WindowGPPFourier.gts: Calculate GPP from O2 time series with gaps, in consecutive...
In GPPFourier: Calculate Gross Primary Production (GPP) from O2 Time Series

Description Usage Arguments Details Value Author(s) References See Also Examples

Calculate GPP from O2 time series in consecutive time-windows of prescribed length. High level function to handle time series with gaps. Short gaps are interpolated, series is split at large gaps.

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WindowGPPFourier.gts(x, dt = difftime(x[2, 1], x[1, 1], units = "days"),
  units = c("days", "hours", "mins"), gapMaxN = 4/24/dt, Width = 14,
  filtWidth = 16, ...)

`x`	Dataframe containing time (POSIXt) in first column and O2 concentrations in the second column.
`dt`	Sampling time step. Either a difftime object or a numerical value. When `dt` is given as a numerical value, `units` have to be provided (defaults to `days`). When omitted `dt` is calculated from the time spacing between the first two samples in the data frame.
`units`	Unit of sampling time step
`gapMaxN`	Minimum number of missing data points to be considered a gap in the time series. Gaps smaller than gapMaxN are interpolated. Defaults to the number of samples corresponding to a 4 hour gap.
`Width`	Width [days] of the time-windows for which GPP is calculated
`filtWidth`	Length of moving average filter [hours] to filter O2
`...`	Other parameters to be passed to WindowGPPFourier()

This high level function analyses multiple regular O2 time series at once, each separated by a gap larger than gapMaxN (the number of . This is convenient e.g. for regular O2 data from a single location, where some data is missing due to sensor maintenance or replacement. x is assumed to contain all these data, pasted together in a single data frame (e.g. by cbinding all individual regular time series or just reading from a single data file). Additionally, missing values within the regular series are allowed and are interpolated with gapfill. Uses WindowGPPFourier.

Average gross primary production in units of oxgen as in x per day.

Tom Cox <tom.cox@uantwerp.be>

Cox T.J.S. et al. (2015) 'Estimating primary production from oxygen time series: a novel approach in the frequency domain', Limnology And Oceanography:Methods 13, 529-552. DOI: 10.1002/lom3.10046

GPPFourierPreprocess, GPPFourier_t, SunRiseSet, WindowGPPFourier

par(mfrow=c(2,1))
plot(Kruibeke, pch=20,xlim=as.POSIXct(c("2010-01-01", "2010-12-31")))
GPPAll_4 <- WindowGPPFourier.gts(Kruibeke, 
                                 gapMaxN = 10, 
                                 Width = 10, 
                                 filtWidth=1*24, 
                                 phi=51.176,
                                 lambda=4.326, 
                                 Detrend=TRUE, 
                                 filter=TRUE, 
                                 filtcorrect=FALSE)
plot(GPPAll_4$time,GPPAll_4$GPP*9.3, 
     col="black", 
    pch=19, 
    type="b", 
    xlab="time", 
    ylab="GPP", 
    xlim=as.POSIXct(c("2010-01-01", "2010-12-31")))