GPP.zip_初级生产力计算_生态系统

#+++ load libraries used in this vignette library(REddyProc) library(dplyr) #+++ Load data with 1 header and 1 unit row from (tab-delimited) text file Dir.s <-paste(system.file(package='REddyProc'),'examples',sep='/') EddyData.F <-fLoadTXTIntoDataframe('AMF_JDS_2018.txt',Dir.s) # note: use\code{fFilterAttr} to subset rows while keeping the units attributes #+++ Add time stamp in POSIX time format EddyDataWithPosix.F<-fConvertTimeToPosix(EddyData.F,'YDH',Year.s='Year',Day.s='DoY',Hour.s='Hour') #+++ Initalize R5 reference class sEddyProc for post-processing of eddy data #+++ with the variables needed for post-processing later EddyProc.C<-sEddyProc$new('JDS',EddyDataWithPosix.F,c('NEE','Rg','Tair','VPD','Ustar')) EddyProc.C$sSetLocationInfo(Lat_deg.n=31.2130184,Long_deg.n=121.9038239,TimeZone_h.n=8) #Location of Jiuduansha #+++ Generate plots of all data in directory \plots (of current R working dir) #EddyProc.C$sPlotHHFluxes('NEE') #EddyProc.C$sPlotFingerprint('Rg') #EddyProc.C$sPlotDiurnalCycle('Tair') #+++ Plot individual months/years to screen (of current R graphics device) #EddyProc.C$sPlotHHFluxesY('NEE', Year.i=2018) EddyProc.C$sPlotFingerprintY('NEE', Year.i=2018) #+++ Fill gaps in variables with MDS gap filling algorithm (without prior ustar filtering) EddyProc.C$sMDSGapFill('NEE', FillAll.b=TRUE) #Fill all values to estimate flux uncertainties EddyProc.C$sMDSGapFill('Rg', FillAll.b=FALSE) #Fill only the gaps for the meteo condition, e.g. 'Rg' #+++ Example plots of filled data to screen or to directory \plots EddyProc.C$sPlotFingerprintY('NEE_f', Year.i=2018) EddyProc.C$sPlotDailySumsY('NEE_f','NEE_fsd', Year.i=2018) #Plot of sums with uncertainties EddyProc.C$sPlotDailySums('NEE_f','NEE_fsd') #+++ Partition NEE into GPP and respiration EddyProc.C$sMDSGapFill('Tair', FillAll.b=FALSE) # Gap-filled Tair (and NEE) needed for partitioning EddyProc.C$sMDSGapFill('VPD', FillAll.b=FALSE) # Gap-filled Tair (and NEE) needed for partitioning EddyProc.C$sMRFluxPartition() # night time partitioning -> Reco, GPP EddyProc.C$sGLFluxPartition() # day time partitioning -> Reco_DT, GPP_DT #EddyProc.C$sGLFluxPartition(controlGLPart.l=partGLControl(isBoundLowerNEEUncertainty=FALSE)) # day time partitioning -> Reco_DT, GPP_DT #plot( EddyProc.C$sTEMP$GPP_DT ~ EddyProc.C$sTEMP$GPP_f); abline(0,1) #plot( -EddyProc.C$sTEMP$GPP_DT + EddyProc.C$sTEMP$Reco_DT ~ EddyProc.C$sTEMP$NEE_f ); abline(0,1) #names(EddyProc.C$sTEMP) # there are some constraints, that might be too strict for some datasets # e.g. in the tropics the required temperature range might be too large. # Its possible to change these constraints #EddyProc.C$sMRFluxPartition(parsE0Regression=list(TempRange.n=2.0, optimAlgorithm="LM") ) #+++ Example plots of calculated GPP and respiration EddyProc.C$sPlotFingerprintY('GPP_f', Year.i=2018) EddyProc.C$sPlotFingerprint('GPP_f') EddyProc.C$sPlotHHFluxesY('Reco', Year.i=2018) EddyProc.C$sPlotHHFluxes('Reco') x <- EddyProc.C$sTEMP write.csv(x,file = "C:/Users/Surface/Desktop/1.csv")