlpha)

Arguments

`isUsingFixedVPD`	boolean scalar: if TRUE, VPD effect set to zero and is not optimized
`isUsingFixedAlpha`	boolean scalar: if TRUE, initial slope is fixed and is not optimized

Details

If subclasses extend the parameter vector, they need to override this method.

Value

integer vector of positions in parameter vector

Author(s)

LightResponseCurveFitter getParameterInitials

Description

return the prior distribution of parameters

Usage

LightResponseCurveFitter_getParameterInitials(thetaPrior)LightResponseCurveFitter_getParameterInitials(thetaPrior)

Arguments

thetaPrior

numeric vector prior estimate of parameters

Value

a numeric matrix (3, nPar) of initial values for fitting parameters

Author(s)

LightResponseCurveFitter getParameterNames

Description

return the parameter names used by this Light Response Curve Function

Usage

LightResponseCurveFitter_getParameterNames()LightResponseCurveFitter_getParameterNames()

Value

string vector of parameter names. Positions are important.

`k`	VPD effect
`beta`	saturation of GPP at high radiation
`alpha`	initial slope
`RRef`	basal respiration (units of provided NEE, usually mumol CO2 m-^-2 s^-2)
`E0`	temperature sensitivity estimated from night-time data (K)

Author(s)

LightResponseCurveFitter getPriorLocation

Description

return the prior distribution of parameters

Usage

LightResponseCurveFitter_getPriorLocation(NEEDay, 
    RRefNight, E0)LightResponseCurveFitter_getPriorLocation(NEEDay, 
    RRefNight, E0)

Arguments

`NEEDay`	numeric vector of daytime NEE
`RRefNight`	numeric scalar of basal respiration estimated from night-time data
`E0`	numeric scalar of night-time estimate of temperature sensitivity

Value

a numeric vector with prior estimates of the parameters

Author(s)

LightResponseCurveFitter getPriorScale

Description

return the prior distribution of parameters

Usage

LightResponseCurveFitter_getPriorScale(thetaPrior, 
    medianRelFluxUncertainty, nRec, ctrl)LightResponseCurveFitter_getPriorScale(thetaPrior, 
    medianRelFluxUncertainty, nRec, ctrl)

Arguments

`thetaPrior`	numeric vector of location of priors
`medianRelFluxUncertainty`	numeric scalar: median across the relative uncertainty of the flux values, i.e. sdNEE / NEE
`nRec`	integer scalar: number of finite observations
`ctrl`	list of further controls, with entry `isLasslopPriorsApplied`

Details

The beta parameter is quite well defined. Hence use a prior with a standard deviation. The specific results are sometimes a bit sensitive to the uncertainty of the beta prior. This uncertainty is set corresponding to 20 times the median relative flux uncertainty. The prior is weighted n times the observations in the cost. Hence, overall it is using a weight of 1 / 20 of the weight of all observations.

However, its not well defined if PAR does not reach saturation. Need to check before applying this prior

Value

a numeric vector with prior estimates of the parameters

Author(s)

LightResponseCurveFitter isParameterInBounds

Description

Check if estimated parameter vector is within reasonable bounds

Usage

LightResponseCurveFitter_isParameterInBounds(theta, 
    sdTheta, RRefNight, ctrl)LightResponseCurveFitter_isParameterInBounds(theta, 
    sdTheta, RRefNight, ctrl)

Arguments

`theta`	estimate of parameter
`sdTheta`	estimate of uncertainty of the parameter
`RRefNight`	numeric scalar: night-time based estimate of basal respiration
`ctrl`	list of further controls

Details

check the Beta bounds that depend on uncertainty: outside if (beta > 100 and sdBeta >= beta)

Value

FALSE if parameters are outside reasonable bounds, TRUE otherwise

Author(s)

LightResponseCurveFitter optimLRC

Description

call the optimization function

Usage

LightResponseCurveFitter_optimLRC(theta, 
    iOpt, sdParameterPrior, ..., ctrl, isUsingHessian)LightResponseCurveFitter_optimLRC(theta, 
    iOpt, sdParameterPrior, ..., ctrl, isUsingHessian)

Arguments

`theta`	numeric vector: starting parameters
`iOpt`	integer vector: positions of parameters to optimize
`sdParameterPrior`	numeric vector: prior uncertainty
`...`	further arguments to the cost function
`ctrl`	list of further controls
`isUsingHessian`	scalar boolean: set to TRUE to compute Hessian at optimum

Value

list of result of optim amended with list

`theta`	numeric vector: optimized parameter vector including the fixed components
`iOpt`	integer vector: position of parameters that have been optimized

Author(s)

LightResponseCurveFitter optimLRCBounds

Description

Optimize parameters with refitting with some fixed parameters if outside bounds

Usage

LightResponseCurveFitter_optimLRCBounds(theta0, 
    parameterPrior, ..., dsDay, lastGoodParameters, 
    ctrl)LightResponseCurveFitter_optimLRCBounds(theta0, 
    parameterPrior, ..., dsDay, lastGoodParameters, 
    ctrl)

Arguments

`theta0`	initial parameter estimate
`parameterPrior`	prior estimate of model parameters
`...`	further parameters to `.optimLRC`,
`dsDay`	argument to `.optimLRC`, here checked for occurrence of high VPD
`lastGoodParameters`	parameters vector of last successful fit
`ctrl`	list of further controls, such as `isNeglectVPDEffect = TRUE`

Details

If parameters alpha or k are outside bounds (Table A1 in Lasslop 2010), refit with some parameters fixed to values from fit of previous window.

No parameters are reported if alpha<0 or RRef < 0 or beta0 < 0 or beta0 > 250

Not parameters are reported if the data did not contain records that are near light saturation. This is checked by comparing the prediction at highest PAR with the beta parameter

Value

list result of optimization as of LightResponseCurveFitter_optimLRCOnAdjustedPrior with entries

`theta`	numeric parameter vector that includes the fixed components
`iOpt`	integer vector of indices of the vector that have been optimized
`convergence`	scalar integer indicating bad conditions on fitting (see `LightResponseCurveFitter_fitLRC`)

Author(s)

LightResponseCurveFitter optimLRCOnAdjustedPrior

Description

Lower bound flux uncertainty and adjust prior uncertainty before calling optimLRC

Usage

LightResponseCurveFitter_optimLRCOnAdjustedPrior(theta, 
    iOpt, dsDay, parameterPrior, ctrl, ...)LightResponseCurveFitter_optimLRCOnAdjustedPrior(theta, 
    iOpt, dsDay, parameterPrior, ctrl, ...)

Arguments

`theta`	numeric vector of starting values
`iOpt`	integer vector: positions of subset of parameters that are optimized
`dsDay`	dataframe of NEE, sdNEE and predictors Rg, VPD and Temp
`parameterPrior`	numeric vector of prior parameter estimates (corresponding to theta) # TODO rename to thetaPrior
`ctrl`	list of further controls
`...`	further arguments to `LightResponseCurveFitter_optimLRC` (passed to `LightResponseCurveFitter_computeCost`)

Details

Only those records are used for optimization where both NEE and sdNEE are finite. In larger settings, already filtered at

Optimization of LRC parameters takes into account the uncertainty of the flux values. In order to avoid very strong leverage, values with a very low uncertainty (< a lower quantile) are assigned the lower quantile is assigned. This procedure downweighs records with a high uncertainty, but does not apply a large leverage for records with a very low uncertainty. Avoid this correction by setting ctrl$isBoundLowerNEEUncertainty = FALSE

The uncertainty of the prior, that maybe derived from fluxes) is allowed to adapt to the uncertainty of the fluxes. This is done in link{LightResponseCurveFitter_getPriorScale}

Value

result of LightResponseCurveFitter_optimLRC with items theta, iOpt and convergence

Author(s)

LightResponseCurveFitter predictGPP

Description

Light Response function for GPP

Usage

LightResponseCurveFitter_predictGPP(Rg, ...)LightResponseCurveFitter_predictGPP(Rg, ...)

Arguments

`Rg`	ppfd [numeric] -> photosynthetic flux density [mumol / m2 / s] or Global Radiation
`...`	further parameters to the LRC

Details

This method must be be implemented by a specific subclass. Currently there are several alternatives:

Rectangular: RectangularLRCFitter_predictGPP
Nonrectangular: NonrectangularLRCFitter_predictGPP
Rectangular: LogisticSigmoidLRCFitter_predictGPP

Value

numeric vector of length(Rg) of GPP

Author(s)

LightResponseCurveFitter predictLRC

Description

Light Response Function

Usage

LightResponseCurveFitter_predictLRC(theta, 
    Rg, VPD, Temp, VPD0 = 10, fixVPD = (k == 
        0), TRef = 15)LightResponseCurveFitter_predictLRC(theta, 
    Rg, VPD, Temp, VPD0 = 10, fixVPD = (k == 
        0), TRef = 15)

Arguments

`theta`	numeric vector of parameters
`Rg`	ppfd [numeric] -> photosynthetic flux density [umol / m2 / s] or Global Radiation
`VPD`	VPD [numeric] -> Vapor Pressure Deficit [hPa]
`Temp`	Temp [degC] -> Temperature [degC]
`VPD0`	VPD0 [hPa] -> Parameters VPD0 fixed to 10 hPa according to Lasslop et al 2010
`fixVPD`	boolean scalar or vector of nrow theta: fixVPD if TRUE the VPD effect is not considered and VPD is not part of the computation
`TRef`	numeric scalar of Temperature (degree Celsius) for reference respiration RRef

Details

Predict ecosystem fluxes (Reco, GPP, NEP = GPP-Reco) for given parameters and environmental conditions.

The VPD effect is included according to Lasslop et al., 2010.

If theta is a matrix, a different row of parameters is used for different entries of other inputs

Author(s)

Class `"LightResponseCurveFitter"`

Description

Base class for fitting parameters to light response curves (LRC)

Concrete classes for the following LRC functions are available:

common rectangular hyperbolic light-response: RectangularLRCFitter-class
nonrectangular hyperbolic light-response: NonrectangularLRCFitter-class
logistic sigmoid light-response: LogisticSigmoidLRCFitter-class

They mostly differ in their prediction of GPP by method LightResponseCurveFitter_predictGPP.

Extends

All reference classes extend and inherit methods from "envRefClass".

Methods

LightResponseCurveFitter_computeLRCGradient(theta, Rg, VPD, Temp, VPD0, fixVPD, TRef):
LightResponseCurveFitter_predictGPP(Rg, ...):
LightResponseCurveFitter_predictLRC(theta, Rg, VPD, Temp, VPD0, fixVPD, TRef):
LightResponseCurveFitter_computeCost(thetaOpt, theta, iOpt, flux, sdFlux, parameterPrior, sdParameterPrior, ...):
LightResponseCurveFitter_optimLRC(theta, iOpt, sdParameterPrior, ..., ctrl, isUsingHessian):
LightResponseCurveFitter_isParameterInBounds(theta, sdTheta, RRefNight, ctrl):
LightResponseCurveFitter_optimLRCOnAdjustedPrior(theta, iOpt, dsDay, parameterPrior, ctrl, ...):
LightResponseCurveFitter_getOptimizedParameterPositions(isUsingFixedVPD, isUsingFixedAlpha):
LightResponseCurveFitter_optimLRCBounds(theta0, parameterPrior, ..., lastGoodParameters, ctrl):
LightResponseCurveFitter_getParameterInitials(thetaPrior):
LightResponseCurveFitter_getPriorScale(thetaPrior, medianRelFluxUncertainty, nRec, ctrl):
LightResponseCurveFitter_getPriorLocation(NEEDay, RRefNight, E0):
LightResponseCurveFitter_fitLRC(dsDay, E0, sdE0, RRefNight, controlGLPart, lastGoodParameters):
LightResponseCurveFitter_getParameterNames():

Author(s)

LogisticSigmoidLRCFitter

Description

Constructs an instance of class LogisticSigmoidLRCFitter-class

Usage

LogisticSigmoidLRCFitter(...)LogisticSigmoidLRCFitter(...)

Arguments

...

not used

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

LogisticSigmoidLRCFitter predictGPP

Description

Logistic Sigmoid Light Response function for GPP

Usage

LogisticSigmoidLRCFitter_predictGPP(Rg, Amax, 
    alpha)LogisticSigmoidLRCFitter_predictGPP(Rg, Amax, 
    alpha)

Arguments

`Rg`	ppfd [numeric] -> photosynthetic flux density [mumol / m2 / s] or Global Radiation
`Amax`	vector of length(Rg): saturation (beta parameter) adjusted for effect of VPD for each line of Rg
`alpha`	numeric scalar or vector of length(Rg): alpha parameter: slope at Rg = 0

Details

GPP <- Amax * tanh(alpha * Rg / Amax)

Value

numeric vector of length(Rg) of GPP

Author(s)

Class `"LogisticSigmoidLRCFitter"`

Description

Logistic sigmoid light-response curve fitting.

Extends

Class "LightResponseCurveFitter", directly.

All reference classes extend and inherit methods from "envRefClass".

Methods

computeGPPGradient(Rg, Amax, alpha):: ~~
predictGPP(Rg, Amax, alpha):: ~~

The following methods are inherited (from the corresponding class): predictGPP ("LightResponseCurveFitter"), getParameterNames ("LightResponseCurveFitter"), fitLRC ("LightResponseCurveFitter"), getPriorLocation ("LightResponseCurveFitter"), getPriorScale ("LightResponseCurveFitter"), getParameterInitials ("LightResponseCurveFitter"), optimLRCBounds ("LightResponseCurveFitter"), getOptimizedParameterPositions ("LightResponseCurveFitter"), optimLRCOnAdjustedPrior ("LightResponseCurveFitter"), isParameterInBounds ("LightResponseCurveFitter"), optimLRC ("LightResponseCurveFitter"), computeCost ("LightResponseCurveFitter"), predictLRC ("LightResponseCurveFitter"), computeLRCGradient ("LightResponseCurveFitter")

NonrectangularLRCFitter

Description

Constructs an instance of class NonrectangularLRCFitter-class

Usage

NonrectangularLRCFitter(...)NonrectangularLRCFitter(...)

Arguments

...

not used.

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

NonrectangularLRCFitter getParameterNames

Description

return the parameter names used by this Light Response Curve Function

Usage

NonrectangularLRCFitter_getParameterNames()NonrectangularLRCFitter_getParameterNames()

Value

string vector of parameter names. Positions are important. Adds sixth parameter, logitconv to the parameters of LightResponseCurveFitter_getParameterNames

logitconf

logit-transformed convexity parameter. The value at original scale is obtained by conv = 1 / (1 + exp(-logitconv))

Author(s)

NonrectangularLRCFitter predictGPP

Description

Nonrectangular hyperbolic Light Response function for GPP

Usage

NonrectangularLRCFitter_predictGPP(Rg, Amax, 
    alpha, conv)NonrectangularLRCFitter_predictGPP(Rg, Amax, 
    alpha, conv)

Arguments

`Rg`	ppfd [numeric] -> photosynthetic flux density [mumol / m2 / s] or Global Radiation
`Amax`	numeric scalar or vector of length(Rg): beta parameter adjusted for VPD effect
`alpha`	numeric scalar or vector of length(Rg): alpha parameter: initial slope
`conv`	numeric scalar or vector of length(Rg): convexity parameter (see details)

Details

This function generalizes the RectangularLRCFitter_predictGPP by adding the convexity parameter conv. For conv -> 0 (logitconv -> -Inf): approaches the rectangular hyperbolic. For conv -> 1 (logitconv -> + Inf): approaches a step function. Expected values of conv are about 0.7-0.9 (Moffat 2012).

Value

numeric vector of length(Rg) of GPP

Author(s)

Class `"NonrectangularLRCFitter"`

Description

Nonrectangular hyperbolic light-response curve fitting.

Extends

Class "LightResponseCurveFitter", directly.

All reference classes extend and inherit methods from "envRefClass".

Methods

computeGPPGradient(Rg, Amax, alpha, logitconv):: ~~
getParameterNames():: ~~
getPriorLocation(NEEDay, RRefNight, E0):: ~~
getPriorScale(thetaPrior, medianRelFluxUncertainty, nRec, ctrl):: ~~
getOptimizedParameterPositions(isUsingFixedVPD, isUsingFixedAlpha):: ~~
predictLRC(theta, Rg, VPD, Temp, VPD0, fixVPD, TRef):: ~~
predictGPP(Rg, Amax, alpha, conv):: ~~
computeLRCGradient(theta, Rg, VPD, Temp, VPD0, fixVPD, TRef):: ~~

The following methods are inherited (from the corresponding class): computeLRCGradient ("LightResponseCurveFitter"), predictGPP ("LightResponseCurveFitter"), predictLRC ("LightResponseCurveFitter"), getOptimizedParameterPositions ("LightResponseCurveFitter"), getPriorScale ("LightResponseCurveFitter"), getPriorLocation ("LightResponseCurveFitter"), getParameterNames ("LightResponseCurveFitter"), fitLRC ("LightResponseCurveFitter"), getParameterInitials ("LightResponseCurveFitter"), optimLRCBounds ("LightResponseCurveFitter"), optimLRCOnAdjustedPrior ("LightResponseCurveFitter"), isParameterInBounds ("LightResponseCurveFitter"), optimLRC ("LightResponseCurveFitter"), computeCost ("LightResponseCurveFitter")

partGLControl

Description

Default list of parameters for Lasslop 2010 daytime flux partitioning For highest compatibility to the pvWave code of G.Lasslop (used by first BGC-online tool) see function partGLControlLasslopCompatible.

Usage

partGLControl(LRCFitConvergenceTolerance = 0.001, 
    nLRCFitConvergenceTolerance = 0.001, 
    nBootUncertainty = 30L, minNRecInDayWindow = 10L, 
    isAssociateParmsToMeanOfValids = TRUE, 
    isLasslopPriorsApplied = TRUE, isUsingLasslopQualityConstraints = FALSE, 
    isSdPredComputed = TRUE, isFilterMeteoQualityFlag = FALSE, 
    isBoundLowerNEEUncertainty = TRUE, fixedTRefAtNightTime = NA, 
    isExtendTRefWindow = TRUE, smoothTempSensEstimateAcrossTime = TRUE, 
    isNeglectPotRadForNight = FALSE, NRHRfunction = FALSE, 
    isNeglectVPDEffect = FALSE, isRefitMissingVPDWithNeglectVPDEffect = TRUE, 
    fixedTempSens = data.frame(E0 = NA_real_, 
        sdE0 = NA_real_, RRef = NA_real_), 
    replaceMissingSdNEEParms = c(perc = 0.2, 
        minSd = 0.7), neglectNEEUncertaintyOnMissing = FALSE, 
    minPropSaturation = NA, useNightimeBasalRespiration = FALSE)partGLControl(LRCFitConvergenceTolerance = 0.001, 
    nLRCFitConvergenceTolerance = 0.001, 
    nBootUncertainty = 30L, minNRecInDayWindow = 10L, 
    isAssociateParmsToMeanOfValids = TRUE, 
    isLasslopPriorsApplied = TRUE, isUsingLasslopQualityConstraints = FALSE, 
    isSdPredComputed = TRUE, isFilterMeteoQualityFlag = FALSE, 
    isBoundLowerNEEUncertainty = TRUE, fixedTRefAtNightTime = NA, 
    isExtendTRefWindow = TRUE, smoothTempSensEstimateAcrossTime = TRUE, 
    isNeglectPotRadForNight = FALSE, NRHRfunction = FALSE, 
    isNeglectVPDEffect = FALSE, isRefitMissingVPDWithNeglectVPDEffect = TRUE, 
    fixedTempSens = data.frame(E0 = NA_real_, 
        sdE0 = NA_real_, RRef = NA_real_), 
    replaceMissingSdNEEParms = c(perc = 0.2, 
        minSd = 0.7), neglectNEEUncertaintyOnMissing = FALSE, 
    minPropSaturation = NA, useNightimeBasalRespiration = FALSE)

Arguments

`LRCFitConvergenceTolerance`	convergence criterion for rectangular light response curve fit. If relative improvement of reducing residual sum of squares between predictions and observations is less than this criterion, assume convergence. Decrease to get more precise parameter estimates, Increase for speedup.
`nLRCFitConvergenceTolerance`	convergence criterion for nonrectangular light response curve fit. Here its a factor of machine tolerance.
`nBootUncertainty`	number of bootstrap samples for estimating uncertainty. Set to zero to derive uncertainty from curvature of a single fit
`minNRecInDayWindow`	Minimum number of data points for regression
`isAssociateParmsToMeanOfValids`	set to FALSE to associate parameters to the first record of the window for interpolation instead of mean across valid records inside a window
`isLasslopPriorsApplied`	set to TRUE to apply strong fixed priors on LRC fitting. Returned parameter estimates claimed valid for some case where not enough data was available
`isUsingLasslopQualityConstraints`	set to TRUE to avoid quality constraints additional to Lasslop 2010
`isSdPredComputed`	set to FALSE to avoid computing standard errors of Reco and GPP for small performance increase
`isFilterMeteoQualityFlag`	set to TRUE to use only records where quality flag of meteo drivers (radiation, temperature, VPD) is zero, i.e. non-gapfilled for parameter estimation. For prediction, the gap-filled value is used always, to produce predictions also for gaps.
`isBoundLowerNEEUncertainty`	set to FALSE to avoid adjustment of very low uncertainties before day-Time fitting that avoids the high leverage those records with unreasonable low uncertainty.
`fixedTRefAtNightTime`	if a finite value (degree Centigrade) is given, it is used instead of median data temperature as reference temperature in estimation of temperature sensitivity from night data
`isExtendTRefWindow`	set to FALSE to avoid successively extending the night-time window in order to estimate a temperature sensitivity where previous estimates failed
`smoothTempSensEstimateAcrossTime`	set to FALSE to use independent estimates of temperature sensitivity on each windows instead of a vector of E0 that is smoothed over time
`isNeglectPotRadForNight`	set to TRUE to not use potential radiation in determining night-time data.
`NRHRfunction`	deprecated: Flag if TRUE use the NRHRF for partitioning; Now use `lrcFitter = NonrectangularLRCFitter()`
`isNeglectVPDEffect`	set to TRUE to avoid using VPD in the computations. This may help when VPD is rarely measured.
`isRefitMissingVPDWithNeglectVPDEffect`	set to FALSE to avoid repeating estimation with `isNeglectVPDEffect = TRUE` trying to predict when VPD is missing
`fixedTempSens`	data.frame of one row or nRow = nWindow corresponding to return value of `partGLFitNightTimeTRespSens` While column `RRef` is used only as a prior and initial value for the daytime-fitting and can be NA, `E0` is used as given temperature sensitivity and varied according to `sdE0` in the bootstrap.
`replaceMissingSdNEEParms`	parameters for replacing missing standard deviation of NEE. see `replaceMissingSdByPercentage`. Default sets missing uncertainty to 20% of NEE but at least 0.7 flux-units (usually mumol CO2 / m2 / s). Specify c(NA, NA) to avoid replacing missings in standard deviation of NEE and to omit those records from LRC fit.
`neglectNEEUncertaintyOnMissing`	If set to TRUE: if there are records with missing uncertainty of NEE inside one window, set all uncertainties to 1. This overrules option replaceMissingSdNEEParms.
`minPropSaturation`	quality criterion for sufficient data in window. If GPP prediction of highest PAR of window is less than minPropSaturation * (GPP at light-saturation, i.e. beta) this indicates that PAR is not sufficiently high to constrain the shape of the LRC
`useNightimeBasalRespiration`	set to TRUE to estimate nighttime respiration based on basal respiration estimated on nighttime data instead of basal respiration estimated from daytime data. This implements the modified daytime method from Keenan 2019 (doi:10.1038/s41559-019-0809-2)

Value

list with entries of given arguments.

Author(s)

Examples

partGLControl(nBootUncertainty = 40L)
partGLControl(nBootUncertainty = 40L)

partGLControlLasslopCompatible

Description

Daytime flux partitioning parms compatible with with the pvWave

Usage

partGLControlLasslopCompatible(nBootUncertainty = 0L, 
    minNRecInDayWindow = 10L, isAssociateParmsToMeanOfValids = FALSE, 
    isLasslopPriorsApplied = TRUE, isUsingLasslopQualityConstraints = TRUE, 
    isBoundLowerNEEUncertainty = FALSE, fixedTRefAtNightTime = 15, 
    isExtendTRefWindow = FALSE, smoothTempSensEstimateAcrossTime = FALSE, 
    isRefitMissingVPDWithNeglectVPDEffect = FALSE, 
    minPropSaturation = NA, isNeglectVPDEffect = FALSE, 
    replaceMissingSdNEEParms = c(NA, NA), 
    neglectNEEUncertaintyOnMissing = TRUE, 
    ...)partGLControlLasslopCompatible(nBootUncertainty = 0L, 
    minNRecInDayWindow = 10L, isAssociateParmsToMeanOfValids = FALSE, 
    isLasslopPriorsApplied = TRUE, isUsingLasslopQualityConstraints = TRUE, 
    isBoundLowerNEEUncertainty = FALSE, fixedTRefAtNightTime = 15, 
    isExtendTRefWindow = FALSE, smoothTempSensEstimateAcrossTime = FALSE, 
    isRefitMissingVPDWithNeglectVPDEffect = FALSE, 
    minPropSaturation = NA, isNeglectVPDEffect = FALSE, 
    replaceMissingSdNEEParms = c(NA, NA), 
    neglectNEEUncertaintyOnMissing = TRUE, 
    ...)

Arguments

`nBootUncertainty`	0: Derive uncertainty from curvature of a single fit, neglecting the uncertainty of previously estimated temperature sensitivity, E0
`minNRecInDayWindow`	Minimum number of 10 valid records for regression in a single window
`isAssociateParmsToMeanOfValids`	associate parameters to the first record of the window for interpolation instead of mean across valid records inside a window
`isLasslopPriorsApplied`	Apply fixed Lasslop priors in LRC fitting.
`isUsingLasslopQualityConstraints`	avoid quality constraints additional to the ones in Lasslop 2010
`isBoundLowerNEEUncertainty`	FALSE: avoid adjustment of very low uncertainties before day-Time fitting that avoids the high leverage those records with unreasonable low uncertainty.
`fixedTRefAtNightTime`	use fixed (degree Centigrade) temperature sensitivity instead of median data temperature as reference temperature in estimation of temperature sensitivity from night data
`isExtendTRefWindow`	avoid successively extending the night-time window in order to estimate a temperature sensitivity where previous estimates failed
`smoothTempSensEstimateAcrossTime`	FALSE: use independent estimates of temperature sensitivity on each windows instead of a vector of E0 that is smoothed over time
`isRefitMissingVPDWithNeglectVPDEffect`	FALSE: avoid repeating estimation with `isNeglectVPDEffect = TRUE`
`minPropSaturation`	NA: avoid quality constraint of sufficient saturation in data This option is overruled, i.e. not considered, if option isUsingLasslopQualityConstraints = TRUE.
`isNeglectVPDEffect`	FALSE: do not neglect VPD effect
`replaceMissingSdNEEParms`	do not replace missing NEE, but see option
`neglectNEEUncertaintyOnMissing`	if there are records with missing uncertainty of NEE inside one window, set all sdNEE to 1. This overrules option replaceMissingSdNEEParms.
`...`	further arguments to `partGLControl`

Author(s)

Examples

partGLControlLasslopCompatible()
partGLControlLasslopCompatible()

partGLExtractStandardData

Description

Relevant columns from original input with defined names

Usage

partGLExtractStandardData(ds, NEEVar = paste0("NEE", 
    suffixDash, "_f"), QFNEEVar = if (!missing(QFNEEVar.s)) QFNEEVar.s else paste0("NEE", 
    suffixDash, "_fqc"), QFNEEValue = if (!missing(QFNEEValue.n)) QFNEEValue.n else 0, 
    NEESdVar = if (!missing(NEESdVar.s)) NEESdVar.s else paste0("NEE", 
        suffixDash, "_fsd"), TempVar = paste0("Tair_f"), 
    QFTempVar = if (!missing(QFTempVar.s)) QFTempVar.s else paste0("Tair_fqc"), 
    QFTempValue = if (!missing(QFTempValue.n)) QFTempValue.n else 0, 
    VPDVar = if (!missing(VPDVar.s)) VPDVar.s else paste0("VPD_f"), 
    QFVPDVar = if (!missing(QFVPDVar.s)) QFVPDVar.s else paste0("VPD_fqc"), 
    QFVPDValue = if (!missing(QFVPDValue.n)) QFVPDValue.n else 0, 
    RadVar = if (!missing(RadVar.s)) RadVar.s else "Rg_f", 
    QFRadVar = if (!missing(QFRadVar.s)) QFRadVar.s else paste0("Rg_fqc"), 
    QFRadValue = if (!missing(QFRadValue.n)) QFRadValue.n else 0, 
    PotRadVar = if (!missing(PotRadVar.s)) PotRadVar.s else "PotRad_NEW", 
    suffix = if (!missing(Suffix.s)) Suffix.s else "", 
    NEEVar.s, QFNEEVar.s, QFNEEValue.n, NEESdVar.s, 
    TempVar.s, QFTempVar.s, QFTempValue.n, 
    VPDVar.s, QFVPDVar.s, QFVPDValue.n, RadVar.s, 
    QFRadVar.s, QFRadValue.n, PotRadVar.s, 
    Suffix.s, controlGLPart = partGLControl())partGLExtractStandardData(ds, NEEVar = paste0("NEE", 
    suffixDash, "_f"), QFNEEVar = if (!missing(QFNEEVar.s)) QFNEEVar.s else paste0("NEE", 
    suffixDash, "_fqc"), QFNEEValue = if (!missing(QFNEEValue.n)) QFNEEValue.n else 0, 
    NEESdVar = if (!missing(NEESdVar.s)) NEESdVar.s else paste0("NEE", 
        suffixDash, "_fsd"), TempVar = paste0("Tair_f"), 
    QFTempVar = if (!missing(QFTempVar.s)) QFTempVar.s else paste0("Tair_fqc"), 
    QFTempValue = if (!missing(QFTempValue.n)) QFTempValue.n else 0, 
    VPDVar = if (!missing(VPDVar.s)) VPDVar.s else paste0("VPD_f"), 
    QFVPDVar = if (!missing(QFVPDVar.s)) QFVPDVar.s else paste0("VPD_fqc"), 
    QFVPDValue = if (!missing(QFVPDValue.n)) QFVPDValue.n else 0, 
    RadVar = if (!missing(RadVar.s)) RadVar.s else "Rg_f", 
    QFRadVar = if (!missing(QFRadVar.s)) QFRadVar.s else paste0("Rg_fqc"), 
    QFRadValue = if (!missing(QFRadValue.n)) QFRadValue.n else 0, 
    PotRadVar = if (!missing(PotRadVar.s)) PotRadVar.s else "PotRad_NEW", 
    suffix = if (!missing(Suffix.s)) Suffix.s else "", 
    NEEVar.s, QFNEEVar.s, QFNEEValue.n, NEESdVar.s, 
    TempVar.s, QFTempVar.s, QFTempValue.n, 
    VPDVar.s, QFVPDVar.s, QFVPDValue.n, RadVar.s, 
    QFRadVar.s, QFRadValue.n, PotRadVar.s, 
    Suffix.s, controlGLPart = partGLControl())

Arguments

`ds`	dataset with all the specified input columns and full days in equidistant times
`NEEVar`	Variable of NEE
`QFNEEVar`	Quality flag of variable
`QFNEEValue`	Value of quality flag for _good_ (original) data
`NEESdVar`	Variable of standard deviation of net ecosystem fluxes
`TempVar`	Filled air or soil temperature variable (degC)
`QFTempVar`	Quality flag of filled temperature variable
`QFTempValue`	Value of temperature quality flag for _good_ (original) data
`VPDVar`	Filled Vapor Pressure Deficit, VPD (hPa)
`QFVPDVar`	Quality flag of filled VPD variable
`QFVPDValue`	Value of VPD quality flag for _good_ (original) data
`RadVar`	Filled radiation variable
`QFRadVar`	Quality flag of filled radiation variable
`QFRadValue`	Value of radiation quality flag for _good_ (original) data
`PotRadVar`	Variable name of potential rad. (W / m2)
`suffix`	string inserted into column names before identifier for NEE column defaults (see `sEddyProc_sMDSGapFillAfterUstar`).
`NEEVar.s`	deprecated
`QFNEEVar.s`	deprecated
`QFNEEValue.n`	deprecated
`NEESdVar.s`	deprecated
`TempVar.s`	deprecated
`QFTempVar.s`	deprecated
`QFTempValue.n`	deprecated
`VPDVar.s`	deprecated
`QFVPDVar.s`	deprecated
`QFVPDValue.n`	deprecated
`RadVar.s`	deprecated
`QFRadVar.s`	deprecated
`QFRadValue.n`	deprecated
`PotRadVar.s`	deprecated
`Suffix.s`	deprecated
`controlGLPart`	further default parameters, see `partGLControl`

Details

The LRC fit usually weights NEE records by its uncertainty. In order to also use records with missing NEESdVar, uncertainty of the missing values is by default set to a conservatively high value, parameterized by controlGLPart$replaceMissingSdNEEParms). Controlled by argument replaceMissingSdNEEParms in partGLControl, but overruled by argument neglectNEEUncertaintyOnMissing.

Value

a data.frame with columns

`sDateTime`	first column of `ds`, usually the time stamp not used, but usually first column is a DateTime is kept for aiding debug
`NEE`	NEE filtered for quality flay
`sdNEE`	standard deviation of NEE with missing values replaced
`Temp`	Temperature, quality filtered if isTRUE(controlGLPart$isFilterMeteoQualityFlag)
`VPD`	Water pressure deficit, quality filtered if isTRUE(controlGLPart$isFilterMeteoQualityFlag)
`Rg`	Incoming radiation
`isDay`	Flag that is true for daytime records
`isNight`	Flag that is true for nighttime records

Author(s)

partitionNEEGL

Description

Partition NEE fluxes into GP and Reco using the daytime method.

Usage

partitionNEEGL(ds, NEEVar = if (!missing(NEEVar.s)) NEEVar.s else paste0("NEE", 
    suffixDash, "_f"), TempVar = if (!missing(TempVar.s)) TempVar.s else "Tair_f", 
    VPDVar = if (!missing(VPDVar.s)) VPDVar.s else "VPD_f", 
    RadVar = if (!missing(RadVar.s)) RadVar.s else "Rg_f", 
    suffix = if (!missing(Suffix.s)) Suffix.s else "", 
    NEEVar.s, TempVar.s, VPDVar.s, RadVar.s, 
    Suffix.s, ..., controlGLPart = partGLControl(), 
    isVerbose = TRUE, nRecInDay = 48L, lrcFitter = RectangularLRCFitter())partitionNEEGL(ds, NEEVar = if (!missing(NEEVar.s)) NEEVar.s else paste0("NEE", 
    suffixDash, "_f"), TempVar = if (!missing(TempVar.s)) TempVar.s else "Tair_f", 
    VPDVar = if (!missing(VPDVar.s)) VPDVar.s else "VPD_f", 
    RadVar = if (!missing(RadVar.s)) RadVar.s else "Rg_f", 
    suffix = if (!missing(Suffix.s)) Suffix.s else "", 
    NEEVar.s, TempVar.s, VPDVar.s, RadVar.s, 
    Suffix.s, ..., controlGLPart = partGLControl(), 
    isVerbose = TRUE, nRecInDay = 48L, lrcFitter = RectangularLRCFitter())

Arguments

`ds`	dataset with all the specified input columns and full days in equidistant times
`NEEVar`	Variable of NEE
`TempVar`	Filled air or soil temperature variable (degC)
`VPDVar`	Filled Vapor Pressure Deficit - VPD - (hPa)
`RadVar`	Filled radiation variable
`suffix`	string inserted into column names before identifier for NEE column defaults (see `sEddyProc_sMDSGapFillAfterUstar`).
`NEEVar.s`	deprecated
`TempVar.s`	deprecated
`VPDVar.s`	deprecated
`RadVar.s`	deprecated
`Suffix.s`	deprecated identifier for NEE column defaults (see `sEddyProc_sMDSGapFillAfterUstar`).
`...`	further arguments to `partGLExtractStandardData`, such as `PotRadVar`
`controlGLPart`	further default parameters, see `partGLControl`
`isVerbose`	set to FALSE to suppress output messages
`nRecInDay`	number of records within one day (for half-hourly data its 48)
`lrcFitter`	R5 class instance responsible for fitting the light response curve. Current possibilities are `RectangularLRCFitter()`, `NonrectangularLRCFitter()`, and `LogisticSigmoidLRCFitter()`.

Details

Daytime-based partitioning of measured net ecosystem fluxes into gross primary production (GPP) and ecosystem respiration (Reco)

The fit to the light-response-curve is done by default using the Rectangular hyperbolic function, as in Lasslop et al. (2010) Alternative fittings can be used by providing the corresponding subclass of LightResponseCurveFitter-class to lrcFitter argument. (see LightResponseCurveFitter_predictGPP)

While the extrapolation uses filled data, the parameter optimization may use only measured data, i.e. with specified quality flag. Even with using filled VPD, there may be large gaps that have not been filled. With the common case where VPD is missing for fitting the LRC, by default (with controlGLPart$isRefitMissingVPDWithNeglectVPDEffect = TRUE) is to redo the estimation of LRC parameters with neglecting the VPD-effect. Next, in the predictions (rows) with missing VPD are then replaced with predictions based on LRC-fits that neglected the VPD effect.

Value

`Reco_DT_<suffix>`	predicted ecosystem respiration: mumol CO2/m2/s
`GPP_DT_<suffix>`	predicted gross primary production mumol CO2/m2/s
`<LRC>`	Further light response curve (LRC) parameters and their standard deviation depend on the used LRC (e.g. for the non-rectangular LRC see `NonrectangularLRCFitter_getParameterNames`). They are estimated for windows and are reported with the first record of the window
`FP_VARnight`	NEE filtered for nighttime records (others NA)
`FP_VARday`	NEE filtered for daytime records (others NA)
`NEW_FP_Temp`	temperature after filtering for quality flag degree Celsius
`NEW_FP_VPD`	vapour pressure deficit after filtering for quality flag, hPa
`FP_RRef_Night`	basal respiration estimated from nighttime (W / m2)
`FP_qc`	quality flag: 0: good parameter fit, 1: some parameters out of range, required refit, 2: next parameter estimate is more than two weeks away
`FP_dRecPar`	records until or after closest record that has a parameter estimate associated
`FP_errorcode`	information why LRC-fit was not successful or was rejected, see result of `LightResponseCurveFitter_fitLRC`
`FP_GPP2000`	predicted GPP at VPD = 0 and PAR = 2000: a surrogate for maximum photosynthetic capacity
`FP_OPT_VPD`	list object of fitting results including iOpt and covParms
`FP_OPT_NoVPD`	same as FP_OPT_VPD holding optimization results with fit neglecting the VPD effect

Author(s)

References

Lasslop G, Reichstein M, Papale D, et al. (2010) Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Global Change Biology, Volume 16, Issue 1, Pages 187-208

POSIXctToBerkeleyJulianDate

Description

convert POSIXct to JulianDate format used in Berkeley release

Usage

POSIXctToBerkeleyJulianDate(sDateTime, tz = getTZone(sDateTime))POSIXctToBerkeleyJulianDate(sDateTime, tz = getTZone(sDateTime))

Arguments

`sDateTime`	POSIXct vector
`tz`	time zone attribute, such as "etc/GMT-1"

Details

In the Berkeley-Release of the Fluxnet data, the time is stored as an number with base10-digits representing YYYYMMddhhmm

Author(s)

Extract basic variables from Ameriflux standard (as of 2022) data.frames

Description

NEE is read from FC, Rg from SW_in, VPD is computed from RH and Tair. Non-storage corrected LE and H are read.

Usage

read_from_ameriflux22(df)
read_from_ameriflux22(df)

Arguments

`df`	data.frame: with columns FC, SW_IN, RH, TA, USTAR, L and E

Value

Data.Frame with columns DateTime, NEE, Rg, Tair, rH, VPD, Ustar, LE, H

extract REddyProc input columns from data.frame in Fluxnet15 format

Description

Column format as described at https://fluxnet.org/data/fluxnet2015-dataset/fullset-data-product/

Usage

read_from_fluxnet15(ds, colname_NEE = "NEE")
read_from_fluxnet15(ds, colname_NEE = "NEE")

Arguments

`ds`	data.frame with columns TIMESTAMP_END (Time YYYYMMDDHHMM), NEE, LE, H, USTAR, TA, TS, VPD, SW_IN and optionally USTAR_QC
`colname_NEE`	name (scalar string) of column that reports NEE observations

Details

If input has numeric column USTAR_QC then USTAR of records with USTAR_QC > 2 are set to NA.

Value

data.frame with additional columns 'DateTime', 'NEE','Ustar' and 'Rg','Tair','Tsoil' if columns 'SW_IN','TA', or 'TS' are present respectively

RectangularLRCFitter

Description

Constructs an instance of class RectangularLRCFitter-class

Usage

RectangularLRCFitter(...)RectangularLRCFitter(...)

Arguments

...

not used.

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

RectangularLRCFitter predictGPP

Description

Rectangular hyperbolic Light Response function for GPP

Usage

RectangularLRCFitter_predictGPP(Rg, Amax, 
    alpha)RectangularLRCFitter_predictGPP(Rg, Amax, 
    alpha)

Arguments

`Rg`	ppfd [numeric] -> photosynthetic flux density [mumol / m2 / s] or Global Radiation
`Amax`	vector of length(Rg): saturation (beta parameter) adjusted for effect of VPD for each line of Rg
`alpha`	numeric scalar or vector of length(Rg): alpha parameter: slope at Rg = 0

Value

numeric vector of length(Rg) of GPP

Author(s)

Class `"RectangularLRCFitter"`

Description

Common rectangular hyperbolic light-response curve fitting.

Extends

Class "LightResponseCurveFitter", directly.

All reference classes extend and inherit methods from "envRefClass".

Methods

computeGPPGradient(Rg, Amax, alpha):: ~~
predictGPP(Rg, Amax, alpha):: ~~

Author(s)

RectangularLRCFitterCVersion

Description

Constructs an instance of class RectangularLRCFitterCVersion-class

Usage

RectangularLRCFitterCVersion(...)RectangularLRCFitterCVersion(...)

Arguments

...

not used.

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

Class `"RectangularLRCFitterCVersion"`

Description

Common rectangular hyperbolic light-response curve fitting, implemented with faster C-based cost function.

Extends

Class "RectangularLRCFitter", directly. Class "LightResponseCurveFitter", by class "RectangularLRCFitter", distance 2.

All reference classes extend and inherit methods from "envRefClass".

Methods

computeCost(thetaOpt, theta, iOpt, flux, sdFlux, parameterPrior, sdParameterPrior, ..., VPD0, fixVPD):: ~~

The following methods are inherited (from the corresponding class): computeCost ("LightResponseCurveFitter"), computeLRCGradient ("LightResponseCurveFitter"), predictGPP ("RectangularLRCFitter"), predictLRC ("LightResponseCurveFitter"), optimLRC ("LightResponseCurveFitter"), isParameterInBounds ("LightResponseCurveFitter"), optimLRCOnAdjustedPrior ("LightResponseCurveFitter"), getOptimizedParameterPositions ("LightResponseCurveFitter"), optimLRCBounds ("LightResponseCurveFitter"), getParameterInitials ("LightResponseCurveFitter"), getPriorScale ("LightResponseCurveFitter"), getPriorLocation ("LightResponseCurveFitter"), fitLRC ("LightResponseCurveFitter"), getParameterNames ("LightResponseCurveFitter"), predictGPP ("LightResponseCurveFitter"), computeGPPGradient ("RectangularLRCFitter")

Get the default units for given variables

Description

Get the default units for given variables

Usage

REddyProc_defaultunits(variable_names)
REddyProc_defaultunits(variable_names)

Arguments

variable_names

string vector of variables to query units for

Value

string vector with units, NA for non-standard variables.

renameVariablesInDataframe

Description

Rename the column names of a data.frame according to a given mapping

Usage

renameVariablesInDataframe(data.F, mapping = getBGC05ToAmerifluxVariableNameMapping())renameVariablesInDataframe(data.F, mapping = getBGC05ToAmerifluxVariableNameMapping())

Arguments

`data.F`	data.frame whose columns should be renamed
`mapping`	named character vector: specifying a renaming (name -> value) of the variables, see e.g. `getAmerifluxToBGC05VariableNameMapping`

Author(s)

RHLightResponseCostC

Description

Computing residual sum of squares for predictions vs. data of NEE implemented in C. See LightResponseCurveFitter_computeCost for a description.

Usage

RHLightResponseCostC(theta, flux, sdFlux,
    parameterPrior, sdParameterPrior, Rg,
    VPD, Temp, VPD0, fixVPD)RHLightResponseCostC(theta, flux, sdFlux,
    parameterPrior, sdParameterPrior, Rg,
    VPD, Temp, VPD0, fixVPD)

Arguments

`theta`	parameter vector with positions as in argument of `LightResponseCurveFitter_getParameterNames`
`flux`	numeric: NEP (-NEE) or GPP time series [umolCO2 / m2 / s], should not contain NA
`sdFlux`	numeric: standard deviation of Flux [umolCO2 / m2 / s], should not contain NA
`parameterPrior`	numeric vector along theta: prior estimate of parameter (range of values)
`sdParameterPrior`	standard deviation of parameterPrior
`Rg`	ppfd [numeric] -> photosynthetic flux density [umol / m2 / s] or Global Radiation
`VPD`	VPD [numeric] -> Vapor Pressure Deficit [hPa]
`Temp`	Temp [degC] -> Temperature [degC]
`VPD0`	VPD0 [hPa] -> Parameters VPD0 fixed to 10 hPa
`fixVPD`	boolean scalar or vector of nrow theta: fixVPD if TRUE the VPD effect is not considered and VPD is not part of the computation

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

sEddyProc

Description

create an instance of class sEddyProc-class

Usage

sEddyProc(...)sEddyProc(...)

Arguments

...

not used.

Author(s)

(Department for Biogeochemical Integration at MPI-BGC, Jena, Germany)

sEddyProc initialize

Description

Initializing sEddyProc class during sEddyProc$new.

Usage

sEddyProc_initialize(ID = ID.s, Data = Data.F, 
    ColNames = c("NEE", "Rg", "Tair", "VPD", 
        "Ustar"), ColPOSIXTime = "DateTime", 
    DTS = if (!missing(DTS.n)) DTS.n else 48, 
    ColNamesNonNumeric = character(0), LatDeg = NA_real_, 
    LongDeg = if (!missing(Long_deg.n)) Long_deg.n else NA_real_, 
    TimeZoneHour = if (!missing(TimeZone_h.n)) TimeZone_h.n else NA_integer_, 
    ID.s, Data.F, ColNames.V.s, ColPOSIXTime.s, 
    DTS.n, ColNamesNonNumeric.V.s, Lat_deg.n, 
    Long_deg.n, TimeZone_h.n, ...)sEddyProc_initialize(ID = ID.s, Data = Data.F, 
    ColNames = c("NEE", "Rg", "Tair", "VPD", 
        "Ustar"), ColPOSIXTime = "DateTime", 
    DTS = if (!missing(DTS.n)) DTS.n else 48, 
    ColNamesNonNumeric = character(0), LatDeg = NA_real_, 
    LongDeg = if (!missing(Long_deg.n)) Long_deg.n else NA_real_, 
    TimeZoneHour = if (!missing(TimeZone_h.n)) TimeZone_h.n else NA_integer_, 
    ID.s, Data.F, ColNames.V.s, ColPOSIXTime.s, 
    DTS.n, ColNamesNonNumeric.V.s, Lat_deg.n, 
    Long_deg.n, TimeZone_h.n, ...)

Arguments

`ID`	String with site ID
`Data`	Data frame with at least three month of (half-)hourly site-level eddy data
`ColNames`	Vector with selected column names, the fewer columns the faster the processing. The default specifies column names assumed in further processing.
`ColPOSIXTime`	Column name with POSIX time stamp
`DTS`	Daily time steps
`ColNamesNonNumeric`	Names of columns that should not be checked for numeric type, e.g. season column
`LatDeg`	Latitude in (decimal) degrees (-90 to + 90)
`LongDeg`	Longitude in (decimal) degrees (-180 to + 180)
`TimeZoneHour`	Time zone: hours shift to UTC, e.g. 1 for Berlin
`ID.s`	deprecated
`Data.F`	deprecated
`ColNames.V.s`	deprecated
`ColPOSIXTime.s`	deprecated
`DTS.n`	deprecated
`ColNamesNonNumeric.V.s`	deprecated
`Lat_deg.n`	deprecated
`Long_deg.n`	deprecated
`TimeZone_h.n`	deprecated
`...`	('...' required for initialization of class fields)

Details

The time stamp must be provided in POSIX format, see also fConvertTimeToPosix. For required properties of the time series, see fCheckHHTimeSeries.

Internally the half-hour time stamp is shifted to the middle of the measurement period (minus 15 minutes or 30 minutes).

All other columns may only contain numeric data. Please use NA as a gap flag for missing data or low quality data not to be used in the processing. The columns are also checked for plausibility with warnings if outside range.

There are several fields initialized within the class.

sID is a string for the site ID.

sDATA is a data frame with site data.

sTEMP is a temporal data frame with the processing results.

sINFO is a list containing the time series information:

DIMS: Number of data rows
DTS: Number of daily time steps (24 or 48)
Y.START: Starting year
Y.END: Ending year
Y.NUMS: Number of years
Y.NAME: Name for years

sUSTAR_SCEN a data.frame with first column the season, and other columns different uStar threshold estimates, as returned by usGetAnnualSeasonUStarMap

sLOCATION is a list of information on site location and timezone (see sEddyProc_sSetLocationInfo).

sTEMP is a data frame used only temporally.

Value

Initialized fields of sEddyProc.

Author(s)

sEddyProc sApplyUStarScen

Description

apply a function with changing the suffix argument

Usage

sEddyProc_sApplyUStarScen(FUN, ..., uStarScenKeep = character(0), 
    warnOnOtherErrors = FALSE, uStarSuffixes = .self$sGetUstarSuffixes())sEddyProc_sApplyUStarScen(FUN, ..., uStarScenKeep = character(0), 
    warnOnOtherErrors = FALSE, uStarSuffixes = .self$sGetUstarSuffixes())

Arguments

`FUN`	function to be applied
`...`	further arguments to FUN
`uStarScenKeep`	Scalar string specifying the scenario for which to keep parameters. If not specified defaults to the first entry in `uStarSuffixes`.
`warnOnOtherErrors`	Set to only display a warning on errors in uStarScenarios other than uStarScenKeep instead of stopping.
`uStarSuffixes`	Vector of suffixed << describing the uStar scenarios

Details

When repeating computations, some of the output variables maybe replaced. Argument uStarKeep allows to select the scenario which is computed last, and hence to which output columns refer to.

Author(s)

sEddyProc sCalcPotRadiation

Description

compute potential radiation from position and time

Usage

sEddyProc_sCalcPotRadiation(useSolartime = TRUE, 
    useSolartime.b)sEddyProc_sCalcPotRadiation(useSolartime = TRUE, 
    useSolartime.b)

Arguments

`useSolartime`	by default corrects hour (given in local winter time) for latitude to solar time(where noon is exactly at 12:00). Set this to FALSE to directly use local winter time
`useSolartime.b`	by default corrects hour (given in local winter time)

Value

column PotRad_NEW in sTEMP

Author(s)

sEddyProc sEstimateUstarScenarios

Description

Estimate the distribution of u* threshold by bootstrapping over data

Usage

sEddyProc_sEstimateUstarScenarios(ctrlUstarEst = usControlUstarEst(), 
    ctrlUstarSub = usControlUstarSubsetting(), 
    UstarColName = "Ustar", NEEColName = "NEE", 
    TempColName = "Tair", RgColName = "Rg", 
    ..., seasonFactor = usCreateSeasonFactorMonth(sDATA$sDateTime), 
    nSample = 200L, probs = c(0.05, 0.5, 
        0.95), isVerbose = TRUE, suppressWarningsAfterFirst = TRUE)sEddyProc_sEstimateUstarScenarios(ctrlUstarEst = usControlUstarEst(), 
    ctrlUstarSub = usControlUstarSubsetting(), 
    UstarColName = "Ustar", NEEColName = "NEE", 
    TempColName = "Tair", RgColName = "Rg", 
    ..., seasonFactor = usCreateSeasonFactorMonth(sDATA$sDateTime), 
    nSample = 200L, probs = c(0.05, 0.5, 
        0.95), isVerbose = TRUE, suppressWarningsAfterFirst = TRUE)

Arguments

`ctrlUstarEst`	control parameters for estimating uStar on a single binned series, see `usControlUstarEst`
`ctrlUstarSub`	control parameters for subsetting time series (number of temperature and Ustar classes ...), see `usControlUstarSubsetting`
`UstarColName`	column name for UStar
`NEEColName`	column name for NEE
`TempColName`	column name for air temperature
`RgColName`	column name for solar radiation for omitting night time data
`...`	further arguments to `sEddyProc_sEstUstarThreshold`
`seasonFactor`	factor of seasons to split (data is resampled only within the seasons)
`nSample`	the number of repetitions in the bootstrap
`probs`	the quantiles of the bootstrap sample to return. Default is the 5%, median and 95% of the bootstrap
`isVerbose`	set to FALSE to omit printing progress
`suppressWarningsAfterFirst`	set to FALSE to show also warnings for all bootstrap estimates instead of only the first bootstrap sample

Details

The choice of the criterion for sufficiently turbulent conditions (u * > chosen threshold) introduces large uncertainties in calculations based on gap-filled Eddy data. Hence, it is good practice to compare derived quantities based on gap-filled data using a range of u * threshold estimates.

This method explores the probability density of the threshold by repeating its estimation on a bootstrapped sample. By default it returns the 90% confidence interval (argument probs). For larger intervals the sample number need to be increased (argument probs).

Quality Assurance: If more than ctrlUstarEst$minValidBootProp (default 40%) did not report a threshold, no quantiles (i.e. NA) are reported.

Value

updated class. Request results by sEddyProc_sGetEstimatedUstarThresholdDistribution

Author(s)

sEddyProc$sEstUstarThreshold - Estimating ustar threshold

Description

Calling usEstUstarThreshold for class data and storing results

Usage

sEddyProc_sEstUstarThold(UstarColName = "Ustar", 
    NEEColName = "NEE", TempColName = "Tair", 
    RgColName = "Rg", ..., seasonFactor = usCreateSeasonFactorMonth(sDATA$sDateTime))sEddyProc_sEstUstarThold(UstarColName = "Ustar", 
    NEEColName = "NEE", TempColName = "Tair", 
    RgColName = "Rg", ..., seasonFactor = usCreateSeasonFactorMonth(sDATA$sDateTime))

Arguments

`UstarColName`	column name for UStar
`NEEColName`	column name for NEE
`TempColName`	column name for air temperature
`RgColName`	column name for solar radiation for omitting night time data
`...`	further arguments to `usEstUstarThreshold`
`seasonFactor`	factor of seasons to split

Value

result component uStarTh of usEstUstarThreshold. In addition the result is stored in class variable sUSTAR_DETAILS.

Author(s)

sEddyProc$sEstUstarThreshold - Estimating ustar threshold

Description

Calling usEstUstarThreshold for class data and storing results

Usage

sEddyProc_sEstUstarThreshold(UstarColName = "Ustar", 
    NEEColName = "NEE", TempColName = "Tair", 
    RgColName = "Rg", ..., isWarnDeprecated = TRUE)sEddyProc_sEstUstarThreshold(UstarColName = "Ustar", 
    NEEColName = "NEE", TempColName = "Tair", 
    RgColName = "Rg", ..., isWarnDeprecated = TRUE)

Arguments

`UstarColName`	column name for UStar
`NEEColName`	column name for NEE
`TempColName`	column name for air temperature
`RgColName`	column name for solar radiation for omitting night time data
`...`	further arguments to `usEstUstarThreshold`
`isWarnDeprecated`	set to FALSE to avoid deprecated warning.

Value

result of usEstUstarThreshold. In addition the result is stored in class variable sUSTAR_DETAILS and the bins as additional columns to sTemp

Author(s)

sEddyProc sEstUstarThresholdDistribution

Description

Estimate the distribution of u* threshold by bootstrapping over data

Usage

sEddyProc_sEstUstarThresholdDistribution(...)sEddyProc_sEstUstarThresholdDistribution(...)

Arguments

...

further parameters to sEddyProc_sEstimateUstarScenarios

Details

This method returns the results directly, without modifying the class. It is there for portability reasons. Recommended is using method sEddyProc_sEstimateUstarScenarios to update the class and then getting the results from the class by sEddyProc_sGetEstimatedUstarThresholdDistribution.

Value

result of sEddyProc_sGetEstimatedUstarThresholdDistribution

Author(s)

sEddyProc sExportData

Description

Export class internal sDATA data frame

Usage

sEddyProc_sExportData()sEddyProc_sExportData()

Value

Return data frame sDATA with time stamp shifted back to original.

Author(s)

sEddyProc sExportResults

Description

Export class internal sTEMP data frame with result columns

Usage

sEddyProc_sExportResults(isListColumnsExported = FALSE)sEddyProc_sExportResults(isListColumnsExported = FALSE)

Arguments

isListColumnsExported

if TRUE export list columns in addition to numeric columns, such as the covariance matrices of the the day-time-partitioning LRC fits

Value

Return data frame sTEMP with results.

Author(s)

sEddyProc$sFillInit - Initialize gap filling

Description

Initializes data frame sTEMP for newly generated gap filled data and qualifiers.

Usage

sEddyProc_sFillInit(Var.s, QFVar.s = "none", 
    QFValue.n = NA_real_, FillAll.b = TRUE)sEddyProc_sFillInit(Var.s, QFVar.s = "none", 
    QFValue.n = NA_real_, FillAll.b = TRUE)

Arguments

`Var.s`	Variable to be filled
`QFVar.s`	Quality flag of variable to be filled
`QFValue.n`	Value of quality flag for _good_ (original) data, other data is set to missing
`FillAll.b`	Fill all values to estimate uncertainties

Details

Description of newly generated variables with gap filled data and qualifiers:

VAR_orig - Original values used for gap filling
VAR_f - Original values and gaps filled with mean of selected datapoints (condition depending on gap filling method)
VAR_fqc - Quality flag assigned depending on gap filling method and window length (0 = original data, 1 = most reliable, 2 = medium, 3 = least reliable)
VAR_fall - All values considered as gaps (for uncertainty estimates)
VAR_fall_qc - Quality flag assigned depending on gap filling method and window length (1 = most reliable, 2 = medium, 3 = least reliable)
VAR_fnum - Number of datapoints used for gap-filling
VAR_fsd - Standard deviation of datapoints used for gap filling (uncertainty)
VAR_fmeth - Method used for gap filling (1 = similar meteo condition (sFillLUT with Rg, VPD, Tair), 2 = similar meteo (sFillLUT with Rg only), 3 = mean diurnal course (sFillMDC))
VAR_fwin - Full window length used for gap filling

Long gaps (larger than 60 days) are not filled.

Author(s)

sEddyProc sFillLUT

Description

Look-Up Table (LUT) algorithm of up to five conditions within prescribed window size

Usage

sEddyProc_sFillLUT(WinDays.i, V1.s = "none", 
    T1.n = NA_real_, V2.s = "none", T2.n = NA_real_, 
    V3.s = "none", T3.n = NA_real_, V4.s = "none", 
    T4.n = NA_real_, V5.s = "none", T5.n = NA_real_, 
    Verbose.b = TRUE, calculate_gapstats = calculate_gapstats_Reichstein05)sEddyProc_sFillLUT(WinDays.i, V1.s = "none", 
    T1.n = NA_real_, V2.s = "none", T2.n = NA_real_, 
    V3.s = "none", T3.n = NA_real_, V4.s = "none", 
    T4.n = NA_real_, V5.s = "none", T5.n = NA_real_, 
    Verbose.b = TRUE, calculate_gapstats = calculate_gapstats_Reichstein05)

Arguments

`WinDays.i`	Window size for filling in days
`V1.s`	Condition variable 1
`T1.n`	Tolerance interval 1
`V2.s`	Condition variable 2
`T2.n`	Tolerance interval 2
`V3.s`	Condition variable 3
`T3.n`	Tolerance interval 3
`V4.s`	Condition variable 4
`T4.n`	Tolerance interval 4
`V5.s`	Condition variable 5
`T5.n`	Tolerance interval 5
`Verbose.b`	Print status information to screen
`calculate_gapstats`	function computing gap-statistics

Details

Quality flags

1: at least one variable and nDay <= 14
2: three variables and nDay in [14,56) or one variable and nDay in [14,28)
3: three variables and nDay > 56 or one variable and nDay > 28

Value

LUT filling results in sTEMP data frame.

Author(s)

sEddyProc sFillMDC

Description

Mean Diurnal Course (MDC) algorithm based on average values within +/- one hour of adjacent days

Usage

sEddyProc_sFillMDC(WinDays.i, Verbose.b = TRUE)sEddyProc_sFillMDC(WinDays.i, Verbose.b = TRUE)

Arguments

`WinDays.i`	Window size for filling in days
`Verbose.b`	Print status information to screen

Details

Quality flag

1: nDay <= 1
2: nDay [2,5)
3: nDay > 5

Value

MDC filling results in sTEMP data frame.

Author(s)

Estimate VPD from daily minimum temperature

Description

of the data in the class function using estimate_vpd_from_dew.

Usage

sEddyProc_sFillVPDFromDew(...)
sEddyProc_sFillVPDFromDew(...)

Arguments

...

further arguments to estimate_vpd_from_dew

Value

side effect of updated column VPDfromDew in class

sEddyProc sGetData

Description

Get class internal sDATA data frame

Usage

sEddyProc_sGetData()sEddyProc_sGetData()

Value

Return data frame sDATA.

Author(s)

sEddyProc sGetEstimatedUstarThresholdDistribution

Description

return the results of sEddyProc_sEstimateUstarScenarios

Usage

sEddyProc_sGetEstimatedUstarThresholdDistribution()sEddyProc_sGetEstimatedUstarThresholdDistribution()

Value

A data.frame with columns aggregationMode, year, and UStar estimate based on the non-resampled data. The other columns correspond to the quantiles of Ustar estimate for given probabilities (argument probs) based on the distribution of estimates using resampled the data.

Author(s)

sEddyProc sGetUstarScenarios

Description

get the current uStar processing scenarios

Usage

sEddyProc_sGetUstarScenarios()sEddyProc_sGetUstarScenarios()

Details

the associated suffixes can be retrieved by colnames(myClass$sGetUstarScenarios())[-1]

Value

a data.frame with first column listing each season and other column a scenario of uStar thresholds.

Author(s)

sEddyProc sGetUstarSuffixes

Description

get the current uStar suffixes

Usage

sEddyProc_sGetUstarSuffixes()sEddyProc_sGetUstarSuffixes()

Value

a character vector of suffixes. If no uStar thresholds have been estimated, returns character(0)

Author(s)

sEddyProc sGLFluxPartition

Description

Daytime-based Flux partitioning after Lasslop et al. (2010)

Usage

sEddyProc_sGLFluxPartition(..., debug = list(useLocaltime = FALSE), 
    debug.l, isWarnReplaceColumns = TRUE)sEddyProc_sGLFluxPartition(..., debug = list(useLocaltime = FALSE), 
    debug.l, isWarnReplaceColumns = TRUE)

Arguments

`...`	arguments to `partitionNEEGL` in addition to the dataset such as `suffix`
`debug`	List with debugging control. useLocaltime if TRUE use local time zone instead of geo-solar time to compute potential radiation
`debug.l`	deprecated, renamed to debug
`isWarnReplaceColumns`	set to FALSE to avoid the warning on replacing output columns

Details

Daytime-based partitioning of measured net ecosystem fluxes into gross primary production (GPP) and ecosystem respiration (Reco)

Value

Flux partitioning results are in sTEMP data frame of the class.

Author(s)

MM, TW Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

References

sEddyProc sGLFluxPartitionUStarScens

Description

Flux partitioning after Lasslop et al. (2010)

Usage

sEddyProc_sGLFluxPartitionUStarScens(..., 
    isWarnReplaceColumns = FALSE, warnOnOtherErrors = FALSE, 
    controlGLPart = partGLControl())sEddyProc_sGLFluxPartitionUStarScens(..., 
    isWarnReplaceColumns = FALSE, warnOnOtherErrors = FALSE, 
    controlGLPart = partGLControl())

Arguments

`...`	arguments to `sEddyProc_sGLFluxPartition`
`isWarnReplaceColumns`	overriding default to avoid the warning on replacing output columns, because this is intended when processing several uStar scenarios.
`warnOnOtherErrors`	Set to TRUE to only display a warning on errors in uStarScenarios other than uStarScenKeep instead of stopping.
`controlGLPart`	further default parameters

Details

Daytime-based partitioning of measured net ecosystem fluxes into gross primary production (GPP) and ecosystem respiration (Reco) for all u* threshold scenarios.

argument uStarScenKeep in ... is a scalar string specifying the scenario for which to keep parameters (see sEddyProc_sApplyUStarScen. Defaults to the first scenario, which is usually the uStar without bootstrap: "uStar". For the uStarScenKeep, a full set of output columns is returned. For the other scenarios, the bootstrap of GPP uncertainty is omitted and columns "FP_<x>" are overridden.

Author(s)

sEddyProc sMDSGapFill

Description

MDS gap filling algorithm adapted after the PV-Wave code and paper by Markus Reichstein.

Usage

sEddyProc_sMDSGapFill(Var = Var.s, QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    V1 = if (!missing(V1.s)) V1.s else "Rg", 
    T1 = if (!missing(T1.n)) T1.n else 50, 
    V2 = if (!missing(V2.s)) V2.s else "VPD", 
    T2 = if (!missing(T2.n)) T2.n else 5, 
    V3 = if (!missing(V3.s)) V3.s else "Tair", 
    T3 = if (!missing(T3.n)) T3.n else 2.5, 
    FillAll = if (!missing(FillAll.b)) FillAll.b else TRUE, 
    isVerbose = if (!missing(Verbose.b)) Verbose.b else TRUE, 
    suffix = if (!missing(Suffix.s)) Suffix.s else "", 
    minNWarnRunLength = if (Var == "NEE") 4 * 
        .self$sINFO$DTS/24 else NA_integer_, 
    Var.s, QFVar.s, QFValue.n, V1.s, T1.n, 
    V2.s, T2.n, V3.s, T3.n, FillAll.b, Verbose.b, 
    Suffix.s, method = "Reichstein05")sEddyProc_sMDSGapFill(Var = Var.s, QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    V1 = if (!missing(V1.s)) V1.s else "Rg", 
    T1 = if (!missing(T1.n)) T1.n else 50, 
    V2 = if (!missing(V2.s)) V2.s else "VPD", 
    T2 = if (!missing(T2.n)) T2.n else 5, 
    V3 = if (!missing(V3.s)) V3.s else "Tair", 
    T3 = if (!missing(T3.n)) T3.n else 2.5, 
    FillAll = if (!missing(FillAll.b)) FillAll.b else TRUE, 
    isVerbose = if (!missing(Verbose.b)) Verbose.b else TRUE, 
    suffix = if (!missing(Suffix.s)) Suffix.s else "", 
    minNWarnRunLength = if (Var == "NEE") 4 * 
        .self$sINFO$DTS/24 else NA_integer_, 
    Var.s, QFVar.s, QFValue.n, V1.s, T1.n, 
    V2.s, T2.n, V3.s, T3.n, FillAll.b, Verbose.b, 
    Suffix.s, method = "Reichstein05")

Arguments

`Var`	Variable to be filled
`QFVar`	Quality flag of variable to be filled
`QFValue`	Value of quality flag for _good_ (original) data, other data is set to missing
`V1`	Condition variable 1 (default: Global radiation 'Rg' in W m-2)
`T1`	Tolerance interval 1 (default: 50 W m-2)
`V2`	Condition variable 2 (default: Vapour pressure deficit 'VPD' in hPa)
`T2`	Tolerance interval 2 (default: 5 hPa)
`V3`	Condition variable 3 (default: Air temperature 'Tair' in degC)
`T3`	Tolerance interval 3 (default: 2.5 degC)
`FillAll`	Fill all values to estimate uncertainties
`isVerbose`	Print status information to screen
`suffix`	String suffix needed for different processing setups on the same dataset (for explanations see below)
`minNWarnRunLength`	scalar integer: warn if number of subsequent numerically equal values exceeds this number. Set to Inf or NA for no warnings. defaults for "NEE" to records across 4 hours and no warning for others.
`Var.s`	deprecated
`QFVar.s`	deprecated
`QFValue.n`	deprecated
`V1.s`	deprecated
`T1.n`	deprecated
`V2.s`	deprecated
`T2.n`	deprecated
`V3.s`	deprecated
`T3.n`	deprecated
`FillAll.b`	deprecated
`Verbose.b`	deprecated
`Suffix.s`	deprecated
`method`	specify "Vekuri23" to use the skewness-bias reducing variant

Details

Different processing setups on the same dataset: Attention: When processing the same site data set with different setups for the gap filling or flux partitioning (e.g. due to different ustar filters), a string suffix is needed! This suffix is added to the result column names to distinguish the results of the different setups.

Value

Gap filling results in sTEMP data frame (with renamed columns).

Author(s)

References

Reichstein, M. et al. (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11, 1424-1439.

sEddyProc sMDSGapFillAfterUstar

Description

sEddyProc$sMDSGapFillAfterUstar - MDS gap filling algorithm after u* filtering

Usage

sEddyProc_sMDSGapFillAfterUstar(fluxVar, 
    uStarVar = "Ustar", uStarTh = .self$sGetUstarScenarios()[, 
        c("season", uStarSuffix), drop = FALSE], 
    uStarSuffix = "uStar", isFlagEntryAfterLowTurbulence = FALSE, 
    isFilterDayTime = FALSE, swThr = 10, 
    RgColName = "Rg", ...)sEddyProc_sMDSGapFillAfterUstar(fluxVar, 
    uStarVar = "Ustar", uStarTh = .self$sGetUstarScenarios()[, 
        c("season", uStarSuffix), drop = FALSE], 
    uStarSuffix = "uStar", isFlagEntryAfterLowTurbulence = FALSE, 
    isFilterDayTime = FALSE, swThr = 10, 
    RgColName = "Rg", ...)

Arguments

`fluxVar`	Flux variable to gap fill after ustar filtering
`uStarVar`	Column name of friction velocity u * (ms-1), default 'Ustar'
`uStarTh`	data.frame with first column, season names, and second column estimates of uStar Threshold. Alternatively, a single value to be used as threshold for all records If only one value is given, it is used for all records.
`uStarSuffix`	Different suffixes required are for different u * scenarios
`isFlagEntryAfterLowTurbulence`	Set to TRUE for flagging the first entry after low turbulence as bad condition (by value of 2).
`isFilterDayTime`	Set to TRUE to also filter day-time values, default only filters night-time data
`swThr`	threshold of solar radiation below which data is marked as night time respiration.
`RgColName`	Column name of incoming short wave radiation
`...`	Other arguments passed to `sEddyProc_sMDSGapFill`

Details

Calling sEddyProc_sMDSGapFill after filtering for (provided) friction velocity u*

The u* threshold(s) are provided with argument uStarTh for filtering the conditions of low turbulence. After filtering, the data is gap filled using the MDS algorithm sEddyProc_sMDSGapFill.

With isFlagEntryAfterLowTurbulence set to TRUE, to be more conservative, in addition to the data acquired when uStar is below the threshold, the first half hour measured with good turbulence conditions after a period with low turbulence is also removed (Papale et al. 2006).

Value

Vector with quality flag from filtering (here 0: good data , 1: low turbulence, 2: first half hour after low turbulence , 3: no threshold available, 4: missing uStar value) Gap filling results are in sTEMP data frame (with renamed columns) that can be retrieved by sEddyProc_sExportResults.

Author(s)

sEddyProc sMDSGapFillAfterUStarDistr

Description

gapfilling for several filters of estimated friction velocity Ustar thresholds.

Usage

sEddyProc_sMDSGapFillAfterUStarDistr(..., 
    uStarTh, uStarSuffixes = colnames(uStarTh)[-1])sEddyProc_sMDSGapFillAfterUStarDistr(..., 
    uStarTh, uStarSuffixes = colnames(uStarTh)[-1])

Arguments

`...`	other arguments to `sEddyProc_sMDSGapFillAfterUstar` and `sEddyProc_sMDSGapFill` such as `fluxVar`
`uStarTh`	data.frame with first column, season names, and remaining columns different estimates of uStar Threshold. If the data.frame has only one row, then each uStar threshold estimate is applied to the entire dataset. Entries in first column must match levels in argument `seasonFactor`
`uStarSuffixes`	String vector to distinguish result columns for different ustar values. Its length must correspond to column numbers in `UstarThres.m.n`.

Details

This method is superseded by sEddyProc_sMDSGapFillUStarScens and only there for backward portability.

Author(s)

sEddyProc sMDSGapFillUStarScens

Description

gapfilling for several filters of estimated friction velocity Ustar thresholds.

Usage

sEddyProc_sMDSGapFillUStarScens(...)sEddyProc_sMDSGapFillUStarScens(...)

Arguments

...

other arguments to sEddyProc_sMDSGapFillAfterUstar and sEddyProc_sMDSGapFill such as fluxVar

Details

sEddyProc$sMDSGapFillUStarDistr: calling sEddyProc_sMDSGapFillAfterUstar for several filters of friction velocity Ustar.

The scenarios need to be set before by sEddyProc_sSetUstarScenarios or accepting the defaults annual estimates of link{sEddyProc_sEstimateUstarScenarios}.

Then the difference between output columns NEE_U05_f and NEE_U95_f corresponds to the uncertainty introduced by the uncertain estimate of the u* threshold.

Value

Matrix (columns correspond to u* Scenarios) with quality flag from filtering ustar (0 - good data, 1 - filtered data)

Gap filling results in sTEMP data frame (with renamed columns), that can be retrieved by sEddyProc_sExportResults. Each of the outputs is calculated for several u* r-estimates and distinguished by a suffix after the variable. E.g. with an an entry "U05" in uStarSuffixes in sEddyProc_sSetUstarScenarios the corresponding filled NEE can be found in output column "NEE_U05_f".

Author(s)

sEddyProc sMRFluxPartition

Description

Nighttime-based partitioning of net ecosystem fluxes into gross fluxes GPP and REco

Usage

sEddyProc_sMRFluxPartition(FluxVar = if (missing(FluxVar.s)) "NEE_f" else FluxVar.s, 
    QFFluxVar = if (missing(QFFluxVar.s)) "NEE_fqc" else QFFluxVar.s, 
    QFFluxValue = if (missing(QFFluxValue.n)) 0L else QFFluxValue.n, 
    TempVar = if (missing(TempVar.s)) "Tair_f" else TempVar.s, 
    QFTempVar = if (missing(QFTempVar.s)) "Tair_fqc" else QFTempVar.s, 
    QFTempValue = if (missing(QFTempValue.n)) 0 else QFTempValue.n, 
    RadVar = if (missing(RadVar.s)) "Rg" else RadVar.s, 
    TRef = if (missing(T_ref.n)) 273.15 + 
        15 else T_ref.n, suffix = if (missing(Suffix.s)) "" else Suffix.s, 
    FluxVar.s, QFFluxVar.s, QFFluxValue.n, 
    TempVar.s, QFTempVar.s, QFTempValue.n, 
    RadVar.s, T_ref.n, Suffix.s, debug.l, 
    debug = if (!missing(debug.l)) debug.l else list(useLocaltime = FALSE), 
    parsE0Regression = list())sEddyProc_sMRFluxPartition(FluxVar = if (missing(FluxVar.s)) "NEE_f" else FluxVar.s, 
    QFFluxVar = if (missing(QFFluxVar.s)) "NEE_fqc" else QFFluxVar.s, 
    QFFluxValue = if (missing(QFFluxValue.n)) 0L else QFFluxValue.n, 
    TempVar = if (missing(TempVar.s)) "Tair_f" else TempVar.s, 
    QFTempVar = if (missing(QFTempVar.s)) "Tair_fqc" else QFTempVar.s, 
    QFTempValue = if (missing(QFTempValue.n)) 0 else QFTempValue.n, 
    RadVar = if (missing(RadVar.s)) "Rg" else RadVar.s, 
    TRef = if (missing(T_ref.n)) 273.15 + 
        15 else T_ref.n, suffix = if (missing(Suffix.s)) "" else Suffix.s, 
    FluxVar.s, QFFluxVar.s, QFFluxValue.n, 
    TempVar.s, QFTempVar.s, QFTempValue.n, 
    RadVar.s, T_ref.n, Suffix.s, debug.l, 
    debug = if (!missing(debug.l)) debug.l else list(useLocaltime = FALSE), 
    parsE0Regression = list())

Arguments

`FluxVar`	Variable name of column with original and filled net ecosystem fluxes (NEE)
`QFFluxVar`	Quality flag of NEE variable
`QFFluxValue`	Value of quality flag for _good_ (original) data
`TempVar`	Filled air- or soil temperature variable (degC)
`QFTempVar`	Quality flag of filled temperature variable
`QFTempValue`	Value of temperature quality flag for _good_ (original) data
`RadVar`	Unfilled (original) radiation variable
`TRef`	Reference temperature in Kelvin (degK) used in `fLloydTaylor` for regressing Flux and Temperature
`suffix`	String suffix needed for different processing setups on the same dataset (for explanations see below)
`FluxVar.s`	deprecated
`QFFluxVar.s`	deprecated
`QFFluxValue.n`	deprecated
`TempVar.s`	deprecated
`QFTempVar.s`	deprecated
`QFTempValue.n`	deprecated
`RadVar.s`	deprecated
`T_ref.n`	deprecated
`Suffix.s`	deprecated
`debug.l`	deprecated
`debug`	List with debugging control (passed also to `sEddyProc_sRegrE0fromShortTerm` for providing `fixedE0 = myE0`). useLocaltime see details on solar vs local time
`parsE0Regression`	list with further parameters passed down to `sEddyProc_sRegrE0fromShortTerm` and `fRegrE0fromShortTerm`, such as `TempRange`

Details

Description of newly generated variables with partitioning results:

PotRad - Potential radiation
FP_NEEnight - Good (original) NEE nighttime fluxes used for flux partitioning
FP_Temp - Good (original) temperature measurements used for flux partitioning
E_0 - Estimated temperature sensitivity
R_ref - Estimated reference respiration
Reco - Estimated ecosystem respiration
GPP_f - Estimated gross primary production

Background: This partitioning is based on the regression of nighttime respiration with temperature using the Lloyd-Taylor-Function fLloydTaylor. First the temperature sensitivity E_0 is estimated from short term data, see sEddyProc_sRegrE0fromShortTerm. Next the reference temperature R_ref is estimated for successive periods throughout the whole dataset (see sEddyProc_sRegrRref). These estimates are then used to calculate the respiration during daytime and nighttime and with this GPP. Attention: Gap filling of the net ecosystem fluxes (NEE) and temperature measurements (Tair or Tsoil) is required prior to the partitioning!

Selection of daytime data based on solar time: The respiration-temperature regression is very sensitive to the selection of night- and daytime data. Nighttime is selected by a combined threshold of current solar radiation and potential radiation. The current implementation calculates potential radiation based on exact solar time, based on latitude and longitude. (see fCalcPotRadiation) Therefore it might differ from implementations that use local winter clock time instead.

Different processing setups on the same dataset: Attention: When processing the same site data set with different setups for the gap filling or flux partitioning (e.g. due to different ustar filters), a string suffix is needed! This suffix is added to the result column names to distinguish the results of the different setups. If a suffix is provided and if the defaults for FluxVar and QFFluxVar are used, the suffix will be added to their variable names (e.g. 'NEE_f' will be renamed to 'NEE_uStar_f' and 'NEE_fqc' to 'NEE_uStar_fqc' for the suffix = 'uStar'). Currently, this works only with defaults of FluxVar = 'NEE_f' and QFFluxVar = 'NEE_fqc'.

Value

Flux partitioning results (see variables in details) in sTEMP data frame (with renamed columns). On success, return value is NULL. On failure an integer scalar error code is returned: -111 if regression of E_0 failed due to insufficient relationship in the data.

Author(s)

References

sEddyProc sMRFluxPartitionUStarScens

Description

Flux partitioning after Reichstein et al. (2005)

Usage

sEddyProc_sMRFluxPartitionUStarScens(..., 
    uStarScenKeep = character(0))sEddyProc_sMRFluxPartitionUStarScens(..., 
    uStarScenKeep = character(0))

Arguments

`...`	arguments to `sEddyProc_sMRFluxPartition`
`uStarScenKeep`	Scalar string specifying the scenario for which to keep parameters (see `sEddyProc_sApplyUStarScen`. Defaults to the first scenario.

Details

Nighttime-based partitioning of measured net ecosystem fluxes into gross primary production (GPP) and ecosystem respiration (Reco) for all u* threshold scenarios.

Value

NULL, it adds output columns in the class

Author(s)

sEddyProc$sPlotDailySums - Image with daily sums of each year

Description

Generates image in specified format ('pdf' or 'png') with daily sums, see also sEddyProc_sPlotDailySumsY.

Usage

sEddyProc_sPlotDailySums(Var = Var.s, VarUnc = "none", 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    unit = if (!missing(unit.s)) unit.s else "gC/m2/day", 
    ..., Var.s, VarUnc.s, Format.s, Dir.s, 
    unit.s)sEddyProc_sPlotDailySums(Var = Var.s, VarUnc = "none", 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    unit = if (!missing(unit.s)) unit.s else "gC/m2/day", 
    ..., Var.s, VarUnc.s, Format.s, Dir.s, 
    unit.s)

Arguments

`Var`	(Filled) variable to plot
`VarUnc`	Uncertainty estimates for variable
`Format`	Graphics file format ('pdf' or 'png')
`Dir`	Directory for plotting
`unit`	unit of the daily sums
`...`	further arguments to `sEddyProc_sPlotDailySumsY`, such as `timeFactor` and `massFactor`.
`Var.s`	deprecated
`VarUnc.s`	deprecated
`Format.s`	deprecated
`Dir.s`	deprecated
`unit.s`	deprecated

Author(s)

KS, AMM Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

sEddyProc sPlotDailySumsY

Description

This function first computes the average flux for each day. If the original unit is not "per day", then it need to be converted to "per day" by argument timeFactor. Furthermore, a change of the mass unit is provided by argument massFactor. The default parameters assume original units of mumol CO2 / m2 / second and convert to gC / m2 / day. The conversion factors allow plotting variables with different units

Usage

sEddyProc_sPlotDailySumsY(Var, VarUnc = "none", 
    Year, timeFactor = 3600 * 24, massFactor = (44.0096/1e+06) * 
        (12.011/44.0096), unit = "gC/m2/day", 
    dts = sINFO$DTS, data = cbind(sDATA, 
        sTEMP))sEddyProc_sPlotDailySumsY(Var, VarUnc = "none", 
    Year, timeFactor = 3600 * 24, massFactor = (44.0096/1e+06) * 
        (12.011/44.0096), unit = "gC/m2/day", 
    dts = sINFO$DTS, data = cbind(sDATA, 
        sTEMP))

Arguments

`Var`	(Filled) variable to plot
`VarUnc`	Uncertainty estimates for variable
`Year`	Year to plot
`timeFactor`	time conversion factor with default per second to per day
`massFactor`	mass conversion factor with default from mumol CO2 to g C
`unit`	unit of the daily sums
`dts`	numeric integer
`data`	data.frame with variables to plot

Author(s)

AMM, KS Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

sEddyProc sPlotDiurnalCycle

Description

Generates image in specified format ('pdf' or 'png') with diurnal cycles.

Usage

sEddyProc_sPlotDiurnalCycle(Var = Var.s, 
    QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    data = cbind(sDATA, sTEMP), dts = sINFO$DTS, 
    Var.s, QFVar.s, QFValue.n, Format.s, 
    Dir.s)sEddyProc_sPlotDiurnalCycle(Var = Var.s, 
    QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    data = cbind(sDATA, sTEMP), dts = sINFO$DTS, 
    Var.s, QFVar.s, QFValue.n, Format.s, 
    Dir.s)

Arguments

`Var`	Variable to plot
`QFVar`	Quality flag of variable to be filled
`QFValue`	Value of quality flag for data to plot
`Format`	Graphics file format (e.g. 'pdf', 'png')
`Dir`	Directory for plotting
`data`	data.frame with variables to plot
`dts`	numeric integer
`Var.s`	deprecated
`QFVar.s`	deprecated
`QFValue.n`	deprecated
`Format.s`	deprecated
`Dir.s`	deprecated

Author(s)

sEddyProc sPlotDiurnalCycleM

Description

The diurnal cycles of a single month are plotted to the current device, scaled to all data. Each year is plotted as a different (coloured) line.

Usage

sEddyProc_sPlotDiurnalCycleM(Var = Var.s, 
    QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Month = Month.i, Legend = if (!missing(Legend.b)) Legend.b else T, 
    data = cbind(sDATA, sTEMP), dts = sINFO$DTS, 
    Var.s, QFVar.s = "none", QFValue.n = NA_real_, 
    Month.i, Legend.b = T)sEddyProc_sPlotDiurnalCycleM(Var = Var.s, 
    QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Month = Month.i, Legend = if (!missing(Legend.b)) Legend.b else T, 
    data = cbind(sDATA, sTEMP), dts = sINFO$DTS, 
    Var.s, QFVar.s = "none", QFValue.n = NA_real_, 
    Month.i, Legend.b = T)

Arguments

`Var`	Variable to plot
`QFVar`	Quality flag of variable to be filled
`QFValue`	Value of quality flag for data to plot
`Month`	Month to plot
`Legend`	Plot with legend
`data`	data.frame with variables to plot
`dts`	numeric integer
`Var.s`	Variable to plot
`QFVar.s`	Quality flag of variable to be filled
`QFValue.n`	Value of quality flag for data to plot
`Month.i`	Month to plot
`Legend.b`	Plot with legend

Author(s)

sEddyProc sPlotFingerprint

Description

Generates fingerprint in file

Usage

sEddyProc_sPlotFingerprint(Var = Var.s, QFVar = "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    ..., Var.s, QFVar.s = "none", QFValue.n = NA_real_, 
    Format.s = "pdf", Dir.s = "plots")sEddyProc_sPlotFingerprint(Var = Var.s, QFVar = "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    ..., Var.s, QFVar.s = "none", QFValue.n = NA_real_, 
    Format.s = "pdf", Dir.s = "plots")

Arguments

`Var`	Variable to plot
`QFVar`	Quality flag of variable to be filled
`QFValue`	Value of quality flag for data to plot
`Format`	Graphics file format (e.g. 'pdf', 'png')
`Dir`	Directory for plotting
`...`	further arguments to `sEddyProc_sPlotFingerprintY`
`Var.s`	Variable to plot
`QFVar.s`	Quality flag of variable to be filled
`QFValue.n`	Value of quality flag for data to plot
`Format.s`	Graphics file format (e.g. 'pdf', 'png')
`Dir.s`	Directory for plotting

Author(s)

sEddyProc sPlotFingerprintY

Description

Plot fingerprint for a single year scaled to all data.

Usage

sEddyProc_sPlotFingerprintY(Var = Var.s, 
    QFVar = "none", QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Year = Year.i, onlyLegend = if (!missing(Legend.b)) Legend.b else F, 
    colors = if (!missing(Col.V)) Col.V else colorRampPalette(c("#00007F", 
        "blue", "#007FFF", "cyan", "#7FFF7F", 
        "yellow", "#FF7F00", "red", "#7F0000"))(50), 
    valueLimits = range(Plot.V.n, na.rm = TRUE), 
    data = cbind(sDATA, sTEMP), dts = sINFO$DTS, 
    Var.s, QFVar.s, QFValue.n, Year.i, Legend.b, 
    Col.V)sEddyProc_sPlotFingerprintY(Var = Var.s, 
    QFVar = "none", QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Year = Year.i, onlyLegend = if (!missing(Legend.b)) Legend.b else F, 
    colors = if (!missing(Col.V)) Col.V else colorRampPalette(c("#00007F", 
        "blue", "#007FFF", "cyan", "#7FFF7F", 
        "yellow", "#FF7F00", "red", "#7F0000"))(50), 
    valueLimits = range(Plot.V.n, na.rm = TRUE), 
    data = cbind(sDATA, sTEMP), dts = sINFO$DTS, 
    Var.s, QFVar.s, QFValue.n, Year.i, Legend.b, 
    Col.V)

Arguments

`Var`	Variable to plot
`QFVar`	Quality flag of variable to be filled
`QFValue`	Value of quality flag for data to plot
`Year`	Year to plot
`onlyLegend`	Plot only legend
`colors`	Color palette for fingerprint plot (can be also defined by user), i.e. color scale argument to `image`
`valueLimits`	values outside this range will be set to the range borders to avoid distorting colour scale e.g. `valueLimits = quantile(EddyProc.C$sDATA$NEE, prob = c( 0.05, 0.95), na.rm = TRUE)`
`data`	data.frame with variables to plot
`dts`	numeric integer of hours in day
`Var.s`	deprecated
`QFVar.s`	deprecated
`QFValue.n`	deprecated
`Year.i`	deprecated
`Legend.b`	deprecated
`Col.V`	deprecated

Author(s)

sEddyProc sPlotHHFluxes

Description

Produce image-plot with half-hourly fluxes for each year

Usage

sEddyProc_sPlotHHFluxes(Var = Var.s, QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    Var.s, QFVar.s, QFValue.n, Format.s, 
    Dir.s)sEddyProc_sPlotHHFluxes(Var = Var.s, QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Format = if (!missing(Format.s)) Format.s else "pdf", 
    Dir = if (!missing(Dir.s)) Dir.s else "plots", 
    Var.s, QFVar.s, QFValue.n, Format.s, 
    Dir.s)

Arguments

`Var`	Variable to plot
`QFVar`	Quality flag of variable to be filled
`QFValue`	Value of quality flag for data to plot
`Format`	Graphics file format (e.g. 'pdf', 'png')
`Dir`	Directory for plotting
`Var.s`	deprecated
`QFVar.s`	deprecated
`QFValue.n`	deprecated
`Format.s`	deprecated
`Dir.s`	deprecated

Details

Generates image in specified format ('pdf' or 'png') with half-hourly fluxes and their daily means, see also sEddyProc_sPlotHHFluxesY.

Author(s)

sEddyProc sPlotHHFluxesY

Description

Plot half-hourly fluxes for a single year scaled to all data.

Usage

sEddyProc_sPlotHHFluxesY(Var = Var.s, QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Year = Year.i, data = cbind(sDATA, sTEMP), 
    dts = sINFO$DTS, Var.s, QFVar.s, QFValue.n, 
    Year.i)sEddyProc_sPlotHHFluxesY(Var = Var.s, QFVar = if (!missing(QFVar.s)) QFVar.s else "none", 
    QFValue = if (!missing(QFValue.n)) QFValue.n else NA_real_, 
    Year = Year.i, data = cbind(sDATA, sTEMP), 
    dts = sINFO$DTS, Var.s, QFVar.s, QFValue.n, 
    Year.i)

Arguments

`Var`	Variable to plot
`QFVar`	Quality flag of variable to be filled
`QFValue`	Value of quality flag for data to plot
`Year`	Year to plot
`data`	data.frame with variables to plot
`dts`	numeric integer
`Var.s`	deprecated
`QFVar.s`	deprecated
`QFValue.n`	deprecated
`Year.i`	deprecated

Author(s)

sEddyProc sPlotNEEVersusUStarForSeason

Description

Generates image in specified format ('pdf' or 'png')

Usage

sEddyProc_sPlotNEEVersusUStarForSeason(season = levels(data$season)[1], 
    format = "pdf", dir = "plots", UstarColName = "Ustar", 
    NEEColName = "NEE", TempColName = "Tair", 
    WInch = 16 * 0.394, HInchSingle = 6 * 
        0.394, ..., data = cbind(sDATA, sTEMP, 
        sUSTAR_DETAILS$bins[, c("uStarBin", 
            "tempBin")]))sEddyProc_sPlotNEEVersusUStarForSeason(season = levels(data$season)[1], 
    format = "pdf", dir = "plots", UstarColName = "Ustar", 
    NEEColName = "NEE", TempColName = "Tair", 
    WInch = 16 * 0.394, HInchSingle = 6 * 
        0.394, ..., data = cbind(sDATA, sTEMP, 
        sUSTAR_DETAILS$bins[, c("uStarBin", 
            "tempBin")]))

Arguments

`season`	string of season, i.e. time period to plot
`format`	string of Graphics file format ('pdf' or 'png')
`dir`	string of Directory for plotting
`UstarColName`	column name for UStar
`NEEColName`	column name for NEE
`TempColName`	column name for air temperature
`WInch`	width of the plot in inches, defaults to 16cm
`HInchSingle`	height of a subplot in inches, defaults to 6cm
`...`	other arguments to `.plotNEEVersusUStarTempClass`, such as xlab and ylab axis label strings
`data`	a data.frame with variables to plot

Author(s)

sEddyProc sSetLocationInfo

Description

set Location and time Zone information to sLOCATION

Usage

sEddyProc_sSetLocationInfo(LatDeg = if (!missing(Lat_deg.n)) Lat_deg.n else NA_real_, 
    LongDeg = if (!missing(Long_deg.n)) Long_deg.n else NA_real_, 
    TimeZoneHour = if (!missing(TimeZone_h.n)) TimeZone_h.n else NA_integer_, 
    Lat_deg.n, Long_deg.n, TimeZone_h.n)sEddyProc_sSetLocationInfo(LatDeg = if (!missing(Lat_deg.n)) Lat_deg.n else NA_real_, 
    LongDeg = if (!missing(Long_deg.n)) Long_deg.n else NA_real_, 
    TimeZoneHour = if (!missing(TimeZone_h.n)) TimeZone_h.n else NA_integer_, 
    Lat_deg.n, Long_deg.n, TimeZone_h.n)

Arguments

`LatDeg`	Latitude in (decimal) degrees (-90 to + 90)
`LongDeg`	Longitude in (decimal) degrees (-180 to + 180)
`TimeZoneHour`	Time zone: hours shift to UTC, e.g. 1 for Berlin
`Lat_deg.n`	deprecated
`Long_deg.n`	deprecated
`TimeZone_h.n`	deprecated

Author(s)

sEddyProc sSetUstarScenarios

Description

set uStar processing scenarios

Usage

sEddyProc_sSetUstarScenarios(uStarTh, uStarSuffixes = colnames(uStarTh)[-1])sEddyProc_sSetUstarScenarios(uStarTh, uStarSuffixes = colnames(uStarTh)[-1])

Arguments

`uStarTh`	data.frame as returned by `usGetAnnualSeasonUStarMap` or `usGetSeasonalSeasonUStarMap`: First column, season names, and remaining columns different estimates of uStar Threshold. If `uStarTh` has only one row, then each uStar threshold estimate is applied to the entire dataset. Entries in first column must match levels in argument `seasonFactor` of `sEddyProc_sEstUstarThresholdDistribution`
`uStarSuffixes`	the suffixes appended to result column names by default the column names of uStarTh unless its first season column

Author(s)

sEddyProc sSetUStarSeasons

Description

Defining seasons for the uStar threshold estimation

Usage

sEddyProc_sSetUStarSeasons(seasonFactor = usCreateSeasonFactorMonth(sDATA$sDateTime))sEddyProc_sSetUStarSeasons(seasonFactor = usCreateSeasonFactorMonth(sDATA$sDateTime))

Arguments

seasonFactor

factor for subsetting times with different uStar threshold (see details)

Value

class with updated seasonFactor

Author(s)

sEddyProc sTKFluxPartition

Description

Modified daytime-based Flux partitioning after Keenan et al. (2019)

Usage

sEddyProc_sTKFluxPartition(..., controlGLPart = partGLControl())sEddyProc_sTKFluxPartition(..., controlGLPart = partGLControl())

Arguments

`...`	arguments to `sEddyProc_sGLFluxPartition` in addition to the dataset
`controlGLPart`	further default parameters, such as `suffix`

Value

Flux partitioning results are in sTEMP data frame of the class.

Author(s)

sEddyProc sTKFluxPartitionUStarScens

Description

Flux partitioning after Keenan et al., 2019

Usage

sEddyProc_sTKFluxPartitionUStarScens(..., 
    uStarScenKeep = character(0))sEddyProc_sTKFluxPartitionUStarScens(..., 
    uStarScenKeep = character(0))

Arguments

`...`	arguments to `sEddyProc_sTKFluxPartition`
`uStarScenKeep`	Scalar string specifying the scenario for which to keep parameters (see `sEddyProc_sApplyUStarScen`. Defaults to the first scenario.

Details

Daytime-based partitioning of measured net ecosystem fluxes into gross primary production (GPP) and ecosystem respiration (Reco) for all u* threshold scenarios.

Note

Currently only experimental.

Author(s)

Add columns reporting the uStar threshold for each scenario to sDATA

Description

Add columns reporting the uStar threshold for each scenario to sDATA

Usage

sEddyProc_update_ustarthreshold_columns()
sEddyProc_update_ustarthreshold_columns()

Value

side effect in .self$sDATA new columns Ustar_Thresh_<ustarsuffix>

sEddyProc useAnnualUStarThresholds

Description

use seasonal estimates of uStar thresholds

Usage

sEddyProc_useAnnualUStarThresholds()sEddyProc_useAnnualUStarThresholds()

Author(s)

sEddyProc useSeaonsalUStarThresholds

Description

use seasonal estimates of uStar thresholds

Usage

sEddyProc_useSeaonsalUStarThresholds()sEddyProc_useSeaonsalUStarThresholds()

Author(s)

Class `"sEddyProc"`

Description

R5 reference class for processing of site-level half-hourly eddy data

Extends

All reference classes extend and inherit methods from "envRefClass".

Fields

private, not to be accessed directly:

sID:: Object of class character with Site ID
sDATA:: Object of class data.frame with (fixed) site data
sINFO:: Object of class list with site information
sLOCATION:: Object of class list with site location information
sTEMP:: Object of class data.frame of (temporary) result data
sUSTAR:: Object of class list with results form uStar Threshold estimation

Methods

Setup, import and export

sEddyProc_initialize(ID.s, Data.F, ColNames.V.s, ColPOSIXTime.s, DTS.n, ColNamesNonNumeric.V.s, Lat_deg.n, Long_deg.n, TimeZone_h.n, ...)
sEddyProc_sSetLocationInfo(Lat_deg.n, Long_deg.n, TimeZone_h.n)
sEddyProc_sExportResults(isListColumnsExported)
sEddyProc_sExportData()
sEddyProc_sGetData()

uStar threshold estimation

sEddyProc_sEstUstarThresholdDistribution(ctrlUstarEst.l, ctrlUstarSub.l, UstarColName, NEEColName, TempColName, RgColName, ..., seasonFactor, seasonFactorsYear, nSample, probs, verbose.b)
sEddyProc_sEstUstarThold(UstarColName, NEEColName, TempColName, RgColName, ...)
sEddyProc_sPlotNEEVersusUStarForSeason(season.s, Format.s, Dir.s, UstarColName, NEEColName, TempColName, WInch, HInchSingle, ...)

Gapfilling

sEddyProc_sCalcPotRadiation(useSolartime.b)
sEddyProc_sMDSGapFill(Var.s, QFVar.s, QFValue.n, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n, FillAll.b, Verbose.b, Suffix.s)
sEddyProc_sMDSGapFillAfterUStarDistr(..., UstarThres.df, UstarSuffix.V.s)
sEddyProc_sMDSGapFillAfterUstar(FluxVar.s, UstarVar.s, UstarThres.df, UstarSuffix.s, FlagEntryAfterLowTurbulence.b, isFilterDayTime, swThr, RgColName, ...)
sEddyProc_sFillMDC(WinDays.i, Verbose.b)
sEddyProc_sFillLUT(WinDays.i, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n, V4.s, T4.n, V5.s, T5.n, Verbose.b)
sEddyProc_sFillInit(Var.s, QFVar.s, QFValue.n, FillAll.b)

Flux partitioning

sEddyProc_sMRFluxPartition(FluxVar.s, QFFluxVar.s, QFFluxValue.n, TempVar.s, QFTempVar.s, QFTempValue.n, RadVar.s, Lat_deg.n, Long_deg.n, TimeZone_h.n, T_ref.n, Suffix.s, debug.l, parsE0Regression)
sEddyProc_sGLFluxPartition(..., debug.l, isWarnReplaceColumns)

Plotting

sEddyProc_sPlotDailySums(Var.s, VarUnc.s, Format.s, Dir.s, unit.s, ...)
sEddyProc_sPlotDailySumsY(Var.s, VarUnc.s, Year.i, timeFactor.n, massFactor.n, unit.s)
sEddyProc_sPlotHHFluxes(Var.s, QFVar.s, QFValue.n, Format.s, Dir.s)
sEddyProc_sPlotHHFluxesY(Var.s, QFVar.s, QFValue.n, Year.i)
sEddyProc_sPlotDiurnalCycle(Var.s, QFVar.s, QFValue.n, Format.s, Dir.s)
sEddyProc_sPlotFingerprint(Var.s, QFVar.s, QFValue.n, Format.s, Dir.s, ...)
sEddyProc_sPlotFingerprintY(Var.s, QFVar.s, QFValue.n, Year.i, Legend.b, Col.V, valueLimits)

Note

for examples see useCase vignette

Author(s)

AM, TW

usControlUstarEst

Description

Default list of parameters for determining UStar of a single binned series

Usage

usControlUstarEst(ustPlateauFwd = 10, ustPlateauBack = 6, 
    plateauCrit = 0.95, corrCheck = 0.5, 
    firstUStarMeanCheck = 0.2, isOmitNoThresholdBins = TRUE, 
    isUsingCPTSeveralT = FALSE, isUsingCPT = FALSE, 
    minValidUStarTempClassesProp = 0.2, minValidBootProp = 0.4, 
    minNuStarPlateau = 3L)usControlUstarEst(ustPlateauFwd = 10, ustPlateauBack = 6, 
    plateauCrit = 0.95, corrCheck = 0.5, 
    firstUStarMeanCheck = 0.2, isOmitNoThresholdBins = TRUE, 
    isUsingCPTSeveralT = FALSE, isUsingCPT = FALSE, 
    minValidUStarTempClassesProp = 0.2, minValidBootProp = 0.4, 
    minNuStarPlateau = 3L)

Arguments

`ustPlateauFwd`	number of subsequent uStar bin values to compare to in fwd mode
`ustPlateauBack`	number of subsequent uStar bin values to compare to in back mode
`plateauCrit`	significant differences between a uStar value and the mean of a "plateau"
`corrCheck`	threshold value for correlation between Tair and u * data
`firstUStarMeanCheck`	if first uStar bin average of a class is already larger than this value, the temperature class is skipped.
`isOmitNoThresholdBins`	if TRUE, bins where no threshold was found are ignored. Set to FALSE to report highest uStar bin for these cases
`isUsingCPTSeveralT`	set to TRUE to use change point detection without binning uStar but with additionally changed aggregation scheme for several temperature classifications
`isUsingCPT`	set to TRUE to use change point detection without binning uStar before in usual aggregation method (good for comparing methods, but not recommended, overruled by isUsingCPTSeveralT = TRUE)
`minValidUStarTempClassesProp`	seasons, in which only less than this proportion of temperature classes a threshold was detected, are excluded from aggregation
`minValidBootProp`	minimum proportion of bootstrap samples for which a threshold was detected. Below this proportion NA quantiles are reported.
`minNuStarPlateau`	minimum number of records in plateau, threshold must be larger than mean of this many bins

Author(s)

Examples

usControlUstarEst()
usControlUstarEst()

usControlUstarSubsetting

Description

Default list of parameters for subsetting the data for uStarThreshold estimation

Usage

usControlUstarSubsetting(taClasses = 7, UstarClasses = 20, 
    swThr = 10, minRecordsWithinTemp = 100, 
    minRecordsWithinSeason = 160, minRecordsWithinYear = 3000, 
    isUsingOneBigSeasonOnFewRecords = TRUE)usControlUstarSubsetting(taClasses = 7, UstarClasses = 20, 
    swThr = 10, minRecordsWithinTemp = 100, 
    minRecordsWithinSeason = 160, minRecordsWithinYear = 3000, 
    isUsingOneBigSeasonOnFewRecords = TRUE)

Arguments

`taClasses`	set number of air temperature classes
`UstarClasses`	set number of Ustar classes
`swThr`	nighttime data threshold for solar radiation [Wm-2]
`minRecordsWithinTemp`	integer scalar: the minimum number of Records within one Temperature-class
`minRecordsWithinSeason`	integer scalar: the minimum number of Records within one season
`minRecordsWithinYear`	integer scalar: the minimum number of Records within one year
`isUsingOneBigSeasonOnFewRecords`	boolean scalar: set to FALSE to avoid aggregating all seasons on too few records

Author(s)

Examples

usControlUstarSubsetting()
usControlUstarSubsetting()

usCreateSeasonFactorMonth

Description

Compute year-spanning Seasonfactor by starting month

Usage

usCreateSeasonFactorMonth(dates, month = as.POSIXlt(dates)$mon + 
    1L, year = as.POSIXlt(dates)$year + 1900L, 
    startMonth = c(3, 6, 9, 12))usCreateSeasonFactorMonth(dates, month = as.POSIXlt(dates)$mon + 
    1L, year = as.POSIXlt(dates)$year + 1900L, 
    startMonth = c(3, 6, 9, 12))

Arguments

`dates`	POSIXct vector of length of the data set to be filled, specifying the center-time of each record
`month`	integer (1-12) vector of length of the data set to be filled, specifying the month for each record
`year`	integer vector of length of the data set to be filled, specifying the year
`startMonth`	integer vector specifying the starting month for each season, counting from one. Default is (Dez, Jan, Feb)(Mar, April, May)(June, July, August), (Sept, Oct, Nov)

Details

Compute factors to denote the season for uStar-Filtering by specifying starting months, with continuous seasons spanning year boundaries If Jan is not a starting month, then the first months of each year will be part of the last period in the year. E.g. with the default the fourth period of the first year consists of Jan, Feb, Dec.

REddyProc internally works with a timestamp 15 minutes after the start of each half hour. When providing the dates argument, user may shift the start time by dates = myDataset$DateTime + 15 * 60

Value

Integer vector length(dates), with each unique value representing one season

Author(s)

usCreateSeasonFactorMonthWithinYear

Description

Compute year-bounded Seasonfactor by starting month

Usage

usCreateSeasonFactorMonthWithinYear(dates, 
    month = as.POSIXlt(dates)$mon + 1, year = as.POSIXlt(dates)$year + 
        1900, startMonth = c(3, 6, 9, 12))usCreateSeasonFactorMonthWithinYear(dates, 
    month = as.POSIXlt(dates)$mon + 1, year = as.POSIXlt(dates)$year + 
        1900, startMonth = c(3, 6, 9, 12))

Arguments

`dates`	POSIXct vector of length of the data set to be filled, specifying the center-time of each record
`month`	integer (1-12) vector of length of the data set to be filled, specifying the month for each record
`year`	integer vector of length of the data set to be filled, specifying the year
`startMonth`	integer vector specifying the starting month for each season, counting from one. Default is (Dez, Jan, Feb)(Mar, April, May)(June, July, August), (Sept, Oct, Nov)

Details

Calculate factors to denote the season for uStar-Filtering by specifying starting months, with seasons not spanning year boundaries If Jan is not a starting month, then the first months of each year will be part of the last period in the year. E.g. with the default the fourth period of the first year consists of Jan, Feb, Dec.

Value

Integer vector length(dates), with each unique value representing one season

Author(s)

usCreateSeasonFactorYday

Description

Compute year-spanning Seasonfactor by starting year-day

Usage

usCreateSeasonFactorYday(dates, yday = as.POSIXlt(dates)$yday + 
    1L, year = as.POSIXlt(dates)$year + 1900L, 
    startYday = c(335, 60, 152, 244))usCreateSeasonFactorYday(dates, yday = as.POSIXlt(dates)$yday + 
    1L, year = as.POSIXlt(dates)$year + 1900L, 
    startYday = c(335, 60, 152, 244))

Arguments

`dates`	POSIXct vector of length of the data set to be filled, specifying the center-time of each record
`yday`	integer (1-366) vector of length of the data set to be filled, specifying the day of the year (1..366) for each record
`year`	integer vector of length of the data set to be filled, specifying the year
`startYday`	integer vector (1-366) specifying the starting yearDay for each season in increasing order

Details

With default parameterization, dates are assumed to denote begin or center of the eddy time period. If working with dates that denote the end of the period, use yday = as.POSIXlt(fGetBeginOfEddyPeriod(dates))$yday

Value

Integer vector of length nrow(ds), each unique class representing one season

Author(s)

usCreateSeasonFactorYdayYear

Description

Compute year-spanning Seasonfactor by starting year and yearday

Usage

usCreateSeasonFactorYdayYear(dates, yday = as.POSIXlt(dates)$yday + 
    1L, year = as.POSIXlt(dates)$year + 1900L, 
    starts)usCreateSeasonFactorYdayYear(dates, yday = as.POSIXlt(dates)$yday + 
    1L, year = as.POSIXlt(dates)$year + 1900L, 
    starts)

Arguments

`dates`	POSIXct vector of length of the data set to be filled, specifying the center-time of each record
`yday`	integer (1-366) vector of length of the data set to be filled, specifying the day of the year (1..366) for each record
`year`	integer vector of length of the data set to be filled, specifying the year
`starts`	data.frame with first column specifying the starting yday (integer 1-366) and second column the year (integer e.g. 1998) for each season in increasing order

Details

Value

Integer vector of length nrow(ds), each unique class representing one season

Author(s)

usEstUstarThreshold - Estimating ustar threshold

Description

Estimate the Ustar threshold by aggregating the estimates for seasonal and temperature subsets.

Usage

usEstUstarThreshold(ds, seasonFactor = usCreateSeasonFactorMonth(ds$sDateTime), 
    yearOfSeasonFactor = usGetYearOfSeason(seasonFactor, 
        ds$sDateTime), ctrlUstarEst = usControlUstarEst(), 
    ctrlUstarSub = usControlUstarSubsetting(), 
    fEstimateUStarBinned = usEstUstarThresholdSingleFw2Binned, 
    isCleaned = FALSE, isInBootstrap = FALSE)usEstUstarThreshold(ds, seasonFactor = usCreateSeasonFactorMonth(ds$sDateTime), 
    yearOfSeasonFactor = usGetYearOfSeason(seasonFactor, 
        ds$sDateTime), ctrlUstarEst = usControlUstarEst(), 
    ctrlUstarSub = usControlUstarSubsetting(), 
    fEstimateUStarBinned = usEstUstarThresholdSingleFw2Binned, 
    isCleaned = FALSE, isInBootstrap = FALSE)

Arguments

`ds`	data.frame with columns "sDateTime", "Ustar", "NEE", "Tair", and "Rg"
`seasonFactor`	factor for subsetting times (see details)
`yearOfSeasonFactor`	named integer vector: for each seasonFactor level, get the year (aggregation period) that this season belongs to
`ctrlUstarEst`	control parameters for estimating uStar on a single binned series, see `usControlUstarEst`
`ctrlUstarSub`	control parameters for subsetting time series (number of temperature and Ustar classes ...), see `usControlUstarSubsetting`
`fEstimateUStarBinned`	function to estimate UStar on a single binned series, see `usEstUstarThresholdSingleFw2Binned`
`isCleaned`	set to TRUE, if the data was cleaned already, to avoid expensive call to `usGetValidUstarIndices`.
`isInBootstrap`	set to TRUE if this is called from `sEddyProc_sEstimateUstarScenarios` to avoid further bootstraps in change-point detection

Details

The threshold for sufficiently turbulent conditions u * (Ustar) is estimated for different subsets of the time series. From the estimates for each season (each value in seasonFactor) the maximum of all seasons of one year is reported as estimate for this year. Within each season the time series is split by temperature classes. Among these Ustar estimates, the median is reported as season value.

In order to split the seasons, the uses must provide a vector with argument seasonFactor. All positions with the same factor, belong to the same season. It is conveniently generated by one of the following functions:

usCreateSeasonFactorMonth (default DJF-MAM-JJA-SON with December from previous to January of the year)
usCreateSeasonFactorMonthWithinYear (default DJF-MAM-JJA-SON with December from the same year)
usCreateSeasonFactorYday for a refined specification of season starts.
usCreateSeasonFactorYdayYear for specifying different seasons season between years.

The estimation of Ustar on a single binned series can be selected argument fEstimateUStarBinned.

usEstUstarThresholdSingleFw1Binned
usEstUstarThresholdSingleFw2Binned (default)

This function is called by

sEddyProc_sEstUstarThold which stores the result in the class variables (sUSTAR and sDATA).
sEddyProc_sEstUstarThresholdDistribution which additionally estimates median and confidence intervals for each year by bootstrapping the original data within seasons.

For inspecting the NEE~uStar relationship plotting is provided by sEddyProc_sPlotNEEVersusUStarForSeason

change point detection (CPT) method

With specifying ctrlUstarEst = usControlUstarEst(isUsingCPTSeveralT = TRUE) change point detection is applied instead of the moving point test (e.g. with Fw2Binned).

The sometimes sensitive binning of uStar values within a temperature class is avoided. Further, possible spurious thresholds are avoid by testing that the model with a threshold fits the data better than a model without a threshold using a likelihood ratio test. In addition, with CPT seasons are excluded where a threshold was detected in only less than ctrlUstarEst$minValidUStarTempClassesProp (default 20%) of the temperature classes.

Note, that this method often gives higher estimates of the u * threshold.

One-big-season fallback: If there are too few records within one year, of when no season yielded a finite u * Threshold estimate, then the yearly u * Th is estimated by pooling the data from seasons within one seasonYear. The user can suppress using pooled data on few records by providing option ctrlUstarSub$isUsingOneBigSeasonOnFewRecords = FALSE (see usControlUstarSubsetting)

Value

A list with entries data.frame with columns "aggregationMode", "seasonYear", "season", "uStar" with rows for "single": the entire aggregate (median across years) , "seasonYear": each year (maximum across seasons or estimate on pooled data) , "season": each season (median across temperature classes)

`seasonYear`	data.frame listing results for year with columns "seasonYear" , "uStarMaxSeason" the maximum across seasonal estimates within the year , "uStarPooled" the estimate based on data pooled across the year (only calculated on few valid records or on uStarMaxSeason was nonfinite) , "nRec" number of valid records (only if the pooled estimate was calculated) , "uStarAggr" chosen estimate, corresponding to uStarPooled if this was calculated, or uStarMaxSeason or uStarTh across years if the former was non-finite
`season`	data.frame listing results for each season , "nRec" the number of valid records , "uStarSeasonEst" the estimate for based on data within the season (median across temperature classes) , "uStarAggr" chose estimate, corresponding to uStarSeasonEst, or the yearly seasonYear$uStarAggr, if the former was non-finite
`tempInSeason`	numeric matrix (nTemp x nAggSeason): estimates for each temperature subset for each season
`bins`	columns `season`, `tempBin` and `uStarBin` for each record of input `ds` reporting classes of similar environmental conditions that the record belongs to.

Author(s)

TW, OM Department for Biogeochemical Integration at MPI-BGC, Jena, Germany <[email protected]> [cph], Thomas Wutzler <[email protected]> [aut, cre], Markus Reichstein <[email protected]> [aut], Antje Maria Moffat <[email protected]> [aut, trl], Olaf Menzer <[email protected]> [ctb], Mirco Migliavacca <[email protected]> [aut], Kerstin Sickel <[email protected]> [ctb, trl], Ladislav <U+0160>igut <[email protected]> [ctb]

References

Ustar filtering following the idea in Papale, D. et al. (2006) Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3(4): 571-583.

usEstUstarThresholdSingleFw1Binned

Description

estimate the Ustar threshold for single subset, using FW1 algorithm

Usage

usEstUstarThresholdSingleFw1Binned(Ust_bins.f, 
    ctrlUstarEst = usControlUstarEst())usEstUstarThresholdSingleFw1Binned(Ust_bins.f, 
    ctrlUstarEst = usControlUstarEst())

Arguments

`Ust_bins.f`	data.frame with columns NEE_avg and Ust_avg, of Ustar bins
`ctrlUstarEst`	parameter list, see `usControlUstarEst` for defaults and description

Details

Relying on binned NEE and Ustar

Author(s)

References

inspired by Papale 2006

usEstUstarThresholdSingleFw2Binned

Description

estimate the Ustar threshold for single subset, using FW2 algorithm

Usage

usEstUstarThresholdSingleFw2Binned(Ust_bins.f, 
    ctrlUstarEst = usControlUstarEst())usEstUstarThresholdSingleFw2Binned(Ust_bins.f, 
    ctrlUstarEst = usControlUstarEst())

Arguments

`Ust_bins.f`	data.frame with column s NEE_avg and Ust_avg, of Ustar bins
`ctrlUstarEst`	parameter list, see `usControlUstarEst` for defaults and description

Details

Demand that threshold is higher than ctrlUstarEst$minNuStarPlateau records. If fewer records

Author(s)

usGetAnnualSeasonUStarMap

Description

extract mapping season -> uStar columns from Distribution result

Usage

usGetAnnualSeasonUStarMap(uStarTh)usGetAnnualSeasonUStarMap(uStarTh)

Arguments

uStarTh

result of sEddyProc_sEstUstarThresholdDistribution or sEddyProc_sEstUstarThreshold$uStarTh

Value

a data frame with first column the season, and other columns different uStar threshold estimates

Author(s)

usGetSeasonalSeasonUStarMap

Description

extract mapping season -> uStar columns from Distribution result

Usage

usGetSeasonalSeasonUStarMap(uStarTh)usGetSeasonalSeasonUStarMap(uStarTh)

Arguments

uStarTh

result of sEddyProc_sEstUstarThresholdDistribution or sEddyProc_sEstUstarThreshold$uStarTh

Details

from result of sEddyProc_sEstUstarThresholdDistribution

Value

a data frame with first column the season, and other columns different uStar threshold estimates

Author(s)

usGetYearOfSeason

Description

determine the year of the record of middle of seasons

Usage

usGetYearOfSeason(seasonFactor, sDateTime.v)usGetYearOfSeason(seasonFactor, sDateTime.v)

Arguments

`seasonFactor`	factor vector of length data: for each record which season it belongs to
`sDateTime.v`	POSIX.t vector of length data: for each record: center of half-hour period (corresponding to sDATA$sDateTime)

Value

named integer vector, with names corresponding to seasons

Package 'REddyProc'

Help Index

Post Processing of (Half-)Hourly Eddy-Covariance Measurements

Description

Details

Author(s)

References

BerkeleyJulianDateToPOSIXct

Description

Usage

Arguments

Details

Author(s)

See Also

Eddy covariance data from Gebesee crop site, Germany

Description

Usage

Format

Details

Source

Estimate VPD from assuming dewpoint at daily minimum temperature

Description

Usage

Arguments

Details

Value

Eddy covariance data from Tharandt, Germany

Description

Usage

Format

Source

extract processing results with columns corresponding to Fluxnet15 release

Description

Usage

Arguments

Value

fCalcAVPfromVMFandPress

Description

Usage

Arguments

Value

Author(s)

fCalcETfromLE

Description

Usage

Arguments

Value

Author(s)

fCalcExtRadiation

Description

Usage

Arguments

Value

Author(s)

fCalcPotRadiation

Description

Usage

Arguments

Value

Author(s)

Examples

fCalcRHfromAVPandTair

Description

Usage

Arguments

Value

Author(s)

fCalcSVPfromTair

Description

Usage

Arguments

Value

Author(s)

fCalcVPDfromRHandTair

Description

Usage

Arguments

Value

Author(s)

fCheckHHTimeSeries