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)
## [1] "NEE_f"
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)
## [1] "NEE_uStar_f"
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())
## aggregationMode seasonYear season uStar
## 1 single NA <NA> 0.4162500
## 2 year 1998 <NA> 0.4162500
## 3 season 1998 1998001 0.4162500
## 4 season 1998 1998003 0.4162500
## 5 season 1998 1998006 0.3520000
## 6 season 1998 1998009 0.3369231
## 7 season 1998 1998012 0.1740000
# 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')
## Warning in .self$sGetUstarScenarios(): uStar scenarios not set yet. Setting to
## annual mapping.
grep("NEE.*_f$",names(EProc$sExportResults()), value = TRUE)
## [1] "NEE_uStar_f"
Choosing a different u* threshold effects filtering and the subsequent processing steps of gap-filling, and flux-partitioning. In order to quantify the uncertainty due to not exactly knowing the u* threshold, these processing steps should be repeated for different threshold scenarios, and the spread across the results should be investigated.
First, the quantiles of the threshold distribution are estimated by bootstrap.
EProc <- sEddyProc$new(
'DE-Tha', EddyDataWithPosix, c('NEE','Rg','Tair','VPD', 'Ustar'))
## New sEddyProc class for site 'DE-Tha'
EProc$sEstimateUstarScenarios(
nSample = 100L, probs = c(0.05, 0.5, 0.95))
##
## Estimated UStar distribution of:
## uStar 5% 50% 95%
## 1 0.41625 0.3735357 0.45 0.6294264
## by using 100 bootstrap samples and controls:
## taClasses UstarClasses
## 7 20
## swThr minRecordsWithinTemp
## 10 100
## minRecordsWithinSeason minRecordsWithinYear
## 160 3000
## isUsingOneBigSeasonOnFewRecords
## 1
# inspect the thresholds to be used by default
EProc$sGetUstarScenarios()
## season uStar U05 U50 U95
## 1 1998001 0.41625 0.3735357 0.45 0.6294264
## 2 1998003 0.41625 0.3735357 0.45 0.6294264
## 3 1998006 0.41625 0.3735357 0.45 0.6294264
## 4 1998009 0.41625 0.3735357 0.45 0.6294264
## 5 1998012 0.41625 0.3735357 0.45 0.6294264
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$sGetEstimatedUstarThresholdDistribution())
## aggregationMode seasonYear season uStar 5% 50% 95%
## 1 single NA <NA> 0.4162500 0.3735357 0.4500000 0.6294264
## 2 year 1998 <NA> 0.4162500 0.3735357 0.4500000 0.6294264
## 3 season 1998 1998001 0.4162500 0.3735357 0.4500000 0.6294264
## 4 season 1998 1998003 0.4162500 0.3221496 0.4059256 0.5481313
## 5 season 1998 1998006 0.3520000 0.2881929 0.3900000 0.4652625
## 6 season 1998 1998009 0.3369231 0.2362100 0.3754221 0.5361841
## 7 season 1998 1998012 0.1740000 0.2096167 0.4239423 0.5891127
In conjunction with method usGetSeasonalSeasonUStarMap and
sEddyProc_sSetUstarScenarios this can be used
to set seasonally different u* threshold.
However, this common case supported by method
sEddyProc_useSeaonsalUStarThresholds.
#EProc$sSetUstarScenarios(
# usGetSeasonalSeasonUStarMap(uStarThAgg)[,-2])
EProc$useSeaonsalUStarThresholds()
# inspect the changed thresholds to be used
EProc$sGetUstarScenarios()
## season uStar U05 U50 U95
## 3 1998001 0.4162500 0.3735357 0.4500000 0.6294264
## 4 1998003 0.4162500 0.3221496 0.4059256 0.5481313
## 5 1998006 0.3520000 0.2881929 0.3900000 0.4652625
## 6 1998009 0.3369231 0.2362100 0.3754221 0.5361841
## 7 1998012 0.1740000 0.2096167 0.4239423 0.5891127
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$sExportResults()), value = TRUE)
## [1] "NEE_uStar_f" "NEE_U05_f" "NEE_U50_f" "NEE_U95_f"
EProc$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)
## [1] "GPP_U05_f" "GPP_U50_f" "GPP_U95_f" "GPP_uStar_f"
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').