#isDevelopMode <- TRUE
if(!exists("isDevelopMode")) library(REddyProc)
set.seed(0815) # for reproducible results
First, the data is loaded. This example uses data that has been downloaded
from http://www.europe-fluxdata.eu
and preprocessed by fLoadEuroFlux16, where the DateTime Column has been created,
and the variables renamed to the BGC-convention (e.g. Tair instead of Ta).
data(DEGebExample)
summary(DEGebExample)
## DateTime NEE Ustar
## Min. :2004-01-01 00:30:00 Min. :-49.919 Min. :0.0000
## 1st Qu.:2004-10-01 00:22:30 1st Qu.: -1.864 1st Qu.:0.0640
## Median :2005-07-02 00:15:00 Median : 0.635 Median :0.1490
## Mean :2005-07-02 00:15:00 Mean : -1.935 Mean :0.1884
## 3rd Qu.:2006-04-02 00:07:30 3rd Qu.: 1.834 3rd Qu.:0.2800
## Max. :2007-01-01 00:00:00 Max. : 19.008 Max. :2.0450
## NA's :21849 NA's :1149
## Tair rH Rg
## Min. :-16.710 Min. : 15.87 Min. : 0.00
## 1st Qu.: 3.360 1st Qu.: 66.61 1st Qu.: 0.00
## Median : 9.970 Median : 79.10 Median : 2.04
## Mean : 9.664 Mean : 75.24 Mean : 124.71
## 3rd Qu.: 15.520 3rd Qu.: 87.07 3rd Qu.: 176.03
## Max. : 34.680 Max. :100.00 Max. :1046.03
## NA's :1
VPD was not given with the original dataset and is calculated from Tair and rH.
DEGebExample$VPD <- fCalcVPDfromRHandTair(DEGebExample$rH, DEGebExample$Tair)
EddyProc.C <- sEddyProc$new('DE-Geb', DEGebExample, c('NEE','Rg','Tair','VPD', 'Ustar'))
The site is a crop site. The harvesting times are visible as sharp edges in the plots of NEE.
The micrometeorological conditions differ between the different cropping periods,
because the friction at the surface differs.
Also not the cropping periods do not correspond very well to seasons.
Hence, for the estimation of uStar-Thresholds, we apply a user-defined splitting
of uStar-seasons. With function usCreateSeasonFactorYdayYear we provide the starting
points of the seasons.
Note that, here, the seasons are not constrained within one calendaryear. There are other variants of a user-specified season that do respect calendaryear boundaries, or that let seasons start at the same day each year.
seasonStarts <- as.data.frame( do.call( rbind, list(
c(70,2004)
,c(210,2004)
,c(320,2004)
,c(70,2005)
,c(180,2005)
,c(320,2005)
,c(120,2006)
,c(305,2006)
)))
seasonFactor <- usCreateSeasonFactorYdayYear(DEGebExample$DateTime, starts=seasonStarts)
plot( NEE ~ DateTime, DEGebExample )
seasonStartsDate <- fConvertTimeToPosix( data.frame(Year=seasonStarts[,2]
, DoY=seasonStarts[,1], Hour=12),'YDH',Year.s="Year", Day.s="DoY",Hour.s="Hour")
abline( v=seasonStartsDate$DateTime)
The user-specific seasoning is provided to the gap-filling by the argument seasonFactor.v.
(uStarTh <- EddyProc.C$sEstUstarThreshold(seasonFactor.v=seasonFactor)$uStarTh)
## aggregationMode seasonYear season uStar
## 1 single NA <NA> 0.15788889
## 2 year 2004 <NA> 0.13766667
## 3 year 2005 <NA> 0.15788889
## 4 year 2006 <NA> 0.25094444
## 5 season 2004 2004001 0.13766667
## 6 season 2004 2004070 0.12287500
## 7 season 2004 2004210 0.08925000
## 8 season 2005 2004320 0.15788889
## 9 season 2005 2005070 0.12533333
## 10 season 2005 2005180 0.12900000
## 11 season 2006 2005320 0.04842361
## 12 season 2006 2006120 0.07390909
## 13 season 2006 2006305 0.25094444
# estimation can be inspected by plotting the saturation of NEE with UStar for the temperatures of one season
#EddyProc.C$sPlotNEEVersusUStarForSeason( levels(uStarTh$season)[2] )
Note that there is an estimate for each season. Further, an annual estimate is obtained by taking the maximum across the seasons, and the overall estimate is the mean across the years.
By default the gap-filling uses annually aggregated estimates of uStar-Threshold.
This usually works for sites with continuous vegetation cover.
For the crop-site of this example, we will use a different threshold for each of the defined seasons.
This is achieved by providing the seasonal estimates with argument UstarThres.df.
The season factor has already been stored with the class when calling EddyProc.C$sEstUstarThreshold.
(UstarThres.df <- usGetSeaonalSeasonUStarMappingFromDistributionResult(uStarTh))
## season uStar
## 5 2004001 0.13766667
## 6 2004070 0.12287500
## 7 2004210 0.08925000
## 8 2004320 0.15788889
## 9 2005070 0.12533333
## 10 2005180 0.12900000
## 11 2005320 0.04842361
## 12 2006120 0.07390909
## 13 2006305 0.25094444
EddyProc.C$sMDSGapFillAfterUstar('NEE', FillAll.b=FALSE, UstarThres.df=UstarThres.df, Verbose.b=FALSE)
With a lower estimate of uStar threshold, more records with lower NEE a kept in the dataset instead of marked as gaps. Therefore annual estimate of NEE will decrease with decreasing uStar Threshold. Also the partitioning of the net-flux to GPP and REco is sensitive to inclusion of data at dawn period with conditions of low uStar.
In order to quantify this uncertainty, a lower, median and upper estimates of uStar are obtained from a bootstrapped sample of half-hourly NEE measurements. The Gap-Filling and computation of derived quantities such as GPP are then repeated for different estimates of the uStar Threshold.
# here, because of speed only 30 samples instead of 100, and 10% and 90% percentile instead of 5%,50%, and 95%
# uStarRes <- EddyProc.C$sEstUstarThresholdDistribution( seasonFactor.v=seasonFactor )
uStarRes <- EddyProc.C$sEstUstarThresholdDistribution( seasonFactor.v=seasonFactor
, nSample=30L, probs =c(0.1,0.9))
(UstarThres.df <- usGetSeaonalSeasonUStarMappingFromDistributionResult(uStarRes))
## season uStar U10 U90
## 5 2004001 0.13766667 0.09421545 0.18925333
## 6 2004070 0.12287500 0.09852917 0.12300000
## 7 2004210 0.08925000 0.08879167 0.18845972
## 8 2004320 0.15788889 0.11723849 0.17302044
## 9 2005070 0.12533333 0.11859643 0.17075952
## 10 2005180 0.12900000 0.08351500 0.14246250
## 11 2005320 0.04842361 0.04836622 0.08997022
## 12 2006120 0.07390909 0.05912727 0.10045385
## 13 2006305 0.25094444 0.14389375 0.25849444
EddyProc.C$sMDSGapFillAfterUStarDistr('NEE', FillAll.b=FALSE, UstarThres.df=UstarThres.df)
Additional output columns are produced for each uStar quantile.
grep("^NEE.*_f$", colnames( EddyProc.C$sExportResults()), value=TRUE )
## [1] "NEE_WithUstar_f" "NEE_uStar_f" "NEE_U10_f" "NEE_U90_f"
In order to feed results of different uStar Threshold estimates into the Gap-Filling, the argument suffix.s is used. The output columns of the Gap-Filling carry the same suffix.
EddyProc.C$sMDSGapFill('Tair', FillAll.b = FALSE)
for( suffix in c('U10', 'U90')){
EddyProc.C$sMRFluxPartition(Lat_deg.n=51.1, Long_deg.n=10.9, TimeZone_h.n=1, Suffix.s = suffix)
}
grep("U10", colnames(EddyProc.C$sExportResults()), value = TRUE)
## [1] "Ustar_U10_Thres" "Ustar_U10_fqc" "NEE_U10_orig"
## [4] "NEE_U10_f" "NEE_U10_fqc" "NEE_U10_fall"
## [7] "NEE_U10_fall_qc" "NEE_U10_fnum" "NEE_U10_fsd"
## [10] "NEE_U10_fmeth" "NEE_U10_fwin" "PotRad_U10"
## [13] "FP_NEEnight_U10" "FP_Temp_U10" "E_0_U10"
## [16] "R_ref_U10" "Reco_U10" "GPP_U10_f"
## [19] "GPP_U10_fqc"
The package also provides another method of estimating the point where NEE saturates with increasing uStar.
With the ChangePointDetection (CPT) method, the data is not binned by classes of uStar but the changepoint
is estimated based on the entire subset within one seasons and one temperature class.
The user selects this method by specifying argument ctrlUstarEst.l=usControlUstarEst(isUsingCPTSeveralT = TRUE)
to EstUstarThreshold or sEstUstarThresholdDistribution.
The CPT method is usually yields higher thresholds and marks more data as Gap.
EddySetups.C <- sEddyProc$new('DE-Geb', DEGebExample, c('NEE','Rg','Tair','VPD', 'Ustar'))
resUStar <- EddySetups.C$sEstUstarThreshold(
ctrlUstarEst.l=usControlUstarEst(isUsingCPTSeveralT = TRUE)
,seasonFactor.v=seasonFactor
)$uStarTh
(UstarThresCP.df <- usGetSeaonalSeasonUStarMappingFromDistributionResult(resUStar))
## season uStar
## 5 2004001 0.5452155
## 6 2004070 0.1206278
## 7 2004210 0.2082677
## 8 2004320 0.1387257
## 9 2005070 0.1387257
## 10 2005180 0.1387257
## 11 2005320 0.3419706
## 12 2006120 0.3419706
## 13 2006305 0.3419706
#UstarThres.df