# do not execute on CRAN: # https://stackoverflow.com/questions/28961431/computationally-heavy-r-vignettes is_check <- ("CheckExEnv" %in% search()) || any(c("_R_CHECK_TIMINGS_", "_R_CHECK_LICENSE_") %in% names(Sys.getenv())) knitr::opts_chunk$set(eval = !is_check)
library(knitr) #rmarkdown::render("vignettes/uStarCases.Rmd","md_document") opts_knit$set(root.dir = '..') opts_chunk$set( #, fig.align = "center" #, fig.width = 3.27, fig.height = 2.5, dev.args = list(pointsize = 10) #,cache = TRUE #, fig.width = 4.3, fig.height = 3.2, dev.args = list(pointsize = 10) #, fig.width = 6.3, fig.height = 6.2, dev.args = list(pointsize = 10) # works with html but causes problems with latex #,out.extra = 'style = "display:block; margin: auto"' ) knit_hooks$set(spar = function(before, options, envir) { if (before) { par(las = 1 ) #also y axis labels horizontal par(mar = c(2.0,3.3,0,0) + 0.3 ) #margins par(tck = 0.02 ) #axe-tick length inside plots par(mgp = c(1.1,0.2,0) ) #positioning of axis title, axis labels, axis } })
#themeTw <- theme_bw(base_size = 10) + theme(axis.title = element_text(size = 9))
The recommended way of dealing with the uncertain uStar threshold for filtering
the half-hourly data, is to repeat all the processing steps with several
bootstrapped estimates of the threshold as in vignette('useCase')
.
First, some setup.
#+++ load libraries used in this vignette library(REddyProc) library(dplyr) #+++ define directory for outputs outDir <- tempdir() # CRAN policy dictates to write only to this dir in examples #outDir <- "out" # to write to subdirectory of current users dir #+++ Add time stamp in POSIX time format to example data # and filter long runs of equal NEE values EddyDataWithPosix <- fConvertTimeToPosix( filterLongRuns(Example_DETha98, "NEE") , 'YDH', Year = 'Year', Day = 'DoY', Hour = 'Hour')
Subsequent processing steps can be performed without further uStar filtering
using sEddyProc_sMDSGapFill
. Corresponding result columns then have
no uStar specific suffix.
EProc <- sEddyProc$new( 'DE-Tha', EddyDataWithPosix, c('NEE','Rg','Tair','VPD', 'Ustar')) EProc$sMDSGapFill('NEE') grep("NEE.*_f$",names(EProc$sExportResults()), value = TRUE)
The user can provide value for uStar-filtering before gapfilling, using
sEddyProc_sMDSGapFillAfterUstar
. Output columns for this uStar scenario use
the suffix as specified by argument uStarSuffix
which defaults to "uStar".
The friction velocity, uStar, needs to be in column named "Ustar" of the input dataset.
EProc <- sEddyProc$new( 'DE-Tha', EddyDataWithPosix, c('NEE','Rg','Tair','VPD', 'Ustar')) uStar <- 0.46 EProc$sMDSGapFillAfterUstar('NEE', uStarTh = uStar) grep("NEE.*_f$",names(EProc$sExportResults()), value = TRUE)
The uStar threshold can be estimated from the uStar-NEE relationship from the data without estimating its uncertainty by a bootstrap.
EProc <- sEddyProc$new( 'DE-Tha', EddyDataWithPosix, c('NEE','Rg','Tair','VPD', 'Ustar')) # estimating the thresholds based on the data (without bootstrap) (uStarTh <- EProc$sEstUstarThold()) # may plot saturation of NEE with UStar for a specified season to pdf EProc$sPlotNEEVersusUStarForSeason(levels(uStarTh$season)[3], dir = outDir )
Next, the annual estimate is used as the default in gap-filling.
Output columns use the suffix as specified by argument uSstarSuffix
which defaults to "uStar".
#EProc$useAnnualUStarThresholds() EProc$sMDSGapFillAfterUstar('NEE') grep("NEE.*_f$",names(EProc$sExportResults()), value = TRUE)
Choosing a different u threshold effects filtering and the subsequent processing steps of gap-filling, and flux-partitioning. In order to quantify the uncertainty due to not exactly knowing the u threshold, these processing steps should be repeated for different threshold scenarios, and the spread across the results should be investigated.
First, the quantiles of the threshold distribution are estimated by bootstrap.
EProc <- sEddyProc$new( 'DE-Tha', EddyDataWithPosix, c('NEE','Rg','Tair','VPD', 'Ustar')) EProc$sEstimateUstarScenarios( nSample = 100L, probs = c(0.05, 0.5, 0.95)) # inspect the thresholds to be used by default EProc$sGetUstarScenarios()
By default the annually aggregated threshold estimates are used for each season
within one year as in the original method publication.
To see the estimates for different aggregation levels,
use method sEddyProc_sGetEstimatedUstarThresholdDistribution
:
(uStarThAgg <- EProc$sGetEstimatedUstarThresholdDistribution())
In conjunction with method usGetSeasonalSeasonUStarMap
and
sEddyProc_sSetUstarScenarios
this can be used
to set seasonally different u* threshold.
However, this common case supported by method
sEddyProc_useSeaonsalUStarThresholds
.
#EProc$sSetUstarScenarios( # usGetSeasonalSeasonUStarMap(uStarThAgg)[,-2]) EProc$useSeaonsalUStarThresholds() # inspect the changed thresholds to be used EProc$sGetUstarScenarios()
Several function whose name ends with 'UstarScens' perform the subsequent processing steps for all uStar scenarios. They operate and create columns that differ between threshold scenarios by a suffix.
EProc$sMDSGapFillUStarScens("NEE") grep("NEE_.*_f$",names(EProc$sExportResults()), value = TRUE)
EProc$sSetLocationInfo(LatDeg = 51.0, LongDeg = 13.6, TimeZoneHour = 1) EProc$sMDSGapFill('Tair', FillAll = FALSE, minNWarnRunLength = NA) EProc$sMDSGapFill('Rg', FillAll = FALSE, minNWarnRunLength = NA) EProc$sMDSGapFill('VPD', FillAll = FALSE, minNWarnRunLength = NA) EProc$sMRFluxPartitionUStarScens() grep("GPP_.*_f$",names(EProc$sExportResults()), value = TRUE) if (FALSE) { # run only interactively, because it takes long EProc$sGLFluxPartitionUStarScens(uStarScenKeep = "U50") grep("GPP_DT_.*_f$",names(EProc$sExportResults()), value = TRUE) }
The argument uStarScenKeep = "U50"
specifies that the outputs that
are not distinguished by the suffix, e.g. FP_GPP2000
, should be reported for the
median u* threshold scenario with suffix U50
, instead of the default first scenario.
A more advanced case of user-specified seasons for
uStar threshold estimate is given in vignette('DEGebExample')
.
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